MAINT: regenerate with cython 0.20 and fix a warning it emits

Fixed warning is "Non-trivial type declarators in shared declaration".

2 files changed, 8080 insertions, 8066 deletions

diff --git a/numpy/random/mtrand/mtrand.c b/numpy/random/mtrand/mtrand.c
index a508efba1..951513aea 100644
--- a/numpy/random/mtrand/mtrand.c
+++ b/numpy/random/mtrand/mtrand.c
@@ -1,4 +1,4 @@
-/* Generated by Cython 0.19 on Fri Jun 28 16:46:58 2013 */
+/* Generated by Cython 0.20 on Sun Jan 19 12:46:03 2014 */
 
 #define PY_SSIZE_T_CLEAN
 #ifndef CYTHON_USE_PYLONG_INTERNALS
@@ -19,6 +19,7 @@
 #elif PY_VERSION_HEX < 0x02040000
     #error Cython requires Python 2.4+.
 #else
+#define CYTHON_ABI "0_20"
 #include <stddef.h> /* For offsetof */
 #ifndef offsetof
 #define offsetof(type, member) ( (size_t) & ((type*)0) -> member )
@@ -53,6 +54,9 @@
 #define CYTHON_COMPILING_IN_PYPY 0
 #define CYTHON_COMPILING_IN_CPYTHON 1
 #endif
+#if CYTHON_COMPILING_IN_PYPY
+#define Py_OptimizeFlag 0
+#endif
 #if PY_VERSION_HEX < 0x02050000
   typedef int Py_ssize_t;
   #define PY_SSIZE_T_MAX INT_MAX
@@ -60,7 +64,7 @@
   #define PY_FORMAT_SIZE_T ""
   #define CYTHON_FORMAT_SSIZE_T ""
   #define PyInt_FromSsize_t(z) PyInt_FromLong(z)
-  #define PyInt_AsSsize_t(o)   __Pyx_PyInt_AsInt(o)
+  #define PyInt_AsSsize_t(o)   __Pyx_PyInt_As_int(o)
   #define PyNumber_Index(o)    ((PyNumber_Check(o) && !PyFloat_Check(o)) ? PyNumber_Int(o) : \
                                 (PyErr_Format(PyExc_TypeError, \
                                               "expected index value, got %.200s", Py_TYPE(o)->tp_name), \
@@ -113,12 +117,14 @@
   #define __Pyx_BUILTIN_MODULE_NAME "__builtin__"
   #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \
           PyCode_New(a, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
+  #define __Pyx_DefaultClassType PyClass_Type
 #else
   #define __Pyx_BUILTIN_MODULE_NAME "builtins"
   #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \
           PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
+  #define __Pyx_DefaultClassType PyType_Type
 #endif
-#if PY_MAJOR_VERSION < 3 && PY_MINOR_VERSION < 6
+#if PY_VERSION_HEX < 0x02060000
   #define PyUnicode_FromString(s) PyUnicode_Decode(s, strlen(s), "UTF-8", "strict")
 #endif
 #if PY_MAJOR_VERSION >= 3
@@ -131,19 +137,38 @@
 #if PY_VERSION_HEX < 0x02060000
   #define Py_TPFLAGS_HAVE_VERSION_TAG 0
 #endif
+#if PY_VERSION_HEX < 0x02060000 && !defined(Py_TPFLAGS_IS_ABSTRACT)
+  #define Py_TPFLAGS_IS_ABSTRACT 0
+#endif
+#if PY_VERSION_HEX < 0x030400a1 && !defined(Py_TPFLAGS_HAVE_FINALIZE)
+  #define Py_TPFLAGS_HAVE_FINALIZE 0
+#endif
 #if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND)
   #define CYTHON_PEP393_ENABLED 1
   #define __Pyx_PyUnicode_READY(op)       (likely(PyUnicode_IS_READY(op)) ? \
                                               0 : _PyUnicode_Ready((PyObject *)(op)))
   #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_LENGTH(u)
   #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i)
+  #define __Pyx_PyUnicode_KIND(u)         PyUnicode_KIND(u)
+  #define __Pyx_PyUnicode_DATA(u)         PyUnicode_DATA(u)
   #define __Pyx_PyUnicode_READ(k, d, i)   PyUnicode_READ(k, d, i)
 #else
   #define CYTHON_PEP393_ENABLED 0
   #define __Pyx_PyUnicode_READY(op)       (0)
   #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_SIZE(u)
   #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i]))
-  #define __Pyx_PyUnicode_READ(k, d, i)   ((k=k), (Py_UCS4)(((Py_UNICODE*)d)[i]))
+  #define __Pyx_PyUnicode_KIND(u)         (sizeof(Py_UNICODE))
+  #define __Pyx_PyUnicode_DATA(u)         ((void*)PyUnicode_AS_UNICODE(u))
+  #define __Pyx_PyUnicode_READ(k, d, i)   ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i]))
+#endif
+#define __Pyx_PyString_FormatSafe(a, b)  ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b))
+#define __Pyx_PyUnicode_FormatSafe(a, b)  ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b))
+#define __Pyx_PyUnicode_Concat(a, b)  ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ? \
+    PyNumber_Add(a, b) : PyUnicode_Concat(a, b))
+#if PY_MAJOR_VERSION >= 3
+  #define __Pyx_PyString_Format(a, b)  PyUnicode_Format(a, b)
+#else
+  #define __Pyx_PyString_Format(a, b)  PyString_Format(a, b)
 #endif
 #if PY_MAJOR_VERSION >= 3
   #define PyBaseString_Type            PyUnicode_Type
@@ -176,7 +201,7 @@
 #else
   #define __Pyx_PyBaseString_Check(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj) || \
                                          PyString_Check(obj) || PyUnicode_Check(obj))
-  #define __Pyx_PyBaseString_CheckExact(obj) (Py_TYPE(obj) == &PyBaseString_Type)
+  #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj))
 #endif
 #if PY_VERSION_HEX < 0x02060000
   #define PySet_Check(obj)             PyObject_TypeCheck(obj, &PySet_Type)
@@ -201,6 +226,7 @@
   #define PyInt_AsSsize_t              PyLong_AsSsize_t
   #define PyInt_AsUnsignedLongMask     PyLong_AsUnsignedLongMask
   #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask
+  #define PyNumber_Int                 PyNumber_Long
 #endif
 #if PY_MAJOR_VERSION >= 3
   #define PyBoolObject                 PyLongObject
@@ -261,6 +287,17 @@
     #define CYTHON_INLINE
   #endif
 #endif
+#ifndef CYTHON_RESTRICT
+  #if defined(__GNUC__)
+    #define CYTHON_RESTRICT __restrict__
+  #elif defined(_MSC_VER) && _MSC_VER >= 1400
+    #define CYTHON_RESTRICT __restrict
+  #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
+    #define CYTHON_RESTRICT restrict
+  #else
+    #define CYTHON_RESTRICT
+  #endif
+#endif
 #ifdef NAN
 #define __PYX_NAN() ((float) NAN)
 #else
@@ -334,8 +371,20 @@ typedef struct {PyObject **p; char *s; const Py_ssize_t n; const char* encoding;
 #define __PYX_DEFAULT_STRING_ENCODING ""
 #define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString
 #define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize
+#define __Pyx_fits_Py_ssize_t(v, type, is_signed)  (    \
+    (sizeof(type) < sizeof(Py_ssize_t))  ||             \
+    (sizeof(type) > sizeof(Py_ssize_t) &&               \
+          likely(v < (type)PY_SSIZE_T_MAX ||            \
+                 v == (type)PY_SSIZE_T_MAX)  &&         \
+          (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||       \
+                                v == (type)PY_SSIZE_T_MIN)))  ||  \
+    (sizeof(type) == sizeof(Py_ssize_t) &&              \
+          (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||        \
+                               v == (type)PY_SSIZE_T_MAX)))  )
 static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject*);
 static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length);
+#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s))
+#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l)
 #define __Pyx_PyBytes_FromString        PyBytes_FromString
 #define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize
 static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(char*);
@@ -346,9 +395,11 @@ static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(char*);
     #define __Pyx_PyStr_FromString        __Pyx_PyUnicode_FromString
     #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize
 #endif
+#define __Pyx_PyObject_AsSString(s)    ((signed char*) __Pyx_PyObject_AsString(s))
 #define __Pyx_PyObject_AsUString(s)    ((unsigned char*) __Pyx_PyObject_AsString(s))
 #define __Pyx_PyObject_FromUString(s)  __Pyx_PyObject_FromString((char*)s)
 #define __Pyx_PyBytes_FromUString(s)   __Pyx_PyBytes_FromString((char*)s)
+#define __Pyx_PyByteArray_FromUString(s)   __Pyx_PyByteArray_FromString((char*)s)
 #define __Pyx_PyStr_FromUString(s)     __Pyx_PyStr_FromString((char*)s)
 #define __Pyx_PyUnicode_FromUString(s) __Pyx_PyUnicode_FromString((char*)s)
 #if PY_MAJOR_VERSION < 3
@@ -370,7 +421,6 @@ static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*);
 static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x);
 static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*);
 static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t);
-static CYTHON_INLINE size_t __Pyx_PyInt_AsSize_t(PyObject*);
 #if CYTHON_COMPILING_IN_CPYTHON
 #define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x))
 #else
@@ -379,7 +429,7 @@ static CYTHON_INLINE size_t __Pyx_PyInt_AsSize_t(PyObject*);
 #define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x))
 #if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII
 static int __Pyx_sys_getdefaultencoding_not_ascii;
-static int __Pyx_init_sys_getdefaultencoding_params() {
+static int __Pyx_init_sys_getdefaultencoding_params(void) {
     PyObject* sys = NULL;
     PyObject* default_encoding = NULL;
     PyObject* ascii_chars_u = NULL;
@@ -404,7 +454,7 @@ static int __Pyx_init_sys_getdefaultencoding_params() {
         if (ascii_chars_b == NULL || strncmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) {
             PyErr_Format(
                 PyExc_ValueError,
-                "This module compiled with c_string_encoding=ascii, but default encoding '%s' is not a superset of ascii.",
+                "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.",
                 default_encoding_c);
             goto bad;
         }
@@ -428,7 +478,7 @@ bad:
 #define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL)
 #if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT
 static char* __PYX_DEFAULT_STRING_ENCODING;
-static int __Pyx_init_sys_getdefaultencoding_params() {
+static int __Pyx_init_sys_getdefaultencoding_params(void) {
     PyObject* sys = NULL;
     PyObject* default_encoding = NULL;
     char* default_encoding_c;
@@ -486,7 +536,7 @@ struct __pyx_obj_6mtrand_RandomState;
 
 /* "mtrand.pyx":107
  *     long rk_logseries(rk_state *state, double p)
- *
+ * 
  * ctypedef double (* rk_cont0)(rk_state *state)             # <<<<<<<<<<<<<<
  * ctypedef double (* rk_cont1)(rk_state *state, double a)
  * ctypedef double (* rk_cont2)(rk_state *state, double a, double b)
@@ -494,7 +544,7 @@ struct __pyx_obj_6mtrand_RandomState;
 typedef double (*__pyx_t_6mtrand_rk_cont0)(rk_state *);
 
 /* "mtrand.pyx":108
- *
+ * 
  * ctypedef double (* rk_cont0)(rk_state *state)
  * ctypedef double (* rk_cont1)(rk_state *state, double a)             # <<<<<<<<<<<<<<
  * ctypedef double (* rk_cont2)(rk_state *state, double a, double b)
@@ -507,7 +557,7 @@ typedef double (*__pyx_t_6mtrand_rk_cont1)(rk_state *, double);
  * ctypedef double (* rk_cont1)(rk_state *state, double a)
  * ctypedef double (* rk_cont2)(rk_state *state, double a, double b)             # <<<<<<<<<<<<<<
  * ctypedef double (* rk_cont3)(rk_state *state, double a, double b, double c)
- *
+ * 
  */
 typedef double (*__pyx_t_6mtrand_rk_cont2)(rk_state *, double, double);
 
@@ -515,14 +565,14 @@ typedef double (*__pyx_t_6mtrand_rk_cont2)(rk_state *, double, double);
  * ctypedef double (* rk_cont1)(rk_state *state, double a)
  * ctypedef double (* rk_cont2)(rk_state *state, double a, double b)
  * ctypedef double (* rk_cont3)(rk_state *state, double a, double b, double c)             # <<<<<<<<<<<<<<
- *
+ * 
  * ctypedef long (* rk_disc0)(rk_state *state)
  */
 typedef double (*__pyx_t_6mtrand_rk_cont3)(rk_state *, double, double, double);
 
 /* "mtrand.pyx":112
  * ctypedef double (* rk_cont3)(rk_state *state, double a, double b, double c)
- *
+ * 
  * ctypedef long (* rk_disc0)(rk_state *state)             # <<<<<<<<<<<<<<
  * ctypedef long (* rk_discnp)(rk_state *state, long n, double p)
  * ctypedef long (* rk_discdd)(rk_state *state, double n, double p)
@@ -530,7 +580,7 @@ typedef double (*__pyx_t_6mtrand_rk_cont3)(rk_state *, double, double, double);
 typedef long (*__pyx_t_6mtrand_rk_disc0)(rk_state *);
 
 /* "mtrand.pyx":113
- *
+ * 
  * ctypedef long (* rk_disc0)(rk_state *state)
  * ctypedef long (* rk_discnp)(rk_state *state, long n, double p)             # <<<<<<<<<<<<<<
  * ctypedef long (* rk_discdd)(rk_state *state, double n, double p)
@@ -552,7 +602,7 @@ typedef long (*__pyx_t_6mtrand_rk_discdd)(rk_state *, double, double);
  * ctypedef long (* rk_discdd)(rk_state *state, double n, double p)
  * ctypedef long (* rk_discnmN)(rk_state *state, long n, long m, long N)             # <<<<<<<<<<<<<<
  * ctypedef long (* rk_discd)(rk_state *state, double a)
- *
+ * 
  */
 typedef long (*__pyx_t_6mtrand_rk_discnmN)(rk_state *, long, long, long);
 
@@ -560,14 +610,14 @@ typedef long (*__pyx_t_6mtrand_rk_discnmN)(rk_state *, long, long, long);
  * ctypedef long (* rk_discdd)(rk_state *state, double n, double p)
  * ctypedef long (* rk_discnmN)(rk_state *state, long n, long m, long N)
  * ctypedef long (* rk_discd)(rk_state *state, double a)             # <<<<<<<<<<<<<<
- *
- *
+ * 
+ * 
  */
 typedef long (*__pyx_t_6mtrand_rk_discd)(rk_state *, double);
 
 /* "mtrand.pyx":525
  *     return sum
- *
+ * 
  * cdef class RandomState:             # <<<<<<<<<<<<<<
  *     """
  *     RandomState(seed=None)
@@ -628,6 +678,14 @@ struct __pyx_obj_6mtrand_RandomState {
   #define __Pyx_XGOTREF(r)
   #define __Pyx_XGIVEREF(r)
 #endif /* CYTHON_REFNANNY */
+#define __Pyx_XDECREF_SET(r, v) do {                            \
+        PyObject *tmp = (PyObject *) r;                         \
+        r = v; __Pyx_XDECREF(tmp);                              \
+    } while (0)
+#define __Pyx_DECREF_SET(r, v) do {                             \
+        PyObject *tmp = (PyObject *) r;                         \
+        r = v; __Pyx_DECREF(tmp);                               \
+    } while (0)
 #define __Pyx_CLEAR(r)    do { PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);} while(0)
 #define __Pyx_XCLEAR(r)   do { if((r) != NULL) {PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);}} while(0)
 
@@ -664,26 +722,39 @@ static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[], \
 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*/
 
-#define __Pyx_GetItemInt(o, i, size, to_py_func, is_list, wraparound, boundscheck) \
-    (((size) <= sizeof(Py_ssize_t)) ? \
-    __Pyx_GetItemInt_Fast(o, i, is_list, wraparound, boundscheck) : \
-    __Pyx_GetItemInt_Generic(o, to_py_func(i)))
-#define __Pyx_GetItemInt_List(o, i, size, to_py_func, is_list, wraparound, boundscheck) \
-    (((size) <= sizeof(Py_ssize_t)) ? \
-    __Pyx_GetItemInt_List_Fast(o, i, wraparound, boundscheck) : \
-    __Pyx_GetItemInt_Generic(o, to_py_func(i)))
+#define __Pyx_GetItemInt(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck) \
+    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ? \
+    __Pyx_GetItemInt_Fast(o, (Py_ssize_t)i, is_list, wraparound, boundscheck) : \
+    (is_list ? (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL) : \
+               __Pyx_GetItemInt_Generic(o, to_py_func(i))))
+#define __Pyx_GetItemInt_List(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck) \
+    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ? \
+    __Pyx_GetItemInt_List_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) : \
+    (PyErr_SetString(PyExc_IndexError, "list index out of range"), (PyObject*)NULL))
 static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i,
                                                               int wraparound, int boundscheck);
-#define __Pyx_GetItemInt_Tuple(o, i, size, to_py_func, is_list, wraparound, boundscheck) \
-    (((size) <= sizeof(Py_ssize_t)) ? \
-    __Pyx_GetItemInt_Tuple_Fast(o, i, wraparound, boundscheck) : \
-    __Pyx_GetItemInt_Generic(o, to_py_func(i)))
+#define __Pyx_GetItemInt_Tuple(o, i, type, is_signed, to_py_func, is_list, wraparound, boundscheck) \
+    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ? \
+    __Pyx_GetItemInt_Tuple_Fast(o, (Py_ssize_t)i, wraparound, boundscheck) : \
+    (PyErr_SetString(PyExc_IndexError, "tuple index out of range"), (PyObject*)NULL))
 static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i,
                                                               int wraparound, int boundscheck);
 static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j);
 static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i,
                                                      int is_list, int wraparound, int boundscheck);
 
+#include <string.h>
+
+static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals); /*proto*/
+
+static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals); /*proto*/
+
+#if PY_MAJOR_VERSION >= 3
+#define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals
+#else
+#define __Pyx_PyString_Equals __Pyx_PyBytes_Equals
+#endif
+
 static CYTHON_INLINE PyObject* __Pyx_PyObject_GetSlice(
         PyObject* obj, Py_ssize_t cstart, Py_ssize_t cstop,
         PyObject** py_start, PyObject** py_stop, PyObject** py_slice,
@@ -697,8 +768,13 @@ static CYTHON_INLINE int __Pyx_IterFinish(void); /*proto*/
 
 static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected); /*proto*/
 
+static CYTHON_INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb); /*proto*/
+static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb); /*proto*/
+
 static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); /*proto*/
 
+static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); /*proto*/
+
 #define __Pyx_PyObject_DelSlice(obj, cstart, cstop, py_start, py_stop, py_slice, has_cstart, has_cstop, wraparound) \
     __Pyx_PyObject_SetSlice(obj, (PyObject*)NULL, cstart, cstop, py_start, py_stop, py_slice, has_cstart, has_cstop, wraparound)
 static CYTHON_INLINE int __Pyx_PyObject_SetSlice(
@@ -725,8 +801,6 @@ static CYTHON_INLINE int __Pyx_PyObject_SetAttrStr(PyObject* obj, PyObject* attr
 
 static CYTHON_INLINE int __Pyx_CheckKeywordStrings(PyObject *kwdict, const char* function_name, int kw_allowed); /*proto*/
 
-static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); /*proto*/
-
 #if CYTHON_COMPILING_IN_CPYTHON
 static CYTHON_INLINE int __Pyx_PyList_Append(PyObject* list, PyObject* x) {
     PyListObject* L = (PyListObject*) list;
@@ -757,54 +831,30 @@ static PyObject* __Pyx_ImportFrom(PyObject* module, PyObject* name); /*proto*/
   #define __PYX_FORCE_INIT_THREADS 0
 #endif
 
-#define __Pyx_SetItemInt(o, i, v, size, to_py_func, is_list, wraparound, boundscheck) \
-    (((size) <= sizeof(Py_ssize_t)) ? \
-    __Pyx_SetItemInt_Fast(o, i, v, is_list, wraparound, boundscheck) : \
-    __Pyx_SetItemInt_Generic(o, to_py_func(i), v))
+#define __Pyx_SetItemInt(o, i, v, type, is_signed, to_py_func, is_list, wraparound, boundscheck) \
+    (__Pyx_fits_Py_ssize_t(i, type, is_signed) ? \
+    __Pyx_SetItemInt_Fast(o, (Py_ssize_t)i, v, is_list, wraparound, boundscheck) : \
+    (is_list ? (PyErr_SetString(PyExc_IndexError, "list assignment index out of range"), -1) : \
+               __Pyx_SetItemInt_Generic(o, to_py_func(i), v)))
 static CYTHON_INLINE int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v);
 static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v,
                                                int is_list, int wraparound, int boundscheck);
 
-static CYTHON_INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb); /*proto*/
-static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb); /*proto*/
-
 static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); /*proto*/
 
-static CYTHON_INLINE npy_intp __Pyx_PyInt_from_py_npy_intp(PyObject *);
-
-static CYTHON_INLINE PyObject *__Pyx_PyInt_to_py_npy_intp(npy_intp);
-
-static CYTHON_INLINE unsigned char __Pyx_PyInt_AsUnsignedChar(PyObject *);
-
-static CYTHON_INLINE unsigned short __Pyx_PyInt_AsUnsignedShort(PyObject *);
-
-static CYTHON_INLINE unsigned int __Pyx_PyInt_AsUnsignedInt(PyObject *);
-
-static CYTHON_INLINE char __Pyx_PyInt_AsChar(PyObject *);
-
-static CYTHON_INLINE short __Pyx_PyInt_AsShort(PyObject *);
-
-static CYTHON_INLINE int __Pyx_PyInt_AsInt(PyObject *);
-
-static CYTHON_INLINE signed char __Pyx_PyInt_AsSignedChar(PyObject *);
+static CYTHON_INLINE npy_intp __Pyx_PyInt_As_npy_intp(PyObject *);
 
-static CYTHON_INLINE signed short __Pyx_PyInt_AsSignedShort(PyObject *);
+static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value);
 
-static CYTHON_INLINE signed int __Pyx_PyInt_AsSignedInt(PyObject *);
+static CYTHON_INLINE unsigned long __Pyx_PyInt_As_unsigned_long(PyObject *);
 
-static CYTHON_INLINE int __Pyx_PyInt_AsLongDouble(PyObject *);
+static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value);
 
-static CYTHON_INLINE unsigned long __Pyx_PyInt_AsUnsignedLong(PyObject *);
+static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *);
 
-static CYTHON_INLINE unsigned PY_LONG_LONG __Pyx_PyInt_AsUnsignedLongLong(PyObject *);
+static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *);
 
-static CYTHON_INLINE long __Pyx_PyInt_AsLong(PyObject *);
-
-static CYTHON_INLINE PY_LONG_LONG __Pyx_PyInt_AsLongLong(PyObject *);
-
-static CYTHON_INLINE signed long __Pyx_PyInt_AsSignedLong(PyObject *);
-
-static CYTHON_INLINE signed PY_LONG_LONG __Pyx_PyInt_AsSignedLongLong(PyObject *);
+static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_intp(npy_intp value);
 
 static int __Pyx_check_binary_version(void);
 
@@ -843,11 +893,11 @@ static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); /*proto*/
 /* Module declarations from 'numpy' */
 
 /* Module declarations from 'mtrand' */
-static PyTypeObject *__pyx_ptype_6mtrand_dtype = 0;
 static PyTypeObject *__pyx_ptype_6mtrand_ndarray = 0;
-static PyTypeObject *__pyx_ptype_6mtrand_flatiter = 0;
 static PyTypeObject *__pyx_ptype_6mtrand_broadcast = 0;
 static PyTypeObject *__pyx_ptype_6mtrand_RandomState = 0;
+static PyTypeObject *__pyx_ptype_6mtrand_flatiter = 0;
+static PyTypeObject *__pyx_ptype_6mtrand_dtype = 0;
 static PyObject *__pyx_f_6mtrand_cont0_array(rk_state *, __pyx_t_6mtrand_rk_cont0, PyObject *); /*proto*/
 static PyObject *__pyx_f_6mtrand_cont1_array_sc(rk_state *, __pyx_t_6mtrand_rk_cont1, PyObject *, double); /*proto*/
 static PyObject *__pyx_f_6mtrand_cont1_array(rk_state *, __pyx_t_6mtrand_rk_cont1, PyObject *, PyArrayObject *); /*proto*/
@@ -925,711 +975,692 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_100dirichlet(struct __pyx_obj_6m
 static PyObject *__pyx_pf_6mtrand_11RandomState_102shuffle(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_x); /* proto */
 static PyObject *__pyx_pf_6mtrand_11RandomState_104permutation(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_x); /* proto */
 static PyObject *__pyx_tp_new_6mtrand_RandomState(PyTypeObject *t, PyObject *a, PyObject *k); /*proto*/
-static char __pyx_k_1[] = "size is not compatible with inputs";
-static char __pyx_k_9[] = "algorithm must be 'MT19937'";
-static char __pyx_k_13[] = "state must be 624 longs";
-static char __pyx_k_15[] = "low >= high";
-static char __pyx_k_18[] = "a must be 1-dimensional or an integer";
-static char __pyx_k_20[] = "a must be greater than 0";
-static char __pyx_k_22[] = "a must be 1-dimensional";
-static char __pyx_k_24[] = "a must be non-empty";
-static char __pyx_k_26[] = "p must be 1-dimensional";
-static char __pyx_k_28[] = "a and p must have same size";
-static char __pyx_k_30[] = "probabilities are not non-negative";
-static char __pyx_k_32[] = "probabilities do not sum to 1";
-static char __pyx_k_34[] = "Cannot take a larger sample than population when 'replace=False'";
-static char __pyx_k_36[] = "Fewer non-zero entries in p than size";
-static char __pyx_k_44[] = "scale <= 0";
-static char __pyx_k_47[] = "a <= 0";
-static char __pyx_k_49[] = "b <= 0";
-static char __pyx_k_56[] = "shape <= 0";
-static char __pyx_k_66[] = "dfnum <= 0";
-static char __pyx_k_68[] = "dfden <= 0";
-static char __pyx_k_70[] = "dfnum <= 1";
-static char __pyx_k_73[] = "nonc < 0";
-static char __pyx_k_78[] = "df <= 0";
-static char __pyx_k_82[] = "nonc <= 0";
-static char __pyx_k_84[] = "df <= 1";
-static char __pyx_k_89[] = "kappa < 0";
-static char __pyx_k__a[] = "a";
-static char __pyx_k__b[] = "b";
-static char __pyx_k__f[] = "f";
-static char __pyx_k__l[] = "l";
-static char __pyx_k__n[] = "n";
-static char __pyx_k__p[] = "p";
-static char __pyx_k_112[] = "sigma <= 0";
-static char __pyx_k_114[] = "sigma <= 0.0";
-static char __pyx_k_118[] = "scale <= 0.0";
-static char __pyx_k_120[] = "mean <= 0";
-static char __pyx_k_123[] = "mean <= 0.0";
-static char __pyx_k_126[] = "left > mode";
-static char __pyx_k_128[] = "mode > right";
-static char __pyx_k_130[] = "left == right";
-static char __pyx_k_135[] = "n < 0";
-static char __pyx_k_137[] = "p < 0";
-static char __pyx_k_139[] = "p > 1";
-static char __pyx_k_144[] = "n <= 0";
-static char __pyx_k_152[] = "lam < 0";
-static char __pyx_k_154[] = "lam value too large";
-static char __pyx_k_157[] = "lam value too large.";
-static char __pyx_k_159[] = "a <= 1.0";
-static char __pyx_k_162[] = "p < 0.0";
-static char __pyx_k_164[] = "p > 1.0";
-static char __pyx_k_168[] = "ngood < 0";
-static char __pyx_k_170[] = "nbad < 0";
-static char __pyx_k_172[] = "nsample < 1";
-static char __pyx_k_174[] = "ngood + nbad < nsample";
-static char __pyx_k_180[] = "p <= 0.0";
-static char __pyx_k_182[] = "p >= 1.0";
-static char __pyx_k_186[] = "mean must be 1 dimensional";
-static char __pyx_k_188[] = "cov must be 2 dimensional and square";
-static char __pyx_k_190[] = "mean and cov must have same length";
-static char __pyx_k_193[] = "numpy.dual";
-static char __pyx_k_194[] = "sum(pvals[:-1]) > 1.0";
-static char __pyx_k_199[] = "standard_exponential";
-static char __pyx_k_200[] = "noncentral_chisquare";
-static char __pyx_k_201[] = "RandomState.random_sample (line 722)";
-static char __pyx_k_202[] = "\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_203[] = "RandomState.tomaxint (line 765)";
-static char __pyx_k_204[] = "\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_205[] = "RandomState.randint (line 812)";
-static char __pyx_k_206[] = "\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_207[] = "RandomState.bytes (line 892)";
-static char __pyx_k_208[] = "\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_209[] = "RandomState.choice (line 920)";
-static char __pyx_k_210[] = "\n        choice(a, size=None, replace=True, p=None)\n\n        Generates a random sample from a given 1-D array\n\n                .. versionadded:: 1.7.0\n\n        Parameters\n        -----------\n        a : 1-D array-like or int\n            If an ndarray, a random sample is generated from its elements.\n            If an int, the random sample is generated as if a was np.arange(n)\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single value is\n            returned.\n        replace : boolean, optional\n            Whether the sample is with or without replacement\n        p : 1-D array-like, optional\n            The probabilities associated with each entry in a.\n            If not given the sample assumes a uniform distribtion over all\n            entries in a.\n\n        Returns\n        --------\n        samples : 1-D ndarray, shape (size,)\n            The generated random samples\n\n        Raises\n        -------\n        ValueError\n            If a is an int and less than zero, if a or p are not 1-dimensional,\n            if a is an array-like of size 0, if p is not a vector of\n            probabilities, if a and p have different lengths, or if\n            replace=False and the sample size is greater than the population\n            size\n\n        See Also\n        ---------\n        randint, shuffle, permutation\n\n        Examples\n        ---------\n        Generate a uniform random sample from np.arange(5) of size 3:\n\n        >>> np.random.choice(5, 3)\n        array([0, 3, 4])\n        >>> #This is equivalent to np.random.randint(0,5,3)\n\n        Generate a non-uniform random sample from np.arange(5) of size 3:\n\n        >>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])\n        array([3, 3, 0])\n\n        Generate a uniform random sample from np.arange(5) of size 3 without\n        replacement:\n\n        >>> np.random.choice(5, 3, replace=False)\n        array([3,1,0])\n    ""    >>> #This is equivalent to np.random.shuffle(np.arange(5))[:3]\n\n        Generate a non-uniform random sample from np.arange(5) of size\n        3 without replacement:\n\n        >>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])\n        array([2, 3, 0])\n\n        Any of the above can be repeated with an arbitrary array-like\n        instead of just integers. For instance:\n\n        >>> aa_milne_arr = ['pooh', 'rabbit', 'piglet', 'Christopher']\n        >>> np.random.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3])\n        array(['pooh', 'pooh', 'pooh', 'Christopher', 'piglet'],\n              dtype='|S11')\n\n        ";
-static char __pyx_k_211[] = "RandomState.uniform (line 1092)";
-static char __pyx_k_212[] = "\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_213[] = "RandomState.rand (line 1179)";
-static char __pyx_k_214[] = "\n        rand(d0, d1, ..., dn)\n\n        Random values in a given shape.\n\n        Create an array of the given shape and propagate it with\n        random samples from a uniform distribution\n        over ``[0, 1)``.\n\n        Parameters\n        ----------\n        d0, d1, ..., dn : int, optional\n            The dimensions of the returned array, should all be positive.\n            If no argument is given a single Python float is returned.\n\n        Returns\n        -------\n        out : ndarray, shape ``(d0, d1, ..., dn)``\n            Random values.\n\n        See Also\n        --------\n        random\n\n        Notes\n        -----\n        This is a convenience function. If you want an interface that\n        takes a shape-tuple as the first argument, refer to\n        np.random.random_sample .\n\n        Examples\n        --------\n        >>> np.random.rand(3,2)\n        array([[ 0.14022471,  0.96360618],  #random\n               [ 0.37601032,  0.25528411],  #random\n               [ 0.49313049,  0.94909878]]) #random\n\n        ";
-static char __pyx_k_215[] = "RandomState.randn (line 1223)";
-static char __pyx_k_216[] = "\n        randn(d0, d1, ..., dn)\n\n        Return a sample (or samples) from the \"standard normal\" distribution.\n\n        If positive, int_like or int-convertible arguments are provided,\n        `randn` generates an array of shape ``(d0, d1, ..., dn)``, filled\n        with random floats sampled from a univariate \"normal\" (Gaussian)\n        distribution of mean 0 and variance 1 (if any of the :math:`d_i` are\n        floats, they are first converted to integers by truncation). A single\n        float randomly sampled from the distribution is returned if no\n        argument is provided.\n\n        This is a convenience function.  If you want an interface that takes a\n        tuple as the first argument, use `numpy.random.standard_normal` instead.\n\n        Parameters\n        ----------\n        d0, d1, ..., dn : int, optional\n            The dimensions of the returned array, should be all positive.\n            If no argument is given a single Python float is returned.\n\n        Returns\n        -------\n        Z : ndarray or float\n            A ``(d0, d1, ..., dn)``-shaped array of floating-point samples from\n            the standard normal distribution, or a single such float if\n            no parameters were supplied.\n\n        See Also\n        --------\n        random.standard_normal : Similar, but takes a tuple as its argument.\n\n        Notes\n        -----\n        For random samples from :math:`N(\\mu, \\sigma^2)`, use:\n\n        ``sigma * np.random.randn(...) + mu``\n\n        Examples\n        --------\n        >>> np.random.randn()\n        2.1923875335537315 #random\n\n        Two-by-four array of samples from N(3, 6.25):\n\n        >>> 2.5 * np.random.randn(2, 4) + 3\n        array([[-4.49401501,  4.00950034, -1.81814867,  7.29718677],  #random\n               [ 0.39924804,  4.68456316,  4.99394529,  4.84057254]]) #random\n\n        ";
-static char __pyx_k_217[] = "RandomState.random_integers (line 1280)";
-static char __pyx_k_218[] = "\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_219[] = "RandomState.standard_normal (line 1358)";
-static char __pyx_k_220[] = "\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_221[] = "RandomState.normal (line 1390)";
-static char __pyx_k_222[] = "\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_223[] = "RandomState.standard_exponential (line 1603)";
-static char __pyx_k_224[] = "\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_225[] = "RandomState.standard_gamma (line 1631)";
-static char __pyx_k_226[] = "\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_227[] = "RandomState.gamma (line 1713)";
-static char __pyx_k_228[] = "\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_229[] = "RandomState.f (line 1804)";
-static char __pyx_k_230[] = "\n        f(dfnum, dfden, size=None)\n\n        Draw samples from a F distribution.\n\n        Samples are drawn from an F distribution with specified parameters,\n        `dfnum` (degrees of freedom in numerator) and `dfden` (degrees of freedom\n        in denominator), where both parameters should be greater than zero.\n\n        The random variate of the F distribution (also known as the\n        Fisher distribution) is a continuous probability distribution\n        that arises in ANOVA tests, and is the ratio of two chi-square\n        variates.\n\n        Parameters\n        ----------\n        dfnum : float\n            Degrees of freedom in numerator. Should be greater than zero.\n        dfden : float\n            Degrees of freedom in denominator. Should be greater than zero.\n        size : {tuple, int}, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``,\n            then ``m * n * k`` samples are drawn. By default only one sample\n            is returned.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n            Samples from the Fisher distribution.\n\n        See Also\n        --------\n        scipy.stats.distributions.f : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The F statistic is used to compare in-group variances to between-group\n        variances. Calculating the distribution depends on the sampling, and\n        so it is a function of the respective degrees of freedom in the\n        problem.  The variable `dfnum` is the number of samples minus one, the\n        between-groups degrees of freedom, while `dfden` is the within-groups\n        degrees of freedom, the sum of the number of samples in each group\n        minus the number of groups.\n\n        References\n        ----------\n        .. [1] Glantz, Stanton A. \"Primer of Biostatistics.\", McGraw-Hill,\n               Fifth Edition, 2002.""\n        .. [2] Wikipedia, \"F-distribution\",\n               http://en.wikipedia.org/wiki/F-distribution\n\n        Examples\n        --------\n        An example from Glantz[1], pp 47-40.\n        Two groups, children of diabetics (25 people) and children from people\n        without diabetes (25 controls). Fasting blood glucose was measured,\n        case group had a mean value of 86.1, controls had a mean value of\n        82.2. Standard deviations were 2.09 and 2.49 respectively. Are these\n        data consistent with the null hypothesis that the parents diabetic\n        status does not affect their children's blood glucose levels?\n        Calculating the F statistic from the data gives a value of 36.01.\n\n        Draw samples from the distribution:\n\n        >>> dfnum = 1. # between group degrees of freedom\n        >>> dfden = 48. # within groups degrees of freedom\n        >>> s = np.random.f(dfnum, dfden, 1000)\n\n        The lower bound for the top 1% of the samples is :\n\n        >>> sort(s)[-10]\n        7.61988120985\n\n        So there is about a 1% chance that the F statistic will exceed 7.62,\n        the measured value is 36, so the null hypothesis is rejected at the 1%\n        level.\n\n        ";
-static char __pyx_k_231[] = "RandomState.noncentral_f (line 1906)";
-static char __pyx_k_232[] = "\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_233[] = "RandomState.chisquare (line 2001)";
-static char __pyx_k_234[] = "\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_235[] = "RandomState.noncentral_chisquare (line 2079)";
-static char __pyx_k_236[] = "\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_237[] = "RandomState.standard_cauchy (line 2171)";
-static char __pyx_k_238[] = "\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_239[] = "RandomState.standard_t (line 2232)";
-static char __pyx_k_240[] = "\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_241[] = "RandomState.vonmises (line 2333)";
-static char __pyx_k_242[] = "\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_243[] = "RandomState.pareto (line 2427)";
-static char __pyx_k_244[] = "\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_245[] = "RandomState.weibull (line 2523)";
-static char __pyx_k_246[] = "\n        weibull(a, size=None)\n\n        Weibull distribution.\n\n        Draw samples from a 1-parameter Weibull distribution with the given\n        shape parameter `a`.\n\n        .. math:: X = (-ln(U))^{1/a}\n\n        Here, U is drawn from the uniform distribution over (0,1].\n\n        The more common 2-parameter Weibull, including a scale parameter\n        :math:`\\lambda` is just :math:`X = \\lambda(-ln(U))^{1/a}`.\n\n        Parameters\n        ----------\n        a : float\n            Shape of the distribution.\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        See Also\n        --------\n        scipy.stats.distributions.weibull_max\n        scipy.stats.distributions.weibull_min\n        scipy.stats.distributions.genextreme\n        gumbel\n\n        Notes\n        -----\n        The Weibull (or Type III asymptotic extreme value distribution for smallest\n        values, SEV Type III, or Rosin-Rammler distribution) is one of a class of\n        Generalized Extreme Value (GEV) distributions used in modeling extreme\n        value problems.  This class includes the Gumbel and Frechet distributions.\n\n        The probability density for the Weibull distribution is\n\n        .. math:: p(x) = \\frac{a}\n                         {\\lambda}(\\frac{x}{\\lambda})^{a-1}e^{-(x/\\lambda)^a},\n\n        where :math:`a` is the shape and :math:`\\lambda` the scale.\n\n        The function has its peak (the mode) at\n        :math:`\\lambda(\\frac{a-1}{a})^{1/a}`.\n\n        When ``a = 1``, the Weibull distribution reduces to the exponential\n        distribution.\n\n        References\n        ----------\n        .. [1] Waloddi Weibull, Professor, Royal Technical University, Stockholm,\n               1939 \"A Statistical Theory Of The Strength Of Materials\",\n               Ingeniorsvetenskapsakademiens Handlingar Nr 151, 1939,\n               General""stabens Litografiska Anstalts Forlag, Stockholm.\n        .. [2] Waloddi Weibull, 1951 \"A Statistical Distribution Function of Wide\n               Applicability\",  Journal Of Applied Mechanics ASME Paper.\n        .. [3] Wikipedia, \"Weibull distribution\",\n               http://en.wikipedia.org/wiki/Weibull_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a = 5. # shape\n        >>> s = np.random.weibull(a, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> x = np.arange(1,100.)/50.\n        >>> def weib(x,n,a):\n        ...     return (a / n) * (x / n)**(a - 1) * np.exp(-(x / n)**a)\n\n        >>> count, bins, ignored = plt.hist(np.random.weibull(5.,1000))\n        >>> x = np.arange(1,100.)/50.\n        >>> scale = count.max()/weib(x, 1., 5.).max()\n        >>> plt.plot(x, weib(x, 1., 5.)*scale)\n        >>> plt.show()\n\n        ";
-static char __pyx_k_247[] = "RandomState.power (line 2623)";
-static char __pyx_k_248[] = "\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_249[] = "RandomState.laplace (line 2732)";
-static char __pyx_k_250[] = "\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_251[] = "RandomState.gumbel (line 2822)";
-static char __pyx_k_252[] = "\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_253[] = "RandomState.logistic (line 2953)";
-static char __pyx_k_254[] = "\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_255[] = "RandomState.lognormal (line 3041)";
-static char __pyx_k_256[] = "\n        lognormal(mean=0.0, sigma=1.0, size=None)\n\n        Return samples drawn from a log-normal distribution.\n\n        Draw samples from a log-normal distribution with specified mean,\n        standard deviation, and array shape.  Note that the mean and standard\n        deviation are not the values for the distribution itself, but of the\n        underlying normal distribution it is derived from.\n\n        Parameters\n        ----------\n        mean : float\n            Mean value of the underlying normal distribution\n        sigma : float, > 0.\n            Standard deviation of the underlying normal distribution\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : ndarray or float\n            The desired samples. An array of the same shape as `size` if given,\n            if `size` is None a float is returned.\n\n        See Also\n        --------\n        scipy.stats.lognorm : probability density function, distribution,\n            cumulative density function, etc.\n\n        Notes\n        -----\n        A variable `x` has a log-normal distribution if `log(x)` is normally\n        distributed.  The probability density function for the log-normal\n        distribution is:\n\n        .. math:: p(x) = \\frac{1}{\\sigma x \\sqrt{2\\pi}}\n                         e^{(-\\frac{(ln(x)-\\mu)^2}{2\\sigma^2})}\n\n        where :math:`\\mu` is the mean and :math:`\\sigma` is the standard\n        deviation of the normally distributed logarithm of the variable.\n        A log-normal distribution results if a random variable is the *product*\n        of a large number of independent, identically-distributed variables in\n        the same way that a normal distribution results if the variable is the\n        *sum* of a large number of independent, identically-distributed\n        variables.\n\n        Reference""s\n        ----------\n        Limpert, E., Stahel, W. A., and Abbt, M., \"Log-normal Distributions\n        across the Sciences: Keys and Clues,\" *BioScience*, Vol. 51, No. 5,\n        May, 2001.  http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf\n\n        Reiss, R.D. and Thomas, M., *Statistical Analysis of Extreme Values*,\n        Basel: Birkhauser Verlag, 2001, pp. 31-32.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, sigma = 3., 1. # mean and standard deviation\n        >>> s = np.random.lognormal(mu, sigma, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='mid')\n\n        >>> x = np.linspace(min(bins), max(bins), 10000)\n        >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n        ...        / (x * sigma * np.sqrt(2 * np.pi)))\n\n        >>> plt.plot(x, pdf, linewidth=2, color='r')\n        >>> plt.axis('tight')\n        >>> plt.show()\n\n        Demonstrate that taking the products of random samples from a uniform\n        distribution can be fit well by a log-normal probability density function.\n\n        >>> # Generate a thousand samples: each is the product of 100 random\n        >>> # values, drawn from a normal distribution.\n        >>> b = []\n        >>> for i in range(1000):\n        ...    a = 10. + np.random.random(100)\n        ...    b.append(np.product(a))\n\n        >>> b = np.array(b) / np.min(b) # scale values to be positive\n        >>> count, bins, ignored = plt.hist(b, 100, normed=True, align='center')\n        >>> sigma = np.std(np.log(b))\n        >>> mu = np.mean(np.log(b))\n\n        >>> x = np.linspace(min(bins), max(bins), 10000)\n        >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n        ...        / (x * sigma * np.sqrt(2 * np.pi)))\n\n        >>> plt.plot(x, pdf, co""lor='r', linewidth=2)\n        >>> plt.show()\n\n        ";
-static char __pyx_k_257[] = "RandomState.rayleigh (line 3162)";
-static char __pyx_k_258[] = "\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_259[] = "RandomState.wald (line 3234)";
-static char __pyx_k_260[] = "\n        wald(mean, scale, size=None)\n\n        Draw samples from a Wald, or Inverse Gaussian, distribution.\n\n        As the scale approaches infinity, the distribution becomes more like a\n        Gaussian.\n\n        Some references claim that the Wald is an Inverse Gaussian with mean=1, but\n        this is by no means universal.\n\n        The Inverse Gaussian distribution was first studied in relationship to\n        Brownian motion. In 1956 M.C.K. Tweedie used the name Inverse Gaussian\n        because there is an inverse relationship between the time to cover a unit\n        distance and distance covered in unit time.\n\n        Parameters\n        ----------\n        mean : scalar\n            Distribution mean, should be > 0.\n        scale : scalar\n            Scale parameter, should be >= 0.\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single value is\n            returned.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            Drawn sample, all greater than zero.\n\n        Notes\n        -----\n        The probability density function for the Wald distribution is\n\n        .. math:: P(x;mean,scale) = \\sqrt{\\frac{scale}{2\\pi x^3}}e^\n                                    \\frac{-scale(x-mean)^2}{2\\cdotp mean^2x}\n\n        As noted above the Inverse Gaussian distribution first arise from attempts\n        to model Brownian Motion. It is also a competitor to the Weibull for use in\n        reliability modeling and modeling stock returns and interest rate\n        processes.\n\n        References\n        ----------\n        .. [1] Brighton Webs Ltd., Wald Distribution,\n              http://www.brighton-webs.co.uk/distributions/wald.asp\n        .. [2] Chhikara, Raj S., and Folks, J. Leroy, \"The Inverse Gaussian\n              Distribution: Theory : Methodology, and Applications\", CRC Press,\n              1988.\n        .. [3] Wikipedia, \"Wald distribu""tion\"\n              http://en.wikipedia.org/wiki/Wald_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram:\n\n        >>> import matplotlib.pyplot as plt\n        >>> h = plt.hist(np.random.wald(3, 2, 100000), bins=200, normed=True)\n        >>> plt.show()\n\n        ";
-static char __pyx_k_261[] = "RandomState.triangular (line 3320)";
-static char __pyx_k_262[] = "\n        triangular(left, mode, right, size=None)\n\n        Draw samples from the triangular distribution.\n\n        The triangular distribution is a continuous probability distribution with\n        lower limit left, peak at mode, and upper limit right. Unlike the other\n        distributions, these parameters directly define the shape of the pdf.\n\n        Parameters\n        ----------\n        left : scalar\n            Lower limit.\n        mode : scalar\n            The value where the peak of the distribution occurs.\n            The value should fulfill the condition ``left <= mode <= right``.\n        right : scalar\n            Upper limit, should be larger than `left`.\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single value is\n            returned.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            The returned samples all lie in the interval [left, right].\n\n        Notes\n        -----\n        The probability density function for the Triangular distribution is\n\n        .. math:: P(x;l, m, r) = \\begin{cases}\n                  \\frac{2(x-l)}{(r-l)(m-l)}& \\text{for $l \\leq x \\leq m$},\\\\\n                  \\frac{2(m-x)}{(r-l)(r-m)}& \\text{for $m \\leq x \\leq r$},\\\\\n                  0& \\text{otherwise}.\n                  \\end{cases}\n\n        The triangular distribution is often used in ill-defined problems where the\n        underlying distribution is not known, but some knowledge of the limits and\n        mode exists. Often it is used in simulations.\n\n        References\n        ----------\n        .. [1] Wikipedia, \"Triangular distribution\"\n              http://en.wikipedia.org/wiki/Triangular_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram:\n\n        >>> import matplotlib.pyplot as plt\n        >>> h = plt.hist(np.random.triangular(-3, 0, 8, 100000), bins=""200,\n        ...              normed=True)\n        >>> plt.show()\n\n        ";
-static char __pyx_k_263[] = "RandomState.binomial (line 3408)";
-static char __pyx_k_264[] = "\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_265[] = "RandomState.negative_binomial (line 3516)";
-static char __pyx_k_266[] = "\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_267[] = "RandomState.poisson (line 3611)";
-static char __pyx_k_268[] = "\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_269[] = "RandomState.zipf (line 3682)";
-static char __pyx_k_270[] = "\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_271[] = "RandomState.geometric (line 3770)";
-static char __pyx_k_272[] = "\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_273[] = "RandomState.hypergeometric (line 3836)";
-static char __pyx_k_274[] = "\n        hypergeometric(ngood, nbad, nsample, size=None)\n\n        Draw samples from a Hypergeometric distribution.\n\n        Samples are drawn from a Hypergeometric distribution with specified\n        parameters, ngood (ways to make a good selection), nbad (ways to make\n        a bad selection), and nsample = number of items sampled, which is less\n        than or equal to the sum ngood + nbad.\n\n        Parameters\n        ----------\n        ngood : int or array_like\n            Number of ways to make a good selection.  Must be nonnegative.\n        nbad : int or array_like\n            Number of ways to make a bad selection.  Must be nonnegative.\n        nsample : int or array_like\n            Number of items sampled.  Must be at least 1 and at most\n            ``ngood + nbad``.\n        size : int or tuple of int\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            The values are all integers in  [0, n].\n\n        See Also\n        --------\n        scipy.stats.distributions.hypergeom : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Hypergeometric distribution is\n\n        .. math:: P(x) = \\frac{\\binom{m}{n}\\binom{N-m}{n-x}}{\\binom{N}{n}},\n\n        where :math:`0 \\le x \\le m` and :math:`n+m-N \\le x \\le n`\n\n        for P(x) the probability of x successes, n = ngood, m = nbad, and\n        N = number of samples.\n\n        Consider an urn with black and white marbles in it, ngood of them\n        black and nbad are white. If you draw nsample balls without\n        replacement, then the Hypergeometric distribution describes the\n        distribution of black balls in the drawn sample.\n\n        Note that this distribution is very similar to the Binomial\n        distrib""ution, except that in this case, samples are drawn without\n        replacement, whereas in the Binomial case samples are drawn with\n        replacement (or the sample space is infinite). As the sample space\n        becomes large, this distribution approaches the Binomial.\n\n        References\n        ----------\n        .. [1] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n               and Quigley, 1972.\n        .. [2] Weisstein, Eric W. \"Hypergeometric Distribution.\" From\n               MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/HypergeometricDistribution.html\n        .. [3] Wikipedia, \"Hypergeometric-distribution\",\n               http://en.wikipedia.org/wiki/Hypergeometric-distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> ngood, nbad, nsamp = 100, 2, 10\n        # number of good, number of bad, and number of samples\n        >>> s = np.random.hypergeometric(ngood, nbad, nsamp, 1000)\n        >>> hist(s)\n        #   note that it is very unlikely to grab both bad items\n\n        Suppose you have an urn with 15 white and 15 black marbles.\n        If you pull 15 marbles at random, how likely is it that\n        12 or more of them are one color?\n\n        >>> s = np.random.hypergeometric(15, 15, 15, 100000)\n        >>> sum(s>=12)/100000. + sum(s<=3)/100000.\n        #   answer = 0.003 ... pretty unlikely!\n\n        ";
-static char __pyx_k_275[] = "RandomState.logseries (line 3955)";
-static char __pyx_k_276[] = "\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_277[] = "RandomState.multivariate_normal (line 4050)";
-static char __pyx_k_278[] = "\n        multivariate_normal(mean, cov[, size])\n\n        Draw random samples from a multivariate normal distribution.\n\n        The multivariate normal, multinormal or Gaussian distribution is a\n        generalization of the one-dimensional normal distribution to higher\n        dimensions.  Such a distribution is specified by its mean and\n        covariance matrix.  These parameters are analogous to the mean\n        (average or \"center\") and variance (standard deviation, or \"width,\"\n        squared) of the one-dimensional normal distribution.\n\n        Parameters\n        ----------\n        mean : 1-D array_like, of length N\n            Mean of the N-dimensional distribution.\n        cov : 2-D array_like, of shape (N, N)\n            Covariance matrix of the distribution.  Must be symmetric and\n            positive semi-definite for \"physically meaningful\" results.\n        size : int or tuple of ints, optional\n            Given a shape of, for example, ``(m,n,k)``, ``m*n*k`` samples are\n            generated, and packed in an `m`-by-`n`-by-`k` arrangement.  Because\n            each sample is `N`-dimensional, the output shape is ``(m,n,k,N)``.\n            If no shape is specified, a single (`N`-D) sample is returned.\n\n        Returns\n        -------\n        out : ndarray\n            The drawn samples, of shape *size*, if that was provided.  If not,\n            the shape is ``(N,)``.\n\n            In other words, each entry ``out[i,j,...,:]`` is an N-dimensional\n            value drawn from the distribution.\n\n        Notes\n        -----\n        The mean is a coordinate in N-dimensional space, which represents the\n        location where samples are most likely to be generated.  This is\n        analogous to the peak of the bell curve for the one-dimensional or\n        univariate normal distribution.\n\n        Covariance indicates the level to which two variables vary together.\n        From the multivariate normal distribution, w""e draw N-dimensional\n        samples, :math:`X = [x_1, x_2, ... x_N]`.  The covariance matrix\n        element :math:`C_{ij}` is the covariance of :math:`x_i` and :math:`x_j`.\n        The element :math:`C_{ii}` is the variance of :math:`x_i` (i.e. its\n        \"spread\").\n\n        Instead of specifying the full covariance matrix, popular\n        approximations include:\n\n          - Spherical covariance (*cov* is a multiple of the identity matrix)\n          - Diagonal covariance (*cov* has non-negative elements, and only on\n            the diagonal)\n\n        This geometrical property can be seen in two dimensions by plotting\n        generated data-points:\n\n        >>> mean = [0,0]\n        >>> cov = [[1,0],[0,100]] # diagonal covariance, points lie on x or y-axis\n\n        >>> import matplotlib.pyplot as plt\n        >>> x,y = np.random.multivariate_normal(mean,cov,5000).T\n        >>> plt.plot(x,y,'x'); plt.axis('equal'); plt.show()\n\n        Note that the covariance matrix must be non-negative definite.\n\n        References\n        ----------\n        Papoulis, A., *Probability, Random Variables, and Stochastic Processes*,\n        3rd ed., New York: McGraw-Hill, 1991.\n\n        Duda, R. O., Hart, P. E., and Stork, D. G., *Pattern Classification*,\n        2nd ed., New York: Wiley, 2001.\n\n        Examples\n        --------\n        >>> mean = (1,2)\n        >>> cov = [[1,0],[1,0]]\n        >>> x = np.random.multivariate_normal(mean,cov,(3,3))\n        >>> x.shape\n        (3, 3, 2)\n\n        The following is probably true, given that 0.6 is roughly twice the\n        standard deviation:\n\n        >>> print list( (x[0,0,:] - mean) < 0.6 )\n        [True, True]\n\n        ";
-static char __pyx_k_279[] = "RandomState.multinomial (line 4182)";
-static char __pyx_k_280[] = "\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_281[] = "RandomState.dirichlet (line 4275)";
-static char __pyx_k_282[] = "\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_283[] = "RandomState.shuffle (line 4391)";
-static char __pyx_k_284[] = "\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_285[] = "RandomState.permutation (line 4449)";
-static char __pyx_k_286[] = "\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        ";
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-static char __pyx_k__low[] = "low";
-static char __pyx_k__max[] = "max";
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-static char __pyx_k__copy[] = "copy";
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-static char __pyx_k__rand[] = "rand";
-static char __pyx_k__seed[] = "seed";
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-static char __pyx_k__size[] = "size";
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-static char __pyx_k__sqrt[] = "sqrt";
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-static char __pyx_k__dtype[] = "dtype";
-static char __pyx_k__empty[] = "empty";
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-static char __pyx_k__gamma[] = "gamma";
-static char __pyx_k__iinfo[] = "iinfo";
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-static char __pyx_k__kappa[] = "kappa";
-static char __pyx_k__ndmin[] = "ndmin";
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-static char __pyx_k__right[] = "right";
-static char __pyx_k__scale[] = "scale";
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-static char __pyx_k__sigma[] = "sigma";
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-static char __pyx_k__arange[] = "arange";
-static char __pyx_k__choice[] = "choice";
-static char __pyx_k__cumsum[] = "cumsum";
-static char __pyx_k__double[] = "double";
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-static char __pyx_k__random[] = "random";
-static char __pyx_k__reduce[] = "reduce";
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-static char __pyx_k__geometric[] = "geometric";
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-static char __pyx_k__lognormal[] = "lognormal";
-static char __pyx_k__logseries[] = "logseries";
-static char __pyx_k__set_state[] = "set_state";
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-static char __pyx_k__exponential[] = "exponential";
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-static char __pyx_k__permutation[] = "permutation";
-static char __pyx_k__noncentral_f[] = "noncentral_f";
-static char __pyx_k__return_index[] = "return_index";
-static char __pyx_k__searchsorted[] = "searchsorted";
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-static char __pyx_k__random_integers[] = "random_integers";
-static char __pyx_k__standard_cauchy[] = "standard_cauchy";
-static char __pyx_k__standard_normal[] = "standard_normal";
-static char __pyx_k__negative_binomial[] = "negative_binomial";
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-static char __pyx_k__multivariate_normal[] = "multivariate_normal";
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-static PyObject *__pyx_kp_s_32;
-static PyObject *__pyx_kp_s_34;
-static PyObject *__pyx_kp_s_36;
-static PyObject *__pyx_kp_s_44;
-static PyObject *__pyx_kp_s_47;
-static PyObject *__pyx_kp_s_49;
-static PyObject *__pyx_kp_s_56;
-static PyObject *__pyx_kp_s_66;
-static PyObject *__pyx_kp_s_68;
-static PyObject *__pyx_kp_s_70;
-static PyObject *__pyx_kp_s_73;
-static PyObject *__pyx_kp_s_78;
-static PyObject *__pyx_kp_s_82;
-static PyObject *__pyx_kp_s_84;
-static PyObject *__pyx_kp_s_89;
-static PyObject *__pyx_kp_s_9;
-static PyObject *__pyx_n_s__MT19937;
-static PyObject *__pyx_n_s__TypeError;
-static PyObject *__pyx_n_s__ValueError;
-static PyObject *__pyx_n_s____RandomState_ctor;
-static PyObject *__pyx_n_s____import__;
-static PyObject *__pyx_n_s____main__;
-static PyObject *__pyx_n_s____test__;
-static PyObject *__pyx_n_s___rand;
-static PyObject *__pyx_n_s__a;
-static PyObject *__pyx_n_s__add;
-static PyObject *__pyx_n_s__allclose;
-static PyObject *__pyx_n_s__alpha;
-static PyObject *__pyx_n_s__any;
-static PyObject *__pyx_n_s__arange;
-static PyObject *__pyx_n_s__array;
-static PyObject *__pyx_n_s__asarray;
-static PyObject *__pyx_n_s__b;
-static PyObject *__pyx_n_s__beta;
-static PyObject *__pyx_n_s__binomial;
-static PyObject *__pyx_n_s__bytes;
-static PyObject *__pyx_n_s__chisquare;
-static PyObject *__pyx_n_s__choice;
-static PyObject *__pyx_n_s__copy;
-static PyObject *__pyx_n_s__cov;
-static PyObject *__pyx_n_s__cumsum;
-static PyObject *__pyx_n_s__df;
-static PyObject *__pyx_n_s__dfden;
-static PyObject *__pyx_n_s__dfnum;
-static PyObject *__pyx_n_s__dirichlet;
-static PyObject *__pyx_n_s__dot;
-static PyObject *__pyx_n_s__double;
-static PyObject *__pyx_n_s__dtype;
-static PyObject *__pyx_n_s__empty;
-static PyObject *__pyx_n_s__equal;
-static PyObject *__pyx_n_s__exponential;
-static PyObject *__pyx_n_s__f;
-static PyObject *__pyx_n_s__float64;
-static PyObject *__pyx_n_s__gamma;
-static PyObject *__pyx_n_s__geometric;
-static PyObject *__pyx_n_s__get_state;
-static PyObject *__pyx_n_s__greater;
-static PyObject *__pyx_n_s__greater_equal;
-static PyObject *__pyx_n_s__gumbel;
-static PyObject *__pyx_n_s__high;
-static PyObject *__pyx_n_s__hypergeometric;
-static PyObject *__pyx_n_s__iinfo;
-static PyObject *__pyx_n_s__index;
-static PyObject *__pyx_n_s__int;
-static PyObject *__pyx_n_s__integer;
-static PyObject *__pyx_n_s__intp;
-static PyObject *__pyx_n_s__item;
-static PyObject *__pyx_n_s__kappa;
-static PyObject *__pyx_n_s__l;
-static PyObject *__pyx_n_s__lam;
-static PyObject *__pyx_n_s__laplace;
-static PyObject *__pyx_n_s__left;
-static PyObject *__pyx_n_s__less;
-static PyObject *__pyx_n_s__less_equal;
-static PyObject *__pyx_n_s__loc;
-static PyObject *__pyx_n_s__logistic;
-static PyObject *__pyx_n_s__lognormal;
-static PyObject *__pyx_n_s__logseries;
-static PyObject *__pyx_n_s__low;
-static PyObject *__pyx_n_s__max;
-static PyObject *__pyx_n_s__mean;
-static PyObject *__pyx_n_s__mode;
-static PyObject *__pyx_n_s__mu;
-static PyObject *__pyx_n_s__multinomial;
-static PyObject *__pyx_n_s__multiply;
-static PyObject *__pyx_n_s__multivariate_normal;
-static PyObject *__pyx_n_s__n;
-static PyObject *__pyx_n_s__nbad;
-static PyObject *__pyx_n_s__ndarray;
-static PyObject *__pyx_n_s__ndim;
-static PyObject *__pyx_n_s__ndmin;
-static PyObject *__pyx_n_s__negative_binomial;
-static PyObject *__pyx_n_s__ngood;
-static PyObject *__pyx_n_s__nonc;
-static PyObject *__pyx_n_s__noncentral_f;
-static PyObject *__pyx_n_s__normal;
-static PyObject *__pyx_n_s__np;
-static PyObject *__pyx_n_s__nsample;
-static PyObject *__pyx_n_s__numpy;
-static PyObject *__pyx_n_s__operator;
-static PyObject *__pyx_n_s__p;
-static PyObject *__pyx_n_s__pareto;
-static PyObject *__pyx_n_s__permutation;
-static PyObject *__pyx_n_s__poisson;
-static PyObject *__pyx_n_s__poisson_lam_max;
-static PyObject *__pyx_n_s__power;
-static PyObject *__pyx_n_s__prod;
-static PyObject *__pyx_n_s__pvals;
-static PyObject *__pyx_n_s__rand;
-static PyObject *__pyx_n_s__randint;
-static PyObject *__pyx_n_s__randn;
-static PyObject *__pyx_n_s__random;
-static PyObject *__pyx_n_s__random_integers;
-static PyObject *__pyx_n_s__random_sample;
-static PyObject *__pyx_n_s__ravel;
-static PyObject *__pyx_n_s__rayleigh;
-static PyObject *__pyx_n_s__reduce;
-static PyObject *__pyx_n_s__replace;
-static PyObject *__pyx_n_s__return_index;
-static PyObject *__pyx_n_s__right;
-static PyObject *__pyx_n_s__scale;
-static PyObject *__pyx_n_s__searchsorted;
-static PyObject *__pyx_n_s__seed;
-static PyObject *__pyx_n_s__set_state;
-static PyObject *__pyx_n_s__shape;
-static PyObject *__pyx_n_s__shuffle;
-static PyObject *__pyx_n_s__side;
-static PyObject *__pyx_n_s__sigma;
-static PyObject *__pyx_n_s__size;
-static PyObject *__pyx_n_s__sort;
-static PyObject *__pyx_n_s__sqrt;
-static PyObject *__pyx_n_s__standard_cauchy;
-static PyObject *__pyx_n_s__standard_gamma;
-static PyObject *__pyx_n_s__standard_normal;
-static PyObject *__pyx_n_s__standard_t;
-static PyObject *__pyx_n_s__subtract;
-static PyObject *__pyx_n_s__sum;
-static PyObject *__pyx_n_s__svd;
-static PyObject *__pyx_n_s__take;
-static PyObject *__pyx_n_s__triangular;
-static PyObject *__pyx_n_s__uint;
-static PyObject *__pyx_n_s__uint32;
-static PyObject *__pyx_n_s__uniform;
-static PyObject *__pyx_n_s__unique;
-static PyObject *__pyx_n_s__vonmises;
-static PyObject *__pyx_n_s__wald;
-static PyObject *__pyx_n_s__weibull;
-static PyObject *__pyx_n_s__zeros;
-static PyObject *__pyx_n_s__zipf;
+static 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_df[] = "df";
+static char __pyx_k_mu[] = "mu";
+static char __pyx_k_np[] = "np";
+static char __pyx_k_a_0[] = "a <= 0";
+static char __pyx_k_add[] = "add";
+static char __pyx_k_any[] = "any";
+static char __pyx_k_b_0[] = "b <= 0";
+static char __pyx_k_cov[] = "cov";
+static char __pyx_k_dot[] = "dot";
+static char __pyx_k_int[] = "int";
+static char __pyx_k_lam[] = "lam";
+static char __pyx_k_loc[] = "loc";
+static char __pyx_k_low[] = "low";
+static char __pyx_k_max[] = "max";
+static char __pyx_k_n_0[] = "n < 0";
+static char __pyx_k_p_0[] = "p < 0";
+static char __pyx_k_p_1[] = "p > 1";
+static char __pyx_k_svd[] = "svd";
+static char __pyx_k_beta[] = "beta";
+static char __pyx_k_copy[] = "copy";
+static char __pyx_k_df_0[] = "df <= 0";
+static char __pyx_k_df_1[] = "df <= 1";
+static char __pyx_k_high[] = "high";
+static char __pyx_k_intp[] = "intp";
+static char __pyx_k_item[] = "item";
+static char __pyx_k_left[] = "left";
+static char __pyx_k_less[] = "less";
+static char __pyx_k_main[] = "__main__";
+static char __pyx_k_mean[] = "mean";
+static char __pyx_k_mode[] = "mode";
+static char __pyx_k_nbad[] = "nbad";
+static char __pyx_k_ndim[] = "ndim";
+static char __pyx_k_nonc[] = "nonc";
+static char __pyx_k_prod[] = "prod";
+static char __pyx_k_rand[] = "rand";
+static char __pyx_k_seed[] = "seed";
+static char __pyx_k_side[] = "side";
+static char __pyx_k_size[] = "size";
+static char __pyx_k_sort[] = "sort";
+static char __pyx_k_sqrt[] = "sqrt";
+static char __pyx_k_take[] = "take";
+static char __pyx_k_test[] = "__test__";
+static char __pyx_k_uint[] = "uint";
+static char __pyx_k_wald[] = "wald";
+static char __pyx_k_zipf[] = "zipf";
+static char __pyx_k_a_1_0[] = "a <= 1.0";
+static char __pyx_k_alpha[] = "alpha";
+static char __pyx_k_array[] = "array";
+static char __pyx_k_bytes[] = "bytes";
+static char __pyx_k_dfden[] = "dfden";
+static char __pyx_k_dfnum[] = "dfnum";
+static char __pyx_k_dtype[] = "dtype";
+static char __pyx_k_empty[] = "empty";
+static char __pyx_k_equal[] = "equal";
+static char __pyx_k_gamma[] = "gamma";
+static char __pyx_k_iinfo[] = "iinfo";
+static char __pyx_k_index[] = "index";
+static char __pyx_k_kappa[] = "kappa";
+static char __pyx_k_lam_0[] = "lam < 0";
+static char __pyx_k_n_0_2[] = "n <= 0";
+static char __pyx_k_ngood[] = "ngood";
+static char __pyx_k_numpy[] = "numpy";
+static char __pyx_k_p_0_0[] = "p < 0.0";
+static char __pyx_k_p_1_0[] = "p > 1.0";
+static char __pyx_k_power[] = "power";
+static char __pyx_k_pvals[] = "pvals";
+static char __pyx_k_randn[] = "randn";
+static char __pyx_k_ravel[] = "ravel";
+static char __pyx_k_right[] = "right";
+static char __pyx_k_scale[] = "scale";
+static char __pyx_k_shape[] = "shape";
+static char __pyx_k_sigma[] = "sigma";
+static char __pyx_k_zeros[] = "zeros";
+static char __pyx_k_arange[] = "arange";
+static char __pyx_k_choice[] = "choice";
+static char __pyx_k_cumsum[] = "cumsum";
+static char __pyx_k_gumbel[] = "gumbel";
+static char __pyx_k_import[] = "__import__";
+static char __pyx_k_mean_0[] = "mean <= 0";
+static char __pyx_k_nbad_0[] = "nbad < 0";
+static char __pyx_k_nonc_0[] = "nonc < 0";
+static char __pyx_k_normal[] = "normal";
+static char __pyx_k_pareto[] = "pareto";
+static char __pyx_k_rand_2[] = "_rand";
+static char __pyx_k_random[] = "random";
+static char __pyx_k_reduce[] = "reduce";
+static char __pyx_k_uint32[] = "uint32";
+static char __pyx_k_unique[] = "unique";
+static char __pyx_k_MT19937[] = "MT19937";
+static char __pyx_k_asarray[] = "asarray";
+static char __pyx_k_dfden_0[] = "dfden <= 0";
+static char __pyx_k_dfnum_0[] = "dfnum <= 0";
+static char __pyx_k_dfnum_1[] = "dfnum <= 1";
+static char __pyx_k_float64[] = "float64";
+static char __pyx_k_greater[] = "greater";
+static char __pyx_k_integer[] = "integer";
+static char __pyx_k_kappa_0[] = "kappa < 0";
+static char __pyx_k_laplace[] = "laplace";
+static char __pyx_k_ndarray[] = "ndarray";
+static char __pyx_k_ngood_0[] = "ngood < 0";
+static char __pyx_k_nsample[] = "nsample";
+static char __pyx_k_p_0_0_2[] = "p <= 0.0";
+static char __pyx_k_p_1_0_2[] = "p >= 1.0";
+static char __pyx_k_poisson[] = "poisson";
+static char __pyx_k_randint[] = "randint";
+static char __pyx_k_replace[] = "replace";
+static char __pyx_k_scale_0[] = "scale <= 0";
+static char __pyx_k_shape_0[] = "shape <= 0";
+static char __pyx_k_shuffle[] = "shuffle";
+static char __pyx_k_sigma_0[] = "sigma <= 0";
+static char __pyx_k_uniform[] = "uniform";
+static char __pyx_k_weibull[] = "weibull";
+static char __pyx_k_binomial[] = "binomial";
+static char __pyx_k_logistic[] = "logistic";
+static char __pyx_k_low_high[] = "low >= high";
+static char __pyx_k_mean_0_0[] = "mean <= 0.0";
+static char __pyx_k_multiply[] = "multiply";
+static char __pyx_k_nonc_0_2[] = "nonc <= 0";
+static char __pyx_k_operator[] = "operator";
+static char __pyx_k_rayleigh[] = "rayleigh";
+static char __pyx_k_subtract[] = "subtract";
+static char __pyx_k_vonmises[] = "vonmises";
+static char __pyx_k_TypeError[] = "TypeError";
+static char __pyx_k_chisquare[] = "chisquare";
+static char __pyx_k_dirichlet[] = "dirichlet";
+static char __pyx_k_geometric[] = "geometric";
+static char __pyx_k_get_state[] = "get_state";
+static char __pyx_k_left_mode[] = "left > mode";
+static char __pyx_k_lognormal[] = "lognormal";
+static char __pyx_k_logseries[] = "logseries";
+static char __pyx_k_nsample_1[] = "nsample < 1";
+static char __pyx_k_scale_0_0[] = "scale <= 0.0";
+static char __pyx_k_set_state[] = "set_state";
+static char __pyx_k_sigma_0_0[] = "sigma <= 0.0";
+static char __pyx_k_ValueError[] = "ValueError";
+static char __pyx_k_left_right[] = "left == right";
+static char __pyx_k_less_equal[] = "less_equal";
+static char __pyx_k_logical_or[] = "logical_or";
+static char __pyx_k_mode_right[] = "mode > right";
+static char __pyx_k_numpy_dual[] = "numpy.dual";
+static char __pyx_k_standard_t[] = "standard_t";
+static char __pyx_k_triangular[] = "triangular";
+static char __pyx_k_exponential[] = "exponential";
+static char __pyx_k_multinomial[] = "multinomial";
+static char __pyx_k_permutation[] = "permutation";
+static char __pyx_k_noncentral_f[] = "noncentral_f";
+static char __pyx_k_return_index[] = "return_index";
+static char __pyx_k_searchsorted[] = "searchsorted";
+static char __pyx_k_count_nonzero[] = "count_nonzero";
+static char __pyx_k_greater_equal[] = "greater_equal";
+static char __pyx_k_random_sample[] = "random_sample";
+static char __pyx_k_hypergeometric[] = "hypergeometric";
+static char __pyx_k_standard_gamma[] = "standard_gamma";
+static char __pyx_k_poisson_lam_max[] = "poisson_lam_max";
+static char __pyx_k_random_integers[] = "random_integers";
+static char __pyx_k_standard_cauchy[] = "standard_cauchy";
+static char __pyx_k_standard_normal[] = "standard_normal";
+static char __pyx_k_sum_pvals_1_1_0[] = "sum(pvals[:-1]) > 1.0";
+static char __pyx_k_RandomState_ctor[] = "__RandomState_ctor";
+static char __pyx_k_negative_binomial[] = "negative_binomial";
+static char __pyx_k_ngood_nbad_nsample[] = "ngood + nbad < nsample";
+static char __pyx_k_a_must_be_non_empty[] = "a must be non-empty";
+static char __pyx_k_lam_value_too_large[] = "lam value too large";
+static char __pyx_k_multivariate_normal[] = "multivariate_normal";
+static char __pyx_k_noncentral_chisquare[] = "noncentral_chisquare";
+static char __pyx_k_standard_exponential[] = "standard_exponential";
+static char __pyx_k_lam_value_too_large_2[] = "lam value too large.";
+static char __pyx_k_RandomState_f_line_1807[] = "RandomState.f (line 1807)";
+static char __pyx_k_a_must_be_1_dimensional[] = "a must be 1-dimensional";
+static char __pyx_k_p_must_be_1_dimensional[] = "p must be 1-dimensional";
+static char __pyx_k_state_must_be_624_longs[] = "state must be 624 longs";
+static char __pyx_k_a_must_be_greater_than_0[] = "a must be greater than 0";
+static char __pyx_k_algorithm_must_be_MT19937[] = "algorithm must be 'MT19937'";
+static char __pyx_k_RandomState_bytes_line_892[] = "RandomState.bytes (line 892)";
+static char __pyx_k_RandomState_rand_line_1182[] = "RandomState.rand (line 1182)";
+static char __pyx_k_RandomState_wald_line_3237[] = "RandomState.wald (line 3237)";
+static char __pyx_k_RandomState_zipf_line_3685[] = "RandomState.zipf (line 3685)";
+static char __pyx_k_mean_must_be_1_dimensional[] = "mean must be 1 dimensional";
+static char __pyx_k_RandomState_choice_line_920[] = "RandomState.choice (line 920)";
+static char __pyx_k_RandomState_gamma_line_1716[] = "RandomState.gamma (line 1716)";
+static char __pyx_k_RandomState_power_line_2626[] = "RandomState.power (line 2626)";
+static char __pyx_k_RandomState_randn_line_1226[] = "RandomState.randn (line 1226)";
+static char __pyx_k_a_and_p_must_have_same_size[] = "a and p must have same size";
+static char __pyx_k_RandomState_gumbel_line_2825[] = "RandomState.gumbel (line 2825)";
+static char __pyx_k_RandomState_normal_line_1393[] = "RandomState.normal (line 1393)";
+static char __pyx_k_RandomState_pareto_line_2430[] = "RandomState.pareto (line 2430)";
+static char __pyx_k_RandomState_randint_line_812[] = "RandomState.randint (line 812)";
+static char __pyx_k_RandomState_laplace_line_2735[] = "RandomState.laplace (line 2735)";
+static char __pyx_k_RandomState_poisson_line_3614[] = "RandomState.poisson (line 3614)";
+static char __pyx_k_RandomState_shuffle_line_4395[] = "RandomState.shuffle (line 4395)";
+static char __pyx_k_RandomState_tomaxint_line_765[] = "RandomState.tomaxint (line 765)";
+static char __pyx_k_RandomState_uniform_line_1095[] = "RandomState.uniform (line 1095)";
+static char __pyx_k_RandomState_weibull_line_2526[] = "RandomState.weibull (line 2526)";
+static char __pyx_k_probabilities_do_not_sum_to_1[] = "probabilities do not sum to 1";
+static char __pyx_k_RandomState_binomial_line_3411[] = "RandomState.binomial (line 3411)";
+static char __pyx_k_RandomState_logistic_line_2956[] = "RandomState.logistic (line 2956)";
+static char __pyx_k_RandomState_rayleigh_line_3165[] = "RandomState.rayleigh (line 3165)";
+static char __pyx_k_RandomState_vonmises_line_2336[] = "RandomState.vonmises (line 2336)";
+static char __pyx_k_dirichlet_alpha_size_None_Draw[] = "\n        dirichlet(alpha, size=None)\n\n        Draw samples from the Dirichlet distribution.\n\n        Draw `size` samples of dimension k from a Dirichlet distribution. A\n        Dirichlet-distributed random variable can be seen as a multivariate\n        generalization of a Beta distribution. Dirichlet pdf is the conjugate\n        prior of a multinomial in Bayesian inference.\n\n        Parameters\n        ----------\n        alpha : array\n            Parameter of the distribution (k dimension for sample of\n            dimension k).\n        size : 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_laplace_loc_0_0_scale_1_0_size[] = "\n        laplace(loc=0.0, scale=1.0, size=None)\n\n        Draw samples from the Laplace or double exponential distribution with\n        specified location (or mean) and scale (decay).\n\n        The Laplace distribution is similar to the Gaussian/normal distribution,\n        but is sharper at the peak and has fatter tails. It represents the\n        difference between two independent, identically distributed exponential\n        random variables.\n\n        Parameters\n        ----------\n        loc : float\n            The position, :math:`\\mu`, of the distribution peak.\n        scale : float\n            :math:`\\lambda`, the exponential decay.\n\n        Notes\n        -----\n        It has the probability density function\n\n        .. math:: f(x; \\mu, \\lambda) = \\frac{1}{2\\lambda}\n                                       \\exp\\left(-\\frac{|x - \\mu|}{\\lambda}\\right).\n\n        The first law of Laplace, from 1774, states that the frequency of an error\n        can be expressed as an exponential function of the absolute magnitude of\n        the error, which leads to the Laplace distribution. For many problems in\n        Economics and Health sciences, this distribution seems to model the data\n        better than the standard Gaussian distribution\n\n\n        References\n        ----------\n        .. [1] Abramowitz, M. and Stegun, I. A. (Eds.). Handbook of Mathematical\n               Functions with Formulas, Graphs, and Mathematical Tables, 9th\n               printing.  New York: Dover, 1972.\n\n        .. [2] The Laplace distribution and generalizations\n               By Samuel Kotz, Tomasz J. Kozubowski, Krzysztof Podgorski,\n               Birkhauser, 2001.\n\n        .. [3] Weisstein, Eric W. \"Laplace Distribution.\"\n               From MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/LaplaceDistribution.html\n\n        .. [4] Wikipedia, \"Laplace distribution\",\n               http://en.wikipedia.org/wik""i/Laplace_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution\n\n        >>> loc, scale = 0., 1.\n        >>> s = np.random.laplace(loc, scale, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n        >>> x = np.arange(-8., 8., .01)\n        >>> pdf = np.exp(-abs(x-loc)/scale)/(2.*scale)\n        >>> plt.plot(x, pdf)\n\n        Plot Gaussian for comparison:\n\n        >>> g = (1/(scale * np.sqrt(2 * np.pi)) * \n        ...      np.exp(-(x - loc)**2 / (2 * scale**2)))\n        >>> plt.plot(x,g)\n\n        ";
+static char __pyx_k_permutation_x_Randomly_permute[] = "\n        permutation(x)\n\n        Randomly permute a sequence, or return a permuted range.\n\n        If `x` is a multi-dimensional array, it is only shuffled along its\n        first index.\n\n        Parameters\n        ----------\n        x : int or array_like\n            If `x` is an integer, randomly permute ``np.arange(x)``.\n            If `x` is an array, make a copy and shuffle the elements\n            randomly.\n\n        Returns\n        -------\n        out : ndarray\n            Permuted sequence or array range.\n\n        Examples\n        --------\n        >>> np.random.permutation(10)\n        array([1, 7, 4, 3, 0, 9, 2, 5, 8, 6])\n\n        >>> np.random.permutation([1, 4, 9, 12, 15])\n        array([15,  1,  9,  4, 12])\n\n        >>> arr = np.arange(9).reshape((3, 3))\n        >>> np.random.permutation(arr)\n        array([[6, 7, 8],\n               [0, 1, 2],\n               [3, 4, 5]])\n\n        ";
+static char __pyx_k_poisson_lam_1_0_size_None_Draw[] = "\n        poisson(lam=1.0, size=None)\n\n        Draw samples from a Poisson distribution.\n\n        The Poisson distribution is the limit of the Binomial\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_rand_d0_d1_dn_Random_values_in[] = "\n        rand(d0, d1, ..., dn)\n\n        Random values in a given shape.\n\n        Create an array of the given shape and propagate it with\n        random samples from a uniform distribution\n        over ``[0, 1)``.\n\n        Parameters\n        ----------\n        d0, d1, ..., dn : int, optional\n            The dimensions of the returned array, should all be positive.\n            If no argument is given a single Python float is returned.\n\n        Returns\n        -------\n        out : ndarray, shape ``(d0, d1, ..., dn)``\n            Random values.\n\n        See Also\n        --------\n        random\n\n        Notes\n        -----\n        This is a convenience function. If you want an interface that\n        takes a shape-tuple as the first argument, refer to\n        np.random.random_sample .\n\n        Examples\n        --------\n        >>> np.random.rand(3,2)\n        array([[ 0.14022471,  0.96360618],  #random\n               [ 0.37601032,  0.25528411],  #random\n               [ 0.49313049,  0.94909878]]) #random\n\n        ";
+static char __pyx_k_randn_d0_d1_dn_Return_a_sample[] = "\n        randn(d0, d1, ..., dn)\n\n        Return a sample (or samples) from the \"standard normal\" distribution.\n\n        If positive, int_like or int-convertible arguments are provided,\n        `randn` generates an array of shape ``(d0, d1, ..., dn)``, filled\n        with random floats sampled from a univariate \"normal\" (Gaussian)\n        distribution of mean 0 and variance 1 (if any of the :math:`d_i` are\n        floats, they are first converted to integers by truncation). A single\n        float randomly sampled from the distribution is returned if no\n        argument is provided.\n\n        This is a convenience function.  If you want an interface that takes a\n        tuple as the first argument, use `numpy.random.standard_normal` instead.\n\n        Parameters\n        ----------\n        d0, d1, ..., dn : int, optional\n            The dimensions of the returned array, should be all positive.\n            If no argument is given a single Python float is returned.\n\n        Returns\n        -------\n        Z : ndarray or float\n            A ``(d0, d1, ..., dn)``-shaped array of floating-point samples from\n            the standard normal distribution, or a single such float if\n            no parameters were supplied.\n\n        See Also\n        --------\n        random.standard_normal : Similar, but takes a tuple as its argument.\n\n        Notes\n        -----\n        For random samples from :math:`N(\\mu, \\sigma^2)`, use:\n\n        ``sigma * np.random.randn(...) + mu``\n\n        Examples\n        --------\n        >>> np.random.randn()\n        2.1923875335537315 #random\n\n        Two-by-four array of samples from N(3, 6.25):\n\n        >>> 2.5 * np.random.randn(2, 4) + 3\n        array([[-4.49401501,  4.00950034, -1.81814867,  7.29718677],  #random\n               [ 0.39924804,  4.68456316,  4.99394529,  4.84057254]]) #random\n\n        ";
+static char __pyx_k_random_sample_size_None_Return[] = "\n        random_sample(size=None)\n\n        Return random floats in the half-open interval [0.0, 1.0).\n\n        Results are from the \"continuous uniform\" distribution over the\n        stated interval.  To sample :math:`Unif[a, b), b > a` multiply\n        the output of `random_sample` by `(b-a)` and add `a`::\n\n          (b - a) * random_sample() + a\n\n        Parameters\n        ----------\n        size : int or tuple of ints, optional\n            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_shuffle_x_Modify_a_sequence_in[] = "\n        shuffle(x)\n\n        Modify a sequence in-place by shuffling its contents.\n\n        Parameters\n        ----------\n        x : array_like\n            The array or list to be shuffled.\n\n        Returns\n        -------\n        None\n\n        Examples\n        --------\n        >>> arr = np.arange(10)\n        >>> np.random.shuffle(arr)\n        >>> arr\n        [1 7 5 2 9 4 3 6 0 8]\n\n        This function only shuffles the array along the first index of a\n        multi-dimensional array:\n\n        >>> arr = np.arange(9).reshape((3, 3))\n        >>> np.random.shuffle(arr)\n        >>> arr\n        array([[3, 4, 5],\n               [6, 7, 8],\n               [0, 1, 2]])\n\n        ";
+static char __pyx_k_standard_exponential_size_None[] = "\n        standard_exponential(size=None)\n\n        Draw samples from the standard exponential distribution.\n\n        `standard_exponential` is identical to the exponential distribution\n        with a scale parameter of 1.\n\n        Parameters\n        ----------\n        size : int or tuple of ints\n            Shape of the output.\n\n        Returns\n        -------\n        out : float or ndarray\n            Drawn samples.\n\n        Examples\n        --------\n        Output a 3x8000 array:\n\n        >>> n = np.random.standard_exponential((3, 8000))\n\n        ";
+static char __pyx_k_standard_gamma_shape_size_None[] = "\n        standard_gamma(shape, size=None)\n\n        Draw samples from a Standard Gamma distribution.\n\n        Samples are drawn from a Gamma distribution with specified parameters,\n        shape (sometimes designated \"k\") and scale=1.\n\n        Parameters\n        ----------\n        shape : float\n            Parameter, should be > 0.\n        size : int or tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            The drawn samples.\n\n        See Also\n        --------\n        scipy.stats.distributions.gamma : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Gamma distribution is\n\n        .. math:: p(x) = x^{k-1}\\frac{e^{-x/\\theta}}{\\theta^k\\Gamma(k)},\n\n        where :math:`k` is the shape and :math:`\\theta` the scale,\n        and :math:`\\Gamma` is the Gamma function.\n\n        The Gamma distribution is often used to model the times to failure of\n        electronic components, and arises naturally in processes for which the\n        waiting times between Poisson distributed events are relevant.\n\n        References\n        ----------\n        .. [1] Weisstein, Eric W. \"Gamma Distribution.\" From MathWorld--A\n               Wolfram Web Resource.\n               http://mathworld.wolfram.com/GammaDistribution.html\n        .. [2] Wikipedia, \"Gamma-distribution\",\n               http://en.wikipedia.org/wiki/Gamma-distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> shape, scale = 2., 1. # mean and width\n        >>> s = np.random.standard_gamma(shape, 1000000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt""\n        >>> import scipy.special as sps\n        >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n        >>> y = bins**(shape-1) * ((np.exp(-bins/scale))/ \\\n        ...                       (sps.gamma(shape) * scale**shape))\n        >>> plt.plot(bins, y, linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
+static char __pyx_k_wald_mean_scale_size_None_Draw[] = "\n        wald(mean, scale, size=None)\n\n        Draw samples from a Wald, or Inverse Gaussian, distribution.\n\n        As the scale approaches infinity, the distribution becomes more like a\n        Gaussian.\n\n        Some references claim that the Wald is an Inverse Gaussian with mean=1, but\n        this is by no means universal.\n\n        The Inverse Gaussian distribution was first studied in relationship to\n        Brownian motion. In 1956 M.C.K. Tweedie used the name Inverse Gaussian\n        because there is an inverse relationship between the time to cover a unit\n        distance and distance covered in unit time.\n\n        Parameters\n        ----------\n        mean : scalar\n            Distribution mean, should be > 0.\n        scale : scalar\n            Scale parameter, should be >= 0.\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single value is\n            returned.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            Drawn sample, all greater than zero.\n\n        Notes\n        -----\n        The probability density function for the Wald distribution is\n\n        .. math:: P(x;mean,scale) = \\sqrt{\\frac{scale}{2\\pi x^3}}e^\n                                    \\frac{-scale(x-mean)^2}{2\\cdotp mean^2x}\n\n        As noted above the Inverse Gaussian distribution first arise from attempts\n        to model Brownian Motion. It is also a competitor to the Weibull for use in\n        reliability modeling and modeling stock returns and interest rate\n        processes.\n\n        References\n        ----------\n        .. [1] Brighton Webs Ltd., Wald Distribution,\n              http://www.brighton-webs.co.uk/distributions/wald.asp\n        .. [2] Chhikara, Raj S., and Folks, J. Leroy, \"The Inverse Gaussian\n              Distribution: Theory : Methodology, and Applications\", CRC Press,\n              1988.\n        .. [3] Wikipedia, \"Wald distribu""tion\"\n              http://en.wikipedia.org/wiki/Wald_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram:\n\n        >>> import matplotlib.pyplot as plt\n        >>> h = plt.hist(np.random.wald(3, 2, 100000), bins=200, normed=True)\n        >>> plt.show()\n\n        ";
+static char __pyx_k_RandomState_chisquare_line_2004[] = "RandomState.chisquare (line 2004)";
+static char __pyx_k_RandomState_dirichlet_line_4278[] = "RandomState.dirichlet (line 4278)";
+static char __pyx_k_RandomState_geometric_line_3773[] = "RandomState.geometric (line 3773)";
+static char __pyx_k_RandomState_hypergeometric_line[] = "RandomState.hypergeometric (line 3839)";
+static char __pyx_k_RandomState_lognormal_line_3044[] = "RandomState.lognormal (line 3044)";
+static char __pyx_k_RandomState_logseries_line_3958[] = "RandomState.logseries (line 3958)";
+static char __pyx_k_RandomState_multivariate_normal[] = "RandomState.multivariate_normal (line 4053)";
+static char __pyx_k_RandomState_standard_gamma_line[] = "RandomState.standard_gamma (line 1634)";
+static char __pyx_k_binomial_n_p_size_None_Draw_sam[] = "\n        binomial(n, p, size=None)\n\n        Draw samples from a binomial distribution.\n\n        Samples are drawn from a Binomial distribution with specified\n        parameters, n trials and p probability of success where\n        n an integer >= 0 and p is in the interval [0,1]. (n may be\n        input as a float, but it is truncated to an integer in use)\n\n        Parameters\n        ----------\n        n : float (but truncated to an integer)\n                parameter, >= 0.\n        p : float\n                parameter, >= 0 and <=1.\n        size : {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_bytes_length_Return_random_byte[] = "\n        bytes(length)\n\n        Return random bytes.\n\n        Parameters\n        ----------\n        length : int\n            Number of random bytes.\n\n        Returns\n        -------\n        out : str\n            String of length `length`.\n\n        Examples\n        --------\n        >>> np.random.bytes(10)\n        ' eh\\x85\\x022SZ\\xbf\\xa4' #random\n\n        ";
+static char __pyx_k_chisquare_df_size_None_Draw_sam[] = "\n        chisquare(df, size=None)\n\n        Draw samples from a chi-square distribution.\n\n        When `df` independent random variables, each with standard normal\n        distributions (mean 0, variance 1), are squared and summed, the\n        resulting distribution is chi-square (see Notes).  This distribution\n        is often used in hypothesis testing.\n\n        Parameters\n        ----------\n        df : int\n             Number of degrees of freedom.\n        size : 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_choice_a_size_None_replace_True[] = "\n        choice(a, size=None, replace=True, p=None)\n\n        Generates a random sample from a given 1-D array\n\n                .. versionadded:: 1.7.0\n\n        Parameters\n        -----------\n        a : 1-D array-like or int\n            If an ndarray, a random sample is generated from its elements.\n            If an int, the random sample is generated as if a was np.arange(n)\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single value is\n            returned.\n        replace : boolean, optional\n            Whether the sample is with or without replacement\n        p : 1-D array-like, optional\n            The probabilities associated with each entry in a.\n            If not given the sample assumes a uniform distribtion over all\n            entries in a.\n\n        Returns\n        --------\n        samples : 1-D ndarray, shape (size,)\n            The generated random samples\n\n        Raises\n        -------\n        ValueError\n            If a is an int and less than zero, if a or p are not 1-dimensional,\n            if a is an array-like of size 0, if p is not a vector of\n            probabilities, if a and p have different lengths, or if\n            replace=False and the sample size is greater than the population\n            size\n\n        See Also\n        ---------\n        randint, shuffle, permutation\n\n        Examples\n        ---------\n        Generate a uniform random sample from np.arange(5) of size 3:\n\n        >>> np.random.choice(5, 3)\n        array([0, 3, 4])\n        >>> #This is equivalent to np.random.randint(0,5,3)\n\n        Generate a non-uniform random sample from np.arange(5) of size 3:\n\n        >>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])\n        array([3, 3, 0])\n\n        Generate a uniform random sample from np.arange(5) of size 3 without\n        replacement:\n\n        >>> np.random.choice(5, 3, replace=False)\n        array([3,1,0])\n    ""    >>> #This is equivalent to np.random.shuffle(np.arange(5))[:3]\n\n        Generate a non-uniform random sample from np.arange(5) of size\n        3 without replacement:\n\n        >>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])\n        array([2, 3, 0])\n\n        Any of the above can be repeated with an arbitrary array-like\n        instead of just integers. For instance:\n\n        >>> aa_milne_arr = ['pooh', 'rabbit', 'piglet', 'Christopher']\n        >>> np.random.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3])\n        array(['pooh', 'pooh', 'pooh', 'Christopher', 'piglet'],\n              dtype='|S11')\n\n        ";
+static char __pyx_k_f_dfnum_dfden_size_None_Draw_sa[] = "\n        f(dfnum, dfden, size=None)\n\n        Draw samples from a F distribution.\n\n        Samples are drawn from an F distribution with specified parameters,\n        `dfnum` (degrees of freedom in numerator) and `dfden` (degrees of freedom\n        in denominator), where both parameters should be greater than zero.\n\n        The random variate of the F distribution (also known as the\n        Fisher distribution) is a continuous probability distribution\n        that arises in ANOVA tests, and is the ratio of two chi-square\n        variates.\n\n        Parameters\n        ----------\n        dfnum : float\n            Degrees of freedom in numerator. Should be greater than zero.\n        dfden : float\n            Degrees of freedom in denominator. Should be greater than zero.\n        size : {tuple, int}, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``,\n            then ``m * n * k`` samples are drawn. By default only one sample\n            is returned.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n            Samples from the Fisher distribution.\n\n        See Also\n        --------\n        scipy.stats.distributions.f : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The F statistic is used to compare in-group variances to between-group\n        variances. Calculating the distribution depends on the sampling, and\n        so it is a function of the respective degrees of freedom in the\n        problem.  The variable `dfnum` is the number of samples minus one, the\n        between-groups degrees of freedom, while `dfden` is the within-groups\n        degrees of freedom, the sum of the number of samples in each group\n        minus the number of groups.\n\n        References\n        ----------\n        .. [1] Glantz, Stanton A. \"Primer of Biostatistics.\", McGraw-Hill,\n               Fifth Edition, 2002.""\n        .. [2] Wikipedia, \"F-distribution\",\n               http://en.wikipedia.org/wiki/F-distribution\n\n        Examples\n        --------\n        An example from Glantz[1], pp 47-40.\n        Two groups, children of diabetics (25 people) and children from people\n        without diabetes (25 controls). Fasting blood glucose was measured,\n        case group had a mean value of 86.1, controls had a mean value of\n        82.2. Standard deviations were 2.09 and 2.49 respectively. Are these\n        data consistent with the null hypothesis that the parents diabetic\n        status does not affect their children's blood glucose levels?\n        Calculating the F statistic from the data gives a value of 36.01.\n\n        Draw samples from the distribution:\n\n        >>> dfnum = 1. # between group degrees of freedom\n        >>> dfden = 48. # within groups degrees of freedom\n        >>> s = np.random.f(dfnum, dfden, 1000)\n\n        The lower bound for the top 1% of the samples is :\n\n        >>> sort(s)[-10]\n        7.61988120985\n\n        So there is about a 1% chance that the F statistic will exceed 7.62,\n        the measured value is 36, so the null hypothesis is rejected at the 1%\n        level.\n\n        ";
+static char __pyx_k_gamma_shape_scale_1_0_size_None[] = "\n        gamma(shape, scale=1.0, size=None)\n\n        Draw samples from a Gamma distribution.\n\n        Samples are drawn from a Gamma distribution with specified parameters,\n        `shape` (sometimes designated \"k\") and `scale` (sometimes designated\n        \"theta\"), where both parameters are > 0.\n\n        Parameters\n        ----------\n        shape : scalar > 0\n            The shape of the gamma distribution.\n        scale : scalar > 0, optional\n            The scale of the gamma distribution.  Default is equal to 1.\n        size : shape_tuple, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray, float\n            Returns one sample unless `size` parameter is specified.\n\n        See Also\n        --------\n        scipy.stats.distributions.gamma : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Gamma distribution is\n\n        .. math:: p(x) = x^{k-1}\\frac{e^{-x/\\theta}}{\\theta^k\\Gamma(k)},\n\n        where :math:`k` is the shape and :math:`\\theta` the scale,\n        and :math:`\\Gamma` is the Gamma function.\n\n        The Gamma distribution is often used to model the times to failure of\n        electronic components, and arises naturally in processes for which the\n        waiting times between Poisson distributed events are relevant.\n\n        References\n        ----------\n        .. [1] Weisstein, Eric W. \"Gamma Distribution.\" From MathWorld--A\n               Wolfram Web Resource.\n               http://mathworld.wolfram.com/GammaDistribution.html\n        .. [2] Wikipedia, \"Gamma-distribution\",\n               http://en.wikipedia.org/wiki/Gamma-distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> shape, scale = 2.,"" 2. # mean and dispersion\n        >>> s = np.random.gamma(shape, scale, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> import scipy.special as sps\n        >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n        >>> y = bins**(shape-1)*(np.exp(-bins/scale) /\n        ...                      (sps.gamma(shape)*scale**shape))\n        >>> plt.plot(bins, y, linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
+static char __pyx_k_geometric_p_size_None_Draw_samp[] = "\n        geometric(p, size=None)\n\n        Draw samples from the geometric distribution.\n\n        Bernoulli trials are experiments with one of two outcomes:\n        success or failure (an example of such an experiment is flipping\n        a coin).  The geometric distribution models the number of trials\n        that must be run in order to achieve success.  It is therefore\n        supported on the positive integers, ``k = 1, 2, ...``.\n\n        The probability mass function of the geometric distribution is\n\n        .. math:: f(k) = (1 - p)^{k - 1} p\n\n        where `p` is the probability of success of an individual trial.\n\n        Parameters\n        ----------\n        p : float\n            The probability of success of an individual trial.\n        size : tuple of ints\n            Number of values to draw from the distribution.  The output\n            is shaped according to `size`.\n\n        Returns\n        -------\n        out : ndarray\n            Samples from the geometric distribution, shaped according to\n            `size`.\n\n        Examples\n        --------\n        Draw ten thousand values from the geometric distribution,\n        with the probability of an individual success equal to 0.35:\n\n        >>> z = np.random.geometric(p=0.35, size=10000)\n\n        How many trials succeeded after a single run?\n\n        >>> (z == 1).sum() / 10000.\n        0.34889999999999999 #random\n\n        ";
+static char __pyx_k_gumbel_loc_0_0_scale_1_0_size_N[] = "\n        gumbel(loc=0.0, scale=1.0, size=None)\n\n        Gumbel distribution.\n\n        Draw samples from a Gumbel distribution with specified location and scale.\n        For more information on the Gumbel distribution, see Notes and References\n        below.\n\n        Parameters\n        ----------\n        loc : float\n            The location of the mode of the distribution.\n        scale : float\n            The scale parameter of the distribution.\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray\n            The samples\n\n        See Also\n        --------\n        scipy.stats.gumbel_l\n        scipy.stats.gumbel_r\n        scipy.stats.genextreme\n            probability density function, distribution, or cumulative density\n            function, etc. for each of the above\n        weibull\n\n        Notes\n        -----\n        The Gumbel (or Smallest Extreme Value (SEV) or the Smallest Extreme Value\n        Type I) distribution is one of a class of Generalized Extreme Value (GEV)\n        distributions used in modeling extreme value problems.  The Gumbel is a\n        special case of the Extreme Value Type I distribution for maximums from\n        distributions with \"exponential-like\" tails.\n\n        The probability density for the Gumbel distribution is\n\n        .. math:: p(x) = \\frac{e^{-(x - \\mu)/ \\beta}}{\\beta} e^{ -e^{-(x - \\mu)/\n                  \\beta}},\n\n        where :math:`\\mu` is the mode, a location parameter, and :math:`\\beta` is\n        the scale parameter.\n\n        The Gumbel (named for German mathematician Emil Julius Gumbel) was used\n        very early in the hydrology literature, for modeling the occurrence of\n        flood events. It is also used for modeling maximum wind speed and rainfall\n        rates.  It is a \"fat-tailed\" distribution - the ""probability of an event in\n        the tail of the distribution is larger than if one used a Gaussian, hence\n        the surprisingly frequent occurrence of 100-year floods. Floods were\n        initially modeled as a Gaussian process, which underestimated the frequency\n        of extreme events.\n\n\n        It is one of a class of extreme value distributions, the Generalized\n        Extreme Value (GEV) distributions, which also includes the Weibull and\n        Frechet.\n\n        The function has a mean of :math:`\\mu + 0.57721\\beta` and a variance of\n        :math:`\\frac{\\pi^2}{6}\\beta^2`.\n\n        References\n        ----------\n        Gumbel, E. J., *Statistics of Extremes*, New York: Columbia University\n        Press, 1958.\n\n        Reiss, R.-D. and Thomas, M., *Statistical Analysis of Extreme Values from\n        Insurance, Finance, Hydrology and Other Fields*, Basel: Birkhauser Verlag,\n        2001.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, beta = 0, 0.1 # location and scale\n        >>> s = np.random.gumbel(mu, beta, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n        >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n        ...          * np.exp( -np.exp( -(bins - mu) /beta) ),\n        ...          linewidth=2, color='r')\n        >>> plt.show()\n\n        Show how an extreme value distribution can arise from a Gaussian process\n        and compare to a Gaussian:\n\n        >>> means = []\n        >>> maxima = []\n        >>> for i in range(0,1000) :\n        ...    a = np.random.normal(mu, beta, 1000)\n        ...    means.append(a.mean())\n        ...    maxima.append(a.max())\n        >>> count, bins, ignored = plt.hist(maxima, 30, normed=True)\n        >>> beta = np.std(maxima)*np.pi/np.sqrt(6)""\n        >>> mu = np.mean(maxima) - 0.57721*beta\n        >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n        ...          * np.exp(-np.exp(-(bins - mu)/beta)),\n        ...          linewidth=2, color='r')\n        >>> plt.plot(bins, 1/(beta * np.sqrt(2 * np.pi))\n        ...          * np.exp(-(bins - mu)**2 / (2 * beta**2)),\n        ...          linewidth=2, color='g')\n        >>> plt.show()\n\n        ";
+static char __pyx_k_hypergeometric_ngood_nbad_nsamp[] = "\n        hypergeometric(ngood, nbad, nsample, size=None)\n\n        Draw samples from a Hypergeometric distribution.\n\n        Samples are drawn from a Hypergeometric distribution with specified\n        parameters, ngood (ways to make a good selection), nbad (ways to make\n        a bad selection), and nsample = number of items sampled, which is less\n        than or equal to the sum ngood + nbad.\n\n        Parameters\n        ----------\n        ngood : int or array_like\n            Number of ways to make a good selection.  Must be nonnegative.\n        nbad : int or array_like\n            Number of ways to make a bad selection.  Must be nonnegative.\n        nsample : int or array_like\n            Number of items sampled.  Must be at least 1 and at most\n            ``ngood + nbad``.\n        size : int or tuple of int\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            The values are all integers in  [0, n].\n\n        See Also\n        --------\n        scipy.stats.distributions.hypergeom : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Hypergeometric distribution is\n\n        .. math:: P(x) = \\frac{\\binom{m}{n}\\binom{N-m}{n-x}}{\\binom{N}{n}},\n\n        where :math:`0 \\le x \\le m` and :math:`n+m-N \\le x \\le n`\n\n        for P(x) the probability of x successes, n = ngood, m = nbad, and\n        N = number of samples.\n\n        Consider an urn with black and white marbles in it, ngood of them\n        black and nbad are white. If you draw nsample balls without\n        replacement, then the Hypergeometric distribution describes the\n        distribution of black balls in the drawn sample.\n\n        Note that this distribution is very similar to the Binomial\n        distrib""ution, except that in this case, samples are drawn without\n        replacement, whereas in the Binomial case samples are drawn with\n        replacement (or the sample space is infinite). As the sample space\n        becomes large, this distribution approaches the Binomial.\n\n        References\n        ----------\n        .. [1] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n               and Quigley, 1972.\n        .. [2] Weisstein, Eric W. \"Hypergeometric Distribution.\" From\n               MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/HypergeometricDistribution.html\n        .. [3] Wikipedia, \"Hypergeometric-distribution\",\n               http://en.wikipedia.org/wiki/Hypergeometric-distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> ngood, nbad, nsamp = 100, 2, 10\n        # number of good, number of bad, and number of samples\n        >>> s = np.random.hypergeometric(ngood, nbad, nsamp, 1000)\n        >>> hist(s)\n        #   note that it is very unlikely to grab both bad items\n\n        Suppose you have an urn with 15 white and 15 black marbles.\n        If you pull 15 marbles at random, how likely is it that\n        12 or more of them are one color?\n\n        >>> s = np.random.hypergeometric(15, 15, 15, 100000)\n        >>> sum(s>=12)/100000. + sum(s<=3)/100000.\n        #   answer = 0.003 ... pretty unlikely!\n\n        ";
+static char __pyx_k_logistic_loc_0_0_scale_1_0_size[] = "\n        logistic(loc=0.0, scale=1.0, size=None)\n\n        Draw samples from a Logistic distribution.\n\n        Samples are drawn from a Logistic distribution with specified\n        parameters, loc (location or mean, also median), and scale (>0).\n\n        Parameters\n        ----------\n        loc : float\n\n        scale : float > 0.\n\n        size : {tuple, int}\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n                  where the values are all integers in  [0, n].\n\n        See Also\n        --------\n        scipy.stats.distributions.logistic : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Logistic distribution is\n\n        .. math:: P(x) = P(x) = \\frac{e^{-(x-\\mu)/s}}{s(1+e^{-(x-\\mu)/s})^2},\n\n        where :math:`\\mu` = location and :math:`s` = scale.\n\n        The Logistic distribution is used in Extreme Value problems where it\n        can act as a mixture of Gumbel distributions, in Epidemiology, and by\n        the World Chess Federation (FIDE) where it is used in the Elo ranking\n        system, assuming the performance of each player is a logistically\n        distributed random variable.\n\n        References\n        ----------\n        .. [1] Reiss, R.-D. and Thomas M. (2001), Statistical Analysis of Extreme\n               Values, from Insurance, Finance, Hydrology and Other Fields,\n               Birkhauser Verlag, Basel, pp 132-133.\n        .. [2] Weisstein, Eric W. \"Logistic Distribution.\" From\n               MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/LogisticDistribution.html\n        .. [3] Wikipedia, \"Logistic-distribution\",\n               http://en.wikipedia.org/wiki/Logistic-distribution\n\n        Examples\n   ""     --------\n        Draw samples from the distribution:\n\n        >>> loc, scale = 10, 1\n        >>> s = np.random.logistic(loc, scale, 10000)\n        >>> count, bins, ignored = plt.hist(s, bins=50)\n\n        #   plot against distribution\n\n        >>> def logist(x, loc, scale):\n        ...     return exp((loc-x)/scale)/(scale*(1+exp((loc-x)/scale))**2)\n        >>> plt.plot(bins, logist(bins, loc, scale)*count.max()/\\\n        ... logist(bins, loc, scale).max())\n        >>> plt.show()\n\n        ";
+static char __pyx_k_lognormal_mean_0_0_sigma_1_0_si[] = "\n        lognormal(mean=0.0, sigma=1.0, size=None)\n\n        Return samples drawn from a log-normal distribution.\n\n        Draw samples from a log-normal distribution with specified mean,\n        standard deviation, and array shape.  Note that the mean and standard\n        deviation are not the values for the distribution itself, but of the\n        underlying normal distribution it is derived from.\n\n        Parameters\n        ----------\n        mean : float\n            Mean value of the underlying normal distribution\n        sigma : float, > 0.\n            Standard deviation of the underlying normal distribution\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : ndarray or float\n            The desired samples. An array of the same shape as `size` if given,\n            if `size` is None a float is returned.\n\n        See Also\n        --------\n        scipy.stats.lognorm : probability density function, distribution,\n            cumulative density function, etc.\n\n        Notes\n        -----\n        A variable `x` has a log-normal distribution if `log(x)` is normally\n        distributed.  The probability density function for the log-normal\n        distribution is:\n\n        .. math:: p(x) = \\frac{1}{\\sigma x \\sqrt{2\\pi}}\n                         e^{(-\\frac{(ln(x)-\\mu)^2}{2\\sigma^2})}\n\n        where :math:`\\mu` is the mean and :math:`\\sigma` is the standard\n        deviation of the normally distributed logarithm of the variable.\n        A log-normal distribution results if a random variable is the *product*\n        of a large number of independent, identically-distributed variables in\n        the same way that a normal distribution results if the variable is the\n        *sum* of a large number of independent, identically-distributed\n        variables.\n\n        Reference""s\n        ----------\n        Limpert, E., Stahel, W. A., and Abbt, M., \"Log-normal Distributions\n        across the Sciences: Keys and Clues,\" *BioScience*, Vol. 51, No. 5,\n        May, 2001.  http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf\n\n        Reiss, R.D. and Thomas, M., *Statistical Analysis of Extreme Values*,\n        Basel: Birkhauser Verlag, 2001, pp. 31-32.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, sigma = 3., 1. # mean and standard deviation\n        >>> s = np.random.lognormal(mu, sigma, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='mid')\n\n        >>> x = np.linspace(min(bins), max(bins), 10000)\n        >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n        ...        / (x * sigma * np.sqrt(2 * np.pi)))\n\n        >>> plt.plot(x, pdf, linewidth=2, color='r')\n        >>> plt.axis('tight')\n        >>> plt.show()\n\n        Demonstrate that taking the products of random samples from a uniform\n        distribution can be fit well by a log-normal probability density function.\n\n        >>> # Generate a thousand samples: each is the product of 100 random\n        >>> # values, drawn from a normal distribution.\n        >>> b = []\n        >>> for i in range(1000):\n        ...    a = 10. + np.random.random(100)\n        ...    b.append(np.product(a))\n\n        >>> b = np.array(b) / np.min(b) # scale values to be positive\n        >>> count, bins, ignored = plt.hist(b, 100, normed=True, align='center')\n        >>> sigma = np.std(np.log(b))\n        >>> mu = np.mean(np.log(b))\n\n        >>> x = np.linspace(min(bins), max(bins), 10000)\n        >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n        ...        / (x * sigma * np.sqrt(2 * np.pi)))\n\n        >>> plt.plot(x, pdf, co""lor='r', linewidth=2)\n        >>> plt.show()\n\n        ";
+static char __pyx_k_logseries_p_size_None_Draw_samp[] = "\n        logseries(p, size=None)\n\n        Draw samples from a Logarithmic Series distribution.\n\n        Samples are drawn from a Log Series distribution with specified\n        parameter, p (probability, 0 < p < 1).\n\n        Parameters\n        ----------\n        loc : float\n\n        scale : float > 0.\n\n        size : {tuple, int}\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n                  where the values are all integers in  [0, n].\n\n        See Also\n        --------\n        scipy.stats.distributions.logser : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Log Series distribution is\n\n        .. math:: P(k) = \\frac{-p^k}{k \\ln(1-p)},\n\n        where p = probability.\n\n        The Log Series distribution is frequently used to represent species\n        richness and occurrence, first proposed by Fisher, Corbet, and\n        Williams in 1943 [2].  It may also be used to model the numbers of\n        occupants seen in cars [3].\n\n        References\n        ----------\n        .. [1] Buzas, Martin A.; Culver, Stephen J.,  Understanding regional\n               species diversity through the log series distribution of\n               occurrences: BIODIVERSITY RESEARCH Diversity & Distributions,\n               Volume 5, Number 5, September 1999 , pp. 187-195(9).\n        .. [2] Fisher, R.A,, A.S. Corbet, and C.B. Williams. 1943. The\n               relation between the number of species and the number of\n               individuals in a random sample of an animal population.\n               Journal of Animal Ecology, 12:42-58.\n        .. [3] D. J. Hand, F. Daly, D. Lunn, E. Ostrowski, A Handbook of Small\n               Data Sets, CRC Press, 1994.\n        .. [4] Wikipedia, \"Log""arithmic-distribution\",\n               http://en.wikipedia.org/wiki/Logarithmic-distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a = .6\n        >>> s = np.random.logseries(a, 10000)\n        >>> count, bins, ignored = plt.hist(s)\n\n        #   plot against distribution\n\n        >>> def logseries(k, p):\n        ...     return -p**k/(k*log(1-p))\n        >>> plt.plot(bins, logseries(bins, a)*count.max()/\n                     logseries(bins, a).max(), 'r')\n        >>> plt.show()\n\n        ";
+static char __pyx_k_multinomial_n_pvals_size_None_D[] = "\n        multinomial(n, pvals, size=None)\n\n        Draw samples from a multinomial distribution.\n\n        The multinomial distribution is a multivariate generalisation of the\n        binomial distribution.  Take an experiment with one of ``p``\n        possible outcomes.  An example of such an experiment is throwing a dice,\n        where the outcome can be 1 through 6.  Each sample drawn from the\n        distribution represents `n` such experiments.  Its values,\n        ``X_i = [X_0, X_1, ..., X_p]``, represent the number of times the outcome\n        was ``i``.\n\n        Parameters\n        ----------\n        n : int\n            Number of experiments.\n        pvals : sequence of floats, length p\n            Probabilities of each of the ``p`` different outcomes.  These\n            should sum to 1 (however, the last element is always assumed to\n            account for the remaining probability, as long as\n            ``sum(pvals[:-1]) <= 1)``.\n        size : tuple of ints\n            Given a `size` of ``(M, N, K)``, then ``M*N*K`` samples are drawn,\n            and the output shape becomes ``(M, N, K, p)``, since each sample\n            has shape ``(p,)``.\n\n        Examples\n        --------\n        Throw a dice 20 times:\n\n        >>> np.random.multinomial(20, [1/6.]*6, size=1)\n        array([[4, 1, 7, 5, 2, 1]])\n\n        It landed 4 times on 1, once on 2, etc.\n\n        Now, throw the dice 20 times, and 20 times again:\n\n        >>> np.random.multinomial(20, [1/6.]*6, size=2)\n        array([[3, 4, 3, 3, 4, 3],\n               [2, 4, 3, 4, 0, 7]])\n\n        For the first run, we threw 3 times 1, 4 times 2, etc.  For the second,\n        we threw 2 times 1, 4 times 2, etc.\n\n        A loaded dice is more likely to land on number 6:\n\n        >>> np.random.multinomial(100, [1/7.]*5)\n        array([13, 16, 13, 16, 42])\n\n        ";
+static char __pyx_k_multivariate_normal_mean_cov_si[] = "\n        multivariate_normal(mean, cov[, size])\n\n        Draw random samples from a multivariate normal distribution.\n\n        The multivariate normal, multinormal or Gaussian distribution is a\n        generalization of the one-dimensional normal distribution to higher\n        dimensions.  Such a distribution is specified by its mean and\n        covariance matrix.  These parameters are analogous to the mean\n        (average or \"center\") and variance (standard deviation, or \"width,\"\n        squared) of the one-dimensional normal distribution.\n\n        Parameters\n        ----------\n        mean : 1-D array_like, of length N\n            Mean of the N-dimensional distribution.\n        cov : 2-D array_like, of shape (N, N)\n            Covariance matrix of the distribution.  Must be symmetric and\n            positive semi-definite for \"physically meaningful\" results.\n        size : int or tuple of ints, optional\n            Given a shape of, for example, ``(m,n,k)``, ``m*n*k`` samples are\n            generated, and packed in an `m`-by-`n`-by-`k` arrangement.  Because\n            each sample is `N`-dimensional, the output shape is ``(m,n,k,N)``.\n            If no shape is specified, a single (`N`-D) sample is returned.\n\n        Returns\n        -------\n        out : ndarray\n            The drawn samples, of shape *size*, if that was provided.  If not,\n            the shape is ``(N,)``.\n\n            In other words, each entry ``out[i,j,...,:]`` is an N-dimensional\n            value drawn from the distribution.\n\n        Notes\n        -----\n        The mean is a coordinate in N-dimensional space, which represents the\n        location where samples are most likely to be generated.  This is\n        analogous to the peak of the bell curve for the one-dimensional or\n        univariate normal distribution.\n\n        Covariance indicates the level to which two variables vary together.\n        From the multivariate normal distribution, w""e draw N-dimensional\n        samples, :math:`X = [x_1, x_2, ... x_N]`.  The covariance matrix\n        element :math:`C_{ij}` is the covariance of :math:`x_i` and :math:`x_j`.\n        The element :math:`C_{ii}` is the variance of :math:`x_i` (i.e. its\n        \"spread\").\n\n        Instead of specifying the full covariance matrix, popular\n        approximations include:\n\n          - Spherical covariance (*cov* is a multiple of the identity matrix)\n          - Diagonal covariance (*cov* has non-negative elements, and only on\n            the diagonal)\n\n        This geometrical property can be seen in two dimensions by plotting\n        generated data-points:\n\n        >>> mean = [0,0]\n        >>> cov = [[1,0],[0,100]] # diagonal covariance, points lie on x or y-axis\n\n        >>> import matplotlib.pyplot as plt\n        >>> x,y = np.random.multivariate_normal(mean,cov,5000).T\n        >>> plt.plot(x,y,'x'); plt.axis('equal'); plt.show()\n\n        Note that the covariance matrix must be non-negative definite.\n\n        References\n        ----------\n        Papoulis, A., *Probability, Random Variables, and Stochastic Processes*,\n        3rd ed., New York: McGraw-Hill, 1991.\n\n        Duda, R. O., Hart, P. E., and Stork, D. G., *Pattern Classification*,\n        2nd ed., New York: Wiley, 2001.\n\n        Examples\n        --------\n        >>> mean = (1,2)\n        >>> cov = [[1,0],[1,0]]\n        >>> x = np.random.multivariate_normal(mean,cov,(3,3))\n        >>> x.shape\n        (3, 3, 2)\n\n        The following is probably true, given that 0.6 is roughly twice the\n        standard deviation:\n\n        >>> print list( (x[0,0,:] - mean) < 0.6 )\n        [True, True]\n\n        ";
+static char __pyx_k_negative_binomial_n_p_size_None[] = "\n        negative_binomial(n, p, size=None)\n\n        Draw samples from a negative_binomial distribution.\n\n        Samples are drawn from a negative_Binomial distribution with specified\n        parameters, `n` trials and `p` probability of success where `n` is an\n        integer > 0 and `p` is in the interval [0, 1].\n\n        Parameters\n        ----------\n        n : int\n            Parameter, > 0.\n        p : float\n            Parameter, >= 0 and <=1.\n        size : int or tuple of ints\n            Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : int or ndarray of ints\n            Drawn samples.\n\n        Notes\n        -----\n        The probability density for the Negative Binomial distribution is\n\n        .. math:: P(N;n,p) = \\binom{N+n-1}{n-1}p^{n}(1-p)^{N},\n\n        where :math:`n-1` is the number of successes, :math:`p` is the probability\n        of success, and :math:`N+n-1` is the number of trials.\n\n        The negative binomial distribution gives the probability of n-1 successes\n        and N failures in N+n-1 trials, and success on the (N+n)th trial.\n\n        If one throws a die repeatedly until the third time a \"1\" appears, then the\n        probability distribution of the number of non-\"1\"s that appear before the\n        third \"1\" is a negative binomial distribution.\n\n        References\n        ----------\n        .. [1] Weisstein, Eric W. \"Negative Binomial Distribution.\" From\n               MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/NegativeBinomialDistribution.html\n        .. [2] Wikipedia, \"Negative binomial distribution\",\n               http://en.wikipedia.org/wiki/Negative_binomial_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        A real world example. A company drills wild-cat oil exploration well""s, each\n        with an estimated probability of success of 0.1.  What is the probability\n        of having one success for each successive well, that is what is the\n        probability of a single success after drilling 5 wells, after 6 wells,\n        etc.?\n\n        >>> s = np.random.negative_binomial(1, 0.1, 100000)\n        >>> for i in range(1, 11):\n        ...    probability = sum(s<i) / 100000.\n        ...    print i, \"wells drilled, probability of one success =\", probability\n\n        ";
+static char __pyx_k_noncentral_chisquare_df_nonc_si[] = "\n        noncentral_chisquare(df, nonc, size=None)\n\n        Draw samples from a noncentral chi-square distribution.\n\n        The noncentral :math:`\\chi^2` distribution is a generalisation of\n        the :math:`\\chi^2` distribution.\n\n        Parameters\n        ----------\n        df : int\n            Degrees of freedom, should be >= 1.\n        nonc : float\n            Non-centrality, should be > 0.\n        size : int or tuple of ints\n            Shape of the output.\n\n        Notes\n        -----\n        The probability density function for the noncentral Chi-square distribution\n        is\n\n        .. math:: P(x;df,nonc) = \\sum^{\\infty}_{i=0}\n                               \\frac{e^{-nonc/2}(nonc/2)^{i}}{i!}P_{Y_{df+2i}}(x),\n\n        where :math:`Y_{q}` is the Chi-square with q degrees of freedom.\n\n        In Delhi (2007), it is noted that the noncentral chi-square is useful in\n        bombing and coverage problems, the probability of killing the point target\n        given by the noncentral chi-squared distribution.\n\n        References\n        ----------\n        .. [1] Delhi, M.S. Holla, \"On a noncentral chi-square distribution in the\n               analysis of weapon systems effectiveness\", Metrika, Volume 15,\n               Number 1 / December, 1970.\n        .. [2] Wikipedia, \"Noncentral chi-square distribution\"\n               http://en.wikipedia.org/wiki/Noncentral_chi-square_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram\n\n        >>> import matplotlib.pyplot as plt\n        >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),\n        ...                   bins=200, normed=True)\n        >>> plt.show()\n\n        Draw values from a noncentral chisquare with very small noncentrality,\n        and compare to a chisquare.\n\n        >>> plt.figure()\n        >>> values = plt.hist(np.random.noncentral_chisquare(3, .0000001, 100000),\n     ""   ...                   bins=np.arange(0., 25, .1), normed=True)\n        >>> values2 = plt.hist(np.random.chisquare(3, 100000),\n        ...                    bins=np.arange(0., 25, .1), normed=True)\n        >>> plt.plot(values[1][0:-1], values[0]-values2[0], 'ob')\n        >>> plt.show()\n\n        Demonstrate how large values of non-centrality lead to a more symmetric\n        distribution.\n\n        >>> plt.figure()\n        >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),\n        ...                   bins=200, normed=True)\n        >>> plt.show()\n\n        ";
+static char __pyx_k_noncentral_f_dfnum_dfden_nonc_s[] = "\n        noncentral_f(dfnum, dfden, nonc, size=None)\n\n        Draw samples from the noncentral F distribution.\n\n        Samples are drawn from an F distribution with specified parameters,\n        `dfnum` (degrees of freedom in numerator) and `dfden` (degrees of\n        freedom in denominator), where both parameters > 1.\n        `nonc` is the non-centrality parameter.\n\n        Parameters\n        ----------\n        dfnum : int\n            Parameter, should be > 1.\n        dfden : int\n            Parameter, should be > 1.\n        nonc : float\n            Parameter, should be >= 0.\n        size : int or tuple of ints\n            Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : scalar or ndarray\n            Drawn samples.\n\n        Notes\n        -----\n        When calculating the power of an experiment (power = probability of\n        rejecting the null hypothesis when a specific alternative is true) the\n        non-central F statistic becomes important.  When the null hypothesis is\n        true, the F statistic follows a central F distribution. When the null\n        hypothesis is not true, then it follows a non-central F statistic.\n\n        References\n        ----------\n        Weisstein, Eric W. \"Noncentral F-Distribution.\" From MathWorld--A Wolfram\n        Web Resource.  http://mathworld.wolfram.com/NoncentralF-Distribution.html\n\n        Wikipedia, \"Noncentral F distribution\",\n        http://en.wikipedia.org/wiki/Noncentral_F-distribution\n\n        Examples\n        --------\n        In a study, testing for a specific alternative to the null hypothesis\n        requires use of the Noncentral F distribution. We need to calculate the\n        area in the tail of the distribution that exceeds the value of the F\n        distribution for the null hypothesis.  We'll plot the two probability\n        distributions for comp""arison.\n\n        >>> dfnum = 3 # between group deg of freedom\n        >>> dfden = 20 # within groups degrees of freedom\n        >>> nonc = 3.0\n        >>> nc_vals = np.random.noncentral_f(dfnum, dfden, nonc, 1000000)\n        >>> NF = np.histogram(nc_vals, bins=50, normed=True)\n        >>> c_vals = np.random.f(dfnum, dfden, 1000000)\n        >>> F = np.histogram(c_vals, bins=50, normed=True)\n        >>> plt.plot(F[1][1:], F[0])\n        >>> plt.plot(NF[1][1:], NF[0])\n        >>> plt.show()\n\n        ";
+static char __pyx_k_normal_loc_0_0_scale_1_0_size_N[] = "\n        normal(loc=0.0, scale=1.0, size=None)\n\n        Draw random samples from a normal (Gaussian) distribution.\n\n        The probability density function of the normal distribution, first\n        derived by De Moivre and 200 years later by both Gauss and Laplace\n        independently [2]_, is often called the bell curve because of\n        its characteristic shape (see the example below).\n\n        The normal distributions occurs often in nature.  For example, it\n        describes the commonly occurring distribution of samples influenced\n        by a large number of tiny, random disturbances, each with its own\n        unique distribution [2]_.\n\n        Parameters\n        ----------\n        loc : float\n            Mean (\"centre\") of the distribution.\n        scale : float\n            Standard deviation (spread or \"width\") of the distribution.\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        See Also\n        --------\n        scipy.stats.distributions.norm : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Gaussian distribution is\n\n        .. math:: p(x) = \\frac{1}{\\sqrt{ 2 \\pi \\sigma^2 }}\n                         e^{ - \\frac{ (x - \\mu)^2 } {2 \\sigma^2} },\n\n        where :math:`\\mu` is the mean and :math:`\\sigma` the standard deviation.\n        The square of the standard deviation, :math:`\\sigma^2`, is called the\n        variance.\n\n        The function has its peak at the mean, and its \"spread\" increases with\n        the standard deviation (the function reaches 0.607 times its maximum at\n        :math:`x + \\sigma` and :math:`x - \\sigma` [2]_).  This implies that\n        `numpy.random.normal` is more likely to return samples lying close to the\n        mean, rather than those far away.\n""\n        References\n        ----------\n        .. [1] Wikipedia, \"Normal distribution\",\n               http://en.wikipedia.org/wiki/Normal_distribution\n        .. [2] P. R. Peebles Jr., \"Central Limit Theorem\" in \"Probability, Random\n               Variables and Random Signal Principles\", 4th ed., 2001,\n               pp. 51, 51, 125.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, sigma = 0, 0.1 # mean and standard deviation\n        >>> s = np.random.normal(mu, sigma, 1000)\n\n        Verify the mean and the variance:\n\n        >>> abs(mu - np.mean(s)) < 0.01\n        True\n\n        >>> abs(sigma - np.std(s, ddof=1)) < 0.01\n        True\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n        >>> plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *\n        ...                np.exp( - (bins - mu)**2 / (2 * sigma**2) ),\n        ...          linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
+static char __pyx_k_pareto_a_size_None_Draw_samples[] = "\n        pareto(a, size=None)\n\n        Draw samples from a Pareto II or Lomax distribution with specified shape.\n\n        The Lomax or Pareto II distribution is a shifted Pareto distribution. The\n        classical Pareto distribution can be obtained from the Lomax distribution\n        by adding the location parameter m, see below. The smallest value of the\n        Lomax distribution is zero while for the classical Pareto distribution it\n        is m, where the standard Pareto distribution has location m=1.\n        Lomax can also be considered as a simplified version of the Generalized\n        Pareto distribution (available in SciPy), with the scale set to one and\n        the location set to zero.\n\n        The Pareto distribution must be greater than zero, and is unbounded above.\n        It is also known as the \"80-20 rule\".  In this distribution, 80 percent of\n        the weights are in the lowest 20 percent of the range, while the other 20\n        percent fill the remaining 80 percent of the range.\n\n        Parameters\n        ----------\n        shape : float, > 0.\n            Shape of the distribution.\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        See Also\n        --------\n        scipy.stats.distributions.lomax.pdf : probability density function,\n            distribution or cumulative density function, etc.\n        scipy.stats.distributions.genpareto.pdf : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Pareto distribution is\n\n        .. math:: p(x) = \\frac{am^a}{x^{a+1}}\n\n        where :math:`a` is the shape and :math:`m` the location\n\n        The Pareto distribution, named after the Italian economist Vilfredo Pareto,\n        is a power law probability distribution useful in many real world probl""ems.\n        Outside the field of economics it is generally referred to as the Bradford\n        distribution. Pareto developed the distribution to describe the\n        distribution of wealth in an economy.  It has also found use in insurance,\n        web page access statistics, oil field sizes, and many other problems,\n        including the download frequency for projects in Sourceforge [1].  It is\n        one of the so-called \"fat-tailed\" distributions.\n\n\n        References\n        ----------\n        .. [1] Francis Hunt and Paul Johnson, On the Pareto Distribution of\n               Sourceforge projects.\n        .. [2] Pareto, V. (1896). Course of Political Economy. Lausanne.\n        .. [3] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme\n               Values, Birkhauser Verlag, Basel, pp 23-30.\n        .. [4] Wikipedia, \"Pareto distribution\",\n               http://en.wikipedia.org/wiki/Pareto_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a, m = 3., 1. # shape and mode\n        >>> s = np.random.pareto(a, 1000) + m\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='center')\n        >>> fit = a*m**a/bins**(a+1)\n        >>> plt.plot(bins, max(count)*fit/max(fit),linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
+static char __pyx_k_power_a_size_None_Draws_samples[] = "\n        power(a, size=None)\n\n        Draws samples in [0, 1] from a power distribution with positive\n        exponent a - 1.\n\n        Also known as the power function distribution.\n\n        Parameters\n        ----------\n        a : float\n            parameter, > 0\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n                    ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n            The returned samples lie in [0, 1].\n\n        Raises\n        ------\n        ValueError\n            If a<1.\n\n        Notes\n        -----\n        The probability density function is\n\n        .. math:: P(x; a) = ax^{a-1}, 0 \\le x \\le 1, a>0.\n\n        The power function distribution is just the inverse of the Pareto\n        distribution. It may also be seen as a special case of the Beta\n        distribution.\n\n        It is used, for example, in modeling the over-reporting of insurance\n        claims.\n\n        References\n        ----------\n        .. [1] Christian Kleiber, Samuel Kotz, \"Statistical size distributions\n               in economics and actuarial sciences\", Wiley, 2003.\n        .. [2] Heckert, N. A. and Filliben, James J. (2003). NIST Handbook 148:\n               Dataplot Reference Manual, Volume 2: Let Subcommands and Library\n               Functions\", National Institute of Standards and Technology Handbook\n               Series, June 2003.\n               http://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/powpdf.pdf\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a = 5. # shape\n        >>> samples = 1000\n        >>> s = np.random.power(a, samples)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, bins=""30)\n        >>> x = np.linspace(0, 1, 100)\n        >>> y = a*x**(a-1.)\n        >>> normed_y = samples*np.diff(bins)[0]*y\n        >>> plt.plot(x, normed_y)\n        >>> plt.show()\n\n        Compare the power function distribution to the inverse of the Pareto.\n\n        >>> from scipy import stats\n        >>> rvs = np.random.power(5, 1000000)\n        >>> rvsp = np.random.pareto(5, 1000000)\n        >>> xx = np.linspace(0,1,100)\n        >>> powpdf = stats.powerlaw.pdf(xx,5)\n\n        >>> plt.figure()\n        >>> plt.hist(rvs, bins=50, normed=True)\n        >>> plt.plot(xx,powpdf,'r-')\n        >>> plt.title('np.random.power(5)')\n\n        >>> plt.figure()\n        >>> plt.hist(1./(1.+rvsp), bins=50, normed=True)\n        >>> plt.plot(xx,powpdf,'r-')\n        >>> plt.title('inverse of 1 + np.random.pareto(5)')\n\n        >>> plt.figure()\n        >>> plt.hist(1./(1.+rvsp), bins=50, normed=True)\n        >>> plt.plot(xx,powpdf,'r-')\n        >>> plt.title('inverse of stats.pareto(5)')\n\n        ";
+static char __pyx_k_randint_low_high_None_size_None[] = "\n        randint(low, high=None, size=None)\n\n        Return random integers from `low` (inclusive) to `high` (exclusive).\n\n        Return random integers from the \"discrete uniform\" distribution in the\n        \"half-open\" interval [`low`, `high`). If `high` is None (the default),\n        then results are from [0, `low`).\n\n        Parameters\n        ----------\n        low : int\n            Lowest (signed) integer to be drawn from the distribution (unless\n            ``high=None``, in which case this parameter is the *highest* such\n            integer).\n        high : int, optional\n            If provided, one above the largest (signed) integer to be drawn\n            from the distribution (see above for behavior if ``high=None``).\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single int is\n            returned.\n\n        Returns\n        -------\n        out : int or ndarray of ints\n            `size`-shaped array of random integers from the appropriate\n            distribution, or a single such random int if `size` not provided.\n\n        See Also\n        --------\n        random.random_integers : similar to `randint`, only for the closed\n            interval [`low`, `high`], and 1 is the lowest value if `high` is\n            omitted. In particular, this other one is the one to use to generate\n            uniformly distributed discrete non-integers.\n\n        Examples\n        --------\n        >>> np.random.randint(2, size=10)\n        array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0])\n        >>> np.random.randint(1, size=10)\n        array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n\n        Generate a 2 x 4 array of ints between 0 and 4, inclusive:\n\n        >>> np.random.randint(5, size=(2, 4))\n        array([[4, 0, 2, 1],\n               [3, 2, 2, 0]])\n\n        ";
+static char __pyx_k_random_integers_low_high_None_s[] = "\n        random_integers(low, high=None, size=None)\n\n        Return random integers between `low` and `high`, inclusive.\n\n        Return random integers from the \"discrete uniform\" distribution in the\n        closed interval [`low`, `high`].  If `high` is None (the default),\n        then results are from [1, `low`].\n\n        Parameters\n        ----------\n        low : int\n            Lowest (signed) integer to be drawn from the distribution (unless\n            ``high=None``, in which case this parameter is the *highest* such\n            integer).\n        high : int, optional\n            If provided, the largest (signed) integer to be drawn from the\n            distribution (see above for behavior if ``high=None``).\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single int is returned.\n\n        Returns\n        -------\n        out : int or ndarray of ints\n            `size`-shaped array of random integers from the appropriate\n            distribution, or a single such random int if `size` not provided.\n\n        See Also\n        --------\n        random.randint : Similar to `random_integers`, only for the half-open\n            interval [`low`, `high`), and 0 is the lowest value if `high` is\n            omitted.\n\n        Notes\n        -----\n        To sample from N evenly spaced floating-point numbers between a and b,\n        use::\n\n          a + (b - a) * (np.random.random_integers(N) - 1) / (N - 1.)\n\n        Examples\n        --------\n        >>> np.random.random_integers(5)\n        4\n        >>> type(np.random.random_integers(5))\n        <type 'int'>\n        >>> np.random.random_integers(5, size=(3.,2.))\n        array([[5, 4],\n               [3, 3],\n               [4, 5]])\n\n        Choose five random numbers from the set of five evenly-spaced\n        numbers between 0 and 2.5, inclusive (*i.e.*, from the set\n        :math:`{0, 5/8, 10/8, 15/8, 20/8}`):\n""\n        >>> 2.5 * (np.random.random_integers(5, size=(5,)) - 1) / 4.\n        array([ 0.625,  1.25 ,  0.625,  0.625,  2.5  ])\n\n        Roll two six sided dice 1000 times and sum the results:\n\n        >>> d1 = np.random.random_integers(1, 6, 1000)\n        >>> d2 = np.random.random_integers(1, 6, 1000)\n        >>> dsums = d1 + d2\n\n        Display results as a histogram:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(dsums, 11, normed=True)\n        >>> plt.show()\n\n        ";
+static char __pyx_k_rayleigh_scale_1_0_size_None_Dr[] = "\n        rayleigh(scale=1.0, size=None)\n\n        Draw samples from a Rayleigh distribution.\n\n        The :math:`\\chi` and Weibull distributions are generalizations of the\n        Rayleigh.\n\n        Parameters\n        ----------\n        scale : scalar\n            Scale, also equals the mode. Should be >= 0.\n        size : int or tuple of ints, optional\n            Shape of the output. Default is None, in which case a single\n            value is returned.\n\n        Notes\n        -----\n        The probability density function for the Rayleigh distribution is\n\n        .. math:: P(x;scale) = \\frac{x}{scale^2}e^{\\frac{-x^2}{2 \\cdotp scale^2}}\n\n        The Rayleigh distribution arises if the wind speed and wind direction are\n        both gaussian variables, then the vector wind velocity forms a Rayleigh\n        distribution. The Rayleigh distribution is used to model the expected\n        output from wind turbines.\n\n        References\n        ----------\n        .. [1] Brighton Webs Ltd., Rayleigh Distribution,\n              http://www.brighton-webs.co.uk/distributions/rayleigh.asp\n        .. [2] Wikipedia, \"Rayleigh distribution\"\n              http://en.wikipedia.org/wiki/Rayleigh_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram\n\n        >>> values = hist(np.random.rayleigh(3, 100000), bins=200, normed=True)\n\n        Wave heights tend to follow a Rayleigh distribution. If the mean wave\n        height is 1 meter, what fraction of waves are likely to be larger than 3\n        meters?\n\n        >>> meanvalue = 1\n        >>> modevalue = np.sqrt(2 / np.pi) * meanvalue\n        >>> s = np.random.rayleigh(modevalue, 1000000)\n\n        The percentage of waves larger than 3 meters is:\n\n        >>> 100.*sum(s>3)/1000000.\n        0.087300000000000003\n\n        ";
+static char __pyx_k_standard_cauchy_size_None_Stand[] = "\n        standard_cauchy(size=None)\n\n        Standard Cauchy distribution with mode = 0.\n\n        Also known as the Lorentz distribution.\n\n        Parameters\n        ----------\n        size : int or tuple of ints\n            Shape of the output.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            The drawn samples.\n\n        Notes\n        -----\n        The probability density function for the full Cauchy distribution is\n\n        .. math:: P(x; x_0, \\gamma) = \\frac{1}{\\pi \\gamma \\bigl[ 1+\n                  (\\frac{x-x_0}{\\gamma})^2 \\bigr] }\n\n        and the Standard Cauchy distribution just sets :math:`x_0=0` and\n        :math:`\\gamma=1`\n\n        The Cauchy distribution arises in the solution to the driven harmonic\n        oscillator problem, and also describes spectral line broadening. It\n        also describes the distribution of values at which a line tilted at\n        a random angle will cut the x axis.\n\n        When studying hypothesis tests that assume normality, seeing how the\n        tests perform on data from a Cauchy distribution is a good indicator of\n        their sensitivity to a heavy-tailed distribution, since the Cauchy looks\n        very much like a Gaussian distribution, but with heavier tails.\n\n        References\n        ----------\n        .. [1] NIST/SEMATECH e-Handbook of Statistical Methods, \"Cauchy\n              Distribution\",\n              http://www.itl.nist.gov/div898/handbook/eda/section3/eda3663.htm\n        .. [2] Weisstein, Eric W. \"Cauchy Distribution.\" From MathWorld--A\n              Wolfram Web Resource.\n              http://mathworld.wolfram.com/CauchyDistribution.html\n        .. [3] Wikipedia, \"Cauchy distribution\"\n              http://en.wikipedia.org/wiki/Cauchy_distribution\n\n        Examples\n        --------\n        Draw samples and plot the distribution:\n\n        >>> s = np.random.standard_cauchy(1000000)\n        >>> s = s[(s>-25) & (s<""25)]  # truncate distribution so it plots well\n        >>> plt.hist(s, bins=100)\n        >>> plt.show()\n\n        ";
+static char __pyx_k_standard_normal_size_None_Retur[] = "\n        standard_normal(size=None)\n\n        Returns samples from a Standard Normal distribution (mean=0, stdev=1).\n\n        Parameters\n        ----------\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single value is\n            returned.\n\n        Returns\n        -------\n        out : float or ndarray\n            Drawn samples.\n\n        Examples\n        --------\n        >>> s = np.random.standard_normal(8000)\n        >>> s\n        array([ 0.6888893 ,  0.78096262, -0.89086505, ...,  0.49876311, #random\n               -0.38672696, -0.4685006 ])                               #random\n        >>> s.shape\n        (8000,)\n        >>> s = np.random.standard_normal(size=(3, 4, 2))\n        >>> s.shape\n        (3, 4, 2)\n\n        ";
+static char __pyx_k_standard_t_df_size_None_Standar[] = "\n        standard_t(df, size=None)\n\n        Standard Student's t distribution with df degrees of freedom.\n\n        A special case of the hyperbolic distribution.\n        As `df` gets large, the result resembles that of the standard normal\n        distribution (`standard_normal`).\n\n        Parameters\n        ----------\n        df : int\n            Degrees of freedom, should be > 0.\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single value is\n            returned.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            Drawn samples.\n\n        Notes\n        -----\n        The probability density function for the t distribution is\n\n        .. math:: P(x, df) = \\frac{\\Gamma(\\frac{df+1}{2})}{\\sqrt{\\pi df}\n                  \\Gamma(\\frac{df}{2})}\\Bigl( 1+\\frac{x^2}{df} \\Bigr)^{-(df+1)/2}\n\n        The t test is based on an assumption that the data come from a Normal\n        distribution. The t test provides a way to test whether the sample mean\n        (that is the mean calculated from the data) is a good estimate of the true\n        mean.\n\n        The derivation of the t-distribution was forst published in 1908 by William\n        Gisset while working for the Guinness Brewery in Dublin. Due to proprietary\n        issues, he had to publish under a pseudonym, and so he used the name\n        Student.\n\n        References\n        ----------\n        .. [1] Dalgaard, Peter, \"Introductory Statistics With R\",\n               Springer, 2002.\n        .. [2] Wikipedia, \"Student's t-distribution\"\n               http://en.wikipedia.org/wiki/Student's_t-distribution\n\n        Examples\n        --------\n        From Dalgaard page 83 [1]_, suppose the daily energy intake for 11\n        women in Kj is:\n\n        >>> intake = np.array([5260., 5470, 5640, 6180, 6390, 6515, 6805, 7515, \\\n        ...                    7515, 8230, 8770])\n\n        Doe""s their energy intake deviate systematically from the recommended\n        value of 7725 kJ?\n\n        We have 10 degrees of freedom, so is the sample mean within 95% of the\n        recommended value?\n\n        >>> s = np.random.standard_t(10, size=100000)\n        >>> np.mean(intake)\n        6753.636363636364\n        >>> intake.std(ddof=1)\n        1142.1232221373727\n\n        Calculate the t statistic, setting the ddof parameter to the unbiased\n        value so the divisor in the standard deviation will be degrees of\n        freedom, N-1.\n\n        >>> t = (np.mean(intake)-7725)/(intake.std(ddof=1)/np.sqrt(len(intake)))\n        >>> import matplotlib.pyplot as plt\n        >>> h = plt.hist(s, bins=100, normed=True)\n\n        For a one-sided t-test, how far out in the distribution does the t\n        statistic appear?\n\n        >>> >>> np.sum(s<t) / float(len(s))\n        0.0090699999999999999  #random\n\n        So the p-value is about 0.009, which says the null hypothesis has a\n        probability of about 99% of being true.\n\n        ";
+static char __pyx_k_tomaxint_size_None_Random_integ[] = "\n        tomaxint(size=None)\n\n        Random integers between 0 and ``sys.maxint``, inclusive.\n\n        Return a sample of uniformly distributed random integers in the interval\n        [0, ``sys.maxint``].\n\n        Parameters\n        ----------\n        size : tuple of ints, int, optional\n            Shape of output.  If this is, for example, (m,n,k), m*n*k samples\n            are generated.  If no shape is specified, a single sample is\n            returned.\n\n        Returns\n        -------\n        out : ndarray\n            Drawn samples, with shape `size`.\n\n        See Also\n        --------\n        randint : Uniform sampling over a given half-open interval of integers.\n        random_integers : Uniform sampling over a given closed interval of\n            integers.\n\n        Examples\n        --------\n        >>> RS = np.random.mtrand.RandomState() # need a RandomState object\n        >>> RS.tomaxint((2,2,2))\n        array([[[1170048599, 1600360186],\n                [ 739731006, 1947757578]],\n               [[1871712945,  752307660],\n                [1601631370, 1479324245]]])\n        >>> import sys\n        >>> sys.maxint\n        2147483647\n        >>> RS.tomaxint((2,2,2)) < sys.maxint\n        array([[[ True,  True],\n                [ True,  True]],\n               [[ True,  True],\n                [ True,  True]]], dtype=bool)\n\n        ";
+static char __pyx_k_triangular_left_mode_right_size[] = "\n        triangular(left, mode, right, size=None)\n\n        Draw samples from the triangular distribution.\n\n        The triangular distribution is a continuous probability distribution with\n        lower limit left, peak at mode, and upper limit right. Unlike the other\n        distributions, these parameters directly define the shape of the pdf.\n\n        Parameters\n        ----------\n        left : scalar\n            Lower limit.\n        mode : scalar\n            The value where the peak of the distribution occurs.\n            The value should fulfill the condition ``left <= mode <= right``.\n        right : scalar\n            Upper limit, should be larger than `left`.\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single value is\n            returned.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            The returned samples all lie in the interval [left, right].\n\n        Notes\n        -----\n        The probability density function for the Triangular distribution is\n\n        .. math:: P(x;l, m, r) = \\begin{cases}\n                  \\frac{2(x-l)}{(r-l)(m-l)}& \\text{for $l \\leq x \\leq m$},\\\\\n                  \\frac{2(m-x)}{(r-l)(r-m)}& \\text{for $m \\leq x \\leq r$},\\\\\n                  0& \\text{otherwise}.\n                  \\end{cases}\n\n        The triangular distribution is often used in ill-defined problems where the\n        underlying distribution is not known, but some knowledge of the limits and\n        mode exists. Often it is used in simulations.\n\n        References\n        ----------\n        .. [1] Wikipedia, \"Triangular distribution\"\n              http://en.wikipedia.org/wiki/Triangular_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram:\n\n        >>> import matplotlib.pyplot as plt\n        >>> h = plt.hist(np.random.triangular(-3, 0, 8, 100000), bins=""200,\n        ...              normed=True)\n        >>> plt.show()\n\n        ";
+static char __pyx_k_uniform_low_0_0_high_1_0_size_1[] = "\n        uniform(low=0.0, high=1.0, size=1)\n\n        Draw samples from a uniform distribution.\n\n        Samples are uniformly distributed over the half-open interval\n        ``[low, high)`` (includes low, but excludes high).  In other words,\n        any value within the given interval is equally likely to be drawn\n        by `uniform`.\n\n        Parameters\n        ----------\n        low : float, optional\n            Lower boundary of the output interval.  All values generated will be\n            greater than or equal to low.  The default value is 0.\n        high : float\n            Upper boundary of the output interval.  All values generated will be\n            less than high.  The default value is 1.0.\n        size : int or tuple of ints, optional\n            Shape of output.  If the given size is, for example, (m,n,k),\n            m*n*k samples are generated.  If no shape is specified, a single sample\n            is returned.\n\n        Returns\n        -------\n        out : ndarray\n            Drawn samples, with shape `size`.\n\n        See Also\n        --------\n        randint : Discrete uniform distribution, yielding integers.\n        random_integers : Discrete uniform distribution over the closed\n                          interval ``[low, high]``.\n        random_sample : Floats uniformly distributed over ``[0, 1)``.\n        random : Alias for `random_sample`.\n        rand : Convenience function that accepts dimensions as input, e.g.,\n               ``rand(2,2)`` would generate a 2-by-2 array of floats,\n               uniformly distributed over ``[0, 1)``.\n\n        Notes\n        -----\n        The probability density function of the uniform distribution is\n\n        .. math:: p(x) = \\frac{1}{b - a}\n\n        anywhere within the interval ``[a, b)``, and zero elsewhere.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> s = np.random.uniform(-1,0,1000)\n\n        All values are w""ithin the given interval:\n\n        >>> np.all(s >= -1)\n        True\n        >>> np.all(s < 0)\n        True\n\n        Display the histogram of the samples, along with the\n        probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 15, normed=True)\n        >>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
+static char __pyx_k_vonmises_mu_kappa_size_None_Dra[] = "\n        vonmises(mu, kappa, size=None)\n\n        Draw samples from a von Mises distribution.\n\n        Samples are drawn from a von Mises distribution with specified mode\n        (mu) and dispersion (kappa), on the interval [-pi, pi].\n\n        The von Mises distribution (also known as the circular normal\n        distribution) is a continuous probability distribution on the unit\n        circle.  It may be thought of as the circular analogue of the normal\n        distribution.\n\n        Parameters\n        ----------\n        mu : float\n            Mode (\"center\") of the distribution.\n        kappa : float\n            Dispersion of the distribution, has to be >=0.\n        size : int or tuple of int\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : scalar or ndarray\n            The returned samples, which are in the interval [-pi, pi].\n\n        See Also\n        --------\n        scipy.stats.distributions.vonmises : probability density function,\n            distribution, or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the von Mises distribution is\n\n        .. math:: p(x) = \\frac{e^{\\kappa cos(x-\\mu)}}{2\\pi I_0(\\kappa)},\n\n        where :math:`\\mu` is the mode and :math:`\\kappa` the dispersion,\n        and :math:`I_0(\\kappa)` is the modified Bessel function of order 0.\n\n        The von Mises is named for Richard Edler von Mises, who was born in\n        Austria-Hungary, in what is now the Ukraine.  He fled to the United\n        States in 1939 and became a professor at Harvard.  He worked in\n        probability theory, aerodynamics, fluid mechanics, and philosophy of\n        science.\n\n        References\n        ----------\n        Abramowitz, M. and Stegun, I. A. (ed.), *Handbook of Mathematical\n        Functions*, New York: Dover, 1965.\n\n      ""  von Mises, R., *Mathematical Theory of Probability and Statistics*,\n        New York: Academic Press, 1964.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, kappa = 0.0, 4.0 # mean and dispersion\n        >>> s = np.random.vonmises(mu, kappa, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> import scipy.special as sps\n        >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n        >>> x = np.arange(-np.pi, np.pi, 2*np.pi/50.)\n        >>> y = -np.exp(kappa*np.cos(x-mu))/(2*np.pi*sps.jn(0,kappa))\n        >>> plt.plot(x, y/max(y), linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
+static char __pyx_k_weibull_a_size_None_Weibull_dis[] = "\n        weibull(a, size=None)\n\n        Weibull distribution.\n\n        Draw samples from a 1-parameter Weibull distribution with the given\n        shape parameter `a`.\n\n        .. math:: X = (-ln(U))^{1/a}\n\n        Here, U is drawn from the uniform distribution over (0,1].\n\n        The more common 2-parameter Weibull, including a scale parameter\n        :math:`\\lambda` is just :math:`X = \\lambda(-ln(U))^{1/a}`.\n\n        Parameters\n        ----------\n        a : float\n            Shape of the distribution.\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        See Also\n        --------\n        scipy.stats.distributions.weibull_max\n        scipy.stats.distributions.weibull_min\n        scipy.stats.distributions.genextreme\n        gumbel\n\n        Notes\n        -----\n        The Weibull (or Type III asymptotic extreme value distribution for smallest\n        values, SEV Type III, or Rosin-Rammler distribution) is one of a class of\n        Generalized Extreme Value (GEV) distributions used in modeling extreme\n        value problems.  This class includes the Gumbel and Frechet distributions.\n\n        The probability density for the Weibull distribution is\n\n        .. math:: p(x) = \\frac{a}\n                         {\\lambda}(\\frac{x}{\\lambda})^{a-1}e^{-(x/\\lambda)^a},\n\n        where :math:`a` is the shape and :math:`\\lambda` the scale.\n\n        The function has its peak (the mode) at\n        :math:`\\lambda(\\frac{a-1}{a})^{1/a}`.\n\n        When ``a = 1``, the Weibull distribution reduces to the exponential\n        distribution.\n\n        References\n        ----------\n        .. [1] Waloddi Weibull, Professor, Royal Technical University, Stockholm,\n               1939 \"A Statistical Theory Of The Strength Of Materials\",\n               Ingeniorsvetenskapsakademiens Handlingar Nr 151, 1939,\n               General""stabens Litografiska Anstalts Forlag, Stockholm.\n        .. [2] Waloddi Weibull, 1951 \"A Statistical Distribution Function of Wide\n               Applicability\",  Journal Of Applied Mechanics ASME Paper.\n        .. [3] Wikipedia, \"Weibull distribution\",\n               http://en.wikipedia.org/wiki/Weibull_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a = 5. # shape\n        >>> s = np.random.weibull(a, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> x = np.arange(1,100.)/50.\n        >>> def weib(x,n,a):\n        ...     return (a / n) * (x / n)**(a - 1) * np.exp(-(x / n)**a)\n\n        >>> count, bins, ignored = plt.hist(np.random.weibull(5.,1000))\n        >>> x = np.arange(1,100.)/50.\n        >>> scale = count.max()/weib(x, 1., 5.).max()\n        >>> plt.plot(x, weib(x, 1., 5.)*scale)\n        >>> plt.show()\n\n        ";
+static char __pyx_k_zipf_a_size_None_Draw_samples_f[] = "\n        zipf(a, size=None)\n\n        Draw samples from a Zipf distribution.\n\n        Samples are drawn from a Zipf distribution with specified parameter\n        `a` > 1.\n\n        The Zipf distribution (also known as the zeta distribution) is a\n        continuous probability distribution that satisfies Zipf's law: the\n        frequency of an item is inversely proportional to its rank in a\n        frequency table.\n\n        Parameters\n        ----------\n        a : float > 1\n            Distribution parameter.\n        size : int or tuple of int, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn; a single integer is equivalent in\n            its result to providing a mono-tuple, i.e., a 1-D array of length\n            *size* is returned.  The default is None, in which case a single\n            scalar is returned.\n\n        Returns\n        -------\n        samples : scalar or ndarray\n            The returned samples are greater than or equal to one.\n\n        See Also\n        --------\n        scipy.stats.distributions.zipf : probability density function,\n            distribution, or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Zipf distribution is\n\n        .. math:: p(x) = \\frac{x^{-a}}{\\zeta(a)},\n\n        where :math:`\\zeta` is the Riemann Zeta function.\n\n        It is named for the American linguist George Kingsley Zipf, who noted\n        that the frequency of any word in a sample of a language is inversely\n        proportional to its rank in the frequency table.\n\n        References\n        ----------\n        Zipf, G. K., *Selected Studies of the Principle of Relative Frequency\n        in Language*, Cambridge, MA: Harvard Univ. Press, 1932.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a = 2. # parameter\n        >>> s = np.random.zipf""(a, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> import scipy.special as sps\n        Truncate s values at 50 so plot is interesting\n        >>> count, bins, ignored = plt.hist(s[s<50], 50, normed=True)\n        >>> x = np.arange(1., 50.)\n        >>> y = x**(-a)/sps.zetac(a)\n        >>> plt.plot(x, y/max(y), linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
+static char __pyx_k_Cannot_take_a_larger_sample_than[] = "Cannot take a larger sample than population when 'replace=False'";
+static char __pyx_k_Fewer_non_zero_entries_in_p_than[] = "Fewer non-zero entries in p than size";
+static char __pyx_k_RandomState_multinomial_line_418[] = "RandomState.multinomial (line 4185)";
+static char __pyx_k_RandomState_negative_binomial_li[] = "RandomState.negative_binomial (line 3519)";
+static char __pyx_k_RandomState_noncentral_chisquare[] = "RandomState.noncentral_chisquare (line 2082)";
+static char __pyx_k_RandomState_noncentral_f_line_19[] = "RandomState.noncentral_f (line 1909)";
+static char __pyx_k_RandomState_permutation_line_445[] = "RandomState.permutation (line 4453)";
+static char __pyx_k_RandomState_random_integers_line[] = "RandomState.random_integers (line 1283)";
+static char __pyx_k_RandomState_random_sample_line_7[] = "RandomState.random_sample (line 722)";
+static char __pyx_k_RandomState_standard_cauchy_line[] = "RandomState.standard_cauchy (line 2174)";
+static char __pyx_k_RandomState_standard_exponential[] = "RandomState.standard_exponential (line 1606)";
+static char __pyx_k_RandomState_standard_normal_line[] = "RandomState.standard_normal (line 1361)";
+static char __pyx_k_RandomState_standard_t_line_2235[] = "RandomState.standard_t (line 2235)";
+static char __pyx_k_RandomState_triangular_line_3323[] = "RandomState.triangular (line 3323)";
+static char __pyx_k_a_must_be_1_dimensional_or_an_in[] = "a must be 1-dimensional or an integer";
+static char __pyx_k_cov_must_be_2_dimensional_and_sq[] = "cov must be 2 dimensional and square";
+static char __pyx_k_mean_and_cov_must_have_same_leng[] = "mean and cov must have same length";
+static char __pyx_k_probabilities_are_not_non_negati[] = "probabilities are not non-negative";
+static char __pyx_k_size_is_not_compatible_with_inpu[] = "size is not compatible with inputs";
+static PyObject *__pyx_kp_s_Cannot_take_a_larger_sample_than;
+static PyObject *__pyx_kp_s_Fewer_non_zero_entries_in_p_than;
+static PyObject *__pyx_n_s_MT19937;
+static PyObject *__pyx_kp_u_RandomState_binomial_line_3411;
+static PyObject *__pyx_kp_u_RandomState_bytes_line_892;
+static PyObject *__pyx_kp_u_RandomState_chisquare_line_2004;
+static PyObject *__pyx_kp_u_RandomState_choice_line_920;
+static PyObject *__pyx_n_s_RandomState_ctor;
+static PyObject *__pyx_kp_u_RandomState_dirichlet_line_4278;
+static PyObject *__pyx_kp_u_RandomState_f_line_1807;
+static PyObject *__pyx_kp_u_RandomState_gamma_line_1716;
+static PyObject *__pyx_kp_u_RandomState_geometric_line_3773;
+static PyObject *__pyx_kp_u_RandomState_gumbel_line_2825;
+static PyObject *__pyx_kp_u_RandomState_hypergeometric_line;
+static PyObject *__pyx_kp_u_RandomState_laplace_line_2735;
+static PyObject *__pyx_kp_u_RandomState_logistic_line_2956;
+static PyObject *__pyx_kp_u_RandomState_lognormal_line_3044;
+static PyObject *__pyx_kp_u_RandomState_logseries_line_3958;
+static PyObject *__pyx_kp_u_RandomState_multinomial_line_418;
+static PyObject *__pyx_kp_u_RandomState_multivariate_normal;
+static PyObject *__pyx_kp_u_RandomState_negative_binomial_li;
+static PyObject *__pyx_kp_u_RandomState_noncentral_chisquare;
+static PyObject *__pyx_kp_u_RandomState_noncentral_f_line_19;
+static PyObject *__pyx_kp_u_RandomState_normal_line_1393;
+static PyObject *__pyx_kp_u_RandomState_pareto_line_2430;
+static PyObject *__pyx_kp_u_RandomState_permutation_line_445;
+static PyObject *__pyx_kp_u_RandomState_poisson_line_3614;
+static PyObject *__pyx_kp_u_RandomState_power_line_2626;
+static PyObject *__pyx_kp_u_RandomState_rand_line_1182;
+static PyObject *__pyx_kp_u_RandomState_randint_line_812;
+static PyObject *__pyx_kp_u_RandomState_randn_line_1226;
+static PyObject *__pyx_kp_u_RandomState_random_integers_line;
+static PyObject *__pyx_kp_u_RandomState_random_sample_line_7;
+static PyObject *__pyx_kp_u_RandomState_rayleigh_line_3165;
+static PyObject *__pyx_kp_u_RandomState_shuffle_line_4395;
+static PyObject *__pyx_kp_u_RandomState_standard_cauchy_line;
+static PyObject *__pyx_kp_u_RandomState_standard_exponential;
+static PyObject *__pyx_kp_u_RandomState_standard_gamma_line;
+static PyObject *__pyx_kp_u_RandomState_standard_normal_line;
+static PyObject *__pyx_kp_u_RandomState_standard_t_line_2235;
+static PyObject *__pyx_kp_u_RandomState_tomaxint_line_765;
+static PyObject *__pyx_kp_u_RandomState_triangular_line_3323;
+static PyObject *__pyx_kp_u_RandomState_uniform_line_1095;
+static PyObject *__pyx_kp_u_RandomState_vonmises_line_2336;
+static PyObject *__pyx_kp_u_RandomState_wald_line_3237;
+static PyObject *__pyx_kp_u_RandomState_weibull_line_2526;
+static PyObject *__pyx_kp_u_RandomState_zipf_line_3685;
+static PyObject *__pyx_n_s_TypeError;
+static PyObject *__pyx_n_s_ValueError;
+static PyObject *__pyx_n_s_a;
+static PyObject *__pyx_kp_s_a_0;
+static PyObject *__pyx_kp_s_a_1_0;
+static PyObject *__pyx_kp_s_a_and_p_must_have_same_size;
+static PyObject *__pyx_kp_s_a_must_be_1_dimensional;
+static PyObject *__pyx_kp_s_a_must_be_1_dimensional_or_an_in;
+static PyObject *__pyx_kp_s_a_must_be_greater_than_0;
+static PyObject *__pyx_kp_s_a_must_be_non_empty;
+static PyObject *__pyx_n_s_add;
+static PyObject *__pyx_kp_s_algorithm_must_be_MT19937;
+static PyObject *__pyx_n_s_alpha;
+static PyObject *__pyx_n_s_any;
+static PyObject *__pyx_n_s_arange;
+static PyObject *__pyx_n_s_array;
+static PyObject *__pyx_n_s_asarray;
+static PyObject *__pyx_n_s_b;
+static PyObject *__pyx_kp_s_b_0;
+static PyObject *__pyx_n_s_beta;
+static PyObject *__pyx_n_s_binomial;
+static PyObject *__pyx_kp_u_binomial_n_p_size_None_Draw_sam;
+static PyObject *__pyx_n_s_bytes;
+static PyObject *__pyx_kp_u_bytes_length_Return_random_byte;
+static PyObject *__pyx_n_s_chisquare;
+static PyObject *__pyx_kp_u_chisquare_df_size_None_Draw_sam;
+static PyObject *__pyx_n_s_choice;
+static PyObject *__pyx_kp_u_choice_a_size_None_replace_True;
+static PyObject *__pyx_n_s_copy;
+static PyObject *__pyx_n_s_count_nonzero;
+static PyObject *__pyx_n_s_cov;
+static PyObject *__pyx_kp_s_cov_must_be_2_dimensional_and_sq;
+static PyObject *__pyx_n_s_cumsum;
+static PyObject *__pyx_n_s_df;
+static PyObject *__pyx_kp_s_df_0;
+static PyObject *__pyx_kp_s_df_1;
+static PyObject *__pyx_n_s_dfden;
+static PyObject *__pyx_kp_s_dfden_0;
+static PyObject *__pyx_n_s_dfnum;
+static PyObject *__pyx_kp_s_dfnum_0;
+static PyObject *__pyx_kp_s_dfnum_1;
+static PyObject *__pyx_n_s_dirichlet;
+static PyObject *__pyx_kp_u_dirichlet_alpha_size_None_Draw;
+static PyObject *__pyx_n_s_dot;
+static PyObject *__pyx_n_s_dtype;
+static PyObject *__pyx_n_s_empty;
+static PyObject *__pyx_n_s_equal;
+static PyObject *__pyx_n_s_exponential;
+static PyObject *__pyx_n_s_f;
+static PyObject *__pyx_kp_u_f_dfnum_dfden_size_None_Draw_sa;
+static PyObject *__pyx_n_s_float64;
+static PyObject *__pyx_n_s_gamma;
+static PyObject *__pyx_kp_u_gamma_shape_scale_1_0_size_None;
+static PyObject *__pyx_n_s_geometric;
+static PyObject *__pyx_kp_u_geometric_p_size_None_Draw_samp;
+static PyObject *__pyx_n_s_get_state;
+static PyObject *__pyx_n_s_greater;
+static PyObject *__pyx_n_s_greater_equal;
+static PyObject *__pyx_n_s_gumbel;
+static PyObject *__pyx_kp_u_gumbel_loc_0_0_scale_1_0_size_N;
+static PyObject *__pyx_n_s_high;
+static PyObject *__pyx_n_s_hypergeometric;
+static PyObject *__pyx_kp_u_hypergeometric_ngood_nbad_nsamp;
+static PyObject *__pyx_n_s_iinfo;
+static PyObject *__pyx_n_s_import;
+static PyObject *__pyx_n_s_index;
+static PyObject *__pyx_n_s_int;
+static PyObject *__pyx_n_s_integer;
+static PyObject *__pyx_n_s_intp;
+static PyObject *__pyx_n_s_item;
+static PyObject *__pyx_n_s_kappa;
+static PyObject *__pyx_kp_s_kappa_0;
+static PyObject *__pyx_n_s_l;
+static PyObject *__pyx_n_s_lam;
+static PyObject *__pyx_kp_s_lam_0;
+static PyObject *__pyx_kp_s_lam_value_too_large;
+static PyObject *__pyx_kp_s_lam_value_too_large_2;
+static PyObject *__pyx_n_s_laplace;
+static PyObject *__pyx_kp_u_laplace_loc_0_0_scale_1_0_size;
+static PyObject *__pyx_n_s_left;
+static PyObject *__pyx_kp_s_left_mode;
+static PyObject *__pyx_kp_s_left_right;
+static PyObject *__pyx_n_s_less;
+static PyObject *__pyx_n_s_less_equal;
+static PyObject *__pyx_n_s_loc;
+static PyObject *__pyx_n_s_logical_or;
+static PyObject *__pyx_n_s_logistic;
+static PyObject *__pyx_kp_u_logistic_loc_0_0_scale_1_0_size;
+static PyObject *__pyx_n_s_lognormal;
+static PyObject *__pyx_kp_u_lognormal_mean_0_0_sigma_1_0_si;
+static PyObject *__pyx_n_s_logseries;
+static PyObject *__pyx_kp_u_logseries_p_size_None_Draw_samp;
+static PyObject *__pyx_n_s_low;
+static PyObject *__pyx_kp_s_low_high;
+static PyObject *__pyx_n_s_main;
+static PyObject *__pyx_n_s_max;
+static PyObject *__pyx_n_s_mean;
+static PyObject *__pyx_kp_s_mean_0;
+static PyObject *__pyx_kp_s_mean_0_0;
+static PyObject *__pyx_kp_s_mean_and_cov_must_have_same_leng;
+static PyObject *__pyx_kp_s_mean_must_be_1_dimensional;
+static PyObject *__pyx_n_s_mode;
+static PyObject *__pyx_kp_s_mode_right;
+static PyObject *__pyx_n_s_mu;
+static PyObject *__pyx_n_s_multinomial;
+static PyObject *__pyx_kp_u_multinomial_n_pvals_size_None_D;
+static PyObject *__pyx_n_s_multiply;
+static PyObject *__pyx_n_s_multivariate_normal;
+static PyObject *__pyx_kp_u_multivariate_normal_mean_cov_si;
+static PyObject *__pyx_n_s_n;
+static PyObject *__pyx_kp_s_n_0;
+static PyObject *__pyx_kp_s_n_0_2;
+static PyObject *__pyx_n_s_nbad;
+static PyObject *__pyx_kp_s_nbad_0;
+static PyObject *__pyx_n_s_ndarray;
+static PyObject *__pyx_n_s_ndim;
+static PyObject *__pyx_n_s_negative_binomial;
+static PyObject *__pyx_kp_u_negative_binomial_n_p_size_None;
+static PyObject *__pyx_n_s_ngood;
+static PyObject *__pyx_kp_s_ngood_0;
+static PyObject *__pyx_kp_s_ngood_nbad_nsample;
+static PyObject *__pyx_n_s_nonc;
+static PyObject *__pyx_kp_s_nonc_0;
+static PyObject *__pyx_kp_s_nonc_0_2;
+static PyObject *__pyx_n_s_noncentral_chisquare;
+static PyObject *__pyx_kp_u_noncentral_chisquare_df_nonc_si;
+static PyObject *__pyx_n_s_noncentral_f;
+static PyObject *__pyx_kp_u_noncentral_f_dfnum_dfden_nonc_s;
+static PyObject *__pyx_n_s_normal;
+static PyObject *__pyx_kp_u_normal_loc_0_0_scale_1_0_size_N;
+static PyObject *__pyx_n_s_np;
+static PyObject *__pyx_n_s_nsample;
+static PyObject *__pyx_kp_s_nsample_1;
+static PyObject *__pyx_n_s_numpy;
+static PyObject *__pyx_n_s_numpy_dual;
+static PyObject *__pyx_n_s_operator;
+static PyObject *__pyx_n_s_p;
+static PyObject *__pyx_kp_s_p_0;
+static PyObject *__pyx_kp_s_p_0_0;
+static PyObject *__pyx_kp_s_p_0_0_2;
+static PyObject *__pyx_kp_s_p_1;
+static PyObject *__pyx_kp_s_p_1_0;
+static PyObject *__pyx_kp_s_p_1_0_2;
+static PyObject *__pyx_kp_s_p_must_be_1_dimensional;
+static PyObject *__pyx_n_s_pareto;
+static PyObject *__pyx_kp_u_pareto_a_size_None_Draw_samples;
+static PyObject *__pyx_n_s_permutation;
+static PyObject *__pyx_kp_u_permutation_x_Randomly_permute;
+static PyObject *__pyx_n_s_poisson;
+static PyObject *__pyx_kp_u_poisson_lam_1_0_size_None_Draw;
+static PyObject *__pyx_n_s_poisson_lam_max;
+static PyObject *__pyx_n_s_power;
+static PyObject *__pyx_kp_u_power_a_size_None_Draws_samples;
+static PyObject *__pyx_kp_s_probabilities_are_not_non_negati;
+static PyObject *__pyx_kp_s_probabilities_do_not_sum_to_1;
+static PyObject *__pyx_n_s_prod;
+static PyObject *__pyx_n_s_pvals;
+static PyObject *__pyx_n_s_rand;
+static PyObject *__pyx_n_s_rand_2;
+static PyObject *__pyx_kp_u_rand_d0_d1_dn_Random_values_in;
+static PyObject *__pyx_n_s_randint;
+static PyObject *__pyx_kp_u_randint_low_high_None_size_None;
+static PyObject *__pyx_n_s_randn;
+static PyObject *__pyx_kp_u_randn_d0_d1_dn_Return_a_sample;
+static PyObject *__pyx_n_s_random;
+static PyObject *__pyx_n_s_random_integers;
+static PyObject *__pyx_kp_u_random_integers_low_high_None_s;
+static PyObject *__pyx_n_s_random_sample;
+static PyObject *__pyx_kp_u_random_sample_size_None_Return;
+static PyObject *__pyx_n_s_ravel;
+static PyObject *__pyx_n_s_rayleigh;
+static PyObject *__pyx_kp_u_rayleigh_scale_1_0_size_None_Dr;
+static PyObject *__pyx_n_s_reduce;
+static PyObject *__pyx_n_s_replace;
+static PyObject *__pyx_n_s_return_index;
+static PyObject *__pyx_n_s_right;
+static PyObject *__pyx_n_s_scale;
+static PyObject *__pyx_kp_s_scale_0;
+static PyObject *__pyx_kp_s_scale_0_0;
+static PyObject *__pyx_n_s_searchsorted;
+static PyObject *__pyx_n_s_seed;
+static PyObject *__pyx_n_s_set_state;
+static PyObject *__pyx_n_s_shape;
+static PyObject *__pyx_kp_s_shape_0;
+static PyObject *__pyx_n_s_shuffle;
+static PyObject *__pyx_kp_u_shuffle_x_Modify_a_sequence_in;
+static PyObject *__pyx_n_s_side;
+static PyObject *__pyx_n_s_sigma;
+static PyObject *__pyx_kp_s_sigma_0;
+static PyObject *__pyx_kp_s_sigma_0_0;
+static PyObject *__pyx_n_s_size;
+static PyObject *__pyx_kp_s_size_is_not_compatible_with_inpu;
+static PyObject *__pyx_n_s_sort;
+static PyObject *__pyx_n_s_sqrt;
+static PyObject *__pyx_n_s_standard_cauchy;
+static PyObject *__pyx_kp_u_standard_cauchy_size_None_Stand;
+static PyObject *__pyx_n_s_standard_exponential;
+static PyObject *__pyx_kp_u_standard_exponential_size_None;
+static PyObject *__pyx_n_s_standard_gamma;
+static PyObject *__pyx_kp_u_standard_gamma_shape_size_None;
+static PyObject *__pyx_n_s_standard_normal;
+static PyObject *__pyx_kp_u_standard_normal_size_None_Retur;
+static PyObject *__pyx_n_s_standard_t;
+static PyObject *__pyx_kp_u_standard_t_df_size_None_Standar;
+static PyObject *__pyx_kp_s_state_must_be_624_longs;
+static PyObject *__pyx_n_s_subtract;
+static PyObject *__pyx_kp_s_sum_pvals_1_1_0;
+static PyObject *__pyx_n_s_svd;
+static PyObject *__pyx_n_s_take;
+static PyObject *__pyx_n_s_test;
+static PyObject *__pyx_kp_u_tomaxint_size_None_Random_integ;
+static PyObject *__pyx_n_s_triangular;
+static PyObject *__pyx_kp_u_triangular_left_mode_right_size;
+static PyObject *__pyx_n_s_uint;
+static PyObject *__pyx_n_s_uint32;
+static PyObject *__pyx_n_s_uniform;
+static PyObject *__pyx_kp_u_uniform_low_0_0_high_1_0_size_1;
+static PyObject *__pyx_n_s_unique;
+static PyObject *__pyx_n_s_vonmises;
+static PyObject *__pyx_kp_u_vonmises_mu_kappa_size_None_Dra;
+static PyObject *__pyx_n_s_wald;
+static PyObject *__pyx_kp_u_wald_mean_scale_size_None_Draw;
+static PyObject *__pyx_n_s_weibull;
+static PyObject *__pyx_kp_u_weibull_a_size_None_Weibull_dis;
+static PyObject *__pyx_n_s_zeros;
+static PyObject *__pyx_n_s_zipf;
+static PyObject *__pyx_kp_u_zipf_a_size_None_Draw_samples_f;
+static PyObject *__pyx_float_0_0;
+static PyObject *__pyx_float_1_0;
 static PyObject *__pyx_int_0;
 static PyObject *__pyx_int_1;
 static PyObject *__pyx_int_3;
 static PyObject *__pyx_int_5;
 static PyObject *__pyx_int_10;
 static PyObject *__pyx_int_624;
-static PyObject *__pyx_k_17;
-static PyObject *__pyx_k_40;
-static PyObject *__pyx_k_41;
-static PyObject *__pyx_k_42;
-static PyObject *__pyx_k_43;
-static PyObject *__pyx_k_53;
-static PyObject *__pyx_k_59;
-static PyObject *__pyx_k_98;
-static PyObject *__pyx_k_99;
-static PyObject *__pyx_k_102;
-static PyObject *__pyx_k_103;
-static PyObject *__pyx_k_106;
-static PyObject *__pyx_k_107;
-static PyObject *__pyx_k_110;
-static PyObject *__pyx_k_111;
-static PyObject *__pyx_k_116;
-static PyObject *__pyx_k_151;
-static PyObject *__pyx_k_tuple_2;
-static PyObject *__pyx_k_tuple_3;
-static PyObject *__pyx_k_tuple_4;
-static PyObject *__pyx_k_tuple_5;
-static PyObject *__pyx_k_tuple_6;
-static PyObject *__pyx_k_tuple_7;
-static PyObject *__pyx_k_tuple_8;
-static PyObject *__pyx_k_slice_11;
-static PyObject *__pyx_k_slice_12;
-static PyObject *__pyx_k_tuple_10;
-static PyObject *__pyx_k_tuple_14;
-static PyObject *__pyx_k_tuple_16;
-static PyObject *__pyx_k_tuple_19;
-static PyObject *__pyx_k_tuple_21;
-static PyObject *__pyx_k_tuple_23;
-static PyObject *__pyx_k_tuple_25;
-static PyObject *__pyx_k_tuple_27;
-static PyObject *__pyx_k_tuple_29;
-static PyObject *__pyx_k_tuple_31;
-static PyObject *__pyx_k_tuple_33;
-static PyObject *__pyx_k_tuple_35;
-static PyObject *__pyx_k_tuple_37;
-static PyObject *__pyx_k_tuple_38;
-static PyObject *__pyx_k_tuple_39;
-static PyObject *__pyx_k_tuple_45;
-static PyObject *__pyx_k_tuple_46;
-static PyObject *__pyx_k_tuple_48;
-static PyObject *__pyx_k_tuple_50;
-static PyObject *__pyx_k_tuple_51;
-static PyObject *__pyx_k_tuple_52;
-static PyObject *__pyx_k_tuple_54;
-static PyObject *__pyx_k_tuple_55;
-static PyObject *__pyx_k_tuple_57;
-static PyObject *__pyx_k_tuple_58;
-static PyObject *__pyx_k_tuple_60;
-static PyObject *__pyx_k_tuple_61;
-static PyObject *__pyx_k_tuple_62;
-static PyObject *__pyx_k_tuple_63;
-static PyObject *__pyx_k_tuple_64;
-static PyObject *__pyx_k_tuple_65;
-static PyObject *__pyx_k_tuple_67;
-static PyObject *__pyx_k_tuple_69;
-static PyObject *__pyx_k_tuple_71;
-static PyObject *__pyx_k_tuple_72;
-static PyObject *__pyx_k_tuple_74;
-static PyObject *__pyx_k_tuple_75;
-static PyObject *__pyx_k_tuple_76;
-static PyObject *__pyx_k_tuple_77;
-static PyObject *__pyx_k_tuple_79;
-static PyObject *__pyx_k_tuple_80;
-static PyObject *__pyx_k_tuple_81;
-static PyObject *__pyx_k_tuple_83;
-static PyObject *__pyx_k_tuple_85;
-static PyObject *__pyx_k_tuple_86;
-static PyObject *__pyx_k_tuple_87;
-static PyObject *__pyx_k_tuple_88;
-static PyObject *__pyx_k_tuple_90;
-static PyObject *__pyx_k_tuple_91;
-static PyObject *__pyx_k_tuple_92;
-static PyObject *__pyx_k_tuple_93;
-static PyObject *__pyx_k_tuple_94;
-static PyObject *__pyx_k_tuple_95;
-static PyObject *__pyx_k_tuple_96;
-static PyObject *__pyx_k_tuple_97;
-static PyObject *__pyx_k_slice_192;
-static PyObject *__pyx_k_slice_196;
-static PyObject *__pyx_k_tuple_100;
-static PyObject *__pyx_k_tuple_101;
-static PyObject *__pyx_k_tuple_104;
-static PyObject *__pyx_k_tuple_105;
-static PyObject *__pyx_k_tuple_108;
-static PyObject *__pyx_k_tuple_109;
-static PyObject *__pyx_k_tuple_113;
-static PyObject *__pyx_k_tuple_115;
-static PyObject *__pyx_k_tuple_117;
-static PyObject *__pyx_k_tuple_119;
-static PyObject *__pyx_k_tuple_121;
-static PyObject *__pyx_k_tuple_122;
-static PyObject *__pyx_k_tuple_124;
-static PyObject *__pyx_k_tuple_125;
-static PyObject *__pyx_k_tuple_127;
-static PyObject *__pyx_k_tuple_129;
-static PyObject *__pyx_k_tuple_131;
-static PyObject *__pyx_k_tuple_132;
-static PyObject *__pyx_k_tuple_133;
-static PyObject *__pyx_k_tuple_134;
-static PyObject *__pyx_k_tuple_136;
-static PyObject *__pyx_k_tuple_138;
-static PyObject *__pyx_k_tuple_140;
-static PyObject *__pyx_k_tuple_141;
-static PyObject *__pyx_k_tuple_142;
-static PyObject *__pyx_k_tuple_143;
-static PyObject *__pyx_k_tuple_145;
-static PyObject *__pyx_k_tuple_146;
-static PyObject *__pyx_k_tuple_147;
-static PyObject *__pyx_k_tuple_148;
-static PyObject *__pyx_k_tuple_149;
-static PyObject *__pyx_k_tuple_150;
-static PyObject *__pyx_k_tuple_153;
-static PyObject *__pyx_k_tuple_155;
-static PyObject *__pyx_k_tuple_156;
-static PyObject *__pyx_k_tuple_158;
-static PyObject *__pyx_k_tuple_160;
-static PyObject *__pyx_k_tuple_161;
-static PyObject *__pyx_k_tuple_163;
-static PyObject *__pyx_k_tuple_165;
-static PyObject *__pyx_k_tuple_166;
-static PyObject *__pyx_k_tuple_167;
-static PyObject *__pyx_k_tuple_169;
-static PyObject *__pyx_k_tuple_171;
-static PyObject *__pyx_k_tuple_173;
-static PyObject *__pyx_k_tuple_175;
-static PyObject *__pyx_k_tuple_176;
-static PyObject *__pyx_k_tuple_177;
-static PyObject *__pyx_k_tuple_178;
-static PyObject *__pyx_k_tuple_179;
-static PyObject *__pyx_k_tuple_181;
-static PyObject *__pyx_k_tuple_183;
-static PyObject *__pyx_k_tuple_184;
-static PyObject *__pyx_k_tuple_185;
-static PyObject *__pyx_k_tuple_187;
-static PyObject *__pyx_k_tuple_189;
-static PyObject *__pyx_k_tuple_191;
-static PyObject *__pyx_k_tuple_195;
-static PyObject *__pyx_k_tuple_197;
-static PyObject *__pyx_k_tuple_198;
+static PyObject *__pyx_tuple_;
+static PyObject *__pyx_slice__9;
+static PyObject *__pyx_tuple__2;
+static PyObject *__pyx_tuple__3;
+static PyObject *__pyx_tuple__4;
+static PyObject *__pyx_tuple__5;
+static PyObject *__pyx_tuple__6;
+static PyObject *__pyx_tuple__7;
+static PyObject *__pyx_tuple__8;
+static PyObject *__pyx_slice__10;
+static PyObject *__pyx_tuple__11;
+static PyObject *__pyx_tuple__12;
+static PyObject *__pyx_tuple__13;
+static PyObject *__pyx_tuple__14;
+static PyObject *__pyx_tuple__15;
+static PyObject *__pyx_tuple__16;
+static PyObject *__pyx_tuple__17;
+static PyObject *__pyx_tuple__18;
+static PyObject *__pyx_tuple__19;
+static PyObject *__pyx_tuple__20;
+static PyObject *__pyx_tuple__21;
+static PyObject *__pyx_tuple__22;
+static PyObject *__pyx_tuple__23;
+static PyObject *__pyx_tuple__24;
+static PyObject *__pyx_tuple__25;
+static PyObject *__pyx_tuple__26;
+static PyObject *__pyx_tuple__27;
+static PyObject *__pyx_tuple__28;
+static PyObject *__pyx_tuple__29;
+static PyObject *__pyx_tuple__30;
+static PyObject *__pyx_tuple__31;
+static PyObject *__pyx_tuple__32;
+static PyObject *__pyx_tuple__33;
+static PyObject *__pyx_tuple__34;
+static PyObject *__pyx_tuple__35;
+static PyObject *__pyx_tuple__36;
+static PyObject *__pyx_tuple__37;
+static PyObject *__pyx_tuple__38;
+static PyObject *__pyx_tuple__39;
+static PyObject *__pyx_tuple__40;
+static PyObject *__pyx_tuple__41;
+static PyObject *__pyx_tuple__42;
+static PyObject *__pyx_tuple__43;
+static PyObject *__pyx_tuple__44;
+static PyObject *__pyx_tuple__45;
+static PyObject *__pyx_tuple__46;
+static PyObject *__pyx_tuple__47;
+static PyObject *__pyx_tuple__48;
+static PyObject *__pyx_tuple__49;
+static PyObject *__pyx_tuple__50;
+static PyObject *__pyx_tuple__51;
+static PyObject *__pyx_tuple__52;
+static PyObject *__pyx_tuple__53;
+static PyObject *__pyx_tuple__54;
+static PyObject *__pyx_tuple__55;
+static PyObject *__pyx_tuple__56;
+static PyObject *__pyx_tuple__57;
+static PyObject *__pyx_tuple__58;
+static PyObject *__pyx_tuple__59;
+static PyObject *__pyx_tuple__60;
+static PyObject *__pyx_tuple__61;
+static PyObject *__pyx_tuple__62;
+static PyObject *__pyx_tuple__63;
+static PyObject *__pyx_tuple__64;
+static PyObject *__pyx_tuple__65;
+static PyObject *__pyx_tuple__66;
+static PyObject *__pyx_tuple__67;
+static PyObject *__pyx_tuple__68;
+static PyObject *__pyx_tuple__69;
+static PyObject *__pyx_tuple__70;
+static PyObject *__pyx_tuple__71;
+static PyObject *__pyx_tuple__72;
+static PyObject *__pyx_tuple__73;
+static PyObject *__pyx_tuple__74;
+static PyObject *__pyx_tuple__75;
+static PyObject *__pyx_tuple__76;
+static PyObject *__pyx_tuple__77;
+static PyObject *__pyx_tuple__78;
+static PyObject *__pyx_tuple__79;
+static PyObject *__pyx_tuple__80;
+static PyObject *__pyx_tuple__81;
+static PyObject *__pyx_tuple__82;
+static PyObject *__pyx_tuple__83;
+static PyObject *__pyx_tuple__84;
+static PyObject *__pyx_tuple__85;
+static PyObject *__pyx_tuple__86;
+static PyObject *__pyx_tuple__87;
+static PyObject *__pyx_tuple__88;
+static PyObject *__pyx_tuple__89;
+static PyObject *__pyx_tuple__90;
+static PyObject *__pyx_tuple__91;
+static PyObject *__pyx_tuple__92;
+static PyObject *__pyx_tuple__93;
+static PyObject *__pyx_tuple__94;
+static PyObject *__pyx_tuple__95;
+static PyObject *__pyx_tuple__96;
+static PyObject *__pyx_tuple__97;
+static PyObject *__pyx_tuple__98;
+static PyObject *__pyx_tuple__99;
+static PyObject *__pyx_slice__122;
+static PyObject *__pyx_slice__124;
+static PyObject *__pyx_tuple__100;
+static PyObject *__pyx_tuple__101;
+static PyObject *__pyx_tuple__102;
+static PyObject *__pyx_tuple__103;
+static PyObject *__pyx_tuple__104;
+static PyObject *__pyx_tuple__105;
+static PyObject *__pyx_tuple__106;
+static PyObject *__pyx_tuple__107;
+static PyObject *__pyx_tuple__108;
+static PyObject *__pyx_tuple__109;
+static PyObject *__pyx_tuple__110;
+static PyObject *__pyx_tuple__111;
+static PyObject *__pyx_tuple__112;
+static PyObject *__pyx_tuple__113;
+static PyObject *__pyx_tuple__114;
+static PyObject *__pyx_tuple__115;
+static PyObject *__pyx_tuple__116;
+static PyObject *__pyx_tuple__117;
+static PyObject *__pyx_tuple__118;
+static PyObject *__pyx_tuple__119;
+static PyObject *__pyx_tuple__120;
+static PyObject *__pyx_tuple__121;
+static PyObject *__pyx_tuple__123;
+static PyObject *__pyx_tuple__125;
+static PyObject *__pyx_tuple__126;
 
 /* "mtrand.pyx":129
  * import operator
- *
+ * 
  * cdef object cont0_array(rk_state *state, rk_cont0 func, object size):             # <<<<<<<<<<<<<<
  *     cdef double *array_data
  *     cdef ndarray array "arrayObject"
@@ -1643,10 +1674,11 @@ static PyObject *__pyx_f_6mtrand_cont0_array(rk_state *__pyx_v_state, __pyx_t_6m
   PyObject *__pyx_r = NULL;
   __Pyx_RefNannyDeclarations
   int __pyx_t_1;
-  PyObject *__pyx_t_2 = NULL;
+  int __pyx_t_2;
   PyObject *__pyx_t_3 = NULL;
   PyObject *__pyx_t_4 = NULL;
-  npy_intp __pyx_t_5;
+  PyObject *__pyx_t_5 = NULL;
+  npy_intp __pyx_t_6;
   int __pyx_lineno = 0;
   const char *__pyx_filename = NULL;
   int __pyx_clineno = 0;
@@ -1654,28 +1686,28 @@ static PyObject *__pyx_f_6mtrand_cont0_array(rk_state *__pyx_v_state, __pyx_t_6m
 
   /* "mtrand.pyx":135
  *     cdef npy_intp i
- *
+ * 
  *     if size is None:             # <<<<<<<<<<<<<<
  *         return func(state)
  *     else:
  */
   __pyx_t_1 = (__pyx_v_size == Py_None);
-  if (__pyx_t_1) {
+  __pyx_t_2 = (__pyx_t_1 != 0);
+  if (__pyx_t_2) {
 
     /* "mtrand.pyx":136
- *
+ * 
  *     if size is None:
  *         return func(state)             # <<<<<<<<<<<<<<
  *     else:
  *         array = <ndarray>np.empty(size, np.float64)
  */
     __Pyx_XDECREF(__pyx_r);
-    __pyx_t_2 = PyFloat_FromDouble(__pyx_v_func(__pyx_v_state)); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 136; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    __Pyx_GOTREF(__pyx_t_2);
-    __pyx_r = __pyx_t_2;
-    __pyx_t_2 = 0;
+    __pyx_t_3 = PyFloat_FromDouble(__pyx_v_func(__pyx_v_state)); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 136; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __Pyx_GOTREF(__pyx_t_3);
+    __pyx_r = __pyx_t_3;
+    __pyx_t_3 = 0;
     goto __pyx_L0;
-    goto __pyx_L3;
   }
   /*else*/ {
 
@@ -1686,33 +1718,33 @@ static PyObject *__pyx_f_6mtrand_cont0_array(rk_state *__pyx_v_state, __pyx_t_6m
  *         length = PyArray_SIZE(array)
  *         array_data = <double *>PyArray_DATA(array)
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 static void __pyx_pw_6mtrand_11RandomState_3__dealloc__(PyObject *__pyx_v_self); /*proto*/
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+ * 
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- *
+ * 
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+  /* "mtrand.pyx":638
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+ * 
+ */
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-  __Pyx_GIVEREF(((PyObject *)__pyx_n_s__MT19937));
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   __pyx_t_1 = 0;
   __pyx_t_2 = 0;
   __pyx_t_3 = 0;
-  __pyx_r = ((PyObject *)__pyx_t_4);
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-  __pyx_r = Py_None; __Pyx_INCREF(Py_None);
-  goto __pyx_L0;
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+ *         get_state()
+ */
+
+  /* function exit code */
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@@ -5671,6 +5869,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_6get_state(struct __pyx_obj_6mtr
   return __pyx_r;
 }
 
+/* "mtrand.pyx":641
+ *             self.internal_state.has_gauss, self.internal_state.gauss)
+ * 
+ *     def set_state(self, state):             # <<<<<<<<<<<<<<
+ *         """
+ *         set_state(state)
+ */
+
 /* Python wrapper */
 static PyObject *__pyx_pw_6mtrand_11RandomState_9set_state(PyObject *__pyx_v_self, PyObject *__pyx_v_state); /*proto*/
 static char __pyx_doc_6mtrand_11RandomState_8set_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        ";
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 static PyObject *__pyx_pw_6mtrand_11RandomState_11__getstate__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/
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-  __pyx_r = Py_None; __Pyx_INCREF(Py_None);
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 }
 
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 /* Python wrapper */
 static PyObject *__pyx_pw_6mtrand_11RandomState_21randint(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
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 static PyObject *__pyx_pw_6mtrand_11RandomState_23bytes(PyObject *__pyx_v_self, PyObject *__pyx_arg_length); /*proto*/
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 /* Python wrapper */
 static PyObject *__pyx_pw_6mtrand_11RandomState_27uniform(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
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+  __pyx_t_2 = __Pyx_GetModuleGlobalName(__pyx_n_s_np); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1176; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_t_4 = PyObject_Call(__pyx_t_3, __pyx_t_2, NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1176; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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- *
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+  __pyx_t_4 = PyArray_EnsureArray(__pyx_v_temp); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1179; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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- *
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-  __pyx_r = Py_None; __Pyx_INCREF(Py_None);
-  goto __pyx_L0;
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+ * 
+ * 
+ *     def uniform(self, low=0.0, high=1.0, size=None):             # <<<<<<<<<<<<<<
+ *         """
+ *         uniform(low=0.0, high=1.0, size=1)
+ */
+
+  /* function exit code */
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@@ -8716,6 +8930,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_26uniform(struct __pyx_obj_6mtra
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 }
 
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 static PyObject *__pyx_pw_6mtrand_11RandomState_29rand(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
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+ * 
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 static PyObject *__pyx_pw_6mtrand_11RandomState_31randn(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
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 static PyObject *__pyx_pw_6mtrand_11RandomState_33random_integers(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
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 static PyObject *__pyx_pw_6mtrand_11RandomState_37normal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
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 }
 
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 }
 
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 static PyObject *__pyx_pw_6mtrand_11RandomState_47gamma(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
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+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1906; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   }
-  __pyx_L7:;
 
-  /* "mtrand.pyx":1904
+  /* "mtrand.pyx":1907
  *         if np.any(np.less_equal(odfden, 0.0)):
  *             raise ValueError("dfden <= 0")
  *         return cont2_array(self.internal_state, rk_f, size, odfnum, odfden)             # <<<<<<<<<<<<<<
- *
+ * 
  *     def noncentral_f(self, dfnum, dfden, nonc, size=None):
  */
   __Pyx_XDECREF(__pyx_r);
-  __pyx_t_2 = __pyx_f_6mtrand_cont2_array(__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 = 1904; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_2);
-  __pyx_r = __pyx_t_2;
-  __pyx_t_2 = 0;
+  __pyx_t_4 = __pyx_f_6mtrand_cont2_array(__pyx_v_self->internal_state, rk_f, __pyx_v_size, __pyx_v_odfnum, __pyx_v_odfden); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1907; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_4);
+  __pyx_r = __pyx_t_4;
+  __pyx_t_4 = 0;
   goto __pyx_L0;
 
-  __pyx_r = Py_None; __Pyx_INCREF(Py_None);
-  goto __pyx_L0;
+  /* "mtrand.pyx":1807
+ *         return cont2_array(self.internal_state, rk_gamma, size, oshape, oscale)
+ * 
+ *     def f(self, dfnum, dfden, size=None):             # <<<<<<<<<<<<<<
+ *         """
+ *         f(dfnum, dfden, size=None)
+ */
+
+  /* function exit code */
   __pyx_L1_error:;
   __Pyx_XDECREF(__pyx_t_2);
   __Pyx_XDECREF(__pyx_t_3);
@@ -11281,6 +11530,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_48f(struct __pyx_obj_6mtrand_Ran
   return __pyx_r;
 }
 
+/* "mtrand.pyx":1909
+ *         return cont2_array(self.internal_state, rk_f, size, odfnum, odfden)
+ * 
+ *     def noncentral_f(self, dfnum, dfden, nonc, size=None):             # <<<<<<<<<<<<<<
+ *         """
+ *         noncentral_f(dfnum, dfden, nonc, size=None)
+ */
+
 /* Python wrapper */
 static PyObject *__pyx_pw_6mtrand_11RandomState_51noncentral_f(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
 static char __pyx_doc_6mtrand_11RandomState_50noncentral_f[] = "\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        ";
@@ -11296,16 +11553,8 @@ static PyObject *__pyx_pw_6mtrand_11RandomState_51noncentral_f(PyObject *__pyx_v
   __Pyx_RefNannyDeclarations
   __Pyx_RefNannySetupContext("noncentral_f (wrapper)", 0);
   {
-    static PyObject **__pyx_pyargnames[] = {&__pyx_n_s__dfnum,&__pyx_n_s__dfden,&__pyx_n_s__nonc,&__pyx_n_s__size,0};
+    static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_dfnum,&__pyx_n_s_dfden,&__pyx_n_s_nonc,&__pyx_n_s_size,0};
     PyObject* values[4] = {0,0,0,0};
-
-    /* "mtrand.pyx":1906
- *         return cont2_array(self.internal_state, rk_f, size, odfnum, odfden)
- *
- *     def noncentral_f(self, dfnum, dfden, nonc, size=None):             # <<<<<<<<<<<<<<
- *         """
- *         noncentral_f(dfnum, dfden, nonc, size=None)
- */
     values[3] = ((PyObject *)Py_None);
     if (unlikely(__pyx_kwds)) {
       Py_ssize_t kw_args;
@@ -11321,26 +11570,26 @@ static PyObject *__pyx_pw_6mtrand_11RandomState_51noncentral_f(PyObject *__pyx_v
       kw_args = PyDict_Size(__pyx_kwds);
       switch (pos_args) {
         case  0:
-        if (likely((values[0] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__dfnum)) != 0)) kw_args--;
+        if (likely((values[0] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_dfnum)) != 0)) kw_args--;
         else goto __pyx_L5_argtuple_error;
         case  1:
-        if (likely((values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__dfden)) != 0)) kw_args--;
+        if (likely((values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_dfden)) != 0)) kw_args--;
         else {
-          __Pyx_RaiseArgtupleInvalid("noncentral_f", 0, 3, 4, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1906; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+          __Pyx_RaiseArgtupleInvalid("noncentral_f", 0, 3, 4, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1909; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
         }
         case  2:
-        if (likely((values[2] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__nonc)) != 0)) kw_args--;
+        if (likely((values[2] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_nonc)) != 0)) kw_args--;
         else {
-          __Pyx_RaiseArgtupleInvalid("noncentral_f", 0, 3, 4, 2); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1906; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+          __Pyx_RaiseArgtupleInvalid("noncentral_f", 0, 3, 4, 2); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1909; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
         }
         case  3:
         if (kw_args > 0) {
-          PyObject* value = PyDict_GetItem(__pyx_kwds, __pyx_n_s__size);
+          PyObject* value = PyDict_GetItem(__pyx_kwds, __pyx_n_s_size);
           if (value) { values[3] = value; kw_args--; }
         }
       }
       if (unlikely(kw_args > 0)) {
-        if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "noncentral_f") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1906; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+        if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "noncentral_f") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1909; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
       }
     } else {
       switch (PyTuple_GET_SIZE(__pyx_args)) {
@@ -11359,13 +11608,15 @@ static PyObject *__pyx_pw_6mtrand_11RandomState_51noncentral_f(PyObject *__pyx_v
   }
   goto __pyx_L4_argument_unpacking_done;
   __pyx_L5_argtuple_error:;
-  __Pyx_RaiseArgtupleInvalid("noncentral_f", 0, 3, 4, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1906; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+  __Pyx_RaiseArgtupleInvalid("noncentral_f", 0, 3, 4, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1909; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
   __pyx_L3_error:;
   __Pyx_AddTraceback("mtrand.RandomState.noncentral_f", __pyx_clineno, __pyx_lineno, __pyx_filename);
   __Pyx_RefNannyFinishContext();
   return NULL;
   __pyx_L4_argument_unpacking_done:;
   __pyx_r = __pyx_pf_6mtrand_11RandomState_50noncentral_f(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self), __pyx_v_dfnum, __pyx_v_dfden, __pyx_v_nonc, __pyx_v_size);
+
+  /* function exit code */
   __Pyx_RefNannyFinishContext();
   return __pyx_r;
 }
@@ -11389,17 +11640,17 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_50noncentral_f(struct __pyx_obj_
   int __pyx_clineno = 0;
   __Pyx_RefNannySetupContext("noncentral_f", 0);
 
-  /* "mtrand.pyx":1973
+  /* "mtrand.pyx":1976
  *         cdef double fdfnum, fdfden, fnonc
- *
+ * 
  *         fdfnum = PyFloat_AsDouble(dfnum)             # <<<<<<<<<<<<<<
  *         fdfden = PyFloat_AsDouble(dfden)
  *         fnonc = PyFloat_AsDouble(nonc)
  */
   __pyx_v_fdfnum = PyFloat_AsDouble(__pyx_v_dfnum);
 
-  /* "mtrand.pyx":1974
- *
+  /* "mtrand.pyx":1977
+ * 
  *         fdfnum = PyFloat_AsDouble(dfnum)
  *         fdfden = PyFloat_AsDouble(dfden)             # <<<<<<<<<<<<<<
  *         fnonc = PyFloat_AsDouble(nonc)
@@ -11407,7 +11658,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_50noncentral_f(struct __pyx_obj_
  */
   __pyx_v_fdfden = PyFloat_AsDouble(__pyx_v_dfden);
 
-  /* "mtrand.pyx":1975
+  /* "mtrand.pyx":1978
  *         fdfnum = PyFloat_AsDouble(dfnum)
  *         fdfden = PyFloat_AsDouble(dfden)
  *         fnonc = PyFloat_AsDouble(nonc)             # <<<<<<<<<<<<<<
@@ -11416,136 +11667,128 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_50noncentral_f(struct __pyx_obj_
  */
   __pyx_v_fnonc = PyFloat_AsDouble(__pyx_v_nonc);
 
-  /* "mtrand.pyx":1976
+  /* "mtrand.pyx":1979
  *         fdfden = PyFloat_AsDouble(dfden)
  *         fnonc = PyFloat_AsDouble(nonc)
  *         if not PyErr_Occurred():             # <<<<<<<<<<<<<<
  *             if fdfnum <= 1:
  *                 raise ValueError("dfnum <= 1")
  */
-  __pyx_t_1 = (!PyErr_Occurred());
+  __pyx_t_1 = ((!(PyErr_Occurred() != 0)) != 0);
   if (__pyx_t_1) {
 
-    /* "mtrand.pyx":1977
+    /* "mtrand.pyx":1980
  *         fnonc = PyFloat_AsDouble(nonc)
  *         if not PyErr_Occurred():
  *             if fdfnum <= 1:             # <<<<<<<<<<<<<<
  *                 raise ValueError("dfnum <= 1")
  *             if fdfden <= 0:
  */
-    __pyx_t_1 = (__pyx_v_fdfnum <= 1.0);
+    __pyx_t_1 = ((__pyx_v_fdfnum <= 1.0) != 0);
     if (__pyx_t_1) {
 
-      /* "mtrand.pyx":1978
+      /* "mtrand.pyx":1981
  *         if not PyErr_Occurred():
  *             if fdfnum <= 1:
  *                 raise ValueError("dfnum <= 1")             # <<<<<<<<<<<<<<
  *             if fdfden <= 0:
  *                 raise ValueError("dfden <= 0")
  */
-      __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_71), 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, __pyx_tuple__43, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1981; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
       __Pyx_GOTREF(__pyx_t_2);
       __Pyx_Raise(__pyx_t_2, 0, 0, 0);
       __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1978; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      goto __pyx_L4;
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1981; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     }
-    __pyx_L4:;
 
-    /* "mtrand.pyx":1979
+    /* "mtrand.pyx":1982
  *             if fdfnum <= 1:
  *                 raise ValueError("dfnum <= 1")
  *             if fdfden <= 0:             # <<<<<<<<<<<<<<
  *                 raise ValueError("dfden <= 0")
  *             if fnonc < 0:
  */
-    __pyx_t_1 = (__pyx_v_fdfden <= 0.0);
+    __pyx_t_1 = ((__pyx_v_fdfden <= 0.0) != 0);
     if (__pyx_t_1) {
 
-      /* "mtrand.pyx":1980
+      /* "mtrand.pyx":1983
  *                 raise ValueError("dfnum <= 1")
  *             if fdfden <= 0:
  *                 raise ValueError("dfden <= 0")             # <<<<<<<<<<<<<<
  *             if fnonc < 0:
  *                 raise ValueError("nonc < 0")
  */
-      __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_72), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1980; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__44, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1983; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
       __Pyx_GOTREF(__pyx_t_2);
       __Pyx_Raise(__pyx_t_2, 0, 0, 0);
       __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1980; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      goto __pyx_L5;
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1983; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     }
-    __pyx_L5:;
 
-    /* "mtrand.pyx":1981
+    /* "mtrand.pyx":1984
  *             if fdfden <= 0:
  *                 raise ValueError("dfden <= 0")
  *             if fnonc < 0:             # <<<<<<<<<<<<<<
  *                 raise ValueError("nonc < 0")
  *             return cont3_array_sc(self.internal_state, rk_noncentral_f, size,
  */
-    __pyx_t_1 = (__pyx_v_fnonc < 0.0);
+    __pyx_t_1 = ((__pyx_v_fnonc < 0.0) != 0);
     if (__pyx_t_1) {
 
-      /* "mtrand.pyx":1982
+      /* "mtrand.pyx":1985
  *                 raise ValueError("dfden <= 0")
  *             if fnonc < 0:
  *                 raise ValueError("nonc < 0")             # <<<<<<<<<<<<<<
  *             return cont3_array_sc(self.internal_state, rk_noncentral_f, size,
  *                                   fdfnum, fdfden, fnonc)
  */
-      __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_74), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1982; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__45, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1985; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
       __Pyx_GOTREF(__pyx_t_2);
       __Pyx_Raise(__pyx_t_2, 0, 0, 0);
       __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1982; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      goto __pyx_L6;
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1985; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     }
-    __pyx_L6:;
 
-    /* "mtrand.pyx":1983
+    /* "mtrand.pyx":1986
  *             if fnonc < 0:
  *                 raise ValueError("nonc < 0")
  *             return cont3_array_sc(self.internal_state, rk_noncentral_f, size,             # <<<<<<<<<<<<<<
  *                                   fdfnum, fdfden, fnonc)
- *
+ * 
  */
     __Pyx_XDECREF(__pyx_r);
 
-    /* "mtrand.pyx":1984
+    /* "mtrand.pyx":1987
  *                 raise ValueError("nonc < 0")
  *             return cont3_array_sc(self.internal_state, rk_noncentral_f, size,
  *                                   fdfnum, fdfden, fnonc)             # <<<<<<<<<<<<<<
- *
+ * 
  *         PyErr_Clear()
  */
-    __pyx_t_2 = __pyx_f_6mtrand_cont3_array_sc(__pyx_v_self->internal_state, rk_noncentral_f, __pyx_v_size, __pyx_v_fdfnum, __pyx_v_fdfden, __pyx_v_fnonc); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1983; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_t_2 = __pyx_f_6mtrand_cont3_array_sc(__pyx_v_self->internal_state, rk_noncentral_f, __pyx_v_size, __pyx_v_fdfnum, __pyx_v_fdfden, __pyx_v_fnonc); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1986; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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     __pyx_r = __pyx_t_2;
     __pyx_t_2 = 0;
     goto __pyx_L0;
-    goto __pyx_L3;
   }
-  __pyx_L3:;
 
-  /* "mtrand.pyx":1986
+  /* "mtrand.pyx":1989
  *                                   fdfnum, fdfden, fnonc)
- *
+ * 
  *         PyErr_Clear()             # <<<<<<<<<<<<<<
- *
+ * 
  *         odfnum = <ndarray>PyArray_FROM_OTF(dfnum, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  */
   PyErr_Clear();
 
-  /* "mtrand.pyx":1988
+  /* "mtrand.pyx":1991
  *         PyErr_Clear()
- *
+ * 
  *         odfnum = <ndarray>PyArray_FROM_OTF(dfnum, NPY_DOUBLE, NPY_ARRAY_ALIGNED)             # <<<<<<<<<<<<<<
  *         odfden = <ndarray>PyArray_FROM_OTF(dfden, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         ononc = <ndarray>PyArray_FROM_OTF(nonc, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  */
-  __pyx_t_2 = PyArray_FROM_OTF(__pyx_v_dfnum, NPY_DOUBLE, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1988; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_2 = PyArray_FROM_OTF(__pyx_v_dfnum, NPY_DOUBLE, NPY_ARRAY_ALIGNED); 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_t_3 = __pyx_t_2;
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@@ -11553,14 +11796,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_50noncentral_f(struct __pyx_obj_
   __pyx_v_odfnum = ((PyArrayObject *)__pyx_t_3);
   __pyx_t_3 = 0;
 
-  /* "mtrand.pyx":1989
- *
+  /* "mtrand.pyx":1992
+ * 
  *         odfnum = <ndarray>PyArray_FROM_OTF(dfnum, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         odfden = <ndarray>PyArray_FROM_OTF(dfden, NPY_DOUBLE, NPY_ARRAY_ALIGNED)             # <<<<<<<<<<<<<<
  *         ononc = <ndarray>PyArray_FROM_OTF(nonc, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
- *
+ * 
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 static char __pyx_doc_6mtrand_11RandomState_54noncentral_chisquare[] = "\n        noncentral_chisquare(df, nonc, size=None)\n\n        Draw samples from a noncentral chi-square distribution.\n\n        The noncentral :math:`\\chi^2` distribution is a generalisation of\n        the :math:`\\chi^2` distribution.\n\n        Parameters\n        ----------\n        df : int\n            Degrees of freedom, should be >= 1.\n        nonc : float\n            Non-centrality, should be > 0.\n        size : int or tuple of ints\n            Shape of the output.\n\n        Notes\n        -----\n        The probability density function for the noncentral Chi-square distribution\n        is\n\n        .. math:: P(x;df,nonc) = \\sum^{\\infty}_{i=0}\n                               \\frac{e^{-nonc/2}(nonc/2)^{i}}{i!}P_{Y_{df+2i}}(x),\n\n        where :math:`Y_{q}` is the Chi-square with q degrees of freedom.\n\n        In Delhi (2007), it is noted that the noncentral chi-square is useful in\n        bombing and coverage problems, the probability of killing the point target\n        given by the noncentral chi-squared distribution.\n\n        References\n        ----------\n        .. [1] Delhi, M.S. Holla, \"On a noncentral chi-square distribution in the\n               analysis of weapon systems effectiveness\", Metrika, Volume 15,\n               Number 1 / December, 1970.\n        .. [2] Wikipedia, \"Noncentral chi-square distribution\"\n               http://en.wikipedia.org/wiki/Noncentral_chi-square_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram\n\n        >>> import matplotlib.pyplot as plt\n        >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),\n        ...                   bins=200, normed=True)\n        >>> plt.show()\n\n        Draw values from a noncentral chisquare with very small noncentrality,\n        and compare to a chisquare.\n\n        >>> plt.figure()\n        >>> values = plt.hist(np.random.noncentral_chisquare(3, .0000001, 100000),\n     ""   ...                   bins=np.arange(0., 25, .1), normed=True)\n        >>> values2 = plt.hist(np.random.chisquare(3, 100000),\n        ...                    bins=np.arange(0., 25, .1), normed=True)\n        >>> plt.plot(values[1][0:-1], values[0]-values2[0], 'ob')\n        >>> plt.show()\n\n        Demonstrate how large values of non-centrality lead to a more symmetric\n        distribution.\n\n        >>> plt.figure()\n        >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),\n        ...                   bins=200, normed=True)\n        >>> plt.show()\n\n        ";
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 static PyObject *__pyx_pw_6mtrand_11RandomState_57standard_cauchy(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
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@@ -13124,6 +13369,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_60vonmises(struct __pyx_obj_6mtr
   return __pyx_r;
 }
 
+/* "mtrand.pyx":2430
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+ * 
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 /* Python wrapper */
 static PyObject *__pyx_pw_6mtrand_11RandomState_63pareto(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
 static char __pyx_doc_6mtrand_11RandomState_62pareto[] = "\n        pareto(a, size=None)\n\n        Draw samples from a Pareto II or Lomax distribution with specified shape.\n\n        The Lomax or Pareto II distribution is a shifted Pareto distribution. The\n        classical Pareto distribution can be obtained from the Lomax distribution\n        by adding the location parameter m, see below. The smallest value of the\n        Lomax distribution is zero while for the classical Pareto distribution it\n        is m, where the standard Pareto distribution has location m=1.\n        Lomax can also be considered as a simplified version of the Generalized\n        Pareto distribution (available in SciPy), with the scale set to one and\n        the location set to zero.\n\n        The Pareto distribution must be greater than zero, and is unbounded above.\n        It is also known as the \"80-20 rule\".  In this distribution, 80 percent of\n        the weights are in the lowest 20 percent of the range, while the other 20\n        percent fill the remaining 80 percent of the range.\n\n        Parameters\n        ----------\n        shape : float, > 0.\n            Shape of the distribution.\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        See Also\n        --------\n        scipy.stats.distributions.lomax.pdf : probability density function,\n            distribution or cumulative density function, etc.\n        scipy.stats.distributions.genpareto.pdf : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Pareto distribution is\n\n        .. math:: p(x) = \\frac{am^a}{x^{a+1}}\n\n        where :math:`a` is the shape and :math:`m` the location\n\n        The Pareto distribution, named after the Italian economist Vilfredo Pareto,\n        is a power law probability distribution useful in many real world probl""ems.\n        Outside the field of economics it is generally referred to as the Bradford\n        distribution. Pareto developed the distribution to describe the\n        distribution of wealth in an economy.  It has also found use in insurance,\n        web page access statistics, oil field sizes, and many other problems,\n        including the download frequency for projects in Sourceforge [1].  It is\n        one of the so-called \"fat-tailed\" distributions.\n\n\n        References\n        ----------\n        .. [1] Francis Hunt and Paul Johnson, On the Pareto Distribution of\n               Sourceforge projects.\n        .. [2] Pareto, V. (1896). Course of Political Economy. Lausanne.\n        .. [3] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme\n               Values, Birkhauser Verlag, Basel, pp 23-30.\n        .. [4] Wikipedia, \"Pareto distribution\",\n               http://en.wikipedia.org/wiki/Pareto_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a, m = 3., 1. # shape and mode\n        >>> s = np.random.pareto(a, 1000) + m\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='center')\n        >>> fit = a*m**a/bins**(a+1)\n        >>> plt.plot(bins, max(count)*fit/max(fit),linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
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 static char __pyx_doc_6mtrand_11RandomState_64weibull[] = "\n        weibull(a, size=None)\n\n        Weibull distribution.\n\n        Draw samples from a 1-parameter Weibull distribution with the given\n        shape parameter `a`.\n\n        .. math:: X = (-ln(U))^{1/a}\n\n        Here, U is drawn from the uniform distribution over (0,1].\n\n        The more common 2-parameter Weibull, including a scale parameter\n        :math:`\\lambda` is just :math:`X = \\lambda(-ln(U))^{1/a}`.\n\n        Parameters\n        ----------\n        a : float\n            Shape of the distribution.\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        See Also\n        --------\n        scipy.stats.distributions.weibull_max\n        scipy.stats.distributions.weibull_min\n        scipy.stats.distributions.genextreme\n        gumbel\n\n        Notes\n        -----\n        The Weibull (or Type III asymptotic extreme value distribution for smallest\n        values, SEV Type III, or Rosin-Rammler distribution) is one of a class of\n        Generalized Extreme Value (GEV) distributions used in modeling extreme\n        value problems.  This class includes the Gumbel and Frechet distributions.\n\n        The probability density for the Weibull distribution is\n\n        .. math:: p(x) = \\frac{a}\n                         {\\lambda}(\\frac{x}{\\lambda})^{a-1}e^{-(x/\\lambda)^a},\n\n        where :math:`a` is the shape and :math:`\\lambda` the scale.\n\n        The function has its peak (the mode) at\n        :math:`\\lambda(\\frac{a-1}{a})^{1/a}`.\n\n        When ``a = 1``, the Weibull distribution reduces to the exponential\n        distribution.\n\n        References\n        ----------\n        .. [1] Waloddi Weibull, Professor, Royal Technical University, Stockholm,\n               1939 \"A Statistical Theory Of The Strength Of Materials\",\n               Ingeniorsvetenskapsakademiens Handlingar Nr 151, 1939,\n               General""stabens Litografiska Anstalts Forlag, Stockholm.\n        .. [2] Waloddi Weibull, 1951 \"A Statistical Distribution Function of Wide\n               Applicability\",  Journal Of Applied Mechanics ASME Paper.\n        .. [3] Wikipedia, \"Weibull distribution\",\n               http://en.wikipedia.org/wiki/Weibull_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a = 5. # shape\n        >>> s = np.random.weibull(a, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> x = np.arange(1,100.)/50.\n        >>> def weib(x,n,a):\n        ...     return (a / n) * (x / n)**(a - 1) * np.exp(-(x / n)**a)\n\n        >>> count, bins, ignored = plt.hist(np.random.weibull(5.,1000))\n        >>> x = np.arange(1,100.)/50.\n        >>> scale = count.max()/weib(x, 1., 5.).max()\n        >>> plt.plot(x, weib(x, 1., 5.)*scale)\n        >>> plt.show()\n\n        ";
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 static PyObject *__pyx_pw_6mtrand_11RandomState_67power(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
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+    __Pyx_Raise(__pyx_t_5, 0, 0, 0);
+    __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2732; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  __pyx_r = __pyx_t_3;
-  __pyx_t_3 = 0;
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+  __Pyx_GOTREF(__pyx_t_5);
+  __pyx_r = __pyx_t_5;
+  __pyx_t_5 = 0;
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-  __pyx_r = Py_None; __Pyx_INCREF(Py_None);
-  goto __pyx_L0;
+  /* "mtrand.pyx":2626
+ *         return cont1_array(self.internal_state, rk_weibull, size, oa)
+ * 
+ *     def power(self, a, size=None):             # <<<<<<<<<<<<<<
+ *         """
+ *         power(a, size=None)
+ */
+
+  /* function exit code */
   __pyx_L1_error:;
   __Pyx_XDECREF(__pyx_t_2);
   __Pyx_XDECREF(__pyx_t_3);
@@ -13910,9 +14158,17 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_66power(struct __pyx_obj_6mtrand
   return __pyx_r;
 }
 
+/* "mtrand.pyx":2735
+ *         return cont1_array(self.internal_state, rk_power, size, oa)
+ * 
+ *     def laplace(self, loc=0.0, scale=1.0, size=None):             # <<<<<<<<<<<<<<
+ *         """
+ *         laplace(loc=0.0, scale=1.0, size=None)
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+
 /* Python wrapper */
 static PyObject *__pyx_pw_6mtrand_11RandomState_69laplace(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_68laplace[] = "\n        laplace(loc=0.0, scale=1.0, size=None)\n\n        Draw samples from the Laplace or double exponential distribution with\n        specified location (or mean) and scale (decay).\n\n        The Laplace distribution is similar to the Gaussian/normal distribution,\n        but is sharper at the peak and has fatter tails. It represents the\n        difference between two independent, identically distributed exponential\n        random variables.\n\n        Parameters\n        ----------\n        loc : float\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_doc_6mtrand_11RandomState_68laplace[] = "\n        laplace(loc=0.0, scale=1.0, size=None)\n\n        Draw samples from the Laplace or double exponential distribution with\n        specified location (or mean) and scale (decay).\n\n        The Laplace distribution is similar to the Gaussian/normal distribution,\n        but is sharper at the peak and has fatter tails. It represents the\n        difference between two independent, identically distributed exponential\n        random variables.\n\n        Parameters\n        ----------\n        loc : float\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_pw_6mtrand_11RandomState_69laplace(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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         if (kw_args > 0) {
-          PyObject* value = PyDict_GetItem(__pyx_kwds, __pyx_n_s__loc);
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+  __Pyx_RaiseArgtupleInvalid("laplace", 0, 0, 3, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2735; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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+
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@@ -14011,17 +14261,17 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_68laplace(struct __pyx_obj_6mtra
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-  /* "mtrand.pyx":2808
+  /* "mtrand.pyx":2811
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+ * 
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  *         fscale = PyFloat_AsDouble(scale)
  *         if not PyErr_Occurred():
  */
   __pyx_v_floc = PyFloat_AsDouble(__pyx_v_loc);
 
-  /* "mtrand.pyx":2809
- *
+  /* "mtrand.pyx":2812
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  *         floc = PyFloat_AsDouble(loc)
  *         fscale = PyFloat_AsDouble(scale)             # <<<<<<<<<<<<<<
  *         if not PyErr_Occurred():
@@ -14029,170 +14279,169 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_68laplace(struct __pyx_obj_6mtra
  */
   __pyx_v_fscale = PyFloat_AsDouble(__pyx_v_scale);
 
-  /* "mtrand.pyx":2810
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  *         fscale = PyFloat_AsDouble(scale)
  *         if not PyErr_Occurred():             # <<<<<<<<<<<<<<
  *             if fscale <= 0:
  *                 raise ValueError("scale <= 0")
  */
-  __pyx_t_1 = (!PyErr_Occurred());
+  __pyx_t_1 = ((!(PyErr_Occurred() != 0)) != 0);
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-    /* "mtrand.pyx":2811
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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 <= 0.0);
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-      /* "mtrand.pyx":2812
+      /* "mtrand.pyx":2815
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  *             if fscale <= 0:
  *                 raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
  *             return cont2_array_sc(self.internal_state, rk_laplace, size, floc, fscale)
- *
+ * 
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-      __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 = 2812; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__65, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2815; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2812; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      goto __pyx_L4;
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  *                 raise ValueError("scale <= 0")
  *             return cont2_array_sc(self.internal_state, rk_laplace, size, floc, fscale)             # <<<<<<<<<<<<<<
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+ * 
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+    __pyx_t_2 = __pyx_f_6mtrand_cont2_array_sc(__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 = 2816; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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     goto __pyx_L0;
-    goto __pyx_L3;
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-  /* "mtrand.pyx":2815
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- *
+ * 
  *         PyErr_Clear()             # <<<<<<<<<<<<<<
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  *         oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
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-  /* "mtrand.pyx":2816
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-  /* "mtrand.pyx":2817
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 static char __pyx_doc_6mtrand_11RandomState_70gumbel[] = "\n        gumbel(loc=0.0, scale=1.0, size=None)\n\n        Gumbel distribution.\n\n        Draw samples from a Gumbel distribution with specified location and scale.\n        For more information on the Gumbel distribution, see Notes and References\n        below.\n\n        Parameters\n        ----------\n        loc : float\n            The location of the mode of the distribution.\n        scale : float\n            The scale parameter of the distribution.\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray\n            The samples\n\n        See Also\n        --------\n        scipy.stats.gumbel_l\n        scipy.stats.gumbel_r\n        scipy.stats.genextreme\n            probability density function, distribution, or cumulative density\n            function, etc. for each of the above\n        weibull\n\n        Notes\n        -----\n        The Gumbel (or Smallest Extreme Value (SEV) or the Smallest Extreme Value\n        Type I) distribution is one of a class of Generalized Extreme Value (GEV)\n        distributions used in modeling extreme value problems.  The Gumbel is a\n        special case of the Extreme Value Type I distribution for maximums from\n        distributions with \"exponential-like\" tails.\n\n        The probability density for the Gumbel distribution is\n\n        .. math:: p(x) = \\frac{e^{-(x - \\mu)/ \\beta}}{\\beta} e^{ -e^{-(x - \\mu)/\n                  \\beta}},\n\n        where :math:`\\mu` is the mode, a location parameter, and :math:`\\beta` is\n        the scale parameter.\n\n        The Gumbel (named for German mathematician Emil Julius Gumbel) was used\n        very early in the hydrology literature, for modeling the occurrence of\n        flood events. It is also used for modeling maximum wind speed and rainfall\n        rates.  It is a \"fat-tailed\" distribution - the ""probability of an event in\n        the tail of the distribution is larger than if one used a Gaussian, hence\n        the surprisingly frequent occurrence of 100-year floods. Floods were\n        initially modeled as a Gaussian process, which underestimated the frequency\n        of extreme events.\n\n\n        It is one of a class of extreme value distributions, the Generalized\n        Extreme Value (GEV) distributions, which also includes the Weibull and\n        Frechet.\n\n        The function has a mean of :math:`\\mu + 0.57721\\beta` and a variance of\n        :math:`\\frac{\\pi^2}{6}\\beta^2`.\n\n        References\n        ----------\n        Gumbel, E. J., *Statistics of Extremes*, New York: Columbia University\n        Press, 1958.\n\n        Reiss, R.-D. and Thomas, M., *Statistical Analysis of Extreme Values from\n        Insurance, Finance, Hydrology and Other Fields*, Basel: Birkhauser Verlag,\n        2001.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, beta = 0, 0.1 # location and scale\n        >>> s = np.random.gumbel(mu, beta, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n        >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n        ...          * np.exp( -np.exp( -(bins - mu) /beta) ),\n        ...          linewidth=2, color='r')\n        >>> plt.show()\n\n        Show how an extreme value distribution can arise from a Gaussian process\n        and compare to a Gaussian:\n\n        >>> means = []\n        >>> maxima = []\n        >>> for i in range(0,1000) :\n        ...    a = np.random.normal(mu, beta, 1000)\n        ...    means.append(a.mean())\n        ...    maxima.append(a.max())\n        >>> count, bins, ignored = plt.hist(maxima, 30, normed=True)\n        >>> beta = np.std(maxima)*np.pi/np.sqrt(6)""\n        >>> mu = np.mean(maxima) - 0.57721*beta\n        >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n        ...          * np.exp(-np.exp(-(bins - mu)/beta)),\n        ...          linewidth=2, color='r')\n        >>> plt.plot(bins, 1/(beta * np.sqrt(2 * np.pi))\n        ...          * np.exp(-(bins - mu)**2 / (2 * beta**2)),\n        ...          linewidth=2, color='g')\n        >>> plt.show()\n\n        ";
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 static char __pyx_doc_6mtrand_11RandomState_74lognormal[] = "\n        lognormal(mean=0.0, sigma=1.0, size=None)\n\n        Return samples drawn from a log-normal distribution.\n\n        Draw samples from a log-normal distribution with specified mean,\n        standard deviation, and array shape.  Note that the mean and standard\n        deviation are not the values for the distribution itself, but of the\n        underlying normal distribution it is derived from.\n\n        Parameters\n        ----------\n        mean : float\n            Mean value of the underlying normal distribution\n        sigma : float, > 0.\n            Standard deviation of the underlying normal distribution\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : ndarray or float\n            The desired samples. An array of the same shape as `size` if given,\n            if `size` is None a float is returned.\n\n        See Also\n        --------\n        scipy.stats.lognorm : probability density function, distribution,\n            cumulative density function, etc.\n\n        Notes\n        -----\n        A variable `x` has a log-normal distribution if `log(x)` is normally\n        distributed.  The probability density function for the log-normal\n        distribution is:\n\n        .. math:: p(x) = \\frac{1}{\\sigma x \\sqrt{2\\pi}}\n                         e^{(-\\frac{(ln(x)-\\mu)^2}{2\\sigma^2})}\n\n        where :math:`\\mu` is the mean and :math:`\\sigma` is the standard\n        deviation of the normally distributed logarithm of the variable.\n        A log-normal distribution results if a random variable is the *product*\n        of a large number of independent, identically-distributed variables in\n        the same way that a normal distribution results if the variable is the\n        *sum* of a large number of independent, identically-distributed\n        variables.\n\n        Reference""s\n        ----------\n        Limpert, E., Stahel, W. A., and Abbt, M., \"Log-normal Distributions\n        across the Sciences: Keys and Clues,\" *BioScience*, Vol. 51, No. 5,\n        May, 2001.  http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf\n\n        Reiss, R.D. and Thomas, M., *Statistical Analysis of Extreme Values*,\n        Basel: Birkhauser Verlag, 2001, pp. 31-32.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, sigma = 3., 1. # mean and standard deviation\n        >>> s = np.random.lognormal(mu, sigma, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='mid')\n\n        >>> x = np.linspace(min(bins), max(bins), 10000)\n        >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n        ...        / (x * sigma * np.sqrt(2 * np.pi)))\n\n        >>> plt.plot(x, pdf, linewidth=2, color='r')\n        >>> plt.axis('tight')\n        >>> plt.show()\n\n        Demonstrate that taking the products of random samples from a uniform\n        distribution can be fit well by a log-normal probability density function.\n\n        >>> # Generate a thousand samples: each is the product of 100 random\n        >>> # values, drawn from a normal distribution.\n        >>> b = []\n        >>> for i in range(1000):\n        ...    a = 10. + np.random.random(100)\n        ...    b.append(np.product(a))\n\n        >>> b = np.array(b) / np.min(b) # scale values to be positive\n        >>> count, bins, ignored = plt.hist(b, 100, normed=True, align='center')\n        >>> sigma = np.std(np.log(b))\n        >>> mu = np.mean(np.log(b))\n\n        >>> x = np.linspace(min(bins), max(bins), 10000)\n        >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n        ...        / (x * sigma * np.sqrt(2 * np.pi)))\n\n        >>> plt.plot(x, pdf, co""lor='r', linewidth=2)\n        >>> plt.show()\n\n        ";
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 static PyObject *__pyx_pw_6mtrand_11RandomState_81triangular(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
 static char __pyx_doc_6mtrand_11RandomState_80triangular[] = "\n        triangular(left, mode, right, size=None)\n\n        Draw samples from the triangular distribution.\n\n        The triangular distribution is a continuous probability distribution with\n        lower limit left, peak at mode, and upper limit right. Unlike the other\n        distributions, these parameters directly define the shape of the pdf.\n\n        Parameters\n        ----------\n        left : scalar\n            Lower limit.\n        mode : scalar\n            The value where the peak of the distribution occurs.\n            The value should fulfill the condition ``left <= mode <= right``.\n        right : scalar\n            Upper limit, should be larger than `left`.\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single value is\n            returned.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            The returned samples all lie in the interval [left, right].\n\n        Notes\n        -----\n        The probability density function for the Triangular distribution is\n\n        .. math:: P(x;l, m, r) = \\begin{cases}\n                  \\frac{2(x-l)}{(r-l)(m-l)}& \\text{for $l \\leq x \\leq m$},\\\\\n                  \\frac{2(m-x)}{(r-l)(r-m)}& \\text{for $m \\leq x \\leq r$},\\\\\n                  0& \\text{otherwise}.\n                  \\end{cases}\n\n        The triangular distribution is often used in ill-defined problems where the\n        underlying distribution is not known, but some knowledge of the limits and\n        mode exists. Often it is used in simulations.\n\n        References\n        ----------\n        .. [1] Wikipedia, \"Triangular distribution\"\n              http://en.wikipedia.org/wiki/Triangular_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram:\n\n        >>> import matplotlib.pyplot as plt\n        >>> h = plt.hist(np.random.triangular(-3, 0, 8, 100000), bins=""200,\n        ...              normed=True)\n        >>> plt.show()\n\n        ";
@@ -15761,16 +16018,8 @@ static PyObject *__pyx_pw_6mtrand_11RandomState_81triangular(PyObject *__pyx_v_s
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   __Pyx_RefNannySetupContext("triangular (wrapper)", 0);
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-    static PyObject **__pyx_pyargnames[] = {&__pyx_n_s__left,&__pyx_n_s__mode,&__pyx_n_s__right,&__pyx_n_s__size,0};
+    static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_left,&__pyx_n_s_mode,&__pyx_n_s_right,&__pyx_n_s_size,0};
     PyObject* values[4] = {0,0,0,0};
-
-    /* "mtrand.pyx":3320
- *
- *
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- */
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@@ -15786,26 +16035,26 @@ static PyObject *__pyx_pw_6mtrand_11RandomState_81triangular(PyObject *__pyx_v_s
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       switch (pos_args) {
         case  0:
-        if (likely((values[0] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__left)) != 0)) kw_args--;
+        if (likely((values[0] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_left)) != 0)) kw_args--;
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         case  1:
-        if (likely((values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__mode)) != 0)) kw_args--;
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+          __Pyx_RaiseArgtupleInvalid("triangular", 0, 3, 4, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3323; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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-        if (likely((values[2] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__right)) != 0)) kw_args--;
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-          __Pyx_RaiseArgtupleInvalid("triangular", 0, 3, 4, 2); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3320; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+          __Pyx_RaiseArgtupleInvalid("triangular", 0, 3, 4, 2); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3323; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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-          PyObject* value = PyDict_GetItem(__pyx_kwds, __pyx_n_s__size);
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+        if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "triangular") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3323; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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@@ -15824,13 +16073,15 @@ static PyObject *__pyx_pw_6mtrand_11RandomState_81triangular(PyObject *__pyx_v_s
   }
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-  __Pyx_RaiseArgtupleInvalid("triangular", 0, 3, 4, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3320; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+  __Pyx_RaiseArgtupleInvalid("triangular", 0, 3, 4, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3323; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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+
+  /* function exit code */
   __Pyx_RefNannyFinishContext();
   return __pyx_r;
 }
@@ -15854,17 +16105,17 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_80triangular(struct __pyx_obj_6m
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-  /* "mtrand.pyx":3380
+  /* "mtrand.pyx":3383
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+ * 
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  *         fright = PyFloat_AsDouble(right)
  *         fmode = PyFloat_AsDouble(mode)
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-  /* "mtrand.pyx":3381
- *
+  /* "mtrand.pyx":3384
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  *         fleft = PyFloat_AsDouble(left)
  *         fright = PyFloat_AsDouble(right)             # <<<<<<<<<<<<<<
  *         fmode = PyFloat_AsDouble(mode)
@@ -15872,7 +16123,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_80triangular(struct __pyx_obj_6m
  */
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-  /* "mtrand.pyx":3382
+  /* "mtrand.pyx":3385
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  *         fright = PyFloat_AsDouble(right)
  *         fmode = PyFloat_AsDouble(mode)             # <<<<<<<<<<<<<<
@@ -15881,136 +16132,128 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_80triangular(struct __pyx_obj_6m
  */
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-  /* "mtrand.pyx":3383
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  *         fmode = PyFloat_AsDouble(mode)
  *         if not PyErr_Occurred():             # <<<<<<<<<<<<<<
  *             if fleft > fmode:
  *                 raise ValueError("left > mode")
  */
-  __pyx_t_1 = (!PyErr_Occurred());
+  __pyx_t_1 = ((!(PyErr_Occurred() != 0)) != 0);
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-    /* "mtrand.pyx":3384
+    /* "mtrand.pyx":3387
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  *             if fleft > fmode:             # <<<<<<<<<<<<<<
  *                 raise ValueError("left > mode")
  *             if fmode > fright:
  */
-    __pyx_t_1 = (__pyx_v_fleft > __pyx_v_fmode);
+    __pyx_t_1 = ((__pyx_v_fleft > __pyx_v_fmode) != 0);
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-      /* "mtrand.pyx":3385
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  *                 raise ValueError("left > mode")             # <<<<<<<<<<<<<<
  *             if fmode > fright:
  *                 raise ValueError("mode > right")
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+      __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__79, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3388; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3385; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      goto __pyx_L4;
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3388; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    /* "mtrand.pyx":3386
+    /* "mtrand.pyx":3389
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  *                 raise ValueError("left > mode")
  *             if fmode > fright:             # <<<<<<<<<<<<<<
  *                 raise ValueError("mode > right")
  *             if fleft == fright:
  */
-    __pyx_t_1 = (__pyx_v_fmode > __pyx_v_fright);
+    __pyx_t_1 = ((__pyx_v_fmode > __pyx_v_fright) != 0);
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-      /* "mtrand.pyx":3387
+      /* "mtrand.pyx":3390
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  *             if fmode > fright:
  *                 raise ValueError("mode > right")             # <<<<<<<<<<<<<<
  *             if fleft == fright:
  *                 raise ValueError("left == right")
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-      __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_129), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3387; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__80, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3390; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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       __Pyx_Raise(__pyx_t_2, 0, 0, 0);
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3387; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      goto __pyx_L5;
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3390; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     }
-    __pyx_L5:;
 
-    /* "mtrand.pyx":3388
+    /* "mtrand.pyx":3391
  *             if fmode > fright:
  *                 raise ValueError("mode > right")
  *             if fleft == fright:             # <<<<<<<<<<<<<<
  *                 raise ValueError("left == right")
  *             return cont3_array_sc(self.internal_state, rk_triangular, size, fleft,
  */
-    __pyx_t_1 = (__pyx_v_fleft == __pyx_v_fright);
+    __pyx_t_1 = ((__pyx_v_fleft == __pyx_v_fright) != 0);
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-      /* "mtrand.pyx":3389
+      /* "mtrand.pyx":3392
  *                 raise ValueError("mode > right")
  *             if fleft == fright:
  *                 raise ValueError("left == right")             # <<<<<<<<<<<<<<
  *             return cont3_array_sc(self.internal_state, rk_triangular, size, fleft,
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+      __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__81, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3392; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3389; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      goto __pyx_L6;
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3392; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    /* "mtrand.pyx":3390
+    /* "mtrand.pyx":3393
  *             if fleft == fright:
  *                 raise ValueError("left == right")
  *             return cont3_array_sc(self.internal_state, rk_triangular, size, fleft,             # <<<<<<<<<<<<<<
  *                                   fmode, fright)
- *
+ * 
  */
     __Pyx_XDECREF(__pyx_r);
 
-    /* "mtrand.pyx":3391
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  *             return cont3_array_sc(self.internal_state, rk_triangular, size, fleft,
  *                                   fmode, fright)             # <<<<<<<<<<<<<<
- *
+ * 
  *         PyErr_Clear()
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+    __pyx_t_2 = __pyx_f_6mtrand_cont3_array_sc(__pyx_v_self->internal_state, rk_triangular, __pyx_v_size, __pyx_v_fleft, __pyx_v_fmode, __pyx_v_fright); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3393; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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     goto __pyx_L0;
-    goto __pyx_L3;
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-  __pyx_L3:;
 
-  /* "mtrand.pyx":3393
+  /* "mtrand.pyx":3396
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  *         oleft = <ndarray>PyArray_FROM_OTF(left, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
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   PyErr_Clear();
 
-  /* "mtrand.pyx":3394
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+  /* "mtrand.pyx":3397
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  *         PyErr_Clear()
  *         oleft = <ndarray>PyArray_FROM_OTF(left, NPY_DOUBLE, NPY_ARRAY_ALIGNED)             # <<<<<<<<<<<<<<
  *         omode = <ndarray>PyArray_FROM_OTF(mode, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         oright = <ndarray>PyArray_FROM_OTF(right, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
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-  __pyx_t_2 = PyArray_FROM_OTF(__pyx_v_left, NPY_DOUBLE, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3394; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_2 = PyArray_FROM_OTF(__pyx_v_left, NPY_DOUBLE, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3397; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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@@ -16018,14 +16261,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_80triangular(struct __pyx_obj_6m
   __pyx_v_oleft = ((PyArrayObject *)__pyx_t_3);
   __pyx_t_3 = 0;
 
-  /* "mtrand.pyx":3395
+  /* "mtrand.pyx":3398
  *         PyErr_Clear()
  *         oleft = <ndarray>PyArray_FROM_OTF(left, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         omode = <ndarray>PyArray_FROM_OTF(mode, NPY_DOUBLE, NPY_ARRAY_ALIGNED)             # <<<<<<<<<<<<<<
  *         oright = <ndarray>PyArray_FROM_OTF(right, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
- *
+ * 
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+  __pyx_t_3 = PyArray_FROM_OTF(__pyx_v_mode, NPY_DOUBLE, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3398; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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@@ -16033,14 +16276,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_80triangular(struct __pyx_obj_6m
   __pyx_v_omode = ((PyArrayObject *)__pyx_t_2);
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-  /* "mtrand.pyx":3396
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  *         omode = <ndarray>PyArray_FROM_OTF(mode, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         oright = <ndarray>PyArray_FROM_OTF(right, NPY_DOUBLE, NPY_ARRAY_ALIGNED)             # <<<<<<<<<<<<<<
- *
+ * 
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+  __pyx_t_2 = PyArray_FROM_OTF(__pyx_v_right, NPY_DOUBLE, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3399; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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@@ -16048,24 +16291,24 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_80triangular(struct __pyx_obj_6m
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-  /* "mtrand.pyx":3398
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-  __pyx_t_3 = __Pyx_GetModuleGlobalName(__pyx_n_s__np); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3398; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_3 = __Pyx_GetModuleGlobalName(__pyx_n_s_np); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3401; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_3, __pyx_n_s_any); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3401; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3403; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    goto __pyx_L9;
+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3406; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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  *         return cont3_array(self.internal_state, rk_triangular, size, oleft,
  *             omode, oright)             # <<<<<<<<<<<<<<
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-  __pyx_r = Py_None; __Pyx_INCREF(Py_None);
-  goto __pyx_L0;
+  /* "mtrand.pyx":3323
+ * 
+ * 
+ *     def triangular(self, left, mode, right, size=None):             # <<<<<<<<<<<<<<
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+ *         triangular(left, mode, right, size=None)
+ */
+
+  /* function exit code */
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@@ -16262,6 +16506,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_80triangular(struct __pyx_obj_6m
   return __pyx_r;
 }
 
+/* "mtrand.pyx":3411
+ * 
+ *     # Complicated, discrete distributions:
+ *     def binomial(self, n, p, size=None):             # <<<<<<<<<<<<<<
+ *         """
+ *         binomial(n, p, size=None)
+ */
+
 /* Python wrapper */
 static PyObject *__pyx_pw_6mtrand_11RandomState_83binomial(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
 static char __pyx_doc_6mtrand_11RandomState_82binomial[] = "\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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-        if (likely((values[0] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__n)) != 0)) kw_args--;
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+          __Pyx_RaiseArgtupleInvalid("binomial", 0, 2, 3, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3411; __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 = 3411; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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+  /* function exit code */
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@@ -16359,17 +16605,17 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_82binomial(struct __pyx_obj_6mtr
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  *         ln = PyInt_AsLong(n)
  *         if not PyErr_Occurred():
  */
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-  /* "mtrand.pyx":3494
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+  /* "mtrand.pyx":3497
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  *         ln = PyInt_AsLong(n)             # <<<<<<<<<<<<<<
  *         if not PyErr_Occurred():
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-  /* "mtrand.pyx":3495
+  /* "mtrand.pyx":3498
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  *         ln = PyInt_AsLong(n)
  *         if not PyErr_Occurred():             # <<<<<<<<<<<<<<
  *             if ln < 0:
  *                 raise ValueError("n < 0")
  */
-  __pyx_t_1 = (!PyErr_Occurred());
+  __pyx_t_1 = ((!(PyErr_Occurred() != 0)) != 0);
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-    /* "mtrand.pyx":3496
+    /* "mtrand.pyx":3499
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  *         if not PyErr_Occurred():
  *             if ln < 0:             # <<<<<<<<<<<<<<
  *                 raise ValueError("n < 0")
  *             if fp < 0:
  */
-    __pyx_t_1 = (__pyx_v_ln < 0);
+    __pyx_t_1 = ((__pyx_v_ln < 0) != 0);
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-      /* "mtrand.pyx":3497
+      /* "mtrand.pyx":3500
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  *             if ln < 0:
  *                 raise ValueError("n < 0")             # <<<<<<<<<<<<<<
  *             if fp < 0:
  *                 raise ValueError("p < 0")
  */
-      __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_136), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3497; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__85, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3500; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-      goto __pyx_L4;
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  */
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  *             elif fp > 1:
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  *                 raise ValueError("p > 1")
  *             return discnp_array_sc(self.internal_state, rk_binomial, size, ln, fp)
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-    __pyx_t_1 = (__pyx_v_fp > 1.0);
+    __pyx_t_1 = ((__pyx_v_fp > 1.0) != 0);
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-      /* "mtrand.pyx":3501
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+      __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__87, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3504; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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  *         PyErr_Clear()
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-    goto __pyx_L3;
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  *         PyErr_Clear()             # <<<<<<<<<<<<<<
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-  /* "mtrand.pyx":3506
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 /* Python wrapper */
 static PyObject *__pyx_pw_6mtrand_11RandomState_85negative_binomial(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
 static char __pyx_doc_6mtrand_11RandomState_84negative_binomial[] = "\n        negative_binomial(n, p, size=None)\n\n        Draw samples from a negative_binomial distribution.\n\n        Samples are drawn from a negative_Binomial distribution with specified\n        parameters, `n` trials and `p` probability of success where `n` is an\n        integer > 0 and `p` is in the interval [0, 1].\n\n        Parameters\n        ----------\n        n : int\n            Parameter, > 0.\n        p : float\n            Parameter, >= 0 and <=1.\n        size : int or tuple of ints\n            Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : int or ndarray of ints\n            Drawn samples.\n\n        Notes\n        -----\n        The probability density for the Negative Binomial distribution is\n\n        .. math:: P(N;n,p) = \\binom{N+n-1}{n-1}p^{n}(1-p)^{N},\n\n        where :math:`n-1` is the number of successes, :math:`p` is the probability\n        of success, and :math:`N+n-1` is the number of trials.\n\n        The negative binomial distribution gives the probability of n-1 successes\n        and N failures in N+n-1 trials, and success on the (N+n)th trial.\n\n        If one throws a die repeatedly until the third time a \"1\" appears, then the\n        probability distribution of the number of non-\"1\"s that appear before the\n        third \"1\" is a negative binomial distribution.\n\n        References\n        ----------\n        .. [1] Weisstein, Eric W. \"Negative Binomial Distribution.\" From\n               MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/NegativeBinomialDistribution.html\n        .. [2] Wikipedia, \"Negative binomial distribution\",\n               http://en.wikipedia.org/wiki/Negative_binomial_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        A real world example. A company drills wild-cat oil exploration well""s, each\n        with an estimated probability of success of 0.1.  What is the probability\n        of having one success for each successive well, that is what is the\n        probability of a single success after drilling 5 wells, after 6 wells,\n        etc.?\n\n        >>> s = np.random.negative_binomial(1, 0.1, 100000)\n        >>> for i in range(1, 11):\n        ...    probability = sum(s<i) / 100000.\n        ...    print i, \"wells drilled, probability of one success =\", probability\n\n        ";
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+      __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__91, NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3593; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-      /* "mtrand.pyx":3592
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+
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 }
 
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 /* Python wrapper */
 static PyObject *__pyx_pw_6mtrand_11RandomState_87poisson(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
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-      /* "mtrand.pyx":3668
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  *             if lam > self.poisson_lam_max:
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3668; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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  *             if lam > self.poisson_lam_max:             # <<<<<<<<<<<<<<
  *                 raise ValueError("lam value too large")
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  *                 raise ValueError("lam value too large")             # <<<<<<<<<<<<<<
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+ *         return discd_array(self.internal_state, rk_zipf, size, oa)
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 }
 
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+ * 
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+ */
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 static PyObject *__pyx_pw_6mtrand_11RandomState_93hypergeometric(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
 static char __pyx_doc_6mtrand_11RandomState_92hypergeometric[] = "\n        hypergeometric(ngood, nbad, nsample, size=None)\n\n        Draw samples from a Hypergeometric distribution.\n\n        Samples are drawn from a Hypergeometric distribution with specified\n        parameters, ngood (ways to make a good selection), nbad (ways to make\n        a bad selection), and nsample = number of items sampled, which is less\n        than or equal to the sum ngood + nbad.\n\n        Parameters\n        ----------\n        ngood : int or array_like\n            Number of ways to make a good selection.  Must be nonnegative.\n        nbad : int or array_like\n            Number of ways to make a bad selection.  Must be nonnegative.\n        nsample : int or array_like\n            Number of items sampled.  Must be at least 1 and at most\n            ``ngood + nbad``.\n        size : int or tuple of int\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            The values are all integers in  [0, n].\n\n        See Also\n        --------\n        scipy.stats.distributions.hypergeom : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Hypergeometric distribution is\n\n        .. math:: P(x) = \\frac{\\binom{m}{n}\\binom{N-m}{n-x}}{\\binom{N}{n}},\n\n        where :math:`0 \\le x \\le m` and :math:`n+m-N \\le x \\le n`\n\n        for P(x) the probability of x successes, n = ngood, m = nbad, and\n        N = number of samples.\n\n        Consider an urn with black and white marbles in it, ngood of them\n        black and nbad are white. If you draw nsample balls without\n        replacement, then the Hypergeometric distribution describes the\n        distribution of black balls in the drawn sample.\n\n        Note that this distribution is very similar to the Binomial\n        distrib""ution, except that in this case, samples are drawn without\n        replacement, whereas in the Binomial case samples are drawn with\n        replacement (or the sample space is infinite). As the sample space\n        becomes large, this distribution approaches the Binomial.\n\n        References\n        ----------\n        .. [1] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n               and Quigley, 1972.\n        .. [2] Weisstein, Eric W. \"Hypergeometric Distribution.\" From\n               MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/HypergeometricDistribution.html\n        .. [3] Wikipedia, \"Hypergeometric-distribution\",\n               http://en.wikipedia.org/wiki/Hypergeometric-distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> ngood, nbad, nsamp = 100, 2, 10\n        # number of good, number of bad, and number of samples\n        >>> s = np.random.hypergeometric(ngood, nbad, nsamp, 1000)\n        >>> hist(s)\n        #   note that it is very unlikely to grab both bad items\n\n        Suppose you have an urn with 15 white and 15 black marbles.\n        If you pull 15 marbles at random, how likely is it that\n        12 or more of them are one color?\n\n        >>> s = np.random.hypergeometric(15, 15, 15, 100000)\n        >>> sum(s>=12)/100000. + sum(s<=3)/100000.\n        #   answer = 0.003 ... pretty unlikely!\n\n        ";
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@@ -18279,17 +18512,17 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_92hypergeometric(struct __pyx_ob
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-  /* "mtrand.pyx":3924
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-  /* "mtrand.pyx":3925
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  *         lnbad = PyInt_AsLong(nbad)             # <<<<<<<<<<<<<<
  *         lnsample = PyInt_AsLong(nsample)
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-  /* "mtrand.pyx":3926
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  *         lnsample = PyInt_AsLong(nsample)             # <<<<<<<<<<<<<<
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-    /* "mtrand.pyx":3928
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-      /* "mtrand.pyx":3931
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+    __pyx_t_1 = (((__pyx_v_lngood + __pyx_v_lnbad) < __pyx_v_lnsample) != 0);
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-      /* "mtrand.pyx":3935
+      /* "mtrand.pyx":3938
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3935; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-      goto __pyx_L7;
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3938; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    /* "mtrand.pyx":3936
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+ * 
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     __Pyx_XDECREF(__pyx_r);
 
-    /* "mtrand.pyx":3937
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-  /* "mtrand.pyx":3939
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-  /* "mtrand.pyx":3941
+  /* "mtrand.pyx":3944
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-  /* "mtrand.pyx":3942
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-    /* "mtrand.pyx":3945
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 static PyObject *__pyx_pw_6mtrand_11RandomState_95logseries(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
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+    __pyx_t_2 = __Pyx_PyInt_From_npy_intp(__pyx_v_d); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4254; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+    __pyx_t_3 = PyTuple_New(2); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4254; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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     __Pyx_GIVEREF(__pyx_t_2);
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-    __pyx_v_shape = ((PyObject *)__pyx_t_3);
+    __pyx_v_shape = __pyx_t_3;
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     goto __pyx_L4;
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-    /* "mtrand.pyx":4253
+    /* "mtrand.pyx":4256
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  *         else:
  *             shape = size + (d,)             # <<<<<<<<<<<<<<
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+    __pyx_t_2 = PyTuple_New(1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4256; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+    __pyx_t_3 = PyNumber_Add(__pyx_v_size, __pyx_t_2); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4256; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     __Pyx_GOTREF(__pyx_t_3);
-    __Pyx_DECREF(((PyObject *)__pyx_t_2)); __pyx_t_2 = 0;
+    __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
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-  /* "mtrand.pyx":4255
+  /* "mtrand.pyx":4258
  *             shape = size + (d,)
- *
+ * 
  *         multin = np.zeros(shape, int)             # <<<<<<<<<<<<<<
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  *         mnix = <long*>PyArray_DATA(mnarr)
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-  __pyx_t_3 = __Pyx_GetModuleGlobalName(__pyx_n_s__np); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4255; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_3 = __Pyx_GetModuleGlobalName(__pyx_n_s_np); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4258; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_3, __pyx_n_s__zeros); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4255; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_2 = __Pyx_PyObject_GetAttrStr(__pyx_t_3, __pyx_n_s_zeros); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4258; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  __pyx_t_3 = PyTuple_New(2); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4255; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_3 = PyTuple_New(2); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4258; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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   PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_v_shape);
@@ -20124,24 +20344,26 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_98multinomial(struct __pyx_obj_6
   __Pyx_INCREF(((PyObject *)((PyObject*)(&PyInt_Type))));
   PyTuple_SET_ITEM(__pyx_t_3, 1, ((PyObject *)((PyObject*)(&PyInt_Type))));
   __Pyx_GIVEREF(((PyObject *)((PyObject*)(&PyInt_Type))));
-  __pyx_t_5 = PyObject_Call(__pyx_t_2, ((PyObject *)__pyx_t_3), NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4255; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_t_5);
+  __pyx_t_6 = PyObject_Call(__pyx_t_2, __pyx_t_3, NULL); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4258; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_t_6);
   __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
-  __Pyx_DECREF(((PyObject *)__pyx_t_3)); __pyx_t_3 = 0;
-  __pyx_v_multin = __pyx_t_5;
-  __pyx_t_5 = 0;
+  __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
+  __pyx_v_multin = __pyx_t_6;
+  __pyx_t_6 = 0;
 
-  /* "mtrand.pyx":4256
- *
+  /* "mtrand.pyx":4259
+ * 
  *         multin = np.zeros(shape, int)
  *         mnarr = <ndarray>multin             # <<<<<<<<<<<<<<
  *         mnix = <long*>PyArray_DATA(mnarr)
  *         i = 0
  */
-  __Pyx_INCREF(((PyObject *)((PyArrayObject *)__pyx_v_multin)));
-  arrayObject_mnarr = ((PyArrayObject *)__pyx_v_multin);
+  __pyx_t_6 = __pyx_v_multin;
+  __Pyx_INCREF(__pyx_t_6);
+  arrayObject_mnarr = ((PyArrayObject *)__pyx_t_6);
+  __pyx_t_6 = 0;
 
-  /* "mtrand.pyx":4257
+  /* "mtrand.pyx":4260
  *         multin = np.zeros(shape, int)
  *         mnarr = <ndarray>multin
  *         mnix = <long*>PyArray_DATA(mnarr)             # <<<<<<<<<<<<<<
@@ -20150,7 +20372,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_98multinomial(struct __pyx_obj_6
  */
   __pyx_v_mnix = ((long *)PyArray_DATA(arrayObject_mnarr));
 
-  /* "mtrand.pyx":4258
+  /* "mtrand.pyx":4261
  *         mnarr = <ndarray>multin
  *         mnix = <long*>PyArray_DATA(mnarr)
  *         i = 0             # <<<<<<<<<<<<<<
@@ -20159,7 +20381,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_98multinomial(struct __pyx_obj_6
  */
   __pyx_v_i = 0;
 
-  /* "mtrand.pyx":4259
+  /* "mtrand.pyx":4262
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  *         i = 0
  *         while i < PyArray_SIZE(mnarr):             # <<<<<<<<<<<<<<
@@ -20167,10 +20389,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_98multinomial(struct __pyx_obj_6
  *             dn = n
  */
   while (1) {
-    __pyx_t_4 = (__pyx_v_i < PyArray_SIZE(arrayObject_mnarr));
+    __pyx_t_4 = ((__pyx_v_i < PyArray_SIZE(arrayObject_mnarr)) != 0);
     if (!__pyx_t_4) break;
 
-    /* "mtrand.pyx":4260
+    /* "mtrand.pyx":4263
  *         i = 0
  *         while i < PyArray_SIZE(mnarr):
  *             Sum = 1.0             # <<<<<<<<<<<<<<
@@ -20179,7 +20401,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_98multinomial(struct __pyx_obj_6
  */
     __pyx_v_Sum = 1.0;
 
-    /* "mtrand.pyx":4261
+    /* "mtrand.pyx":4264
  *         while i < PyArray_SIZE(mnarr):
  *             Sum = 1.0
  *             dn = n             # <<<<<<<<<<<<<<
@@ -20188,17 +20410,17 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_98multinomial(struct __pyx_obj_6
  */
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-    /* "mtrand.pyx":4262
+    /* "mtrand.pyx":4265
  *             Sum = 1.0
  *             dn = n
  *             for j from 0 <= j < d-1:             # <<<<<<<<<<<<<<
  *                 mnix[i+j] = rk_binomial(self.internal_state, dn, pix[j]/Sum)
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  */
-    __pyx_t_6 = (__pyx_v_d - 1);
-    for (__pyx_v_j = 0; __pyx_v_j < __pyx_t_6; __pyx_v_j++) {
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+    for (__pyx_v_j = 0; __pyx_v_j < __pyx_t_7; __pyx_v_j++) {
 
-      /* "mtrand.pyx":4263
+      /* "mtrand.pyx":4266
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  *             for j from 0 <= j < d-1:
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@@ -20209,15 +20431,15 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_98multinomial(struct __pyx_obj_6
         #ifdef WITH_THREAD
         PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();
         #endif
-        PyErr_Format(PyExc_ZeroDivisionError, "float division");
+        PyErr_SetString(PyExc_ZeroDivisionError, "float division");
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-        {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4263; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+        {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4266; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
       }
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-      /* "mtrand.pyx":4264
+      /* "mtrand.pyx":4267
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  *                 mnix[i+j] = rk_binomial(self.internal_state, dn, pix[j]/Sum)
  *                 dn = dn - mnix[i+j]             # <<<<<<<<<<<<<<
@@ -20226,17 +20448,17 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_98multinomial(struct __pyx_obj_6
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-      /* "mtrand.pyx":4265
+      /* "mtrand.pyx":4268
  *                 mnix[i+j] = rk_binomial(self.internal_state, dn, pix[j]/Sum)
  *                 dn = dn - mnix[i+j]
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  *                 Sum = Sum - pix[j]
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-      __pyx_t_4 = (__pyx_v_dn <= 0);
+      __pyx_t_4 = ((__pyx_v_dn <= 0) != 0);
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-        /* "mtrand.pyx":4266
+        /* "mtrand.pyx":4269
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@@ -20244,11 +20466,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_98multinomial(struct __pyx_obj_6
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-        goto __pyx_L9;
       }
-      __pyx_L9:;
 
-      /* "mtrand.pyx":4267
+      /* "mtrand.pyx":4270
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  *                 Sum = Sum - pix[j]             # <<<<<<<<<<<<<<
@@ -20259,21 +20479,21 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_98multinomial(struct __pyx_obj_6
     }
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+    /* "mtrand.pyx":4271
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  *                 mnix[i+d-1] = dn
- *
+ * 
  */
-    __pyx_t_4 = (__pyx_v_dn > 0);
+    __pyx_t_4 = ((__pyx_v_dn > 0) != 0);
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-      /* "mtrand.pyx":4269
+      /* "mtrand.pyx":4272
  *                 Sum = Sum - pix[j]
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- *
+ * 
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@@ -20281,21 +20501,21 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_98multinomial(struct __pyx_obj_6
     }
     __pyx_L10:;
 
-    /* "mtrand.pyx":4271
+    /* "mtrand.pyx":4274
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- *
+ * 
  *             i = i + d             # <<<<<<<<<<<<<<
- *
+ * 
  *         return multin
  */
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-  /* "mtrand.pyx":4273
+  /* "mtrand.pyx":4276
  *             i = i + d
- *
+ * 
  *         return multin             # <<<<<<<<<<<<<<
- *
+ * 
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@@ -20303,12 +20523,19 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_98multinomial(struct __pyx_obj_6
   __pyx_r = __pyx_v_multin;
   goto __pyx_L0;
 
-  __pyx_r = Py_None; __Pyx_INCREF(Py_None);
-  goto __pyx_L0;
+  /* "mtrand.pyx":4185
+ *         return x
+ * 
+ *     def multinomial(self, npy_intp n, object pvals, size=None):             # <<<<<<<<<<<<<<
+ *         """
+ *         multinomial(n, pvals, size=None)
+ */
+
+  /* function exit code */
   __pyx_L1_error:;
   __Pyx_XDECREF(__pyx_t_2);
   __Pyx_XDECREF(__pyx_t_3);
-  __Pyx_XDECREF(__pyx_t_5);
+  __Pyx_XDECREF(__pyx_t_6);
   __Pyx_AddTraceback("mtrand.RandomState.multinomial", __pyx_clineno, __pyx_lineno, __pyx_filename);
   __pyx_r = NULL;
   __pyx_L0:;
@@ -20321,6 +20548,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_98multinomial(struct __pyx_obj_6
   return __pyx_r;
 }
 
+/* "mtrand.pyx":4278
+ *         return multin
+ * 
+ *     def dirichlet(self, object alpha, size=None):             # <<<<<<<<<<<<<<
+ *         """
+ *         dirichlet(alpha, size=None)
+ */
+
 /* Python wrapper */
 static PyObject *__pyx_pw_6mtrand_11RandomState_101dirichlet(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
 static char __pyx_doc_6mtrand_11RandomState_100dirichlet[] = "\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        ";
@@ -20334,16 +20569,8 @@ static PyObject *__pyx_pw_6mtrand_11RandomState_101dirichlet(PyObject *__pyx_v_s
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+    static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_alpha,&__pyx_n_s_size,0};
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-
-    /* "mtrand.pyx":4275
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- *
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- */
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@@ -20357,16 +20584,16 @@ static PyObject *__pyx_pw_6mtrand_11RandomState_101dirichlet(PyObject *__pyx_v_s
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       switch (pos_args) {
         case  0:
-        if (likely((values[0] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__alpha)) != 0)) kw_args--;
+        if (likely((values[0] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_alpha)) != 0)) kw_args--;
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-          PyObject* value = PyDict_GetItem(__pyx_kwds, __pyx_n_s__size);
+          PyObject* value = PyDict_GetItem(__pyx_kwds, __pyx_n_s_size);
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+        if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "dirichlet") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4278; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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+  __Pyx_RaiseArgtupleInvalid("dirichlet", 0, 1, 2, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4278; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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     --Py_REFCNT(o);
     PyErr_Restore(etype, eval, etb);
   }
@@ -21210,104 +21481,6 @@ static PyMethodDef __pyx_methods_6mtrand_RandomState[] = {
   {0, 0, 0, 0}
 };
 
-static PyNumberMethods __pyx_tp_as_number_RandomState = {
-  0, /*nb_add*/
-  0, /*nb_subtract*/
-  0, /*nb_multiply*/
-  #if PY_MAJOR_VERSION < 3
-  0, /*nb_divide*/
-  #endif
-  0, /*nb_remainder*/
-  0, /*nb_divmod*/
-  0, /*nb_power*/
-  0, /*nb_negative*/
-  0, /*nb_positive*/
-  0, /*nb_absolute*/
-  0, /*nb_nonzero*/
-  0, /*nb_invert*/
-  0, /*nb_lshift*/
-  0, /*nb_rshift*/
-  0, /*nb_and*/
-  0, /*nb_xor*/
-  0, /*nb_or*/
-  #if PY_MAJOR_VERSION < 3
-  0, /*nb_coerce*/
-  #endif
-  0, /*nb_int*/
-  #if PY_MAJOR_VERSION < 3
-  0, /*nb_long*/
-  #else
-  0, /*reserved*/
-  #endif
-  0, /*nb_float*/
-  #if PY_MAJOR_VERSION < 3
-  0, /*nb_oct*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*nb_hex*/
-  #endif
-  0, /*nb_inplace_add*/
-  0, /*nb_inplace_subtract*/
-  0, /*nb_inplace_multiply*/
-  #if PY_MAJOR_VERSION < 3
-  0, /*nb_inplace_divide*/
-  #endif
-  0, /*nb_inplace_remainder*/
-  0, /*nb_inplace_power*/
-  0, /*nb_inplace_lshift*/
-  0, /*nb_inplace_rshift*/
-  0, /*nb_inplace_and*/
-  0, /*nb_inplace_xor*/
-  0, /*nb_inplace_or*/
-  0, /*nb_floor_divide*/
-  0, /*nb_true_divide*/
-  0, /*nb_inplace_floor_divide*/
-  0, /*nb_inplace_true_divide*/
-  #if PY_VERSION_HEX >= 0x02050000
-  0, /*nb_index*/
-  #endif
-};
-
-static PySequenceMethods __pyx_tp_as_sequence_RandomState = {
-  0, /*sq_length*/
-  0, /*sq_concat*/
-  0, /*sq_repeat*/
-  0, /*sq_item*/
-  0, /*sq_slice*/
-  0, /*sq_ass_item*/
-  0, /*sq_ass_slice*/
-  0, /*sq_contains*/
-  0, /*sq_inplace_concat*/
-  0, /*sq_inplace_repeat*/
-};
-
-static PyMappingMethods __pyx_tp_as_mapping_RandomState = {
-  0, /*mp_length*/
-  0, /*mp_subscript*/
-  0, /*mp_ass_subscript*/
-};
-
-static PyBufferProcs __pyx_tp_as_buffer_RandomState = {
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getreadbuffer*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getwritebuffer*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getsegcount*/
-  #endif
-  #if PY_MAJOR_VERSION < 3
-  0, /*bf_getcharbuffer*/
-  #endif
-  #if PY_VERSION_HEX >= 0x02060000
-  0, /*bf_getbuffer*/
-  #endif
-  #if PY_VERSION_HEX >= 0x02060000
-  0, /*bf_releasebuffer*/
-  #endif
-};
-
 static PyTypeObject __pyx_type_6mtrand_RandomState = {
   PyVarObject_HEAD_INIT(0, 0)
   __Pyx_NAMESTR("mtrand.RandomState"), /*tp_name*/
@@ -21323,17 +21496,17 @@ static PyTypeObject __pyx_type_6mtrand_RandomState = {
   0, /*reserved*/
   #endif
   0, /*tp_repr*/
-  &__pyx_tp_as_number_RandomState, /*tp_as_number*/
-  &__pyx_tp_as_sequence_RandomState, /*tp_as_sequence*/
-  &__pyx_tp_as_mapping_RandomState, /*tp_as_mapping*/
+  0, /*tp_as_number*/
+  0, /*tp_as_sequence*/
+  0, /*tp_as_mapping*/
   0, /*tp_hash*/
   0, /*tp_call*/
   0, /*tp_str*/
   0, /*tp_getattro*/
   0, /*tp_setattro*/
-  &__pyx_tp_as_buffer_RandomState, /*tp_as_buffer*/
+  0, /*tp_as_buffer*/
   Py_TPFLAGS_DEFAULT|Py_TPFLAGS_HAVE_VERSION_TAG|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*/
+  __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 : {None, int, 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*/
   0, /*tp_richcompare*/
@@ -21362,6 +21535,9 @@ static PyTypeObject __pyx_type_6mtrand_RandomState = {
   #if PY_VERSION_HEX >= 0x02060000
   0, /*tp_version_tag*/
   #endif
+  #if PY_VERSION_HEX >= 0x030400a1
+  0, /*tp_finalize*/
+  #endif
 };
 
 static PyMethodDef __pyx_methods[] = {
@@ -21387,287 +21563,285 @@ static struct PyModuleDef __pyx_moduledef = {
 #endif
 
 static __Pyx_StringTabEntry __pyx_string_tab[] = {
-  {&__pyx_kp_s_1, __pyx_k_1, sizeof(__pyx_k_1), 0, 0, 1, 0},
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-  {&__pyx_kp_s_118, __pyx_k_118, sizeof(__pyx_k_118), 0, 0, 1, 0},
-  {&__pyx_kp_s_120, __pyx_k_120, sizeof(__pyx_k_120), 0, 0, 1, 0},
-  {&__pyx_kp_s_123, __pyx_k_123, sizeof(__pyx_k_123), 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_130, __pyx_k_130, sizeof(__pyx_k_130), 0, 0, 1, 0},
-  {&__pyx_kp_s_135, __pyx_k_135, sizeof(__pyx_k_135), 0, 0, 1, 0},
-  {&__pyx_kp_s_137, __pyx_k_137, sizeof(__pyx_k_137), 0, 0, 1, 0},
-  {&__pyx_kp_s_139, __pyx_k_139, sizeof(__pyx_k_139), 0, 0, 1, 0},
-  {&__pyx_kp_s_144, __pyx_k_144, sizeof(__pyx_k_144), 0, 0, 1, 0},
-  {&__pyx_kp_s_15, __pyx_k_15, sizeof(__pyx_k_15), 0, 0, 1, 0},
-  {&__pyx_kp_s_152, __pyx_k_152, sizeof(__pyx_k_152), 0, 0, 1, 0},
-  {&__pyx_kp_s_154, __pyx_k_154, sizeof(__pyx_k_154), 0, 0, 1, 0},
-  {&__pyx_kp_s_157, __pyx_k_157, sizeof(__pyx_k_157), 0, 0, 1, 0},
-  {&__pyx_kp_s_159, __pyx_k_159, sizeof(__pyx_k_159), 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_kp_s_168, __pyx_k_168, sizeof(__pyx_k_168), 0, 0, 1, 0},
-  {&__pyx_kp_s_170, __pyx_k_170, sizeof(__pyx_k_170), 0, 0, 1, 0},
-  {&__pyx_kp_s_172, __pyx_k_172, sizeof(__pyx_k_172), 0, 0, 1, 0},
-  {&__pyx_kp_s_174, __pyx_k_174, sizeof(__pyx_k_174), 0, 0, 1, 0},
-  {&__pyx_kp_s_18, __pyx_k_18, sizeof(__pyx_k_18), 0, 0, 1, 0},
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-  {&__pyx_kp_s_182, __pyx_k_182, sizeof(__pyx_k_182), 0, 0, 1, 0},
-  {&__pyx_kp_s_186, __pyx_k_186, sizeof(__pyx_k_186), 0, 0, 1, 0},
-  {&__pyx_kp_s_188, __pyx_k_188, sizeof(__pyx_k_188), 0, 0, 1, 0},
-  {&__pyx_kp_s_190, __pyx_k_190, sizeof(__pyx_k_190), 0, 0, 1, 0},
-  {&__pyx_n_s_193, __pyx_k_193, sizeof(__pyx_k_193), 0, 0, 1, 1},
-  {&__pyx_kp_s_194, __pyx_k_194, sizeof(__pyx_k_194), 0, 0, 1, 0},
-  {&__pyx_n_s_199, __pyx_k_199, sizeof(__pyx_k_199), 0, 0, 1, 1},
-  {&__pyx_kp_s_20, __pyx_k_20, sizeof(__pyx_k_20), 0, 0, 1, 0},
-  {&__pyx_n_s_200, __pyx_k_200, sizeof(__pyx_k_200), 0, 0, 1, 1},
-  {&__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_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},
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-  {&__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},
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-  {&__pyx_kp_u_258, __pyx_k_258, sizeof(__pyx_k_258), 0, 1, 0, 0},
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-  {&__pyx_kp_u_263, __pyx_k_263, sizeof(__pyx_k_263), 0, 1, 0, 0},
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+  {&__pyx_kp_u_RandomState_random_integers_line, __pyx_k_RandomState_random_integers_line, sizeof(__pyx_k_RandomState_random_integers_line), 0, 1, 0, 0},
+  {&__pyx_kp_u_RandomState_random_sample_line_7, __pyx_k_RandomState_random_sample_line_7, sizeof(__pyx_k_RandomState_random_sample_line_7), 0, 1, 0, 0},
+  {&__pyx_kp_u_RandomState_rayleigh_line_3165, __pyx_k_RandomState_rayleigh_line_3165, sizeof(__pyx_k_RandomState_rayleigh_line_3165), 0, 1, 0, 0},
+  {&__pyx_kp_u_RandomState_shuffle_line_4395, __pyx_k_RandomState_shuffle_line_4395, sizeof(__pyx_k_RandomState_shuffle_line_4395), 0, 1, 0, 0},
+  {&__pyx_kp_u_RandomState_standard_cauchy_line, __pyx_k_RandomState_standard_cauchy_line, sizeof(__pyx_k_RandomState_standard_cauchy_line), 0, 1, 0, 0},
+  {&__pyx_kp_u_RandomState_standard_exponential, __pyx_k_RandomState_standard_exponential, sizeof(__pyx_k_RandomState_standard_exponential), 0, 1, 0, 0},
+  {&__pyx_kp_u_RandomState_standard_gamma_line, __pyx_k_RandomState_standard_gamma_line, sizeof(__pyx_k_RandomState_standard_gamma_line), 0, 1, 0, 0},
+  {&__pyx_kp_u_RandomState_standard_normal_line, __pyx_k_RandomState_standard_normal_line, sizeof(__pyx_k_RandomState_standard_normal_line), 0, 1, 0, 0},
+  {&__pyx_kp_u_RandomState_standard_t_line_2235, __pyx_k_RandomState_standard_t_line_2235, sizeof(__pyx_k_RandomState_standard_t_line_2235), 0, 1, 0, 0},
+  {&__pyx_kp_u_RandomState_tomaxint_line_765, __pyx_k_RandomState_tomaxint_line_765, sizeof(__pyx_k_RandomState_tomaxint_line_765), 0, 1, 0, 0},
+  {&__pyx_kp_u_RandomState_triangular_line_3323, __pyx_k_RandomState_triangular_line_3323, sizeof(__pyx_k_RandomState_triangular_line_3323), 0, 1, 0, 0},
+  {&__pyx_kp_u_RandomState_uniform_line_1095, __pyx_k_RandomState_uniform_line_1095, sizeof(__pyx_k_RandomState_uniform_line_1095), 0, 1, 0, 0},
+  {&__pyx_kp_u_RandomState_vonmises_line_2336, __pyx_k_RandomState_vonmises_line_2336, sizeof(__pyx_k_RandomState_vonmises_line_2336), 0, 1, 0, 0},
+  {&__pyx_kp_u_RandomState_wald_line_3237, __pyx_k_RandomState_wald_line_3237, sizeof(__pyx_k_RandomState_wald_line_3237), 0, 1, 0, 0},
+  {&__pyx_kp_u_RandomState_weibull_line_2526, __pyx_k_RandomState_weibull_line_2526, sizeof(__pyx_k_RandomState_weibull_line_2526), 0, 1, 0, 0},
+  {&__pyx_kp_u_RandomState_zipf_line_3685, __pyx_k_RandomState_zipf_line_3685, sizeof(__pyx_k_RandomState_zipf_line_3685), 0, 1, 0, 0},
+  {&__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_a, __pyx_k_a, sizeof(__pyx_k_a), 0, 0, 1, 1},
+  {&__pyx_kp_s_a_0, __pyx_k_a_0, sizeof(__pyx_k_a_0), 0, 0, 1, 0},
+  {&__pyx_kp_s_a_1_0, __pyx_k_a_1_0, sizeof(__pyx_k_a_1_0), 0, 0, 1, 0},
+  {&__pyx_kp_s_a_and_p_must_have_same_size, __pyx_k_a_and_p_must_have_same_size, sizeof(__pyx_k_a_and_p_must_have_same_size), 0, 0, 1, 0},
+  {&__pyx_kp_s_a_must_be_1_dimensional, __pyx_k_a_must_be_1_dimensional, sizeof(__pyx_k_a_must_be_1_dimensional), 0, 0, 1, 0},
+  {&__pyx_kp_s_a_must_be_1_dimensional_or_an_in, __pyx_k_a_must_be_1_dimensional_or_an_in, sizeof(__pyx_k_a_must_be_1_dimensional_or_an_in), 0, 0, 1, 0},
+  {&__pyx_kp_s_a_must_be_greater_than_0, __pyx_k_a_must_be_greater_than_0, sizeof(__pyx_k_a_must_be_greater_than_0), 0, 0, 1, 0},
+  {&__pyx_kp_s_a_must_be_non_empty, __pyx_k_a_must_be_non_empty, sizeof(__pyx_k_a_must_be_non_empty), 0, 0, 1, 0},
+  {&__pyx_n_s_add, __pyx_k_add, sizeof(__pyx_k_add), 0, 0, 1, 1},
+  {&__pyx_kp_s_algorithm_must_be_MT19937, __pyx_k_algorithm_must_be_MT19937, sizeof(__pyx_k_algorithm_must_be_MT19937), 0, 0, 1, 0},
+  {&__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_arange, __pyx_k_arange, sizeof(__pyx_k_arange), 0, 0, 1, 1},
+  {&__pyx_n_s_array, __pyx_k_array, sizeof(__pyx_k_array), 0, 0, 1, 1},
+  {&__pyx_n_s_asarray, __pyx_k_asarray, sizeof(__pyx_k_asarray), 0, 0, 1, 1},
+  {&__pyx_n_s_b, __pyx_k_b, sizeof(__pyx_k_b), 0, 0, 1, 1},
+  {&__pyx_kp_s_b_0, __pyx_k_b_0, sizeof(__pyx_k_b_0), 0, 0, 1, 0},
+  {&__pyx_n_s_beta, __pyx_k_beta, sizeof(__pyx_k_beta), 0, 0, 1, 1},
+  {&__pyx_n_s_binomial, __pyx_k_binomial, sizeof(__pyx_k_binomial), 0, 0, 1, 1},
+  {&__pyx_kp_u_binomial_n_p_size_None_Draw_sam, __pyx_k_binomial_n_p_size_None_Draw_sam, sizeof(__pyx_k_binomial_n_p_size_None_Draw_sam), 0, 1, 0, 0},
+  {&__pyx_n_s_bytes, __pyx_k_bytes, sizeof(__pyx_k_bytes), 0, 0, 1, 1},
+  {&__pyx_kp_u_bytes_length_Return_random_byte, __pyx_k_bytes_length_Return_random_byte, sizeof(__pyx_k_bytes_length_Return_random_byte), 0, 1, 0, 0},
+  {&__pyx_n_s_chisquare, __pyx_k_chisquare, sizeof(__pyx_k_chisquare), 0, 0, 1, 1},
+  {&__pyx_kp_u_chisquare_df_size_None_Draw_sam, __pyx_k_chisquare_df_size_None_Draw_sam, sizeof(__pyx_k_chisquare_df_size_None_Draw_sam), 0, 1, 0, 0},
+  {&__pyx_n_s_choice, __pyx_k_choice, sizeof(__pyx_k_choice), 0, 0, 1, 1},
+  {&__pyx_kp_u_choice_a_size_None_replace_True, __pyx_k_choice_a_size_None_replace_True, sizeof(__pyx_k_choice_a_size_None_replace_True), 0, 1, 0, 0},
+  {&__pyx_n_s_copy, __pyx_k_copy, sizeof(__pyx_k_copy), 0, 0, 1, 1},
+  {&__pyx_n_s_count_nonzero, __pyx_k_count_nonzero, sizeof(__pyx_k_count_nonzero), 0, 0, 1, 1},
+  {&__pyx_n_s_cov, __pyx_k_cov, sizeof(__pyx_k_cov), 0, 0, 1, 1},
+  {&__pyx_kp_s_cov_must_be_2_dimensional_and_sq, __pyx_k_cov_must_be_2_dimensional_and_sq, sizeof(__pyx_k_cov_must_be_2_dimensional_and_sq), 0, 0, 1, 0},
+  {&__pyx_n_s_cumsum, __pyx_k_cumsum, sizeof(__pyx_k_cumsum), 0, 0, 1, 1},
+  {&__pyx_n_s_df, __pyx_k_df, sizeof(__pyx_k_df), 0, 0, 1, 1},
+  {&__pyx_kp_s_df_0, __pyx_k_df_0, sizeof(__pyx_k_df_0), 0, 0, 1, 0},
+  {&__pyx_kp_s_df_1, __pyx_k_df_1, sizeof(__pyx_k_df_1), 0, 0, 1, 0},
+  {&__pyx_n_s_dfden, __pyx_k_dfden, sizeof(__pyx_k_dfden), 0, 0, 1, 1},
+  {&__pyx_kp_s_dfden_0, __pyx_k_dfden_0, sizeof(__pyx_k_dfden_0), 0, 0, 1, 0},
+  {&__pyx_n_s_dfnum, __pyx_k_dfnum, sizeof(__pyx_k_dfnum), 0, 0, 1, 1},
+  {&__pyx_kp_s_dfnum_0, __pyx_k_dfnum_0, sizeof(__pyx_k_dfnum_0), 0, 0, 1, 0},
+  {&__pyx_kp_s_dfnum_1, __pyx_k_dfnum_1, sizeof(__pyx_k_dfnum_1), 0, 0, 1, 0},
+  {&__pyx_n_s_dirichlet, __pyx_k_dirichlet, sizeof(__pyx_k_dirichlet), 0, 0, 1, 1},
+  {&__pyx_kp_u_dirichlet_alpha_size_None_Draw, __pyx_k_dirichlet_alpha_size_None_Draw, sizeof(__pyx_k_dirichlet_alpha_size_None_Draw), 0, 1, 0, 0},
+  {&__pyx_n_s_dot, __pyx_k_dot, sizeof(__pyx_k_dot), 0, 0, 1, 1},
+  {&__pyx_n_s_dtype, __pyx_k_dtype, sizeof(__pyx_k_dtype), 0, 0, 1, 1},
+  {&__pyx_n_s_empty, __pyx_k_empty, sizeof(__pyx_k_empty), 0, 0, 1, 1},
+  {&__pyx_n_s_equal, __pyx_k_equal, sizeof(__pyx_k_equal), 0, 0, 1, 1},
+  {&__pyx_n_s_exponential, __pyx_k_exponential, sizeof(__pyx_k_exponential), 0, 0, 1, 1},
+  {&__pyx_n_s_f, __pyx_k_f, sizeof(__pyx_k_f), 0, 0, 1, 1},
+  {&__pyx_kp_u_f_dfnum_dfden_size_None_Draw_sa, __pyx_k_f_dfnum_dfden_size_None_Draw_sa, sizeof(__pyx_k_f_dfnum_dfden_size_None_Draw_sa), 0, 1, 0, 0},
+  {&__pyx_n_s_float64, __pyx_k_float64, sizeof(__pyx_k_float64), 0, 0, 1, 1},
+  {&__pyx_n_s_gamma, __pyx_k_gamma, sizeof(__pyx_k_gamma), 0, 0, 1, 1},
+  {&__pyx_kp_u_gamma_shape_scale_1_0_size_None, __pyx_k_gamma_shape_scale_1_0_size_None, sizeof(__pyx_k_gamma_shape_scale_1_0_size_None), 0, 1, 0, 0},
+  {&__pyx_n_s_geometric, __pyx_k_geometric, sizeof(__pyx_k_geometric), 0, 0, 1, 1},
+  {&__pyx_kp_u_geometric_p_size_None_Draw_samp, __pyx_k_geometric_p_size_None_Draw_samp, sizeof(__pyx_k_geometric_p_size_None_Draw_samp), 0, 1, 0, 0},
+  {&__pyx_n_s_get_state, __pyx_k_get_state, sizeof(__pyx_k_get_state), 0, 0, 1, 1},
+  {&__pyx_n_s_greater, __pyx_k_greater, sizeof(__pyx_k_greater), 0, 0, 1, 1},
+  {&__pyx_n_s_greater_equal, __pyx_k_greater_equal, sizeof(__pyx_k_greater_equal), 0, 0, 1, 1},
+  {&__pyx_n_s_gumbel, __pyx_k_gumbel, sizeof(__pyx_k_gumbel), 0, 0, 1, 1},
+  {&__pyx_kp_u_gumbel_loc_0_0_scale_1_0_size_N, __pyx_k_gumbel_loc_0_0_scale_1_0_size_N, sizeof(__pyx_k_gumbel_loc_0_0_scale_1_0_size_N), 0, 1, 0, 0},
+  {&__pyx_n_s_high, __pyx_k_high, sizeof(__pyx_k_high), 0, 0, 1, 1},
+  {&__pyx_n_s_hypergeometric, __pyx_k_hypergeometric, sizeof(__pyx_k_hypergeometric), 0, 0, 1, 1},
+  {&__pyx_kp_u_hypergeometric_ngood_nbad_nsamp, __pyx_k_hypergeometric_ngood_nbad_nsamp, sizeof(__pyx_k_hypergeometric_ngood_nbad_nsamp), 0, 1, 0, 0},
+  {&__pyx_n_s_iinfo, __pyx_k_iinfo, sizeof(__pyx_k_iinfo), 0, 0, 1, 1},
+  {&__pyx_n_s_import, __pyx_k_import, sizeof(__pyx_k_import), 0, 0, 1, 1},
+  {&__pyx_n_s_index, __pyx_k_index, sizeof(__pyx_k_index), 0, 0, 1, 1},
+  {&__pyx_n_s_int, __pyx_k_int, sizeof(__pyx_k_int), 0, 0, 1, 1},
+  {&__pyx_n_s_integer, __pyx_k_integer, sizeof(__pyx_k_integer), 0, 0, 1, 1},
+  {&__pyx_n_s_intp, __pyx_k_intp, sizeof(__pyx_k_intp), 0, 0, 1, 1},
+  {&__pyx_n_s_item, __pyx_k_item, sizeof(__pyx_k_item), 0, 0, 1, 1},
+  {&__pyx_n_s_kappa, __pyx_k_kappa, sizeof(__pyx_k_kappa), 0, 0, 1, 1},
+  {&__pyx_kp_s_kappa_0, __pyx_k_kappa_0, sizeof(__pyx_k_kappa_0), 0, 0, 1, 0},
+  {&__pyx_n_s_l, __pyx_k_l, sizeof(__pyx_k_l), 0, 0, 1, 1},
+  {&__pyx_n_s_lam, __pyx_k_lam, sizeof(__pyx_k_lam), 0, 0, 1, 1},
+  {&__pyx_kp_s_lam_0, __pyx_k_lam_0, sizeof(__pyx_k_lam_0), 0, 0, 1, 0},
+  {&__pyx_kp_s_lam_value_too_large, __pyx_k_lam_value_too_large, sizeof(__pyx_k_lam_value_too_large), 0, 0, 1, 0},
+  {&__pyx_kp_s_lam_value_too_large_2, __pyx_k_lam_value_too_large_2, sizeof(__pyx_k_lam_value_too_large_2), 0, 0, 1, 0},
+  {&__pyx_n_s_laplace, __pyx_k_laplace, sizeof(__pyx_k_laplace), 0, 0, 1, 1},
+  {&__pyx_kp_u_laplace_loc_0_0_scale_1_0_size, __pyx_k_laplace_loc_0_0_scale_1_0_size, sizeof(__pyx_k_laplace_loc_0_0_scale_1_0_size), 0, 1, 0, 0},
+  {&__pyx_n_s_left, __pyx_k_left, sizeof(__pyx_k_left), 0, 0, 1, 1},
+  {&__pyx_kp_s_left_mode, __pyx_k_left_mode, sizeof(__pyx_k_left_mode), 0, 0, 1, 0},
+  {&__pyx_kp_s_left_right, __pyx_k_left_right, sizeof(__pyx_k_left_right), 0, 0, 1, 0},
+  {&__pyx_n_s_less, __pyx_k_less, sizeof(__pyx_k_less), 0, 0, 1, 1},
+  {&__pyx_n_s_less_equal, __pyx_k_less_equal, sizeof(__pyx_k_less_equal), 0, 0, 1, 1},
+  {&__pyx_n_s_loc, __pyx_k_loc, sizeof(__pyx_k_loc), 0, 0, 1, 1},
+  {&__pyx_n_s_logical_or, __pyx_k_logical_or, sizeof(__pyx_k_logical_or), 0, 0, 1, 1},
+  {&__pyx_n_s_logistic, __pyx_k_logistic, sizeof(__pyx_k_logistic), 0, 0, 1, 1},
+  {&__pyx_kp_u_logistic_loc_0_0_scale_1_0_size, __pyx_k_logistic_loc_0_0_scale_1_0_size, sizeof(__pyx_k_logistic_loc_0_0_scale_1_0_size), 0, 1, 0, 0},
+  {&__pyx_n_s_lognormal, __pyx_k_lognormal, sizeof(__pyx_k_lognormal), 0, 0, 1, 1},
+  {&__pyx_kp_u_lognormal_mean_0_0_sigma_1_0_si, __pyx_k_lognormal_mean_0_0_sigma_1_0_si, sizeof(__pyx_k_lognormal_mean_0_0_sigma_1_0_si), 0, 1, 0, 0},
+  {&__pyx_n_s_logseries, __pyx_k_logseries, sizeof(__pyx_k_logseries), 0, 0, 1, 1},
+  {&__pyx_kp_u_logseries_p_size_None_Draw_samp, __pyx_k_logseries_p_size_None_Draw_samp, sizeof(__pyx_k_logseries_p_size_None_Draw_samp), 0, 1, 0, 0},
+  {&__pyx_n_s_low, __pyx_k_low, sizeof(__pyx_k_low), 0, 0, 1, 1},
+  {&__pyx_kp_s_low_high, __pyx_k_low_high, sizeof(__pyx_k_low_high), 0, 0, 1, 0},
+  {&__pyx_n_s_main, __pyx_k_main, sizeof(__pyx_k_main), 0, 0, 1, 1},
+  {&__pyx_n_s_max, __pyx_k_max, sizeof(__pyx_k_max), 0, 0, 1, 1},
+  {&__pyx_n_s_mean, __pyx_k_mean, sizeof(__pyx_k_mean), 0, 0, 1, 1},
+  {&__pyx_kp_s_mean_0, __pyx_k_mean_0, sizeof(__pyx_k_mean_0), 0, 0, 1, 0},
+  {&__pyx_kp_s_mean_0_0, __pyx_k_mean_0_0, sizeof(__pyx_k_mean_0_0), 0, 0, 1, 0},
+  {&__pyx_kp_s_mean_and_cov_must_have_same_leng, __pyx_k_mean_and_cov_must_have_same_leng, sizeof(__pyx_k_mean_and_cov_must_have_same_leng), 0, 0, 1, 0},
+  {&__pyx_kp_s_mean_must_be_1_dimensional, __pyx_k_mean_must_be_1_dimensional, sizeof(__pyx_k_mean_must_be_1_dimensional), 0, 0, 1, 0},
+  {&__pyx_n_s_mode, __pyx_k_mode, sizeof(__pyx_k_mode), 0, 0, 1, 1},
+  {&__pyx_kp_s_mode_right, __pyx_k_mode_right, sizeof(__pyx_k_mode_right), 0, 0, 1, 0},
+  {&__pyx_n_s_mu, __pyx_k_mu, sizeof(__pyx_k_mu), 0, 0, 1, 1},
+  {&__pyx_n_s_multinomial, __pyx_k_multinomial, sizeof(__pyx_k_multinomial), 0, 0, 1, 1},
+  {&__pyx_kp_u_multinomial_n_pvals_size_None_D, __pyx_k_multinomial_n_pvals_size_None_D, sizeof(__pyx_k_multinomial_n_pvals_size_None_D), 0, 1, 0, 0},
+  {&__pyx_n_s_multiply, __pyx_k_multiply, sizeof(__pyx_k_multiply), 0, 0, 1, 1},
+  {&__pyx_n_s_multivariate_normal, __pyx_k_multivariate_normal, sizeof(__pyx_k_multivariate_normal), 0, 0, 1, 1},
+  {&__pyx_kp_u_multivariate_normal_mean_cov_si, __pyx_k_multivariate_normal_mean_cov_si, sizeof(__pyx_k_multivariate_normal_mean_cov_si), 0, 1, 0, 0},
+  {&__pyx_n_s_n, __pyx_k_n, sizeof(__pyx_k_n), 0, 0, 1, 1},
+  {&__pyx_kp_s_n_0, __pyx_k_n_0, sizeof(__pyx_k_n_0), 0, 0, 1, 0},
+  {&__pyx_kp_s_n_0_2, __pyx_k_n_0_2, sizeof(__pyx_k_n_0_2), 0, 0, 1, 0},
+  {&__pyx_n_s_nbad, __pyx_k_nbad, sizeof(__pyx_k_nbad), 0, 0, 1, 1},
+  {&__pyx_kp_s_nbad_0, __pyx_k_nbad_0, sizeof(__pyx_k_nbad_0), 0, 0, 1, 0},
+  {&__pyx_n_s_ndarray, __pyx_k_ndarray, sizeof(__pyx_k_ndarray), 0, 0, 1, 1},
+  {&__pyx_n_s_ndim, __pyx_k_ndim, sizeof(__pyx_k_ndim), 0, 0, 1, 1},
+  {&__pyx_n_s_negative_binomial, __pyx_k_negative_binomial, sizeof(__pyx_k_negative_binomial), 0, 0, 1, 1},
+  {&__pyx_kp_u_negative_binomial_n_p_size_None, __pyx_k_negative_binomial_n_p_size_None, sizeof(__pyx_k_negative_binomial_n_p_size_None), 0, 1, 0, 0},
+  {&__pyx_n_s_ngood, __pyx_k_ngood, sizeof(__pyx_k_ngood), 0, 0, 1, 1},
+  {&__pyx_kp_s_ngood_0, __pyx_k_ngood_0, sizeof(__pyx_k_ngood_0), 0, 0, 1, 0},
+  {&__pyx_kp_s_ngood_nbad_nsample, __pyx_k_ngood_nbad_nsample, sizeof(__pyx_k_ngood_nbad_nsample), 0, 0, 1, 0},
+  {&__pyx_n_s_nonc, __pyx_k_nonc, sizeof(__pyx_k_nonc), 0, 0, 1, 1},
+  {&__pyx_kp_s_nonc_0, __pyx_k_nonc_0, sizeof(__pyx_k_nonc_0), 0, 0, 1, 0},
+  {&__pyx_kp_s_nonc_0_2, __pyx_k_nonc_0_2, sizeof(__pyx_k_nonc_0_2), 0, 0, 1, 0},
+  {&__pyx_n_s_noncentral_chisquare, __pyx_k_noncentral_chisquare, sizeof(__pyx_k_noncentral_chisquare), 0, 0, 1, 1},
+  {&__pyx_kp_u_noncentral_chisquare_df_nonc_si, __pyx_k_noncentral_chisquare_df_nonc_si, sizeof(__pyx_k_noncentral_chisquare_df_nonc_si), 0, 1, 0, 0},
+  {&__pyx_n_s_noncentral_f, __pyx_k_noncentral_f, sizeof(__pyx_k_noncentral_f), 0, 0, 1, 1},
+  {&__pyx_kp_u_noncentral_f_dfnum_dfden_nonc_s, __pyx_k_noncentral_f_dfnum_dfden_nonc_s, sizeof(__pyx_k_noncentral_f_dfnum_dfden_nonc_s), 0, 1, 0, 0},
+  {&__pyx_n_s_normal, __pyx_k_normal, sizeof(__pyx_k_normal), 0, 0, 1, 1},
+  {&__pyx_kp_u_normal_loc_0_0_scale_1_0_size_N, __pyx_k_normal_loc_0_0_scale_1_0_size_N, sizeof(__pyx_k_normal_loc_0_0_scale_1_0_size_N), 0, 1, 0, 0},
+  {&__pyx_n_s_np, __pyx_k_np, sizeof(__pyx_k_np), 0, 0, 1, 1},
+  {&__pyx_n_s_nsample, __pyx_k_nsample, sizeof(__pyx_k_nsample), 0, 0, 1, 1},
+  {&__pyx_kp_s_nsample_1, __pyx_k_nsample_1, sizeof(__pyx_k_nsample_1), 0, 0, 1, 0},
+  {&__pyx_n_s_numpy, __pyx_k_numpy, sizeof(__pyx_k_numpy), 0, 0, 1, 1},
+  {&__pyx_n_s_numpy_dual, __pyx_k_numpy_dual, sizeof(__pyx_k_numpy_dual), 0, 0, 1, 1},
+  {&__pyx_n_s_operator, __pyx_k_operator, sizeof(__pyx_k_operator), 0, 0, 1, 1},
+  {&__pyx_n_s_p, __pyx_k_p, sizeof(__pyx_k_p), 0, 0, 1, 1},
+  {&__pyx_kp_s_p_0, __pyx_k_p_0, sizeof(__pyx_k_p_0), 0, 0, 1, 0},
+  {&__pyx_kp_s_p_0_0, __pyx_k_p_0_0, sizeof(__pyx_k_p_0_0), 0, 0, 1, 0},
+  {&__pyx_kp_s_p_0_0_2, __pyx_k_p_0_0_2, sizeof(__pyx_k_p_0_0_2), 0, 0, 1, 0},
+  {&__pyx_kp_s_p_1, __pyx_k_p_1, sizeof(__pyx_k_p_1), 0, 0, 1, 0},
+  {&__pyx_kp_s_p_1_0, __pyx_k_p_1_0, sizeof(__pyx_k_p_1_0), 0, 0, 1, 0},
+  {&__pyx_kp_s_p_1_0_2, __pyx_k_p_1_0_2, sizeof(__pyx_k_p_1_0_2), 0, 0, 1, 0},
+  {&__pyx_kp_s_p_must_be_1_dimensional, __pyx_k_p_must_be_1_dimensional, sizeof(__pyx_k_p_must_be_1_dimensional), 0, 0, 1, 0},
+  {&__pyx_n_s_pareto, __pyx_k_pareto, sizeof(__pyx_k_pareto), 0, 0, 1, 1},
+  {&__pyx_kp_u_pareto_a_size_None_Draw_samples, __pyx_k_pareto_a_size_None_Draw_samples, sizeof(__pyx_k_pareto_a_size_None_Draw_samples), 0, 1, 0, 0},
+  {&__pyx_n_s_permutation, __pyx_k_permutation, sizeof(__pyx_k_permutation), 0, 0, 1, 1},
+  {&__pyx_kp_u_permutation_x_Randomly_permute, __pyx_k_permutation_x_Randomly_permute, sizeof(__pyx_k_permutation_x_Randomly_permute), 0, 1, 0, 0},
+  {&__pyx_n_s_poisson, __pyx_k_poisson, sizeof(__pyx_k_poisson), 0, 0, 1, 1},
+  {&__pyx_kp_u_poisson_lam_1_0_size_None_Draw, __pyx_k_poisson_lam_1_0_size_None_Draw, sizeof(__pyx_k_poisson_lam_1_0_size_None_Draw), 0, 1, 0, 0},
+  {&__pyx_n_s_poisson_lam_max, __pyx_k_poisson_lam_max, sizeof(__pyx_k_poisson_lam_max), 0, 0, 1, 1},
+  {&__pyx_n_s_power, __pyx_k_power, sizeof(__pyx_k_power), 0, 0, 1, 1},
+  {&__pyx_kp_u_power_a_size_None_Draws_samples, __pyx_k_power_a_size_None_Draws_samples, sizeof(__pyx_k_power_a_size_None_Draws_samples), 0, 1, 0, 0},
+  {&__pyx_kp_s_probabilities_are_not_non_negati, __pyx_k_probabilities_are_not_non_negati, sizeof(__pyx_k_probabilities_are_not_non_negati), 0, 0, 1, 0},
+  {&__pyx_kp_s_probabilities_do_not_sum_to_1, __pyx_k_probabilities_do_not_sum_to_1, sizeof(__pyx_k_probabilities_do_not_sum_to_1), 0, 0, 1, 0},
+  {&__pyx_n_s_prod, __pyx_k_prod, sizeof(__pyx_k_prod), 0, 0, 1, 1},
+  {&__pyx_n_s_pvals, __pyx_k_pvals, sizeof(__pyx_k_pvals), 0, 0, 1, 1},
+  {&__pyx_n_s_rand, __pyx_k_rand, sizeof(__pyx_k_rand), 0, 0, 1, 1},
+  {&__pyx_n_s_rand_2, __pyx_k_rand_2, sizeof(__pyx_k_rand_2), 0, 0, 1, 1},
+  {&__pyx_kp_u_rand_d0_d1_dn_Random_values_in, __pyx_k_rand_d0_d1_dn_Random_values_in, sizeof(__pyx_k_rand_d0_d1_dn_Random_values_in), 0, 1, 0, 0},
+  {&__pyx_n_s_randint, __pyx_k_randint, sizeof(__pyx_k_randint), 0, 0, 1, 1},
+  {&__pyx_kp_u_randint_low_high_None_size_None, __pyx_k_randint_low_high_None_size_None, sizeof(__pyx_k_randint_low_high_None_size_None), 0, 1, 0, 0},
+  {&__pyx_n_s_randn, __pyx_k_randn, sizeof(__pyx_k_randn), 0, 0, 1, 1},
+  {&__pyx_kp_u_randn_d0_d1_dn_Return_a_sample, __pyx_k_randn_d0_d1_dn_Return_a_sample, sizeof(__pyx_k_randn_d0_d1_dn_Return_a_sample), 0, 1, 0, 0},
+  {&__pyx_n_s_random, __pyx_k_random, sizeof(__pyx_k_random), 0, 0, 1, 1},
+  {&__pyx_n_s_random_integers, __pyx_k_random_integers, sizeof(__pyx_k_random_integers), 0, 0, 1, 1},
+  {&__pyx_kp_u_random_integers_low_high_None_s, __pyx_k_random_integers_low_high_None_s, sizeof(__pyx_k_random_integers_low_high_None_s), 0, 1, 0, 0},
+  {&__pyx_n_s_random_sample, __pyx_k_random_sample, sizeof(__pyx_k_random_sample), 0, 0, 1, 1},
+  {&__pyx_kp_u_random_sample_size_None_Return, __pyx_k_random_sample_size_None_Return, sizeof(__pyx_k_random_sample_size_None_Return), 0, 1, 0, 0},
+  {&__pyx_n_s_ravel, __pyx_k_ravel, sizeof(__pyx_k_ravel), 0, 0, 1, 1},
+  {&__pyx_n_s_rayleigh, __pyx_k_rayleigh, sizeof(__pyx_k_rayleigh), 0, 0, 1, 1},
+  {&__pyx_kp_u_rayleigh_scale_1_0_size_None_Dr, __pyx_k_rayleigh_scale_1_0_size_None_Dr, sizeof(__pyx_k_rayleigh_scale_1_0_size_None_Dr), 0, 1, 0, 0},
+  {&__pyx_n_s_reduce, __pyx_k_reduce, sizeof(__pyx_k_reduce), 0, 0, 1, 1},
+  {&__pyx_n_s_replace, __pyx_k_replace, sizeof(__pyx_k_replace), 0, 0, 1, 1},
+  {&__pyx_n_s_return_index, __pyx_k_return_index, sizeof(__pyx_k_return_index), 0, 0, 1, 1},
+  {&__pyx_n_s_right, __pyx_k_right, sizeof(__pyx_k_right), 0, 0, 1, 1},
+  {&__pyx_n_s_scale, __pyx_k_scale, sizeof(__pyx_k_scale), 0, 0, 1, 1},
+  {&__pyx_kp_s_scale_0, __pyx_k_scale_0, sizeof(__pyx_k_scale_0), 0, 0, 1, 0},
+  {&__pyx_kp_s_scale_0_0, __pyx_k_scale_0_0, sizeof(__pyx_k_scale_0_0), 0, 0, 1, 0},
+  {&__pyx_n_s_searchsorted, __pyx_k_searchsorted, sizeof(__pyx_k_searchsorted), 0, 0, 1, 1},
+  {&__pyx_n_s_seed, __pyx_k_seed, sizeof(__pyx_k_seed), 0, 0, 1, 1},
+  {&__pyx_n_s_set_state, __pyx_k_set_state, sizeof(__pyx_k_set_state), 0, 0, 1, 1},
+  {&__pyx_n_s_shape, __pyx_k_shape, sizeof(__pyx_k_shape), 0, 0, 1, 1},
+  {&__pyx_kp_s_shape_0, __pyx_k_shape_0, sizeof(__pyx_k_shape_0), 0, 0, 1, 0},
+  {&__pyx_n_s_shuffle, __pyx_k_shuffle, sizeof(__pyx_k_shuffle), 0, 0, 1, 1},
+  {&__pyx_kp_u_shuffle_x_Modify_a_sequence_in, __pyx_k_shuffle_x_Modify_a_sequence_in, sizeof(__pyx_k_shuffle_x_Modify_a_sequence_in), 0, 1, 0, 0},
+  {&__pyx_n_s_side, __pyx_k_side, sizeof(__pyx_k_side), 0, 0, 1, 1},
+  {&__pyx_n_s_sigma, __pyx_k_sigma, sizeof(__pyx_k_sigma), 0, 0, 1, 1},
+  {&__pyx_kp_s_sigma_0, __pyx_k_sigma_0, sizeof(__pyx_k_sigma_0), 0, 0, 1, 0},
+  {&__pyx_kp_s_sigma_0_0, __pyx_k_sigma_0_0, sizeof(__pyx_k_sigma_0_0), 0, 0, 1, 0},
+  {&__pyx_n_s_size, __pyx_k_size, sizeof(__pyx_k_size), 0, 0, 1, 1},
+  {&__pyx_kp_s_size_is_not_compatible_with_inpu, __pyx_k_size_is_not_compatible_with_inpu, sizeof(__pyx_k_size_is_not_compatible_with_inpu), 0, 0, 1, 0},
+  {&__pyx_n_s_sort, __pyx_k_sort, sizeof(__pyx_k_sort), 0, 0, 1, 1},
+  {&__pyx_n_s_sqrt, __pyx_k_sqrt, sizeof(__pyx_k_sqrt), 0, 0, 1, 1},
+  {&__pyx_n_s_standard_cauchy, __pyx_k_standard_cauchy, sizeof(__pyx_k_standard_cauchy), 0, 0, 1, 1},
+  {&__pyx_kp_u_standard_cauchy_size_None_Stand, __pyx_k_standard_cauchy_size_None_Stand, sizeof(__pyx_k_standard_cauchy_size_None_Stand), 0, 1, 0, 0},
+  {&__pyx_n_s_standard_exponential, __pyx_k_standard_exponential, sizeof(__pyx_k_standard_exponential), 0, 0, 1, 1},
+  {&__pyx_kp_u_standard_exponential_size_None, __pyx_k_standard_exponential_size_None, sizeof(__pyx_k_standard_exponential_size_None), 0, 1, 0, 0},
+  {&__pyx_n_s_standard_gamma, __pyx_k_standard_gamma, sizeof(__pyx_k_standard_gamma), 0, 0, 1, 1},
+  {&__pyx_kp_u_standard_gamma_shape_size_None, __pyx_k_standard_gamma_shape_size_None, sizeof(__pyx_k_standard_gamma_shape_size_None), 0, 1, 0, 0},
+  {&__pyx_n_s_standard_normal, __pyx_k_standard_normal, sizeof(__pyx_k_standard_normal), 0, 0, 1, 1},
+  {&__pyx_kp_u_standard_normal_size_None_Retur, __pyx_k_standard_normal_size_None_Retur, sizeof(__pyx_k_standard_normal_size_None_Retur), 0, 1, 0, 0},
+  {&__pyx_n_s_standard_t, __pyx_k_standard_t, sizeof(__pyx_k_standard_t), 0, 0, 1, 1},
+  {&__pyx_kp_u_standard_t_df_size_None_Standar, __pyx_k_standard_t_df_size_None_Standar, sizeof(__pyx_k_standard_t_df_size_None_Standar), 0, 1, 0, 0},
+  {&__pyx_kp_s_state_must_be_624_longs, __pyx_k_state_must_be_624_longs, sizeof(__pyx_k_state_must_be_624_longs), 0, 0, 1, 0},
+  {&__pyx_n_s_subtract, __pyx_k_subtract, sizeof(__pyx_k_subtract), 0, 0, 1, 1},
+  {&__pyx_kp_s_sum_pvals_1_1_0, __pyx_k_sum_pvals_1_1_0, sizeof(__pyx_k_sum_pvals_1_1_0), 0, 0, 1, 0},
+  {&__pyx_n_s_svd, __pyx_k_svd, sizeof(__pyx_k_svd), 0, 0, 1, 1},
+  {&__pyx_n_s_take, __pyx_k_take, sizeof(__pyx_k_take), 0, 0, 1, 1},
+  {&__pyx_n_s_test, __pyx_k_test, sizeof(__pyx_k_test), 0, 0, 1, 1},
+  {&__pyx_kp_u_tomaxint_size_None_Random_integ, __pyx_k_tomaxint_size_None_Random_integ, sizeof(__pyx_k_tomaxint_size_None_Random_integ), 0, 1, 0, 0},
+  {&__pyx_n_s_triangular, __pyx_k_triangular, sizeof(__pyx_k_triangular), 0, 0, 1, 1},
+  {&__pyx_kp_u_triangular_left_mode_right_size, __pyx_k_triangular_left_mode_right_size, sizeof(__pyx_k_triangular_left_mode_right_size), 0, 1, 0, 0},
+  {&__pyx_n_s_uint, __pyx_k_uint, sizeof(__pyx_k_uint), 0, 0, 1, 1},
+  {&__pyx_n_s_uint32, __pyx_k_uint32, sizeof(__pyx_k_uint32), 0, 0, 1, 1},
+  {&__pyx_n_s_uniform, __pyx_k_uniform, sizeof(__pyx_k_uniform), 0, 0, 1, 1},
+  {&__pyx_kp_u_uniform_low_0_0_high_1_0_size_1, __pyx_k_uniform_low_0_0_high_1_0_size_1, sizeof(__pyx_k_uniform_low_0_0_high_1_0_size_1), 0, 1, 0, 0},
+  {&__pyx_n_s_unique, __pyx_k_unique, sizeof(__pyx_k_unique), 0, 0, 1, 1},
+  {&__pyx_n_s_vonmises, __pyx_k_vonmises, sizeof(__pyx_k_vonmises), 0, 0, 1, 1},
+  {&__pyx_kp_u_vonmises_mu_kappa_size_None_Dra, __pyx_k_vonmises_mu_kappa_size_None_Dra, sizeof(__pyx_k_vonmises_mu_kappa_size_None_Dra), 0, 1, 0, 0},
+  {&__pyx_n_s_wald, __pyx_k_wald, sizeof(__pyx_k_wald), 0, 0, 1, 1},
+  {&__pyx_kp_u_wald_mean_scale_size_None_Draw, __pyx_k_wald_mean_scale_size_None_Draw, sizeof(__pyx_k_wald_mean_scale_size_None_Draw), 0, 1, 0, 0},
+  {&__pyx_n_s_weibull, __pyx_k_weibull, sizeof(__pyx_k_weibull), 0, 0, 1, 1},
+  {&__pyx_kp_u_weibull_a_size_None_Weibull_dis, __pyx_k_weibull_a_size_None_Weibull_dis, sizeof(__pyx_k_weibull_a_size_None_Weibull_dis), 0, 1, 0, 0},
+  {&__pyx_n_s_zeros, __pyx_k_zeros, sizeof(__pyx_k_zeros), 0, 0, 1, 1},
+  {&__pyx_n_s_zipf, __pyx_k_zipf, sizeof(__pyx_k_zipf), 0, 0, 1, 1},
+  {&__pyx_kp_u_zipf_a_size_None_Draw_samples_f, __pyx_k_zipf_a_size_None_Draw_samples_f, sizeof(__pyx_k_zipf_a_size_None_Draw_samples_f), 0, 1, 0, 0},
   {0, 0, 0, 0, 0, 0, 0}
 };
 static int __Pyx_InitCachedBuiltins(void) {
-  __pyx_builtin_ValueError = __Pyx_GetBuiltinName(__pyx_n_s__ValueError); if (!__pyx_builtin_ValueError) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 186; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __pyx_builtin_TypeError = __Pyx_GetBuiltinName(__pyx_n_s__TypeError); if (!__pyx_builtin_TypeError) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 701; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_builtin_ValueError = __Pyx_GetBuiltinName(__pyx_n_s_ValueError); if (!__pyx_builtin_ValueError) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 186; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_builtin_TypeError = __Pyx_GetBuiltinName(__pyx_n_s_TypeError); if (!__pyx_builtin_TypeError) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 701; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   return 0;
   __pyx_L1_error:;
   return -1;
@@ -21684,9 +21858,9 @@ static int __Pyx_InitCachedConstants(void) {
  *         for i from 0 <= i < multi.size:
  *             oa_data = <double *>PyArray_MultiIter_DATA(multi, 1)
  */
-  __pyx_k_tuple_2 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_1)); if (unlikely(!__pyx_k_tuple_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 186; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_2);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_2));
+  __pyx_tuple_ = PyTuple_Pack(1, __pyx_kp_s_size_is_not_compatible_with_inpu); if (unlikely(!__pyx_tuple_)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 186; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple_);
+  __Pyx_GIVEREF(__pyx_tuple_);
 
   /* "mtrand.pyx":235
  *         multi = <broadcast>PyArray_MultiIterNew(3, <void*>array, <void *>oa, <void *>ob)
@@ -21695,9 +21869,9 @@ static int __Pyx_InitCachedConstants(void) {
  *         for i from 0 <= i < multi.size:
  *             oa_data = <double *>PyArray_MultiIter_DATA(multi, 1)
  */
-  __pyx_k_tuple_3 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_1)); if (unlikely(!__pyx_k_tuple_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 235; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_3);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_3));
+  __pyx_tuple__2 = PyTuple_Pack(1, __pyx_kp_s_size_is_not_compatible_with_inpu); if (unlikely(!__pyx_tuple__2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 235; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__2);
+  __Pyx_GIVEREF(__pyx_tuple__2);
 
   /* "mtrand.pyx":290
  *                                                 <void *>ob, <void *>oc)
@@ -21706,9 +21880,9 @@ static int __Pyx_InitCachedConstants(void) {
  *         for i from 0 <= i < multi.size:
  *             oa_data = <double *>PyArray_MultiIter_DATA(multi, 1)
  */
-  __pyx_k_tuple_4 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_1)); if (unlikely(!__pyx_k_tuple_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 290; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_4);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_4));
+  __pyx_tuple__3 = PyTuple_Pack(1, __pyx_kp_s_size_is_not_compatible_with_inpu); if (unlikely(!__pyx_tuple__3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 290; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__3);
+  __Pyx_GIVEREF(__pyx_tuple__3);
 
   /* "mtrand.pyx":354
  *         multi = <broadcast>PyArray_MultiIterNew(3, <void*>array, <void *>on, <void *>op)
@@ -21717,9 +21891,9 @@ static int __Pyx_InitCachedConstants(void) {
  *         for i from 0 <= i < multi.size:
  *             on_data = <long *>PyArray_MultiIter_DATA(multi, 1)
  */
-  __pyx_k_tuple_5 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_1)); if (unlikely(!__pyx_k_tuple_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 354; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_5);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_5));
+  __pyx_tuple__4 = PyTuple_Pack(1, __pyx_kp_s_size_is_not_compatible_with_inpu); if (unlikely(!__pyx_tuple__4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 354; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__4);
+  __Pyx_GIVEREF(__pyx_tuple__4);
 
   /* "mtrand.pyx":403
  *         multi = <broadcast>PyArray_MultiIterNew(3, <void*>array, <void *>on, <void *>op)
@@ -21728,9 +21902,9 @@ static int __Pyx_InitCachedConstants(void) {
  *         for i from 0 <= i < multi.size:
  *             on_data = <double *>PyArray_MultiIter_DATA(multi, 1)
  */
-  __pyx_k_tuple_6 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_1)); if (unlikely(!__pyx_k_tuple_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 403; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_6);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_6));
+  __pyx_tuple__5 = PyTuple_Pack(1, __pyx_kp_s_size_is_not_compatible_with_inpu); if (unlikely(!__pyx_tuple__5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 403; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__5);
+  __Pyx_GIVEREF(__pyx_tuple__5);
 
   /* "mtrand.pyx":457
  *                                                 <void *>oN)
@@ -21739,9 +21913,9 @@ static int __Pyx_InitCachedConstants(void) {
  *         for i from 0 <= i < multi.size:
  *             on_data = <long *>PyArray_MultiIter_DATA(multi, 1)
  */
-  __pyx_k_tuple_7 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_1)); if (unlikely(!__pyx_k_tuple_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 457; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_7);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_7));
+  __pyx_tuple__6 = PyTuple_Pack(1, __pyx_kp_s_size_is_not_compatible_with_inpu); if (unlikely(!__pyx_tuple__6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 457; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__6);
+  __Pyx_GIVEREF(__pyx_tuple__6);
 
   /* "mtrand.pyx":506
  *         multi = <broadcast>PyArray_MultiIterNew(2, <void *>array, <void *>oa)
@@ -21750,9 +21924,9 @@ static int __Pyx_InitCachedConstants(void) {
  *         for i from 0 <= i < multi.size:
  *             oa_data = <double *>PyArray_MultiIter_DATA(multi, 1)
  */
-  __pyx_k_tuple_8 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_1)); if (unlikely(!__pyx_k_tuple_8)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 506; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_8);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_8));
+  __pyx_tuple__7 = PyTuple_Pack(1, __pyx_kp_s_size_is_not_compatible_with_inpu); if (unlikely(!__pyx_tuple__7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 506; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__7);
+  __Pyx_GIVEREF(__pyx_tuple__7);
 
   /* "mtrand.pyx":692
  *         algorithm_name = state[0]
@@ -21761,9 +21935,9 @@ static int __Pyx_InitCachedConstants(void) {
  *         key, pos = state[1:3]
  *         if len(state) == 3:
  */
-  __pyx_k_tuple_10 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_9)); if (unlikely(!__pyx_k_tuple_10)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 692; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_10);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_10));
+  __pyx_tuple__8 = PyTuple_Pack(1, __pyx_kp_s_algorithm_must_be_MT19937); if (unlikely(!__pyx_tuple__8)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 692; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__8);
+  __Pyx_GIVEREF(__pyx_tuple__8);
 
   /* "mtrand.pyx":693
  *         if algorithm_name != 'MT19937':
@@ -21772,9 +21946,9 @@ static int __Pyx_InitCachedConstants(void) {
  *         if len(state) == 3:
  *             has_gauss = 0
  */
-  __pyx_k_slice_11 = PySlice_New(__pyx_int_1, __pyx_int_3, Py_None); if (unlikely(!__pyx_k_slice_11)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 693; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_slice_11);
-  __Pyx_GIVEREF(__pyx_k_slice_11);
+  __pyx_slice__9 = PySlice_New(__pyx_int_1, __pyx_int_3, Py_None); if (unlikely(!__pyx_slice__9)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 693; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_slice__9);
+  __Pyx_GIVEREF(__pyx_slice__9);
 
   /* "mtrand.pyx":698
  *             cached_gaussian = 0.0
@@ -21783,9 +21957,9 @@ static int __Pyx_InitCachedConstants(void) {
  *         try:
  *             obj = <ndarray>PyArray_ContiguousFromObject(key, NPY_ULONG, 1, 1)
  */
-  __pyx_k_slice_12 = PySlice_New(__pyx_int_3, __pyx_int_5, Py_None); if (unlikely(!__pyx_k_slice_12)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 698; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_slice_12);
-  __Pyx_GIVEREF(__pyx_k_slice_12);
+  __pyx_slice__10 = PySlice_New(__pyx_int_3, __pyx_int_5, Py_None); if (unlikely(!__pyx_slice__10)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 698; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_slice__10);
+  __Pyx_GIVEREF(__pyx_slice__10);
 
   /* "mtrand.pyx":705
  *             obj = <ndarray>PyArray_ContiguousFromObject(key, NPY_LONG, 1, 1)
@@ -21794,20 +21968,20 @@ static int __Pyx_InitCachedConstants(void) {
  *         memcpy(<void*>(self.internal_state.key), <void*>PyArray_DATA(obj), 624*sizeof(long))
  *         self.internal_state.pos = pos
  */
-  __pyx_k_tuple_14 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_13)); if (unlikely(!__pyx_k_tuple_14)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 705; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_14);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_14));
+  __pyx_tuple__11 = PyTuple_Pack(1, __pyx_kp_s_state_must_be_624_longs); if (unlikely(!__pyx_tuple__11)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 705; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__11);
+  __Pyx_GIVEREF(__pyx_tuple__11);
 
   /* "mtrand.pyx":877
- *
+ * 
  *         if lo >= hi :
  *             raise ValueError("low >= high")             # <<<<<<<<<<<<<<
- *
+ * 
  *         diff = <unsigned long>hi - <unsigned long>lo - 1UL
  */
-  __pyx_k_tuple_16 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_15)); if (unlikely(!__pyx_k_tuple_16)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 877; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_16);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_16));
+  __pyx_tuple__12 = PyTuple_Pack(1, __pyx_kp_s_low_high); if (unlikely(!__pyx_tuple__12)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 877; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__12);
+  __Pyx_GIVEREF(__pyx_tuple__12);
 
   /* "mtrand.pyx":1004
  *                 pop_size = operator.index(a.item())
@@ -21816,9 +21990,9 @@ static int __Pyx_InitCachedConstants(void) {
  *             if pop_size <= 0:
  *                 raise ValueError("a must be greater than 0")
  */
-  __pyx_k_tuple_19 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_18)); if (unlikely(!__pyx_k_tuple_19)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1004; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_19);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_19));
+  __pyx_tuple__13 = PyTuple_Pack(1, __pyx_kp_s_a_must_be_1_dimensional_or_an_in); if (unlikely(!__pyx_tuple__13)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1004; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__13);
+  __Pyx_GIVEREF(__pyx_tuple__13);
 
   /* "mtrand.pyx":1006
  *                 raise ValueError("a must be 1-dimensional or an integer")
@@ -21827,9 +22001,9 @@ static int __Pyx_InitCachedConstants(void) {
  *         elif a.ndim != 1:
  *             raise ValueError("a must be 1-dimensional")
  */
-  __pyx_k_tuple_21 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_20)); if (unlikely(!__pyx_k_tuple_21)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1006; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_21);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_21));
+  __pyx_tuple__14 = PyTuple_Pack(1, __pyx_kp_s_a_must_be_greater_than_0); if (unlikely(!__pyx_tuple__14)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1006; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__14);
+  __Pyx_GIVEREF(__pyx_tuple__14);
 
   /* "mtrand.pyx":1008
  *                 raise ValueError("a must be greater than 0")
@@ -21838,1222 +22012,1222 @@ static int __Pyx_InitCachedConstants(void) {
  *         else:
  *             pop_size = a.shape[0]
  */
-  __pyx_k_tuple_23 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_22)); if (unlikely(!__pyx_k_tuple_23)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1008; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_23);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_23));
+  __pyx_tuple__15 = PyTuple_Pack(1, __pyx_kp_s_a_must_be_1_dimensional); if (unlikely(!__pyx_tuple__15)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1008; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__15);
+  __Pyx_GIVEREF(__pyx_tuple__15);
 
   /* "mtrand.pyx":1012
  *             pop_size = a.shape[0]
  *             if pop_size is 0:
  *                 raise ValueError("a must be non-empty")             # <<<<<<<<<<<<<<
- *
+ * 
  *         if None != p:
  */
-  __pyx_k_tuple_25 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_24)); if (unlikely(!__pyx_k_tuple_25)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1012; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_25);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_25));
+  __pyx_tuple__16 = PyTuple_Pack(1, __pyx_kp_s_a_must_be_non_empty); if (unlikely(!__pyx_tuple__16)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1012; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__16);
+  __Pyx_GIVEREF(__pyx_tuple__16);
 
-  /* "mtrand.pyx":1017
- *             p = np.array(p, dtype=np.double, ndmin=1, copy=False)
+  /* "mtrand.pyx":1020
+ * 
  *             if p.ndim != 1:
  *                 raise ValueError("p must be 1-dimensional")             # <<<<<<<<<<<<<<
  *             if p.size != pop_size:
  *                 raise ValueError("a and p must have same size")
  */
-  __pyx_k_tuple_27 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_26)); if (unlikely(!__pyx_k_tuple_27)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1017; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_27);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_27));
+  __pyx_tuple__17 = PyTuple_Pack(1, __pyx_kp_s_p_must_be_1_dimensional); if (unlikely(!__pyx_tuple__17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1020; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__17);
+  __Pyx_GIVEREF(__pyx_tuple__17);
 
-  /* "mtrand.pyx":1019
+  /* "mtrand.pyx":1022
  *                 raise ValueError("p must be 1-dimensional")
  *             if p.size != pop_size:
  *                 raise ValueError("a and p must have same size")             # <<<<<<<<<<<<<<
- *             if np.any(p < 0):
+ *             if np.logical_or.reduce(p < 0):
  *                 raise ValueError("probabilities are not non-negative")
  */
-  __pyx_k_tuple_29 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_28)); if (unlikely(!__pyx_k_tuple_29)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1019; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_29);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_29));
+  __pyx_tuple__18 = PyTuple_Pack(1, __pyx_kp_s_a_and_p_must_have_same_size); if (unlikely(!__pyx_tuple__18)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1022; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__18);
+  __Pyx_GIVEREF(__pyx_tuple__18);
 
-  /* "mtrand.pyx":1021
+  /* "mtrand.pyx":1024
  *                 raise ValueError("a and p must have same size")
- *             if np.any(p < 0):
+ *             if np.logical_or.reduce(p < 0):
  *                 raise ValueError("probabilities are not non-negative")             # <<<<<<<<<<<<<<
- *             if not np.allclose(p.sum(), 1):
+ *             if abs(kahan_sum(pix, d) - 1.) > 1e-8:
  *                 raise ValueError("probabilities do not sum to 1")
  */
-  __pyx_k_tuple_31 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_30)); if (unlikely(!__pyx_k_tuple_31)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1021; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_31);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_31));
+  __pyx_tuple__19 = PyTuple_Pack(1, __pyx_kp_s_probabilities_are_not_non_negati); if (unlikely(!__pyx_tuple__19)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1024; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__19);
+  __Pyx_GIVEREF(__pyx_tuple__19);
 
-  /* "mtrand.pyx":1023
+  /* "mtrand.pyx":1026
  *                 raise ValueError("probabilities are not non-negative")
- *             if not np.allclose(p.sum(), 1):
+ *             if abs(kahan_sum(pix, d) - 1.) > 1e-8:
  *                 raise ValueError("probabilities do not sum to 1")             # <<<<<<<<<<<<<<
- *
+ * 
  *         shape = size
  */
-  __pyx_k_tuple_33 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_32)); if (unlikely(!__pyx_k_tuple_33)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1023; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_33);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_33));
+  __pyx_tuple__20 = PyTuple_Pack(1, __pyx_kp_s_probabilities_do_not_sum_to_1); if (unlikely(!__pyx_tuple__20)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1026; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__20);
+  __Pyx_GIVEREF(__pyx_tuple__20);
 
-  /* "mtrand.pyx":1043
+  /* "mtrand.pyx":1046
  *         else:
  *             if size > pop_size:
  *                 raise ValueError("Cannot take a larger sample than "             # <<<<<<<<<<<<<<
  *                                  "population when 'replace=False'")
- *
+ * 
  */
-  __pyx_k_tuple_35 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_34)); if (unlikely(!__pyx_k_tuple_35)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1043; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_35);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_35));
+  __pyx_tuple__21 = PyTuple_Pack(1, __pyx_kp_s_Cannot_take_a_larger_sample_than); if (unlikely(!__pyx_tuple__21)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1046; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__21);
+  __Pyx_GIVEREF(__pyx_tuple__21);
 
-  /* "mtrand.pyx":1048
+  /* "mtrand.pyx":1051
  *             if None != p:
- *                 if np.sum(p > 0) < size:
+ *                 if np.count_nonzero(p > 0) < size:
  *                     raise ValueError("Fewer non-zero entries in p than size")             # <<<<<<<<<<<<<<
  *                 n_uniq = 0
  *                 p = p.copy()
  */
-  __pyx_k_tuple_37 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_36)); if (unlikely(!__pyx_k_tuple_37)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1048; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_37);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_37));
+  __pyx_tuple__22 = PyTuple_Pack(1, __pyx_kp_s_Fewer_non_zero_entries_in_p_than); if (unlikely(!__pyx_tuple__22)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1051; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__22);
+  __Pyx_GIVEREF(__pyx_tuple__22);
 
-  /* "mtrand.pyx":1073
+  /* "mtrand.pyx":1076
  *         if shape is None and isinstance(idx, np.ndarray):
  *             # In most cases a scalar will have been made an array
  *             idx = idx.item(0)             # <<<<<<<<<<<<<<
- *
+ * 
  *         #Use samples as indices for a if a is array-like
  */
-  __pyx_k_tuple_38 = PyTuple_Pack(1, __pyx_int_0); if (unlikely(!__pyx_k_tuple_38)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1073; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_38);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_38));
+  __pyx_tuple__23 = PyTuple_Pack(1, __pyx_int_0); if (unlikely(!__pyx_tuple__23)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1076; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__23);
+  __Pyx_GIVEREF(__pyx_tuple__23);
 
-  /* "mtrand.pyx":1085
+  /* "mtrand.pyx":1088
  *             # array, taking into account that np.array(item) may not work
  *             # for object arrays.
  *             res = np.empty((), dtype=a.dtype)             # <<<<<<<<<<<<<<
  *             res[()] = a[idx]
  *             return res
  */
-  __pyx_k_tuple_39 = PyTuple_Pack(1, ((PyObject *)__pyx_empty_tuple)); if (unlikely(!__pyx_k_tuple_39)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1085; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_39);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_39));
+  __pyx_tuple__24 = PyTuple_Pack(1, __pyx_empty_tuple); if (unlikely(!__pyx_tuple__24)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1088; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__24);
+  __Pyx_GIVEREF(__pyx_tuple__24);
 
-  /* "mtrand.pyx":1479
+  /* "mtrand.pyx":1482
  *         if not PyErr_Occurred():
  *             if fscale <= 0:
  *                 raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
  *             return cont2_array_sc(self.internal_state, rk_normal, size, floc, fscale)
- *
+ * 
  */
-  __pyx_k_tuple_45 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_44)); if (unlikely(!__pyx_k_tuple_45)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1479; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_45);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_45));
+  __pyx_tuple__25 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__25)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1482; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__25);
+  __Pyx_GIVEREF(__pyx_tuple__25);
 
-  /* "mtrand.pyx":1487
+  /* "mtrand.pyx":1490
  *         oscale = <ndarray>PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(oscale, 0)):
  *             raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
  *         return cont2_array(self.internal_state, rk_normal, size, oloc, oscale)
- *
+ * 
  */
-  __pyx_k_tuple_46 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_44)); if (unlikely(!__pyx_k_tuple_46)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1487; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_46);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_46));
+  __pyx_tuple__26 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__26)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1490; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__26);
+  __Pyx_GIVEREF(__pyx_tuple__26);
 
-  /* "mtrand.pyx":1534
+  /* "mtrand.pyx":1537
  *         if not PyErr_Occurred():
  *             if fa <= 0:
  *                 raise ValueError("a <= 0")             # <<<<<<<<<<<<<<
  *             if fb <= 0:
  *                 raise ValueError("b <= 0")
  */
-  __pyx_k_tuple_48 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_47)); if (unlikely(!__pyx_k_tuple_48)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1534; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_48);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_48));
+  __pyx_tuple__27 = PyTuple_Pack(1, __pyx_kp_s_a_0); if (unlikely(!__pyx_tuple__27)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1537; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__27);
+  __Pyx_GIVEREF(__pyx_tuple__27);
 
-  /* "mtrand.pyx":1536
+  /* "mtrand.pyx":1539
  *                 raise ValueError("a <= 0")
  *             if fb <= 0:
  *                 raise ValueError("b <= 0")             # <<<<<<<<<<<<<<
  *             return cont2_array_sc(self.internal_state, rk_beta, size, fa, fb)
- *
+ * 
  */
-  __pyx_k_tuple_50 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_49)); if (unlikely(!__pyx_k_tuple_50)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1536; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_50);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_50));
+  __pyx_tuple__28 = PyTuple_Pack(1, __pyx_kp_s_b_0); if (unlikely(!__pyx_tuple__28)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1539; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__28);
+  __Pyx_GIVEREF(__pyx_tuple__28);
 
-  /* "mtrand.pyx":1544
+  /* "mtrand.pyx":1547
  *         ob = <ndarray>PyArray_FROM_OTF(b, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(oa, 0)):
  *             raise ValueError("a <= 0")             # <<<<<<<<<<<<<<
  *         if np.any(np.less_equal(ob, 0)):
  *             raise ValueError("b <= 0")
  */
-  __pyx_k_tuple_51 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_47)); if (unlikely(!__pyx_k_tuple_51)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1544; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_51);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_51));
+  __pyx_tuple__29 = PyTuple_Pack(1, __pyx_kp_s_a_0); if (unlikely(!__pyx_tuple__29)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1547; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__29);
+  __Pyx_GIVEREF(__pyx_tuple__29);
 
-  /* "mtrand.pyx":1546
+  /* "mtrand.pyx":1549
  *             raise ValueError("a <= 0")
  *         if np.any(np.less_equal(ob, 0)):
  *             raise ValueError("b <= 0")             # <<<<<<<<<<<<<<
  *         return cont2_array(self.internal_state, rk_beta, size, oa, ob)
- *
+ * 
  */
-  __pyx_k_tuple_52 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_49)); if (unlikely(!__pyx_k_tuple_52)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1546; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_52);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_52));
+  __pyx_tuple__30 = PyTuple_Pack(1, __pyx_kp_s_b_0); if (unlikely(!__pyx_tuple__30)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1549; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__30);
+  __Pyx_GIVEREF(__pyx_tuple__30);
 
-  /* "mtrand.pyx":1593
+  /* "mtrand.pyx":1596
  *         if not PyErr_Occurred():
  *             if fscale <= 0:
  *                 raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
  *             return cont1_array_sc(self.internal_state, rk_exponential, size, fscale)
- *
+ * 
  */
-  __pyx_k_tuple_54 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_44)); if (unlikely(!__pyx_k_tuple_54)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1593; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_54);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_54));
+  __pyx_tuple__31 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__31)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1596; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__31);
+  __Pyx_GIVEREF(__pyx_tuple__31);
 
-  /* "mtrand.pyx":1600
+  /* "mtrand.pyx":1603
  *         oscale = <ndarray> PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(oscale, 0.0)):
  *             raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
  *         return cont1_array(self.internal_state, rk_exponential, size, oscale)
- *
+ * 
  */
-  __pyx_k_tuple_55 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_44)); if (unlikely(!__pyx_k_tuple_55)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1600; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_55);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_55));
+  __pyx_tuple__32 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__32)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1603; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__32);
+  __Pyx_GIVEREF(__pyx_tuple__32);
 
-  /* "mtrand.pyx":1704
+  /* "mtrand.pyx":1707
  *         if not PyErr_Occurred():
  *             if fshape <= 0:
  *                 raise ValueError("shape <= 0")             # <<<<<<<<<<<<<<
  *             return cont1_array_sc(self.internal_state, rk_standard_gamma, size, fshape)
- *
+ * 
  */
-  __pyx_k_tuple_57 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_56)); if (unlikely(!__pyx_k_tuple_57)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1704; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_57);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_57));
+  __pyx_tuple__33 = PyTuple_Pack(1, __pyx_kp_s_shape_0); if (unlikely(!__pyx_tuple__33)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1707; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__33);
+  __Pyx_GIVEREF(__pyx_tuple__33);
 
-  /* "mtrand.pyx":1710
+  /* "mtrand.pyx":1713
  *         oshape = <ndarray> PyArray_FROM_OTF(shape, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(oshape, 0.0)):
  *             raise ValueError("shape <= 0")             # <<<<<<<<<<<<<<
  *         return cont1_array(self.internal_state, rk_standard_gamma, size, oshape)
- *
+ * 
  */
-  __pyx_k_tuple_58 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_56)); if (unlikely(!__pyx_k_tuple_58)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1710; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_58);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_58));
+  __pyx_tuple__34 = PyTuple_Pack(1, __pyx_kp_s_shape_0); if (unlikely(!__pyx_tuple__34)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1713; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__34);
+  __Pyx_GIVEREF(__pyx_tuple__34);
 
-  /* "mtrand.pyx":1790
+  /* "mtrand.pyx":1793
  *         if not PyErr_Occurred():
  *             if fshape <= 0:
  *                 raise ValueError("shape <= 0")             # <<<<<<<<<<<<<<
  *             if fscale <= 0:
  *                 raise ValueError("scale <= 0")
  */
-  __pyx_k_tuple_60 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_56)); if (unlikely(!__pyx_k_tuple_60)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1790; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_60);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_60));
+  __pyx_tuple__35 = PyTuple_Pack(1, __pyx_kp_s_shape_0); if (unlikely(!__pyx_tuple__35)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1793; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__35);
+  __Pyx_GIVEREF(__pyx_tuple__35);
 
-  /* "mtrand.pyx":1792
+  /* "mtrand.pyx":1795
  *                 raise ValueError("shape <= 0")
  *             if fscale <= 0:
  *                 raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
  *             return cont2_array_sc(self.internal_state, rk_gamma, size, fshape, fscale)
- *
+ * 
  */
-  __pyx_k_tuple_61 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_44)); if (unlikely(!__pyx_k_tuple_61)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1792; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_61);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_61));
+  __pyx_tuple__36 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__36)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1795; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__36);
+  __Pyx_GIVEREF(__pyx_tuple__36);
 
-  /* "mtrand.pyx":1799
+  /* "mtrand.pyx":1802
  *         oscale = <ndarray>PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(oshape, 0.0)):
  *             raise ValueError("shape <= 0")             # <<<<<<<<<<<<<<
  *         if np.any(np.less_equal(oscale, 0.0)):
  *             raise ValueError("scale <= 0")
  */
-  __pyx_k_tuple_62 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_56)); if (unlikely(!__pyx_k_tuple_62)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1799; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_62);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_62));
+  __pyx_tuple__37 = PyTuple_Pack(1, __pyx_kp_s_shape_0); if (unlikely(!__pyx_tuple__37)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1802; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__37);
+  __Pyx_GIVEREF(__pyx_tuple__37);
 
-  /* "mtrand.pyx":1801
+  /* "mtrand.pyx":1804
  *             raise ValueError("shape <= 0")
  *         if np.any(np.less_equal(oscale, 0.0)):
  *             raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
  *         return cont2_array(self.internal_state, rk_gamma, size, oshape, oscale)
- *
+ * 
  */
-  __pyx_k_tuple_63 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_44)); if (unlikely(!__pyx_k_tuple_63)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1801; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_63);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_63));
+  __pyx_tuple__38 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__38)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1804; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__38);
+  __Pyx_GIVEREF(__pyx_tuple__38);
 
-  /* "mtrand.pyx":1891
+  /* "mtrand.pyx":1894
  *         if not PyErr_Occurred():
  *             if fdfnum <= 0:
  *                 raise ValueError("shape <= 0")             # <<<<<<<<<<<<<<
  *             if fdfden <= 0:
  *                 raise ValueError("scale <= 0")
  */
-  __pyx_k_tuple_64 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_56)); if (unlikely(!__pyx_k_tuple_64)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1891; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_64);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_64));
+  __pyx_tuple__39 = PyTuple_Pack(1, __pyx_kp_s_shape_0); if (unlikely(!__pyx_tuple__39)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1894; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__39);
+  __Pyx_GIVEREF(__pyx_tuple__39);
 
-  /* "mtrand.pyx":1893
+  /* "mtrand.pyx":1896
  *                 raise ValueError("shape <= 0")
  *             if fdfden <= 0:
  *                 raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
  *             return cont2_array_sc(self.internal_state, rk_f, size, fdfnum, fdfden)
- *
+ * 
  */
-  __pyx_k_tuple_65 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_44)); if (unlikely(!__pyx_k_tuple_65)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1893; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_65);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_65));
+  __pyx_tuple__40 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__40)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1896; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__40);
+  __Pyx_GIVEREF(__pyx_tuple__40);
 
-  /* "mtrand.pyx":1901
+  /* "mtrand.pyx":1904
  *         odfden = <ndarray>PyArray_FROM_OTF(dfden, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(odfnum, 0.0)):
  *             raise ValueError("dfnum <= 0")             # <<<<<<<<<<<<<<
  *         if np.any(np.less_equal(odfden, 0.0)):
  *             raise ValueError("dfden <= 0")
  */
-  __pyx_k_tuple_67 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_66)); if (unlikely(!__pyx_k_tuple_67)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1901; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_67);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_67));
+  __pyx_tuple__41 = PyTuple_Pack(1, __pyx_kp_s_dfnum_0); if (unlikely(!__pyx_tuple__41)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1904; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__41);
+  __Pyx_GIVEREF(__pyx_tuple__41);
 
-  /* "mtrand.pyx":1903
+  /* "mtrand.pyx":1906
  *             raise ValueError("dfnum <= 0")
  *         if np.any(np.less_equal(odfden, 0.0)):
  *             raise ValueError("dfden <= 0")             # <<<<<<<<<<<<<<
  *         return cont2_array(self.internal_state, rk_f, size, odfnum, odfden)
- *
+ * 
  */
-  __pyx_k_tuple_69 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_68)); if (unlikely(!__pyx_k_tuple_69)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1903; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_69);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_69));
+  __pyx_tuple__42 = PyTuple_Pack(1, __pyx_kp_s_dfden_0); if (unlikely(!__pyx_tuple__42)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1906; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__42);
+  __Pyx_GIVEREF(__pyx_tuple__42);
 
-  /* "mtrand.pyx":1978
+  /* "mtrand.pyx":1981
  *         if not PyErr_Occurred():
  *             if fdfnum <= 1:
  *                 raise ValueError("dfnum <= 1")             # <<<<<<<<<<<<<<
  *             if fdfden <= 0:
  *                 raise ValueError("dfden <= 0")
  */
-  __pyx_k_tuple_71 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_70)); if (unlikely(!__pyx_k_tuple_71)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1978; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_71);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_71));
+  __pyx_tuple__43 = PyTuple_Pack(1, __pyx_kp_s_dfnum_1); if (unlikely(!__pyx_tuple__43)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1981; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__43);
+  __Pyx_GIVEREF(__pyx_tuple__43);
 
-  /* "mtrand.pyx":1980
+  /* "mtrand.pyx":1983
  *                 raise ValueError("dfnum <= 1")
  *             if fdfden <= 0:
  *                 raise ValueError("dfden <= 0")             # <<<<<<<<<<<<<<
  *             if fnonc < 0:
  *                 raise ValueError("nonc < 0")
  */
-  __pyx_k_tuple_72 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_68)); if (unlikely(!__pyx_k_tuple_72)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1980; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_72);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_72));
+  __pyx_tuple__44 = PyTuple_Pack(1, __pyx_kp_s_dfden_0); if (unlikely(!__pyx_tuple__44)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1983; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__44);
+  __Pyx_GIVEREF(__pyx_tuple__44);
 
-  /* "mtrand.pyx":1982
+  /* "mtrand.pyx":1985
  *                 raise ValueError("dfden <= 0")
  *             if fnonc < 0:
  *                 raise ValueError("nonc < 0")             # <<<<<<<<<<<<<<
  *             return cont3_array_sc(self.internal_state, rk_noncentral_f, size,
  *                                   fdfnum, fdfden, fnonc)
  */
-  __pyx_k_tuple_74 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_73)); if (unlikely(!__pyx_k_tuple_74)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1982; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_74);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_74));
+  __pyx_tuple__45 = PyTuple_Pack(1, __pyx_kp_s_nonc_0); if (unlikely(!__pyx_tuple__45)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1985; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__45);
+  __Pyx_GIVEREF(__pyx_tuple__45);
 
-  /* "mtrand.pyx":1993
- *
+  /* "mtrand.pyx":1996
+ * 
  *         if np.any(np.less_equal(odfnum, 1.0)):
  *             raise ValueError("dfnum <= 1")             # <<<<<<<<<<<<<<
  *         if np.any(np.less_equal(odfden, 0.0)):
  *             raise ValueError("dfden <= 0")
  */
-  __pyx_k_tuple_75 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_70)); if (unlikely(!__pyx_k_tuple_75)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1993; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_75);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_75));
+  __pyx_tuple__46 = PyTuple_Pack(1, __pyx_kp_s_dfnum_1); if (unlikely(!__pyx_tuple__46)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1996; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__46);
+  __Pyx_GIVEREF(__pyx_tuple__46);
 
-  /* "mtrand.pyx":1995
+  /* "mtrand.pyx":1998
  *             raise ValueError("dfnum <= 1")
  *         if np.any(np.less_equal(odfden, 0.0)):
  *             raise ValueError("dfden <= 0")             # <<<<<<<<<<<<<<
  *         if np.any(np.less(ononc, 0.0)):
  *             raise ValueError("nonc < 0")
  */
-  __pyx_k_tuple_76 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_68)); if (unlikely(!__pyx_k_tuple_76)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1995; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_76);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_76));
+  __pyx_tuple__47 = PyTuple_Pack(1, __pyx_kp_s_dfden_0); if (unlikely(!__pyx_tuple__47)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1998; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__47);
+  __Pyx_GIVEREF(__pyx_tuple__47);
 
-  /* "mtrand.pyx":1997
+  /* "mtrand.pyx":2000
  *             raise ValueError("dfden <= 0")
  *         if np.any(np.less(ononc, 0.0)):
  *             raise ValueError("nonc < 0")             # <<<<<<<<<<<<<<
  *         return cont3_array(self.internal_state, rk_noncentral_f, size, odfnum,
  *             odfden, ononc)
  */
-  __pyx_k_tuple_77 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_73)); if (unlikely(!__pyx_k_tuple_77)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1997; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_77);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_77));
+  __pyx_tuple__48 = PyTuple_Pack(1, __pyx_kp_s_nonc_0); if (unlikely(!__pyx_tuple__48)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2000; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__48);
+  __Pyx_GIVEREF(__pyx_tuple__48);
 
-  /* "mtrand.pyx":2069
+  /* "mtrand.pyx":2072
  *         if not PyErr_Occurred():
  *             if fdf <= 0:
  *                 raise ValueError("df <= 0")             # <<<<<<<<<<<<<<
  *             return cont1_array_sc(self.internal_state, rk_chisquare, size, fdf)
- *
+ * 
  */
-  __pyx_k_tuple_79 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_78)); if (unlikely(!__pyx_k_tuple_79)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2069; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_79);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_79));
+  __pyx_tuple__49 = PyTuple_Pack(1, __pyx_kp_s_df_0); if (unlikely(!__pyx_tuple__49)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2072; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__49);
+  __Pyx_GIVEREF(__pyx_tuple__49);
 
-  /* "mtrand.pyx":2076
+  /* "mtrand.pyx":2079
  *         odf = <ndarray>PyArray_FROM_OTF(df, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(odf, 0.0)):
  *             raise ValueError("df <= 0")             # <<<<<<<<<<<<<<
  *         return cont1_array(self.internal_state, rk_chisquare, size, odf)
- *
+ * 
  */
-  __pyx_k_tuple_80 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_78)); if (unlikely(!__pyx_k_tuple_80)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2076; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_80);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_80));
+  __pyx_tuple__50 = PyTuple_Pack(1, __pyx_kp_s_df_0); if (unlikely(!__pyx_tuple__50)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2079; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__50);
+  __Pyx_GIVEREF(__pyx_tuple__50);
 
-  /* "mtrand.pyx":2154
+  /* "mtrand.pyx":2157
  *         if not PyErr_Occurred():
  *             if fdf <= 1:
  *                 raise ValueError("df <= 0")             # <<<<<<<<<<<<<<
  *             if fnonc <= 0:
  *                 raise ValueError("nonc <= 0")
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-  __pyx_k_tuple_81 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_78)); if (unlikely(!__pyx_k_tuple_81)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2154; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_81);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_81));
+  __pyx_tuple__51 = PyTuple_Pack(1, __pyx_kp_s_df_0); if (unlikely(!__pyx_tuple__51)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2157; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__51);
+  __Pyx_GIVEREF(__pyx_tuple__51);
 
-  /* "mtrand.pyx":2156
+  /* "mtrand.pyx":2159
  *                 raise ValueError("df <= 0")
  *             if fnonc <= 0:
  *                 raise ValueError("nonc <= 0")             # <<<<<<<<<<<<<<
  *             return cont2_array_sc(self.internal_state, rk_noncentral_chisquare,
  *                                   size, fdf, fnonc)
  */
-  __pyx_k_tuple_83 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_82)); if (unlikely(!__pyx_k_tuple_83)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2156; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_83);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_83));
+  __pyx_tuple__52 = PyTuple_Pack(1, __pyx_kp_s_nonc_0_2); if (unlikely(!__pyx_tuple__52)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2159; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__52);
+  __Pyx_GIVEREF(__pyx_tuple__52);
 
-  /* "mtrand.pyx":2165
+  /* "mtrand.pyx":2168
  *         ononc = <ndarray>PyArray_FROM_OTF(nonc, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(odf, 0.0)):
  *             raise ValueError("df <= 1")             # <<<<<<<<<<<<<<
  *         if np.any(np.less_equal(ononc, 0.0)):
  *             raise ValueError("nonc < 0")
  */
-  __pyx_k_tuple_85 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_84)); if (unlikely(!__pyx_k_tuple_85)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2165; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_85);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_85));
+  __pyx_tuple__53 = PyTuple_Pack(1, __pyx_kp_s_df_1); if (unlikely(!__pyx_tuple__53)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2168; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__53);
+  __Pyx_GIVEREF(__pyx_tuple__53);
 
-  /* "mtrand.pyx":2167
+  /* "mtrand.pyx":2170
  *             raise ValueError("df <= 1")
  *         if np.any(np.less_equal(ononc, 0.0)):
  *             raise ValueError("nonc < 0")             # <<<<<<<<<<<<<<
  *         return cont2_array(self.internal_state, rk_noncentral_chisquare, size,
  *             odf, ononc)
  */
-  __pyx_k_tuple_86 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_73)); if (unlikely(!__pyx_k_tuple_86)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2167; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_86);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_86));
+  __pyx_tuple__54 = PyTuple_Pack(1, __pyx_kp_s_nonc_0); if (unlikely(!__pyx_tuple__54)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2170; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__54);
+  __Pyx_GIVEREF(__pyx_tuple__54);
 
-  /* "mtrand.pyx":2323
+  /* "mtrand.pyx":2326
  *         if not PyErr_Occurred():
  *             if fdf <= 0:
  *                 raise ValueError("df <= 0")             # <<<<<<<<<<<<<<
  *             return cont1_array_sc(self.internal_state, rk_standard_t, size, fdf)
- *
+ * 
  */
-  __pyx_k_tuple_87 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_78)); if (unlikely(!__pyx_k_tuple_87)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2323; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_87);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_87));
+  __pyx_tuple__55 = PyTuple_Pack(1, __pyx_kp_s_df_0); if (unlikely(!__pyx_tuple__55)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2326; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__55);
+  __Pyx_GIVEREF(__pyx_tuple__55);
 
-  /* "mtrand.pyx":2330
+  /* "mtrand.pyx":2333
  *         odf = <ndarray> PyArray_FROM_OTF(df, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(odf, 0.0)):
  *             raise ValueError("df <= 0")             # <<<<<<<<<<<<<<
  *         return cont1_array(self.internal_state, rk_standard_t, size, odf)
- *
+ * 
  */
-  __pyx_k_tuple_88 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_78)); if (unlikely(!__pyx_k_tuple_88)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2330; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_88);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_88));
+  __pyx_tuple__56 = PyTuple_Pack(1, __pyx_kp_s_df_0); if (unlikely(!__pyx_tuple__56)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2333; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__56);
+  __Pyx_GIVEREF(__pyx_tuple__56);
 
-  /* "mtrand.pyx":2416
+  /* "mtrand.pyx":2419
  *         if not PyErr_Occurred():
  *             if fkappa < 0:
  *                 raise ValueError("kappa < 0")             # <<<<<<<<<<<<<<
  *             return cont2_array_sc(self.internal_state, rk_vonmises, size, fmu, fkappa)
- *
+ * 
  */
-  __pyx_k_tuple_90 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_89)); if (unlikely(!__pyx_k_tuple_90)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2416; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_90);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_90));
+  __pyx_tuple__57 = PyTuple_Pack(1, __pyx_kp_s_kappa_0); if (unlikely(!__pyx_tuple__57)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2419; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__57);
+  __Pyx_GIVEREF(__pyx_tuple__57);
 
-  /* "mtrand.pyx":2424
+  /* "mtrand.pyx":2427
  *         okappa = <ndarray> PyArray_FROM_OTF(kappa, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less(okappa, 0.0)):
  *             raise ValueError("kappa < 0")             # <<<<<<<<<<<<<<
  *         return cont2_array(self.internal_state, rk_vonmises, size, omu, okappa)
- *
+ * 
  */
-  __pyx_k_tuple_91 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_89)); if (unlikely(!__pyx_k_tuple_91)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2424; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_91);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_91));
+  __pyx_tuple__58 = PyTuple_Pack(1, __pyx_kp_s_kappa_0); if (unlikely(!__pyx_tuple__58)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2427; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__58);
+  __Pyx_GIVEREF(__pyx_tuple__58);
 
-  /* "mtrand.pyx":2513
+  /* "mtrand.pyx":2516
  *         if not PyErr_Occurred():
  *             if fa <= 0:
  *                 raise ValueError("a <= 0")             # <<<<<<<<<<<<<<
  *             return cont1_array_sc(self.internal_state, rk_pareto, size, fa)
- *
+ * 
  */
-  __pyx_k_tuple_92 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_47)); if (unlikely(!__pyx_k_tuple_92)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2513; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_92);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_92));
+  __pyx_tuple__59 = PyTuple_Pack(1, __pyx_kp_s_a_0); if (unlikely(!__pyx_tuple__59)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2516; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__59);
+  __Pyx_GIVEREF(__pyx_tuple__59);
 
-  /* "mtrand.pyx":2520
+  /* "mtrand.pyx":2523
  *         oa = <ndarray>PyArray_FROM_OTF(a, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(oa, 0.0)):
  *             raise ValueError("a <= 0")             # <<<<<<<<<<<<<<
  *         return cont1_array(self.internal_state, rk_pareto, size, oa)
- *
+ * 
  */
-  __pyx_k_tuple_93 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_47)); if (unlikely(!__pyx_k_tuple_93)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2520; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_93);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_93));
+  __pyx_tuple__60 = PyTuple_Pack(1, __pyx_kp_s_a_0); if (unlikely(!__pyx_tuple__60)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2523; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__60);
+  __Pyx_GIVEREF(__pyx_tuple__60);
 
-  /* "mtrand.pyx":2613
+  /* "mtrand.pyx":2616
  *         if not PyErr_Occurred():
  *             if fa <= 0:
  *                 raise ValueError("a <= 0")             # <<<<<<<<<<<<<<
  *             return cont1_array_sc(self.internal_state, rk_weibull, size, fa)
- *
+ * 
  */
-  __pyx_k_tuple_94 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_47)); if (unlikely(!__pyx_k_tuple_94)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2613; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_94);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_94));
+  __pyx_tuple__61 = PyTuple_Pack(1, __pyx_kp_s_a_0); if (unlikely(!__pyx_tuple__61)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2616; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__61);
+  __Pyx_GIVEREF(__pyx_tuple__61);
 
-  /* "mtrand.pyx":2620
+  /* "mtrand.pyx":2623
  *         oa = <ndarray>PyArray_FROM_OTF(a, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(oa, 0.0)):
  *             raise ValueError("a <= 0")             # <<<<<<<<<<<<<<
  *         return cont1_array(self.internal_state, rk_weibull, size, oa)
- *
+ * 
  */
-  __pyx_k_tuple_95 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_47)); if (unlikely(!__pyx_k_tuple_95)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2620; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_95);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_95));
+  __pyx_tuple__62 = PyTuple_Pack(1, __pyx_kp_s_a_0); if (unlikely(!__pyx_tuple__62)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2623; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__62);
+  __Pyx_GIVEREF(__pyx_tuple__62);
 
-  /* "mtrand.pyx":2722
+  /* "mtrand.pyx":2725
  *         if not PyErr_Occurred():
  *             if fa <= 0:
  *                 raise ValueError("a <= 0")             # <<<<<<<<<<<<<<
  *             return cont1_array_sc(self.internal_state, rk_power, size, fa)
- *
+ * 
  */
-  __pyx_k_tuple_96 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_47)); if (unlikely(!__pyx_k_tuple_96)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2722; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_96);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_96));
+  __pyx_tuple__63 = PyTuple_Pack(1, __pyx_kp_s_a_0); if (unlikely(!__pyx_tuple__63)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2725; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__63);
+  __Pyx_GIVEREF(__pyx_tuple__63);
 
-  /* "mtrand.pyx":2729
+  /* "mtrand.pyx":2732
  *         oa = <ndarray>PyArray_FROM_OTF(a, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(oa, 0.0)):
  *             raise ValueError("a <= 0")             # <<<<<<<<<<<<<<
  *         return cont1_array(self.internal_state, rk_power, size, oa)
- *
+ * 
  */
-  __pyx_k_tuple_97 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_47)); if (unlikely(!__pyx_k_tuple_97)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2729; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_97);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_97));
+  __pyx_tuple__64 = PyTuple_Pack(1, __pyx_kp_s_a_0); if (unlikely(!__pyx_tuple__64)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2732; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__64);
+  __Pyx_GIVEREF(__pyx_tuple__64);
 
-  /* "mtrand.pyx":2812
+  /* "mtrand.pyx":2815
  *         if not PyErr_Occurred():
  *             if fscale <= 0:
  *                 raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
  *             return cont2_array_sc(self.internal_state, rk_laplace, size, floc, fscale)
- *
+ * 
  */
-  __pyx_k_tuple_100 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_44)); if (unlikely(!__pyx_k_tuple_100)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2812; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_100);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_100));
+  __pyx_tuple__65 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__65)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2815; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__65);
+  __Pyx_GIVEREF(__pyx_tuple__65);
 
-  /* "mtrand.pyx":2819
+  /* "mtrand.pyx":2822
  *         oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(oscale, 0.0)):
  *             raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
  *         return cont2_array(self.internal_state, rk_laplace, size, oloc, oscale)
- *
+ * 
  */
-  __pyx_k_tuple_101 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_44)); if (unlikely(!__pyx_k_tuple_101)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2819; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_101);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_101));
+  __pyx_tuple__66 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__66)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2822; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__66);
+  __Pyx_GIVEREF(__pyx_tuple__66);
 
-  /* "mtrand.pyx":2943
+  /* "mtrand.pyx":2946
  *         if not PyErr_Occurred():
  *             if fscale <= 0:
  *                 raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
  *             return cont2_array_sc(self.internal_state, rk_gumbel, size, floc, fscale)
- *
+ * 
  */
-  __pyx_k_tuple_104 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_44)); if (unlikely(!__pyx_k_tuple_104)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2943; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_104);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_104));
+  __pyx_tuple__67 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__67)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2946; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__67);
+  __Pyx_GIVEREF(__pyx_tuple__67);
 
-  /* "mtrand.pyx":2950
+  /* "mtrand.pyx":2953
  *         oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(oscale, 0.0)):
  *             raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
  *         return cont2_array(self.internal_state, rk_gumbel, size, oloc, oscale)
- *
+ * 
  */
-  __pyx_k_tuple_105 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_44)); if (unlikely(!__pyx_k_tuple_105)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2950; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_105);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_105));
+  __pyx_tuple__68 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__68)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2953; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__68);
+  __Pyx_GIVEREF(__pyx_tuple__68);
 
-  /* "mtrand.pyx":3031
+  /* "mtrand.pyx":3034
  *         if not PyErr_Occurred():
  *             if fscale <= 0:
  *                 raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
  *             return cont2_array_sc(self.internal_state, rk_logistic, size, floc, fscale)
- *
+ * 
  */
-  __pyx_k_tuple_108 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_44)); if (unlikely(!__pyx_k_tuple_108)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3031; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_108);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_108));
+  __pyx_tuple__69 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__69)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3034; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__69);
+  __Pyx_GIVEREF(__pyx_tuple__69);
 
-  /* "mtrand.pyx":3038
+  /* "mtrand.pyx":3041
  *         oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(oscale, 0.0)):
  *             raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
  *         return cont2_array(self.internal_state, rk_logistic, size, oloc, oscale)
- *
+ * 
  */
-  __pyx_k_tuple_109 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_44)); if (unlikely(!__pyx_k_tuple_109)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3038; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_109);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_109));
+  __pyx_tuple__70 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__70)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3041; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__70);
+  __Pyx_GIVEREF(__pyx_tuple__70);
 
-  /* "mtrand.pyx":3151
+  /* "mtrand.pyx":3154
  *         if not PyErr_Occurred():
  *             if fsigma <= 0:
  *                 raise ValueError("sigma <= 0")             # <<<<<<<<<<<<<<
  *             return cont2_array_sc(self.internal_state, rk_lognormal, size, fmean, fsigma)
- *
+ * 
  */
-  __pyx_k_tuple_113 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_112)); if (unlikely(!__pyx_k_tuple_113)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3151; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_113);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_113));
+  __pyx_tuple__71 = PyTuple_Pack(1, __pyx_kp_s_sigma_0); if (unlikely(!__pyx_tuple__71)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3154; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__71);
+  __Pyx_GIVEREF(__pyx_tuple__71);
 
-  /* "mtrand.pyx":3159
+  /* "mtrand.pyx":3162
  *         osigma = PyArray_FROM_OTF(sigma, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(osigma, 0.0)):
  *             raise ValueError("sigma <= 0.0")             # <<<<<<<<<<<<<<
  *         return cont2_array(self.internal_state, rk_lognormal, size, omean, osigma)
- *
+ * 
  */
-  __pyx_k_tuple_115 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_114)); if (unlikely(!__pyx_k_tuple_115)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3159; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_115);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_115));
+  __pyx_tuple__72 = PyTuple_Pack(1, __pyx_kp_s_sigma_0_0); if (unlikely(!__pyx_tuple__72)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3162; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__72);
+  __Pyx_GIVEREF(__pyx_tuple__72);
 
-  /* "mtrand.pyx":3224
+  /* "mtrand.pyx":3227
  *         if not PyErr_Occurred():
  *             if fscale <= 0:
  *                 raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
  *             return cont1_array_sc(self.internal_state, rk_rayleigh, size, fscale)
- *
+ * 
  */
-  __pyx_k_tuple_117 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_44)); if (unlikely(!__pyx_k_tuple_117)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3224; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_117);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_117));
+  __pyx_tuple__73 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__73)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3227; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__73);
+  __Pyx_GIVEREF(__pyx_tuple__73);
 
-  /* "mtrand.pyx":3231
+  /* "mtrand.pyx":3234
  *         oscale = <ndarray>PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(oscale, 0.0)):
  *             raise ValueError("scale <= 0.0")             # <<<<<<<<<<<<<<
  *         return cont1_array(self.internal_state, rk_rayleigh, size, oscale)
- *
+ * 
  */
-  __pyx_k_tuple_119 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_118)); if (unlikely(!__pyx_k_tuple_119)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3231; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_119);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_119));
+  __pyx_tuple__74 = PyTuple_Pack(1, __pyx_kp_s_scale_0_0); if (unlikely(!__pyx_tuple__74)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3234; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__74);
+  __Pyx_GIVEREF(__pyx_tuple__74);
 
-  /* "mtrand.pyx":3304
+  /* "mtrand.pyx":3307
  *         if not PyErr_Occurred():
  *             if fmean <= 0:
  *                 raise ValueError("mean <= 0")             # <<<<<<<<<<<<<<
  *             if fscale <= 0:
  *                 raise ValueError("scale <= 0")
  */
-  __pyx_k_tuple_121 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_120)); if (unlikely(!__pyx_k_tuple_121)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3304; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_121);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_121));
+  __pyx_tuple__75 = PyTuple_Pack(1, __pyx_kp_s_mean_0); if (unlikely(!__pyx_tuple__75)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3307; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__75);
+  __Pyx_GIVEREF(__pyx_tuple__75);
 
-  /* "mtrand.pyx":3306
+  /* "mtrand.pyx":3309
  *                 raise ValueError("mean <= 0")
  *             if fscale <= 0:
  *                 raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
  *             return cont2_array_sc(self.internal_state, rk_wald, size, fmean, fscale)
- *
+ * 
  */
-  __pyx_k_tuple_122 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_44)); if (unlikely(!__pyx_k_tuple_122)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3306; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_122);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_122));
+  __pyx_tuple__76 = PyTuple_Pack(1, __pyx_kp_s_scale_0); if (unlikely(!__pyx_tuple__76)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3309; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__76);
+  __Pyx_GIVEREF(__pyx_tuple__76);
 
-  /* "mtrand.pyx":3313
+  /* "mtrand.pyx":3316
  *         oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(omean,0.0)):
  *             raise ValueError("mean <= 0.0")             # <<<<<<<<<<<<<<
  *         elif np.any(np.less_equal(oscale,0.0)):
  *             raise ValueError("scale <= 0.0")
  */
-  __pyx_k_tuple_124 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_123)); if (unlikely(!__pyx_k_tuple_124)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3313; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_124);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_124));
+  __pyx_tuple__77 = PyTuple_Pack(1, __pyx_kp_s_mean_0_0); if (unlikely(!__pyx_tuple__77)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3316; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__77);
+  __Pyx_GIVEREF(__pyx_tuple__77);
 
-  /* "mtrand.pyx":3315
+  /* "mtrand.pyx":3318
  *             raise ValueError("mean <= 0.0")
  *         elif np.any(np.less_equal(oscale,0.0)):
  *             raise ValueError("scale <= 0.0")             # <<<<<<<<<<<<<<
  *         return cont2_array(self.internal_state, rk_wald, size, omean, oscale)
- *
+ * 
  */
-  __pyx_k_tuple_125 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_118)); if (unlikely(!__pyx_k_tuple_125)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3315; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_125);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_125));
+  __pyx_tuple__78 = PyTuple_Pack(1, __pyx_kp_s_scale_0_0); if (unlikely(!__pyx_tuple__78)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3318; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__78);
+  __Pyx_GIVEREF(__pyx_tuple__78);
 
-  /* "mtrand.pyx":3385
+  /* "mtrand.pyx":3388
  *         if not PyErr_Occurred():
  *             if fleft > fmode:
  *                 raise ValueError("left > mode")             # <<<<<<<<<<<<<<
  *             if fmode > fright:
  *                 raise ValueError("mode > right")
  */
-  __pyx_k_tuple_127 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_126)); if (unlikely(!__pyx_k_tuple_127)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3385; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_127);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_127));
+  __pyx_tuple__79 = PyTuple_Pack(1, __pyx_kp_s_left_mode); if (unlikely(!__pyx_tuple__79)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3388; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__79);
+  __Pyx_GIVEREF(__pyx_tuple__79);
 
-  /* "mtrand.pyx":3387
+  /* "mtrand.pyx":3390
  *                 raise ValueError("left > mode")
  *             if fmode > fright:
  *                 raise ValueError("mode > right")             # <<<<<<<<<<<<<<
  *             if fleft == fright:
  *                 raise ValueError("left == right")
  */
-  __pyx_k_tuple_129 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_128)); if (unlikely(!__pyx_k_tuple_129)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3387; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_129);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_129));
+  __pyx_tuple__80 = PyTuple_Pack(1, __pyx_kp_s_mode_right); if (unlikely(!__pyx_tuple__80)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3390; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__80);
+  __Pyx_GIVEREF(__pyx_tuple__80);
 
-  /* "mtrand.pyx":3389
+  /* "mtrand.pyx":3392
  *                 raise ValueError("mode > right")
  *             if fleft == fright:
  *                 raise ValueError("left == right")             # <<<<<<<<<<<<<<
  *             return cont3_array_sc(self.internal_state, rk_triangular, size, fleft,
  *                                   fmode, fright)
  */
-  __pyx_k_tuple_131 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_130)); if (unlikely(!__pyx_k_tuple_131)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3389; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_131);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_131));
+  __pyx_tuple__81 = PyTuple_Pack(1, __pyx_kp_s_left_right); if (unlikely(!__pyx_tuple__81)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3392; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__81);
+  __Pyx_GIVEREF(__pyx_tuple__81);
 
-  /* "mtrand.pyx":3399
- *
+  /* "mtrand.pyx":3402
+ * 
  *         if np.any(np.greater(oleft, omode)):
  *             raise ValueError("left > mode")             # <<<<<<<<<<<<<<
  *         if np.any(np.greater(omode, oright)):
  *             raise ValueError("mode > right")
  */
-  __pyx_k_tuple_132 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_126)); if (unlikely(!__pyx_k_tuple_132)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3399; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_132);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_132));
+  __pyx_tuple__82 = PyTuple_Pack(1, __pyx_kp_s_left_mode); if (unlikely(!__pyx_tuple__82)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3402; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__82);
+  __Pyx_GIVEREF(__pyx_tuple__82);
 
-  /* "mtrand.pyx":3401
+  /* "mtrand.pyx":3404
  *             raise ValueError("left > mode")
  *         if np.any(np.greater(omode, oright)):
  *             raise ValueError("mode > right")             # <<<<<<<<<<<<<<
  *         if np.any(np.equal(oleft, oright)):
  *             raise ValueError("left == right")
  */
-  __pyx_k_tuple_133 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_128)); if (unlikely(!__pyx_k_tuple_133)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3401; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_133);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_133));
+  __pyx_tuple__83 = PyTuple_Pack(1, __pyx_kp_s_mode_right); if (unlikely(!__pyx_tuple__83)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3404; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__83);
+  __Pyx_GIVEREF(__pyx_tuple__83);
 
-  /* "mtrand.pyx":3403
+  /* "mtrand.pyx":3406
  *             raise ValueError("mode > right")
  *         if np.any(np.equal(oleft, oright)):
  *             raise ValueError("left == right")             # <<<<<<<<<<<<<<
  *         return cont3_array(self.internal_state, rk_triangular, size, oleft,
  *             omode, oright)
  */
-  __pyx_k_tuple_134 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_130)); if (unlikely(!__pyx_k_tuple_134)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3403; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_134);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_134));
+  __pyx_tuple__84 = PyTuple_Pack(1, __pyx_kp_s_left_right); if (unlikely(!__pyx_tuple__84)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3406; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__84);
+  __Pyx_GIVEREF(__pyx_tuple__84);
 
-  /* "mtrand.pyx":3497
+  /* "mtrand.pyx":3500
  *         if not PyErr_Occurred():
  *             if ln < 0:
  *                 raise ValueError("n < 0")             # <<<<<<<<<<<<<<
  *             if fp < 0:
  *                 raise ValueError("p < 0")
  */
-  __pyx_k_tuple_136 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_135)); if (unlikely(!__pyx_k_tuple_136)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3497; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_136);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_136));
+  __pyx_tuple__85 = PyTuple_Pack(1, __pyx_kp_s_n_0); if (unlikely(!__pyx_tuple__85)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3500; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__85);
+  __Pyx_GIVEREF(__pyx_tuple__85);
 
-  /* "mtrand.pyx":3499
+  /* "mtrand.pyx":3502
  *                 raise ValueError("n < 0")
  *             if fp < 0:
  *                 raise ValueError("p < 0")             # <<<<<<<<<<<<<<
  *             elif fp > 1:
  *                 raise ValueError("p > 1")
  */
-  __pyx_k_tuple_138 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_137)); if (unlikely(!__pyx_k_tuple_138)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3499; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_138);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_138));
+  __pyx_tuple__86 = PyTuple_Pack(1, __pyx_kp_s_p_0); if (unlikely(!__pyx_tuple__86)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3502; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__86);
+  __Pyx_GIVEREF(__pyx_tuple__86);
 
-  /* "mtrand.pyx":3501
+  /* "mtrand.pyx":3504
  *                 raise ValueError("p < 0")
  *             elif fp > 1:
  *                 raise ValueError("p > 1")             # <<<<<<<<<<<<<<
  *             return discnp_array_sc(self.internal_state, rk_binomial, size, ln, fp)
- *
+ * 
  */
-  __pyx_k_tuple_140 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_139)); if (unlikely(!__pyx_k_tuple_140)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3501; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_140);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_140));
+  __pyx_tuple__87 = PyTuple_Pack(1, __pyx_kp_s_p_1); if (unlikely(!__pyx_tuple__87)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3504; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__87);
+  __Pyx_GIVEREF(__pyx_tuple__87);
 
-  /* "mtrand.pyx":3509
+  /* "mtrand.pyx":3512
  *         op = <ndarray>PyArray_FROM_OTF(p, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less(n, 0)):
  *             raise ValueError("n < 0")             # <<<<<<<<<<<<<<
  *         if np.any(np.less(p, 0)):
  *             raise ValueError("p < 0")
  */
-  __pyx_k_tuple_141 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_135)); if (unlikely(!__pyx_k_tuple_141)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3509; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_141);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_141));
+  __pyx_tuple__88 = PyTuple_Pack(1, __pyx_kp_s_n_0); if (unlikely(!__pyx_tuple__88)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3512; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__88);
+  __Pyx_GIVEREF(__pyx_tuple__88);
 
-  /* "mtrand.pyx":3511
+  /* "mtrand.pyx":3514
  *             raise ValueError("n < 0")
  *         if np.any(np.less(p, 0)):
  *             raise ValueError("p < 0")             # <<<<<<<<<<<<<<
  *         if np.any(np.greater(p, 1)):
  *             raise ValueError("p > 1")
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-  __pyx_k_tuple_142 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_137)); if (unlikely(!__pyx_k_tuple_142)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3511; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_142);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_142));
+  __pyx_tuple__89 = PyTuple_Pack(1, __pyx_kp_s_p_0); if (unlikely(!__pyx_tuple__89)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3514; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__89);
+  __Pyx_GIVEREF(__pyx_tuple__89);
 
-  /* "mtrand.pyx":3513
+  /* "mtrand.pyx":3516
  *             raise ValueError("p < 0")
  *         if np.any(np.greater(p, 1)):
  *             raise ValueError("p > 1")             # <<<<<<<<<<<<<<
  *         return discnp_array(self.internal_state, rk_binomial, size, on, op)
- *
+ * 
  */
-  __pyx_k_tuple_143 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_139)); if (unlikely(!__pyx_k_tuple_143)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3513; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_143);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_143));
+  __pyx_tuple__90 = PyTuple_Pack(1, __pyx_kp_s_p_1); if (unlikely(!__pyx_tuple__90)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3516; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__90);
+  __Pyx_GIVEREF(__pyx_tuple__90);
 
-  /* "mtrand.pyx":3590
+  /* "mtrand.pyx":3593
  *         if not PyErr_Occurred():
  *             if fn <= 0:
  *                 raise ValueError("n <= 0")             # <<<<<<<<<<<<<<
  *             if fp < 0:
  *                 raise ValueError("p < 0")
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-  __pyx_k_tuple_145 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_144)); if (unlikely(!__pyx_k_tuple_145)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3590; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_145);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_145));
+  __pyx_tuple__91 = PyTuple_Pack(1, __pyx_kp_s_n_0_2); if (unlikely(!__pyx_tuple__91)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3593; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__91);
+  __Pyx_GIVEREF(__pyx_tuple__91);
 
-  /* "mtrand.pyx":3592
+  /* "mtrand.pyx":3595
  *                 raise ValueError("n <= 0")
  *             if fp < 0:
  *                 raise ValueError("p < 0")             # <<<<<<<<<<<<<<
  *             elif fp > 1:
  *                 raise ValueError("p > 1")
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-  __pyx_k_tuple_146 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_137)); if (unlikely(!__pyx_k_tuple_146)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3592; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_146);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_146));
+  __pyx_tuple__92 = PyTuple_Pack(1, __pyx_kp_s_p_0); if (unlikely(!__pyx_tuple__92)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3595; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__92);
+  __Pyx_GIVEREF(__pyx_tuple__92);
 
-  /* "mtrand.pyx":3594
+  /* "mtrand.pyx":3597
  *                 raise ValueError("p < 0")
  *             elif fp > 1:
  *                 raise ValueError("p > 1")             # <<<<<<<<<<<<<<
  *             return discdd_array_sc(self.internal_state, rk_negative_binomial,
  *                                    size, fn, fp)
  */
-  __pyx_k_tuple_147 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_139)); if (unlikely(!__pyx_k_tuple_147)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3594; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_147);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_147));
+  __pyx_tuple__93 = PyTuple_Pack(1, __pyx_kp_s_p_1); if (unlikely(!__pyx_tuple__93)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3597; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__93);
+  __Pyx_GIVEREF(__pyx_tuple__93);
 
-  /* "mtrand.pyx":3603
+  /* "mtrand.pyx":3606
  *         op = <ndarray>PyArray_FROM_OTF(p, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(n, 0)):
  *             raise ValueError("n <= 0")             # <<<<<<<<<<<<<<
  *         if np.any(np.less(p, 0)):
  *             raise ValueError("p < 0")
  */
-  __pyx_k_tuple_148 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_144)); if (unlikely(!__pyx_k_tuple_148)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3603; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_148);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_148));
+  __pyx_tuple__94 = PyTuple_Pack(1, __pyx_kp_s_n_0_2); if (unlikely(!__pyx_tuple__94)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3606; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__94);
+  __Pyx_GIVEREF(__pyx_tuple__94);
 
-  /* "mtrand.pyx":3605
+  /* "mtrand.pyx":3608
  *             raise ValueError("n <= 0")
  *         if np.any(np.less(p, 0)):
  *             raise ValueError("p < 0")             # <<<<<<<<<<<<<<
  *         if np.any(np.greater(p, 1)):
  *             raise ValueError("p > 1")
  */
-  __pyx_k_tuple_149 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_137)); if (unlikely(!__pyx_k_tuple_149)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3605; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_149);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_149));
+  __pyx_tuple__95 = PyTuple_Pack(1, __pyx_kp_s_p_0); if (unlikely(!__pyx_tuple__95)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3608; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__95);
+  __Pyx_GIVEREF(__pyx_tuple__95);
 
-  /* "mtrand.pyx":3607
+  /* "mtrand.pyx":3610
  *             raise ValueError("p < 0")
  *         if np.any(np.greater(p, 1)):
  *             raise ValueError("p > 1")             # <<<<<<<<<<<<<<
  *         return discdd_array(self.internal_state, rk_negative_binomial, size,
  *                             on, op)
  */
-  __pyx_k_tuple_150 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_139)); if (unlikely(!__pyx_k_tuple_150)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3607; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_150);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_150));
+  __pyx_tuple__96 = PyTuple_Pack(1, __pyx_kp_s_p_1); if (unlikely(!__pyx_tuple__96)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3610; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__96);
+  __Pyx_GIVEREF(__pyx_tuple__96);
 
-  /* "mtrand.pyx":3668
+  /* "mtrand.pyx":3671
  *         if not PyErr_Occurred():
  *             if lam < 0:
  *                 raise ValueError("lam < 0")             # <<<<<<<<<<<<<<
  *             if lam > self.poisson_lam_max:
  *                 raise ValueError("lam value too large")
  */
-  __pyx_k_tuple_153 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_152)); if (unlikely(!__pyx_k_tuple_153)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3668; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_153);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_153));
+  __pyx_tuple__97 = PyTuple_Pack(1, __pyx_kp_s_lam_0); if (unlikely(!__pyx_tuple__97)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3671; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__97);
+  __Pyx_GIVEREF(__pyx_tuple__97);
 
-  /* "mtrand.pyx":3670
+  /* "mtrand.pyx":3673
  *                 raise ValueError("lam < 0")
  *             if lam > self.poisson_lam_max:
  *                 raise ValueError("lam value too large")             # <<<<<<<<<<<<<<
  *             return discd_array_sc(self.internal_state, rk_poisson, size, flam)
- *
+ * 
  */
-  __pyx_k_tuple_155 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_154)); if (unlikely(!__pyx_k_tuple_155)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3670; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_155);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_155));
+  __pyx_tuple__98 = PyTuple_Pack(1, __pyx_kp_s_lam_value_too_large); if (unlikely(!__pyx_tuple__98)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3673; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__98);
+  __Pyx_GIVEREF(__pyx_tuple__98);
 
-  /* "mtrand.pyx":3677
+  /* "mtrand.pyx":3680
  *         olam = <ndarray>PyArray_FROM_OTF(lam, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less(olam, 0)):
  *             raise ValueError("lam < 0")             # <<<<<<<<<<<<<<
  *         if np.any(np.greater(olam, self.poisson_lam_max)):
  *             raise ValueError("lam value too large.")
  */
-  __pyx_k_tuple_156 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_152)); if (unlikely(!__pyx_k_tuple_156)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3677; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_156);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_156));
+  __pyx_tuple__99 = PyTuple_Pack(1, __pyx_kp_s_lam_0); if (unlikely(!__pyx_tuple__99)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3680; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__99);
+  __Pyx_GIVEREF(__pyx_tuple__99);
 
-  /* "mtrand.pyx":3679
+  /* "mtrand.pyx":3682
  *             raise ValueError("lam < 0")
  *         if np.any(np.greater(olam, self.poisson_lam_max)):
  *             raise ValueError("lam value too large.")             # <<<<<<<<<<<<<<
  *         return discd_array(self.internal_state, rk_poisson, size, olam)
- *
+ * 
  */
-  __pyx_k_tuple_158 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_157)); if (unlikely(!__pyx_k_tuple_158)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3679; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_158);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_158));
+  __pyx_tuple__100 = PyTuple_Pack(1, __pyx_kp_s_lam_value_too_large_2); if (unlikely(!__pyx_tuple__100)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3682; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__100);
+  __Pyx_GIVEREF(__pyx_tuple__100);
 
-  /* "mtrand.pyx":3760
+  /* "mtrand.pyx":3763
  *         if not PyErr_Occurred():
  *             if fa <= 1.0:
  *                 raise ValueError("a <= 1.0")             # <<<<<<<<<<<<<<
  *             return discd_array_sc(self.internal_state, rk_zipf, size, fa)
- *
+ * 
  */
-  __pyx_k_tuple_160 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_159)); if (unlikely(!__pyx_k_tuple_160)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3760; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_160);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_160));
+  __pyx_tuple__101 = PyTuple_Pack(1, __pyx_kp_s_a_1_0); if (unlikely(!__pyx_tuple__101)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3763; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__101);
+  __Pyx_GIVEREF(__pyx_tuple__101);
 
-  /* "mtrand.pyx":3767
+  /* "mtrand.pyx":3770
  *         oa = <ndarray>PyArray_FROM_OTF(a, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(oa, 1.0)):
  *             raise ValueError("a <= 1.0")             # <<<<<<<<<<<<<<
  *         return discd_array(self.internal_state, rk_zipf, size, oa)
- *
+ * 
  */
-  __pyx_k_tuple_161 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_159)); if (unlikely(!__pyx_k_tuple_161)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3767; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_161);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_161));
+  __pyx_tuple__102 = PyTuple_Pack(1, __pyx_kp_s_a_1_0); if (unlikely(!__pyx_tuple__102)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3770; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__102);
+  __Pyx_GIVEREF(__pyx_tuple__102);
 
-  /* "mtrand.pyx":3821
+  /* "mtrand.pyx":3824
  *         if not PyErr_Occurred():
  *             if fp < 0.0:
  *                 raise ValueError("p < 0.0")             # <<<<<<<<<<<<<<
  *             if fp > 1.0:
  *                 raise ValueError("p > 1.0")
  */
-  __pyx_k_tuple_163 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_162)); if (unlikely(!__pyx_k_tuple_163)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3821; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_163);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_163));
+  __pyx_tuple__103 = PyTuple_Pack(1, __pyx_kp_s_p_0_0); if (unlikely(!__pyx_tuple__103)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3824; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__103);
+  __Pyx_GIVEREF(__pyx_tuple__103);
 
-  /* "mtrand.pyx":3823
+  /* "mtrand.pyx":3826
  *                 raise ValueError("p < 0.0")
  *             if fp > 1.0:
  *                 raise ValueError("p > 1.0")             # <<<<<<<<<<<<<<
  *             return discd_array_sc(self.internal_state, rk_geometric, size, fp)
- *
+ * 
  */
-  __pyx_k_tuple_165 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_164)); if (unlikely(!__pyx_k_tuple_165)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3823; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_165);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_165));
+  __pyx_tuple__104 = PyTuple_Pack(1, __pyx_kp_s_p_1_0); if (unlikely(!__pyx_tuple__104)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3826; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__104);
+  __Pyx_GIVEREF(__pyx_tuple__104);
 
-  /* "mtrand.pyx":3831
+  /* "mtrand.pyx":3834
  *         op = <ndarray>PyArray_FROM_OTF(p, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less(op, 0.0)):
  *             raise ValueError("p < 0.0")             # <<<<<<<<<<<<<<
  *         if np.any(np.greater(op, 1.0)):
  *             raise ValueError("p > 1.0")
  */
-  __pyx_k_tuple_166 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_162)); if (unlikely(!__pyx_k_tuple_166)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3831; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_166);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_166));
+  __pyx_tuple__105 = PyTuple_Pack(1, __pyx_kp_s_p_0_0); if (unlikely(!__pyx_tuple__105)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3834; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__105);
+  __Pyx_GIVEREF(__pyx_tuple__105);
 
-  /* "mtrand.pyx":3833
+  /* "mtrand.pyx":3836
  *             raise ValueError("p < 0.0")
  *         if np.any(np.greater(op, 1.0)):
  *             raise ValueError("p > 1.0")             # <<<<<<<<<<<<<<
  *         return discd_array(self.internal_state, rk_geometric, size, op)
- *
+ * 
  */
-  __pyx_k_tuple_167 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_164)); if (unlikely(!__pyx_k_tuple_167)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3833; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_167);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_167));
+  __pyx_tuple__106 = PyTuple_Pack(1, __pyx_kp_s_p_1_0); if (unlikely(!__pyx_tuple__106)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3836; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__106);
+  __Pyx_GIVEREF(__pyx_tuple__106);
 
-  /* "mtrand.pyx":3929
+  /* "mtrand.pyx":3932
  *         if not PyErr_Occurred():
  *             if lngood < 0:
  *                 raise ValueError("ngood < 0")             # <<<<<<<<<<<<<<
  *             if lnbad < 0:
  *                 raise ValueError("nbad < 0")
  */
-  __pyx_k_tuple_169 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_168)); if (unlikely(!__pyx_k_tuple_169)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3929; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_169);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_169));
+  __pyx_tuple__107 = PyTuple_Pack(1, __pyx_kp_s_ngood_0); if (unlikely(!__pyx_tuple__107)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3932; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__107);
+  __Pyx_GIVEREF(__pyx_tuple__107);
 
-  /* "mtrand.pyx":3931
+  /* "mtrand.pyx":3934
  *                 raise ValueError("ngood < 0")
  *             if lnbad < 0:
  *                 raise ValueError("nbad < 0")             # <<<<<<<<<<<<<<
  *             if lnsample < 1:
  *                 raise ValueError("nsample < 1")
  */
-  __pyx_k_tuple_171 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_170)); if (unlikely(!__pyx_k_tuple_171)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3931; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_171);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_171));
+  __pyx_tuple__108 = PyTuple_Pack(1, __pyx_kp_s_nbad_0); if (unlikely(!__pyx_tuple__108)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3934; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__108);
+  __Pyx_GIVEREF(__pyx_tuple__108);
 
-  /* "mtrand.pyx":3933
+  /* "mtrand.pyx":3936
  *                 raise ValueError("nbad < 0")
  *             if lnsample < 1:
  *                 raise ValueError("nsample < 1")             # <<<<<<<<<<<<<<
  *             if lngood + lnbad < lnsample:
  *                 raise ValueError("ngood + nbad < nsample")
  */
-  __pyx_k_tuple_173 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_172)); if (unlikely(!__pyx_k_tuple_173)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3933; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_173);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_173));
+  __pyx_tuple__109 = PyTuple_Pack(1, __pyx_kp_s_nsample_1); if (unlikely(!__pyx_tuple__109)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3936; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__109);
+  __Pyx_GIVEREF(__pyx_tuple__109);
 
-  /* "mtrand.pyx":3935
+  /* "mtrand.pyx":3938
  *                 raise ValueError("nsample < 1")
  *             if lngood + lnbad < lnsample:
  *                 raise ValueError("ngood + nbad < nsample")             # <<<<<<<<<<<<<<
  *             return discnmN_array_sc(self.internal_state, rk_hypergeometric, size,
  *                                     lngood, lnbad, lnsample)
  */
-  __pyx_k_tuple_175 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_174)); if (unlikely(!__pyx_k_tuple_175)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3935; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_175);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_175));
+  __pyx_tuple__110 = PyTuple_Pack(1, __pyx_kp_s_ngood_nbad_nsample); if (unlikely(!__pyx_tuple__110)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3938; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__110);
+  __Pyx_GIVEREF(__pyx_tuple__110);
 
-  /* "mtrand.pyx":3945
+  /* "mtrand.pyx":3948
  *         onsample = <ndarray>PyArray_FROM_OTF(nsample, NPY_LONG, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less(ongood, 0)):
  *             raise ValueError("ngood < 0")             # <<<<<<<<<<<<<<
  *         if np.any(np.less(onbad, 0)):
  *             raise ValueError("nbad < 0")
  */
-  __pyx_k_tuple_176 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_168)); if (unlikely(!__pyx_k_tuple_176)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3945; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_176);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_176));
+  __pyx_tuple__111 = PyTuple_Pack(1, __pyx_kp_s_ngood_0); if (unlikely(!__pyx_tuple__111)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3948; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__111);
+  __Pyx_GIVEREF(__pyx_tuple__111);
 
-  /* "mtrand.pyx":3947
+  /* "mtrand.pyx":3950
  *             raise ValueError("ngood < 0")
  *         if np.any(np.less(onbad, 0)):
  *             raise ValueError("nbad < 0")             # <<<<<<<<<<<<<<
  *         if np.any(np.less(onsample, 1)):
  *             raise ValueError("nsample < 1")
  */
-  __pyx_k_tuple_177 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_170)); if (unlikely(!__pyx_k_tuple_177)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3947; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_177);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_177));
+  __pyx_tuple__112 = PyTuple_Pack(1, __pyx_kp_s_nbad_0); if (unlikely(!__pyx_tuple__112)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3950; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__112);
+  __Pyx_GIVEREF(__pyx_tuple__112);
 
-  /* "mtrand.pyx":3949
+  /* "mtrand.pyx":3952
  *             raise ValueError("nbad < 0")
  *         if np.any(np.less(onsample, 1)):
  *             raise ValueError("nsample < 1")             # <<<<<<<<<<<<<<
  *         if np.any(np.less(np.add(ongood, onbad),onsample)):
  *             raise ValueError("ngood + nbad < nsample")
  */
-  __pyx_k_tuple_178 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_172)); if (unlikely(!__pyx_k_tuple_178)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3949; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_178);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_178));
+  __pyx_tuple__113 = PyTuple_Pack(1, __pyx_kp_s_nsample_1); if (unlikely(!__pyx_tuple__113)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3952; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__113);
+  __Pyx_GIVEREF(__pyx_tuple__113);
 
-  /* "mtrand.pyx":3951
+  /* "mtrand.pyx":3954
  *             raise ValueError("nsample < 1")
  *         if np.any(np.less(np.add(ongood, onbad),onsample)):
  *             raise ValueError("ngood + nbad < nsample")             # <<<<<<<<<<<<<<
  *         return discnmN_array(self.internal_state, rk_hypergeometric, size,
  *             ongood, onbad, onsample)
  */
-  __pyx_k_tuple_179 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_174)); if (unlikely(!__pyx_k_tuple_179)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3951; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_179);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_179));
+  __pyx_tuple__114 = PyTuple_Pack(1, __pyx_kp_s_ngood_nbad_nsample); if (unlikely(!__pyx_tuple__114)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3954; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__114);
+  __Pyx_GIVEREF(__pyx_tuple__114);
 
-  /* "mtrand.pyx":4035
+  /* "mtrand.pyx":4038
  *         if not PyErr_Occurred():
  *             if fp <= 0.0:
  *                 raise ValueError("p <= 0.0")             # <<<<<<<<<<<<<<
  *             if fp >= 1.0:
  *                 raise ValueError("p >= 1.0")
  */
-  __pyx_k_tuple_181 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_180)); if (unlikely(!__pyx_k_tuple_181)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4035; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_181);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_181));
+  __pyx_tuple__115 = PyTuple_Pack(1, __pyx_kp_s_p_0_0_2); if (unlikely(!__pyx_tuple__115)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4038; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__115);
+  __Pyx_GIVEREF(__pyx_tuple__115);
 
-  /* "mtrand.pyx":4037
+  /* "mtrand.pyx":4040
  *                 raise ValueError("p <= 0.0")
  *             if fp >= 1.0:
  *                 raise ValueError("p >= 1.0")             # <<<<<<<<<<<<<<
  *             return discd_array_sc(self.internal_state, rk_logseries, size, fp)
- *
+ * 
  */
-  __pyx_k_tuple_183 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_182)); if (unlikely(!__pyx_k_tuple_183)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4037; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_183);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_183));
+  __pyx_tuple__116 = PyTuple_Pack(1, __pyx_kp_s_p_1_0_2); if (unlikely(!__pyx_tuple__116)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4040; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__116);
+  __Pyx_GIVEREF(__pyx_tuple__116);
 
-  /* "mtrand.pyx":4044
+  /* "mtrand.pyx":4047
  *         op = <ndarray>PyArray_FROM_OTF(p, NPY_DOUBLE, NPY_ARRAY_ALIGNED)
  *         if np.any(np.less_equal(op, 0.0)):
  *             raise ValueError("p <= 0.0")             # <<<<<<<<<<<<<<
  *         if np.any(np.greater_equal(op, 1.0)):
  *             raise ValueError("p >= 1.0")
  */
-  __pyx_k_tuple_184 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_180)); if (unlikely(!__pyx_k_tuple_184)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4044; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_184);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_184));
+  __pyx_tuple__117 = PyTuple_Pack(1, __pyx_kp_s_p_0_0_2); if (unlikely(!__pyx_tuple__117)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4047; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__117);
+  __Pyx_GIVEREF(__pyx_tuple__117);
 
-  /* "mtrand.pyx":4046
+  /* "mtrand.pyx":4049
  *             raise ValueError("p <= 0.0")
  *         if np.any(np.greater_equal(op, 1.0)):
  *             raise ValueError("p >= 1.0")             # <<<<<<<<<<<<<<
  *         return discd_array(self.internal_state, rk_logseries, size, op)
- *
+ * 
  */
-  __pyx_k_tuple_185 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_182)); if (unlikely(!__pyx_k_tuple_185)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4046; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_185);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_185));
+  __pyx_tuple__118 = PyTuple_Pack(1, __pyx_kp_s_p_1_0_2); if (unlikely(!__pyx_tuple__118)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4049; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__118);
+  __Pyx_GIVEREF(__pyx_tuple__118);
 
-  /* "mtrand.pyx":4149
+  /* "mtrand.pyx":4152
  *             shape = size
  *         if len(mean.shape) != 1:
  *                raise ValueError("mean must be 1 dimensional")             # <<<<<<<<<<<<<<
  *         if (len(cov.shape) != 2) or (cov.shape[0] != cov.shape[1]):
  *                raise ValueError("cov must be 2 dimensional and square")
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-  __pyx_k_tuple_187 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_186)); if (unlikely(!__pyx_k_tuple_187)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4149; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_187);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_187));
+  __pyx_tuple__119 = PyTuple_Pack(1, __pyx_kp_s_mean_must_be_1_dimensional); if (unlikely(!__pyx_tuple__119)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4152; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__119);
+  __Pyx_GIVEREF(__pyx_tuple__119);
 
-  /* "mtrand.pyx":4151
+  /* "mtrand.pyx":4154
  *                raise ValueError("mean must be 1 dimensional")
  *         if (len(cov.shape) != 2) or (cov.shape[0] != cov.shape[1]):
  *                raise ValueError("cov must be 2 dimensional and square")             # <<<<<<<<<<<<<<
  *         if mean.shape[0] != cov.shape[0]:
  *                raise ValueError("mean and cov must have same length")
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-  __pyx_k_tuple_189 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_188)); if (unlikely(!__pyx_k_tuple_189)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4151; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_189);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_189));
+  __pyx_tuple__120 = PyTuple_Pack(1, __pyx_kp_s_cov_must_be_2_dimensional_and_sq); if (unlikely(!__pyx_tuple__120)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4154; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__120);
+  __Pyx_GIVEREF(__pyx_tuple__120);
 
-  /* "mtrand.pyx":4153
+  /* "mtrand.pyx":4156
  *                raise ValueError("cov must be 2 dimensional and square")
  *         if mean.shape[0] != cov.shape[0]:
  *                raise ValueError("mean and cov must have same length")             # <<<<<<<<<<<<<<
  *         # Compute shape of output
  *         if isinstance(shape, (int, long, np.integer)):
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-  __pyx_k_tuple_191 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_190)); if (unlikely(!__pyx_k_tuple_191)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4153; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_191);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_191));
+  __pyx_tuple__121 = PyTuple_Pack(1, __pyx_kp_s_mean_and_cov_must_have_same_leng); if (unlikely(!__pyx_tuple__121)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4156; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__121);
+  __Pyx_GIVEREF(__pyx_tuple__121);
 
-  /* "mtrand.pyx":4157
+  /* "mtrand.pyx":4160
  *         if isinstance(shape, (int, long, np.integer)):
  *             shape = [shape]
  *         final_shape = list(shape[:])             # <<<<<<<<<<<<<<
  *         final_shape.append(mean.shape[0])
  *         # Create a matrix of independent standard normally distributed random
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-  __pyx_k_slice_192 = PySlice_New(Py_None, Py_None, Py_None); if (unlikely(!__pyx_k_slice_192)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4157; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_slice_192);
-  __Pyx_GIVEREF(__pyx_k_slice_192);
+  __pyx_slice__122 = PySlice_New(Py_None, Py_None, Py_None); if (unlikely(!__pyx_slice__122)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4160; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_slice__122);
+  __Pyx_GIVEREF(__pyx_slice__122);
 
-  /* "mtrand.pyx":4246
- *
+  /* "mtrand.pyx":4249
+ * 
  *         if kahan_sum(pix, d-1) > (1.0 + 1e-12):
  *             raise ValueError("sum(pvals[:-1]) > 1.0")             # <<<<<<<<<<<<<<
- *
+ * 
  *         if size is None:
  */
-  __pyx_k_tuple_195 = PyTuple_Pack(1, ((PyObject *)__pyx_kp_s_194)); if (unlikely(!__pyx_k_tuple_195)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4246; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_195);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_195));
+  __pyx_tuple__123 = PyTuple_Pack(1, __pyx_kp_s_sum_pvals_1_1_0); if (unlikely(!__pyx_tuple__123)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4249; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__123);
+  __Pyx_GIVEREF(__pyx_tuple__123);
 
-  /* "mtrand.pyx":4433
+  /* "mtrand.pyx":4437
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  *             buf = np.empty(x.shape[1:], dtype=x.dtype)             # <<<<<<<<<<<<<<
  *             while i > 0:
  *                 j = rk_interval(i, self.internal_state)
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-  __pyx_k_slice_196 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_k_slice_196)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4433; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_slice_196);
-  __Pyx_GIVEREF(__pyx_k_slice_196);
+  __pyx_slice__124 = PySlice_New(__pyx_int_1, Py_None, Py_None); if (unlikely(!__pyx_slice__124)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4437; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_slice__124);
+  __Pyx_GIVEREF(__pyx_slice__124);
 
   /* "mtrand.pyx":559
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  *     cdef rk_state *internal_state
  *     poisson_lam_max = np.iinfo('l').max - np.sqrt(np.iinfo('l').max)*10             # <<<<<<<<<<<<<<
- *
+ * 
  *     def __init__(self, seed=None):
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-  __pyx_k_tuple_197 = PyTuple_Pack(1, ((PyObject *)__pyx_n_s__l)); if (unlikely(!__pyx_k_tuple_197)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 559; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(__pyx_k_tuple_197);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_197));
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-  __Pyx_GOTREF(__pyx_k_tuple_198);
-  __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_198));
+  __pyx_tuple__125 = PyTuple_Pack(1, __pyx_n_s_l); if (unlikely(!__pyx_tuple__125)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 559; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__125);
+  __Pyx_GIVEREF(__pyx_tuple__125);
+  __pyx_tuple__126 = PyTuple_Pack(1, __pyx_n_s_l); if (unlikely(!__pyx_tuple__126)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 559; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_GOTREF(__pyx_tuple__126);
+  __Pyx_GIVEREF(__pyx_tuple__126);
   __Pyx_RefNannyFinishContext();
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   __pyx_L1_error:;
@@ -23063,12 +23237,14 @@ static int __Pyx_InitCachedConstants(void) {
 
 static int __Pyx_InitGlobals(void) {
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-  __pyx_int_0 = PyInt_FromLong(0); if (unlikely(!__pyx_int_0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
-  __pyx_int_1 = PyInt_FromLong(1); if (unlikely(!__pyx_int_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
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-  __pyx_int_5 = PyInt_FromLong(5); if (unlikely(!__pyx_int_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
-  __pyx_int_10 = PyInt_FromLong(10); if (unlikely(!__pyx_int_10)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
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+  __pyx_float_1_0 = PyFloat_FromDouble(1.0); if (unlikely(!__pyx_float_1_0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_int_0 = PyInt_FromLong(0); if (unlikely(!__pyx_int_0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+ * 
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+  __pyx_t_4 = __Pyx_GetModuleGlobalName(__pyx_n_s_rand_2); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4523; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_t_1 = __Pyx_GetModuleGlobalName(__pyx_n_s_rand_2); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4524; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  if (PyDict_SetItem(__pyx_t_1, ((PyObject *)__pyx_kp_u_239), ((PyObject *)__pyx_kp_u_240)) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, ((PyObject *)__pyx_kp_u_241), ((PyObject *)__pyx_kp_u_242)) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, ((PyObject *)__pyx_kp_u_243), ((PyObject *)__pyx_kp_u_244)) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, ((PyObject *)__pyx_kp_u_245), ((PyObject *)__pyx_kp_u_246)) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  if (PyDict_SetItem(__pyx_t_1, ((PyObject *)__pyx_kp_u_253), ((PyObject *)__pyx_kp_u_254)) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, ((PyObject *)__pyx_kp_u_255), ((PyObject *)__pyx_kp_u_256)) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, ((PyObject *)__pyx_kp_u_257), ((PyObject *)__pyx_kp_u_258)) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, ((PyObject *)__pyx_kp_u_259), ((PyObject *)__pyx_kp_u_260)) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, ((PyObject *)__pyx_kp_u_261), ((PyObject *)__pyx_kp_u_262)) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, ((PyObject *)__pyx_kp_u_263), ((PyObject *)__pyx_kp_u_264)) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, ((PyObject *)__pyx_kp_u_265), ((PyObject *)__pyx_kp_u_266)) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, ((PyObject *)__pyx_kp_u_267), ((PyObject *)__pyx_kp_u_268)) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, ((PyObject *)__pyx_kp_u_269), ((PyObject *)__pyx_kp_u_270)) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, ((PyObject *)__pyx_kp_u_271), ((PyObject *)__pyx_kp_u_272)) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, ((PyObject *)__pyx_kp_u_273), ((PyObject *)__pyx_kp_u_274)) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, ((PyObject *)__pyx_kp_u_275), ((PyObject *)__pyx_kp_u_276)) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, ((PyObject *)__pyx_kp_u_277), ((PyObject *)__pyx_kp_u_278)) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, ((PyObject *)__pyx_kp_u_279), ((PyObject *)__pyx_kp_u_280)) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, ((PyObject *)__pyx_kp_u_281), ((PyObject *)__pyx_kp_u_282)) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, ((PyObject *)__pyx_kp_u_283), ((PyObject *)__pyx_kp_u_284)) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_t_1, ((PyObject *)__pyx_kp_u_285), ((PyObject *)__pyx_kp_u_286)) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  if (PyDict_SetItem(__pyx_d, __pyx_n_s____test__, ((PyObject *)__pyx_t_1)) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_DECREF(((PyObject *)__pyx_t_1)); __pyx_t_1 = 0;
+  __Pyx_GOTREF(__pyx_t_1);
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_random_sample_line_7, __pyx_kp_u_random_sample_size_None_Return) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_tomaxint_line_765, __pyx_kp_u_tomaxint_size_None_Random_integ) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_randint_line_812, __pyx_kp_u_randint_low_high_None_size_None) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_bytes_line_892, __pyx_kp_u_bytes_length_Return_random_byte) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_choice_line_920, __pyx_kp_u_choice_a_size_None_replace_True) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_uniform_line_1095, __pyx_kp_u_uniform_low_0_0_high_1_0_size_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_rand_line_1182, __pyx_kp_u_rand_d0_d1_dn_Random_values_in) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_randn_line_1226, __pyx_kp_u_randn_d0_d1_dn_Return_a_sample) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_random_integers_line, __pyx_kp_u_random_integers_low_high_None_s) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_standard_normal_line, __pyx_kp_u_standard_normal_size_None_Retur) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_normal_line_1393, __pyx_kp_u_normal_loc_0_0_scale_1_0_size_N) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_standard_exponential, __pyx_kp_u_standard_exponential_size_None) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_standard_gamma_line, __pyx_kp_u_standard_gamma_shape_size_None) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_gamma_line_1716, __pyx_kp_u_gamma_shape_scale_1_0_size_None) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_f_line_1807, __pyx_kp_u_f_dfnum_dfden_size_None_Draw_sa) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_noncentral_f_line_19, __pyx_kp_u_noncentral_f_dfnum_dfden_nonc_s) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_chisquare_line_2004, __pyx_kp_u_chisquare_df_size_None_Draw_sam) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_noncentral_chisquare, __pyx_kp_u_noncentral_chisquare_df_nonc_si) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_standard_cauchy_line, __pyx_kp_u_standard_cauchy_size_None_Stand) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_standard_t_line_2235, __pyx_kp_u_standard_t_df_size_None_Standar) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_vonmises_line_2336, __pyx_kp_u_vonmises_mu_kappa_size_None_Dra) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_pareto_line_2430, __pyx_kp_u_pareto_a_size_None_Draw_samples) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_weibull_line_2526, __pyx_kp_u_weibull_a_size_None_Weibull_dis) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_power_line_2626, __pyx_kp_u_power_a_size_None_Draws_samples) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_laplace_line_2735, __pyx_kp_u_laplace_loc_0_0_scale_1_0_size) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_gumbel_line_2825, __pyx_kp_u_gumbel_loc_0_0_scale_1_0_size_N) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_logistic_line_2956, __pyx_kp_u_logistic_loc_0_0_scale_1_0_size) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_lognormal_line_3044, __pyx_kp_u_lognormal_mean_0_0_sigma_1_0_si) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_rayleigh_line_3165, __pyx_kp_u_rayleigh_scale_1_0_size_None_Dr) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_wald_line_3237, __pyx_kp_u_wald_mean_scale_size_None_Draw) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_triangular_line_3323, __pyx_kp_u_triangular_left_mode_right_size) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_binomial_line_3411, __pyx_kp_u_binomial_n_p_size_None_Draw_sam) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_negative_binomial_li, __pyx_kp_u_negative_binomial_n_p_size_None) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_poisson_line_3614, __pyx_kp_u_poisson_lam_1_0_size_None_Draw) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_zipf_line_3685, __pyx_kp_u_zipf_a_size_None_Draw_samples_f) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_geometric_line_3773, __pyx_kp_u_geometric_p_size_None_Draw_samp) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_hypergeometric_line, __pyx_kp_u_hypergeometric_ngood_nbad_nsamp) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_logseries_line_3958, __pyx_kp_u_logseries_p_size_None_Draw_samp) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_multivariate_normal, __pyx_kp_u_multivariate_normal_mean_cov_si) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_multinomial_line_418, __pyx_kp_u_multinomial_n_pvals_size_None_D) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_dirichlet_line_4278, __pyx_kp_u_dirichlet_alpha_size_None_Draw) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_shuffle_line_4395, __pyx_kp_u_shuffle_x_Modify_a_sequence_in) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_t_1, __pyx_kp_u_RandomState_permutation_line_445, __pyx_kp_u_permutation_x_Randomly_permute) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyDict_SetItem(__pyx_d, __pyx_n_s_test, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
   goto __pyx_L0;
   __pyx_L1_error:;
   __Pyx_XDECREF(__pyx_t_1);
@@ -24240,7 +24244,7 @@ static PyObject *__Pyx_GetBuiltinName(PyObject *name) {
 #if PY_MAJOR_VERSION >= 3
             "name '%U' is not defined", name);
 #else
-            "name '%s' is not defined", PyString_AS_STRING(name));
+            "name '%.200s' is not defined", PyString_AS_STRING(name));
 #endif
     }
     return result;
@@ -24380,27 +24384,40 @@ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject
         value = type;
         type = (PyObject*) Py_TYPE(value);
     } else if (PyExceptionClass_Check(type)) {
-        PyObject *args;
-        if (!value)
-            args = PyTuple_New(0);
-        else if (PyTuple_Check(value)) {
-            Py_INCREF(value);
-            args = value;
-        } else
-            args = PyTuple_Pack(1, value);
-        if (!args)
-            goto bad;
-        owned_instance = PyEval_CallObject(type, args);
-        Py_DECREF(args);
-        if (!owned_instance)
-            goto bad;
-        value = owned_instance;
-        if (!PyExceptionInstance_Check(value)) {
-            PyErr_Format(PyExc_TypeError,
-                         "calling %R should have returned an instance of "
-                         "BaseException, not %R",
-                         type, Py_TYPE(value));
-            goto bad;
+        PyObject *instance_class = NULL;
+        if (value && PyExceptionInstance_Check(value)) {
+            instance_class = (PyObject*) Py_TYPE(value);
+            if (instance_class != type) {
+                if (PyObject_IsSubclass(instance_class, type)) {
+                    type = instance_class;
+                } else {
+                    instance_class = NULL;
+                }
+            }
+        }
+        if (!instance_class) {
+            PyObject *args;
+            if (!value)
+                args = PyTuple_New(0);
+            else if (PyTuple_Check(value)) {
+                Py_INCREF(value);
+                args = value;
+            } else
+                args = PyTuple_Pack(1, value);
+            if (!args)
+                goto bad;
+            owned_instance = PyObject_Call(type, args, NULL);
+            Py_DECREF(args);
+            if (!owned_instance)
+                goto bad;
+            value = owned_instance;
+            if (!PyExceptionInstance_Check(value)) {
+                PyErr_Format(PyExc_TypeError,
+                             "calling %R should have returned an instance of "
+                             "BaseException, not %R",
+                             type, Py_TYPE(value));
+                goto bad;
+            }
         }
     } else {
         PyErr_SetString(PyExc_TypeError,
@@ -24545,12 +24562,12 @@ arg_passed_twice:
     goto bad;
 invalid_keyword_type:
     PyErr_Format(PyExc_TypeError,
-        "%s() keywords must be strings", function_name);
+        "%.200s() keywords must be strings", function_name);
     goto bad;
 invalid_keyword:
     PyErr_Format(PyExc_TypeError,
     #if PY_MAJOR_VERSION < 3
-        "%s() got an unexpected keyword argument '%s'",
+        "%.200s() got an unexpected keyword argument '%.200s'",
         function_name, PyString_AsString(key));
     #else
         "%s() got an unexpected keyword argument '%U'",
@@ -24580,7 +24597,7 @@ static void __Pyx_RaiseArgtupleInvalid(
         more_or_less = "exactly";
     }
     PyErr_Format(PyExc_TypeError,
-                 "%s() takes %s %" CYTHON_FORMAT_SSIZE_T "d positional argument%s (%" CYTHON_FORMAT_SSIZE_T "d given)",
+                 "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)",
                  func_name, more_or_less, num_expected,
                  (num_expected == 1) ? "" : "s", num_found);
 }
@@ -24663,6 +24680,128 @@ static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i,
     return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
 }
 
+static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) {
+#if CYTHON_COMPILING_IN_PYPY
+    return PyObject_RichCompareBool(s1, s2, equals);
+#else
+    if (s1 == s2) {
+        return (equals == Py_EQ);
+    } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) {
+        const char *ps1, *ps2;
+        Py_ssize_t length = PyBytes_GET_SIZE(s1);
+        if (length != PyBytes_GET_SIZE(s2))
+            return (equals == Py_NE);
+        ps1 = PyBytes_AS_STRING(s1);
+        ps2 = PyBytes_AS_STRING(s2);
+        if (ps1[0] != ps2[0]) {
+            return (equals == Py_NE);
+        } else if (length == 1) {
+            return (equals == Py_EQ);
+        } else {
+            int result = memcmp(ps1, ps2, (size_t)length);
+            return (equals == Py_EQ) ? (result == 0) : (result != 0);
+        }
+    } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) {
+        return (equals == Py_NE);
+    } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) {
+        return (equals == Py_NE);
+    } else {
+        int result;
+        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
+        if (!py_result)
+            return -1;
+        result = __Pyx_PyObject_IsTrue(py_result);
+        Py_DECREF(py_result);
+        return result;
+    }
+#endif
+}
+
+static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) {
+#if CYTHON_COMPILING_IN_PYPY
+    return PyObject_RichCompareBool(s1, s2, equals);
+#else
+#if PY_MAJOR_VERSION < 3
+    PyObject* owned_ref = NULL;
+#endif
+    int s1_is_unicode, s2_is_unicode;
+    if (s1 == s2) {
+        goto return_eq;
+    }
+    s1_is_unicode = PyUnicode_CheckExact(s1);
+    s2_is_unicode = PyUnicode_CheckExact(s2);
+#if PY_MAJOR_VERSION < 3
+    if ((s1_is_unicode & (!s2_is_unicode)) && PyString_CheckExact(s2)) {
+        owned_ref = PyUnicode_FromObject(s2);
+        if (unlikely(!owned_ref))
+            return -1;
+        s2 = owned_ref;
+        s2_is_unicode = 1;
+    } else if ((s2_is_unicode & (!s1_is_unicode)) && PyString_CheckExact(s1)) {
+        owned_ref = PyUnicode_FromObject(s1);
+        if (unlikely(!owned_ref))
+            return -1;
+        s1 = owned_ref;
+        s1_is_unicode = 1;
+    } else if (((!s2_is_unicode) & (!s1_is_unicode))) {
+        return __Pyx_PyBytes_Equals(s1, s2, equals);
+    }
+#endif
+    if (s1_is_unicode & s2_is_unicode) {
+        Py_ssize_t length;
+        int kind;
+        void *data1, *data2;
+        #if CYTHON_PEP393_ENABLED
+        if (unlikely(PyUnicode_READY(s1) < 0) || unlikely(PyUnicode_READY(s2) < 0))
+            return -1;
+        #endif
+        length = __Pyx_PyUnicode_GET_LENGTH(s1);
+        if (length != __Pyx_PyUnicode_GET_LENGTH(s2)) {
+            goto return_ne;
+        }
+        kind = __Pyx_PyUnicode_KIND(s1);
+        if (kind != __Pyx_PyUnicode_KIND(s2)) {
+            goto return_ne;
+        }
+        data1 = __Pyx_PyUnicode_DATA(s1);
+        data2 = __Pyx_PyUnicode_DATA(s2);
+        if (__Pyx_PyUnicode_READ(kind, data1, 0) != __Pyx_PyUnicode_READ(kind, data2, 0)) {
+            goto return_ne;
+        } else if (length == 1) {
+            goto return_eq;
+        } else {
+            int result = memcmp(data1, data2, length * kind);
+            #if PY_MAJOR_VERSION < 3
+            Py_XDECREF(owned_ref);
+            #endif
+            return (equals == Py_EQ) ? (result == 0) : (result != 0);
+        }
+    } else if ((s1 == Py_None) & s2_is_unicode) {
+        goto return_ne;
+    } else if ((s2 == Py_None) & s1_is_unicode) {
+        goto return_ne;
+    } else {
+        int result;
+        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
+        if (!py_result)
+            return -1;
+        result = __Pyx_PyObject_IsTrue(py_result);
+        Py_DECREF(py_result);
+        return result;
+    }
+return_eq:
+    #if PY_MAJOR_VERSION < 3
+    Py_XDECREF(owned_ref);
+    #endif
+    return (equals == Py_EQ);
+return_ne:
+    #if PY_MAJOR_VERSION < 3
+    Py_XDECREF(owned_ref);
+    #endif
+    return (equals == Py_NE);
+#endif
+}
+
 static CYTHON_INLINE PyObject* __Pyx_PyObject_GetSlice(
         PyObject* obj, Py_ssize_t cstart, Py_ssize_t cstop,
         PyObject** _py_start, PyObject** _py_stop, PyObject** _py_slice,
@@ -24767,7 +24906,7 @@ static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {
 
 static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {
     PyErr_Format(PyExc_ValueError,
-                 "need more than %" CYTHON_FORMAT_SSIZE_T "d value%s to unpack",
+                 "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack",
                  index, (index == 1) ? "" : "s");
 }
 
@@ -24816,6 +24955,37 @@ static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected) {
     return 0;
 }
 
+static CYTHON_INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb) {
+#if CYTHON_COMPILING_IN_CPYTHON
+    PyThreadState *tstate = PyThreadState_GET();
+    *type = tstate->exc_type;
+    *value = tstate->exc_value;
+    *tb = tstate->exc_traceback;
+    Py_XINCREF(*type);
+    Py_XINCREF(*value);
+    Py_XINCREF(*tb);
+#else
+    PyErr_GetExcInfo(type, value, tb);
+#endif
+}
+static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb) {
+#if CYTHON_COMPILING_IN_CPYTHON
+    PyObject *tmp_type, *tmp_value, *tmp_tb;
+    PyThreadState *tstate = PyThreadState_GET();
+    tmp_type = tstate->exc_type;
+    tmp_value = tstate->exc_value;
+    tmp_tb = tstate->exc_traceback;
+    tstate->exc_type = type;
+    tstate->exc_value = value;
+    tstate->exc_traceback = tb;
+    Py_XDECREF(tmp_type);
+    Py_XDECREF(tmp_value);
+    Py_XDECREF(tmp_tb);
+#else
+    PyErr_SetExcInfo(type, value, tb);
+#endif
+}
+
 static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) {
     PyObject *local_type, *local_value, *local_tb;
 #if CYTHON_COMPILING_IN_CPYTHON
@@ -24838,12 +25008,14 @@ static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb)
 #endif
         goto bad;
     #if PY_MAJOR_VERSION >= 3
-    if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0))
-        goto bad;
+    if (local_tb) {
+        if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0))
+            goto bad;
+    }
     #endif
-    Py_INCREF(local_type);
-    Py_INCREF(local_value);
-    Py_INCREF(local_tb);
+    Py_XINCREF(local_tb);
+    Py_XINCREF(local_type);
+    Py_XINCREF(local_value);
     *type = local_type;
     *value = local_value;
     *tb = local_tb;
@@ -24854,8 +25026,6 @@ static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb)
     tstate->exc_type = local_type;
     tstate->exc_value = local_value;
     tstate->exc_traceback = local_tb;
-    /* Make sure tstate is in a consistent state when we XDECREF
-       these objects (DECREF may run arbitrary code). */
     Py_XDECREF(tmp_type);
     Py_XDECREF(tmp_value);
     Py_XDECREF(tmp_tb);
@@ -24873,6 +25043,18 @@ bad:
     return -1;
 }
 
+static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) {
+    if (unlikely(!type)) {
+        PyErr_SetString(PyExc_SystemError, "Missing type object");
+        return 0;
+    }
+    if (likely(PyObject_TypeCheck(obj, type)))
+        return 1;
+    PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s",
+                 Py_TYPE(obj)->tp_name, type->tp_name);
+    return 0;
+}
+
 static CYTHON_INLINE int __Pyx_PyObject_SetSlice(
         PyObject* obj, PyObject* value, Py_ssize_t cstart, Py_ssize_t cstop,
         PyObject** _py_start, PyObject** _py_stop, PyObject** _py_slice,
@@ -24965,7 +25147,7 @@ static CYTHON_INLINE int __Pyx_PyObject_SetSlice(
         return result;
     }
     PyErr_Format(PyExc_TypeError,
-        "'%.200s' object does not support slice %s",
+        "'%.200s' object does not support slice %.10s",
         Py_TYPE(obj)->tp_name, value ? "assignment" : "deletion");
 bad:
     return -1;
@@ -24995,13 +25177,13 @@ static CYTHON_INLINE int __Pyx_CheckKeywordStrings(
     return 1;
 invalid_keyword_type:
     PyErr_Format(PyExc_TypeError,
-        "%s() keywords must be strings", function_name);
+        "%.200s() keywords must be strings", function_name);
     return 0;
 #endif
 invalid_keyword:
     PyErr_Format(PyExc_TypeError,
     #if PY_MAJOR_VERSION < 3
-        "%s() got an unexpected keyword argument '%s'",
+        "%.200s() got an unexpected keyword argument '%.200s'",
         function_name, PyString_AsString(key));
     #else
         "%s() got an unexpected keyword argument '%U'",
@@ -25010,18 +25192,6 @@ invalid_keyword:
     return 0;
 }
 
-static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) {
-    if (unlikely(!type)) {
-        PyErr_Format(PyExc_SystemError, "Missing type object");
-        return 0;
-    }
-    if (likely(PyObject_TypeCheck(obj, type)))
-        return 1;
-    PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s",
-                 Py_TYPE(obj)->tp_name, type->tp_name);
-    return 0;
-}
-
 #if CYTHON_COMPILING_IN_CPYTHON
 static CYTHON_INLINE void __Pyx_crop_slice(Py_ssize_t* _start, Py_ssize_t* _stop, Py_ssize_t* _length) {
     Py_ssize_t start = *_start, stop = *_stop, length = *_length;
@@ -25038,11 +25208,7 @@ static CYTHON_INLINE void __Pyx_crop_slice(Py_ssize_t* _start, Py_ssize_t* _stop
     *_start = start;
     *_stop = stop;
 }
-#if defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L
-static CYTHON_INLINE void __Pyx_copy_object_array(PyObject** restrict src, PyObject** restrict dest, Py_ssize_t length) {
-#else
-static CYTHON_INLINE void __Pyx_copy_object_array(PyObject** src, PyObject** dest, Py_ssize_t length) {
-#endif
+static CYTHON_INLINE void __Pyx_copy_object_array(PyObject** CYTHON_RESTRICT src, PyObject** CYTHON_RESTRICT dest, Py_ssize_t length) {
     PyObject *v;
     Py_ssize_t i;
     for (i = 0; i < length; i++) {
@@ -25145,37 +25311,6 @@ static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObje
     return __Pyx_SetItemInt_Generic(o, PyInt_FromSsize_t(i), v);
 }
 
-static CYTHON_INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb) {
-#if CYTHON_COMPILING_IN_CPYTHON
-    PyThreadState *tstate = PyThreadState_GET();
-    *type = tstate->exc_type;
-    *value = tstate->exc_value;
-    *tb = tstate->exc_traceback;
-    Py_XINCREF(*type);
-    Py_XINCREF(*value);
-    Py_XINCREF(*tb);
-#else
-    PyErr_GetExcInfo(type, value, tb);
-#endif
-}
-static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb) {
-#if CYTHON_COMPILING_IN_CPYTHON
-    PyObject *tmp_type, *tmp_value, *tmp_tb;
-    PyThreadState *tstate = PyThreadState_GET();
-    tmp_type = tstate->exc_type;
-    tmp_value = tstate->exc_value;
-    tmp_tb = tstate->exc_traceback;
-    tstate->exc_type = type;
-    tstate->exc_value = value;
-    tstate->exc_traceback = tb;
-    Py_XDECREF(tmp_type);
-    Py_XDECREF(tmp_value);
-    Py_XDECREF(tmp_tb);
-#else
-    PyErr_SetExcInfo(type, value, tb);
-#endif
-}
-
 static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) {
     PyObject *empty_list = 0;
     PyObject *module = 0;
@@ -25184,7 +25319,7 @@ static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) {
     PyObject *list;
     #if PY_VERSION_HEX < 0x03030000
     PyObject *py_import;
-    py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s____import__);
+    py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import);
     if (!py_import)
         goto bad;
     #endif
@@ -25258,644 +25393,500 @@ bad:
     return module;
 }
 
-static CYTHON_INLINE npy_intp __Pyx_PyInt_from_py_npy_intp(PyObject* x) {
-    const npy_intp neg_one = (npy_intp)-1, const_zero = (npy_intp)0;
-    const int is_unsigned = const_zero < neg_one;
-    if (sizeof(npy_intp) == sizeof(char)) {
-        if (is_unsigned)
-            return (npy_intp)__Pyx_PyInt_AsUnsignedChar(x);
-        else
-            return (npy_intp)__Pyx_PyInt_AsSignedChar(x);
-    } else if (sizeof(npy_intp) == sizeof(short)) {
-        if (is_unsigned)
-            return (npy_intp)__Pyx_PyInt_AsUnsignedShort(x);
-        else
-            return (npy_intp)__Pyx_PyInt_AsSignedShort(x);
-    } else if (sizeof(npy_intp) == sizeof(int)) {
-        if (is_unsigned)
-            return (npy_intp)__Pyx_PyInt_AsUnsignedInt(x);
-        else
-            return (npy_intp)__Pyx_PyInt_AsSignedInt(x);
-    } else if (sizeof(npy_intp) == sizeof(long)) {
-        if (is_unsigned)
-            return (npy_intp)__Pyx_PyInt_AsUnsignedLong(x);
-        else
-            return (npy_intp)__Pyx_PyInt_AsSignedLong(x);
-    } else if (sizeof(npy_intp) == sizeof(PY_LONG_LONG)) {
-        if (is_unsigned)
-            return (npy_intp)__Pyx_PyInt_AsUnsignedLongLong(x);
-        else
-            return (npy_intp)__Pyx_PyInt_AsSignedLongLong(x);
-    }  else {
-        #if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
-        PyErr_SetString(PyExc_RuntimeError,
-                        "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
-        #else
-        npy_intp val;
-        PyObject *v = __Pyx_PyNumber_Int(x);
-        #if PY_MAJOR_VERSION < 3
-        if (likely(v) && !PyLong_Check(v)) {
-            PyObject *tmp = v;
-            v = PyNumber_Long(tmp);
-            Py_DECREF(tmp);
-        }
-        #endif
-        if (likely(v)) {
-            int one = 1; int is_little = (int)*(unsigned char *)&one;
-            unsigned char *bytes = (unsigned char *)&val;
-            int ret = _PyLong_AsByteArray((PyLongObject *)v,
-                                          bytes, sizeof(val),
-                                          is_little, !is_unsigned);
-            Py_DECREF(v);
-            if (likely(!ret))
-                return val;
-        }
-        #endif
-        return (npy_intp)-1;
-    }
-}
-
-static CYTHON_INLINE PyObject *__Pyx_PyInt_to_py_npy_intp(npy_intp val) {
-    const npy_intp neg_one = (npy_intp)-1, const_zero = (npy_intp)0;
-    const int is_unsigned = const_zero < neg_one;
-    if ((sizeof(npy_intp) == sizeof(char))  ||
-        (sizeof(npy_intp) == sizeof(short))) {
-        return PyInt_FromLong((long)val);
-    } else if ((sizeof(npy_intp) == sizeof(int)) ||
-               (sizeof(npy_intp) == sizeof(long))) {
-        if (is_unsigned)
-            return PyLong_FromUnsignedLong((unsigned long)val);
-        else
-            return PyInt_FromLong((long)val);
-    } else if (sizeof(npy_intp) == sizeof(PY_LONG_LONG)) {
-        if (is_unsigned)
-            return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG)val);
-        else
-            return PyLong_FromLongLong((PY_LONG_LONG)val);
-    } else {
-        int one = 1; int little = (int)*(unsigned char *)&one;
-        unsigned char *bytes = (unsigned char *)&val;
-        return _PyLong_FromByteArray(bytes, sizeof(npy_intp),
-                                     little, !is_unsigned);
-    }
-}
-
-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;
-    if (sizeof(unsigned char) < sizeof(long)) {
-        long val = __Pyx_PyInt_AsLong(x);
-        if (unlikely(val != (long)(unsigned char)val)) {
-            if (!unlikely(val == -1 && PyErr_Occurred())) {
-                PyErr_SetString(PyExc_OverflowError,
-                    (is_unsigned && unlikely(val < 0)) ?
-                    "can't convert negative value to unsigned char" :
-                    "value too large to convert to unsigned char");
-            }
-            return (unsigned char)-1;
-        }
-        return (unsigned char)val;
+#define __PYX_VERIFY_RETURN_INT(target_type, func_type, func)             \
+    {                                                                     \
+        func_type value = func(x);                                        \
+        if (sizeof(target_type) < sizeof(func_type)) {                    \
+            if (unlikely(value != (func_type) (target_type) value)) {     \
+                func_type zero = 0;                                       \
+                PyErr_SetString(PyExc_OverflowError,                      \
+                    (is_unsigned && unlikely(value < zero)) ?             \
+                    "can't convert negative value to " #target_type :     \
+                    "value too large to convert to " #target_type);       \
+                return (target_type) -1;                                  \
+            }                                                             \
+        }                                                                 \
+        return (target_type) value;                                       \
     }
-    return (unsigned char)__Pyx_PyInt_AsUnsignedLong(x);
-}
-
-static CYTHON_INLINE unsigned short __Pyx_PyInt_AsUnsignedShort(PyObject* x) {
-    const unsigned short neg_one = (unsigned short)-1, const_zero = 0;
-    const int is_unsigned = neg_one > const_zero;
-    if (sizeof(unsigned short) < sizeof(long)) {
-        long val = __Pyx_PyInt_AsLong(x);
-        if (unlikely(val != (long)(unsigned short)val)) {
-            if (!unlikely(val == -1 && PyErr_Occurred())) {
-                PyErr_SetString(PyExc_OverflowError,
-                    (is_unsigned && unlikely(val < 0)) ?
-                    "can't convert negative value to unsigned short" :
-                    "value too large to convert to unsigned short");
-            }
-            return (unsigned short)-1;
-        }
-        return (unsigned short)val;
-    }
-    return (unsigned short)__Pyx_PyInt_AsUnsignedLong(x);
-}
-
-static CYTHON_INLINE unsigned int __Pyx_PyInt_AsUnsignedInt(PyObject* x) {
-    const unsigned int neg_one = (unsigned int)-1, const_zero = 0;
-    const int is_unsigned = neg_one > const_zero;
-    if (sizeof(unsigned int) < sizeof(long)) {
-        long val = __Pyx_PyInt_AsLong(x);
-        if (unlikely(val != (long)(unsigned int)val)) {
-            if (!unlikely(val == -1 && PyErr_Occurred())) {
-                PyErr_SetString(PyExc_OverflowError,
-                    (is_unsigned && unlikely(val < 0)) ?
-                    "can't convert negative value to unsigned int" :
-                    "value too large to convert to unsigned int");
-            }
-            return (unsigned int)-1;
-        }
-        return (unsigned int)val;
-    }
-    return (unsigned int)__Pyx_PyInt_AsUnsignedLong(x);
-}
-
-static CYTHON_INLINE char __Pyx_PyInt_AsChar(PyObject* x) {
-    const char neg_one = (char)-1, const_zero = 0;
-    const int is_unsigned = neg_one > const_zero;
-    if (sizeof(char) < sizeof(long)) {
-        long val = __Pyx_PyInt_AsLong(x);
-        if (unlikely(val != (long)(char)val)) {
-            if (!unlikely(val == -1 && PyErr_Occurred())) {
-                PyErr_SetString(PyExc_OverflowError,
-                    (is_unsigned && unlikely(val < 0)) ?
-                    "can't convert negative value to char" :
-                    "value too large to convert to char");
-            }
-            return (char)-1;
-        }
-        return (char)val;
-    }
-    return (char)__Pyx_PyInt_AsLong(x);
-}
-
-static CYTHON_INLINE short __Pyx_PyInt_AsShort(PyObject* x) {
-    const short neg_one = (short)-1, const_zero = 0;
-    const int is_unsigned = neg_one > const_zero;
-    if (sizeof(short) < sizeof(long)) {
-        long val = __Pyx_PyInt_AsLong(x);
-        if (unlikely(val != (long)(short)val)) {
-            if (!unlikely(val == -1 && PyErr_Occurred())) {
-                PyErr_SetString(PyExc_OverflowError,
-                    (is_unsigned && unlikely(val < 0)) ?
-                    "can't convert negative value to short" :
-                    "value too large to convert to short");
-            }
-            return (short)-1;
-        }
-        return (short)val;
-    }
-    return (short)__Pyx_PyInt_AsLong(x);
-}
-
-static CYTHON_INLINE int __Pyx_PyInt_AsInt(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 signed char __Pyx_PyInt_AsSignedChar(PyObject* x) {
-    const signed char neg_one = (signed char)-1, const_zero = 0;
-    const int is_unsigned = neg_one > const_zero;
-    if (sizeof(signed char) < sizeof(long)) {
-        long val = __Pyx_PyInt_AsLong(x);
-        if (unlikely(val != (long)(signed char)val)) {
-            if (!unlikely(val == -1 && PyErr_Occurred())) {
-                PyErr_SetString(PyExc_OverflowError,
-                    (is_unsigned && unlikely(val < 0)) ?
-                    "can't convert negative value to signed char" :
-                    "value too large to convert to signed char");
-            }
-            return (signed char)-1;
-        }
-        return (signed char)val;
-    }
-    return (signed char)__Pyx_PyInt_AsSignedLong(x);
-}
-
-static CYTHON_INLINE signed short __Pyx_PyInt_AsSignedShort(PyObject* x) {
-    const signed short neg_one = (signed short)-1, const_zero = 0;
-    const int is_unsigned = neg_one > const_zero;
-    if (sizeof(signed short) < sizeof(long)) {
-        long val = __Pyx_PyInt_AsLong(x);
-        if (unlikely(val != (long)(signed short)val)) {
-            if (!unlikely(val == -1 && PyErr_Occurred())) {
-                PyErr_SetString(PyExc_OverflowError,
-                    (is_unsigned && unlikely(val < 0)) ?
-                    "can't convert negative value to signed short" :
-                    "value too large to convert to signed short");
-            }
-            return (signed short)-1;
-        }
-        return (signed short)val;
-    }
-    return (signed short)__Pyx_PyInt_AsSignedLong(x);
-}
-
-static CYTHON_INLINE signed int __Pyx_PyInt_AsSignedInt(PyObject* x) {
-    const signed int neg_one = (signed int)-1, const_zero = 0;
-    const int is_unsigned = neg_one > const_zero;
-    if (sizeof(signed int) < sizeof(long)) {
-        long val = __Pyx_PyInt_AsLong(x);
-        if (unlikely(val != (long)(signed int)val)) {
-            if (!unlikely(val == -1 && PyErr_Occurred())) {
-                PyErr_SetString(PyExc_OverflowError,
-                    (is_unsigned && unlikely(val < 0)) ?
-                    "can't convert negative value to signed int" :
-                    "value too large to convert to signed int");
-            }
-            return (signed int)-1;
-        }
-        return (signed int)val;
-    }
-    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);
-}
 
 #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
-#if CYTHON_USE_PYLONG_INTERNALS
-#include "longintrepr.h"
-#endif
+ #if CYTHON_USE_PYLONG_INTERNALS
+  #include "longintrepr.h"
+ #endif
 #endif
-static CYTHON_INLINE unsigned long __Pyx_PyInt_AsUnsignedLong(PyObject* x) {
-    const unsigned long neg_one = (unsigned long)-1, const_zero = 0;
+static CYTHON_INLINE npy_intp __Pyx_PyInt_As_npy_intp(PyObject *x) {
+    const npy_intp neg_one = (npy_intp) -1, const_zero = 0;
     const int is_unsigned = neg_one > const_zero;
 #if PY_MAJOR_VERSION < 3
     if (likely(PyInt_Check(x))) {
-        long val = PyInt_AS_LONG(x);
-        if (is_unsigned && unlikely(val < 0)) {
-            PyErr_SetString(PyExc_OverflowError,
-                            "can't convert negative value to unsigned long");
-            return (unsigned long)-1;
+        if (sizeof(npy_intp) < sizeof(long)) {
+            __PYX_VERIFY_RETURN_INT(npy_intp, long, PyInt_AS_LONG)
+        } else {
+            long val = PyInt_AS_LONG(x);
+            if (is_unsigned && unlikely(val < 0)) {
+                PyErr_SetString(PyExc_OverflowError,
+                                "can't convert negative value to npy_intp");
+                return (npy_intp) -1;
+            }
+            return (npy_intp) val;
         }
-        return (unsigned long)val;
     } else
 #endif
     if (likely(PyLong_Check(x))) {
         if (is_unsigned) {
 #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (sizeof(digit) <= sizeof(unsigned long)) {
+ #if CYTHON_USE_PYLONG_INTERNALS
+            if (sizeof(digit) <= sizeof(npy_intp)) {
                 switch (Py_SIZE(x)) {
                     case  0: return 0;
-                    case  1: return (unsigned long) ((PyLongObject*)x)->ob_digit[0];
+                    case  1: return (npy_intp) ((PyLongObject*)x)->ob_digit[0];
                 }
             }
-#endif
+ #endif
 #endif
             if (unlikely(Py_SIZE(x) < 0)) {
                 PyErr_SetString(PyExc_OverflowError,
-                                "can't convert negative value to unsigned long");
-                return (unsigned long)-1;
+                                "can't convert negative value to npy_intp");
+                return (npy_intp) -1;
+            }
+            if (sizeof(npy_intp) <= sizeof(unsigned long)) {
+                __PYX_VERIFY_RETURN_INT(npy_intp, unsigned long, PyLong_AsUnsignedLong)
+            } else if (sizeof(npy_intp) <= sizeof(unsigned long long)) {
+                __PYX_VERIFY_RETURN_INT(npy_intp, unsigned long long, PyLong_AsUnsignedLongLong)
             }
-            return (unsigned long)PyLong_AsUnsignedLong(x);
         } else {
 #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (sizeof(digit) <= sizeof(unsigned long)) {
+ #if CYTHON_USE_PYLONG_INTERNALS
+            if (sizeof(digit) <= sizeof(npy_intp)) {
                 switch (Py_SIZE(x)) {
                     case  0: return 0;
-                    case  1: return +(unsigned long) ((PyLongObject*)x)->ob_digit[0];
-                    case -1: return -(unsigned long) ((PyLongObject*)x)->ob_digit[0];
+                    case  1: return +(npy_intp) ((PyLongObject*)x)->ob_digit[0];
+                    case -1: return -(npy_intp) ((PyLongObject*)x)->ob_digit[0];
                 }
             }
+ #endif
 #endif
+            if (sizeof(npy_intp) <= sizeof(long)) {
+                __PYX_VERIFY_RETURN_INT(npy_intp, long, PyLong_AsLong)
+            } else if (sizeof(npy_intp) <= sizeof(long long)) {
+                __PYX_VERIFY_RETURN_INT(npy_intp, long long, PyLong_AsLongLong)
+            }
+        }
+        {
+#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
+            PyErr_SetString(PyExc_RuntimeError,
+                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
+#else
+            npy_intp val;
+            PyObject *v = __Pyx_PyNumber_Int(x);
+ #if PY_MAJOR_VERSION < 3
+            if (likely(v) && !PyLong_Check(v)) {
+                PyObject *tmp = v;
+                v = PyNumber_Long(tmp);
+                Py_DECREF(tmp);
+            }
+ #endif
+            if (likely(v)) {
+                int one = 1; int is_little = (int)*(unsigned char *)&one;
+                unsigned char *bytes = (unsigned char *)&val;
+                int ret = _PyLong_AsByteArray((PyLongObject *)v,
+                                              bytes, sizeof(val),
+                                              is_little, !is_unsigned);
+                Py_DECREF(v);
+                if (likely(!ret))
+                    return val;
+            }
 #endif
-            return (unsigned long)PyLong_AsLong(x);
+            return (npy_intp) -1;
         }
     } else {
-        unsigned long val;
+        npy_intp val;
         PyObject *tmp = __Pyx_PyNumber_Int(x);
-        if (!tmp) return (unsigned long)-1;
-        val = __Pyx_PyInt_AsUnsignedLong(tmp);
+        if (!tmp) return (npy_intp) -1;
+        val = __Pyx_PyInt_As_npy_intp(tmp);
         Py_DECREF(tmp);
         return val;
     }
 }
 
-#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
-#if CYTHON_USE_PYLONG_INTERNALS
-#include "longintrepr.h"
-#endif
-#endif
-static CYTHON_INLINE unsigned PY_LONG_LONG __Pyx_PyInt_AsUnsignedLongLong(PyObject* x) {
-    const unsigned PY_LONG_LONG neg_one = (unsigned PY_LONG_LONG)-1, const_zero = 0;
+static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) {
+    const long neg_one = (long) -1, const_zero = 0;
     const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        long val = PyInt_AS_LONG(x);
-        if (is_unsigned && unlikely(val < 0)) {
-            PyErr_SetString(PyExc_OverflowError,
-                            "can't convert negative value to unsigned PY_LONG_LONG");
-            return (unsigned PY_LONG_LONG)-1;
-        }
-        return (unsigned PY_LONG_LONG)val;
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (sizeof(digit) <= sizeof(unsigned PY_LONG_LONG)) {
-                switch (Py_SIZE(x)) {
-                    case  0: return 0;
-                    case  1: return (unsigned PY_LONG_LONG) ((PyLongObject*)x)->ob_digit[0];
-                }
-            }
-#endif
-#endif
-            if (unlikely(Py_SIZE(x) < 0)) {
-                PyErr_SetString(PyExc_OverflowError,
-                                "can't convert negative value to unsigned PY_LONG_LONG");
-                return (unsigned PY_LONG_LONG)-1;
-            }
-            return (unsigned PY_LONG_LONG)PyLong_AsUnsignedLongLong(x);
-        } else {
-#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (sizeof(digit) <= sizeof(unsigned PY_LONG_LONG)) {
-                switch (Py_SIZE(x)) {
-                    case  0: return 0;
-                    case  1: return +(unsigned PY_LONG_LONG) ((PyLongObject*)x)->ob_digit[0];
-                    case -1: return -(unsigned PY_LONG_LONG) ((PyLongObject*)x)->ob_digit[0];
-                }
-            }
-#endif
-#endif
-            return (unsigned PY_LONG_LONG)PyLong_AsLongLong(x);
+    if (is_unsigned) {
+        if (sizeof(long) < sizeof(long)) {
+            return PyInt_FromLong((long) value);
+        } else if (sizeof(long) <= sizeof(unsigned long)) {
+            return PyLong_FromUnsignedLong((unsigned long) value);
+        } else if (sizeof(long) <= sizeof(unsigned long long)) {
+            return PyLong_FromUnsignedLongLong((unsigned long long) value);
         }
     } else {
-        unsigned PY_LONG_LONG val;
-        PyObject *tmp = __Pyx_PyNumber_Int(x);
-        if (!tmp) return (unsigned PY_LONG_LONG)-1;
-        val = __Pyx_PyInt_AsUnsignedLongLong(tmp);
-        Py_DECREF(tmp);
-        return val;
+        if (sizeof(long) <= sizeof(long)) {
+            return PyInt_FromLong((long) value);
+        } else if (sizeof(long) <= sizeof(long long)) {
+            return PyLong_FromLongLong((long long) value);
+        }
+    }
+    {
+        int one = 1; int little = (int)*(unsigned char *)&one;
+        unsigned char *bytes = (unsigned char *)&value;
+        return _PyLong_FromByteArray(bytes, sizeof(long),
+                                     little, !is_unsigned);
     }
 }
 
 #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
-#if CYTHON_USE_PYLONG_INTERNALS
-#include "longintrepr.h"
-#endif
+ #if CYTHON_USE_PYLONG_INTERNALS
+  #include "longintrepr.h"
+ #endif
 #endif
-static CYTHON_INLINE long __Pyx_PyInt_AsLong(PyObject* x) {
-    const long neg_one = (long)-1, const_zero = 0;
+static CYTHON_INLINE unsigned long __Pyx_PyInt_As_unsigned_long(PyObject *x) {
+    const unsigned long neg_one = (unsigned long) -1, const_zero = 0;
     const int is_unsigned = neg_one > const_zero;
 #if PY_MAJOR_VERSION < 3
     if (likely(PyInt_Check(x))) {
-        long val = PyInt_AS_LONG(x);
-        if (is_unsigned && unlikely(val < 0)) {
-            PyErr_SetString(PyExc_OverflowError,
-                            "can't convert negative value to long");
-            return (long)-1;
+        if (sizeof(unsigned long) < sizeof(long)) {
+            __PYX_VERIFY_RETURN_INT(unsigned long, long, PyInt_AS_LONG)
+        } else {
+            long val = PyInt_AS_LONG(x);
+            if (is_unsigned && unlikely(val < 0)) {
+                PyErr_SetString(PyExc_OverflowError,
+                                "can't convert negative value to unsigned long");
+                return (unsigned long) -1;
+            }
+            return (unsigned long) val;
         }
-        return (long)val;
     } else
 #endif
     if (likely(PyLong_Check(x))) {
         if (is_unsigned) {
 #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (sizeof(digit) <= sizeof(long)) {
+ #if CYTHON_USE_PYLONG_INTERNALS
+            if (sizeof(digit) <= sizeof(unsigned long)) {
                 switch (Py_SIZE(x)) {
                     case  0: return 0;
-                    case  1: return (long) ((PyLongObject*)x)->ob_digit[0];
+                    case  1: return (unsigned long) ((PyLongObject*)x)->ob_digit[0];
                 }
             }
-#endif
+ #endif
 #endif
             if (unlikely(Py_SIZE(x) < 0)) {
                 PyErr_SetString(PyExc_OverflowError,
-                                "can't convert negative value to long");
-                return (long)-1;
+                                "can't convert negative value to unsigned long");
+                return (unsigned long) -1;
+            }
+            if (sizeof(unsigned long) <= sizeof(unsigned long)) {
+                __PYX_VERIFY_RETURN_INT(unsigned long, unsigned long, PyLong_AsUnsignedLong)
+            } else if (sizeof(unsigned long) <= sizeof(unsigned long long)) {
+                __PYX_VERIFY_RETURN_INT(unsigned long, unsigned long long, PyLong_AsUnsignedLongLong)
             }
-            return (long)PyLong_AsUnsignedLong(x);
         } else {
 #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (sizeof(digit) <= sizeof(long)) {
+ #if CYTHON_USE_PYLONG_INTERNALS
+            if (sizeof(digit) <= sizeof(unsigned long)) {
                 switch (Py_SIZE(x)) {
                     case  0: return 0;
-                    case  1: return +(long) ((PyLongObject*)x)->ob_digit[0];
-                    case -1: return -(long) ((PyLongObject*)x)->ob_digit[0];
+                    case  1: return +(unsigned long) ((PyLongObject*)x)->ob_digit[0];
+                    case -1: return -(unsigned long) ((PyLongObject*)x)->ob_digit[0];
                 }
             }
+ #endif
 #endif
+            if (sizeof(unsigned long) <= sizeof(long)) {
+                __PYX_VERIFY_RETURN_INT(unsigned long, long, PyLong_AsLong)
+            } else if (sizeof(unsigned long) <= sizeof(long long)) {
+                __PYX_VERIFY_RETURN_INT(unsigned long, long long, PyLong_AsLongLong)
+            }
+        }
+        {
+#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
+            PyErr_SetString(PyExc_RuntimeError,
+                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
+#else
+            unsigned long val;
+            PyObject *v = __Pyx_PyNumber_Int(x);
+ #if PY_MAJOR_VERSION < 3
+            if (likely(v) && !PyLong_Check(v)) {
+                PyObject *tmp = v;
+                v = PyNumber_Long(tmp);
+                Py_DECREF(tmp);
+            }
+ #endif
+            if (likely(v)) {
+                int one = 1; int is_little = (int)*(unsigned char *)&one;
+                unsigned char *bytes = (unsigned char *)&val;
+                int ret = _PyLong_AsByteArray((PyLongObject *)v,
+                                              bytes, sizeof(val),
+                                              is_little, !is_unsigned);
+                Py_DECREF(v);
+                if (likely(!ret))
+                    return val;
+            }
 #endif
-            return (long)PyLong_AsLong(x);
+            return (unsigned long) -1;
         }
     } else {
-        long val;
+        unsigned long val;
         PyObject *tmp = __Pyx_PyNumber_Int(x);
-        if (!tmp) return (long)-1;
-        val = __Pyx_PyInt_AsLong(tmp);
+        if (!tmp) return (unsigned long) -1;
+        val = __Pyx_PyInt_As_unsigned_long(tmp);
         Py_DECREF(tmp);
         return val;
     }
 }
 
-#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
-#if CYTHON_USE_PYLONG_INTERNALS
-#include "longintrepr.h"
-#endif
-#endif
-static CYTHON_INLINE PY_LONG_LONG __Pyx_PyInt_AsLongLong(PyObject* x) {
-    const PY_LONG_LONG neg_one = (PY_LONG_LONG)-1, const_zero = 0;
+static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) {
+    const int neg_one = (int) -1, const_zero = 0;
     const int is_unsigned = neg_one > const_zero;
-#if PY_MAJOR_VERSION < 3
-    if (likely(PyInt_Check(x))) {
-        long val = PyInt_AS_LONG(x);
-        if (is_unsigned && unlikely(val < 0)) {
-            PyErr_SetString(PyExc_OverflowError,
-                            "can't convert negative value to PY_LONG_LONG");
-            return (PY_LONG_LONG)-1;
-        }
-        return (PY_LONG_LONG)val;
-    } else
-#endif
-    if (likely(PyLong_Check(x))) {
-        if (is_unsigned) {
-#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (sizeof(digit) <= sizeof(PY_LONG_LONG)) {
-                switch (Py_SIZE(x)) {
-                    case  0: return 0;
-                    case  1: return (PY_LONG_LONG) ((PyLongObject*)x)->ob_digit[0];
-                }
-            }
-#endif
-#endif
-            if (unlikely(Py_SIZE(x) < 0)) {
-                PyErr_SetString(PyExc_OverflowError,
-                                "can't convert negative value to PY_LONG_LONG");
-                return (PY_LONG_LONG)-1;
-            }
-            return (PY_LONG_LONG)PyLong_AsUnsignedLongLong(x);
-        } else {
-#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (sizeof(digit) <= sizeof(PY_LONG_LONG)) {
-                switch (Py_SIZE(x)) {
-                    case  0: return 0;
-                    case  1: return +(PY_LONG_LONG) ((PyLongObject*)x)->ob_digit[0];
-                    case -1: return -(PY_LONG_LONG) ((PyLongObject*)x)->ob_digit[0];
-                }
-            }
-#endif
-#endif
-            return (PY_LONG_LONG)PyLong_AsLongLong(x);
+    if (is_unsigned) {
+        if (sizeof(int) < sizeof(long)) {
+            return PyInt_FromLong((long) value);
+        } else if (sizeof(int) <= sizeof(unsigned long)) {
+            return PyLong_FromUnsignedLong((unsigned long) value);
+        } else if (sizeof(int) <= sizeof(unsigned long long)) {
+            return PyLong_FromUnsignedLongLong((unsigned long long) value);
         }
     } else {
-        PY_LONG_LONG val;
-        PyObject *tmp = __Pyx_PyNumber_Int(x);
-        if (!tmp) return (PY_LONG_LONG)-1;
-        val = __Pyx_PyInt_AsLongLong(tmp);
-        Py_DECREF(tmp);
-        return val;
+        if (sizeof(int) <= sizeof(long)) {
+            return PyInt_FromLong((long) value);
+        } else if (sizeof(int) <= sizeof(long long)) {
+            return PyLong_FromLongLong((long long) value);
+        }
+    }
+    {
+        int one = 1; int little = (int)*(unsigned char *)&one;
+        unsigned char *bytes = (unsigned char *)&value;
+        return _PyLong_FromByteArray(bytes, sizeof(int),
+                                     little, !is_unsigned);
     }
 }
 
 #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
-#if CYTHON_USE_PYLONG_INTERNALS
-#include "longintrepr.h"
+ #if CYTHON_USE_PYLONG_INTERNALS
+  #include "longintrepr.h"
+ #endif
 #endif
-#endif
-static CYTHON_INLINE signed long __Pyx_PyInt_AsSignedLong(PyObject* x) {
-    const signed long neg_one = (signed long)-1, const_zero = 0;
+static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) {
+    const int neg_one = (int) -1, const_zero = 0;
     const int is_unsigned = neg_one > const_zero;
 #if PY_MAJOR_VERSION < 3
     if (likely(PyInt_Check(x))) {
-        long val = PyInt_AS_LONG(x);
-        if (is_unsigned && unlikely(val < 0)) {
-            PyErr_SetString(PyExc_OverflowError,
-                            "can't convert negative value to signed long");
-            return (signed long)-1;
+        if (sizeof(int) < sizeof(long)) {
+            __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG)
+        } else {
+            long val = PyInt_AS_LONG(x);
+            if (is_unsigned && unlikely(val < 0)) {
+                PyErr_SetString(PyExc_OverflowError,
+                                "can't convert negative value to int");
+                return (int) -1;
+            }
+            return (int) val;
         }
-        return (signed long)val;
     } else
 #endif
     if (likely(PyLong_Check(x))) {
         if (is_unsigned) {
 #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (sizeof(digit) <= sizeof(signed long)) {
+ #if CYTHON_USE_PYLONG_INTERNALS
+            if (sizeof(digit) <= sizeof(int)) {
                 switch (Py_SIZE(x)) {
                     case  0: return 0;
-                    case  1: return (signed long) ((PyLongObject*)x)->ob_digit[0];
+                    case  1: return (int) ((PyLongObject*)x)->ob_digit[0];
                 }
             }
-#endif
+ #endif
 #endif
             if (unlikely(Py_SIZE(x) < 0)) {
                 PyErr_SetString(PyExc_OverflowError,
-                                "can't convert negative value to signed long");
-                return (signed long)-1;
+                                "can't convert negative value to int");
+                return (int) -1;
+            }
+            if (sizeof(int) <= sizeof(unsigned long)) {
+                __PYX_VERIFY_RETURN_INT(int, unsigned long, PyLong_AsUnsignedLong)
+            } else if (sizeof(int) <= sizeof(unsigned long long)) {
+                __PYX_VERIFY_RETURN_INT(int, unsigned long long, PyLong_AsUnsignedLongLong)
             }
-            return (signed long)PyLong_AsUnsignedLong(x);
         } else {
 #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (sizeof(digit) <= sizeof(signed long)) {
+ #if CYTHON_USE_PYLONG_INTERNALS
+            if (sizeof(digit) <= sizeof(int)) {
                 switch (Py_SIZE(x)) {
                     case  0: return 0;
-                    case  1: return +(signed long) ((PyLongObject*)x)->ob_digit[0];
-                    case -1: return -(signed long) ((PyLongObject*)x)->ob_digit[0];
+                    case  1: return +(int) ((PyLongObject*)x)->ob_digit[0];
+                    case -1: return -(int) ((PyLongObject*)x)->ob_digit[0];
                 }
             }
+ #endif
 #endif
+            if (sizeof(int) <= sizeof(long)) {
+                __PYX_VERIFY_RETURN_INT(int, long, PyLong_AsLong)
+            } else if (sizeof(int) <= sizeof(long long)) {
+                __PYX_VERIFY_RETURN_INT(int, long long, PyLong_AsLongLong)
+            }
+        }
+        {
+#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
+            PyErr_SetString(PyExc_RuntimeError,
+                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
+#else
+            int val;
+            PyObject *v = __Pyx_PyNumber_Int(x);
+ #if PY_MAJOR_VERSION < 3
+            if (likely(v) && !PyLong_Check(v)) {
+                PyObject *tmp = v;
+                v = PyNumber_Long(tmp);
+                Py_DECREF(tmp);
+            }
+ #endif
+            if (likely(v)) {
+                int one = 1; int is_little = (int)*(unsigned char *)&one;
+                unsigned char *bytes = (unsigned char *)&val;
+                int ret = _PyLong_AsByteArray((PyLongObject *)v,
+                                              bytes, sizeof(val),
+                                              is_little, !is_unsigned);
+                Py_DECREF(v);
+                if (likely(!ret))
+                    return val;
+            }
 #endif
-            return (signed long)PyLong_AsLong(x);
+            return (int) -1;
         }
     } else {
-        signed long val;
+        int val;
         PyObject *tmp = __Pyx_PyNumber_Int(x);
-        if (!tmp) return (signed long)-1;
-        val = __Pyx_PyInt_AsSignedLong(tmp);
+        if (!tmp) return (int) -1;
+        val = __Pyx_PyInt_As_int(tmp);
         Py_DECREF(tmp);
         return val;
     }
 }
 
 #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
-#if CYTHON_USE_PYLONG_INTERNALS
-#include "longintrepr.h"
-#endif
+ #if CYTHON_USE_PYLONG_INTERNALS
+  #include "longintrepr.h"
+ #endif
 #endif
-static CYTHON_INLINE signed PY_LONG_LONG __Pyx_PyInt_AsSignedLongLong(PyObject* x) {
-    const signed PY_LONG_LONG neg_one = (signed PY_LONG_LONG)-1, const_zero = 0;
+static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) {
+    const long neg_one = (long) -1, const_zero = 0;
     const int is_unsigned = neg_one > const_zero;
 #if PY_MAJOR_VERSION < 3
     if (likely(PyInt_Check(x))) {
-        long val = PyInt_AS_LONG(x);
-        if (is_unsigned && unlikely(val < 0)) {
-            PyErr_SetString(PyExc_OverflowError,
-                            "can't convert negative value to signed PY_LONG_LONG");
-            return (signed PY_LONG_LONG)-1;
+        if (sizeof(long) < sizeof(long)) {
+            __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG)
+        } else {
+            long val = PyInt_AS_LONG(x);
+            if (is_unsigned && unlikely(val < 0)) {
+                PyErr_SetString(PyExc_OverflowError,
+                                "can't convert negative value to long");
+                return (long) -1;
+            }
+            return (long) val;
         }
-        return (signed PY_LONG_LONG)val;
     } else
 #endif
     if (likely(PyLong_Check(x))) {
         if (is_unsigned) {
 #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (sizeof(digit) <= sizeof(signed PY_LONG_LONG)) {
+ #if CYTHON_USE_PYLONG_INTERNALS
+            if (sizeof(digit) <= sizeof(long)) {
                 switch (Py_SIZE(x)) {
                     case  0: return 0;
-                    case  1: return (signed PY_LONG_LONG) ((PyLongObject*)x)->ob_digit[0];
+                    case  1: return (long) ((PyLongObject*)x)->ob_digit[0];
                 }
             }
-#endif
+ #endif
 #endif
             if (unlikely(Py_SIZE(x) < 0)) {
                 PyErr_SetString(PyExc_OverflowError,
-                                "can't convert negative value to signed PY_LONG_LONG");
-                return (signed PY_LONG_LONG)-1;
+                                "can't convert negative value to long");
+                return (long) -1;
+            }
+            if (sizeof(long) <= sizeof(unsigned long)) {
+                __PYX_VERIFY_RETURN_INT(long, unsigned long, PyLong_AsUnsignedLong)
+            } else if (sizeof(long) <= sizeof(unsigned long long)) {
+                __PYX_VERIFY_RETURN_INT(long, unsigned long long, PyLong_AsUnsignedLongLong)
             }
-            return (signed PY_LONG_LONG)PyLong_AsUnsignedLongLong(x);
         } else {
 #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
-#if CYTHON_USE_PYLONG_INTERNALS
-            if (sizeof(digit) <= sizeof(signed PY_LONG_LONG)) {
+ #if CYTHON_USE_PYLONG_INTERNALS
+            if (sizeof(digit) <= sizeof(long)) {
                 switch (Py_SIZE(x)) {
                     case  0: return 0;
-                    case  1: return +(signed PY_LONG_LONG) ((PyLongObject*)x)->ob_digit[0];
-                    case -1: return -(signed PY_LONG_LONG) ((PyLongObject*)x)->ob_digit[0];
+                    case  1: return +(long) ((PyLongObject*)x)->ob_digit[0];
+                    case -1: return -(long) ((PyLongObject*)x)->ob_digit[0];
                 }
             }
+ #endif
 #endif
+            if (sizeof(long) <= sizeof(long)) {
+                __PYX_VERIFY_RETURN_INT(long, long, PyLong_AsLong)
+            } else if (sizeof(long) <= sizeof(long long)) {
+                __PYX_VERIFY_RETURN_INT(long, long long, PyLong_AsLongLong)
+            }
+        }
+        {
+#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray)
+            PyErr_SetString(PyExc_RuntimeError,
+                            "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers");
+#else
+            long val;
+            PyObject *v = __Pyx_PyNumber_Int(x);
+ #if PY_MAJOR_VERSION < 3
+            if (likely(v) && !PyLong_Check(v)) {
+                PyObject *tmp = v;
+                v = PyNumber_Long(tmp);
+                Py_DECREF(tmp);
+            }
+ #endif
+            if (likely(v)) {
+                int one = 1; int is_little = (int)*(unsigned char *)&one;
+                unsigned char *bytes = (unsigned char *)&val;
+                int ret = _PyLong_AsByteArray((PyLongObject *)v,
+                                              bytes, sizeof(val),
+                                              is_little, !is_unsigned);
+                Py_DECREF(v);
+                if (likely(!ret))
+                    return val;
+            }
 #endif
-            return (signed PY_LONG_LONG)PyLong_AsLongLong(x);
+            return (long) -1;
         }
     } else {
-        signed PY_LONG_LONG val;
+        long val;
         PyObject *tmp = __Pyx_PyNumber_Int(x);
-        if (!tmp) return (signed PY_LONG_LONG)-1;
-        val = __Pyx_PyInt_AsSignedLongLong(tmp);
+        if (!tmp) return (long) -1;
+        val = __Pyx_PyInt_As_long(tmp);
         Py_DECREF(tmp);
         return val;
     }
 }
 
+static CYTHON_INLINE PyObject* __Pyx_PyInt_From_npy_intp(npy_intp value) {
+    const npy_intp neg_one = (npy_intp) -1, const_zero = 0;
+    const int is_unsigned = neg_one > const_zero;
+    if (is_unsigned) {
+        if (sizeof(npy_intp) < sizeof(long)) {
+            return PyInt_FromLong((long) value);
+        } else if (sizeof(npy_intp) <= sizeof(unsigned long)) {
+            return PyLong_FromUnsignedLong((unsigned long) value);
+        } else if (sizeof(npy_intp) <= sizeof(unsigned long long)) {
+            return PyLong_FromUnsignedLongLong((unsigned long long) value);
+        }
+    } else {
+        if (sizeof(npy_intp) <= sizeof(long)) {
+            return PyInt_FromLong((long) value);
+        } else if (sizeof(npy_intp) <= sizeof(long long)) {
+            return PyLong_FromLongLong((long long) value);
+        }
+    }
+    {
+        int one = 1; int little = (int)*(unsigned char *)&one;
+        unsigned char *bytes = (unsigned char *)&value;
+        return _PyLong_FromByteArray(bytes, sizeof(npy_intp),
+                                     little, !is_unsigned);
+    }
+}
+
 static int __Pyx_check_binary_version(void) {
     char ctversion[4], rtversion[4];
     PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION);
@@ -25960,7 +25951,7 @@ static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class
         goto bad;
     if (!PyType_Check(result)) {
         PyErr_Format(PyExc_TypeError,
-            "%s.%s is not a type object",
+            "%.200s.%.200s is not a type object",
             module_name, class_name);
         goto bad;
     }
@@ -25988,7 +25979,7 @@ static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class
     }
     else if ((size_t)basicsize != size) {
         PyErr_Format(PyExc_ValueError,
-            "%s.%s has the wrong size, try recompiling",
+            "%.200s.%.200s has the wrong size, try recompiling",
             module_name, class_name);
         goto bad;
     }
@@ -26241,10 +26232,16 @@ static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_
 #endif /* PY_VERSION_HEX < 0x03030000 */
     } else
 #endif /* __PYX_DEFAULT_STRING_ENCODING_IS_ASCII  || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT */
+#if PY_VERSION_HEX >= 0x02060000
+    if (PyByteArray_Check(o)) {
+        *length = PyByteArray_GET_SIZE(o);
+        return PyByteArray_AS_STRING(o);
+    } else
+#endif
     {
         char* result;
         int r = PyBytes_AsStringAndSize(o, &result, length);
-        if (r < 0) {
+        if (unlikely(r < 0)) {
             return NULL;
         } else {
             return result;
@@ -26289,7 +26286,7 @@ static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) {
     if (!PyLong_Check(res)) {
 #endif
       PyErr_Format(PyExc_TypeError,
-                   "__%s__ returned non-%s (type %.200s)",
+                   "__%.4s__ returned non-%.4s (type %.200s)",
                    name, name, Py_TYPE(res)->tp_name);
       Py_DECREF(res);
       return NULL;
@@ -26301,9 +26298,35 @@ static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) {
   }
   return res;
 }
+#if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
+ #if CYTHON_USE_PYLONG_INTERNALS
+  #include "longintrepr.h"
+ #endif
+#endif
 static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) {
   Py_ssize_t ival;
-  PyObject* x = PyNumber_Index(b);
+  PyObject *x;
+#if PY_MAJOR_VERSION < 3
+  if (likely(PyInt_CheckExact(b)))
+      return PyInt_AS_LONG(b);
+#endif
+  if (likely(PyLong_CheckExact(b))) {
+    #if CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3
+     #if CYTHON_USE_PYLONG_INTERNALS
+       switch (Py_SIZE(b)) {
+       case -1: return -(sdigit)((PyLongObject*)b)->ob_digit[0];
+       case  0: return 0;
+       case  1: return ((PyLongObject*)b)->ob_digit[0];
+       }
+     #endif
+    #endif
+  #if PY_VERSION_HEX < 0x02060000
+    return PyInt_AsSsize_t(b);
+  #else
+    return PyLong_AsSsize_t(b);
+  #endif
+  }
+  x = PyNumber_Index(b);
   if (!x) return -1;
   ival = PyInt_AsSsize_t(x);
   Py_DECREF(x);
@@ -26322,16 +26345,6 @@ static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) {
    return PyInt_FromSize_t(ival);
 #endif
 }
-static CYTHON_INLINE size_t __Pyx_PyInt_AsSize_t(PyObject* x) {
-   unsigned PY_LONG_LONG val = __Pyx_PyInt_AsUnsignedLongLong(x);
-   if (unlikely(val != (unsigned PY_LONG_LONG)(size_t)val)) {
-       if ((val != (unsigned PY_LONG_LONG)-1) || !PyErr_Occurred())
-           PyErr_SetString(PyExc_OverflowError,
-                           "value too large to convert to size_t");
-       return (size_t)-1;
-   }
-   return (size_t)val;
-}
 
 
 #endif /* Py_PYTHON_H */
diff --git a/numpy/random/mtrand/mtrand.pyx b/numpy/random/mtrand/mtrand.pyx
index 99ce8e5c1..a50fe58b3 100644
--- a/numpy/random/mtrand/mtrand.pyx
+++ b/numpy/random/mtrand/mtrand.pyx
@@ -4357,7 +4357,8 @@ cdef class RandomState:
         cdef npy_intp   k
         cdef npy_intp   totsize
         cdef ndarray    alpha_arr, val_arr
-        cdef double     *alpha_data, *val_data
+        cdef double     *alpha_data
+        cdef double     *val_data
         cdef npy_intp   i, j
         cdef double     acc, invacc