Rebuild mtrand.c with cython and change the script to use cython.

This is needed because some VC++ compilers can't handle long strings.

2 files changed, 11850 insertions, 5251 deletions

diff --git a/numpy/random/mtrand/generate_mtrand_c.py b/numpy/random/mtrand/generate_mtrand_c.py
index 8eb6254f2..a781bce97 100644
--- a/numpy/random/mtrand/generate_mtrand_c.py
+++ b/numpy/random/mtrand/generate_mtrand_c.py
@@ -13,7 +13,9 @@ unused_internal_funcs = ['__Pyx_PrintItem',
                          '__Pyx_CreateClass']
 
 if __name__ == '__main__':
-    os.system('pyrexc mtrand.pyx')
+    # Use cython here so that long docstrings are broken up.
+    # This is needed for some VC++ compilers.
+    os.system('cython mtrand.pyx')
     mtrand_c = open('mtrand.c', 'r')
     processed = open('mtrand_pp.c', 'w')
     unused_funcs_str = '(' + '|'.join(unused_internal_funcs) + ')'
diff --git a/numpy/random/mtrand/mtrand.c b/numpy/random/mtrand/mtrand.c
index de8f23265..a95e7cc70 100644
--- a/numpy/random/mtrand/mtrand.c
+++ b/numpy/random/mtrand/mtrand.c
@@ -1,4 +1,4 @@
-/* 0.9.7.2 on Sat Mar 28 00:52:11 2009 */
+/* Generated by Cython 0.9.8.1.1 on Wed Apr 15 19:12:00 2009 */
 
 #define PY_SSIZE_T_CLEAN
 #include "Python.h"
@@ -6,20 +6,96 @@
 #ifndef PY_LONG_LONG
   #define PY_LONG_LONG LONG_LONG
 #endif
+#ifndef DL_EXPORT
+  #define DL_EXPORT(t) t
+#endif
+#if PY_VERSION_HEX < 0x02040000
+  #define METH_COEXIST 0
+#endif
 #if PY_VERSION_HEX < 0x02050000
   typedef int Py_ssize_t;
   #define PY_SSIZE_T_MAX INT_MAX
   #define PY_SSIZE_T_MIN INT_MIN
   #define PyInt_FromSsize_t(z) PyInt_FromLong(z)
-  #define PyInt_AsSsize_t(o)	PyInt_AsLong(o)
+  #define PyInt_AsSsize_t(o)   PyInt_AsLong(o)
+  #define PyNumber_Index(o)    PyNumber_Int(o)
+  #define PyIndex_Check(o)     PyNumber_Check(o)
+#endif
+#if PY_VERSION_HEX < 0x02060000
+  #define Py_REFCNT(ob) (((PyObject*)(ob))->ob_refcnt)
+  #define Py_TYPE(ob)   (((PyObject*)(ob))->ob_type)
+  #define Py_SIZE(ob)   (((PyVarObject*)(ob))->ob_size)
+  #define PyVarObject_HEAD_INIT(type, size) \
+          PyObject_HEAD_INIT(type) size,
+  #define PyType_Modified(t)
+
+  typedef struct {
+       void *buf;
+       Py_ssize_t len;
+       int readonly;
+       const char *format;
+       int ndim;
+       Py_ssize_t *shape;
+       Py_ssize_t *strides;
+       Py_ssize_t *suboffsets;
+       Py_ssize_t itemsize;
+       void *internal;
+  } Py_buffer;
+
+  #define PyBUF_SIMPLE 0
+  #define PyBUF_WRITABLE 0x0001
+  #define PyBUF_LOCK 0x0002
+  #define PyBUF_FORMAT 0x0004
+  #define PyBUF_ND 0x0008
+  #define PyBUF_STRIDES (0x0010 | PyBUF_ND)
+  #define PyBUF_C_CONTIGUOUS (0x0020 | PyBUF_STRIDES)
+  #define PyBUF_F_CONTIGUOUS (0x0040 | PyBUF_STRIDES)
+  #define PyBUF_ANY_CONTIGUOUS (0x0080 | PyBUF_STRIDES)
+  #define PyBUF_INDIRECT (0x0100 | PyBUF_STRIDES)
+
+#endif
+#if PY_MAJOR_VERSION < 3
+  #define __Pyx_BUILTIN_MODULE_NAME "__builtin__"
+#else
+  #define __Pyx_BUILTIN_MODULE_NAME "builtins"
+#endif
+#if PY_MAJOR_VERSION >= 3
+  #define Py_TPFLAGS_CHECKTYPES 0
+  #define Py_TPFLAGS_HAVE_INDEX 0
+#endif
+#if PY_MAJOR_VERSION >= 3
+  #define PyBaseString_Type            PyUnicode_Type
+  #define PyString_Type                PyBytes_Type
+  #define PyInt_Type                   PyLong_Type
+  #define PyInt_Check(op)              PyLong_Check(op)
+  #define PyInt_CheckExact(op)         PyLong_CheckExact(op)
+  #define PyInt_FromString             PyLong_FromString
+  #define PyInt_FromUnicode            PyLong_FromUnicode
+  #define PyInt_FromLong               PyLong_FromLong
+  #define PyInt_FromSize_t             PyLong_FromSize_t
+  #define PyInt_FromSsize_t            PyLong_FromSsize_t
+  #define PyInt_AsLong                 PyLong_AsLong
+  #define PyInt_AS_LONG                PyLong_AS_LONG
+  #define PyInt_AsSsize_t              PyLong_AsSsize_t
+  #define PyInt_AsUnsignedLongMask     PyLong_AsUnsignedLongMask
+  #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask
+  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_TrueDivide(x,y)
+#else
+  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_Divide(x,y)
+  #define PyBytes_Type                 PyString_Type
 #endif
-#ifndef WIN32
+#if PY_MAJOR_VERSION >= 3
+  #define PyMethod_New(func, self, klass) PyInstanceMethod_New(func)
+#endif
+#if !defined(WIN32) && !defined(MS_WINDOWS)
   #ifndef __stdcall
     #define __stdcall
   #endif
   #ifndef __cdecl
     #define __cdecl
   #endif
+#else
+  #define _USE_MATH_DEFINES
 #endif
 #ifdef __cplusplus
 #define __PYX_EXTERN_C extern "C"
@@ -27,6 +103,7 @@
 #define __PYX_EXTERN_C extern
 #endif
 #include <math.h>
+#define __PYX_HAVE_API__mtrand
 #include "string.h"
 #include "math.h"
 #include "numpy/arrayobject.h"
@@ -35,210 +112,636 @@
 #include "initarray.h"
 
 
-typedef struct {PyObject **p; char *s;} __Pyx_InternTabEntry; /*proto*/
-typedef struct {PyObject **p; char *s; long n;} __Pyx_StringTabEntry; /*proto*/
+#ifdef __GNUC__
+#define INLINE __inline__
+#elif _WIN32
+#define INLINE __inline
+#else
+#define INLINE 
+#endif
+
+typedef struct {PyObject **p; char *s; long n; char is_unicode; char intern; char is_identifier;} __Pyx_StringTabEntry; /*proto*/
+
+
+
+static int __pyx_skip_dispatch = 0;
 
+
+/* Type Conversion Predeclarations */
+
+#if PY_MAJOR_VERSION < 3
+#define __Pyx_PyBytes_FromString PyString_FromString
+#define __Pyx_PyBytes_AsString   PyString_AsString
+#else
+#define __Pyx_PyBytes_FromString PyBytes_FromString
+#define __Pyx_PyBytes_AsString   PyBytes_AsString
+#endif
+
+#define __Pyx_PyBool_FromLong(b) ((b) ? (Py_INCREF(Py_True), Py_True) : (Py_INCREF(Py_False), Py_False))
+static INLINE int __Pyx_PyObject_IsTrue(PyObject* x);
+static INLINE PY_LONG_LONG __pyx_PyInt_AsLongLong(PyObject* x);
+static INLINE unsigned PY_LONG_LONG __pyx_PyInt_AsUnsignedLongLong(PyObject* x);
+static INLINE Py_ssize_t __pyx_PyIndex_AsSsize_t(PyObject* b);
+
+#define __pyx_PyInt_AsLong(x) (PyInt_CheckExact(x) ? PyInt_AS_LONG(x) : PyInt_AsLong(x))
+#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x))
+
+static INLINE unsigned char __pyx_PyInt_unsigned_char(PyObject* x);
+static INLINE unsigned short __pyx_PyInt_unsigned_short(PyObject* x);
+static INLINE char __pyx_PyInt_char(PyObject* x);
+static INLINE short __pyx_PyInt_short(PyObject* x);
+static INLINE int __pyx_PyInt_int(PyObject* x);
+static INLINE long __pyx_PyInt_long(PyObject* x);
+static INLINE signed char __pyx_PyInt_signed_char(PyObject* x);
+static INLINE signed short __pyx_PyInt_signed_short(PyObject* x);
+static INLINE signed int __pyx_PyInt_signed_int(PyObject* x);
+static INLINE signed long __pyx_PyInt_signed_long(PyObject* x);
+static INLINE long double __pyx_PyInt_long_double(PyObject* x);
+#ifdef __GNUC__
+/* Test for GCC > 2.95 */
+#if __GNUC__ > 2 ||               (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)) 
+#define likely(x)   __builtin_expect(!!(x), 1)
+#define unlikely(x) __builtin_expect(!!(x), 0)
+#else /* __GNUC__ > 2 ... */
+#define likely(x)   (x)
+#define unlikely(x) (x)
+#endif /* __GNUC__ > 2 ... */
+#else /* __GNUC__ */
+#define likely(x)   (x)
+#define unlikely(x) (x)
+#endif /* __GNUC__ */
+    
 static PyObject *__pyx_m;
 static PyObject *__pyx_b;
+static PyObject *__pyx_empty_tuple;
 static int __pyx_lineno;
-static char *__pyx_filename;
-static char **__pyx_f;
+static int __pyx_clineno = 0;
+static const char * __pyx_cfilenm= __FILE__;
+static const char *__pyx_filename;
+static const char **__pyx_f;
 
-static int __Pyx_GetStarArgs(PyObject **args, PyObject **kwds, char *kwd_list[],     Py_ssize_t nargs, PyObject **args2, PyObject **kwds2, char rqd_kwds[]); /*proto*/
+static INLINE void __Pyx_RaiseArgtupleTooLong(Py_ssize_t num_expected, Py_ssize_t num_found); /*proto*/
 
-static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list); /*proto*/
+static INLINE int __Pyx_SplitStarArg(PyObject **args, Py_ssize_t nargs, PyObject **args2); /*proto*/
 
-static PyObject *__Pyx_GetName(PyObject *dict, PyObject *name); /*proto*/
+static int __Pyx_CheckKeywordStrings(PyObject *kwdict, const char* function_name, int kw_allowed); /*proto*/
 
-static int __Pyx_SetItemInt(PyObject *o, Py_ssize_t i, PyObject *v); /*proto*/
+static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list); /*proto*/
 
-static PyObject *__Pyx_GetItemInt(PyObject *o, Py_ssize_t i); /*proto*/
+static PyObject *__Pyx_GetName(PyObject *dict, PyObject *name); /*proto*/
 
 static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb); /*proto*/
 
-static PyObject *__Pyx_UnpackItem(PyObject *); /*proto*/
+static INLINE PyObject *__Pyx_GetItemInt(PyObject *o, Py_ssize_t i, int is_unsigned) {
+    PyObject *r;
+    if (PyList_CheckExact(o) && 0 <= i && i < PyList_GET_SIZE(o)) {
+        r = PyList_GET_ITEM(o, i);
+        Py_INCREF(r);
+    }
+    else if (PyTuple_CheckExact(o) && 0 <= i && i < PyTuple_GET_SIZE(o)) {
+        r = PyTuple_GET_ITEM(o, i);
+        Py_INCREF(r);
+    }
+    else if (Py_TYPE(o)->tp_as_sequence && Py_TYPE(o)->tp_as_sequence->sq_item && (likely(i >= 0) || !is_unsigned))
+        r = PySequence_GetItem(o, i);
+    else {
+        PyObject *j = (likely(i >= 0) || !is_unsigned) ? PyInt_FromLong(i) : PyLong_FromUnsignedLongLong((sizeof(unsigned long long) > sizeof(Py_ssize_t) ? (1ULL << (sizeof(Py_ssize_t)*8)) : 0) + i);
+        if (!j)
+            return 0;
+        r = PyObject_GetItem(o, j);
+        Py_DECREF(j);
+    }
+    return r;
+}
+
+static PyObject *__Pyx_UnpackItem(PyObject *, Py_ssize_t index); /*proto*/
 static int __Pyx_EndUnpack(PyObject *); /*proto*/
 
 static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); /*proto*/
 
 static int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); /*proto*/
 
-static int __Pyx_InternStrings(__Pyx_InternTabEntry *t); /*proto*/
+static INLINE PyObject* __Pyx_PyObject_Append(PyObject* L, PyObject* x) {
+    if (likely(PyList_CheckExact(L))) {
+        if (PyList_Append(L, x) < 0) return NULL;
+        Py_INCREF(Py_None);
+        return Py_None; // this is just to have an accurate signature
+    }
+    else {
+        return PyObject_CallMethod(L, "append", "(O)", x);
+    }
+}
 
-static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); /*proto*/
+static INLINE int __Pyx_SetItemInt(PyObject *o, Py_ssize_t i, PyObject *v, int is_unsigned) {
+    int r;
+    if (PyList_CheckExact(o) && 0 <= i && i < PyList_GET_SIZE(o)) {
+        Py_DECREF(PyList_GET_ITEM(o, i));
+        Py_INCREF(v);
+        PyList_SET_ITEM(o, i, v);
+        return 1;
+    }
+    else if (Py_TYPE(o)->tp_as_sequence && Py_TYPE(o)->tp_as_sequence->sq_ass_item && (likely(i >= 0) || !is_unsigned))
+        r = PySequence_SetItem(o, i, v);
+    else {
+        PyObject *j = (likely(i >= 0) || !is_unsigned) ? PyInt_FromLong(i) : PyLong_FromUnsignedLongLong((sizeof(unsigned long long) > sizeof(Py_ssize_t) ? (1ULL << (sizeof(Py_ssize_t)*8)) : 0) + i);
+        if (!j)
+            return -1;
+        r = PyObject_SetItem(o, j, v);
+        Py_DECREF(j);
+    }
+    return r;
+}
 
 static PyTypeObject *__Pyx_ImportType(char *module_name, char *class_name, long size);  /*proto*/
 
 static PyObject *__Pyx_ImportModule(char *name); /*proto*/
 
-static void __Pyx_AddTraceback(char *funcname); /*proto*/
+static void __Pyx_AddTraceback(const char *funcname); /*proto*/
 
-/* Declarations from mtrand */
+static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); /*proto*/
+
+/* Type declarations */
 
 typedef double (*__pyx_t_6mtrand_rk_cont0)(rk_state *);
 
-typedef double (*__pyx_t_6mtrand_rk_cont1)(rk_state *,double);
+typedef double (*__pyx_t_6mtrand_rk_cont1)(rk_state *, double);
 
-typedef double (*__pyx_t_6mtrand_rk_cont2)(rk_state *,double,double);
+typedef double (*__pyx_t_6mtrand_rk_cont2)(rk_state *, double, double);
 
-typedef double (*__pyx_t_6mtrand_rk_cont3)(rk_state *,double,double,double);
+typedef double (*__pyx_t_6mtrand_rk_cont3)(rk_state *, double, double, double);
 
 typedef long (*__pyx_t_6mtrand_rk_disc0)(rk_state *);
 
-typedef long (*__pyx_t_6mtrand_rk_discnp)(rk_state *,long,double);
+typedef long (*__pyx_t_6mtrand_rk_discnp)(rk_state *, long, double);
 
-typedef long (*__pyx_t_6mtrand_rk_discdd)(rk_state *,double,double);
+typedef long (*__pyx_t_6mtrand_rk_discdd)(rk_state *, double, double);
 
-typedef long (*__pyx_t_6mtrand_rk_discnmN)(rk_state *,long,long,long);
+typedef long (*__pyx_t_6mtrand_rk_discnmN)(rk_state *, long, long, long);
 
-typedef long (*__pyx_t_6mtrand_rk_discd)(rk_state *,double);
+typedef long (*__pyx_t_6mtrand_rk_discd)(rk_state *, double);
+
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":519
+ *     return sum
+ * 
+ * cdef class RandomState:             # <<<<<<<<<<<<<<
+ *     """
+ *     RandomState(seed=None)
+ */
 
 struct __pyx_obj_6mtrand_RandomState {
   PyObject_HEAD
   rk_state *internal_state;
 };
+/* 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 PyObject *__pyx_k2;
-static PyObject *__pyx_k3;
-static PyObject *__pyx_k4;
-static PyObject *__pyx_k5;
-static PyObject *__pyx_k6;
-static PyObject *__pyx_k7;
-static PyObject *__pyx_k8;
-static PyObject *__pyx_k9;
-static PyObject *__pyx_k10;
-static PyObject *__pyx_k11;
-static PyObject *__pyx_k12;
-static PyObject *__pyx_k13;
-static PyObject *__pyx_k14;
-static PyObject *__pyx_k15;
-static PyObject *__pyx_k16;
-static PyObject *__pyx_k17;
-static PyObject *__pyx_k18;
-static PyObject *__pyx_k19;
-static PyObject *__pyx_k20;
-static PyObject *__pyx_k21;
-static PyObject *__pyx_k22;
-static PyObject *__pyx_k23;
-static PyObject *__pyx_k24;
-static PyObject *__pyx_k25;
-static PyObject *__pyx_k26;
-static PyObject *__pyx_k27;
-static PyObject *__pyx_k28;
-static PyObject *__pyx_k29;
-static PyObject *__pyx_k30;
-static PyObject *__pyx_k31;
-static PyObject *__pyx_k32;
-static PyObject *__pyx_k33;
-static PyObject *__pyx_k34;
-static PyObject *__pyx_k35;
-static PyObject *__pyx_k36;
-static PyObject *__pyx_k37;
-static PyObject *__pyx_k38;
-static PyObject *__pyx_k39;
-static PyObject *__pyx_k40;
-static PyObject *__pyx_k41;
-static PyObject *__pyx_k42;
-static PyObject *__pyx_k43;
-static PyObject *__pyx_k44;
-static PyObject *__pyx_k45;
-static PyObject *__pyx_k46;
-static PyObject *__pyx_k47;
-static PyObject *__pyx_k48;
-static PyObject *__pyx_k49;
-static PyObject *__pyx_k50;
-static PyObject *__pyx_k51;
-static PyObject *__pyx_k52;
-static PyObject *__pyx_k53;
-static PyObject *__pyx_k54;
-static PyObject *__pyx_k55;
-static PyObject *__pyx_k56;
-static PyObject *__pyx_k57;
-static PyObject *__pyx_k58;
-static PyObject *__pyx_k59;
-static PyObject *__pyx_k60;
-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*/
-static PyObject *__pyx_f_6mtrand_cont2_array_sc(rk_state *,__pyx_t_6mtrand_rk_cont2,PyObject *,double,double); /*proto*/
-static PyObject *__pyx_f_6mtrand_cont2_array(rk_state *,__pyx_t_6mtrand_rk_cont2,PyObject *,PyArrayObject *,PyArrayObject *); /*proto*/
-static PyObject *__pyx_f_6mtrand_cont3_array_sc(rk_state *,__pyx_t_6mtrand_rk_cont3,PyObject *,double,double,double); /*proto*/
-static PyObject *__pyx_f_6mtrand_cont3_array(rk_state *,__pyx_t_6mtrand_rk_cont3,PyObject *,PyArrayObject *,PyArrayObject *,PyArrayObject *); /*proto*/
-static PyObject *__pyx_f_6mtrand_disc0_array(rk_state *,__pyx_t_6mtrand_rk_disc0,PyObject *); /*proto*/
-static PyObject *__pyx_f_6mtrand_discnp_array_sc(rk_state *,__pyx_t_6mtrand_rk_discnp,PyObject *,long,double); /*proto*/
-static PyObject *__pyx_f_6mtrand_discnp_array(rk_state *,__pyx_t_6mtrand_rk_discnp,PyObject *,PyArrayObject *,PyArrayObject *); /*proto*/
-static PyObject *__pyx_f_6mtrand_discdd_array_sc(rk_state *,__pyx_t_6mtrand_rk_discdd,PyObject *,double,double); /*proto*/
-static PyObject *__pyx_f_6mtrand_discdd_array(rk_state *,__pyx_t_6mtrand_rk_discdd,PyObject *,PyArrayObject *,PyArrayObject *); /*proto*/
-static PyObject *__pyx_f_6mtrand_discnmN_array_sc(rk_state *,__pyx_t_6mtrand_rk_discnmN,PyObject *,long,long,long); /*proto*/
-static PyObject *__pyx_f_6mtrand_discnmN_array(rk_state *,__pyx_t_6mtrand_rk_discnmN,PyObject *,PyArrayObject *,PyArrayObject *,PyArrayObject *); /*proto*/
-static PyObject *__pyx_f_6mtrand_discd_array_sc(rk_state *,__pyx_t_6mtrand_rk_discd,PyObject *,double); /*proto*/
-static PyObject *__pyx_f_6mtrand_discd_array(rk_state *,__pyx_t_6mtrand_rk_discd,PyObject *,PyArrayObject *); /*proto*/
-static double __pyx_f_6mtrand_kahan_sum(double *,long); /*proto*/
+static PyObject *__pyx_k_3;
+static PyObject *__pyx_k_4;
+static PyObject *__pyx_k_5;
+static PyObject *__pyx_k_6;
+static PyObject *__pyx_k_7;
+static PyObject *__pyx_k_8;
+static PyObject *__pyx_k_9;
+static PyObject *__pyx_k_10;
+static PyObject *__pyx_k_11;
+static PyObject *__pyx_k_12;
+static PyObject *__pyx_k_13;
+static PyObject *__pyx_k_14;
+static PyObject *__pyx_k_15;
+static PyObject *__pyx_k_16;
+static PyObject *__pyx_k_17;
+static PyObject *__pyx_k_18;
+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*/
+static PyObject *__pyx_f_6mtrand_cont2_array_sc(rk_state *, __pyx_t_6mtrand_rk_cont2, PyObject *, double, double); /*proto*/
+static PyObject *__pyx_f_6mtrand_cont2_array(rk_state *, __pyx_t_6mtrand_rk_cont2, PyObject *, PyArrayObject *, PyArrayObject *); /*proto*/
+static PyObject *__pyx_f_6mtrand_cont3_array_sc(rk_state *, __pyx_t_6mtrand_rk_cont3, PyObject *, double, double, double); /*proto*/
+static PyObject *__pyx_f_6mtrand_cont3_array(rk_state *, __pyx_t_6mtrand_rk_cont3, PyObject *, PyArrayObject *, PyArrayObject *, PyArrayObject *); /*proto*/
+static PyObject *__pyx_f_6mtrand_disc0_array(rk_state *, __pyx_t_6mtrand_rk_disc0, PyObject *); /*proto*/
+static PyObject *__pyx_f_6mtrand_discnp_array_sc(rk_state *, __pyx_t_6mtrand_rk_discnp, PyObject *, long, double); /*proto*/
+static PyObject *__pyx_f_6mtrand_discnp_array(rk_state *, __pyx_t_6mtrand_rk_discnp, PyObject *, PyArrayObject *, PyArrayObject *); /*proto*/
+static PyObject *__pyx_f_6mtrand_discdd_array_sc(rk_state *, __pyx_t_6mtrand_rk_discdd, PyObject *, double, double); /*proto*/
+static PyObject *__pyx_f_6mtrand_discdd_array(rk_state *, __pyx_t_6mtrand_rk_discdd, PyObject *, PyArrayObject *, PyArrayObject *); /*proto*/
+static PyObject *__pyx_f_6mtrand_discnmN_array_sc(rk_state *, __pyx_t_6mtrand_rk_discnmN, PyObject *, long, long, long); /*proto*/
+static PyObject *__pyx_f_6mtrand_discnmN_array(rk_state *, __pyx_t_6mtrand_rk_discnmN, PyObject *, PyArrayObject *, PyArrayObject *, PyArrayObject *); /*proto*/
+static PyObject *__pyx_f_6mtrand_discd_array_sc(rk_state *, __pyx_t_6mtrand_rk_discd, PyObject *, double); /*proto*/
+static PyObject *__pyx_f_6mtrand_discd_array(rk_state *, __pyx_t_6mtrand_rk_discd, PyObject *, PyArrayObject *); /*proto*/
+static double __pyx_f_6mtrand_kahan_sum(double *, long); /*proto*/
 
 
 /* Implementation of mtrand */
-
-
-static PyObject *__pyx_n_numpy;
-static PyObject *__pyx_n_np;
-static PyObject *__pyx_n__rand;
-static PyObject *__pyx_n_seed;
-static PyObject *__pyx_n_get_state;
-static PyObject *__pyx_n_set_state;
-static PyObject *__pyx_n_random_sample;
-static PyObject *__pyx_n_randint;
-static PyObject *__pyx_n_bytes;
-static PyObject *__pyx_n_uniform;
-static PyObject *__pyx_n_rand;
-static PyObject *__pyx_n_randn;
-static PyObject *__pyx_n_random_integers;
-static PyObject *__pyx_n_standard_normal;
-static PyObject *__pyx_n_normal;
-static PyObject *__pyx_n_beta;
-static PyObject *__pyx_n_exponential;
-static PyObject *__pyx_n_standard_exponential;
-static PyObject *__pyx_n_standard_gamma;
-static PyObject *__pyx_n_gamma;
-static PyObject *__pyx_n_f;
-static PyObject *__pyx_n_noncentral_f;
-static PyObject *__pyx_n_chisquare;
-static PyObject *__pyx_n_noncentral_chisquare;
-static PyObject *__pyx_n_standard_cauchy;
-static PyObject *__pyx_n_standard_t;
-static PyObject *__pyx_n_vonmises;
-static PyObject *__pyx_n_pareto;
-static PyObject *__pyx_n_weibull;
-static PyObject *__pyx_n_power;
-static PyObject *__pyx_n_laplace;
-static PyObject *__pyx_n_gumbel;
-static PyObject *__pyx_n_logistic;
-static PyObject *__pyx_n_lognormal;
-static PyObject *__pyx_n_rayleigh;
-static PyObject *__pyx_n_wald;
-static PyObject *__pyx_n_triangular;
-static PyObject *__pyx_n_binomial;
-static PyObject *__pyx_n_negative_binomial;
-static PyObject *__pyx_n_poisson;
-static PyObject *__pyx_n_zipf;
-static PyObject *__pyx_n_geometric;
-static PyObject *__pyx_n_hypergeometric;
-static PyObject *__pyx_n_logseries;
-static PyObject *__pyx_n_multivariate_normal;
-static PyObject *__pyx_n_multinomial;
-static PyObject *__pyx_n_dirichlet;
-static PyObject *__pyx_n_shuffle;
-static PyObject *__pyx_n_permutation;
-
-static PyObject *__pyx_n_empty;
-static PyObject *__pyx_n_float64;
-
-static PyObject *__pyx_f_6mtrand_cont0_array(rk_state *__pyx_v_state,__pyx_t_6mtrand_rk_cont0 __pyx_v_func,PyObject *__pyx_v_size) {
+static PyObject *__pyx_int_624;
+static PyObject *__pyx_int_0;
+static PyObject *__pyx_int_1;
+static char __pyx_k___init__[] = "__init__";
+static PyObject *__pyx_kp___init__;
+static char __pyx_k___dealloc__[] = "__dealloc__";
+static PyObject *__pyx_kp___dealloc__;
+static char __pyx_k_seed[] = "seed";
+static PyObject *__pyx_kp_seed;
+static char __pyx_k_get_state[] = "get_state";
+static PyObject *__pyx_kp_get_state;
+static char __pyx_k_set_state[] = "set_state";
+static PyObject *__pyx_kp_set_state;
+static char __pyx_k___getstate__[] = "__getstate__";
+static PyObject *__pyx_kp___getstate__;
+static char __pyx_k___setstate__[] = "__setstate__";
+static PyObject *__pyx_kp___setstate__;
+static char __pyx_k___reduce__[] = "__reduce__";
+static PyObject *__pyx_kp___reduce__;
+static char __pyx_k_random_sample[] = "random_sample";
+static PyObject *__pyx_kp_random_sample;
+static char __pyx_k_tomaxint[] = "tomaxint";
+static PyObject *__pyx_kp_tomaxint;
+static char __pyx_k_randint[] = "randint";
+static PyObject *__pyx_kp_randint;
+static char __pyx_k_bytes[] = "bytes";
+static PyObject *__pyx_kp_bytes;
+static char __pyx_k_uniform[] = "uniform";
+static PyObject *__pyx_kp_uniform;
+static char __pyx_k_rand[] = "rand";
+static PyObject *__pyx_kp_rand;
+static char __pyx_k_randn[] = "randn";
+static PyObject *__pyx_kp_randn;
+static char __pyx_k_random_integers[] = "random_integers";
+static PyObject *__pyx_kp_random_integers;
+static char __pyx_k_standard_normal[] = "standard_normal";
+static PyObject *__pyx_kp_standard_normal;
+static char __pyx_k_normal[] = "normal";
+static PyObject *__pyx_kp_normal;
+static char __pyx_k_beta[] = "beta";
+static PyObject *__pyx_kp_beta;
+static char __pyx_k_exponential[] = "exponential";
+static PyObject *__pyx_kp_exponential;
+static char __pyx_k_1[] = "standard_exponential";
+static PyObject *__pyx_kp_1;
+static char __pyx_k_standard_gamma[] = "standard_gamma";
+static PyObject *__pyx_kp_standard_gamma;
+static char __pyx_k_gamma[] = "gamma";
+static PyObject *__pyx_kp_gamma;
+static char __pyx_k_f[] = "f";
+static PyObject *__pyx_kp_f;
+static char __pyx_k_noncentral_f[] = "noncentral_f";
+static PyObject *__pyx_kp_noncentral_f;
+static char __pyx_k_chisquare[] = "chisquare";
+static PyObject *__pyx_kp_chisquare;
+static char __pyx_k_2[] = "noncentral_chisquare";
+static PyObject *__pyx_kp_2;
+static char __pyx_k_standard_cauchy[] = "standard_cauchy";
+static PyObject *__pyx_kp_standard_cauchy;
+static char __pyx_k_standard_t[] = "standard_t";
+static PyObject *__pyx_kp_standard_t;
+static char __pyx_k_vonmises[] = "vonmises";
+static PyObject *__pyx_kp_vonmises;
+static char __pyx_k_pareto[] = "pareto";
+static PyObject *__pyx_kp_pareto;
+static char __pyx_k_weibull[] = "weibull";
+static PyObject *__pyx_kp_weibull;
+static char __pyx_k_power[] = "power";
+static PyObject *__pyx_kp_power;
+static char __pyx_k_laplace[] = "laplace";
+static PyObject *__pyx_kp_laplace;
+static char __pyx_k_gumbel[] = "gumbel";
+static PyObject *__pyx_kp_gumbel;
+static char __pyx_k_logistic[] = "logistic";
+static PyObject *__pyx_kp_logistic;
+static char __pyx_k_lognormal[] = "lognormal";
+static PyObject *__pyx_kp_lognormal;
+static char __pyx_k_rayleigh[] = "rayleigh";
+static PyObject *__pyx_kp_rayleigh;
+static char __pyx_k_wald[] = "wald";
+static PyObject *__pyx_kp_wald;
+static char __pyx_k_triangular[] = "triangular";
+static PyObject *__pyx_kp_triangular;
+static char __pyx_k_binomial[] = "binomial";
+static PyObject *__pyx_kp_binomial;
+static char __pyx_k_negative_binomial[] = "negative_binomial";
+static PyObject *__pyx_kp_negative_binomial;
+static char __pyx_k_poisson[] = "poisson";
+static PyObject *__pyx_kp_poisson;
+static char __pyx_k_zipf[] = "zipf";
+static PyObject *__pyx_kp_zipf;
+static char __pyx_k_geometric[] = "geometric";
+static PyObject *__pyx_kp_geometric;
+static char __pyx_k_hypergeometric[] = "hypergeometric";
+static PyObject *__pyx_kp_hypergeometric;
+static char __pyx_k_logseries[] = "logseries";
+static PyObject *__pyx_kp_logseries;
+static char __pyx_k_multivariate_normal[] = "multivariate_normal";
+static PyObject *__pyx_kp_multivariate_normal;
+static char __pyx_k_multinomial[] = "multinomial";
+static PyObject *__pyx_kp_multinomial;
+static char __pyx_k_dirichlet[] = "dirichlet";
+static PyObject *__pyx_kp_dirichlet;
+static char __pyx_k_shuffle[] = "shuffle";
+static PyObject *__pyx_kp_shuffle;
+static char __pyx_k_permutation[] = "permutation";
+static PyObject *__pyx_kp_permutation;
+static char __pyx_k_numpy[] = "numpy";
+static PyObject *__pyx_kp_numpy;
+static char __pyx_k_np[] = "np";
+static PyObject *__pyx_kp_np;
+static char __pyx_k__rand[] = "_rand";
+static PyObject *__pyx_kp__rand;
+static char __pyx_k_empty[] = "empty";
+static PyObject *__pyx_kp_empty;
+static char __pyx_k_19[] = "float64";
+static PyObject *__pyx_kp_19;
+static char __pyx_k_ValueError[] = "ValueError";
+static PyObject *__pyx_kp_ValueError;
+static char __pyx_k_integer[] = "integer";
+static PyObject *__pyx_kp_integer;
+static char __pyx_k_uint[] = "uint";
+static PyObject *__pyx_kp_uint;
+static char __pyx_k_asarray[] = "asarray";
+static PyObject *__pyx_kp_asarray;
+static char __pyx_k_27[] = "uint32";
+static PyObject *__pyx_kp_27;
+static char __pyx_k_28[] = "MT19937";
+static PyObject *__pyx_kp_28;
+static char __pyx_k_29[] = "MT19937";
+static PyObject *__pyx_kp_29;
+static char __pyx_k_TypeError[] = "TypeError";
+static PyObject *__pyx_kp_TypeError;
+static char __pyx_k_random[] = "random";
+static PyObject *__pyx_kp_random;
+static char __pyx_k___RandomState_ctor[] = "__RandomState_ctor";
+static PyObject *__pyx_kp___RandomState_ctor;
+static char __pyx_k_subtract[] = "subtract";
+static PyObject *__pyx_kp_subtract;
+static char __pyx_k_size[] = "size";
+static PyObject *__pyx_kp_size;
+static char __pyx_k_any[] = "any";
+static PyObject *__pyx_kp_any;
+static char __pyx_k_less_equal[] = "less_equal";
+static PyObject *__pyx_kp_less_equal;
+static char __pyx_k_less[] = "less";
+static PyObject *__pyx_kp_less;
+static char __pyx_k_greater[] = "greater";
+static PyObject *__pyx_kp_greater;
+static char __pyx_k_equal[] = "equal";
+static PyObject *__pyx_kp_equal;
+static char __pyx_k_add[] = "add";
+static PyObject *__pyx_kp_add;
+static char __pyx_k_greater_equal[] = "greater_equal";
+static PyObject *__pyx_kp_greater_equal;
+static char __pyx_k_array[] = "array";
+static PyObject *__pyx_kp_array;
+static char __pyx_k_shape[] = "shape";
+static PyObject *__pyx_kp_shape;
+static char __pyx_k_append[] = "append";
+static PyObject *__pyx_kp_append;
+static char __pyx_k_multiply[] = "multiply";
+static PyObject *__pyx_kp_multiply;
+static char __pyx_k_reduce[] = "reduce";
+static PyObject *__pyx_kp_reduce;
+static char __pyx_k_128[] = "numpy.dual";
+static PyObject *__pyx_kp_128;
+static char __pyx_k_svd[] = "svd";
+static PyObject *__pyx_kp_svd;
+static char __pyx_k_dot[] = "dot";
+static PyObject *__pyx_kp_dot;
+static char __pyx_k_sqrt[] = "sqrt";
+static PyObject *__pyx_kp_sqrt;
+static char __pyx_k_zeros[] = "zeros";
+static PyObject *__pyx_kp_zeros;
+static char __pyx_k_130[] = "copy";
+static PyObject *__pyx_kp_130;
+static char __pyx_k_copy[] = "copy";
+static PyObject *__pyx_kp_copy;
+static char __pyx_k_arange[] = "arange";
+static PyObject *__pyx_kp_arange;
+static PyObject *__pyx_builtin_ValueError;
+static PyObject *__pyx_builtin_TypeError;
+static PyObject *__pyx_kp_20;
+static char __pyx_k_20[] = "size is not compatible with inputs";
+static PyObject *__pyx_kp_21;
+static char __pyx_k_21[] = "size is not compatible with inputs";
+static PyObject *__pyx_kp_22;
+static char __pyx_k_22[] = "size is not compatible with inputs";
+static PyObject *__pyx_kp_23;
+static char __pyx_k_23[] = "size is not compatible with inputs";
+static PyObject *__pyx_kp_24;
+static char __pyx_k_24[] = "size is not compatible with inputs";
+static PyObject *__pyx_kp_25;
+static char __pyx_k_25[] = "size is not compatible with inputs";
+static PyObject *__pyx_kp_26;
+static char __pyx_k_26[] = "size is not compatible with inputs";
+static PyObject *__pyx_kp_30;
+static PyObject *__pyx_kp_31;
+static char __pyx_k_30[] = "algorithm must be 'MT19937'";
+static char __pyx_k_31[] = "state must be 624 longs";
+static PyObject *__pyx_kp_32;
+static char __pyx_k_32[] = "low >= high";
+static PyObject *__pyx_kp_33;
+static PyObject *__pyx_kp_34;
+static char __pyx_k_33[] = "scale <= 0";
+static char __pyx_k_34[] = "scale <= 0";
+static PyObject *__pyx_kp_35;
+static PyObject *__pyx_kp_36;
+static PyObject *__pyx_kp_37;
+static PyObject *__pyx_kp_38;
+static char __pyx_k_35[] = "a <= 0";
+static char __pyx_k_36[] = "b <= 0";
+static char __pyx_k_37[] = "a <= 0";
+static char __pyx_k_38[] = "b <= 0";
+static PyObject *__pyx_kp_39;
+static PyObject *__pyx_kp_40;
+static char __pyx_k_39[] = "scale <= 0";
+static char __pyx_k_40[] = "scale <= 0";
+static PyObject *__pyx_kp_41;
+static PyObject *__pyx_kp_42;
+static char __pyx_k_41[] = "shape <= 0";
+static char __pyx_k_42[] = "shape <= 0";
+static PyObject *__pyx_kp_43;
+static PyObject *__pyx_kp_44;
+static PyObject *__pyx_kp_45;
+static PyObject *__pyx_kp_46;
+static char __pyx_k_43[] = "shape <= 0";
+static char __pyx_k_44[] = "scale <= 0";
+static char __pyx_k_45[] = "shape <= 0";
+static char __pyx_k_46[] = "scale <= 0";
+static PyObject *__pyx_kp_47;
+static PyObject *__pyx_kp_48;
+static PyObject *__pyx_kp_49;
+static PyObject *__pyx_kp_50;
+static char __pyx_k_47[] = "shape <= 0";
+static char __pyx_k_48[] = "scale <= 0";
+static char __pyx_k_49[] = "dfnum <= 0";
+static char __pyx_k_50[] = "dfden <= 0";
+static PyObject *__pyx_kp_51;
+static PyObject *__pyx_kp_52;
+static PyObject *__pyx_kp_53;
+static PyObject *__pyx_kp_54;
+static PyObject *__pyx_kp_55;
+static PyObject *__pyx_kp_56;
+static char __pyx_k_51[] = "dfnum <= 1";
+static char __pyx_k_52[] = "dfden <= 0";
+static char __pyx_k_53[] = "nonc < 0";
+static char __pyx_k_54[] = "dfnum <= 1";
+static char __pyx_k_55[] = "dfden <= 0";
+static char __pyx_k_56[] = "nonc < 0";
+static PyObject *__pyx_kp_57;
+static PyObject *__pyx_kp_58;
+static char __pyx_k_57[] = "df <= 0";
+static char __pyx_k_58[] = "df <= 0";
+static PyObject *__pyx_kp_59;
+static PyObject *__pyx_kp_60;
+static PyObject *__pyx_kp_61;
+static PyObject *__pyx_kp_62;
+static char __pyx_k_59[] = "df <= 0";
+static char __pyx_k_60[] = "nonc <= 0";
+static char __pyx_k_61[] = "df <= 1";
+static char __pyx_k_62[] = "nonc < 0";
+static PyObject *__pyx_kp_63;
+static PyObject *__pyx_kp_64;
+static char __pyx_k_63[] = "df <= 0";
+static char __pyx_k_64[] = "df <= 0";
+static PyObject *__pyx_kp_65;
+static PyObject *__pyx_kp_66;
+static char __pyx_k_65[] = "kappa < 0";
+static char __pyx_k_66[] = "kappa < 0";
+static PyObject *__pyx_kp_67;
+static PyObject *__pyx_kp_68;
+static char __pyx_k_67[] = "a <= 0";
+static char __pyx_k_68[] = "a <= 0";
+static PyObject *__pyx_kp_69;
+static PyObject *__pyx_kp_70;
+static char __pyx_k_69[] = "a <= 0";
+static char __pyx_k_70[] = "a <= 0";
+static PyObject *__pyx_kp_71;
+static PyObject *__pyx_kp_72;
+static char __pyx_k_71[] = "a <= 0";
+static char __pyx_k_72[] = "a <= 0";
+static PyObject *__pyx_kp_73;
+static PyObject *__pyx_kp_74;
+static char __pyx_k_73[] = "scale <= 0";
+static char __pyx_k_74[] = "scale <= 0";
+static PyObject *__pyx_kp_75;
+static PyObject *__pyx_kp_76;
+static char __pyx_k_75[] = "scale <= 0";
+static char __pyx_k_76[] = "scale <= 0";
+static PyObject *__pyx_kp_77;
+static PyObject *__pyx_kp_78;
+static char __pyx_k_77[] = "scale <= 0";
+static char __pyx_k_78[] = "scale <= 0";
+static PyObject *__pyx_kp_79;
+static PyObject *__pyx_kp_80;
+static char __pyx_k_79[] = "sigma <= 0";
+static char __pyx_k_80[] = "sigma <= 0.0";
+static PyObject *__pyx_kp_81;
+static PyObject *__pyx_kp_82;
+static char __pyx_k_81[] = "scale <= 0";
+static char __pyx_k_82[] = "scale <= 0.0";
+static PyObject *__pyx_kp_83;
+static PyObject *__pyx_kp_84;
+static PyObject *__pyx_kp_85;
+static PyObject *__pyx_kp_86;
+static char __pyx_k_83[] = "mean <= 0";
+static char __pyx_k_84[] = "scale <= 0";
+static char __pyx_k_85[] = "mean <= 0.0";
+static char __pyx_k_86[] = "scale <= 0.0";
+static PyObject *__pyx_kp_87;
+static PyObject *__pyx_kp_88;
+static PyObject *__pyx_kp_89;
+static PyObject *__pyx_kp_90;
+static PyObject *__pyx_kp_91;
+static PyObject *__pyx_kp_92;
+static char __pyx_k_87[] = "left > mode";
+static char __pyx_k_88[] = "mode > right";
+static char __pyx_k_89[] = "left == right";
+static char __pyx_k_90[] = "left > mode";
+static char __pyx_k_91[] = "mode > right";
+static char __pyx_k_92[] = "left == right";
+static PyObject *__pyx_kp_93;
+static PyObject *__pyx_kp_94;
+static PyObject *__pyx_kp_95;
+static PyObject *__pyx_kp_96;
+static PyObject *__pyx_kp_97;
+static PyObject *__pyx_kp_98;
+static char __pyx_k_93[] = "n <= 0";
+static char __pyx_k_94[] = "p < 0";
+static char __pyx_k_95[] = "p > 1";
+static char __pyx_k_96[] = "n <= 0";
+static char __pyx_k_97[] = "p < 0";
+static char __pyx_k_98[] = "p > 1";
+static PyObject *__pyx_kp_99;
+static PyObject *__pyx_kp_100;
+static PyObject *__pyx_kp_101;
+static PyObject *__pyx_kp_102;
+static PyObject *__pyx_kp_103;
+static PyObject *__pyx_kp_104;
+static char __pyx_k_99[] = "n <= 0";
+static char __pyx_k_100[] = "p < 0";
+static char __pyx_k_101[] = "p > 1";
+static char __pyx_k_102[] = "n <= 0";
+static char __pyx_k_103[] = "p < 0";
+static char __pyx_k_104[] = "p > 1";
+static PyObject *__pyx_kp_105;
+static PyObject *__pyx_kp_106;
+static char __pyx_k_105[] = "lam < 0";
+static char __pyx_k_106[] = "lam < 0";
+static PyObject *__pyx_kp_107;
+static PyObject *__pyx_kp_108;
+static char __pyx_k_107[] = "a <= 1.0";
+static char __pyx_k_108[] = "a <= 1.0";
+static PyObject *__pyx_kp_109;
+static PyObject *__pyx_kp_110;
+static PyObject *__pyx_kp_111;
+static PyObject *__pyx_kp_112;
+static char __pyx_k_109[] = "p < 0.0";
+static char __pyx_k_110[] = "p > 1.0";
+static char __pyx_k_111[] = "p < 0.0";
+static char __pyx_k_112[] = "p > 1.0";
+static PyObject *__pyx_kp_113;
+static PyObject *__pyx_kp_114;
+static PyObject *__pyx_kp_115;
+static PyObject *__pyx_kp_116;
+static PyObject *__pyx_kp_117;
+static PyObject *__pyx_kp_118;
+static PyObject *__pyx_kp_119;
+static PyObject *__pyx_kp_120;
+static char __pyx_k_113[] = "ngood < 1";
+static char __pyx_k_114[] = "nbad < 1";
+static char __pyx_k_115[] = "nsample < 1";
+static char __pyx_k_116[] = "ngood + nbad < nsample";
+static char __pyx_k_117[] = "ngood < 1";
+static char __pyx_k_118[] = "nbad < 1";
+static char __pyx_k_119[] = "nsample < 1";
+static char __pyx_k_120[] = "ngood + nbad < nsample";
+static PyObject *__pyx_kp_121;
+static PyObject *__pyx_kp_122;
+static PyObject *__pyx_kp_123;
+static PyObject *__pyx_kp_124;
+static char __pyx_k_121[] = "p <= 0.0";
+static char __pyx_k_122[] = "p >= 1.0";
+static char __pyx_k_123[] = "p <= 0.0";
+static char __pyx_k_124[] = "p >= 1.0";
+static PyObject *__pyx_kp_125;
+static PyObject *__pyx_kp_126;
+static PyObject *__pyx_kp_127;
+static char __pyx_k_125[] = "mean must be 1 dimensional";
+static char __pyx_k_126[] = "cov must be 2 dimensional and square";
+static char __pyx_k_127[] = "mean and cov must have same length";
+static PyObject *__pyx_kp_129;
+static char __pyx_k_129[] = "sum(pvals[:-1]) > 1.0";
+
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":125
+ * import numpy as np
+ * 
+ * cdef object cont0_array(rk_state *state, rk_cont0 func, object size):             # <<<<<<<<<<<<<<
+ *     cdef double *array_data
+ *     cdef ndarray array "arrayObject"
+ */
+
+static  PyObject *__pyx_f_6mtrand_cont0_array(rk_state *__pyx_v_state, __pyx_t_6mtrand_rk_cont0 __pyx_v_func, PyObject *__pyx_v_size) {
   double *__pyx_v_array_data;
   PyArrayObject *arrayObject;
   long __pyx_v_length;
@@ -248,61 +751,112 @@ static PyObject *__pyx_f_6mtrand_cont0_array(rk_state *__pyx_v_state,__pyx_t_6mt
   PyObject *__pyx_2 = 0;
   PyObject *__pyx_3 = 0;
   PyObject *__pyx_4 = 0;
-  Py_INCREF(__pyx_v_size);
   arrayObject = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":131 */
-  __pyx_1 = __pyx_v_size == Py_None;
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":131
+ *     cdef long i
+ * 
+ *     if size is None:             # <<<<<<<<<<<<<<
+ *         return func(state)
+ *     else:
+ */
+  __pyx_1 = (__pyx_v_size == Py_None);
   if (__pyx_1) {
-    __pyx_2 = PyFloat_FromDouble(__pyx_v_func(__pyx_v_state)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 132; goto __pyx_L1;}
+
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":132
+ * 
+ *     if size is None:
+ *         return func(state)             # <<<<<<<<<<<<<<
+ *     else:
+ *         array = <ndarray>np.empty(size, np.float64)
+ */
+    __pyx_2 = PyFloat_FromDouble(__pyx_v_func(__pyx_v_state)); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 132; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     __pyx_r = __pyx_2;
     __pyx_2 = 0;
     goto __pyx_L0;
-    goto __pyx_L2;
+    goto __pyx_L3;
   }
   /*else*/ {
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":134 */
-    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n_np); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 134; goto __pyx_L1;}
-    __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_n_empty); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 134; goto __pyx_L1;}
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":134
+ *         return func(state)
+ *     else:
+ *         array = <ndarray>np.empty(size, np.float64)             # <<<<<<<<<<<<<<
+ *         length = PyArray_SIZE(array)
+ *         array_data = <double *>array.data
+ */
+    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 134; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_kp_empty); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 134; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     Py_DECREF(__pyx_2); __pyx_2 = 0;
-    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n_np); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 134; goto __pyx_L1;}
-    __pyx_4 = PyObject_GetAttr(__pyx_2, __pyx_n_float64); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 134; goto __pyx_L1;}
+    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 134; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_4 = PyObject_GetAttr(__pyx_2, __pyx_kp_19); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 134; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     Py_DECREF(__pyx_2); __pyx_2 = 0;
-    __pyx_2 = PyTuple_New(2); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 134; goto __pyx_L1;}
+    __pyx_2 = PyTuple_New(2); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 134; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     Py_INCREF(__pyx_v_size);
     PyTuple_SET_ITEM(__pyx_2, 0, __pyx_v_size);
     PyTuple_SET_ITEM(__pyx_2, 1, __pyx_4);
     __pyx_4 = 0;
-    __pyx_4 = PyObject_CallObject(__pyx_3, __pyx_2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 134; goto __pyx_L1;}
+    __pyx_4 = PyObject_Call(__pyx_3, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 134; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     Py_DECREF(__pyx_3); __pyx_3 = 0;
-    Py_DECREF(__pyx_2); __pyx_2 = 0;
+    Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0;
     Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_4)));
     Py_DECREF(((PyObject *)arrayObject));
     arrayObject = ((PyArrayObject *)__pyx_4);
     Py_DECREF(__pyx_4); __pyx_4 = 0;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":135 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":135
+ *     else:
+ *         array = <ndarray>np.empty(size, np.float64)
+ *         length = PyArray_SIZE(array)             # <<<<<<<<<<<<<<
+ *         array_data = <double *>array.data
+ *         for i from 0 <= i < length:
+ */
     __pyx_v_length = PyArray_SIZE(arrayObject);
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":136 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":136
+ *         array = <ndarray>np.empty(size, np.float64)
+ *         length = PyArray_SIZE(array)
+ *         array_data = <double *>array.data             # <<<<<<<<<<<<<<
+ *         for i from 0 <= i < length:
+ *             array_data[i] = func(state)
+ */
     __pyx_v_array_data = ((double *)arrayObject->data);
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":137 */
-    for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; ++__pyx_v_i) {
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":137
+ *         length = PyArray_SIZE(array)
+ *         array_data = <double *>array.data
+ *         for i from 0 <= i < length:             # <<<<<<<<<<<<<<
+ *             array_data[i] = func(state)
+ *         return array
+ */
+    for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; __pyx_v_i++) {
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":138
+ *         array_data = <double *>array.data
+ *         for i from 0 <= i < length:
+ *             array_data[i] = func(state)             # <<<<<<<<<<<<<<
+ *         return array
+ * 
+ */
       (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state);
     }
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":139 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":139
+ *         for i from 0 <= i < length:
+ *             array_data[i] = func(state)
+ *         return array             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
     Py_INCREF(((PyObject *)arrayObject));
     __pyx_r = ((PyObject *)arrayObject);
     goto __pyx_L0;
   }
-  __pyx_L2:;
+  __pyx_L3:;
 
   __pyx_r = Py_None; Py_INCREF(Py_None);
   goto __pyx_L0;
-  __pyx_L1:;
+  __pyx_L1_error:;
   Py_XDECREF(__pyx_2);
   Py_XDECREF(__pyx_3);
   Py_XDECREF(__pyx_4);
@@ -310,11 +864,18 @@ static PyObject *__pyx_f_6mtrand_cont0_array(rk_state *__pyx_v_state,__pyx_t_6mt
   __pyx_r = 0;
   __pyx_L0:;
   Py_DECREF(arrayObject);
-  Py_DECREF(__pyx_v_size);
   return __pyx_r;
 }
 
-static PyObject *__pyx_f_6mtrand_cont1_array_sc(rk_state *__pyx_v_state,__pyx_t_6mtrand_rk_cont1 __pyx_v_func,PyObject *__pyx_v_size,double __pyx_v_a) {
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":142
+ * 
+ * 
+ * cdef object cont1_array_sc(rk_state *state, rk_cont1 func, object size, double a):             # <<<<<<<<<<<<<<
+ *     cdef double *array_data
+ *     cdef ndarray array "arrayObject"
+ */
+
+static  PyObject *__pyx_f_6mtrand_cont1_array_sc(rk_state *__pyx_v_state, __pyx_t_6mtrand_rk_cont1 __pyx_v_func, PyObject *__pyx_v_size, double __pyx_v_a) {
   double *__pyx_v_array_data;
   PyArrayObject *arrayObject;
   long __pyx_v_length;
@@ -324,61 +885,112 @@ static PyObject *__pyx_f_6mtrand_cont1_array_sc(rk_state *__pyx_v_state,__pyx_t_
   PyObject *__pyx_2 = 0;
   PyObject *__pyx_3 = 0;
   PyObject *__pyx_4 = 0;
-  Py_INCREF(__pyx_v_size);
   arrayObject = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":148 */
-  __pyx_1 = __pyx_v_size == Py_None;
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":148
+ *     cdef long i
+ * 
+ *     if size is None:             # <<<<<<<<<<<<<<
+ *         return func(state, a)
+ *     else:
+ */
+  __pyx_1 = (__pyx_v_size == Py_None);
   if (__pyx_1) {
-    __pyx_2 = PyFloat_FromDouble(__pyx_v_func(__pyx_v_state,__pyx_v_a)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 149; goto __pyx_L1;}
+
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":149
+ * 
+ *     if size is None:
+ *         return func(state, a)             # <<<<<<<<<<<<<<
+ *     else:
+ *         array = <ndarray>np.empty(size, np.float64)
+ */
+    __pyx_2 = PyFloat_FromDouble(__pyx_v_func(__pyx_v_state, __pyx_v_a)); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 149; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     __pyx_r = __pyx_2;
     __pyx_2 = 0;
     goto __pyx_L0;
-    goto __pyx_L2;
+    goto __pyx_L3;
   }
   /*else*/ {
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":151 */
-    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n_np); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 151; goto __pyx_L1;}
-    __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_n_empty); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 151; goto __pyx_L1;}
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":151
+ *         return func(state, a)
+ *     else:
+ *         array = <ndarray>np.empty(size, np.float64)             # <<<<<<<<<<<<<<
+ *         length = PyArray_SIZE(array)
+ *         array_data = <double *>array.data
+ */
+    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 151; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_kp_empty); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 151; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     Py_DECREF(__pyx_2); __pyx_2 = 0;
-    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n_np); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 151; goto __pyx_L1;}
-    __pyx_4 = PyObject_GetAttr(__pyx_2, __pyx_n_float64); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 151; goto __pyx_L1;}
+    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 151; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_4 = PyObject_GetAttr(__pyx_2, __pyx_kp_19); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 151; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     Py_DECREF(__pyx_2); __pyx_2 = 0;
-    __pyx_2 = PyTuple_New(2); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 151; goto __pyx_L1;}
+    __pyx_2 = PyTuple_New(2); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 151; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     Py_INCREF(__pyx_v_size);
     PyTuple_SET_ITEM(__pyx_2, 0, __pyx_v_size);
     PyTuple_SET_ITEM(__pyx_2, 1, __pyx_4);
     __pyx_4 = 0;
-    __pyx_4 = PyObject_CallObject(__pyx_3, __pyx_2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 151; goto __pyx_L1;}
+    __pyx_4 = PyObject_Call(__pyx_3, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 151; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     Py_DECREF(__pyx_3); __pyx_3 = 0;
-    Py_DECREF(__pyx_2); __pyx_2 = 0;
+    Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0;
     Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_4)));
     Py_DECREF(((PyObject *)arrayObject));
     arrayObject = ((PyArrayObject *)__pyx_4);
     Py_DECREF(__pyx_4); __pyx_4 = 0;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":152 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":152
+ *     else:
+ *         array = <ndarray>np.empty(size, np.float64)
+ *         length = PyArray_SIZE(array)             # <<<<<<<<<<<<<<
+ *         array_data = <double *>array.data
+ *         for i from 0 <= i < length:
+ */
     __pyx_v_length = PyArray_SIZE(arrayObject);
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":153 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":153
+ *         array = <ndarray>np.empty(size, np.float64)
+ *         length = PyArray_SIZE(array)
+ *         array_data = <double *>array.data             # <<<<<<<<<<<<<<
+ *         for i from 0 <= i < length:
+ *             array_data[i] = func(state, a)
+ */
     __pyx_v_array_data = ((double *)arrayObject->data);
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":154 */
-    for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; ++__pyx_v_i) {
-      (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,__pyx_v_a);
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":154
+ *         length = PyArray_SIZE(array)
+ *         array_data = <double *>array.data
+ *         for i from 0 <= i < length:             # <<<<<<<<<<<<<<
+ *             array_data[i] = func(state, a)
+ *         return array
+ */
+    for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; __pyx_v_i++) {
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":155
+ *         array_data = <double *>array.data
+ *         for i from 0 <= i < length:
+ *             array_data[i] = func(state, a)             # <<<<<<<<<<<<<<
+ *         return array
+ * 
+ */
+      (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state, __pyx_v_a);
     }
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":156 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":156
+ *         for i from 0 <= i < length:
+ *             array_data[i] = func(state, a)
+ *         return array             # <<<<<<<<<<<<<<
+ * 
+ * cdef object cont1_array(rk_state *state, rk_cont1 func, object size, ndarray oa):
+ */
     Py_INCREF(((PyObject *)arrayObject));
     __pyx_r = ((PyObject *)arrayObject);
     goto __pyx_L0;
   }
-  __pyx_L2:;
+  __pyx_L3:;
 
   __pyx_r = Py_None; Py_INCREF(Py_None);
   goto __pyx_L0;
-  __pyx_L1:;
+  __pyx_L1_error:;
   Py_XDECREF(__pyx_2);
   Py_XDECREF(__pyx_3);
   Py_XDECREF(__pyx_4);
@@ -386,15 +998,18 @@ static PyObject *__pyx_f_6mtrand_cont1_array_sc(rk_state *__pyx_v_state,__pyx_t_
   __pyx_r = 0;
   __pyx_L0:;
   Py_DECREF(arrayObject);
-  Py_DECREF(__pyx_v_size);
   return __pyx_r;
 }
 
-static PyObject *__pyx_k61p;
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":158
+ *         return array
+ * 
+ * cdef object cont1_array(rk_state *state, rk_cont1 func, object size, ndarray oa):             # <<<<<<<<<<<<<<
+ *     cdef double *array_data
+ *     cdef double *oa_data
+ */
 
-static char __pyx_k61[] = "size is not compatible with inputs";
-
-static PyObject *__pyx_f_6mtrand_cont1_array(rk_state *__pyx_v_state,__pyx_t_6mtrand_rk_cont1 __pyx_v_func,PyObject *__pyx_v_size,PyArrayObject *__pyx_v_oa) {
+static  PyObject *__pyx_f_6mtrand_cont1_array(rk_state *__pyx_v_state, __pyx_t_6mtrand_rk_cont1 __pyx_v_func, PyObject *__pyx_v_size, PyArrayObject *__pyx_v_oa) {
   double *__pyx_v_array_data;
   double *__pyx_v_oa_data;
   PyArrayObject *arrayObject;
@@ -408,118 +1023,226 @@ static PyObject *__pyx_f_6mtrand_cont1_array(rk_state *__pyx_v_state,__pyx_t_6mt
   PyObject *__pyx_3 = 0;
   PyObject *__pyx_4 = 0;
   npy_intp __pyx_5;
-  Py_INCREF(__pyx_v_size);
-  Py_INCREF(__pyx_v_oa);
   arrayObject = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
   __pyx_v_itera = ((PyArrayIterObject *)Py_None); Py_INCREF(Py_None);
   __pyx_v_multi = ((PyArrayMultiIterObject *)Py_None); Py_INCREF(Py_None);
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":167 */
-  __pyx_1 = __pyx_v_size == Py_None;
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":167
+ *     cdef broadcast multi
+ * 
+ *     if size is None:             # <<<<<<<<<<<<<<
+ *         array = <ndarray>PyArray_SimpleNew(oa.nd, oa.dimensions, NPY_DOUBLE)
+ *         length = PyArray_SIZE(array)
+ */
+  __pyx_1 = (__pyx_v_size == Py_None);
   if (__pyx_1) {
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":168 */
-    __pyx_2 = PyArray_SimpleNew(__pyx_v_oa->nd,__pyx_v_oa->dimensions,NPY_DOUBLE); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 168; goto __pyx_L1;}
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":168
+ * 
+ *     if size is None:
+ *         array = <ndarray>PyArray_SimpleNew(oa.nd, oa.dimensions, NPY_DOUBLE)             # <<<<<<<<<<<<<<
+ *         length = PyArray_SIZE(array)
+ *         array_data = <double *>array.data
+ */
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":169 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":169
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+ *         array_data = <double *>array.data
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+ */
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":170 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":170
+ *         array = <ndarray>PyArray_SimpleNew(oa.nd, oa.dimensions, NPY_DOUBLE)
+ *         length = PyArray_SIZE(array)
+ *         array_data = <double *>array.data             # <<<<<<<<<<<<<<
+ *         itera = <flatiter>PyArray_IterNew(<object>oa)
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+ */
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":171 */
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":171
+ *         length = PyArray_SIZE(array)
+ *         array_data = <double *>array.data
+ *         itera = <flatiter>PyArray_IterNew(<object>oa)             # <<<<<<<<<<<<<<
+ *         for i from 0 <= i < length:
+ *             array_data[i] = func(state, (<double *>(itera.dataptr))[0])
+ */
+    __pyx_2 = PyArray_IterNew(((PyObject *)__pyx_v_oa)); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 171; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":172 */
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-      /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":173 */
-      (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,(((double *)__pyx_v_itera->dataptr)[0]));
-
-      /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":174 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":172
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+ *             array_data[i] = func(state, (<double *>(itera.dataptr))[0])
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+ */
+    for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; __pyx_v_i++) {
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":173
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+ *             array_data[i] = func(state, (<double *>(itera.dataptr))[0])             # <<<<<<<<<<<<<<
+ *             PyArray_ITER_NEXT(itera)
+ *     else:
+ */
+      (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state, (((double *)__pyx_v_itera->dataptr)[0]));
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":174
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+ *             array_data[i] = func(state, (<double *>(itera.dataptr))[0])
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+ *     else:
+ *         array = <ndarray>np.empty(size, np.float64)
+ */
       PyArray_ITER_NEXT(__pyx_v_itera);
     }
-    goto __pyx_L2;
+    goto __pyx_L3;
   }
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":177 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":177
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+ */
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":179
+ *         array_data = <double *>array.data
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":180 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":180
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 181; goto __pyx_L1;}
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+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 181; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":182 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":182
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-      PyArray_MultiIter_NEXTi(__pyx_v_multi,1);
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":183
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":185
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":186 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":186
+ *             array_data[i] = func(state, oa_data[0])
+ *             PyArray_MultiIter_NEXTi(multi, 1)
+ *     return array             # <<<<<<<<<<<<<<
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   __pyx_r = Py_None; Py_INCREF(Py_None);
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-  __pyx_L1:;
+  __pyx_L1_error:;
   Py_XDECREF(__pyx_2);
   Py_XDECREF(__pyx_3);
   Py_XDECREF(__pyx_4);
@@ -529,12 +1252,18 @@ static PyObject *__pyx_f_6mtrand_cont1_array(rk_state *__pyx_v_state,__pyx_t_6mt
   Py_DECREF(arrayObject);
   Py_DECREF(__pyx_v_itera);
   Py_DECREF(__pyx_v_multi);
-  Py_DECREF(__pyx_v_size);
-  Py_DECREF(__pyx_v_oa);
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 }
 
-static PyObject *__pyx_f_6mtrand_cont2_array_sc(rk_state *__pyx_v_state,__pyx_t_6mtrand_rk_cont2 __pyx_v_func,PyObject *__pyx_v_size,double __pyx_v_a,double __pyx_v_b) {
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":188
+ *     return array
+ * 
+ * cdef object cont2_array_sc(rk_state *state, rk_cont2 func, object size, double a,             # <<<<<<<<<<<<<<
+ *                            double b):
+ *     cdef double *array_data
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+static  PyObject *__pyx_f_6mtrand_cont2_array_sc(rk_state *__pyx_v_state, __pyx_t_6mtrand_rk_cont2 __pyx_v_func, PyObject *__pyx_v_size, double __pyx_v_a, double __pyx_v_b) {
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":196
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+    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 198; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_4 = PyObject_GetAttr(__pyx_2, __pyx_kp_19); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 198; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     Py_DECREF(__pyx_2); __pyx_2 = 0;
-    __pyx_2 = PyTuple_New(2); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 198; goto __pyx_L1;}
+    __pyx_2 = PyTuple_New(2); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 198; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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     PyTuple_SET_ITEM(__pyx_2, 0, __pyx_v_size);
     PyTuple_SET_ITEM(__pyx_2, 1, __pyx_4);
     __pyx_4 = 0;
-    __pyx_4 = PyObject_CallObject(__pyx_3, __pyx_2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 198; goto __pyx_L1;}
+    __pyx_4 = PyObject_Call(__pyx_3, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 198; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     Py_DECREF(__pyx_3); __pyx_3 = 0;
-    Py_DECREF(__pyx_2); __pyx_2 = 0;
+    Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0;
     Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_4)));
     Py_DECREF(((PyObject *)arrayObject));
     arrayObject = ((PyArrayObject *)__pyx_4);
     Py_DECREF(__pyx_4); __pyx_4 = 0;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":199 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":199
+ *     else:
+ *         array = <ndarray>np.empty(size, np.float64)
+ *         length = PyArray_SIZE(array)             # <<<<<<<<<<<<<<
+ *         array_data = <double *>array.data
+ *         for i from 0 <= i < length:
+ */
     __pyx_v_length = PyArray_SIZE(arrayObject);
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":200 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":200
+ *         array = <ndarray>np.empty(size, np.float64)
+ *         length = PyArray_SIZE(array)
+ *         array_data = <double *>array.data             # <<<<<<<<<<<<<<
+ *         for i from 0 <= i < length:
+ *             array_data[i] = func(state, a, b)
+ */
     __pyx_v_array_data = ((double *)arrayObject->data);
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":201 */
-    for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; ++__pyx_v_i) {
-      (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,__pyx_v_a,__pyx_v_b);
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":201
+ *         length = PyArray_SIZE(array)
+ *         array_data = <double *>array.data
+ *         for i from 0 <= i < length:             # <<<<<<<<<<<<<<
+ *             array_data[i] = func(state, a, b)
+ *         return array
+ */
+    for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; __pyx_v_i++) {
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":202
+ *         array_data = <double *>array.data
+ *         for i from 0 <= i < length:
+ *             array_data[i] = func(state, a, b)             # <<<<<<<<<<<<<<
+ *         return array
+ * 
+ */
+      (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state, __pyx_v_a, __pyx_v_b);
     }
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":203 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":203
+ *         for i from 0 <= i < length:
+ *             array_data[i] = func(state, a, b)
+ *         return array             # <<<<<<<<<<<<<<
+ * 
+ * 
+ */
     Py_INCREF(((PyObject *)arrayObject));
     __pyx_r = ((PyObject *)arrayObject);
     goto __pyx_L0;
   }
-  __pyx_L2:;
+  __pyx_L3:;
 
   __pyx_r = Py_None; Py_INCREF(Py_None);
   goto __pyx_L0;
-  __pyx_L1:;
+  __pyx_L1_error:;
   Py_XDECREF(__pyx_2);
   Py_XDECREF(__pyx_3);
   Py_XDECREF(__pyx_4);
@@ -606,15 +1386,18 @@ static PyObject *__pyx_f_6mtrand_cont2_array_sc(rk_state *__pyx_v_state,__pyx_t_
   __pyx_r = 0;
   __pyx_L0:;
   Py_DECREF(arrayObject);
-  Py_DECREF(__pyx_v_size);
   return __pyx_r;
 }
 
-static PyObject *__pyx_k62p;
-
-static char __pyx_k62[] = "size is not compatible with inputs";
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":206
+ * 
+ * 
+ * cdef object cont2_array(rk_state *state, rk_cont2 func, object size,             # <<<<<<<<<<<<<<
+ *                         ndarray oa, ndarray ob):
+ *     cdef double *array_data
+ */
 
-static PyObject *__pyx_f_6mtrand_cont2_array(rk_state *__pyx_v_state,__pyx_t_6mtrand_rk_cont2 __pyx_v_func,PyObject *__pyx_v_size,PyArrayObject *__pyx_v_oa,PyArrayObject *__pyx_v_ob) {
+static  PyObject *__pyx_f_6mtrand_cont2_array(rk_state *__pyx_v_state, __pyx_t_6mtrand_rk_cont2 __pyx_v_func, PyObject *__pyx_v_size, PyArrayObject *__pyx_v_oa, PyArrayObject *__pyx_v_ob) {
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-  Py_INCREF(__pyx_v_oa);
-  Py_INCREF(__pyx_v_ob);
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":219 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":219
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":220
+ *         array = <ndarray> PyArray_SimpleNew(multi.nd, multi.dimensions, NPY_DOUBLE)
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-      /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":224 */
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":221
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":224
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+    __pyx_2 = PyTuple_New(2); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 226; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":227 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":227
+ *     else:
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":228
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":229 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":229
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":230
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+ *             oa_data = <double *>PyArray_MultiIter_DATA(multi, 1)
+ */
+      __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 230; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      Py_INCREF(__pyx_kp_21);
+      PyTuple_SET_ITEM(__pyx_2, 0, __pyx_kp_21);
+      __pyx_5 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 230; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0;
       __Pyx_Raise(__pyx_5, 0, 0);
       Py_DECREF(__pyx_5); __pyx_5 = 0;
-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 230; goto __pyx_L1;}
-      goto __pyx_L5;
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 230; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      goto __pyx_L6;
     }
-    __pyx_L5:;
+    __pyx_L6:;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":231 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":231
+ *         if (multi.size != PyArray_SIZE(array)):
+ *             raise ValueError("size is not compatible with inputs")
+ *         for i from 0 <= i < multi.size:             # <<<<<<<<<<<<<<
+ *             oa_data = <double *>PyArray_MultiIter_DATA(multi, 1)
+ *             ob_data = <double *>PyArray_MultiIter_DATA(multi, 2)
+ */
     __pyx_3 = __pyx_v_multi->size;
-    for (__pyx_v_i = 0; __pyx_v_i < __pyx_3; ++__pyx_v_i) {
-
-      /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":232 */
-      __pyx_v_oa_data = ((double *)PyArray_MultiIter_DATA(__pyx_v_multi,1));
-
-      /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":233 */
-      __pyx_v_ob_data = ((double *)PyArray_MultiIter_DATA(__pyx_v_multi,2));
-
-      /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":234 */
-      (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,(__pyx_v_oa_data[0]),(__pyx_v_ob_data[0]));
-
-      /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":235 */
-      PyArray_MultiIter_NEXTi(__pyx_v_multi,1);
-
-      /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":236 */
-      PyArray_MultiIter_NEXTi(__pyx_v_multi,2);
+    for (__pyx_v_i = 0; __pyx_v_i < __pyx_3; __pyx_v_i++) {
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":232
+ *             raise ValueError("size is not compatible with inputs")
+ *         for i from 0 <= i < multi.size:
+ *             oa_data = <double *>PyArray_MultiIter_DATA(multi, 1)             # <<<<<<<<<<<<<<
+ *             ob_data = <double *>PyArray_MultiIter_DATA(multi, 2)
+ *             array_data[i] = func(state, oa_data[0], ob_data[0])
+ */
+      __pyx_v_oa_data = ((double *)PyArray_MultiIter_DATA(__pyx_v_multi, 1));
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":233
+ *         for i from 0 <= i < multi.size:
+ *             oa_data = <double *>PyArray_MultiIter_DATA(multi, 1)
+ *             ob_data = <double *>PyArray_MultiIter_DATA(multi, 2)             # <<<<<<<<<<<<<<
+ *             array_data[i] = func(state, oa_data[0], ob_data[0])
+ *             PyArray_MultiIter_NEXTi(multi, 1)
+ */
+      __pyx_v_ob_data = ((double *)PyArray_MultiIter_DATA(__pyx_v_multi, 2));
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":234
+ *             oa_data = <double *>PyArray_MultiIter_DATA(multi, 1)
+ *             ob_data = <double *>PyArray_MultiIter_DATA(multi, 2)
+ *             array_data[i] = func(state, oa_data[0], ob_data[0])             # <<<<<<<<<<<<<<
+ *             PyArray_MultiIter_NEXTi(multi, 1)
+ *             PyArray_MultiIter_NEXTi(multi, 2)
+ */
+      (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state, (__pyx_v_oa_data[0]), (__pyx_v_ob_data[0]));
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":235
+ *             ob_data = <double *>PyArray_MultiIter_DATA(multi, 2)
+ *             array_data[i] = func(state, oa_data[0], ob_data[0])
+ *             PyArray_MultiIter_NEXTi(multi, 1)             # <<<<<<<<<<<<<<
+ *             PyArray_MultiIter_NEXTi(multi, 2)
+ *     return array
+ */
+      PyArray_MultiIter_NEXTi(__pyx_v_multi, 1);
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":236
+ *             array_data[i] = func(state, oa_data[0], ob_data[0])
+ *             PyArray_MultiIter_NEXTi(multi, 1)
+ *             PyArray_MultiIter_NEXTi(multi, 2)             # <<<<<<<<<<<<<<
+ *     return array
+ * 
+ */
+      PyArray_MultiIter_NEXTi(__pyx_v_multi, 2);
     }
   }
-  __pyx_L2:;
+  __pyx_L3:;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":237 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":237
+ *             PyArray_MultiIter_NEXTi(multi, 1)
+ *             PyArray_MultiIter_NEXTi(multi, 2)
+ *     return array             # <<<<<<<<<<<<<<
+ * 
+ * cdef object cont3_array_sc(rk_state *state, rk_cont3 func, object size, double a,
+ */
   Py_INCREF(((PyObject *)arrayObject));
   __pyx_r = ((PyObject *)arrayObject);
   goto __pyx_L0;
 
   __pyx_r = Py_None; Py_INCREF(Py_None);
   goto __pyx_L0;
-  __pyx_L1:;
+  __pyx_L1_error:;
   Py_XDECREF(__pyx_2);
   Py_XDECREF(__pyx_4);
   Py_XDECREF(__pyx_5);
@@ -757,13 +1665,18 @@ static PyObject *__pyx_f_6mtrand_cont2_array(rk_state *__pyx_v_state,__pyx_t_6mt
   __pyx_L0:;
   Py_DECREF(arrayObject);
   Py_DECREF(__pyx_v_multi);
-  Py_DECREF(__pyx_v_size);
-  Py_DECREF(__pyx_v_oa);
-  Py_DECREF(__pyx_v_ob);
   return __pyx_r;
 }
 
-static PyObject *__pyx_f_6mtrand_cont3_array_sc(rk_state *__pyx_v_state,__pyx_t_6mtrand_rk_cont3 __pyx_v_func,PyObject *__pyx_v_size,double __pyx_v_a,double __pyx_v_b,double __pyx_v_c) {
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":239
+ *     return array
+ * 
+ * cdef object cont3_array_sc(rk_state *state, rk_cont3 func, object size, double a,             # <<<<<<<<<<<<<<
+ *                            double b, double c):
+ * 
+ */
+
+static  PyObject *__pyx_f_6mtrand_cont3_array_sc(rk_state *__pyx_v_state, __pyx_t_6mtrand_rk_cont3 __pyx_v_func, PyObject *__pyx_v_size, double __pyx_v_a, double __pyx_v_b, double __pyx_v_c) {
   double *__pyx_v_array_data;
   PyArrayObject *arrayObject;
   long __pyx_v_length;
@@ -773,61 +1686,112 @@ static PyObject *__pyx_f_6mtrand_cont3_array_sc(rk_state *__pyx_v_state,__pyx_t_
   PyObject *__pyx_2 = 0;
   PyObject *__pyx_3 = 0;
   PyObject *__pyx_4 = 0;
-  Py_INCREF(__pyx_v_size);
   arrayObject = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":247 */
-  __pyx_1 = __pyx_v_size == Py_None;
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":247
+ *     cdef long i
+ * 
+ *     if size is None:             # <<<<<<<<<<<<<<
+ *         return func(state, a, b, c)
+ *     else:
+ */
+  __pyx_1 = (__pyx_v_size == Py_None);
   if (__pyx_1) {
-    __pyx_2 = PyFloat_FromDouble(__pyx_v_func(__pyx_v_state,__pyx_v_a,__pyx_v_b,__pyx_v_c)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 248; goto __pyx_L1;}
+
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":248
+ * 
+ *     if size is None:
+ *         return func(state, a, b, c)             # <<<<<<<<<<<<<<
+ *     else:
+ *         array = <ndarray>np.empty(size, np.float64)
+ */
+    __pyx_2 = PyFloat_FromDouble(__pyx_v_func(__pyx_v_state, __pyx_v_a, __pyx_v_b, __pyx_v_c)); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 248; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     __pyx_r = __pyx_2;
     __pyx_2 = 0;
     goto __pyx_L0;
-    goto __pyx_L2;
+    goto __pyx_L3;
   }
   /*else*/ {
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":250 */
-    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n_np); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 250; goto __pyx_L1;}
-    __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_n_empty); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 250; goto __pyx_L1;}
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":250
+ *         return func(state, a, b, c)
+ *     else:
+ *         array = <ndarray>np.empty(size, np.float64)             # <<<<<<<<<<<<<<
+ *         length = PyArray_SIZE(array)
+ *         array_data = <double *>array.data
+ */
+    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 250; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_kp_empty); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 250; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     Py_DECREF(__pyx_2); __pyx_2 = 0;
-    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n_np); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 250; goto __pyx_L1;}
-    __pyx_4 = PyObject_GetAttr(__pyx_2, __pyx_n_float64); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 250; goto __pyx_L1;}
+    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 250; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_4 = PyObject_GetAttr(__pyx_2, __pyx_kp_19); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 250; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     Py_DECREF(__pyx_2); __pyx_2 = 0;
-    __pyx_2 = PyTuple_New(2); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 250; goto __pyx_L1;}
+    __pyx_2 = PyTuple_New(2); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 250; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     Py_INCREF(__pyx_v_size);
     PyTuple_SET_ITEM(__pyx_2, 0, __pyx_v_size);
     PyTuple_SET_ITEM(__pyx_2, 1, __pyx_4);
     __pyx_4 = 0;
-    __pyx_4 = PyObject_CallObject(__pyx_3, __pyx_2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 250; goto __pyx_L1;}
+    __pyx_4 = PyObject_Call(__pyx_3, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 250; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     Py_DECREF(__pyx_3); __pyx_3 = 0;
-    Py_DECREF(__pyx_2); __pyx_2 = 0;
+    Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0;
     Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_4)));
     Py_DECREF(((PyObject *)arrayObject));
     arrayObject = ((PyArrayObject *)__pyx_4);
     Py_DECREF(__pyx_4); __pyx_4 = 0;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":251 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":251
+ *     else:
+ *         array = <ndarray>np.empty(size, np.float64)
+ *         length = PyArray_SIZE(array)             # <<<<<<<<<<<<<<
+ *         array_data = <double *>array.data
+ *         for i from 0 <= i < length:
+ */
     __pyx_v_length = PyArray_SIZE(arrayObject);
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":252 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":252
+ *         array = <ndarray>np.empty(size, np.float64)
+ *         length = PyArray_SIZE(array)
+ *         array_data = <double *>array.data             # <<<<<<<<<<<<<<
+ *         for i from 0 <= i < length:
+ *             array_data[i] = func(state, a, b, c)
+ */
     __pyx_v_array_data = ((double *)arrayObject->data);
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":253 */
-    for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; ++__pyx_v_i) {
-      (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,__pyx_v_a,__pyx_v_b,__pyx_v_c);
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":253
+ *         length = PyArray_SIZE(array)
+ *         array_data = <double *>array.data
+ *         for i from 0 <= i < length:             # <<<<<<<<<<<<<<
+ *             array_data[i] = func(state, a, b, c)
+ *         return array
+ */
+    for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; __pyx_v_i++) {
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":254
+ *         array_data = <double *>array.data
+ *         for i from 0 <= i < length:
+ *             array_data[i] = func(state, a, b, c)             # <<<<<<<<<<<<<<
+ *         return array
+ * 
+ */
+      (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state, __pyx_v_a, __pyx_v_b, __pyx_v_c);
     }
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":255 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":255
+ *         for i from 0 <= i < length:
+ *             array_data[i] = func(state, a, b, c)
+ *         return array             # <<<<<<<<<<<<<<
+ * 
+ * cdef object cont3_array(rk_state *state, rk_cont3 func, object size, ndarray oa,
+ */
     Py_INCREF(((PyObject *)arrayObject));
     __pyx_r = ((PyObject *)arrayObject);
     goto __pyx_L0;
   }
-  __pyx_L2:;
+  __pyx_L3:;
 
   __pyx_r = Py_None; Py_INCREF(Py_None);
   goto __pyx_L0;
-  __pyx_L1:;
+  __pyx_L1_error:;
   Py_XDECREF(__pyx_2);
   Py_XDECREF(__pyx_3);
   Py_XDECREF(__pyx_4);
@@ -835,15 +1799,18 @@ static PyObject *__pyx_f_6mtrand_cont3_array_sc(rk_state *__pyx_v_state,__pyx_t_
   __pyx_r = 0;
   __pyx_L0:;
   Py_DECREF(arrayObject);
-  Py_DECREF(__pyx_v_size);
   return __pyx_r;
 }
 
-static PyObject *__pyx_k63p;
-
-static char __pyx_k63[] = "size is not compatible with inputs";
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":257
+ *         return array
+ * 
+ * cdef object cont3_array(rk_state *state, rk_cont3 func, object size, ndarray oa,             # <<<<<<<<<<<<<<
+ *     ndarray ob, ndarray oc):
+ * 
+ */
 
-static PyObject *__pyx_f_6mtrand_cont3_array(rk_state *__pyx_v_state,__pyx_t_6mtrand_rk_cont3 __pyx_v_func,PyObject *__pyx_v_size,PyArrayObject *__pyx_v_oa,PyArrayObject *__pyx_v_ob,PyArrayObject *__pyx_v_oc) {
+static  PyObject *__pyx_f_6mtrand_cont3_array(rk_state *__pyx_v_state, __pyx_t_6mtrand_rk_cont3 __pyx_v_func, PyObject *__pyx_v_size, PyArrayObject *__pyx_v_oa, PyArrayObject *__pyx_v_ob, PyArrayObject *__pyx_v_oc) {
   double *__pyx_v_array_data;
   double *__pyx_v_oa_data;
   double *__pyx_v_ob_data;
@@ -857,132 +1824,262 @@ static PyObject *__pyx_f_6mtrand_cont3_array(rk_state *__pyx_v_state,__pyx_t_6mt
   npy_intp __pyx_3;
   PyObject *__pyx_4 = 0;
   PyObject *__pyx_5 = 0;
-  Py_INCREF(__pyx_v_size);
-  Py_INCREF(__pyx_v_oa);
-  Py_INCREF(__pyx_v_ob);
-  Py_INCREF(__pyx_v_oc);
   arrayObject = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
   __pyx_v_multi = ((PyArrayMultiIterObject *)Py_None); Py_INCREF(Py_None);
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":269 */
-  __pyx_1 = __pyx_v_size == Py_None;
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":269
+ *     cdef broadcast multi
+ * 
+ *     if size is None:             # <<<<<<<<<<<<<<
+ *         multi = <broadcast> PyArray_MultiIterNew(3, <void *>oa, <void *>ob, <void *>oc)
+ *         array = <ndarray> PyArray_SimpleNew(multi.nd, multi.dimensions, NPY_DOUBLE)
+ */
+  __pyx_1 = (__pyx_v_size == Py_None);
   if (__pyx_1) {
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":270 */
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":270
+ * 
+ *     if size is None:
+ *         multi = <broadcast> PyArray_MultiIterNew(3, <void *>oa, <void *>ob, <void *>oc)             # <<<<<<<<<<<<<<
+ *         array = <ndarray> PyArray_SimpleNew(multi.nd, multi.dimensions, NPY_DOUBLE)
+ *         array_data = <double *>array.data
+ */
+    __pyx_2 = PyArray_MultiIterNew(3, ((void *)__pyx_v_oa), ((void *)__pyx_v_ob), ((void *)__pyx_v_oc)); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 270; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     Py_INCREF(((PyObject *)((PyArrayMultiIterObject *)__pyx_2)));
     Py_DECREF(((PyObject *)__pyx_v_multi));
     __pyx_v_multi = ((PyArrayMultiIterObject *)__pyx_2);
     Py_DECREF(__pyx_2); __pyx_2 = 0;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":271 */
-    __pyx_2 = PyArray_SimpleNew(__pyx_v_multi->nd,__pyx_v_multi->dimensions,NPY_DOUBLE); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 271; goto __pyx_L1;}
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":271
+ *     if size is None:
+ *         multi = <broadcast> PyArray_MultiIterNew(3, <void *>oa, <void *>ob, <void *>oc)
+ *         array = <ndarray> PyArray_SimpleNew(multi.nd, multi.dimensions, NPY_DOUBLE)             # <<<<<<<<<<<<<<
+ *         array_data = <double *>array.data
+ *         for i from 0 <= i < multi.size:
+ */
+    __pyx_2 = PyArray_SimpleNew(__pyx_v_multi->nd, __pyx_v_multi->dimensions, NPY_DOUBLE); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 271; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_2)));
     Py_DECREF(((PyObject *)arrayObject));
     arrayObject = ((PyArrayObject *)__pyx_2);
     Py_DECREF(__pyx_2); __pyx_2 = 0;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":272 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":272
+ *         multi = <broadcast> PyArray_MultiIterNew(3, <void *>oa, <void *>ob, <void *>oc)
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+ */
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":273 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":273
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+ *             oa_data = <double *>PyArray_MultiIter_DATA(multi, 0)
+ *             ob_data = <double *>PyArray_MultiIter_DATA(multi, 1)
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-      __pyx_v_ob_data = ((double *)PyArray_MultiIter_DATA(__pyx_v_multi,1));
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-      /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":277 */
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-
-      /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":278 */
+    for (__pyx_v_i = 0; __pyx_v_i < __pyx_3; __pyx_v_i++) {
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":274
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+ *             ob_data = <double *>PyArray_MultiIter_DATA(multi, 1)
+ *             oc_data = <double *>PyArray_MultiIter_DATA(multi, 2)
+ */
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":275
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+ *             oa_data = <double *>PyArray_MultiIter_DATA(multi, 0)
+ *             ob_data = <double *>PyArray_MultiIter_DATA(multi, 1)             # <<<<<<<<<<<<<<
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+ *             array_data[i] = func(state, oa_data[0], ob_data[0], oc_data[0])
+ */
+      __pyx_v_ob_data = ((double *)PyArray_MultiIter_DATA(__pyx_v_multi, 1));
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":276
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+ *             ob_data = <double *>PyArray_MultiIter_DATA(multi, 1)
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+ */
+      __pyx_v_oc_data = ((double *)PyArray_MultiIter_DATA(__pyx_v_multi, 2));
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":277
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+ *     else:
+ */
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":278
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+ *         array = <ndarray>np.empty(size, np.float64)
+ */
       PyArray_MultiIter_NEXT(__pyx_v_multi);
     }
-    goto __pyx_L2;
+    goto __pyx_L3;
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-    __pyx_2 = PyTuple_New(2); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 280; goto __pyx_L1;}
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-    __pyx_5 = PyObject_CallObject(__pyx_4, __pyx_2); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 280; goto __pyx_L1;}
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     Py_DECREF(__pyx_4); __pyx_4 = 0;
-    Py_DECREF(__pyx_2); __pyx_2 = 0;
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     Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_5)));
     Py_DECREF(((PyObject *)arrayObject));
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":281 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":281
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+ *                                                 <void *>ob, <void *>oc)
+ */
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":283
+ *         array_data = <double *>array.data
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":284 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":284
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+ */
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-      Py_DECREF(__pyx_2); __pyx_2 = 0;
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":285
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-      goto __pyx_L5;
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+      goto __pyx_L6;
     }
-    __pyx_L5:;
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":286 */
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-
-      /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":290 */
-      (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,(__pyx_v_oa_data[0]),(__pyx_v_ob_data[0]),(__pyx_v_oc_data[0]));
-
-      /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":291 */
+    for (__pyx_v_i = 0; __pyx_v_i < __pyx_3; __pyx_v_i++) {
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":287
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":288
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+ *             ob_data = <double *>PyArray_MultiIter_DATA(multi, 2)             # <<<<<<<<<<<<<<
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+      __pyx_v_ob_data = ((double *)PyArray_MultiIter_DATA(__pyx_v_multi, 2));
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":289
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+ *             ob_data = <double *>PyArray_MultiIter_DATA(multi, 2)
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+ */
+      __pyx_v_oc_data = ((double *)PyArray_MultiIter_DATA(__pyx_v_multi, 3));
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":290
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+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":291
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+ *             array_data[i] = func(state, oa_data[0], ob_data[0], oc_data[0])
+ *             PyArray_MultiIter_NEXT(multi)             # <<<<<<<<<<<<<<
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+ * 
+ */
       PyArray_MultiIter_NEXT(__pyx_v_multi);
     }
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+  __pyx_L3:;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":292 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":292
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+ *             PyArray_MultiIter_NEXT(multi)
+ *     return array             # <<<<<<<<<<<<<<
+ * 
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   goto __pyx_L0;
 
   __pyx_r = Py_None; Py_INCREF(Py_None);
   goto __pyx_L0;
-  __pyx_L1:;
+  __pyx_L1_error:;
   Py_XDECREF(__pyx_2);
   Py_XDECREF(__pyx_4);
   Py_XDECREF(__pyx_5);
@@ -991,14 +2088,18 @@ static PyObject *__pyx_f_6mtrand_cont3_array(rk_state *__pyx_v_state,__pyx_t_6mt
   __pyx_L0:;
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   Py_DECREF(__pyx_v_multi);
-  Py_DECREF(__pyx_v_size);
-  Py_DECREF(__pyx_v_oa);
-  Py_DECREF(__pyx_v_ob);
-  Py_DECREF(__pyx_v_oc);
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+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":294
+ *     return array
+ * 
+ * cdef object disc0_array(rk_state *state, rk_disc0 func, object size):             # <<<<<<<<<<<<<<
+ *     cdef long *array_data
+ *     cdef ndarray array "arrayObject"
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+static  PyObject *__pyx_f_6mtrand_disc0_array(rk_state *__pyx_v_state, __pyx_t_6mtrand_rk_disc0 __pyx_v_func, PyObject *__pyx_v_size) {
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   PyArrayObject *arrayObject;
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-  __pyx_1 = __pyx_v_size == Py_None;
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":300
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+ *     else:
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+
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":301
+ * 
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+ *         array = <ndarray>np.empty(size, int)
+ */
+    __pyx_2 = PyInt_FromLong(__pyx_v_func(__pyx_v_state)); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 301; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     __pyx_r = __pyx_2;
     __pyx_2 = 0;
     goto __pyx_L0;
-    goto __pyx_L2;
+    goto __pyx_L3;
   }
   /*else*/ {
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":303 */
-    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n_np); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 303; goto __pyx_L1;}
-    __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_n_empty); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 303; goto __pyx_L1;}
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":303
+ *         return func(state)
+ *     else:
+ *         array = <ndarray>np.empty(size, int)             # <<<<<<<<<<<<<<
+ *         length = PyArray_SIZE(array)
+ *         array_data = <long *>array.data
+ */
+    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 303; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_kp_empty); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 303; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     Py_DECREF(__pyx_2); __pyx_2 = 0;
-    __pyx_2 = PyTuple_New(2); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 303; goto __pyx_L1;}
+    __pyx_2 = PyTuple_New(2); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 303; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     Py_INCREF(__pyx_v_size);
     PyTuple_SET_ITEM(__pyx_2, 0, __pyx_v_size);
-    Py_INCREF(((PyObject *)(&PyInt_Type)));
-    PyTuple_SET_ITEM(__pyx_2, 1, ((PyObject *)(&PyInt_Type)));
-    __pyx_4 = PyObject_CallObject(__pyx_3, __pyx_2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 303; goto __pyx_L1;}
+    Py_INCREF(((PyObject*)&PyInt_Type));
+    PyTuple_SET_ITEM(__pyx_2, 1, ((PyObject*)&PyInt_Type));
+    __pyx_4 = PyObject_Call(__pyx_3, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 303; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     Py_DECREF(__pyx_3); __pyx_3 = 0;
-    Py_DECREF(__pyx_2); __pyx_2 = 0;
+    Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0;
     Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_4)));
     Py_DECREF(((PyObject *)arrayObject));
     arrayObject = ((PyArrayObject *)__pyx_4);
     Py_DECREF(__pyx_4); __pyx_4 = 0;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":304 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":304
+ *     else:
+ *         array = <ndarray>np.empty(size, int)
+ *         length = PyArray_SIZE(array)             # <<<<<<<<<<<<<<
+ *         array_data = <long *>array.data
+ *         for i from 0 <= i < length:
+ */
     __pyx_v_length = PyArray_SIZE(arrayObject);
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":305 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":305
+ *         array = <ndarray>np.empty(size, int)
+ *         length = PyArray_SIZE(array)
+ *         array_data = <long *>array.data             # <<<<<<<<<<<<<<
+ *         for i from 0 <= i < length:
+ *             array_data[i] = func(state)
+ */
     __pyx_v_array_data = ((long *)arrayObject->data);
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":306 */
-    for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; ++__pyx_v_i) {
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":306
+ *         length = PyArray_SIZE(array)
+ *         array_data = <long *>array.data
+ *         for i from 0 <= i < length:             # <<<<<<<<<<<<<<
+ *             array_data[i] = func(state)
+ *         return array
+ */
+    for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; __pyx_v_i++) {
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":307
+ *         array_data = <long *>array.data
+ *         for i from 0 <= i < length:
+ *             array_data[i] = func(state)             # <<<<<<<<<<<<<<
+ *         return array
+ * 
+ */
       (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state);
     }
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":308 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":308
+ *         for i from 0 <= i < length:
+ *             array_data[i] = func(state)
+ *         return array             # <<<<<<<<<<<<<<
+ * 
+ * cdef object discnp_array_sc(rk_state *state, rk_discnp func, object size, long n, double p):
+ */
     Py_INCREF(((PyObject *)arrayObject));
     __pyx_r = ((PyObject *)arrayObject);
     goto __pyx_L0;
   }
-  __pyx_L2:;
+  __pyx_L3:;
 
   __pyx_r = Py_None; Py_INCREF(Py_None);
   goto __pyx_L0;
-  __pyx_L1:;
+  __pyx_L1_error:;
   Py_XDECREF(__pyx_2);
   Py_XDECREF(__pyx_3);
   Py_XDECREF(__pyx_4);
@@ -1067,11 +2219,18 @@ static PyObject *__pyx_f_6mtrand_disc0_array(rk_state *__pyx_v_state,__pyx_t_6mt
   __pyx_r = 0;
   __pyx_L0:;
   Py_DECREF(arrayObject);
-  Py_DECREF(__pyx_v_size);
   return __pyx_r;
 }
 
-static PyObject *__pyx_f_6mtrand_discnp_array_sc(rk_state *__pyx_v_state,__pyx_t_6mtrand_rk_discnp __pyx_v_func,PyObject *__pyx_v_size,long __pyx_v_n,double __pyx_v_p) {
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":310
+ *         return array
+ * 
+ * cdef object discnp_array_sc(rk_state *state, rk_discnp func, object size, long n, double p):             # <<<<<<<<<<<<<<
+ *     cdef long *array_data
+ *     cdef ndarray array "arrayObject"
+ */
+
+static  PyObject *__pyx_f_6mtrand_discnp_array_sc(rk_state *__pyx_v_state, __pyx_t_6mtrand_rk_discnp __pyx_v_func, PyObject *__pyx_v_size, long __pyx_v_n, double __pyx_v_p) {
   long *__pyx_v_array_data;
   PyArrayObject *arrayObject;
   long __pyx_v_length;
@@ -1081,58 +2240,109 @@ static PyObject *__pyx_f_6mtrand_discnp_array_sc(rk_state *__pyx_v_state,__pyx_t
   PyObject *__pyx_2 = 0;
   PyObject *__pyx_3 = 0;
   PyObject *__pyx_4 = 0;
-  Py_INCREF(__pyx_v_size);
   arrayObject = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":316 */
-  __pyx_1 = __pyx_v_size == Py_None;
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":316
+ *     cdef long i
+ * 
+ *     if size is None:             # <<<<<<<<<<<<<<
+ *         return func(state, n, p)
+ *     else:
+ */
+  __pyx_1 = (__pyx_v_size == Py_None);
   if (__pyx_1) {
-    __pyx_2 = PyInt_FromLong(__pyx_v_func(__pyx_v_state,__pyx_v_n,__pyx_v_p)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 317; goto __pyx_L1;}
+
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":317
+ * 
+ *     if size is None:
+ *         return func(state, n, p)             # <<<<<<<<<<<<<<
+ *     else:
+ *         array = <ndarray>np.empty(size, int)
+ */
+    __pyx_2 = PyInt_FromLong(__pyx_v_func(__pyx_v_state, __pyx_v_n, __pyx_v_p)); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 317; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     __pyx_r = __pyx_2;
     __pyx_2 = 0;
     goto __pyx_L0;
-    goto __pyx_L2;
+    goto __pyx_L3;
   }
   /*else*/ {
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":319 */
-    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n_np); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 319; goto __pyx_L1;}
-    __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_n_empty); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 319; goto __pyx_L1;}
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":319
+ *         return func(state, n, p)
+ *     else:
+ *         array = <ndarray>np.empty(size, int)             # <<<<<<<<<<<<<<
+ *         length = PyArray_SIZE(array)
+ *         array_data = <long *>array.data
+ */
+    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 319; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_kp_empty); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 319; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     Py_DECREF(__pyx_2); __pyx_2 = 0;
-    __pyx_2 = PyTuple_New(2); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 319; goto __pyx_L1;}
+    __pyx_2 = PyTuple_New(2); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 319; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     Py_INCREF(__pyx_v_size);
     PyTuple_SET_ITEM(__pyx_2, 0, __pyx_v_size);
-    Py_INCREF(((PyObject *)(&PyInt_Type)));
-    PyTuple_SET_ITEM(__pyx_2, 1, ((PyObject *)(&PyInt_Type)));
-    __pyx_4 = PyObject_CallObject(__pyx_3, __pyx_2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 319; goto __pyx_L1;}
+    Py_INCREF(((PyObject*)&PyInt_Type));
+    PyTuple_SET_ITEM(__pyx_2, 1, ((PyObject*)&PyInt_Type));
+    __pyx_4 = PyObject_Call(__pyx_3, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 319; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     Py_DECREF(__pyx_3); __pyx_3 = 0;
-    Py_DECREF(__pyx_2); __pyx_2 = 0;
+    Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0;
     Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_4)));
     Py_DECREF(((PyObject *)arrayObject));
     arrayObject = ((PyArrayObject *)__pyx_4);
     Py_DECREF(__pyx_4); __pyx_4 = 0;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":320 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":320
+ *     else:
+ *         array = <ndarray>np.empty(size, int)
+ *         length = PyArray_SIZE(array)             # <<<<<<<<<<<<<<
+ *         array_data = <long *>array.data
+ *         for i from 0 <= i < length:
+ */
     __pyx_v_length = PyArray_SIZE(arrayObject);
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":321 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":321
+ *         array = <ndarray>np.empty(size, int)
+ *         length = PyArray_SIZE(array)
+ *         array_data = <long *>array.data             # <<<<<<<<<<<<<<
+ *         for i from 0 <= i < length:
+ *             array_data[i] = func(state, n, p)
+ */
     __pyx_v_array_data = ((long *)arrayObject->data);
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":322 */
-    for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; ++__pyx_v_i) {
-      (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,__pyx_v_n,__pyx_v_p);
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":322
+ *         length = PyArray_SIZE(array)
+ *         array_data = <long *>array.data
+ *         for i from 0 <= i < length:             # <<<<<<<<<<<<<<
+ *             array_data[i] = func(state, n, p)
+ *         return array
+ */
+    for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; __pyx_v_i++) {
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":323
+ *         array_data = <long *>array.data
+ *         for i from 0 <= i < length:
+ *             array_data[i] = func(state, n, p)             # <<<<<<<<<<<<<<
+ *         return array
+ * 
+ */
+      (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state, __pyx_v_n, __pyx_v_p);
     }
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":324 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":324
+ *         for i from 0 <= i < length:
+ *             array_data[i] = func(state, n, p)
+ *         return array             # <<<<<<<<<<<<<<
+ * 
+ * cdef object discnp_array(rk_state *state, rk_discnp func, object size, ndarray on, ndarray op):
+ */
     Py_INCREF(((PyObject *)arrayObject));
     __pyx_r = ((PyObject *)arrayObject);
     goto __pyx_L0;
   }
-  __pyx_L2:;
+  __pyx_L3:;
 
   __pyx_r = Py_None; Py_INCREF(Py_None);
   goto __pyx_L0;
-  __pyx_L1:;
+  __pyx_L1_error:;
   Py_XDECREF(__pyx_2);
   Py_XDECREF(__pyx_3);
   Py_XDECREF(__pyx_4);
@@ -1140,15 +2350,18 @@ static PyObject *__pyx_f_6mtrand_discnp_array_sc(rk_state *__pyx_v_state,__pyx_t
   __pyx_r = 0;
   __pyx_L0:;
   Py_DECREF(arrayObject);
-  Py_DECREF(__pyx_v_size);
   return __pyx_r;
 }
 
-static PyObject *__pyx_k64p;
-
-static char __pyx_k64[] = "size is not compatible with inputs";
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":326
+ *         return array
+ * 
+ * cdef object discnp_array(rk_state *state, rk_discnp func, object size, ndarray on, ndarray op):             # <<<<<<<<<<<<<<
+ *     cdef long *array_data
+ *     cdef ndarray array "arrayObject"
+ */
 
-static PyObject *__pyx_f_6mtrand_discnp_array(rk_state *__pyx_v_state,__pyx_t_6mtrand_rk_discnp __pyx_v_func,PyObject *__pyx_v_size,PyArrayObject *__pyx_v_on,PyArrayObject *__pyx_v_op) {
+static  PyObject *__pyx_f_6mtrand_discnp_array(rk_state *__pyx_v_state, __pyx_t_6mtrand_rk_discnp __pyx_v_func, PyObject *__pyx_v_size, PyArrayObject *__pyx_v_on, PyArrayObject *__pyx_v_op) {
   long *__pyx_v_array_data;
   PyArrayObject *arrayObject;
   npy_intp __pyx_v_i;
@@ -1161,125 +2374,250 @@ static PyObject *__pyx_f_6mtrand_discnp_array(rk_state *__pyx_v_state,__pyx_t_6m
   npy_intp __pyx_3;
   PyObject *__pyx_4 = 0;
   PyObject *__pyx_5 = 0;
-  Py_INCREF(__pyx_v_size);
-  Py_INCREF(__pyx_v_on);
-  Py_INCREF(__pyx_v_op);
   arrayObject = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
   __pyx_v_multi = ((PyArrayMultiIterObject *)Py_None); Py_INCREF(Py_None);
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":335 */
-  __pyx_1 = __pyx_v_size == Py_None;
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":335
+ *     cdef broadcast multi
+ * 
+ *     if size is None:             # <<<<<<<<<<<<<<
+ *         multi = <broadcast> PyArray_MultiIterNew(2, <void *>on, <void *>op)
+ *         array = <ndarray> PyArray_SimpleNew(multi.nd, multi.dimensions, NPY_LONG)
+ */
+  __pyx_1 = (__pyx_v_size == Py_None);
   if (__pyx_1) {
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":336 */
-    __pyx_2 = PyArray_MultiIterNew(2,((void *)__pyx_v_on),((void *)__pyx_v_op)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 336; goto __pyx_L1;}
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":336
+ * 
+ *     if size is None:
+ *         multi = <broadcast> PyArray_MultiIterNew(2, <void *>on, <void *>op)             # <<<<<<<<<<<<<<
+ *         array = <ndarray> PyArray_SimpleNew(multi.nd, multi.dimensions, NPY_LONG)
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+ */
+    __pyx_2 = PyArray_MultiIterNew(2, ((void *)__pyx_v_on), ((void *)__pyx_v_op)); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 336; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     Py_INCREF(((PyObject *)((PyArrayMultiIterObject *)__pyx_2)));
     Py_DECREF(((PyObject *)__pyx_v_multi));
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     Py_DECREF(__pyx_2); __pyx_2 = 0;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":337 */
-    __pyx_2 = PyArray_SimpleNew(__pyx_v_multi->nd,__pyx_v_multi->dimensions,NPY_LONG); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 337; goto __pyx_L1;}
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":337
+ *     if size is None:
+ *         multi = <broadcast> PyArray_MultiIterNew(2, <void *>on, <void *>op)
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+ */
+    __pyx_2 = PyArray_SimpleNew(__pyx_v_multi->nd, __pyx_v_multi->dimensions, NPY_LONG); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 337; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_2)));
     Py_DECREF(((PyObject *)arrayObject));
     arrayObject = ((PyArrayObject *)__pyx_2);
     Py_DECREF(__pyx_2); __pyx_2 = 0;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":338 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":338
+ *         multi = <broadcast> PyArray_MultiIterNew(2, <void *>on, <void *>op)
+ *         array = <ndarray> PyArray_SimpleNew(multi.nd, multi.dimensions, NPY_LONG)
+ *         array_data = <long *>array.data             # <<<<<<<<<<<<<<
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+ *             on_data = <long *>PyArray_MultiIter_DATA(multi, 0)
+ */
     __pyx_v_array_data = ((long *)arrayObject->data);
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":339 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":339
+ *         array = <ndarray> PyArray_SimpleNew(multi.nd, multi.dimensions, NPY_LONG)
+ *         array_data = <long *>array.data
+ *         for i from 0 <= i < multi.size:             # <<<<<<<<<<<<<<
+ *             on_data = <long *>PyArray_MultiIter_DATA(multi, 0)
+ *             op_data = <double *>PyArray_MultiIter_DATA(multi, 1)
+ */
     __pyx_3 = __pyx_v_multi->size;
-    for (__pyx_v_i = 0; __pyx_v_i < __pyx_3; ++__pyx_v_i) {
-
-      /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":340 */
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-
-      /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":343 */
+    for (__pyx_v_i = 0; __pyx_v_i < __pyx_3; __pyx_v_i++) {
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":340
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":341
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":342
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+ *             op_data = <double *>PyArray_MultiIter_DATA(multi, 1)
+ *             array_data[i] = func(state, on_data[0], op_data[0])             # <<<<<<<<<<<<<<
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+ *     else:
+ */
+      (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state, (__pyx_v_on_data[0]), (__pyx_v_op_data[0]));
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":343
+ *             op_data = <double *>PyArray_MultiIter_DATA(multi, 1)
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+    goto __pyx_L3;
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":345
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+    __pyx_4 = PyObject_GetAttr(__pyx_2, __pyx_kp_empty); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 345; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+    __pyx_2 = PyTuple_New(2); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 345; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    Py_DECREF(__pyx_2); __pyx_2 = 0;
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":347
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":349
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-  __pyx_L2:;
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":357 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":357
+ *             PyArray_MultiIter_NEXTi(multi, 2)
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   __pyx_r = Py_None; Py_INCREF(Py_None);
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   Py_XDECREF(__pyx_5);
@@ -1288,13 +2626,18 @@ static PyObject *__pyx_f_6mtrand_discnp_array(rk_state *__pyx_v_state,__pyx_t_6m
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+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":359
+ *     return array
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     Py_DECREF(__pyx_4); __pyx_4 = 0;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":369 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":369
+ *     else:
+ *         array = <ndarray>np.empty(size, int)
+ *         length = PyArray_SIZE(array)             # <<<<<<<<<<<<<<
+ *         array_data = <long *>array.data
+ *         for i from 0 <= i < length:
+ */
     __pyx_v_length = PyArray_SIZE(arrayObject);
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":370 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":370
+ *         array = <ndarray>np.empty(size, int)
+ *         length = PyArray_SIZE(array)
+ *         array_data = <long *>array.data             # <<<<<<<<<<<<<<
+ *         for i from 0 <= i < length:
+ *             array_data[i] = func(state, n, p)
+ */
     __pyx_v_array_data = ((long *)arrayObject->data);
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":371 */
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-      (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,__pyx_v_n,__pyx_v_p);
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":371
+ *         length = PyArray_SIZE(array)
+ *         array_data = <long *>array.data
+ *         for i from 0 <= i < length:             # <<<<<<<<<<<<<<
+ *             array_data[i] = func(state, n, p)
+ *         return array
+ */
+    for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; __pyx_v_i++) {
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":372
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+ *         for i from 0 <= i < length:
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+ *         return array
+ * 
+ */
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":373 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":373
+ *         for i from 0 <= i < length:
+ *             array_data[i] = func(state, n, p)
+ *         return array             # <<<<<<<<<<<<<<
+ * 
+ * cdef object discdd_array(rk_state *state, rk_discdd func, object size, ndarray on, ndarray op):
+ */
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     goto __pyx_L0;
   }
-  __pyx_L2:;
+  __pyx_L3:;
 
   __pyx_r = Py_None; Py_INCREF(Py_None);
   goto __pyx_L0;
-  __pyx_L1:;
+  __pyx_L1_error:;
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+ *     cdef ndarray array "arrayObject"
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":387
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-      /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":392 */
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":392
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":394 */
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-      PyArray_MultiIter_NEXTi(__pyx_v_multi,1);
-
-      /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":404 */
-      PyArray_MultiIter_NEXTi(__pyx_v_multi,2);
+    for (__pyx_v_i = 0; __pyx_v_i < __pyx_3; __pyx_v_i++) {
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":400
+ *             raise ValueError("size is not compatible with inputs")
+ *         for i from 0 <= i < multi.size:
+ *             on_data = <double *>PyArray_MultiIter_DATA(multi, 1)             # <<<<<<<<<<<<<<
+ *             op_data = <double *>PyArray_MultiIter_DATA(multi, 2)
+ *             array_data[i] = func(state, on_data[0], op_data[0])
+ */
+      __pyx_v_on_data = ((double *)PyArray_MultiIter_DATA(__pyx_v_multi, 1));
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":401
+ *         for i from 0 <= i < multi.size:
+ *             on_data = <double *>PyArray_MultiIter_DATA(multi, 1)
+ *             op_data = <double *>PyArray_MultiIter_DATA(multi, 2)             # <<<<<<<<<<<<<<
+ *             array_data[i] = func(state, on_data[0], op_data[0])
+ *             PyArray_MultiIter_NEXTi(multi, 1)
+ */
+      __pyx_v_op_data = ((double *)PyArray_MultiIter_DATA(__pyx_v_multi, 2));
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":402
+ *             on_data = <double *>PyArray_MultiIter_DATA(multi, 1)
+ *             op_data = <double *>PyArray_MultiIter_DATA(multi, 2)
+ *             array_data[i] = func(state, on_data[0], op_data[0])             # <<<<<<<<<<<<<<
+ *             PyArray_MultiIter_NEXTi(multi, 1)
+ *             PyArray_MultiIter_NEXTi(multi, 2)
+ */
+      (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state, (__pyx_v_on_data[0]), (__pyx_v_op_data[0]));
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":403
+ *             op_data = <double *>PyArray_MultiIter_DATA(multi, 2)
+ *             array_data[i] = func(state, on_data[0], op_data[0])
+ *             PyArray_MultiIter_NEXTi(multi, 1)             # <<<<<<<<<<<<<<
+ *             PyArray_MultiIter_NEXTi(multi, 2)
+ * 
+ */
+      PyArray_MultiIter_NEXTi(__pyx_v_multi, 1);
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":404
+ *             array_data[i] = func(state, on_data[0], op_data[0])
+ *             PyArray_MultiIter_NEXTi(multi, 1)
+ *             PyArray_MultiIter_NEXTi(multi, 2)             # <<<<<<<<<<<<<<
+ * 
+ *     return array
+ */
+      PyArray_MultiIter_NEXTi(__pyx_v_multi, 2);
     }
   }
-  __pyx_L2:;
+  __pyx_L3:;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":406 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":406
+ *             PyArray_MultiIter_NEXTi(multi, 2)
+ * 
+ *     return array             # <<<<<<<<<<<<<<
+ * 
+ * cdef object discnmN_array_sc(rk_state *state, rk_discnmN func, object size,
+ */
   Py_INCREF(((PyObject *)arrayObject));
   __pyx_r = ((PyObject *)arrayObject);
   goto __pyx_L0;
 
   __pyx_r = Py_None; Py_INCREF(Py_None);
   goto __pyx_L0;
-  __pyx_L1:;
+  __pyx_L1_error:;
   Py_XDECREF(__pyx_2);
   Py_XDECREF(__pyx_4);
   Py_XDECREF(__pyx_5);
@@ -1511,13 +3033,18 @@ static PyObject *__pyx_f_6mtrand_discdd_array(rk_state *__pyx_v_state,__pyx_t_6m
   __pyx_L0:;
   Py_DECREF(arrayObject);
   Py_DECREF(__pyx_v_multi);
-  Py_DECREF(__pyx_v_size);
-  Py_DECREF(__pyx_v_on);
-  Py_DECREF(__pyx_v_op);
   return __pyx_r;
 }
 
-static PyObject *__pyx_f_6mtrand_discnmN_array_sc(rk_state *__pyx_v_state,__pyx_t_6mtrand_rk_discnmN __pyx_v_func,PyObject *__pyx_v_size,long __pyx_v_n,long __pyx_v_m,long __pyx_v_N) {
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":408
+ *     return array
+ * 
+ * cdef object discnmN_array_sc(rk_state *state, rk_discnmN func, object size,             # <<<<<<<<<<<<<<
+ *     long n, long m, long N):
+ *     cdef long *array_data
+ */
+
+static  PyObject *__pyx_f_6mtrand_discnmN_array_sc(rk_state *__pyx_v_state, __pyx_t_6mtrand_rk_discnmN __pyx_v_func, PyObject *__pyx_v_size, long __pyx_v_n, long __pyx_v_m, long __pyx_v_N) {
   long *__pyx_v_array_data;
   PyArrayObject *arrayObject;
   long __pyx_v_length;
@@ -1527,58 +3054,109 @@ static PyObject *__pyx_f_6mtrand_discnmN_array_sc(rk_state *__pyx_v_state,__pyx_
   PyObject *__pyx_2 = 0;
   PyObject *__pyx_3 = 0;
   PyObject *__pyx_4 = 0;
-  Py_INCREF(__pyx_v_size);
   arrayObject = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":415 */
-  __pyx_1 = __pyx_v_size == Py_None;
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":415
+ *     cdef long i
+ * 
+ *     if size is None:             # <<<<<<<<<<<<<<
+ *         return func(state, n, m, N)
+ *     else:
+ */
+  __pyx_1 = (__pyx_v_size == Py_None);
   if (__pyx_1) {
-    __pyx_2 = PyInt_FromLong(__pyx_v_func(__pyx_v_state,__pyx_v_n,__pyx_v_m,__pyx_v_N)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 416; goto __pyx_L1;}
+
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":416
+ * 
+ *     if size is None:
+ *         return func(state, n, m, N)             # <<<<<<<<<<<<<<
+ *     else:
+ *         array = <ndarray>np.empty(size, int)
+ */
+    __pyx_2 = PyInt_FromLong(__pyx_v_func(__pyx_v_state, __pyx_v_n, __pyx_v_m, __pyx_v_N)); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 416; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     __pyx_r = __pyx_2;
     __pyx_2 = 0;
     goto __pyx_L0;
-    goto __pyx_L2;
+    goto __pyx_L3;
   }
   /*else*/ {
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":418 */
-    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n_np); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 418; goto __pyx_L1;}
-    __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_n_empty); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 418; goto __pyx_L1;}
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":418
+ *         return func(state, n, m, N)
+ *     else:
+ *         array = <ndarray>np.empty(size, int)             # <<<<<<<<<<<<<<
+ *         length = PyArray_SIZE(array)
+ *         array_data = <long *>array.data
+ */
+    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 418; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_kp_empty); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 418; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     Py_DECREF(__pyx_2); __pyx_2 = 0;
-    __pyx_2 = PyTuple_New(2); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 418; goto __pyx_L1;}
+    __pyx_2 = PyTuple_New(2); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 418; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     Py_INCREF(__pyx_v_size);
     PyTuple_SET_ITEM(__pyx_2, 0, __pyx_v_size);
-    Py_INCREF(((PyObject *)(&PyInt_Type)));
-    PyTuple_SET_ITEM(__pyx_2, 1, ((PyObject *)(&PyInt_Type)));
-    __pyx_4 = PyObject_CallObject(__pyx_3, __pyx_2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 418; goto __pyx_L1;}
+    Py_INCREF(((PyObject*)&PyInt_Type));
+    PyTuple_SET_ITEM(__pyx_2, 1, ((PyObject*)&PyInt_Type));
+    __pyx_4 = PyObject_Call(__pyx_3, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 418; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     Py_DECREF(__pyx_3); __pyx_3 = 0;
-    Py_DECREF(__pyx_2); __pyx_2 = 0;
+    Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0;
     Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_4)));
     Py_DECREF(((PyObject *)arrayObject));
     arrayObject = ((PyArrayObject *)__pyx_4);
     Py_DECREF(__pyx_4); __pyx_4 = 0;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":419 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":419
+ *     else:
+ *         array = <ndarray>np.empty(size, int)
+ *         length = PyArray_SIZE(array)             # <<<<<<<<<<<<<<
+ *         array_data = <long *>array.data
+ *         for i from 0 <= i < length:
+ */
     __pyx_v_length = PyArray_SIZE(arrayObject);
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":420 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":420
+ *         array = <ndarray>np.empty(size, int)
+ *         length = PyArray_SIZE(array)
+ *         array_data = <long *>array.data             # <<<<<<<<<<<<<<
+ *         for i from 0 <= i < length:
+ *             array_data[i] = func(state, n, m, N)
+ */
     __pyx_v_array_data = ((long *)arrayObject->data);
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":421 */
-    for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; ++__pyx_v_i) {
-      (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,__pyx_v_n,__pyx_v_m,__pyx_v_N);
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":421
+ *         length = PyArray_SIZE(array)
+ *         array_data = <long *>array.data
+ *         for i from 0 <= i < length:             # <<<<<<<<<<<<<<
+ *             array_data[i] = func(state, n, m, N)
+ *         return array
+ */
+    for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; __pyx_v_i++) {
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":422
+ *         array_data = <long *>array.data
+ *         for i from 0 <= i < length:
+ *             array_data[i] = func(state, n, m, N)             # <<<<<<<<<<<<<<
+ *         return array
+ * 
+ */
+      (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state, __pyx_v_n, __pyx_v_m, __pyx_v_N);
     }
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":423 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":423
+ *         for i from 0 <= i < length:
+ *             array_data[i] = func(state, n, m, N)
+ *         return array             # <<<<<<<<<<<<<<
+ * 
+ * cdef object discnmN_array(rk_state *state, rk_discnmN func, object size,
+ */
     Py_INCREF(((PyObject *)arrayObject));
     __pyx_r = ((PyObject *)arrayObject);
     goto __pyx_L0;
   }
-  __pyx_L2:;
+  __pyx_L3:;
 
   __pyx_r = Py_None; Py_INCREF(Py_None);
   goto __pyx_L0;
-  __pyx_L1:;
+  __pyx_L1_error:;
   Py_XDECREF(__pyx_2);
   Py_XDECREF(__pyx_3);
   Py_XDECREF(__pyx_4);
@@ -1586,15 +3164,18 @@ static PyObject *__pyx_f_6mtrand_discnmN_array_sc(rk_state *__pyx_v_state,__pyx_
   __pyx_r = 0;
   __pyx_L0:;
   Py_DECREF(arrayObject);
-  Py_DECREF(__pyx_v_size);
   return __pyx_r;
 }
 
-static PyObject *__pyx_k66p;
-
-static char __pyx_k66[] = "size is not compatible with inputs";
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":425
+ *         return array
+ * 
+ * cdef object discnmN_array(rk_state *state, rk_discnmN func, object size,             # <<<<<<<<<<<<<<
+ *     ndarray on, ndarray om, ndarray oN):
+ *     cdef long *array_data
+ */
 
-static PyObject *__pyx_f_6mtrand_discnmN_array(rk_state *__pyx_v_state,__pyx_t_6mtrand_rk_discnmN __pyx_v_func,PyObject *__pyx_v_size,PyArrayObject *__pyx_v_on,PyArrayObject *__pyx_v_om,PyArrayObject *__pyx_v_oN) {
+static  PyObject *__pyx_f_6mtrand_discnmN_array(rk_state *__pyx_v_state, __pyx_t_6mtrand_rk_discnmN __pyx_v_func, PyObject *__pyx_v_size, PyArrayObject *__pyx_v_on, PyArrayObject *__pyx_v_om, PyArrayObject *__pyx_v_oN) {
   long *__pyx_v_array_data;
   long *__pyx_v_on_data;
   long *__pyx_v_om_data;
@@ -1608,129 +3189,259 @@ static PyObject *__pyx_f_6mtrand_discnmN_array(rk_state *__pyx_v_state,__pyx_t_6
   npy_intp __pyx_3;
   PyObject *__pyx_4 = 0;
   PyObject *__pyx_5 = 0;
-  Py_INCREF(__pyx_v_size);
-  Py_INCREF(__pyx_v_on);
-  Py_INCREF(__pyx_v_om);
-  Py_INCREF(__pyx_v_oN);
   arrayObject = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
   __pyx_v_multi = ((PyArrayMultiIterObject *)Py_None); Py_INCREF(Py_None);
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":436 */
-  __pyx_1 = __pyx_v_size == Py_None;
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":436
+ *     cdef broadcast multi
+ * 
+ *     if size is None:             # <<<<<<<<<<<<<<
+ *         multi = <broadcast> PyArray_MultiIterNew(3, <void *>on, <void *>om, <void *>oN)
+ *         array = <ndarray> PyArray_SimpleNew(multi.nd, multi.dimensions, NPY_LONG)
+ */
+  __pyx_1 = (__pyx_v_size == Py_None);
   if (__pyx_1) {
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":437 */
-    __pyx_2 = PyArray_MultiIterNew(3,((void *)__pyx_v_on),((void *)__pyx_v_om),((void *)__pyx_v_oN)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 437; goto __pyx_L1;}
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":437
+ * 
+ *     if size is None:
+ *         multi = <broadcast> PyArray_MultiIterNew(3, <void *>on, <void *>om, <void *>oN)             # <<<<<<<<<<<<<<
+ *         array = <ndarray> PyArray_SimpleNew(multi.nd, multi.dimensions, NPY_LONG)
+ *         array_data = <long *>array.data
+ */
+    __pyx_2 = PyArray_MultiIterNew(3, ((void *)__pyx_v_on), ((void *)__pyx_v_om), ((void *)__pyx_v_oN)); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 437; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     Py_INCREF(((PyObject *)((PyArrayMultiIterObject *)__pyx_2)));
     Py_DECREF(((PyObject *)__pyx_v_multi));
     __pyx_v_multi = ((PyArrayMultiIterObject *)__pyx_2);
     Py_DECREF(__pyx_2); __pyx_2 = 0;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":438 */
-    __pyx_2 = PyArray_SimpleNew(__pyx_v_multi->nd,__pyx_v_multi->dimensions,NPY_LONG); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 438; goto __pyx_L1;}
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":438
+ *     if size is None:
+ *         multi = <broadcast> PyArray_MultiIterNew(3, <void *>on, <void *>om, <void *>oN)
+ *         array = <ndarray> PyArray_SimpleNew(multi.nd, multi.dimensions, NPY_LONG)             # <<<<<<<<<<<<<<
+ *         array_data = <long *>array.data
+ *         for i from 0 <= i < multi.size:
+ */
+    __pyx_2 = PyArray_SimpleNew(__pyx_v_multi->nd, __pyx_v_multi->dimensions, NPY_LONG); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 438; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_2)));
     Py_DECREF(((PyObject *)arrayObject));
     arrayObject = ((PyArrayObject *)__pyx_2);
     Py_DECREF(__pyx_2); __pyx_2 = 0;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":439 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":439
+ *         multi = <broadcast> PyArray_MultiIterNew(3, <void *>on, <void *>om, <void *>oN)
+ *         array = <ndarray> PyArray_SimpleNew(multi.nd, multi.dimensions, NPY_LONG)
+ *         array_data = <long *>array.data             # <<<<<<<<<<<<<<
+ *         for i from 0 <= i < multi.size:
+ *             on_data = <long *>PyArray_MultiIter_DATA(multi, 0)
+ */
     __pyx_v_array_data = ((long *)arrayObject->data);
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":440 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":440
+ *         array = <ndarray> PyArray_SimpleNew(multi.nd, multi.dimensions, NPY_LONG)
+ *         array_data = <long *>array.data
+ *         for i from 0 <= i < multi.size:             # <<<<<<<<<<<<<<
+ *             on_data = <long *>PyArray_MultiIter_DATA(multi, 0)
+ *             om_data = <long *>PyArray_MultiIter_DATA(multi, 1)
+ */
     __pyx_3 = __pyx_v_multi->size;
-    for (__pyx_v_i = 0; __pyx_v_i < __pyx_3; ++__pyx_v_i) {
-
-      /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":441 */
-      __pyx_v_on_data = ((long *)PyArray_MultiIter_DATA(__pyx_v_multi,0));
-
-      /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":442 */
-      __pyx_v_om_data = ((long *)PyArray_MultiIter_DATA(__pyx_v_multi,1));
-
-      /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":443 */
-      __pyx_v_oN_data = ((long *)PyArray_MultiIter_DATA(__pyx_v_multi,2));
-
-      /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":444 */
-      (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,(__pyx_v_on_data[0]),(__pyx_v_om_data[0]),(__pyx_v_oN_data[0]));
-
-      /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":445 */
+    for (__pyx_v_i = 0; __pyx_v_i < __pyx_3; __pyx_v_i++) {
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":441
+ *         array_data = <long *>array.data
+ *         for i from 0 <= i < multi.size:
+ *             on_data = <long *>PyArray_MultiIter_DATA(multi, 0)             # <<<<<<<<<<<<<<
+ *             om_data = <long *>PyArray_MultiIter_DATA(multi, 1)
+ *             oN_data = <long *>PyArray_MultiIter_DATA(multi, 2)
+ */
+      __pyx_v_on_data = ((long *)PyArray_MultiIter_DATA(__pyx_v_multi, 0));
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":442
+ *         for i from 0 <= i < multi.size:
+ *             on_data = <long *>PyArray_MultiIter_DATA(multi, 0)
+ *             om_data = <long *>PyArray_MultiIter_DATA(multi, 1)             # <<<<<<<<<<<<<<
+ *             oN_data = <long *>PyArray_MultiIter_DATA(multi, 2)
+ *             array_data[i] = func(state, on_data[0], om_data[0], oN_data[0])
+ */
+      __pyx_v_om_data = ((long *)PyArray_MultiIter_DATA(__pyx_v_multi, 1));
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":443
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+ *             oN_data = <long *>PyArray_MultiIter_DATA(multi, 2)             # <<<<<<<<<<<<<<
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+ */
+      __pyx_v_oN_data = ((long *)PyArray_MultiIter_DATA(__pyx_v_multi, 2));
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":444
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+ *             array_data[i] = func(state, on_data[0], om_data[0], oN_data[0])             # <<<<<<<<<<<<<<
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+ *     else:
+ */
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+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":445
+ *             oN_data = <long *>PyArray_MultiIter_DATA(multi, 2)
+ *             array_data[i] = func(state, on_data[0], om_data[0], oN_data[0])
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+ */
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-    goto __pyx_L2;
+    goto __pyx_L3;
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":447
+ *             PyArray_MultiIter_NEXT(multi)
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+ *         array = <ndarray>np.empty(size, int)             # <<<<<<<<<<<<<<
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+    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 447; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_4 = PyObject_GetAttr(__pyx_2, __pyx_kp_empty); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 447; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    __pyx_2 = PyTuple_New(2); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 447; goto __pyx_L1;}
+    __pyx_2 = PyTuple_New(2); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 447; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+    Py_INCREF(((PyObject*)&PyInt_Type));
+    PyTuple_SET_ITEM(__pyx_2, 1, ((PyObject*)&PyInt_Type));
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     Py_DECREF(__pyx_4); __pyx_4 = 0;
-    Py_DECREF(__pyx_2); __pyx_2 = 0;
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":448 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":448
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":450
+ *         array_data = <long *>array.data
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+ *                                                 <void *>oN)             # <<<<<<<<<<<<<<
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":451 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":451
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+ */
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":452
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 452; goto __pyx_L1;}
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-    __pyx_L5:;
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-      /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":458 */
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":455
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":458
+ *             oN_data = <long *>PyArray_MultiIter_DATA(multi, 3)
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-  __pyx_L2:;
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":460 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":460
+ *             PyArray_MultiIter_NEXT(multi)
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+ *     return array             # <<<<<<<<<<<<<<
+ * 
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   __pyx_r = Py_None; Py_INCREF(Py_None);
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-  __pyx_L1:;
+  __pyx_L1_error:;
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   Py_XDECREF(__pyx_4);
   Py_XDECREF(__pyx_5);
@@ -1739,14 +3450,18 @@ static PyObject *__pyx_f_6mtrand_discnmN_array(rk_state *__pyx_v_state,__pyx_t_6
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+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":462
+ *     return array
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+ * cdef object discd_array_sc(rk_state *state, rk_discd func, object size, double a):             # <<<<<<<<<<<<<<
+ *     cdef long *array_data
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-  __pyx_1 = __pyx_v_size == Py_None;
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":468
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-    __pyx_2 = PyTuple_New(2); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 471; goto __pyx_L1;}
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     Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_4)));
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":472 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":472
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":473 */
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+ *         length = PyArray_SIZE(array)
+ *         array_data = <long *>array.data             # <<<<<<<<<<<<<<
+ *         for i from 0 <= i < length:
+ *             array_data[i] = func(state, a)
+ */
     __pyx_v_array_data = ((long *)arrayObject->data);
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":474 */
-    for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; ++__pyx_v_i) {
-      (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,__pyx_v_a);
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":474
+ *         length = PyArray_SIZE(array)
+ *         array_data = <long *>array.data
+ *         for i from 0 <= i < length:             # <<<<<<<<<<<<<<
+ *             array_data[i] = func(state, a)
+ *         return array
+ */
+    for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; __pyx_v_i++) {
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":475
+ *         array_data = <long *>array.data
+ *         for i from 0 <= i < length:
+ *             array_data[i] = func(state, a)             # <<<<<<<<<<<<<<
+ *         return array
+ * 
+ */
+      (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state, __pyx_v_a);
     }
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":476 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":476
+ *         for i from 0 <= i < length:
+ *             array_data[i] = func(state, a)
+ *         return array             # <<<<<<<<<<<<<<
+ * 
+ * cdef object discd_array(rk_state *state, rk_discd func, object size, ndarray oa):
+ */
     Py_INCREF(((PyObject *)arrayObject));
     __pyx_r = ((PyObject *)arrayObject);
     goto __pyx_L0;
   }
-  __pyx_L2:;
+  __pyx_L3:;
 
   __pyx_r = Py_None; Py_INCREF(Py_None);
   goto __pyx_L0;
-  __pyx_L1:;
+  __pyx_L1_error:;
   Py_XDECREF(__pyx_2);
   Py_XDECREF(__pyx_3);
   Py_XDECREF(__pyx_4);
@@ -1815,15 +3581,18 @@ static PyObject *__pyx_f_6mtrand_discd_array_sc(rk_state *__pyx_v_state,__pyx_t_
   __pyx_r = 0;
   __pyx_L0:;
   Py_DECREF(arrayObject);
-  Py_DECREF(__pyx_v_size);
   return __pyx_r;
 }
 
-static PyObject *__pyx_k67p;
-
-static char __pyx_k67[] = "size is not compatible with inputs";
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":478
+ *         return array
+ * 
+ * cdef object discd_array(rk_state *state, rk_discd func, object size, ndarray oa):             # <<<<<<<<<<<<<<
+ *     cdef long *array_data
+ *     cdef double *oa_data
+ */
 
-static PyObject *__pyx_f_6mtrand_discd_array(rk_state *__pyx_v_state,__pyx_t_6mtrand_rk_discd __pyx_v_func,PyObject *__pyx_v_size,PyArrayObject *__pyx_v_oa) {
+static  PyObject *__pyx_f_6mtrand_discd_array(rk_state *__pyx_v_state, __pyx_t_6mtrand_rk_discd __pyx_v_func, PyObject *__pyx_v_size, PyArrayObject *__pyx_v_oa) {
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   double *__pyx_v_oa_data;
   PyArrayObject *arrayObject;
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   PyObject *__pyx_4 = 0;
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":487 */
-  __pyx_1 = __pyx_v_size == Py_None;
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":487
+ *     cdef flatiter itera
+ * 
+ *     if size is None:             # <<<<<<<<<<<<<<
+ *         array = <ndarray>PyArray_SimpleNew(oa.nd, oa.dimensions, NPY_LONG)
+ *         length = PyArray_SIZE(array)
+ */
+  __pyx_1 = (__pyx_v_size == Py_None);
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":488 */
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":488
+ * 
+ *     if size is None:
+ *         array = <ndarray>PyArray_SimpleNew(oa.nd, oa.dimensions, NPY_LONG)             # <<<<<<<<<<<<<<
+ *         length = PyArray_SIZE(array)
+ *         array_data = <long *>array.data
+ */
+    __pyx_2 = PyArray_SimpleNew(__pyx_v_oa->nd, __pyx_v_oa->dimensions, NPY_LONG); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 488; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_2)));
     Py_DECREF(((PyObject *)arrayObject));
     arrayObject = ((PyArrayObject *)__pyx_2);
     Py_DECREF(__pyx_2); __pyx_2 = 0;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":489 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":489
+ *     if size is None:
+ *         array = <ndarray>PyArray_SimpleNew(oa.nd, oa.dimensions, NPY_LONG)
+ *         length = PyArray_SIZE(array)             # <<<<<<<<<<<<<<
+ *         array_data = <long *>array.data
+ *         itera = <flatiter>PyArray_IterNew(<object>oa)
+ */
     __pyx_v_length = PyArray_SIZE(arrayObject);
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":490 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":490
+ *         array = <ndarray>PyArray_SimpleNew(oa.nd, oa.dimensions, NPY_LONG)
+ *         length = PyArray_SIZE(array)
+ *         array_data = <long *>array.data             # <<<<<<<<<<<<<<
+ *         itera = <flatiter>PyArray_IterNew(<object>oa)
+ *         for i from 0 <= i < length:
+ */
     __pyx_v_array_data = ((long *)arrayObject->data);
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":491 */
-    __pyx_2 = PyArray_IterNew(((PyObject *)__pyx_v_oa)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 491; goto __pyx_L1;}
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":491
+ *         length = PyArray_SIZE(array)
+ *         array_data = <long *>array.data
+ *         itera = <flatiter>PyArray_IterNew(<object>oa)             # <<<<<<<<<<<<<<
+ *         for i from 0 <= i < length:
+ *             array_data[i] = func(state, (<double *>(itera.dataptr))[0])
+ */
+    __pyx_2 = PyArray_IterNew(((PyObject *)__pyx_v_oa)); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 491; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     Py_INCREF(((PyObject *)((PyArrayIterObject *)__pyx_2)));
     Py_DECREF(((PyObject *)__pyx_v_itera));
     __pyx_v_itera = ((PyArrayIterObject *)__pyx_2);
     Py_DECREF(__pyx_2); __pyx_2 = 0;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":492 */
-    for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; ++__pyx_v_i) {
-
-      /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":493 */
-      (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,(((double *)__pyx_v_itera->dataptr)[0]));
-
-      /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":494 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":492
+ *         array_data = <long *>array.data
+ *         itera = <flatiter>PyArray_IterNew(<object>oa)
+ *         for i from 0 <= i < length:             # <<<<<<<<<<<<<<
+ *             array_data[i] = func(state, (<double *>(itera.dataptr))[0])
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+ */
+    for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; __pyx_v_i++) {
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":493
+ *         itera = <flatiter>PyArray_IterNew(<object>oa)
+ *         for i from 0 <= i < length:
+ *             array_data[i] = func(state, (<double *>(itera.dataptr))[0])             # <<<<<<<<<<<<<<
+ *             PyArray_ITER_NEXT(itera)
+ *     else:
+ */
+      (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state, (((double *)__pyx_v_itera->dataptr)[0]));
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":494
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+ *             array_data[i] = func(state, (<double *>(itera.dataptr))[0])
+ *             PyArray_ITER_NEXT(itera)             # <<<<<<<<<<<<<<
+ *     else:
+ *         array = <ndarray>np.empty(size, int)
+ */
       PyArray_ITER_NEXT(__pyx_v_itera);
     }
-    goto __pyx_L2;
+    goto __pyx_L3;
   }
   /*else*/ {
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":496 */
-    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n_np); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 496; goto __pyx_L1;}
-    __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_n_empty); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 496; goto __pyx_L1;}
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":496
+ *             PyArray_ITER_NEXT(itera)
+ *     else:
+ *         array = <ndarray>np.empty(size, int)             # <<<<<<<<<<<<<<
+ *         array_data = <long *>array.data
+ *         multi = <broadcast>PyArray_MultiIterNew(2, <void *>array, <void *>oa)
+ */
+    __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 496; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_kp_empty); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 496; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     Py_DECREF(__pyx_2); __pyx_2 = 0;
-    __pyx_2 = PyTuple_New(2); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 496; goto __pyx_L1;}
+    __pyx_2 = PyTuple_New(2); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 496; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     Py_INCREF(__pyx_v_size);
     PyTuple_SET_ITEM(__pyx_2, 0, __pyx_v_size);
-    Py_INCREF(((PyObject *)(&PyInt_Type)));
-    PyTuple_SET_ITEM(__pyx_2, 1, ((PyObject *)(&PyInt_Type)));
-    __pyx_4 = PyObject_CallObject(__pyx_3, __pyx_2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 496; goto __pyx_L1;}
+    Py_INCREF(((PyObject*)&PyInt_Type));
+    PyTuple_SET_ITEM(__pyx_2, 1, ((PyObject*)&PyInt_Type));
+    __pyx_4 = PyObject_Call(__pyx_3, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 496; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     Py_DECREF(__pyx_3); __pyx_3 = 0;
-    Py_DECREF(__pyx_2); __pyx_2 = 0;
+    Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0;
     Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_4)));
     Py_DECREF(((PyObject *)arrayObject));
     arrayObject = ((PyArrayObject *)__pyx_4);
     Py_DECREF(__pyx_4); __pyx_4 = 0;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":497 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":497
+ *     else:
+ *         array = <ndarray>np.empty(size, int)
+ *         array_data = <long *>array.data             # <<<<<<<<<<<<<<
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+ *         if (multi.size != PyArray_SIZE(array)):
+ */
     __pyx_v_array_data = ((long *)arrayObject->data);
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":498 */
-    __pyx_3 = PyArray_MultiIterNew(2,((void *)arrayObject),((void *)__pyx_v_oa)); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 498; goto __pyx_L1;}
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":498
+ *         array = <ndarray>np.empty(size, int)
+ *         array_data = <long *>array.data
+ *         multi = <broadcast>PyArray_MultiIterNew(2, <void *>array, <void *>oa)             # <<<<<<<<<<<<<<
+ *         if (multi.size != PyArray_SIZE(array)):
+ *             raise ValueError("size is not compatible with inputs")
+ */
+    __pyx_3 = PyArray_MultiIterNew(2, ((void *)arrayObject), ((void *)__pyx_v_oa)); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 498; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     Py_INCREF(((PyObject *)((PyArrayMultiIterObject *)__pyx_3)));
     Py_DECREF(((PyObject *)__pyx_v_multi));
     __pyx_v_multi = ((PyArrayMultiIterObject *)__pyx_3);
     Py_DECREF(__pyx_3); __pyx_3 = 0;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":499 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":499
+ *         array_data = <long *>array.data
+ *         multi = <broadcast>PyArray_MultiIterNew(2, <void *>array, <void *>oa)
+ *         if (multi.size != PyArray_SIZE(array)):             # <<<<<<<<<<<<<<
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+ *         for i from 0 <= i < multi.size:
+ */
     __pyx_1 = (__pyx_v_multi->size != PyArray_SIZE(arrayObject));
     if (__pyx_1) {
-      __pyx_2 = PyTuple_New(1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 500; goto __pyx_L1;}
-      Py_INCREF(__pyx_k67p);
-      PyTuple_SET_ITEM(__pyx_2, 0, __pyx_k67p);
-      __pyx_4 = PyObject_CallObject(PyExc_ValueError, __pyx_2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 500; goto __pyx_L1;}
-      Py_DECREF(__pyx_2); __pyx_2 = 0;
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":500
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+      Py_INCREF(__pyx_kp_26);
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+      Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0;
       __Pyx_Raise(__pyx_4, 0, 0);
       Py_DECREF(__pyx_4); __pyx_4 = 0;
-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 500; goto __pyx_L1;}
-      goto __pyx_L5;
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 500; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      goto __pyx_L6;
     }
-    __pyx_L5:;
+    __pyx_L6:;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":501 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":501
+ *         if (multi.size != PyArray_SIZE(array)):
+ *             raise ValueError("size is not compatible with inputs")
+ *         for i from 0 <= i < multi.size:             # <<<<<<<<<<<<<<
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     __pyx_5 = __pyx_v_multi->size;
-    for (__pyx_v_i = 0; __pyx_v_i < __pyx_5; ++__pyx_v_i) {
-
-      /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":502 */
-      __pyx_v_oa_data = ((double *)PyArray_MultiIter_DATA(__pyx_v_multi,1));
-
-      /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":503 */
-      (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,(__pyx_v_oa_data[0]));
-
-      /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":504 */
-      PyArray_MultiIter_NEXTi(__pyx_v_multi,1);
+    for (__pyx_v_i = 0; __pyx_v_i < __pyx_5; __pyx_v_i++) {
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":502
+ *             raise ValueError("size is not compatible with inputs")
+ *         for i from 0 <= i < multi.size:
+ *             oa_data = <double *>PyArray_MultiIter_DATA(multi, 1)             # <<<<<<<<<<<<<<
+ *             array_data[i] = func(state, oa_data[0])
+ *             PyArray_MultiIter_NEXTi(multi, 1)
+ */
+      __pyx_v_oa_data = ((double *)PyArray_MultiIter_DATA(__pyx_v_multi, 1));
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":503
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+ *             oa_data = <double *>PyArray_MultiIter_DATA(multi, 1)
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+ *             PyArray_MultiIter_NEXTi(multi, 1)
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+ */
+      (__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state, (__pyx_v_oa_data[0]));
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":504
+ *             oa_data = <double *>PyArray_MultiIter_DATA(multi, 1)
+ *             array_data[i] = func(state, oa_data[0])
+ *             PyArray_MultiIter_NEXTi(multi, 1)             # <<<<<<<<<<<<<<
+ *     return array
+ * 
+ */
+      PyArray_MultiIter_NEXTi(__pyx_v_multi, 1);
     }
   }
-  __pyx_L2:;
+  __pyx_L3:;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":505 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":505
+ *             array_data[i] = func(state, oa_data[0])
+ *             PyArray_MultiIter_NEXTi(multi, 1)
+ *     return array             # <<<<<<<<<<<<<<
+ * 
+ * cdef double kahan_sum(double *darr, long n):
+ */
   Py_INCREF(((PyObject *)arrayObject));
   __pyx_r = ((PyObject *)arrayObject);
   goto __pyx_L0;
 
   __pyx_r = Py_None; Py_INCREF(Py_None);
   goto __pyx_L0;
-  __pyx_L1:;
+  __pyx_L1_error:;
   Py_XDECREF(__pyx_2);
   Py_XDECREF(__pyx_3);
   Py_XDECREF(__pyx_4);
@@ -1955,12 +3832,18 @@ static PyObject *__pyx_f_6mtrand_discd_array(rk_state *__pyx_v_state,__pyx_t_6mt
   Py_DECREF(arrayObject);
   Py_DECREF(__pyx_v_multi);
   Py_DECREF(__pyx_v_itera);
-  Py_DECREF(__pyx_v_size);
-  Py_DECREF(__pyx_v_oa);
   return __pyx_r;
 }
 
-static double __pyx_f_6mtrand_kahan_sum(double *__pyx_v_darr,long __pyx_v_n) {
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":507
+ *     return array
+ * 
+ * cdef double kahan_sum(double *darr, long n):             # <<<<<<<<<<<<<<
+ *     cdef double c, y, t, sum
+ *     cdef long i
+ */
+
+static  double __pyx_f_6mtrand_kahan_sum(double *__pyx_v_darr, long __pyx_v_n) {
   double __pyx_v_c;
   double __pyx_v_y;
   double __pyx_v_t;
@@ -1968,29 +3851,77 @@ static double __pyx_f_6mtrand_kahan_sum(double *__pyx_v_darr,long __pyx_v_n) {
   long __pyx_v_i;
   double __pyx_r;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":510 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":510
+ *     cdef double c, y, t, sum
+ *     cdef long i
+ *     sum = darr[0]             # <<<<<<<<<<<<<<
+ *     c = 0.0
+ *     for i from 1 <= i < n:
+ */
   __pyx_v_sum = (__pyx_v_darr[0]);
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":511 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":511
+ *     cdef long i
+ *     sum = darr[0]
+ *     c = 0.0             # <<<<<<<<<<<<<<
+ *     for i from 1 <= i < n:
+ *         y = darr[i] - c
+ */
   __pyx_v_c = 0.0;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":512 */
-  for (__pyx_v_i = 1; __pyx_v_i < __pyx_v_n; ++__pyx_v_i) {
-
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":513 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":512
+ *     sum = darr[0]
+ *     c = 0.0
+ *     for i from 1 <= i < n:             # <<<<<<<<<<<<<<
+ *         y = darr[i] - c
+ *         t = sum + y
+ */
+  for (__pyx_v_i = 1; __pyx_v_i < __pyx_v_n; __pyx_v_i++) {
+
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":513
+ *     c = 0.0
+ *     for i from 1 <= i < n:
+ *         y = darr[i] - c             # <<<<<<<<<<<<<<
+ *         t = sum + y
+ *         c = (t-sum) - y
+ */
     __pyx_v_y = ((__pyx_v_darr[__pyx_v_i]) - __pyx_v_c);
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":514 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":514
+ *     for i from 1 <= i < n:
+ *         y = darr[i] - c
+ *         t = sum + y             # <<<<<<<<<<<<<<
+ *         c = (t-sum) - y
+ *         sum = t
+ */
     __pyx_v_t = (__pyx_v_sum + __pyx_v_y);
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":515 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":515
+ *         y = darr[i] - c
+ *         t = sum + y
+ *         c = (t-sum) - y             # <<<<<<<<<<<<<<
+ *         sum = t
+ *     return sum
+ */
     __pyx_v_c = ((__pyx_v_t - __pyx_v_sum) - __pyx_v_y);
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":516 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":516
+ *         t = sum + y
+ *         c = (t-sum) - y
+ *         sum = t             # <<<<<<<<<<<<<<
+ *     return sum
+ * 
+ */
     __pyx_v_sum = __pyx_v_t;
   }
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":517 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":517
+ *         c = (t-sum) - y
+ *         sum = t
+ *     return sum             # <<<<<<<<<<<<<<
+ * 
+ * cdef class RandomState:
+ */
   __pyx_r = __pyx_v_sum;
   goto __pyx_L0;
 
@@ -1999,70 +3930,130 @@ static double __pyx_f_6mtrand_kahan_sum(double *__pyx_v_darr,long __pyx_v_n) {
   return __pyx_r;
 }
 
-static int __pyx_f_6mtrand_11RandomState___init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static int __pyx_f_6mtrand_11RandomState___init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":546
+ *     cdef rk_state *internal_state
+ * 
+ *     def __init__(self, seed=None):             # <<<<<<<<<<<<<<
+ *         self.internal_state = <rk_state*>PyMem_Malloc(sizeof(rk_state))
+ * 
+ */
+
+static int __pyx_pf_6mtrand_11RandomState___init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static int __pyx_pf_6mtrand_11RandomState___init__(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
   PyObject *__pyx_v_seed = 0;
   int __pyx_r;
   PyObject *__pyx_1 = 0;
   PyObject *__pyx_2 = 0;
   PyObject *__pyx_3 = 0;
   static char *__pyx_argnames[] = {"seed",0};
-  __pyx_v_seed = __pyx_k2;
-  if (!PyArg_ParseTupleAndKeywords(__pyx_args, __pyx_kwds, "|O", __pyx_argnames, &__pyx_v_seed)) return -1;
-  Py_INCREF(__pyx_v_self);
-  Py_INCREF(__pyx_v_seed);
+  __pyx_v_seed = Py_None;
+  if (likely(!__pyx_kwds) && likely(0 <= PyTuple_GET_SIZE(__pyx_args)) && likely(PyTuple_GET_SIZE(__pyx_args) <= 1)) {
+    if (PyTuple_GET_SIZE(__pyx_args) > 0) {
+      __pyx_v_seed = PyTuple_GET_ITEM(__pyx_args, 0);
+    }
+  }
+  else {
+    if (unlikely(!PyArg_ParseTupleAndKeywords(__pyx_args, __pyx_kwds, "|O", __pyx_argnames, &__pyx_v_seed))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 546; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+  }
+  goto __pyx_L4;
+  __pyx_L3_error:;
+  __Pyx_AddTraceback("mtrand.RandomState.__init__");
+  return -1;
+  __pyx_L4:;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":547 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":547
+ * 
+ *     def __init__(self, seed=None):
+ *         self.internal_state = <rk_state*>PyMem_Malloc(sizeof(rk_state))             # <<<<<<<<<<<<<<
+ * 
+ *         self.seed(seed)
+ */
   ((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state = ((rk_state *)PyMem_Malloc((sizeof(rk_state))));
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":549 */
-  __pyx_1 = PyObject_GetAttr(__pyx_v_self, __pyx_n_seed); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 549; goto __pyx_L1;}
-  __pyx_2 = PyTuple_New(1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 549; goto __pyx_L1;}
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":549
+ *         self.internal_state = <rk_state*>PyMem_Malloc(sizeof(rk_state))
+ * 
+ *         self.seed(seed)             # <<<<<<<<<<<<<<
+ * 
+ *     def __dealloc__(self):
+ */
+  __pyx_1 = PyObject_GetAttr(__pyx_v_self, __pyx_kp_seed); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 549; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 549; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   Py_INCREF(__pyx_v_seed);
   PyTuple_SET_ITEM(__pyx_2, 0, __pyx_v_seed);
-  __pyx_3 = PyObject_CallObject(__pyx_1, __pyx_2); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 549; goto __pyx_L1;}
+  __pyx_3 = PyObject_Call(__pyx_1, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 549; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   Py_DECREF(__pyx_1); __pyx_1 = 0;
-  Py_DECREF(__pyx_2); __pyx_2 = 0;
+  Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0;
   Py_DECREF(__pyx_3); __pyx_3 = 0;
 
   __pyx_r = 0;
   goto __pyx_L0;
-  __pyx_L1:;
+  __pyx_L1_error:;
   Py_XDECREF(__pyx_1);
   Py_XDECREF(__pyx_2);
   Py_XDECREF(__pyx_3);
   __Pyx_AddTraceback("mtrand.RandomState.__init__");
   __pyx_r = -1;
   __pyx_L0:;
-  Py_DECREF(__pyx_v_self);
-  Py_DECREF(__pyx_v_seed);
   return __pyx_r;
 }
 
-static void __pyx_f_6mtrand_11RandomState___dealloc__(PyObject *__pyx_v_self); /*proto*/
-static void __pyx_f_6mtrand_11RandomState___dealloc__(PyObject *__pyx_v_self) {
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":551
+ *         self.seed(seed)
+ * 
+ *     def __dealloc__(self):             # <<<<<<<<<<<<<<
+ *         if self.internal_state != NULL:
+ *             PyMem_Free(self.internal_state)
+ */
+
+static void __pyx_pf_6mtrand_11RandomState___dealloc__(PyObject *__pyx_v_self); /*proto*/
+static void __pyx_pf_6mtrand_11RandomState___dealloc__(PyObject *__pyx_v_self) {
   int __pyx_1;
-  Py_INCREF(__pyx_v_self);
+
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":552
+ * 
+ *     def __dealloc__(self):
+ *         if self.internal_state != NULL:             # <<<<<<<<<<<<<<
+ *             PyMem_Free(self.internal_state)
+ *             self.internal_state = NULL
+ */
   __pyx_1 = (((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state != NULL);
   if (__pyx_1) {
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":553 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":553
+ *     def __dealloc__(self):
+ *         if self.internal_state != NULL:
+ *             PyMem_Free(self.internal_state)             # <<<<<<<<<<<<<<
+ *             self.internal_state = NULL
+ * 
+ */
     PyMem_Free(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state);
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":554 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":554
+ *         if self.internal_state != NULL:
+ *             PyMem_Free(self.internal_state)
+ *             self.internal_state = NULL             # <<<<<<<<<<<<<<
+ * 
+ *     def seed(self, seed=None):
+ */
     ((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state = NULL;
-    goto __pyx_L2;
+    goto __pyx_L5;
   }
-  __pyx_L2:;
+  __pyx_L5:;
 
-  Py_DECREF(__pyx_v_self);
 }
 
-static PyObject *__pyx_n_integer;
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":556
+ *             self.internal_state = NULL
+ * 
+ *     def seed(self, seed=None):             # <<<<<<<<<<<<<<
+ *         """
+ *         seed(seed=None)
+ */
 
-static PyObject *__pyx_f_6mtrand_11RandomState_seed(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static PyObject *__pyx_pf_6mtrand_11RandomState_seed(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
 static char __pyx_doc_6mtrand_11RandomState_seed[] = "\n        seed(seed=None)\n\n        Seed the generator.\n\n        seed can be an integer, an array (or other sequence) of integers of any\n        length, or None. If seed is None, then RandomState will try to read data\n        from /dev/urandom (or the Windows analogue) if available or seed from\n        the clock otherwise.\n\n        ";
-static PyObject *__pyx_f_6mtrand_11RandomState_seed(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_seed(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
   PyObject *__pyx_v_seed = 0;
   rk_error __pyx_v_errcode;
   PyArrayObject *arrayObject_obj;
@@ -2073,190 +4064,290 @@ static PyObject *__pyx_f_6mtrand_11RandomState_seed(PyObject *__pyx_v_self, PyOb
   PyObject *__pyx_3 = 0;
   unsigned long __pyx_4;
   static char *__pyx_argnames[] = {"seed",0};
-  __pyx_v_seed = __pyx_k3;
-  if (!PyArg_ParseTupleAndKeywords(__pyx_args, __pyx_kwds, "|O", __pyx_argnames, &__pyx_v_seed)) return 0;
-  Py_INCREF(__pyx_v_self);
-  Py_INCREF(__pyx_v_seed);
+  __pyx_v_seed = Py_None;
+  if (likely(!__pyx_kwds) && likely(0 <= PyTuple_GET_SIZE(__pyx_args)) && likely(PyTuple_GET_SIZE(__pyx_args) <= 1)) {
+    if (PyTuple_GET_SIZE(__pyx_args) > 0) {
+      __pyx_v_seed = PyTuple_GET_ITEM(__pyx_args, 0);
+    }
+  }
+  else {
+    if (unlikely(!PyArg_ParseTupleAndKeywords(__pyx_args, __pyx_kwds, "|O", __pyx_argnames, &__pyx_v_seed))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 556; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+  }
+  goto __pyx_L4;
+  __pyx_L3_error:;
+  __Pyx_AddTraceback("mtrand.RandomState.seed");
+  return NULL;
+  __pyx_L4:;
   arrayObject_obj = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
   __pyx_v_iseed = Py_None; Py_INCREF(Py_None);
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":570 */
-  __pyx_1 = __pyx_v_seed == Py_None;
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":570
+ *         cdef rk_error errcode
+ *         cdef ndarray obj "arrayObject_obj"
+ *         if seed is None:             # <<<<<<<<<<<<<<
+ *             errcode = rk_randomseed(self.internal_state)
+ *         elif type(seed) is int:
+ */
+  __pyx_1 = (__pyx_v_seed == Py_None);
   if (__pyx_1) {
+
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":571
+ *         cdef ndarray obj "arrayObject_obj"
+ *         if seed is None:
+ *             errcode = rk_randomseed(self.internal_state)             # <<<<<<<<<<<<<<
+ *         elif type(seed) is int:
+ *             rk_seed(seed, self.internal_state)
+ */
     __pyx_v_errcode = rk_randomseed(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state);
-    goto __pyx_L2;
+    goto __pyx_L5;
   }
-  __pyx_2 = PyTuple_New(1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 572; goto __pyx_L1;}
+
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":572
+ *         if seed is None:
+ *             errcode = rk_randomseed(self.internal_state)
+ *         elif type(seed) is int:             # <<<<<<<<<<<<<<
+ *             rk_seed(seed, self.internal_state)
+ *         elif isinstance(seed, np.integer):
+ */
+  __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 572; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   Py_INCREF(__pyx_v_seed);
   PyTuple_SET_ITEM(__pyx_2, 0, __pyx_v_seed);
-  __pyx_3 = PyObject_CallObject(((PyObject *)(&PyType_Type)), __pyx_2); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 572; goto __pyx_L1;}
-  Py_DECREF(__pyx_2); __pyx_2 = 0;
-  __pyx_1 = __pyx_3 == ((PyObject *)(&PyInt_Type));
+  __pyx_3 = PyObject_Call(((PyObject*)&PyType_Type), ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 572; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0;
+  __pyx_1 = (__pyx_3 == ((PyObject*)&PyInt_Type));
   Py_DECREF(__pyx_3); __pyx_3 = 0;
   if (__pyx_1) {
-    __pyx_4 = PyInt_AsUnsignedLongMask(__pyx_v_seed); if (PyErr_Occurred()) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 573; goto __pyx_L1;}
-    rk_seed(__pyx_4,((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state);
-    goto __pyx_L2;
+
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":573
+ *             errcode = rk_randomseed(self.internal_state)
+ *         elif type(seed) is int:
+ *             rk_seed(seed, self.internal_state)             # <<<<<<<<<<<<<<
+ *         elif isinstance(seed, np.integer):
+ *             iseed = int(seed)
+ */
+    __pyx_4 = PyInt_AsUnsignedLongMask(__pyx_v_seed); if (unlikely((__pyx_4 == (unsigned long)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 573; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    rk_seed(__pyx_4, ((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state);
+    goto __pyx_L5;
   }
-  __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n_np); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 574; goto __pyx_L1;}
-  __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_n_integer); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 574; goto __pyx_L1;}
+
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":574
+ *         elif type(seed) is int:
+ *             rk_seed(seed, self.internal_state)
+ *         elif isinstance(seed, np.integer):             # <<<<<<<<<<<<<<
+ *             iseed = int(seed)
+ *             rk_seed(iseed, self.internal_state)
+ */
+  __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 574; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_kp_integer); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 574; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   Py_DECREF(__pyx_2); __pyx_2 = 0;
-  __pyx_1 = PyObject_IsInstance(__pyx_v_seed,__pyx_3); if (__pyx_1 == -1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 574; goto __pyx_L1;}
+  __pyx_1 = PyObject_IsInstance(__pyx_v_seed, __pyx_3); if (unlikely(__pyx_1 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 574; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   Py_DECREF(__pyx_3); __pyx_3 = 0;
   if (__pyx_1) {
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":575 */
-    __pyx_2 = PyTuple_New(1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 575; goto __pyx_L1;}
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":575
+ *             rk_seed(seed, self.internal_state)
+ *         elif isinstance(seed, np.integer):
+ *             iseed = int(seed)             # <<<<<<<<<<<<<<
+ *             rk_seed(iseed, self.internal_state)
+ *         else:
+ */
+    __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 575; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     Py_INCREF(__pyx_v_seed);
     PyTuple_SET_ITEM(__pyx_2, 0, __pyx_v_seed);
-    __pyx_3 = PyObject_CallObject(((PyObject *)(&PyInt_Type)), __pyx_2); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 575; goto __pyx_L1;}
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":578
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+ *     def get_state(self):
+ */
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   goto __pyx_L0;
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-static PyObject *__pyx_f_6mtrand_11RandomState_get_state(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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+ *                 obj.dimensions[0])
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   Py_DECREF(__pyx_2); __pyx_2 = 0;
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   Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_3)));
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-  __pyx_4 = PyFloat_FromDouble(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state->gauss); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 609; goto __pyx_L1;}
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+ *         state = <ndarray>np.asarray(state, np.uint32)
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":607
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+ * 
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+ * 
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-static PyObject *__pyx_f_6mtrand_11RandomState_set_state(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
-  PyObject *__pyx_v_state = 0;
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":611
+ *             self.internal_state.has_gauss, self.internal_state.gauss)
+ * 
+ *     def set_state(self, state):             # <<<<<<<<<<<<<<
+ *         """
+ *         set_state(state)
+ */
+
+static PyObject *__pyx_pf_6mtrand_11RandomState_set_state(PyObject *__pyx_v_self, PyObject *__pyx_v_state); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_set_state[] = "\n        set_state(state)\n\n        Set the state from a tuple.\n\n        Parameters\n        ----------\n        state : tuple(string, list of 624 ints, int, int, float)\n            The `state` tuple is made up of\n\n            1. the string 'MT19937'\n            2. a list of 624 integer keys\n            3. an integer pos\n            4. an integer has_gauss\n            5. and a float for the 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        For backwards compatibility, the following form is also accepted\n        although it is missing some information about the cached Gaussian value.\n\n        state = ('MT19937', int key[624], int pos)\n\n        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_set_state(PyObject *__pyx_v_self, PyObject *__pyx_v_state) {
   PyArrayObject *arrayObject_obj;
   int __pyx_v_pos;
   PyObject *__pyx_v_algorithm_name;
@@ -2267,182 +4358,341 @@ static PyObject *__pyx_f_6mtrand_11RandomState_set_state(PyObject *__pyx_v_self,
   PyObject *__pyx_1 = 0;
   int __pyx_2;
   PyObject *__pyx_3 = 0;
-  Py_ssize_t __pyx_4;
-  PyObject *__pyx_5 = 0;
-  PyObject *__pyx_6 = 0;
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-  Py_INCREF(__pyx_v_self);
-  Py_INCREF(__pyx_v_state);
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+  int __pyx_5;
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+  PyObject *__pyx_7 = 0;
+  double __pyx_8;
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+ *         cdef ndarray obj "arrayObject_obj"
+ *         cdef int pos
+ *         algorithm_name = state[0]             # <<<<<<<<<<<<<<
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+ *         if algorithm_name != 'MT19937':             # <<<<<<<<<<<<<<
+ *             raise ValueError("algorithm must be 'MT19937'")
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+ *             raise ValueError("algorithm must be 'MT19937'")
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+ *         key, pos = state[1:3]
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":653 */
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-    Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_1)));
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     Py_DECREF(((PyObject *)arrayObject_obj));
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   Py_XDECREF(__pyx_1); __pyx_1 = 0;
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-    Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_6)));
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":660
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":670
+ *     # Pickling support:
+ *     def __getstate__(self):
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+ * 
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+static PyObject *__pyx_pf_6mtrand_11RandomState___setstate__(PyObject *__pyx_v_self, PyObject *__pyx_v_state); /*proto*/
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":673
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-  Py_DECREF(__pyx_v_self);
   return __pyx_r;
 }
 
-static PyObject *__pyx_f_6mtrand_11RandomState_random_sample(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":679
+ * 
+ *     # Basic distributions:
+ *     def random_sample(self, size=None):             # <<<<<<<<<<<<<<
+ *         """
+ *         random_sample(size=None)
+ */
+
+static PyObject *__pyx_pf_6mtrand_11RandomState_random_sample(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
 static char __pyx_doc_6mtrand_11RandomState_random_sample[] = "\n        random_sample(size=None)\n\n        Return random floats in the half-open interval [0.0, 1.0).\n\n        Parameters\n        ----------\n        size : shape tuple, optional\n            Defines the shape of the returned array of random floats.\n\n        Returns\n        -------\n        out : ndarray, floats\n            Array of random of floats with shape of `size`.\n\n        ";
-static PyObject *__pyx_f_6mtrand_11RandomState_random_sample(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_random_sample(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
   PyObject *__pyx_v_size = 0;
   PyObject *__pyx_r;
   PyObject *__pyx_1 = 0;
   static char *__pyx_argnames[] = {"size",0};
-  __pyx_v_size = __pyx_k4;
-  if (!PyArg_ParseTupleAndKeywords(__pyx_args, __pyx_kwds, "|O", __pyx_argnames, &__pyx_v_size)) return 0;
-  Py_INCREF(__pyx_v_self);
-  Py_INCREF(__pyx_v_size);
-  __pyx_1 = __pyx_f_6mtrand_cont0_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state,rk_double,__pyx_v_size); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 696; goto __pyx_L1;}
+  __pyx_v_size = Py_None;
+  if (likely(!__pyx_kwds) && likely(0 <= PyTuple_GET_SIZE(__pyx_args)) && likely(PyTuple_GET_SIZE(__pyx_args) <= 1)) {
+    if (PyTuple_GET_SIZE(__pyx_args) > 0) {
+      __pyx_v_size = PyTuple_GET_ITEM(__pyx_args, 0);
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+  }
+  goto __pyx_L4;
+  __pyx_L3_error:;
+  __Pyx_AddTraceback("mtrand.RandomState.random_sample");
+  return NULL;
+  __pyx_L4:;
+
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":696
+ * 
+ *         """
+ *         return cont0_array(self.internal_state, rk_double, size)             # <<<<<<<<<<<<<<
+ * 
+ *     def tomaxint(self, size=None):
+ */
+  __pyx_1 = __pyx_f_6mtrand_cont0_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_double, __pyx_v_size); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 696; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __pyx_r = __pyx_1;
   __pyx_1 = 0;
   goto __pyx_L0;
 
   __pyx_r = Py_None; Py_INCREF(Py_None);
   goto __pyx_L0;
-  __pyx_L1:;
+  __pyx_L1_error:;
   Py_XDECREF(__pyx_1);
   __Pyx_AddTraceback("mtrand.RandomState.random_sample");
-  __pyx_r = 0;
+  __pyx_r = NULL;
   __pyx_L0:;
-  Py_DECREF(__pyx_v_self);
-  Py_DECREF(__pyx_v_size);
   return __pyx_r;
 }
 
-static PyObject *__pyx_f_6mtrand_11RandomState_tomaxint(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":698
+ *         return cont0_array(self.internal_state, rk_double, size)
+ * 
+ *     def tomaxint(self, size=None):             # <<<<<<<<<<<<<<
+ *         """
+ *         tomaxint(size=None)
+ */
+
+static PyObject *__pyx_pf_6mtrand_11RandomState_tomaxint(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
 static char __pyx_doc_6mtrand_11RandomState_tomaxint[] = "\n        tomaxint(size=None)\n\n        Uniformly sample discrete random integers `x` such that\n        ``0 <= x <= sys.maxint``.\n\n        Parameters\n        ----------\n        size : tuple of ints, int, 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 : 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        ";
-static PyObject *__pyx_f_6mtrand_11RandomState_tomaxint(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_tomaxint(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
   PyObject *__pyx_v_size = 0;
   PyObject *__pyx_r;
   PyObject *__pyx_1 = 0;
   static char *__pyx_argnames[] = {"size",0};
-  __pyx_v_size = __pyx_k5;
-  if (!PyArg_ParseTupleAndKeywords(__pyx_args, __pyx_kwds, "|O", __pyx_argnames, &__pyx_v_size)) return 0;
-  Py_INCREF(__pyx_v_self);
-  Py_INCREF(__pyx_v_size);
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+  __pyx_v_size = Py_None;
+  if (likely(!__pyx_kwds) && likely(0 <= PyTuple_GET_SIZE(__pyx_args)) && likely(PyTuple_GET_SIZE(__pyx_args) <= 1)) {
+    if (PyTuple_GET_SIZE(__pyx_args) > 0) {
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+  }
+  goto __pyx_L4;
+  __pyx_L3_error:;
+  __Pyx_AddTraceback("mtrand.RandomState.tomaxint");
+  return NULL;
+  __pyx_L4:;
+
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":724
+ * 
+ *         """
+ *         return disc0_array(self.internal_state, rk_long, size)             # <<<<<<<<<<<<<<
+ * 
+ *     def randint(self, low, high=None, size=None):
+ */
+  __pyx_1 = __pyx_f_6mtrand_disc0_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_long, __pyx_v_size); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 724; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __pyx_r = __pyx_1;
   __pyx_1 = 0;
   goto __pyx_L0;
 
   __pyx_r = Py_None; Py_INCREF(Py_None);
   goto __pyx_L0;
-  __pyx_L1:;
+  __pyx_L1_error:;
   Py_XDECREF(__pyx_1);
   __Pyx_AddTraceback("mtrand.RandomState.tomaxint");
-  __pyx_r = 0;
+  __pyx_r = NULL;
   __pyx_L0:;
-  Py_DECREF(__pyx_v_self);
-  Py_DECREF(__pyx_v_size);
   return __pyx_r;
 }
 
-static PyObject *__pyx_k72p;
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":726
+ *         return disc0_array(self.internal_state, rk_long, size)
+ * 
+ *     def randint(self, low, high=None, size=None):             # <<<<<<<<<<<<<<
+ *         """
+ *         randint(low, high=None, size=None)
+ */
 
-static char __pyx_k72[] = "low >= high";
-
-static PyObject *__pyx_f_6mtrand_11RandomState_randint(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static PyObject *__pyx_pf_6mtrand_11RandomState_randint(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
 static char __pyx_doc_6mtrand_11RandomState_randint[] = "\n        randint(low, high=None, size=None)\n\n        Return random integers x such that low <= x < high.\n\n        If high is None, then 0 <= x < low.\n\n        ";
-static PyObject *__pyx_f_6mtrand_11RandomState_randint(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_randint(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
   PyObject *__pyx_v_low = 0;
   PyObject *__pyx_v_high = 0;
   PyObject *__pyx_v_size = 0;
@@ -2621,167 +4937,319 @@ static PyObject *__pyx_f_6mtrand_11RandomState_randint(PyObject *__pyx_v_self, P
   PyObject *__pyx_4 = 0;
   PyObject *__pyx_5 = 0;
   static char *__pyx_argnames[] = {"low","high","size",0};
-  __pyx_v_high = __pyx_k6;
-  __pyx_v_size = __pyx_k7;
-  if (!PyArg_ParseTupleAndKeywords(__pyx_args, __pyx_kwds, "O|OO", __pyx_argnames, &__pyx_v_low, &__pyx_v_high, &__pyx_v_size)) return 0;
-  Py_INCREF(__pyx_v_self);
-  Py_INCREF(__pyx_v_low);
-  Py_INCREF(__pyx_v_high);
-  Py_INCREF(__pyx_v_size);
+  __pyx_v_high = Py_None;
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+  if (likely(!__pyx_kwds) && likely(1 <= PyTuple_GET_SIZE(__pyx_args)) && likely(PyTuple_GET_SIZE(__pyx_args) <= 3)) {
+    __pyx_v_low = PyTuple_GET_ITEM(__pyx_args, 0);
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+      if (PyTuple_GET_SIZE(__pyx_args) > 2) {
+        __pyx_v_size = PyTuple_GET_ITEM(__pyx_args, 2);
+      }
+    }
+  }
+  else {
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+  }
+  goto __pyx_L4;
+  __pyx_L3_error:;
+  __Pyx_AddTraceback("mtrand.RandomState.randint");
+  return NULL;
+  __pyx_L4:;
   arrayObject = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":741 */
-  __pyx_1 = __pyx_v_high == Py_None;
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":741
+ *         cdef long i
+ * 
+ *         if high is None:             # <<<<<<<<<<<<<<
+ *             lo = 0
+ *             hi = low
+ */
+  __pyx_1 = (__pyx_v_high == Py_None);
   if (__pyx_1) {
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":742 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":742
+ * 
+ *         if high is None:
+ *             lo = 0             # <<<<<<<<<<<<<<
+ *             hi = low
+ *         else:
+ */
     __pyx_v_lo = 0;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":743 */
-    __pyx_2 = PyInt_AsLong(__pyx_v_low); if (PyErr_Occurred()) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 743; goto __pyx_L1;}
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":743
+ *         if high is None:
+ *             lo = 0
+ *             hi = low             # <<<<<<<<<<<<<<
+ *         else:
+ *             lo = low
+ */
+    __pyx_2 = __pyx_PyInt_long(__pyx_v_low); if (unlikely((__pyx_2 == (long)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 743; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     __pyx_v_hi = __pyx_2;
-    goto __pyx_L2;
+    goto __pyx_L5;
   }
   /*else*/ {
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":745 */
-    __pyx_2 = PyInt_AsLong(__pyx_v_low); if (PyErr_Occurred()) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 745; goto __pyx_L1;}
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":745
+ *             hi = low
+ *         else:
+ *             lo = low             # <<<<<<<<<<<<<<
+ *             hi = high
+ * 
+ */
+    __pyx_2 = __pyx_PyInt_long(__pyx_v_low); if (unlikely((__pyx_2 == (long)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 745; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     __pyx_v_lo = __pyx_2;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":746 */
-    __pyx_2 = PyInt_AsLong(__pyx_v_high); if (PyErr_Occurred()) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 746; goto __pyx_L1;}
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":746
+ *         else:
+ *             lo = low
+ *             hi = high             # <<<<<<<<<<<<<<
+ * 
+ *         diff = hi - lo - 1
+ */
+    __pyx_2 = __pyx_PyInt_long(__pyx_v_high); if (unlikely((__pyx_2 == (long)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 746; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     __pyx_v_hi = __pyx_2;
   }
-  __pyx_L2:;
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":748 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":748
+ *             hi = high
+ * 
+ *         diff = hi - lo - 1             # <<<<<<<<<<<<<<
+ *         if diff < 0:
+ *             raise ValueError("low >= high")
+ */
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":749 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":749
+ * 
+ *         diff = hi - lo - 1
+ *         if diff < 0:             # <<<<<<<<<<<<<<
+ *             raise ValueError("low >= high")
+ * 
+ */
   __pyx_1 = (__pyx_v_diff < 0);
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-    __pyx_3 = PyTuple_New(1); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 750; goto __pyx_L1;}
-    Py_INCREF(__pyx_k72p);
-    PyTuple_SET_ITEM(__pyx_3, 0, __pyx_k72p);
-    __pyx_4 = PyObject_CallObject(PyExc_ValueError, __pyx_3); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 750; goto __pyx_L1;}
-    Py_DECREF(__pyx_3); __pyx_3 = 0;
+
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":750
+ *         diff = hi - lo - 1
+ *         if diff < 0:
+ *             raise ValueError("low >= high")             # <<<<<<<<<<<<<<
+ * 
+ *         if size is None:
+ */
+    __pyx_3 = PyTuple_New(1); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 750; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    Py_INCREF(__pyx_kp_32);
+    PyTuple_SET_ITEM(__pyx_3, 0, __pyx_kp_32);
+    __pyx_4 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_3), NULL); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 750; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    Py_DECREF(((PyObject *)__pyx_3)); __pyx_3 = 0;
     __Pyx_Raise(__pyx_4, 0, 0);
     Py_DECREF(__pyx_4); __pyx_4 = 0;
-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 750; goto __pyx_L1;}
-    goto __pyx_L3;
+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 750; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    goto __pyx_L6;
   }
-  __pyx_L3:;
+  __pyx_L6:;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":752 */
-  __pyx_1 = __pyx_v_size == Py_None;
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":752
+ *             raise ValueError("low >= high")
+ * 
+ *         if size is None:             # <<<<<<<<<<<<<<
+ *             return <long>rk_interval(diff, self.internal_state) + lo
+ *         else:
+ */
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   if (__pyx_1) {
-    __pyx_3 = PyInt_FromLong((((long)rk_interval(__pyx_v_diff,((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state)) + __pyx_v_lo)); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 753; goto __pyx_L1;}
+
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":753
+ * 
+ *         if size is None:
+ *             return <long>rk_interval(diff, self.internal_state) + lo             # <<<<<<<<<<<<<<
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+ */
+    __pyx_3 = PyInt_FromLong((((long)rk_interval(__pyx_v_diff, ((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state)) + __pyx_v_lo)); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 753; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     __pyx_r = __pyx_3;
     __pyx_3 = 0;
     goto __pyx_L0;
-    goto __pyx_L4;
+    goto __pyx_L7;
   }
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":755 */
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":755
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+ *         else:
+ *             array = <ndarray>np.empty(size, int)             # <<<<<<<<<<<<<<
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+ */
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+    __pyx_3 = PyObject_GetAttr(__pyx_4, __pyx_kp_empty); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 755; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    __pyx_4 = PyTuple_New(2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 755; goto __pyx_L1;}
+    __pyx_4 = PyTuple_New(2); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 755; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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     Py_DECREF(__pyx_3); __pyx_3 = 0;
-    Py_DECREF(__pyx_4); __pyx_4 = 0;
+    Py_DECREF(((PyObject *)__pyx_4)); __pyx_4 = 0;
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":756 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":756
+ *         else:
+ *             array = <ndarray>np.empty(size, int)
+ *             length = PyArray_SIZE(array)             # <<<<<<<<<<<<<<
+ *             array_data = <long *>array.data
+ *             for i from 0 <= i < length:
+ */
     __pyx_v_length = PyArray_SIZE(arrayObject);
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":757 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":757
+ *             array = <ndarray>np.empty(size, int)
+ *             length = PyArray_SIZE(array)
+ *             array_data = <long *>array.data             # <<<<<<<<<<<<<<
+ *             for i from 0 <= i < length:
+ *                 array_data[i] = lo + <long>rk_interval(diff, self.internal_state)
+ */
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":758 */
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":758
+ *             length = PyArray_SIZE(array)
+ *             array_data = <long *>array.data
+ *             for i from 0 <= i < length:             # <<<<<<<<<<<<<<
+ *                 array_data[i] = lo + <long>rk_interval(diff, self.internal_state)
+ *             return array
+ */
+    for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; __pyx_v_i++) {
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":759
+ *             array_data = <long *>array.data
+ *             for i from 0 <= i < length:
+ *                 array_data[i] = lo + <long>rk_interval(diff, self.internal_state)             # <<<<<<<<<<<<<<
+ *             return array
+ * 
+ */
+      (__pyx_v_array_data[__pyx_v_i]) = (__pyx_v_lo + ((long)rk_interval(__pyx_v_diff, ((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state)));
     }
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":760 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":760
+ *             for i from 0 <= i < length:
+ *                 array_data[i] = lo + <long>rk_interval(diff, self.internal_state)
+ *             return array             # <<<<<<<<<<<<<<
+ * 
+ *     def bytes(self, unsigned int length):
+ */
     Py_INCREF(((PyObject *)arrayObject));
     __pyx_r = ((PyObject *)arrayObject);
     goto __pyx_L0;
   }
-  __pyx_L4:;
+  __pyx_L7:;
 
   __pyx_r = Py_None; Py_INCREF(Py_None);
   goto __pyx_L0;
-  __pyx_L1:;
+  __pyx_L1_error:;
   Py_XDECREF(__pyx_3);
   Py_XDECREF(__pyx_4);
   Py_XDECREF(__pyx_5);
   __Pyx_AddTraceback("mtrand.RandomState.randint");
-  __pyx_r = 0;
+  __pyx_r = NULL;
   __pyx_L0:;
   Py_DECREF(arrayObject);
-  Py_DECREF(__pyx_v_self);
-  Py_DECREF(__pyx_v_low);
-  Py_DECREF(__pyx_v_high);
-  Py_DECREF(__pyx_v_size);
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 }
 
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-static PyObject *__pyx_f_6mtrand_11RandomState_bytes(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":762
+ *             return array
+ * 
+ *     def bytes(self, unsigned int length):             # <<<<<<<<<<<<<<
+ *         """
+ *         bytes(length)
+ */
+
+static PyObject *__pyx_pf_6mtrand_11RandomState_bytes(PyObject *__pyx_v_self, PyObject *__pyx_arg_length); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_bytes[] = "\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 `N`.\n\n        Examples\n        --------\n        >>> np.random.bytes(10)\n        ' eh\\x85\\x022SZ\\xbf\\xa4' #random\n\n        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_bytes(PyObject *__pyx_v_self, PyObject *__pyx_arg_length) {
   unsigned int __pyx_v_length;
   void *__pyx_v_bytes;
   PyObject *__pyx_v_bytestring;
   PyObject *__pyx_r;
   PyObject *__pyx_1 = 0;
-  static char *__pyx_argnames[] = {"length",0};
-  if (!PyArg_ParseTupleAndKeywords(__pyx_args, __pyx_kwds, "I", __pyx_argnames, &__pyx_v_length)) return 0;
-  Py_INCREF(__pyx_v_self);
+  assert(__pyx_arg_length); {
+    __pyx_v_length = PyInt_AsUnsignedLongMask(__pyx_arg_length); if (unlikely((__pyx_v_length == (unsigned int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 762; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+  }
+  goto __pyx_L4;
+  __pyx_L3_error:;
+  __Pyx_AddTraceback("mtrand.RandomState.bytes");
+  return NULL;
+  __pyx_L4:;
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":785 */
-  __pyx_1 = PyString_FromStringAndSize(NULL,__pyx_v_length); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 785; goto __pyx_L1;}
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":785
+ *         """
+ *         cdef void *bytes
+ *         bytestring = PyString_FromStringAndSize(NULL, length)             # <<<<<<<<<<<<<<
+ *         bytes = PyString_AS_STRING(bytestring)
+ *         rk_fill(bytes, length, self.internal_state)
+ */
+  __pyx_1 = PyString_FromStringAndSize(NULL, __pyx_v_length); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 785; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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   __pyx_1 = 0;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":786 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":786
+ *         cdef void *bytes
+ *         bytestring = PyString_FromStringAndSize(NULL, length)
+ *         bytes = PyString_AS_STRING(bytestring)             # <<<<<<<<<<<<<<
+ *         rk_fill(bytes, length, self.internal_state)
+ *         return bytestring
+ */
   __pyx_v_bytes = PyString_AS_STRING(__pyx_v_bytestring);
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":787 */
-  rk_fill(__pyx_v_bytes,__pyx_v_length,((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state);
-
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":788 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":787
+ *         bytestring = PyString_FromStringAndSize(NULL, length)
+ *         bytes = PyString_AS_STRING(bytestring)
+ *         rk_fill(bytes, length, self.internal_state)             # <<<<<<<<<<<<<<
+ *         return bytestring
+ * 
+ */
+  rk_fill(__pyx_v_bytes, __pyx_v_length, ((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state);
+
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":788
+ *         bytes = PyString_AS_STRING(bytestring)
+ *         rk_fill(bytes, length, self.internal_state)
+ *         return bytestring             # <<<<<<<<<<<<<<
+ * 
+ *     def uniform(self, low=0.0, high=1.0, size=None):
+ */
   Py_INCREF(__pyx_v_bytestring);
   __pyx_r = __pyx_v_bytestring;
   goto __pyx_L0;
 
   __pyx_r = Py_None; Py_INCREF(Py_None);
   goto __pyx_L0;
-  __pyx_L1:;
+  __pyx_L1_error:;
   Py_XDECREF(__pyx_1);
   __Pyx_AddTraceback("mtrand.RandomState.bytes");
-  __pyx_r = 0;
+  __pyx_r = NULL;
   __pyx_L0:;
   Py_DECREF(__pyx_v_bytestring);
-  Py_DECREF(__pyx_v_self);
   return __pyx_r;
 }
 
-static PyObject *__pyx_n_subtract;
-
-static PyObject *__pyx_f_6mtrand_11RandomState_uniform(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_uniform[] = "\n        uniform(low=0.0, high=1.0, size=1)\n\n        Draw samples from a uniform distribution.\n\n        Samples are uniformly distributed over the half-open interval\n        ``[low, high)`` (includes low, but excludes high).  In other words,\n        any value within the given interval is equally likely to be drawn\n        by `uniform`.\n\n        Parameters\n        ----------\n        low : float, optional\n            Lower boundary of the output interval.  All values generated will be\n            greater than or equal to low.  The default value is 0.\n        high : float\n            Upper boundary of the output interval.  All values generated will be\n            less than high.  The default value is 1.0.\n        size : tuple of ints, int, 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 interval\n                          ``[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, uniformly\n               distributed over ``[0, 1)``.\n\n        Notes\n        -----\n        The probability density function of the uniform distribution is\n\n        .. math:: p(x) = \\frac{1}{b - a}\n\n        anywhere within the interval ``[a, b)``, and zero elsewhere.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> s = np.random.uniform(-1,0,1000)\n\n        All values are within the given interval:\n\n        >>> np.all(s >= -1)\n        True\n\n        >>> np.all(s < 0)\n        True\n\n        Display the histogram of the samples, along with the\n        probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 15, normed=True)\n        >>> plt.plot(bins, np.ones_like(bins), linewidth=2, color=\'r\')\n        >>> plt.show()\n\n        ";
-static PyObject *__pyx_f_6mtrand_11RandomState_uniform(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":790
+ *         return bytestring
+ * 
+ *     def uniform(self, low=0.0, high=1.0, size=None):             # <<<<<<<<<<<<<<
+ *         """
+ *         uniform(low=0.0, high=1.0, size=1)
+ */
+
+static PyObject *__pyx_pf_6mtrand_11RandomState_uniform(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_uniform[] = "\n""        uniform(low=0.0, high=1.0, size=1)\n""\n""        Draw samples from a uniform distribution.\n""\n""        Samples are uniformly distributed over the half-open interval\n""        ``[low, high)`` (includes low, but excludes high).  In other words,\n""        any value within the given interval is equally likely to be drawn\n""        by `uniform`.\n""\n""        Parameters\n""        ----------\n""        low : float, optional\n""            Lower boundary of the output interval.  All values generated will be\n""            greater than or equal to low.  The default value is 0.\n""        high : float\n""            Upper boundary of the output interval.  All values generated will be\n""            less than high.  The default value is 1.0.\n""        size : tuple of ints, int, 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 interval\n""                          ``[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, uniformly\n""               distributed over ``[0, 1)``.\n""\n""        Notes\n""        -----\n""        The probability density function of the uniform distribution is\n""\n""        .. math:: p(x) = \\frac{1}{b - a}\n""\n""        anywhere within the interval ``[a, b)``, and zero elsewhere.\n""\n""        Examples\n""        --------\n""        Draw samples from the distribution:\n""\n""        >>> s = np.random.uniform(-1,0,1000)\n""\n""        All values are within the given interval:\n""\n""        >>> np.all(s >= -1)\n""        True\n""\n""        >>> np.all(s < 0)\n""        True\n""\n""        Display the histogram of the samples, along with the\n""        probability density function:\n""\n""        >>> import matplotlib.pyplot as plt\n""        >>> count, bins, ignored = plt.hist(s, 15, normed=True)\n""        >>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r')\n""        >>> plt.show()\n""\n""        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_uniform(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
   PyObject *__pyx_v_low = 0;
   PyObject *__pyx_v_high = 0;
   PyObject *__pyx_v_size = 0;
@@ -2797,234 +5265,363 @@ static PyObject *__pyx_f_6mtrand_11RandomState_uniform(PyObject *__pyx_v_self, P
   PyObject *__pyx_3 = 0;
   PyObject *__pyx_4 = 0;
   static char *__pyx_argnames[] = {"low","high","size",0};
-  __pyx_v_low = __pyx_k8;
-  __pyx_v_high = __pyx_k9;
-  __pyx_v_size = __pyx_k10;
-  if (!PyArg_ParseTupleAndKeywords(__pyx_args, __pyx_kwds, "|OOO", __pyx_argnames, &__pyx_v_low, &__pyx_v_high, &__pyx_v_size)) return 0;
-  Py_INCREF(__pyx_v_self);
-  Py_INCREF(__pyx_v_low);
-  Py_INCREF(__pyx_v_high);
-  Py_INCREF(__pyx_v_size);
+  __pyx_v_low = __pyx_k_3;
+  __pyx_v_high = __pyx_k_4;
+  __pyx_v_size = Py_None;
+  if (likely(!__pyx_kwds) && likely(0 <= PyTuple_GET_SIZE(__pyx_args)) && likely(PyTuple_GET_SIZE(__pyx_args) <= 3)) {
+    if (PyTuple_GET_SIZE(__pyx_args) > 0) {
+      __pyx_v_low = PyTuple_GET_ITEM(__pyx_args, 0);
+      if (PyTuple_GET_SIZE(__pyx_args) > 1) {
+        __pyx_v_high = PyTuple_GET_ITEM(__pyx_args, 1);
+        if (PyTuple_GET_SIZE(__pyx_args) > 2) {
+          __pyx_v_size = PyTuple_GET_ITEM(__pyx_args, 2);
+        }
+      }
+    }
+  }
+  else {
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+  }
+  goto __pyx_L4;
+  __pyx_L3_error:;
+  __Pyx_AddTraceback("mtrand.RandomState.uniform");
+  return NULL;
+  __pyx_L4:;
   __pyx_v_olow = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
   __pyx_v_ohigh = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":865 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":865
+ *         cdef object temp
+ * 
+ *         flow = PyFloat_AsDouble(low)             # <<<<<<<<<<<<<<
+ *         fhigh = PyFloat_AsDouble(high)
+ *         if not PyErr_Occurred():
+ */
   __pyx_v_flow = PyFloat_AsDouble(__pyx_v_low);
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":866 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":866
+ * 
+ *         flow = PyFloat_AsDouble(low)
+ *         fhigh = PyFloat_AsDouble(high)             # <<<<<<<<<<<<<<
+ *         if not PyErr_Occurred():
+ *             return cont2_array_sc(self.internal_state, rk_uniform, size, flow, fhigh-flow)
+ */
   __pyx_v_fhigh = PyFloat_AsDouble(__pyx_v_high);
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":867 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":867
+ *         flow = PyFloat_AsDouble(low)
+ *         fhigh = PyFloat_AsDouble(high)
+ *         if not PyErr_Occurred():             # <<<<<<<<<<<<<<
+ *             return cont2_array_sc(self.internal_state, rk_uniform, size, flow, fhigh-flow)
+ *         PyErr_Clear()
+ */
   __pyx_1 = (!PyErr_Occurred());
   if (__pyx_1) {
-    __pyx_2 = __pyx_f_6mtrand_cont2_array_sc(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state,rk_uniform,__pyx_v_size,__pyx_v_flow,(__pyx_v_fhigh - __pyx_v_flow)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 868; goto __pyx_L1;}
+
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":868
+ *         fhigh = PyFloat_AsDouble(high)
+ *         if not PyErr_Occurred():
+ *             return cont2_array_sc(self.internal_state, rk_uniform, size, flow, fhigh-flow)             # <<<<<<<<<<<<<<
+ *         PyErr_Clear()
+ *         olow = <ndarray>PyArray_FROM_OTF(low, NPY_DOUBLE, NPY_ALIGNED)
+ */
+    __pyx_2 = __pyx_f_6mtrand_cont2_array_sc(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_uniform, __pyx_v_size, __pyx_v_flow, (__pyx_v_fhigh - __pyx_v_flow)); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 868; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     __pyx_r = __pyx_2;
     __pyx_2 = 0;
     goto __pyx_L0;
-    goto __pyx_L2;
+    goto __pyx_L5;
   }
-  __pyx_L2:;
+  __pyx_L5:;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":869 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":869
+ *         if not PyErr_Occurred():
+ *             return cont2_array_sc(self.internal_state, rk_uniform, size, flow, fhigh-flow)
+ *         PyErr_Clear()             # <<<<<<<<<<<<<<
+ *         olow = <ndarray>PyArray_FROM_OTF(low, NPY_DOUBLE, NPY_ALIGNED)
+ *         ohigh = <ndarray>PyArray_FROM_OTF(high, NPY_DOUBLE, NPY_ALIGNED)
+ */
   PyErr_Clear();
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":870 */
-  __pyx_2 = PyArray_FROM_OTF(__pyx_v_low,NPY_DOUBLE,NPY_ALIGNED); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 870; goto __pyx_L1;}
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":870
+ *             return cont2_array_sc(self.internal_state, rk_uniform, size, flow, fhigh-flow)
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 static char __pyx_doc_6mtrand_11RandomState_rand[] = "\n        rand(d0, d1, ..., dn)\n\n        Random values in a given shape.\n\n        Create an array of the given shape and propagate it with\n        random samples from a uniform distribution\n        over ``[0, 1)``.\n\n        Parameters\n        ----------\n        d0, d1, ..., dn : int\n            Shape of the output.\n\n        Returns\n        -------\n        out : ndarray, shape ``(d0, d1, ..., dn)``\n            Random values.\n\n        See Also\n        --------\n        random\n\n        Notes\n        -----\n        This is a convenience function. If you want an interface that\n        takes a shape-tuple as the first argument, refer to\n        `random`.\n\n        Examples\n        --------\n        >>> np.random.rand(3,2)\n        array([[ 0.14022471,  0.96360618],  #random\n               [ 0.37601032,  0.25528411],  #random\n               [ 0.49313049,  0.94909878]]) #random\n\n        ";
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 static char __pyx_doc_6mtrand_11RandomState_randn[] = "\n        randn(d0, d1, ..., dn)\n\n        Returns zero-mean, unit-variance Gaussian random numbers in an\n        array of shape (d0, d1, ..., dn).\n\n        Note:  This is a convenience function. If you want an\n                    interface that takes a tuple as the first argument\n                    use numpy.random.standard_normal(shape_tuple).\n\n        ";
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-static PyObject *__pyx_f_6mtrand_11RandomState_standard_normal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":953
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+ */
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-static PyObject *__pyx_f_6mtrand_11RandomState_normal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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+static PyObject *__pyx_pf_6mtrand_11RandomState_normal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_normal[] = "\n""        normal(loc=0.0, scale=1.0, size=None)\n""\n""        Draw random samples from a normal (Gaussian) distribution.\n""\n""        The probability density function of the normal distribution, first\n""        derived by De Moivre and 200 years later by both Gauss and Laplace\n""        independently [2]_, is often called the bell curve because of\n""        its characteristic shape (see the example below).\n""\n""        The normal distributions occurs often in nature.  For example, it\n""        describes the commonly occurring distribution of samples influenced\n""        by a large number of tiny, random disturbances, each with its own\n""        unique distribution [2]_.\n""\n""        Parameters\n""        ----------\n""        loc : float\n""            Mean (\"centre\") of the distribution.\n""        scale : float\n""            Standard deviation (spread or \"width\") of the distribution.\n""        size : tuple of ints\n""            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n""            ``m * n * k`` samples are drawn.\n""\n""        See Also\n""        --------\n""        scipy.stats.distributions.norm : probability density function,\n""            distribution or cumulative density function, etc.\n""\n""        Notes\n""        -----\n""        The probability density for the Gaussian distribution is\n""\n""        .. math:: p(x) = \\frac{1}{\\sqrt{ 2 \\pi \\sigma^2 }}\n""                         e^{ - \\frac{ (x - \\mu)^2 } {2 \\sigma^2} },\n""\n""        where :math:`\\mu` is the mean and :math:`\\sigma` the standard deviation.\n""        The square of the standard deviation, :math:`\\sigma^2`, is called the\n""        variance.\n""\n""        The function has its peak at the mean, and its \"spread\" increases with\n""        the standard deviation (the function reaches 0.607 times its maximum at\n""        :math:`x + \\sigma` and :math:`x - \\sigma` [2]_).  This implies that\n""        `numpy.random.normal` is more likely to return samples lying close to the\n""        mean, rather than those far away.\n""\n""        References\n""        ----------\n""        .. [1] Wikipedia, \"Normal distribution\",\n""               http://en.wikipedia.org/wiki/Normal_distribution\n""        .. [2] P. R. Peebles Jr., \"Central Limit Theorem\" in \"Probability, Random\n""               Variables and Random Signal Principles\", 4th ed., 2001,\n""               pp. 51, 51, 125.\n""\n""        Examples\n""        --------\n""        Draw samples from the distribution:\n""\n""        >>> mu, sigma = 0, 0.1 # mean and standard deviation\n""        >>> s = np.random.normal(mu, sigma, 1000)\n""\n""        Verify the mean and the variance:\n""\n""        >>> abs(mu - np.mean(s)) < 0.01\n""        True\n""\n""        >>> abs(sigma - np.std(s, ddof=1)) < 0.01\n""        True\n""\n""        Display the histogram of the samples, along with\n""        the probability density function:\n""\n""        >>> import matplotlib.pyplot as plt\n""        >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n""        >>> plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *\n""        ...                np.exp( - (bins - mu)**2 / (2 * sigma**2) ),\n""        ...          linewidth=2, color='r')\n""        >>> plt.show()\n""\n""        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_normal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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@@ -3149,143 +5802,226 @@ static PyObject *__pyx_f_6mtrand_11RandomState_normal(PyObject *__pyx_v_self, Py
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   PyObject *__pyx_5 = 0;
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-  __pyx_v_loc = __pyx_k14;
-  __pyx_v_scale = __pyx_k15;
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-  Py_INCREF(__pyx_v_loc);
-  Py_INCREF(__pyx_v_scale);
-  Py_INCREF(__pyx_v_size);
+  __pyx_v_loc = __pyx_k_5;
+  __pyx_v_scale = __pyx_k_6;
+  __pyx_v_size = Py_None;
+  if (likely(!__pyx_kwds) && likely(0 <= PyTuple_GET_SIZE(__pyx_args)) && likely(PyTuple_GET_SIZE(__pyx_args) <= 3)) {
+    if (PyTuple_GET_SIZE(__pyx_args) > 0) {
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+      if (PyTuple_GET_SIZE(__pyx_args) > 1) {
+        __pyx_v_scale = PyTuple_GET_ITEM(__pyx_args, 1);
+        if (PyTuple_GET_SIZE(__pyx_args) > 2) {
+          __pyx_v_size = PyTuple_GET_ITEM(__pyx_args, 2);
+        }
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+  }
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+  }
+  goto __pyx_L4;
+  __pyx_L3_error:;
+  __Pyx_AddTraceback("mtrand.RandomState.normal");
+  return NULL;
+  __pyx_L4:;
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1059 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1059
+ *         cdef double floc, fscale
+ * 
+ *         floc = PyFloat_AsDouble(loc)             # <<<<<<<<<<<<<<
+ *         fscale = PyFloat_AsDouble(scale)
+ *         if not PyErr_Occurred():
+ */
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1060 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1060
+ * 
+ *         floc = PyFloat_AsDouble(loc)
+ *         fscale = PyFloat_AsDouble(scale)             # <<<<<<<<<<<<<<
+ *         if not PyErr_Occurred():
+ *             if fscale <= 0:
+ */
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1061 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1061
+ *         floc = PyFloat_AsDouble(loc)
+ *         fscale = PyFloat_AsDouble(scale)
+ *         if not PyErr_Occurred():             # <<<<<<<<<<<<<<
+ *             if fscale <= 0:
+ *                 raise ValueError("scale <= 0")
+ */
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1062 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1062
+ *         fscale = PyFloat_AsDouble(scale)
+ *         if not PyErr_Occurred():
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-      Py_DECREF(__pyx_2); __pyx_2 = 0;
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1063
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+ *             if fscale <= 0:
+ *                 raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
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+ * 
+ */
+      __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1063; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1063; goto __pyx_L1;}
-      goto __pyx_L3;
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1063; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      goto __pyx_L6;
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-    __pyx_L3:;
+    __pyx_L6:;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1064 */
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1064
+ *             if fscale <= 0:
+ *                 raise ValueError("scale <= 0")
+ *             return cont2_array_sc(self.internal_state, rk_normal, size, floc, fscale)             # <<<<<<<<<<<<<<
+ * 
+ *         PyErr_Clear()
+ */
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     goto __pyx_L0;
-    goto __pyx_L2;
+    goto __pyx_L5;
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-  __pyx_L2:;
+  __pyx_L5:;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1066 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1066
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+ * 
+ *         PyErr_Clear()             # <<<<<<<<<<<<<<
+ * 
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1068
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1069
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-  __pyx_L4:;
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1072
+ *         if np.any(np.less_equal(oscale, 0)):
+ *             raise ValueError("scale <= 0")
+ *         return cont2_array(self.internal_state, rk_normal, size, oloc, oscale)             # <<<<<<<<<<<<<<
+ * 
+ *     def beta(self, a, b, size=None):
+ */
+  __pyx_4 = __pyx_f_6mtrand_cont2_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_normal, __pyx_v_size, __pyx_v_oloc, __pyx_v_oscale); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1072; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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   goto __pyx_L0;
 
   __pyx_r = Py_None; Py_INCREF(Py_None);
   goto __pyx_L0;
-  __pyx_L1:;
+  __pyx_L1_error:;
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   Py_XDECREF(__pyx_3);
   Py_XDECREF(__pyx_4);
   Py_XDECREF(__pyx_5);
   __Pyx_AddTraceback("mtrand.RandomState.normal");
-  __pyx_r = 0;
+  __pyx_r = NULL;
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   Py_DECREF(__pyx_v_oloc);
   Py_DECREF(__pyx_v_oscale);
-  Py_DECREF(__pyx_v_self);
-  Py_DECREF(__pyx_v_loc);
-  Py_DECREF(__pyx_v_scale);
-  Py_DECREF(__pyx_v_size);
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 }
 
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-static PyObject *__pyx_k77p;
-static PyObject *__pyx_k78p;
-static PyObject *__pyx_k79p;
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-static char __pyx_k77[] = "b <= 0";
-static char __pyx_k78[] = "a <= 0";
-static char __pyx_k79[] = "b <= 0";
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1074
+ *         return cont2_array(self.internal_state, rk_normal, size, oloc, oscale)
+ * 
+ *     def beta(self, a, b, size=None):             # <<<<<<<<<<<<<<
+ *         """
+ *         beta(a, b, size=None)
+ */
 
-static PyObject *__pyx_f_6mtrand_11RandomState_beta(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static PyObject *__pyx_pf_6mtrand_11RandomState_beta(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
 static char __pyx_doc_6mtrand_11RandomState_beta[] = "\n        beta(a, b, size=None)\n\n        The Beta distribution over ``[0, 1]``.\n\n        The Beta distribution is a special case of the Dirichlet distribution,\n        and is related to the Gamma distribution.  It has the probability\n        distribution function\n\n        .. math:: f(x; a,b) = \\frac{1}{B(\\alpha, \\beta)} x^{\\alpha - 1}\n                                                         (1 - x)^{\\beta - 1},\n\n        where the normalisation, B, is the beta function,\n\n        .. math:: B(\\alpha, \\beta) = \\int_0^1 t^{\\alpha - 1}\n                                     (1 - t)^{\\beta - 1} dt.\n\n        It is often seen in Bayesian inference and order statistics.\n\n        Parameters\n        ----------\n        a : float\n            Alpha, non-negative.\n        b : float\n            Beta, non-negative.\n        size : tuple of ints, optional\n            The number of samples to draw.  The ouput is packed according to\n            the size given.\n\n        Returns\n        -------\n        out : ndarray\n            Array of the given shape, containing values drawn from a\n            Beta distribution.\n\n        ";
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-  Py_INCREF(__pyx_v_size);
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+  goto __pyx_L4;
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1114 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1114
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+ * 
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+ *         if not PyErr_Occurred():
+ */
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1115 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1115
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1116 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1116
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+ *         fb = PyFloat_AsDouble(b)
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+ */
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1117 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1117
+ *         fb = PyFloat_AsDouble(b)
+ *         if not PyErr_Occurred():
+ *             if fa <= 0:             # <<<<<<<<<<<<<<
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1118
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+ *                 raise ValueError("a <= 0")             # <<<<<<<<<<<<<<
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+ *                 raise ValueError("b <= 0")
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1119
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-      Py_DECREF(__pyx_2); __pyx_2 = 0;
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1120
+ *                 raise ValueError("a <= 0")
+ *             if fb <= 0:
+ *                 raise ValueError("b <= 0")             # <<<<<<<<<<<<<<
+ *             return cont2_array_sc(self.internal_state, rk_beta, size, fa, fb)
+ * 
+ */
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1120; goto __pyx_L1;}
-      goto __pyx_L4;
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1120; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    __pyx_L4:;
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1121
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-    goto __pyx_L2;
+    goto __pyx_L5;
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1123 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1123
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+ * 
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   PyErr_Clear();
 
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+ *         PyErr_Clear()
+ * 
+ *         oa = <ndarray>PyArray_FROM_OTF(a, NPY_DOUBLE, NPY_ALIGNED)             # <<<<<<<<<<<<<<
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1126
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1130
+ *             raise ValueError("a <= 0")
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+ *             raise ValueError("b <= 0")             # <<<<<<<<<<<<<<
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+ * 
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+    Py_DECREF(((PyObject *)__pyx_5)); __pyx_5 = 0;
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+    goto __pyx_L9;
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-  __pyx_L6:;
+  __pyx_L9:;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1131 */
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1131
+ *         if np.any(np.less_equal(ob, 0)):
+ *             raise ValueError("b <= 0")
+ *         return cont2_array(self.internal_state, rk_beta, size, oa, ob)             # <<<<<<<<<<<<<<
+ * 
+ *     def exponential(self, scale=1.0, size=None):
+ */
+  __pyx_2 = __pyx_f_6mtrand_cont2_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_beta, __pyx_v_size, __pyx_v_oa, __pyx_v_ob); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1131; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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   __pyx_r = Py_None; Py_INCREF(Py_None);
   goto __pyx_L0;
-  __pyx_L1:;
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-  Py_DECREF(__pyx_v_size);
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 }
 
-static PyObject *__pyx_k80p;
-static PyObject *__pyx_k81p;
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1133
+ *         return cont2_array(self.internal_state, rk_beta, size, oa, ob)
+ * 
+ *     def exponential(self, scale=1.0, size=None):             # <<<<<<<<<<<<<<
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+ */
 
-static char __pyx_k80[] = "scale <= 0";
-static char __pyx_k81[] = "scale <= 0";
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-static PyObject *__pyx_f_6mtrand_11RandomState_exponential(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static PyObject *__pyx_pf_6mtrand_11RandomState_exponential(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
 static char __pyx_doc_6mtrand_11RandomState_exponential[] = "\n        exponential(scale=1.0, size=None)\n\n        Exponential distribution.\n\n        Its probability density function is\n\n        .. math:: f(x; \\frac{1}{\\beta}) = \\frac{1}{\\beta} \\exp(-\\frac{x}{\\beta}),\n\n        for ``x > 0`` and 0 elsewhere. :math:`\\beta` is the scale parameter,\n        which is the inverse of the rate parameter :math:`\\lambda = 1/\\beta`.\n        The rate parameter is an alternative, widely used parameterization\n        of the exponential distribution [3]_.\n\n        The exponential distribution is a continuous analogue of the\n        geometric distribution.  It describes many common situations, such as\n        the size of raindrops measured over many rainstorms [1]_, or the time\n        between page requests to Wikipedia [2]_.\n\n        Parameters\n        ----------\n        scale : float\n            The scale parameter, :math:`\\beta = 1/\\lambda`.\n        size : tuple of ints\n            Number of samples to draw.  The output is shaped\n            according to `size`.\n\n        References\n        ----------\n        .. [1] Peyton Z. Peebles Jr., \"Probability, Random Variables and\n               Random Signal Principles\", 4th ed, 2001, p. 57.\n        .. [2] \"Poisson Process\", Wikipedia,\n               http://en.wikipedia.org/wiki/Poisson_process\n        .. [3] \"Exponential Distribution, Wikipedia,\n               http://en.wikipedia.org/wiki/Exponential_distribution\n\n        ";
-static PyObject *__pyx_f_6mtrand_11RandomState_exponential(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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-  Py_INCREF(__pyx_v_self);
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-  Py_INCREF(__pyx_v_size);
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+  }
+  goto __pyx_L4;
+  __pyx_L3_error:;
+  __Pyx_AddTraceback("mtrand.RandomState.exponential");
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1174 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1174
+ *         cdef double fscale
+ * 
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+ *         if not PyErr_Occurred():
+ *             if fscale <= 0:
+ */
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1175 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1175
+ * 
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+ *         if not PyErr_Occurred():             # <<<<<<<<<<<<<<
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+ *                 raise ValueError("scale <= 0")
+ */
   __pyx_1 = (!PyErr_Occurred());
   if (__pyx_1) {
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1176 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1176
+ *         fscale = PyFloat_AsDouble(scale)
+ *         if not PyErr_Occurred():
+ *             if fscale <= 0:             # <<<<<<<<<<<<<<
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+ */
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-      __pyx_2 = PyTuple_New(1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1177; goto __pyx_L1;}
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-      Py_DECREF(__pyx_2); __pyx_2 = 0;
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1177
+ *         if not PyErr_Occurred():
+ *             if fscale <= 0:
+ *                 raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
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+ * 
+ */
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+      Py_INCREF(__pyx_kp_39);
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       __Pyx_Raise(__pyx_3, 0, 0);
       Py_DECREF(__pyx_3); __pyx_3 = 0;
-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1177; goto __pyx_L1;}
-      goto __pyx_L3;
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1177; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      goto __pyx_L6;
     }
-    __pyx_L3:;
+    __pyx_L6:;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1178 */
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1178
+ *             if fscale <= 0:
+ *                 raise ValueError("scale <= 0")
+ *             return cont1_array_sc(self.internal_state, rk_exponential, size, fscale)             # <<<<<<<<<<<<<<
+ * 
+ *         PyErr_Clear()
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     __pyx_r = __pyx_2;
     __pyx_2 = 0;
     goto __pyx_L0;
-    goto __pyx_L2;
+    goto __pyx_L5;
   }
-  __pyx_L2:;
+  __pyx_L5:;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1180 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1180
+ *             return cont1_array_sc(self.internal_state, rk_exponential, size, fscale)
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   __pyx_r = Py_None; Py_INCREF(Py_None);
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-  __pyx_L1:;
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   Py_XDECREF(__pyx_4);
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 }
 
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 static char __pyx_doc_6mtrand_11RandomState_standard_exponential[] = "\n        standard_exponential(size=None)\n\n        Standard exponential distribution (scale=1).\n\n        ";
-static PyObject *__pyx_f_6mtrand_11RandomState_standard_exponential(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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 static char __pyx_doc_6mtrand_11RandomState_standard_gamma[] = "\n        standard_gamma(shape, size=None)\n\n        Standard Gamma distribution.\n\n        ";
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-      Py_DECREF(__pyx_2); __pyx_2 = 0;
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-      goto __pyx_L3;
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1210
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1212 */
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1215
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-  __pyx_L4:;
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1216
+ *         if np.any(np.less_equal(oshape, 0.0)):
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-  __pyx_L1:;
+  __pyx_L1_error:;
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-static PyObject *__pyx_k86p;
-static PyObject *__pyx_k87p;
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-static PyObject *__pyx_f_6mtrand_11RandomState_gamma(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1218
+ *         return cont1_array(self.internal_state, rk_standard_gamma, size, oshape)
+ * 
+ *     def gamma(self, shape, scale=1.0, size=None):             # <<<<<<<<<<<<<<
+ *         """
+ *         gamma(shape, scale=1.0, size=None)
+ */
+
+static PyObject *__pyx_pf_6mtrand_11RandomState_gamma(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_gamma[] = "\n""        gamma(shape, scale=1.0, size=None)\n""\n""        Draw samples from a Gamma distribution.\n""\n""        Samples are drawn from a Gamma distribution with specified parameters,\n""        `shape` (sometimes designated \"k\") and `scale` (sometimes designated\n""        \"theta\"), where both parameters are > 0.\n""\n""        Parameters\n""        ----------\n""        shape : scalar > 0\n""            The shape of the gamma distribution.\n""        scale : scalar > 0, optional\n""            The scale of the gamma distribution.  Default is equal to 1.\n""        size : shape_tuple, optional\n""            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n""            ``m * n * k`` samples are drawn.\n""\n""        Returns\n""        -------\n""        out : ndarray, float\n""            Returns one sample unless `size` parameter is specified.\n""\n""        See Also\n""        --------\n""        scipy.stats.distributions.gamma : probability density function,\n""            distribution or cumulative density function, etc.\n""\n""        Notes\n""        -----\n""        The probability density for the Gamma distribution is\n""\n""        .. math:: p(x) = x^{k-1}\\frac{e^{-x/\\theta}}{\\theta^k\\Gamma(k)},\n""\n""        where :math:`k` is the shape and :math:`\\theta` the scale,\n""        and :math:`\\Gamma` is the Gamma function.\n""\n""        The Gamma distribution is often used to model the times to failure of\n""        electronic components, and arises naturally in processes for which the\n""        waiting times between Poisson distributed events are relevant.\n""\n""        References\n""        ----------\n""        .. [1] Weisstein, Eric W. \"Gamma Distribution.\" From MathWorld--A\n""               Wolfram Web Resource.\n""               http://mathworld.wolfram.com/GammaDistribution.html\n""        .. [2] Wikipedia, \"Gamma-distribution\",\n""               http://en.wikipedia.org/wiki/Gamma-distribution\n""\n""        Examples\n""        --------\n""        Draw samples from the distribution:\n""\n""        >>> shape, scale = 2., 2. # mean and dispersion\n""        >>> s = np.random.gamma(shape, scale, 1000)\n""\n""        Display the histogram of the samples, along with\n""        the probability density function:\n""\n""        >>> import matplotlib.pyplot as plt\n""        >>> import scipy.special as sps\n""        >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n""        >>> y = bins**(shape-1)*((exp(-bins/scale))/\\\n""            (sps.gamma(shape)*scale**shape))\n""        >>> plt.plot(bins, y, linewidth=2, color='r')\n""        >>> plt.show()\n""\n""        ";
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1293 */
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+ *         fscale = PyFloat_AsDouble(scale)
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-    PyTuple_SET_ITEM(__pyx_4, 0, __pyx_k87p);
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-    Py_DECREF(__pyx_4); __pyx_4 = 0;
+
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1306
+ *             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_4 = PyTuple_New(1); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1306; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    Py_INCREF(__pyx_kp_46);
+    PyTuple_SET_ITEM(__pyx_4, 0, __pyx_kp_46);
+    __pyx_5 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_4), NULL); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1306; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    Py_DECREF(((PyObject *)__pyx_4)); __pyx_4 = 0;
     __Pyx_Raise(__pyx_5, 0, 0);
     Py_DECREF(__pyx_5); __pyx_5 = 0;
-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1306; goto __pyx_L1;}
-    goto __pyx_L6;
+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1306; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    goto __pyx_L9;
   }
-  __pyx_L6:;
+  __pyx_L9:;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1307 */
-  __pyx_2 = __pyx_f_6mtrand_cont2_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state,rk_gamma,__pyx_v_size,__pyx_v_oshape,__pyx_v_oscale); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1307; goto __pyx_L1;}
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1307
+ *         if np.any(np.less_equal(oscale, 0.0)):
+ *             raise ValueError("scale <= 0")
+ *         return cont2_array(self.internal_state, rk_gamma, size, oshape, oscale)             # <<<<<<<<<<<<<<
+ * 
+ *     def f(self, dfnum, dfden, size=None):
+ */
+  __pyx_2 = __pyx_f_6mtrand_cont2_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_gamma, __pyx_v_size, __pyx_v_oshape, __pyx_v_oscale); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1307; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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   goto __pyx_L0;
 
   __pyx_r = Py_None; Py_INCREF(Py_None);
   goto __pyx_L0;
-  __pyx_L1:;
+  __pyx_L1_error:;
   Py_XDECREF(__pyx_2);
   Py_XDECREF(__pyx_3);
   Py_XDECREF(__pyx_4);
   Py_XDECREF(__pyx_5);
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-  __pyx_r = 0;
+  __pyx_r = NULL;
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   Py_DECREF(__pyx_v_oshape);
   Py_DECREF(__pyx_v_oscale);
-  Py_DECREF(__pyx_v_self);
-  Py_DECREF(__pyx_v_shape);
-  Py_DECREF(__pyx_v_scale);
-  Py_DECREF(__pyx_v_size);
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 }
 
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-static PyObject *__pyx_k89p;
-static PyObject *__pyx_k90p;
-static PyObject *__pyx_k91p;
-
-static char __pyx_k88[] = "shape <= 0";
-static char __pyx_k89[] = "scale <= 0";
-static char __pyx_k90[] = "dfnum <= 0";
-static char __pyx_k91[] = "dfden <= 0";
-
-static PyObject *__pyx_f_6mtrand_11RandomState_f(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_f[] = "\n        f(dfnum, dfden, size=None)\n\n        Draw samples from a F distribution.\n\n        Samples are drawn from an F distribution with specified parameters,\n        `dfnum` (degrees of freedom in numerator) and `dfden` (degrees of freedom\n        in denominator), where both parameters should be greater than zero.\n\n        The random variate of the F distribution (also known as the\n        Fisher distribution) is a continuous probability distribution\n        that arises in ANOVA tests, and is the ratio of two chi-square\n        variates.\n\n        Parameters\n        ----------\n        dfnum : float\n            Degrees of freedom in numerator. Should be greater than zero.\n        dfden : float\n            Degrees of freedom in denominator. Should be greater than zero.\n        size : {tuple, int}, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``,\n            then ``m * n * k`` samples are drawn. By default only one sample\n            is returned.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n            Samples from the Fisher distribution.\n\n        See Also\n        --------\n        scipy.stats.distributions.f : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n\n        The F statistic is used to compare in-group variances to between-group\n        variances. Calculating the distribution depends on the sampling, and\n        so it is a function of the respective degrees of freedom in the\n        problem.  The variable `dfnum` is the number of samples minus one, the\n        between-groups degrees of freedom, while `dfden` is the within-groups\n        degrees of freedom, the sum of the number of samples in each group\n        minus the number of groups.\n\n        References\n        ----------\n        .. [1] Glantz, Stanton A. \"Primer of Biostatistics.\", McGraw-Hill,\n               Fifth Edition, 2002.\n        .. [2] Wikipedia, \"F-distribution\",\n               http://en.wikipedia.org/wiki/F-distribution\n\n        Examples\n        --------\n        An example from Glantz[1], pp 47-40.\n        Two groups, children of diabetics (25 people) and children from people\n        without diabetes (25 controls). Fasting blood glucose was measured,\n        case group had a mean value of 86.1, controls had a mean value of\n        82.2. Standard deviations were 2.09 and 2.49 respectively. Are these\n        data consistent with the null hypothesis that the parents diabetic\n        status does not affect their children\'s blood glucose levels?\n        Calculating the F statistic from the data gives a value of 36.01.\n\n        Draw samples from the distribution:\n\n        >>> dfnum = 1. # between group degrees of freedom\n        >>> dfden = 48. # within groups degrees of freedom\n        >>> s = np.random.f(dfnum, dfden, 1000)\n\n        The lower bound for the top 1% of the samples is :\n\n        >>> sort(s)[-10]\n        7.61988120985\n\n        So there is about a 1% chance that the F statistic will exceed 7.62,\n        the measured value is 36, so the null hypothesis is rejected at the 1%\n        level.\n\n        ";
-static PyObject *__pyx_f_6mtrand_11RandomState_f(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1309
+ *         return cont2_array(self.internal_state, rk_gamma, size, oshape, oscale)
+ * 
+ *     def f(self, dfnum, dfden, size=None):             # <<<<<<<<<<<<<<
+ *         """
+ *         f(dfnum, dfden, size=None)
+ */
+
+static PyObject *__pyx_pf_6mtrand_11RandomState_f(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_f[] = "\n""        f(dfnum, dfden, size=None)\n""\n""        Draw samples from a F distribution.\n""\n""        Samples are drawn from an F distribution with specified parameters,\n""        `dfnum` (degrees of freedom in numerator) and `dfden` (degrees of freedom\n""        in denominator), where both parameters should be greater than zero.\n""\n""        The random variate of the F distribution (also known as the\n""        Fisher distribution) is a continuous probability distribution\n""        that arises in ANOVA tests, and is the ratio of two chi-square\n""        variates.\n""\n""        Parameters\n""        ----------\n""        dfnum : float\n""            Degrees of freedom in numerator. Should be greater than zero.\n""        dfden : float\n""            Degrees of freedom in denominator. Should be greater than zero.\n""        size : {tuple, int}, optional\n""            Output shape.  If the given shape is, e.g., ``(m, n, k)``,\n""            then ``m * n * k`` samples are drawn. By default only one sample\n""            is returned.\n""\n""        Returns\n""        -------\n""        samples : {ndarray, scalar}\n""            Samples from the Fisher distribution.\n""\n""        See Also\n""        --------\n""        scipy.stats.distributions.f : probability density function,\n""            distribution or cumulative density function, etc.\n""\n""        Notes\n""        -----\n""\n""        The F statistic is used to compare in-group variances to between-group\n""        variances. Calculating the distribution depends on the sampling, and\n""        so it is a function of the respective degrees of freedom in the\n""        problem.  The variable `dfnum` is the number of samples minus one, the\n""        between-groups degrees of freedom, while `dfden` is the within-groups\n""        degrees of freedom, the sum of the number of samples in each group\n""        minus the number of groups.\n""\n""        References\n""        ----------\n""        .. [1] Glantz, Stanton A. \"Primer of Biostatistics.\", McGraw-Hill,\n""               Fifth Edition, 2002.\n""        .. [2] Wikipedia, \"F-distribution\",\n""               http://en.wikipedia.org/wiki/F-distribution\n""\n""        Examples\n""        --------\n""        An example from Glantz[1], pp 47-40.\n""        Two groups, children of diabetics (25 people) and children from people\n""        without diabetes (25 controls). Fasting blood glucose was measured,\n""        case group had a mean value of 86.1, controls had a mean value of\n""        82.2. Standard deviations were 2.09 and 2.49 respectively. Are these\n""        data consistent with the null hypothesis that the parents diabetic\n""        status does not affect their children's blood glucose levels?\n""        Calculating the F statistic from the data gives a value of 36.01.\n""\n""        Draw samples from the distribution:\n""\n""        >>> dfnum = 1. # between group degrees of freedom\n""        >>> dfden = 48. # within groups degrees of freedom\n""        >>> s = np.random.f(dfnum, dfden, 1000)\n""\n""        The lower bound for the top 1% of the samples is :\n""\n""        >>> sort(s)[-10]\n""        7.61988120985\n""\n""        So there is about a 1% chance that the F statistic will exceed 7.62,\n""        the measured value is 36, so the null hypothesis is rejected at the 1%\n""        level.\n""\n""        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_f(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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@@ -3988,199 +7112,301 @@ static PyObject *__pyx_f_6mtrand_11RandomState_f(PyObject *__pyx_v_self, PyObjec
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   PyObject *__pyx_5 = 0;
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-  __pyx_v_size = __pyx_k24;
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-  Py_INCREF(__pyx_v_self);
-  Py_INCREF(__pyx_v_dfnum);
-  Py_INCREF(__pyx_v_dfden);
-  Py_INCREF(__pyx_v_size);
+  __pyx_v_size = Py_None;
+  if (likely(!__pyx_kwds) && likely(2 <= PyTuple_GET_SIZE(__pyx_args)) && likely(PyTuple_GET_SIZE(__pyx_args) <= 3)) {
+    __pyx_v_dfnum = PyTuple_GET_ITEM(__pyx_args, 0);
+    __pyx_v_dfden = PyTuple_GET_ITEM(__pyx_args, 1);
+    if (PyTuple_GET_SIZE(__pyx_args) > 2) {
+      __pyx_v_size = PyTuple_GET_ITEM(__pyx_args, 2);
+    }
+  }
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+  }
+  goto __pyx_L4;
+  __pyx_L3_error:;
+  __Pyx_AddTraceback("mtrand.RandomState.f");
+  return NULL;
+  __pyx_L4:;
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1393 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1393
+ *         cdef double fdfnum, fdfden
+ * 
+ *         fdfnum = PyFloat_AsDouble(dfnum)             # <<<<<<<<<<<<<<
+ *         fdfden = PyFloat_AsDouble(dfden)
+ *         if not PyErr_Occurred():
+ */
   __pyx_v_fdfnum = PyFloat_AsDouble(__pyx_v_dfnum);
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1394 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1394
+ * 
+ *         fdfnum = PyFloat_AsDouble(dfnum)
+ *         fdfden = PyFloat_AsDouble(dfden)             # <<<<<<<<<<<<<<
+ *         if not PyErr_Occurred():
+ *             if fdfnum <= 0:
+ */
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1395 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1395
+ *         fdfnum = PyFloat_AsDouble(dfnum)
+ *         fdfden = PyFloat_AsDouble(dfden)
+ *         if not PyErr_Occurred():             # <<<<<<<<<<<<<<
+ *             if fdfnum <= 0:
+ *                 raise ValueError("shape <= 0")
+ */
   __pyx_1 = (!PyErr_Occurred());
   if (__pyx_1) {
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1396 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1396
+ *         fdfden = PyFloat_AsDouble(dfden)
+ *         if not PyErr_Occurred():
+ *             if fdfnum <= 0:             # <<<<<<<<<<<<<<
+ *                 raise ValueError("shape <= 0")
+ *             if fdfden <= 0:
+ */
     __pyx_1 = (__pyx_v_fdfnum <= 0);
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-      __pyx_2 = PyTuple_New(1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1397; goto __pyx_L1;}
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-      __pyx_3 = PyObject_CallObject(PyExc_ValueError, __pyx_2); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1397; goto __pyx_L1;}
-      Py_DECREF(__pyx_2); __pyx_2 = 0;
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1397
+ *         if not PyErr_Occurred():
+ *             if fdfnum <= 0:
+ *                 raise ValueError("shape <= 0")             # <<<<<<<<<<<<<<
+ *             if fdfden <= 0:
+ *                 raise ValueError("scale <= 0")
+ */
+      __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1397; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      Py_INCREF(__pyx_kp_47);
+      PyTuple_SET_ITEM(__pyx_2, 0, __pyx_kp_47);
+      __pyx_3 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1397; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0;
       __Pyx_Raise(__pyx_3, 0, 0);
       Py_DECREF(__pyx_3); __pyx_3 = 0;
-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1397; goto __pyx_L1;}
-      goto __pyx_L3;
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1397; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      goto __pyx_L6;
     }
-    __pyx_L3:;
+    __pyx_L6:;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1398 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1398
+ *             if fdfnum <= 0:
+ *                 raise ValueError("shape <= 0")
+ *             if fdfden <= 0:             # <<<<<<<<<<<<<<
+ *                 raise ValueError("scale <= 0")
+ *             return cont2_array_sc(self.internal_state, rk_f, size, fdfnum, fdfden)
+ */
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-      __pyx_2 = PyTuple_New(1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1399; goto __pyx_L1;}
-      Py_INCREF(__pyx_k89p);
-      PyTuple_SET_ITEM(__pyx_2, 0, __pyx_k89p);
-      __pyx_3 = PyObject_CallObject(PyExc_ValueError, __pyx_2); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1399; goto __pyx_L1;}
-      Py_DECREF(__pyx_2); __pyx_2 = 0;
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1399
+ *                 raise ValueError("shape <= 0")
+ *             if fdfden <= 0:
+ *                 raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
+ *             return cont2_array_sc(self.internal_state, rk_f, size, fdfnum, fdfden)
+ * 
+ */
+      __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1399; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      Py_INCREF(__pyx_kp_48);
+      PyTuple_SET_ITEM(__pyx_2, 0, __pyx_kp_48);
+      __pyx_3 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1399; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0;
       __Pyx_Raise(__pyx_3, 0, 0);
       Py_DECREF(__pyx_3); __pyx_3 = 0;
-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1399; goto __pyx_L1;}
-      goto __pyx_L4;
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1399; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      goto __pyx_L7;
     }
-    __pyx_L4:;
+    __pyx_L7:;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1400 */
-    __pyx_2 = __pyx_f_6mtrand_cont2_array_sc(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state,rk_f,__pyx_v_size,__pyx_v_fdfnum,__pyx_v_fdfden); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1400; goto __pyx_L1;}
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1400
+ *             if fdfden <= 0:
+ *                 raise ValueError("scale <= 0")
+ *             return cont2_array_sc(self.internal_state, rk_f, size, fdfnum, fdfden)             # <<<<<<<<<<<<<<
+ * 
+ *         PyErr_Clear()
+ */
+    __pyx_2 = __pyx_f_6mtrand_cont2_array_sc(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_f, __pyx_v_size, __pyx_v_fdfnum, __pyx_v_fdfden); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1400; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     __pyx_r = __pyx_2;
     __pyx_2 = 0;
     goto __pyx_L0;
-    goto __pyx_L2;
+    goto __pyx_L5;
   }
-  __pyx_L2:;
+  __pyx_L5:;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1402 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1402
+ *             return cont2_array_sc(self.internal_state, rk_f, size, fdfnum, fdfden)
+ * 
+ *         PyErr_Clear()             # <<<<<<<<<<<<<<
+ * 
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+ */
   PyErr_Clear();
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1404 */
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1404
+ *         PyErr_Clear()
+ * 
+ *         odfnum = <ndarray>PyArray_FROM_OTF(dfnum, NPY_DOUBLE, NPY_ALIGNED)             # <<<<<<<<<<<<<<
+ *         odfden = <ndarray>PyArray_FROM_OTF(dfden, NPY_DOUBLE, NPY_ALIGNED)
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+    __pyx_v_nonc = PyTuple_GET_ITEM(__pyx_args, 2);
+    if (PyTuple_GET_SIZE(__pyx_args) > 3) {
+      __pyx_v_size = PyTuple_GET_ITEM(__pyx_args, 3);
+    }
+  }
+  else {
+    if (unlikely(!PyArg_ParseTupleAndKeywords(__pyx_args, __pyx_kwds, "OOO|O", __pyx_argnames, &__pyx_v_dfnum, &__pyx_v_dfden, &__pyx_v_nonc, &__pyx_v_size))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1412; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+  }
+  goto __pyx_L4;
+  __pyx_L3_error:;
+  __Pyx_AddTraceback("mtrand.RandomState.noncentral_f");
+  return NULL;
+  __pyx_L4:;
   __pyx_v_odfnum = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
   __pyx_v_odfden = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
   __pyx_v_ononc = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1422 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1422
+ *         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);
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1423 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1423
+ * 
+ *         fdfnum = PyFloat_AsDouble(dfnum)
+ *         fdfden = PyFloat_AsDouble(dfden)             # <<<<<<<<<<<<<<
+ *         fnonc = PyFloat_AsDouble(nonc)
+ *         if not PyErr_Occurred():
+ */
   __pyx_v_fdfden = PyFloat_AsDouble(__pyx_v_dfden);
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1424 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1424
+ *         fdfnum = PyFloat_AsDouble(dfnum)
+ *         fdfden = PyFloat_AsDouble(dfden)
+ *         fnonc = PyFloat_AsDouble(nonc)             # <<<<<<<<<<<<<<
+ *         if not PyErr_Occurred():
+ *             if fdfnum <= 1:
+ */
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1425 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1425
+ *         fdfden = PyFloat_AsDouble(dfden)
+ *         fnonc = PyFloat_AsDouble(nonc)
+ *         if not PyErr_Occurred():             # <<<<<<<<<<<<<<
+ *             if fdfnum <= 1:
+ *                 raise ValueError("dfnum <= 1")
+ */
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   if (__pyx_1) {
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1426 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1426
+ *         fnonc = PyFloat_AsDouble(nonc)
+ *         if not PyErr_Occurred():
+ *             if fdfnum <= 1:             # <<<<<<<<<<<<<<
+ *                 raise ValueError("dfnum <= 1")
+ *             if fdfden <= 0:
+ */
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-      __pyx_2 = PyTuple_New(1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1427; goto __pyx_L1;}
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-      Py_DECREF(__pyx_2); __pyx_2 = 0;
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1427
+ *         if not PyErr_Occurred():
+ *             if fdfnum <= 1:
+ *                 raise ValueError("dfnum <= 1")             # <<<<<<<<<<<<<<
+ *             if fdfden <= 0:
+ *                 raise ValueError("dfden <= 0")
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+      __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1427; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      Py_INCREF(__pyx_kp_51);
+      PyTuple_SET_ITEM(__pyx_2, 0, __pyx_kp_51);
+      __pyx_3 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1427; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0;
       __Pyx_Raise(__pyx_3, 0, 0);
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1427; goto __pyx_L1;}
-      goto __pyx_L3;
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1427; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      goto __pyx_L6;
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-    __pyx_L3:;
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1428 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1428
+ *             if fdfnum <= 1:
+ *                 raise ValueError("dfnum <= 1")
+ *             if fdfden <= 0:             # <<<<<<<<<<<<<<
+ *                 raise ValueError("dfden <= 0")
+ *             if fnonc < 0:
+ */
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-      Py_DECREF(__pyx_2); __pyx_2 = 0;
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1429
+ *                 raise ValueError("dfnum <= 1")
+ *             if fdfden <= 0:
+ *                 raise ValueError("dfden <= 0")             # <<<<<<<<<<<<<<
+ *             if fnonc < 0:
+ *                 raise ValueError("nonc < 0")
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+      __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1429; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      Py_INCREF(__pyx_kp_52);
+      PyTuple_SET_ITEM(__pyx_2, 0, __pyx_kp_52);
+      __pyx_3 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1429; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-      goto __pyx_L4;
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1429; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1430 */
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1431; goto __pyx_L1;}
-      goto __pyx_L5;
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1431; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      goto __pyx_L8;
     }
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1432 */
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+    __pyx_L8:;
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1433
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-    goto __pyx_L2;
+    goto __pyx_L5;
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-  __pyx_L2:;
+  __pyx_L5:;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1435 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1435
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1437 */
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1437
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+  __pyx_3 = PyArray_FROM_OTF(__pyx_v_dfnum, NPY_DOUBLE, NPY_ALIGNED); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1437; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1438 */
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1438
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1439 */
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1439
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+ *         odfden = <ndarray>PyArray_FROM_OTF(dfden, NPY_DOUBLE, NPY_ALIGNED)
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1441
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-static PyObject *__pyx_f_6mtrand_11RandomState_chisquare(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static PyObject *__pyx_pf_6mtrand_11RandomState_chisquare(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
 static char __pyx_doc_6mtrand_11RandomState_chisquare[] = "\n        chisquare(df, size=None)\n\n        Draw samples from a chi-square distribution.\n\n        When `df` independent random variables, each with standard\n        normal distributions (mean 0, variance 1), are squared and summed,\n        the resulting distribution is chi-square (see Notes).  This\n        distribution 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        .. [1] NIST/SEMATECH e-Handbook of Statistical Methods,\n               http://www.itl.nist.gov/div898/handbook/eda/section3/eda3666.htm\n        .. [2] Wikipedia, \"Chi-square distribution\",\n               http://en.wikipedia.org/wiki/Chi-square_distribution\n\n        Examples\n        --------\n        >>> np.random.chisquare(2,4)\n        array([ 1.89920014,  9.00867716,  3.13710533,  5.62318272])\n\n        ";
-static PyObject *__pyx_f_6mtrand_11RandomState_chisquare(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_chisquare(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
   PyObject *__pyx_v_df = 0;
   PyObject *__pyx_v_size = 0;
   PyArrayObject *__pyx_v_odf;
@@ -4458,127 +7835,196 @@ static PyObject *__pyx_f_6mtrand_11RandomState_chisquare(PyObject *__pyx_v_self,
   PyObject *__pyx_4 = 0;
   PyObject *__pyx_5 = 0;
   static char *__pyx_argnames[] = {"df","size",0};
-  __pyx_v_size = __pyx_k26;
-  if (!PyArg_ParseTupleAndKeywords(__pyx_args, __pyx_kwds, "O|O", __pyx_argnames, &__pyx_v_df, &__pyx_v_size)) return 0;
-  Py_INCREF(__pyx_v_self);
-  Py_INCREF(__pyx_v_df);
-  Py_INCREF(__pyx_v_size);
+  __pyx_v_size = Py_None;
+  if (likely(!__pyx_kwds) && likely(1 <= PyTuple_GET_SIZE(__pyx_args)) && likely(PyTuple_GET_SIZE(__pyx_args) <= 2)) {
+    __pyx_v_df = PyTuple_GET_ITEM(__pyx_args, 0);
+    if (PyTuple_GET_SIZE(__pyx_args) > 1) {
+      __pyx_v_size = PyTuple_GET_ITEM(__pyx_args, 1);
+    }
+  }
+  else {
+    if (unlikely(!PyArg_ParseTupleAndKeywords(__pyx_args, __pyx_kwds, "O|O", __pyx_argnames, &__pyx_v_df, &__pyx_v_size))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1450; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+  }
+  goto __pyx_L4;
+  __pyx_L3_error:;
+  __Pyx_AddTraceback("mtrand.RandomState.chisquare");
+  return NULL;
+  __pyx_L4:;
   __pyx_v_odf = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1517 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1517
+ *         cdef double fdf
+ * 
+ *         fdf = PyFloat_AsDouble(df)             # <<<<<<<<<<<<<<
+ *         if not PyErr_Occurred():
+ *             if fdf <= 0:
+ */
   __pyx_v_fdf = PyFloat_AsDouble(__pyx_v_df);
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1518 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1518
+ * 
+ *         fdf = PyFloat_AsDouble(df)
+ *         if not PyErr_Occurred():             # <<<<<<<<<<<<<<
+ *             if fdf <= 0:
+ *                 raise ValueError("df <= 0")
+ */
   __pyx_1 = (!PyErr_Occurred());
   if (__pyx_1) {
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1519 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1519
+ *         fdf = PyFloat_AsDouble(df)
+ *         if not PyErr_Occurred():
+ *             if fdf <= 0:             # <<<<<<<<<<<<<<
+ *                 raise ValueError("df <= 0")
+ *             return cont1_array_sc(self.internal_state, rk_chisquare, size, fdf)
+ */
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-      __pyx_2 = PyTuple_New(1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1520; goto __pyx_L1;}
-      Py_INCREF(__pyx_k98p);
-      PyTuple_SET_ITEM(__pyx_2, 0, __pyx_k98p);
-      __pyx_3 = PyObject_CallObject(PyExc_ValueError, __pyx_2); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1520; goto __pyx_L1;}
-      Py_DECREF(__pyx_2); __pyx_2 = 0;
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1520
+ *         if not PyErr_Occurred():
+ *             if fdf <= 0:
+ *                 raise ValueError("df <= 0")             # <<<<<<<<<<<<<<
+ *             return cont1_array_sc(self.internal_state, rk_chisquare, size, fdf)
+ * 
+ */
+      __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1520; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      Py_INCREF(__pyx_kp_57);
+      PyTuple_SET_ITEM(__pyx_2, 0, __pyx_kp_57);
+      __pyx_3 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1520; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0;
       __Pyx_Raise(__pyx_3, 0, 0);
       Py_DECREF(__pyx_3); __pyx_3 = 0;
-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1520; goto __pyx_L1;}
-      goto __pyx_L3;
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1520; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      goto __pyx_L6;
     }
-    __pyx_L3:;
+    __pyx_L6:;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1521 */
-    __pyx_2 = __pyx_f_6mtrand_cont1_array_sc(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state,rk_chisquare,__pyx_v_size,__pyx_v_fdf); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1521; goto __pyx_L1;}
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1521
+ *             if fdf <= 0:
+ *                 raise ValueError("df <= 0")
+ *             return cont1_array_sc(self.internal_state, rk_chisquare, size, fdf)             # <<<<<<<<<<<<<<
+ * 
+ *         PyErr_Clear()
+ */
+    __pyx_2 = __pyx_f_6mtrand_cont1_array_sc(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_chisquare, __pyx_v_size, __pyx_v_fdf); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1521; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     __pyx_r = __pyx_2;
     __pyx_2 = 0;
     goto __pyx_L0;
-    goto __pyx_L2;
+    goto __pyx_L5;
   }
-  __pyx_L2:;
+  __pyx_L5:;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1523 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1523
+ *             return cont1_array_sc(self.internal_state, rk_chisquare, size, fdf)
+ * 
+ *         PyErr_Clear()             # <<<<<<<<<<<<<<
+ * 
+ *         odf = <ndarray>PyArray_FROM_OTF(df, NPY_DOUBLE, NPY_ALIGNED)
+ */
   PyErr_Clear();
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1525 */
-  __pyx_3 = PyArray_FROM_OTF(__pyx_v_df,NPY_DOUBLE,NPY_ALIGNED); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1525; goto __pyx_L1;}
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1525
+ *         PyErr_Clear()
+ * 
+ *         odf = <ndarray>PyArray_FROM_OTF(df, NPY_DOUBLE, NPY_ALIGNED)             # <<<<<<<<<<<<<<
+ *         if np.any(np.less_equal(odf, 0.0)):
+ *             raise ValueError("df <= 0")
+ */
+  __pyx_3 = PyArray_FROM_OTF(__pyx_v_df, NPY_DOUBLE, NPY_ALIGNED); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1525; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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   Py_DECREF(((PyObject *)__pyx_v_odf));
   __pyx_v_odf = ((PyArrayObject *)__pyx_3);
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-  __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_n_any); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1526; goto __pyx_L1;}
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1526
+ * 
+ *         odf = <ndarray>PyArray_FROM_OTF(df, NPY_DOUBLE, NPY_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_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1526; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_kp_any); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1526; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   Py_DECREF(__pyx_2); __pyx_2 = 0;
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+  __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1526; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_4 = PyObject_GetAttr(__pyx_2, __pyx_kp_less_equal); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1526; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  __pyx_5 = PyTuple_New(2); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1526; goto __pyx_L1;}
+  __pyx_2 = PyFloat_FromDouble(0.0); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1526; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_5 = PyTuple_New(2); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1526; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_4 = PyTuple_New(1); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1526; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  __pyx_1 = PyObject_IsTrue(__pyx_5); if (__pyx_1 < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1526; goto __pyx_L1;}
+  Py_DECREF(((PyObject *)__pyx_4)); __pyx_4 = 0;
+  __pyx_1 = __Pyx_PyObject_IsTrue(__pyx_5); if (unlikely(__pyx_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1526; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    Py_DECREF(__pyx_2); __pyx_2 = 0;
+
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1527
+ *         odf = <ndarray>PyArray_FROM_OTF(df, NPY_DOUBLE, NPY_ALIGNED)
+ *         if np.any(np.less_equal(odf, 0.0)):
+ *             raise ValueError("df <= 0")             # <<<<<<<<<<<<<<
+ *         return cont1_array(self.internal_state, rk_chisquare, size, odf)
+ * 
+ */
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+    Py_INCREF(__pyx_kp_58);
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+    Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0;
     __Pyx_Raise(__pyx_3, 0, 0);
     Py_DECREF(__pyx_3); __pyx_3 = 0;
-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1527; goto __pyx_L1;}
-    goto __pyx_L4;
+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1527; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    goto __pyx_L7;
   }
-  __pyx_L4:;
+  __pyx_L7:;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1528 */
-  __pyx_4 = __pyx_f_6mtrand_cont1_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state,rk_chisquare,__pyx_v_size,__pyx_v_odf); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1528; goto __pyx_L1;}
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1528
+ *         if np.any(np.less_equal(odf, 0.0)):
+ *             raise ValueError("df <= 0")
+ *         return cont1_array(self.internal_state, rk_chisquare, size, odf)             # <<<<<<<<<<<<<<
+ * 
+ *     def noncentral_chisquare(self, df, nonc, size=None):
+ */
+  __pyx_4 = __pyx_f_6mtrand_cont1_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_chisquare, __pyx_v_size, __pyx_v_odf); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1528; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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   goto __pyx_L0;
 
   __pyx_r = Py_None; Py_INCREF(Py_None);
   goto __pyx_L0;
-  __pyx_L1:;
+  __pyx_L1_error:;
   Py_XDECREF(__pyx_2);
   Py_XDECREF(__pyx_3);
   Py_XDECREF(__pyx_4);
   Py_XDECREF(__pyx_5);
   __Pyx_AddTraceback("mtrand.RandomState.chisquare");
-  __pyx_r = 0;
+  __pyx_r = NULL;
   __pyx_L0:;
   Py_DECREF(__pyx_v_odf);
-  Py_DECREF(__pyx_v_self);
-  Py_DECREF(__pyx_v_df);
-  Py_DECREF(__pyx_v_size);
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 }
 
-static PyObject *__pyx_k100p;
-static PyObject *__pyx_k101p;
-static PyObject *__pyx_k102p;
-static PyObject *__pyx_k103p;
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-static char __pyx_k100[] = "df <= 0";
-static char __pyx_k101[] = "nonc <= 0";
-static char __pyx_k102[] = "df <= 1";
-static char __pyx_k103[] = "nonc < 0";
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1530
+ *         return cont1_array(self.internal_state, rk_chisquare, size, odf)
+ * 
+ *     def noncentral_chisquare(self, df, nonc, size=None):             # <<<<<<<<<<<<<<
+ *         """
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+ */
 
-static PyObject *__pyx_f_6mtrand_11RandomState_noncentral_chisquare(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static PyObject *__pyx_pf_6mtrand_11RandomState_noncentral_chisquare(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
 static char __pyx_doc_6mtrand_11RandomState_noncentral_chisquare[] = "\n        noncentral_chisquare(df, nonc, size=None)\n\n        Draw samples from a noncentral chi-square distribution.\n\n        The noncentral :math:`\\chi^2` distribution is a generalisation of\n        the :math:`\\chi^2` distribution.\n\n        Parameters\n        ----------\n        df : int\n            Degrees of freedom.\n        nonc : float\n            Non-centrality.\n        size : tuple of ints\n            Shape of the output.\n\n        ";
-static PyObject *__pyx_f_6mtrand_11RandomState_noncentral_chisquare(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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@@ -4593,217 +8039,353 @@ static PyObject *__pyx_f_6mtrand_11RandomState_noncentral_chisquare(PyObject *__
   PyObject *__pyx_4 = 0;
   PyObject *__pyx_5 = 0;
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-  __pyx_v_size = __pyx_k27;
-  if (!PyArg_ParseTupleAndKeywords(__pyx_args, __pyx_kwds, "OO|O", __pyx_argnames, &__pyx_v_df, &__pyx_v_nonc, &__pyx_v_size)) return 0;
-  Py_INCREF(__pyx_v_self);
-  Py_INCREF(__pyx_v_df);
-  Py_INCREF(__pyx_v_nonc);
-  Py_INCREF(__pyx_v_size);
+  __pyx_v_size = Py_None;
+  if (likely(!__pyx_kwds) && likely(2 <= PyTuple_GET_SIZE(__pyx_args)) && likely(PyTuple_GET_SIZE(__pyx_args) <= 3)) {
+    __pyx_v_df = PyTuple_GET_ITEM(__pyx_args, 0);
+    __pyx_v_nonc = PyTuple_GET_ITEM(__pyx_args, 1);
+    if (PyTuple_GET_SIZE(__pyx_args) > 2) {
+      __pyx_v_size = PyTuple_GET_ITEM(__pyx_args, 2);
+    }
+  }
+  else {
+    if (unlikely(!PyArg_ParseTupleAndKeywords(__pyx_args, __pyx_kwds, "OO|O", __pyx_argnames, &__pyx_v_df, &__pyx_v_nonc, &__pyx_v_size))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1530; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+  }
+  goto __pyx_L4;
+  __pyx_L3_error:;
+  __Pyx_AddTraceback("mtrand.RandomState.noncentral_chisquare");
+  return NULL;
+  __pyx_L4:;
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1551 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1551
+ *         cdef ndarray odf, ononc
+ *         cdef double fdf, fnonc
+ *         fdf = PyFloat_AsDouble(df)             # <<<<<<<<<<<<<<
+ *         fnonc = PyFloat_AsDouble(nonc)
+ *         if not PyErr_Occurred():
+ */
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1552 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1552
+ *         cdef double fdf, fnonc
+ *         fdf = PyFloat_AsDouble(df)
+ *         fnonc = PyFloat_AsDouble(nonc)             # <<<<<<<<<<<<<<
+ *         if not PyErr_Occurred():
+ *             if fdf <= 1:
+ */
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1553 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1553
+ *         fdf = PyFloat_AsDouble(df)
+ *         fnonc = PyFloat_AsDouble(nonc)
+ *         if not PyErr_Occurred():             # <<<<<<<<<<<<<<
+ *             if fdf <= 1:
+ *                 raise ValueError("df <= 0")
+ */
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   if (__pyx_1) {
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1554 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1554
+ *         fnonc = PyFloat_AsDouble(nonc)
+ *         if not PyErr_Occurred():
+ *             if fdf <= 1:             # <<<<<<<<<<<<<<
+ *                 raise ValueError("df <= 0")
+ *             if fnonc <= 0:
+ */
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-      __pyx_2 = PyTuple_New(1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1555; goto __pyx_L1;}
-      Py_INCREF(__pyx_k100p);
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-      __pyx_3 = PyObject_CallObject(PyExc_ValueError, __pyx_2); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1555; goto __pyx_L1;}
-      Py_DECREF(__pyx_2); __pyx_2 = 0;
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1555
+ *         if not PyErr_Occurred():
+ *             if fdf <= 1:
+ *                 raise ValueError("df <= 0")             # <<<<<<<<<<<<<<
+ *             if fnonc <= 0:
+ *                 raise ValueError("nonc <= 0")
+ */
+      __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1555; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      Py_INCREF(__pyx_kp_59);
+      PyTuple_SET_ITEM(__pyx_2, 0, __pyx_kp_59);
+      __pyx_3 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1555; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0;
       __Pyx_Raise(__pyx_3, 0, 0);
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1555; goto __pyx_L1;}
-      goto __pyx_L3;
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1555; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      goto __pyx_L6;
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-    __pyx_L3:;
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1556 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1556
+ *             if fdf <= 1:
+ *                 raise ValueError("df <= 0")
+ *             if fnonc <= 0:             # <<<<<<<<<<<<<<
+ *                 raise ValueError("nonc <= 0")
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-      Py_INCREF(__pyx_k101p);
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-      Py_DECREF(__pyx_2); __pyx_2 = 0;
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1557
+ *                 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_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1557; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      Py_INCREF(__pyx_kp_60);
+      PyTuple_SET_ITEM(__pyx_2, 0, __pyx_kp_60);
+      __pyx_3 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1557; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0;
       __Pyx_Raise(__pyx_3, 0, 0);
       Py_DECREF(__pyx_3); __pyx_3 = 0;
-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1557; goto __pyx_L1;}
-      goto __pyx_L4;
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1557; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      goto __pyx_L7;
     }
-    __pyx_L4:;
+    __pyx_L7:;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1558 */
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1559
+ *                 raise ValueError("nonc <= 0")
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+ *                                   size, fdf, fnonc)             # <<<<<<<<<<<<<<
+ * 
+ *         PyErr_Clear()
+ */
+    __pyx_2 = __pyx_f_6mtrand_cont2_array_sc(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_noncentral_chisquare, __pyx_v_size, __pyx_v_fdf, __pyx_v_fnonc); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1558; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     __pyx_r = __pyx_2;
     __pyx_2 = 0;
     goto __pyx_L0;
-    goto __pyx_L2;
+    goto __pyx_L5;
   }
-  __pyx_L2:;
+  __pyx_L5:;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1561 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1561
+ *                                   size, fdf, fnonc)
+ * 
+ *         PyErr_Clear()             # <<<<<<<<<<<<<<
+ * 
+ *         odf = <ndarray>PyArray_FROM_OTF(df, NPY_DOUBLE, NPY_ALIGNED)
+ */
   PyErr_Clear();
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1563 */
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1563
+ *         PyErr_Clear()
+ * 
+ *         odf = <ndarray>PyArray_FROM_OTF(df, NPY_DOUBLE, NPY_ALIGNED)             # <<<<<<<<<<<<<<
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+ *         if np.any(np.less_equal(odf, 0.0)):
+ */
+  __pyx_3 = PyArray_FROM_OTF(__pyx_v_df, NPY_DOUBLE, NPY_ALIGNED); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1563; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_3)));
   Py_DECREF(((PyObject *)__pyx_v_odf));
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1564 */
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1564
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+ *         odf = <ndarray>PyArray_FROM_OTF(df, NPY_DOUBLE, NPY_ALIGNED)
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   Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_2)));
   Py_DECREF(((PyObject *)__pyx_v_ononc));
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-  __pyx_2 = PyObject_GetAttr(__pyx_3, __pyx_n_any); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1565; goto __pyx_L1;}
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1565
+ *         odf = <ndarray>PyArray_FROM_OTF(df, NPY_DOUBLE, NPY_ALIGNED)
+ *         ononc = <ndarray>PyArray_FROM_OTF(nonc, NPY_DOUBLE, NPY_ALIGNED)
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+  __pyx_2 = PyObject_GetAttr(__pyx_3, __pyx_kp_any); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1565; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  __pyx_3 = __Pyx_GetName(__pyx_m, __pyx_n_np); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1565; goto __pyx_L1;}
-  __pyx_4 = PyObject_GetAttr(__pyx_3, __pyx_n_less_equal); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1565; goto __pyx_L1;}
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+  __pyx_4 = PyObject_GetAttr(__pyx_3, __pyx_kp_less_equal); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1565; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  __pyx_5 = PyTuple_New(2); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1565; goto __pyx_L1;}
+  __pyx_3 = PyFloat_FromDouble(0.0); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1565; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_5 = PyTuple_New(2); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1565; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_3 = PyObject_Call(__pyx_4, ((PyObject *)__pyx_5), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1565; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  __pyx_4 = PyTuple_New(1); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1565; goto __pyx_L1;}
+  Py_DECREF(((PyObject *)__pyx_5)); __pyx_5 = 0;
+  __pyx_4 = PyTuple_New(1); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1565; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_5 = PyObject_Call(__pyx_2, ((PyObject *)__pyx_4), NULL); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1565; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  __pyx_1 = PyObject_IsTrue(__pyx_5); if (__pyx_1 < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1565; goto __pyx_L1;}
+  Py_DECREF(((PyObject *)__pyx_4)); __pyx_4 = 0;
+  __pyx_1 = __Pyx_PyObject_IsTrue(__pyx_5); if (unlikely(__pyx_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1565; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   Py_DECREF(__pyx_5); __pyx_5 = 0;
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-    Py_INCREF(__pyx_k102p);
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-    Py_DECREF(__pyx_3); __pyx_3 = 0;
+
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1566
+ *         ononc = <ndarray>PyArray_FROM_OTF(nonc, NPY_DOUBLE, NPY_ALIGNED)
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+    __pyx_3 = PyTuple_New(1); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1566; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    Py_INCREF(__pyx_kp_61);
+    PyTuple_SET_ITEM(__pyx_3, 0, __pyx_kp_61);
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+    Py_DECREF(((PyObject *)__pyx_3)); __pyx_3 = 0;
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-    goto __pyx_L5;
+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1566; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    goto __pyx_L8;
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-  __pyx_L5:;
+  __pyx_L8:;
 
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1567
+ *         if np.any(np.less_equal(odf, 0.0)):
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-  __pyx_L6:;
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+ * 
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-  __pyx_L1:;
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-  __pyx_r = 0;
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 static char __pyx_doc_6mtrand_11RandomState_standard_cauchy[] = "\n        standard_cauchy(size=None)\n\n        Standard Cauchy with mode=0.\n\n        ";
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-  __pyx_L1:;
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+  }
+  goto __pyx_L4;
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1591 */
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1592 */
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1593 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1593
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-      Py_DECREF(__pyx_2); __pyx_2 = 0;
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1594
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+      Py_INCREF(__pyx_kp_63);
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+      __pyx_3 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1594; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0;
       __Pyx_Raise(__pyx_3, 0, 0);
       Py_DECREF(__pyx_3); __pyx_3 = 0;
-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1594; goto __pyx_L1;}
-      goto __pyx_L3;
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1594; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      goto __pyx_L6;
     }
-    __pyx_L3:;
+    __pyx_L6:;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1595 */
-    __pyx_2 = __pyx_f_6mtrand_cont1_array_sc(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state,rk_standard_t,__pyx_v_size,__pyx_v_fdf); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1595; goto __pyx_L1;}
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1595
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     __pyx_r = __pyx_2;
     __pyx_2 = 0;
     goto __pyx_L0;
-    goto __pyx_L2;
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1597 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1597
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   PyErr_Clear();
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1599 */
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1599
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-    goto __pyx_L4;
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-  __pyx_L4:;
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1602
+ *         if np.any(np.less_equal(odf, 0.0)):
+ *             raise ValueError("df <= 0")
+ *         return cont1_array(self.internal_state, rk_standard_t, size, odf)             # <<<<<<<<<<<<<<
+ * 
+ *     def vonmises(self, mu, kappa, size=None):
+ */
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-static PyObject *__pyx_k106p;
-static PyObject *__pyx_k107p;
-
-static char __pyx_k106[] = "kappa < 0";
-static char __pyx_k107[] = "kappa < 0";
-
-static PyObject *__pyx_f_6mtrand_11RandomState_vonmises(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_vonmises[] = "\n        vonmises(mu=0.0, kappa=1.0, size=None)\n\n        Draw samples from a von Mises distribution.\n\n        Samples are drawn from a von Mises distribution with specified mode (mu)\n        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 circle. It\n        may be thought of as the circular analogue of the normal distribution.\n\n        Parameters\n        ----------\n        mu : float\n            Mode (\"center\") of the distribution.\n        kappa : float, >= 0.\n            Dispersion of the distribution.\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            The returned samples live on the unit circle [-\\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, named for Richard Edler von Mises, born in\n        Austria-Hungary, in what is now the Ukraine. He fled to the United\n        States in 1939 and became a professor at Harvard. He worked in\n        probability theory, aerodynamics, fluid mechanics, and philosophy of\n        science.\n\n        References\n        ----------\n        .. [1] Abramowitz, M. and Stegun, I. A. (ed.), Handbook of Mathematical\n               Functions, National Bureau of Standards, 1964; reprinted Dover\n               Publications, 1965.\n        .. [2] von Mises, Richard, 1964, Mathematical Theory of Probability\n               and Statistics (New York: Academic Press).\n        .. [3] Wikipedia, \"Von Mises distribution\",\n               http://en.wikipedia.org/wiki/Von_Mises_distribution\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 = arange(-pi, pi, 2*pi/50.)\n        >>> y = -np.exp(kappa*np.cos(x-mu))/(2*pi*sps.jn(0,kappa))\n        >>> plt.plot(x, y/max(y), linewidth=2, color=\'r\')\n        >>> plt.show()\n\n        ";
-static PyObject *__pyx_f_6mtrand_11RandomState_vonmises(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1604
+ *         return cont1_array(self.internal_state, rk_standard_t, size, odf)
+ * 
+ *     def vonmises(self, mu, kappa, size=None):             # <<<<<<<<<<<<<<
+ *         """
+ *         vonmises(mu=0.0, kappa=1.0, size=None)
+ */
+
+static PyObject *__pyx_pf_6mtrand_11RandomState_vonmises(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_vonmises[] = "\n""        vonmises(mu=0.0, kappa=1.0, size=None)\n""\n""        Draw samples from a von Mises distribution.\n""\n""        Samples are drawn from a von Mises distribution with specified mode (mu)\n""        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 circle. It\n""        may be thought of as the circular analogue of the normal distribution.\n""\n""        Parameters\n""        ----------\n""        mu : float\n""            Mode (\"center\") of the distribution.\n""        kappa : float, >= 0.\n""            Dispersion of the distribution.\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""            The returned samples live on the unit circle [-\\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, named for Richard Edler von Mises, born in\n""        Austria-Hungary, in what is now the Ukraine. He fled to the United\n""        States in 1939 and became a professor at Harvard. He worked in\n""        probability theory, aerodynamics, fluid mechanics, and philosophy of\n""        science.\n""\n""        References\n""        ----------\n""        .. [1] Abramowitz, M. and Stegun, I. A. (ed.), Handbook of Mathematical\n""               Functions, National Bureau of Standards, 1964; reprinted Dover\n""               Publications, 1965.\n""        .. [2] von Mises, Richard, 1964, Mathematical Theory of Probability\n""               and Statistics (New York: Academic Press).\n""        .. [3] Wikipedia, \"Von Mises distribution\",\n""               http://en.wikipedia.org/wiki/Von_Mises_distribution\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 = arange(-pi, pi, 2*pi/50.)\n""        >>> y = -np.exp(kappa*np.cos(x-mu))/(2*pi*sps.jn(0,kappa))\n""        >>> plt.plot(x, y/max(y), linewidth=2, color='r')\n""        >>> plt.show()\n""\n""        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_vonmises(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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@@ -4946,137 +8601,221 @@ static PyObject *__pyx_f_6mtrand_11RandomState_vonmises(PyObject *__pyx_v_self,
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-  Py_INCREF(__pyx_v_size);
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1684 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1684
+ *         cdef double fmu, fkappa
+ * 
+ *         fmu = PyFloat_AsDouble(mu)             # <<<<<<<<<<<<<<
+ *         fkappa = PyFloat_AsDouble(kappa)
+ *         if not PyErr_Occurred():
+ */
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1685 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1685
+ * 
+ *         fmu = PyFloat_AsDouble(mu)
+ *         fkappa = PyFloat_AsDouble(kappa)             # <<<<<<<<<<<<<<
+ *         if not PyErr_Occurred():
+ *             if fkappa < 0:
+ */
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1686 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1686
+ *         fmu = PyFloat_AsDouble(mu)
+ *         fkappa = PyFloat_AsDouble(kappa)
+ *         if not PyErr_Occurred():             # <<<<<<<<<<<<<<
+ *             if fkappa < 0:
+ *                 raise ValueError("kappa < 0")
+ */
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   if (__pyx_1) {
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1687 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1687
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+ *         if not PyErr_Occurred():
+ *             if fkappa < 0:             # <<<<<<<<<<<<<<
+ *                 raise ValueError("kappa < 0")
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+ */
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-      Py_DECREF(__pyx_2); __pyx_2 = 0;
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1688
+ *         if not PyErr_Occurred():
+ *             if fkappa < 0:
+ *                 raise ValueError("kappa < 0")             # <<<<<<<<<<<<<<
+ *             return cont2_array_sc(self.internal_state, rk_vonmises, size, fmu, fkappa)
+ * 
+ */
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1689 */
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1689
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+ */
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     goto __pyx_L0;
-    goto __pyx_L2;
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-  __pyx_L2:;
+  __pyx_L5:;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1691 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1691
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+ *         okappa = <ndarray> PyArray_FROM_OTF(kappa, NPY_DOUBLE, NPY_ALIGNED)
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1696
+ *         okappa = <ndarray> PyArray_FROM_OTF(kappa, NPY_DOUBLE, NPY_ALIGNED)
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+ * 
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-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1696; goto __pyx_L1;}
-    goto __pyx_L4;
+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1696; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    goto __pyx_L7;
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-  __pyx_L4:;
+  __pyx_L7:;
 
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1697
+ *         if np.any(np.less(okappa, 0.0)):
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+ *         return cont2_array(self.internal_state, rk_vonmises, size, omu, okappa)             # <<<<<<<<<<<<<<
+ * 
+ *     def pareto(self, a, size=None):
+ */
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   goto __pyx_L0;
-  __pyx_L1:;
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-static char __pyx_k108[] = "a <= 0";
-static char __pyx_k109[] = "a <= 0";
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-static PyObject *__pyx_f_6mtrand_11RandomState_pareto(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_pareto[] = "\n        pareto(a, size=None)\n\n        Draw samples from a Pareto distribution with specified shape.\n\n        This is a simplified version of the Generalized Pareto distribution\n        (available in SciPy), with the scale set to one and the location set to\n        zero. Most authors default the location to one.\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.genpareto.pdf : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Pareto distribution is\n\n        .. math:: p(x) = \\frac{am^a}{x^{a+1}}\n\n        where :math:`a` is the shape and :math:`m` the location\n\n        The Pareto distribution, named after the Italian economist Vilfredo Pareto,\n        is a power law probability distribution useful in many real world problems.\n        Outside the field of economics it is generally referred to as the Bradford\n        distribution. Pareto developed the distribution to describe the\n        distribution of wealth in an economy.  It has also found use in insurance,\n        web page access statistics, oil field sizes, and many other problems,\n        including the download frequency for projects in Sourceforge [1].  It is\n        one of the so-called \"fat-tailed\" distributions.\n\n\n        References\n        ----------\n        .. [1] Francis Hunt and Paul Johnson, On the Pareto Distribution of\n               Sourceforge projects.\n        .. [2] Pareto, V. (1896). Course of Political Economy. Lausanne.\n        .. [3] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme\n               Values, Birkhauser Verlag, Basel, pp 23-30.\n        .. [4] Wikipedia, \"Pareto distribution\",\n               http://en.wikipedia.org/wiki/Pareto_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a, m = 3., 1. # shape and mode\n        >>> s = np.random.pareto(a, 1000) + m\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 100, normed=True, align=\'center\')\n        >>> fit = a*m**a/bins**(a+1)\n        >>> plt.plot(bins, max(count)*fit/max(fit),linewidth=2, color=\'r\')\n        >>> plt.show()\n\n        ";
-static PyObject *__pyx_f_6mtrand_11RandomState_pareto(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1699
+ *         return cont2_array(self.internal_state, rk_vonmises, size, omu, okappa)
+ * 
+ *     def pareto(self, a, size=None):             # <<<<<<<<<<<<<<
+ *         """
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+ */
+
+static PyObject *__pyx_pf_6mtrand_11RandomState_pareto(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_pareto[] = "\n""        pareto(a, size=None)\n""\n""        Draw samples from a Pareto distribution with specified shape.\n""\n""        This is a simplified version of the Generalized Pareto distribution\n""        (available in SciPy), with the scale set to one and the location set to\n""        zero. Most authors default the location to one.\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.genpareto.pdf : probability density function,\n""            distribution or cumulative density function, etc.\n""\n""        Notes\n""        -----\n""        The probability density for the Pareto distribution is\n""\n""        .. math:: p(x) = \\frac{am^a}{x^{a+1}}\n""\n""        where :math:`a` is the shape and :math:`m` the location\n""\n""        The Pareto distribution, named after the Italian economist Vilfredo Pareto,\n""        is a power law probability distribution useful in many real world problems.\n""        Outside the field of economics it is generally referred to as the Bradford\n""        distribution. Pareto developed the distribution to describe the\n""        distribution of wealth in an economy.  It has also found use in insurance,\n""        web page access statistics, oil field sizes, and many other problems,\n""        including the download frequency for projects in Sourceforge [1].  It is\n""        one of the so-called \"fat-tailed\" distributions.\n""\n""\n""        References\n""        ----------\n""        .. [1] Francis Hunt and Paul Johnson, On the Pareto Distribution of\n""               Sourceforge projects.\n""        .. [2] Pareto, V. (1896). Course of Political Economy. Lausanne.\n""        .. [3] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme\n""               Values, Birkhauser Verlag, Basel, pp 23-30.\n""        .. [4] Wikipedia, \"Pareto distribution\",\n""               http://en.wikipedia.org/wiki/Pareto_distribution\n""\n""        Examples\n""        --------\n""        Draw samples from the distribution:\n""\n""        >>> a, m = 3., 1. # shape and mode\n""        >>> s = np.random.pareto(a, 1000) + m\n""\n""        Display the histogram of the samples, along with\n""        the probability density function:\n""\n""        >>> import matplotlib.pyplot as plt\n""        >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='center')\n""        >>> fit = a*m**a/bins**(a+1)\n""        >>> plt.plot(bins, max(count)*fit/max(fit),linewidth=2, color='r')\n""        >>> plt.show()\n""\n""        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_pareto(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1775 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1775
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1776 */
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1777 */
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+    goto __pyx_L5;
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1786
+ *         if np.any(np.less_equal(oa, 0.0)):
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+ * 
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-  __pyx_L1:;
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-static PyObject *__pyx_k111p;
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-static char __pyx_k111[] = "a <= 0";
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-static PyObject *__pyx_f_6mtrand_11RandomState_weibull(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_weibull[] = "\n        weibull(a, size=None)\n\n        Weibull distribution.\n\n        Draw samples from a 1-parameter Weibull distribution with the given\n        shape parameter.\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        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        Parameters\n        ----------\n        a : float\n            Shape of the distribution.\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        See Also\n        --------\n        scipy.stats.distributions.weibull : probability density function,\n            distribution or cumulative density function, etc.\n\n        gumbel, scipy.stats.distributions.genextreme\n\n        Notes\n        -----\n        The probability density for the Weibull distribution is\n\n        .. math:: p(x) = \\frac{a}\n                         {\\lambda}(\\frac{x}{\\lambda})^{a-1}e^{-(x/\\lambda)^a},\n\n        where :math:`a` is the shape and :math:`\\lambda` the scale.\n\n        The function has its peak (the mode) at\n        :math:`\\lambda(\\frac{a-1}{a})^{1/a}`.\n\n        When ``a = 1``, the Weibull distribution reduces to the exponential\n        distribution.\n\n        References\n        ----------\n        .. [1] Waloddi Weibull, Professor, Royal Technical University, Stockholm,\n               1939 \"A Statistical Theory Of The Strength Of Materials\",\n               Ingeniorsvetenskapsakademiens Handlingar Nr 151, 1939,\n               Generalstabens Litografiska Anstalts Forlag, Stockholm.\n        .. [2] Waloddi Weibull, 1951 \"A Statistical Distribution Function of Wide\n               Applicability\",  Journal Of Applied Mechanics ASME Paper.\n        .. [3] Wikipedia, \"Weibull distribution\",\n               http://en.wikipedia.org/wiki/Weibull_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a = 5. # shape\n        >>> s = np.random.weibull(a, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> 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 PyObject *__pyx_f_6mtrand_11RandomState_weibull(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1788
+ *         return cont1_array(self.internal_state, rk_pareto, size, oa)
+ * 
+ *     def weibull(self, a, size=None):             # <<<<<<<<<<<<<<
+ *         """
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+ */
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+static PyObject *__pyx_pf_6mtrand_11RandomState_weibull(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_weibull[] = "\n""        weibull(a, size=None)\n""\n""        Weibull distribution.\n""\n""        Draw samples from a 1-parameter Weibull distribution with the given\n""        shape parameter.\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""        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""        Parameters\n""        ----------\n""        a : float\n""            Shape of the distribution.\n""        size : tuple of ints\n""            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n""            ``m * n * k`` samples are drawn.\n""\n""        See Also\n""        --------\n""        scipy.stats.distributions.weibull : probability density function,\n""            distribution or cumulative density function, etc.\n""\n""        gumbel, scipy.stats.distributions.genextreme\n""\n""        Notes\n""        -----\n""        The probability density for the Weibull distribution is\n""\n""        .. math:: p(x) = \\frac{a}\n""                         {\\lambda}(\\frac{x}{\\lambda})^{a-1}e^{-(x/\\lambda)^a},\n""\n""        where :math:`a` is the shape and :math:`\\lambda` the scale.\n""\n""        The function has its peak (the mode) at\n""        :math:`\\lambda(\\frac{a-1}{a})^{1/a}`.\n""\n""        When ``a = 1``, the Weibull distribution reduces to the exponential\n""        distribution.\n""\n""        References\n""        ----------\n""        .. [1] Waloddi Weibull, Professor, Royal Technical University, Stockholm,\n""               1939 \"A Statistical Theory Of The Strength Of Materials\",\n""               Ingeniorsvetenskapsakademiens Handlingar Nr 151, 1939,\n""               Generalstabens Litografiska Anstalts Forlag, Stockholm.\n""        .. [2] Waloddi Weibull, 1951 \"A Statistical Distribution Function of Wide\n""               Applicability\",  Journal Of Applied Mechanics ASME Paper.\n""        .. [3] Wikipedia, \"Weibull distribution\",\n""               http://en.wikipedia.org/wiki/Weibull_distribution\n""\n""        Examples\n""        --------\n""        Draw samples from the distribution:\n""\n""        >>> a = 5. # shape\n""        >>> s = np.random.weibull(a, 1000)\n""\n""        Display the histogram of the samples, along with\n""        the probability density function:\n""\n""        >>> import matplotlib.pyplot as plt\n""        >>> 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 PyObject *__pyx_pf_6mtrand_11RandomState_weibull(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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@@ -5216,123 +9028,196 @@ static PyObject *__pyx_f_6mtrand_11RandomState_weibull(PyObject *__pyx_v_self, P
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-  Py_INCREF(__pyx_v_self);
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-  Py_INCREF(__pyx_v_size);
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+  }
+  goto __pyx_L4;
+  __pyx_L3_error:;
+  __Pyx_AddTraceback("mtrand.RandomState.weibull");
+  return NULL;
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1874 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1874
+ *         cdef double fa
+ * 
+ *         fa = PyFloat_AsDouble(a)             # <<<<<<<<<<<<<<
+ *         if not PyErr_Occurred():
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1875 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1875
+ * 
+ *         fa = PyFloat_AsDouble(a)
+ *         if not PyErr_Occurred():             # <<<<<<<<<<<<<<
+ *             if fa <= 0:
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+ */
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1876 */
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+ *             if fa <= 0:             # <<<<<<<<<<<<<<
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-      Py_DECREF(__pyx_2); __pyx_2 = 0;
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1877
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+ *             if fa <= 0:
+ *                 raise ValueError("a <= 0")             # <<<<<<<<<<<<<<
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+ * 
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+      Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0;
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1877; goto __pyx_L1;}
-      goto __pyx_L3;
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1877; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      goto __pyx_L6;
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-    __pyx_L3:;
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1878
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+ *         PyErr_Clear()
+ */
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-    goto __pyx_L2;
+    goto __pyx_L5;
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+  __pyx_L5:;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1880 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1880
+ *             return cont1_array_sc(self.internal_state, rk_weibull, size, fa)
+ * 
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+ * 
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1882
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1885
+ *         if np.any(np.less_equal(oa, 0.0)):
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+ * 
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 }
 
-static PyObject *__pyx_k112p;
-static PyObject *__pyx_k113p;
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 static char __pyx_doc_6mtrand_11RandomState_power[] = "\n        power(a, size=None)\n\n        Power distribution.\n\n        ";
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@@ -5344,123 +9229,196 @@ static PyObject *__pyx_f_6mtrand_11RandomState_power(PyObject *__pyx_v_self, PyO
   PyObject *__pyx_4 = 0;
   PyObject *__pyx_5 = 0;
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-  __pyx_v_size = __pyx_k33;
-  if (!PyArg_ParseTupleAndKeywords(__pyx_args, __pyx_kwds, "O|O", __pyx_argnames, &__pyx_v_a, &__pyx_v_size)) return 0;
-  Py_INCREF(__pyx_v_self);
-  Py_INCREF(__pyx_v_a);
-  Py_INCREF(__pyx_v_size);
+  __pyx_v_size = Py_None;
+  if (likely(!__pyx_kwds) && likely(1 <= PyTuple_GET_SIZE(__pyx_args)) && likely(PyTuple_GET_SIZE(__pyx_args) <= 2)) {
+    __pyx_v_a = PyTuple_GET_ITEM(__pyx_args, 0);
+    if (PyTuple_GET_SIZE(__pyx_args) > 1) {
+      __pyx_v_size = PyTuple_GET_ITEM(__pyx_args, 1);
+    }
+  }
+  else {
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+  }
+  goto __pyx_L4;
+  __pyx_L3_error:;
+  __Pyx_AddTraceback("mtrand.RandomState.power");
+  return NULL;
+  __pyx_L4:;
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1897 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1897
+ *         cdef double fa
+ * 
+ *         fa = PyFloat_AsDouble(a)             # <<<<<<<<<<<<<<
+ *         if not PyErr_Occurred():
+ *             if fa <= 0:
+ */
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1898 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1898
+ * 
+ *         fa = PyFloat_AsDouble(a)
+ *         if not PyErr_Occurred():             # <<<<<<<<<<<<<<
+ *             if fa <= 0:
+ *                 raise ValueError("a <= 0")
+ */
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   if (__pyx_1) {
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1899 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1899
+ *         fa = PyFloat_AsDouble(a)
+ *         if not PyErr_Occurred():
+ *             if fa <= 0:             # <<<<<<<<<<<<<<
+ *                 raise ValueError("a <= 0")
+ *             return cont1_array_sc(self.internal_state, rk_power, size, fa)
+ */
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-      __pyx_2 = PyTuple_New(1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1900; goto __pyx_L1;}
-      Py_INCREF(__pyx_k112p);
-      PyTuple_SET_ITEM(__pyx_2, 0, __pyx_k112p);
-      __pyx_3 = PyObject_CallObject(PyExc_ValueError, __pyx_2); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1900; goto __pyx_L1;}
-      Py_DECREF(__pyx_2); __pyx_2 = 0;
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1900
+ *         if not PyErr_Occurred():
+ *             if fa <= 0:
+ *                 raise ValueError("a <= 0")             # <<<<<<<<<<<<<<
+ *             return cont1_array_sc(self.internal_state, rk_power, size, fa)
+ * 
+ */
+      __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1900; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      Py_INCREF(__pyx_kp_71);
+      PyTuple_SET_ITEM(__pyx_2, 0, __pyx_kp_71);
+      __pyx_3 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1900; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0;
       __Pyx_Raise(__pyx_3, 0, 0);
       Py_DECREF(__pyx_3); __pyx_3 = 0;
-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1900; goto __pyx_L1;}
-      goto __pyx_L3;
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1900; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      goto __pyx_L6;
     }
-    __pyx_L3:;
+    __pyx_L6:;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1901 */
-    __pyx_2 = __pyx_f_6mtrand_cont1_array_sc(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state,rk_power,__pyx_v_size,__pyx_v_fa); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1901; goto __pyx_L1;}
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1901
+ *             if fa <= 0:
+ *                 raise ValueError("a <= 0")
+ *             return cont1_array_sc(self.internal_state, rk_power, size, fa)             # <<<<<<<<<<<<<<
+ * 
+ *         PyErr_Clear()
+ */
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     __pyx_r = __pyx_2;
     __pyx_2 = 0;
     goto __pyx_L0;
-    goto __pyx_L2;
+    goto __pyx_L5;
   }
-  __pyx_L2:;
+  __pyx_L5:;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1903 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1903
+ *             return cont1_array_sc(self.internal_state, rk_power, size, fa)
+ * 
+ *         PyErr_Clear()             # <<<<<<<<<<<<<<
+ * 
+ *         oa = <ndarray>PyArray_FROM_OTF(a, NPY_DOUBLE, NPY_ALIGNED)
+ */
   PyErr_Clear();
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1905 */
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1905
+ *         PyErr_Clear()
+ * 
+ *         oa = <ndarray>PyArray_FROM_OTF(a, NPY_DOUBLE, NPY_ALIGNED)             # <<<<<<<<<<<<<<
+ *         if np.any(np.less_equal(oa, 0.0)):
+ *             raise ValueError("a <= 0")
+ */
+  __pyx_3 = PyArray_FROM_OTF(__pyx_v_a, NPY_DOUBLE, NPY_ALIGNED); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1905; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_3)));
   Py_DECREF(((PyObject *)__pyx_v_oa));
   __pyx_v_oa = ((PyArrayObject *)__pyx_3);
   Py_DECREF(__pyx_3); __pyx_3 = 0;
 
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-  __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n_np); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1906; goto __pyx_L1;}
-  __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_n_any); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1906; goto __pyx_L1;}
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1906
+ * 
+ *         oa = <ndarray>PyArray_FROM_OTF(a, NPY_DOUBLE, NPY_ALIGNED)
+ *         if np.any(np.less_equal(oa, 0.0)):             # <<<<<<<<<<<<<<
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+ *         return cont1_array(self.internal_state, rk_power, size, oa)
+ */
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+  __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_kp_any); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1906; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   Py_DECREF(__pyx_2); __pyx_2 = 0;
-  __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n_np); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1906; goto __pyx_L1;}
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+  __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1906; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_4 = PyObject_GetAttr(__pyx_2, __pyx_kp_less_equal); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1906; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  __pyx_5 = PyTuple_New(2); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1906; goto __pyx_L1;}
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+  Py_DECREF(((PyObject *)__pyx_5)); __pyx_5 = 0;
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-  __pyx_5 = PyObject_CallObject(__pyx_3, __pyx_4); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1906; goto __pyx_L1;}
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-  Py_DECREF(__pyx_4); __pyx_4 = 0;
-  __pyx_1 = PyObject_IsTrue(__pyx_5); if (__pyx_1 < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1906; goto __pyx_L1;}
+  Py_DECREF(((PyObject *)__pyx_4)); __pyx_4 = 0;
+  __pyx_1 = __Pyx_PyObject_IsTrue(__pyx_5); if (unlikely(__pyx_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1906; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    Py_DECREF(__pyx_2); __pyx_2 = 0;
+
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1907
+ *         oa = <ndarray>PyArray_FROM_OTF(a, NPY_DOUBLE, NPY_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_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1907; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    Py_INCREF(__pyx_kp_72);
+    PyTuple_SET_ITEM(__pyx_2, 0, __pyx_kp_72);
+    __pyx_3 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1907; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0;
     __Pyx_Raise(__pyx_3, 0, 0);
     Py_DECREF(__pyx_3); __pyx_3 = 0;
-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1907; goto __pyx_L1;}
-    goto __pyx_L4;
+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1907; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    goto __pyx_L7;
   }
-  __pyx_L4:;
+  __pyx_L7:;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1908 */
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1908
+ *         if np.any(np.less_equal(oa, 0.0)):
+ *             raise ValueError("a <= 0")
+ *         return cont1_array(self.internal_state, rk_power, size, oa)             # <<<<<<<<<<<<<<
+ * 
+ *     def laplace(self, loc=0.0, scale=1.0, size=None):
+ */
+  __pyx_4 = __pyx_f_6mtrand_cont1_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_power, __pyx_v_size, __pyx_v_oa); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1908; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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   goto __pyx_L0;
 
   __pyx_r = Py_None; Py_INCREF(Py_None);
   goto __pyx_L0;
-  __pyx_L1:;
+  __pyx_L1_error:;
   Py_XDECREF(__pyx_2);
   Py_XDECREF(__pyx_3);
   Py_XDECREF(__pyx_4);
   Py_XDECREF(__pyx_5);
   __Pyx_AddTraceback("mtrand.RandomState.power");
-  __pyx_r = 0;
+  __pyx_r = NULL;
   __pyx_L0:;
   Py_DECREF(__pyx_v_oa);
-  Py_DECREF(__pyx_v_self);
-  Py_DECREF(__pyx_v_a);
-  Py_DECREF(__pyx_v_size);
   return __pyx_r;
 }
 
-static PyObject *__pyx_k114p;
-static PyObject *__pyx_k115p;
-
-static char __pyx_k114[] = "scale <= 0";
-static char __pyx_k115[] = "scale <= 0";
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1910
+ *         return cont1_array(self.internal_state, rk_power, size, oa)
+ * 
+ *     def laplace(self, loc=0.0, scale=1.0, size=None):             # <<<<<<<<<<<<<<
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+ *         laplace(loc=0.0, scale=1.0, size=None)
+ */
 
-static PyObject *__pyx_f_6mtrand_11RandomState_laplace(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static PyObject *__pyx_pf_6mtrand_11RandomState_laplace(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
 static char __pyx_doc_6mtrand_11RandomState_laplace[] = "\n        laplace(loc=0.0, scale=1.0, size=None)\n\n        Laplace or double exponential distribution.\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 Laplace distribution is similar to the Gaussian/normal distribution,\n        but is sharper at the peak and has fatter tails.\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        ";
-static PyObject *__pyx_f_6mtrand_11RandomState_laplace(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_laplace(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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-  Py_INCREF(__pyx_v_loc);
-  Py_INCREF(__pyx_v_scale);
-  Py_INCREF(__pyx_v_size);
+  __pyx_v_loc = __pyx_k_9;
+  __pyx_v_scale = __pyx_k_10;
+  __pyx_v_size = Py_None;
+  if (likely(!__pyx_kwds) && likely(0 <= PyTuple_GET_SIZE(__pyx_args)) && likely(PyTuple_GET_SIZE(__pyx_args) <= 3)) {
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+  }
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+  __pyx_L3_error:;
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+  return NULL;
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   __pyx_v_oscale = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1935 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1935
+ *         cdef double floc, fscale
+ * 
+ *         floc = PyFloat_AsDouble(loc)             # <<<<<<<<<<<<<<
+ *         fscale = PyFloat_AsDouble(scale)
+ *         if not PyErr_Occurred():
+ */
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1936 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1936
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+ *         fscale = PyFloat_AsDouble(scale)             # <<<<<<<<<<<<<<
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+ */
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1937 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1937
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+ *         fscale = PyFloat_AsDouble(scale)
+ *         if not PyErr_Occurred():             # <<<<<<<<<<<<<<
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+ *                 raise ValueError("scale <= 0")
+ */
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1938 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1938
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1939
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+ */
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1939; goto __pyx_L1;}
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+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1939; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1940
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-    goto __pyx_L2;
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1942 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1942
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+  if (!(__Pyx_TypeTest(__pyx_3, __pyx_ptype_6mtrand_ndarray))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1943; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    Py_DECREF(__pyx_3); __pyx_3 = 0;
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1947
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-
-static PyObject *__pyx_f_6mtrand_11RandomState_gumbel(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_gumbel[] = "\n        gumbel(loc=0.0, scale=1.0, size=None)\n\n        Gumbel distribution.\n\n        Draw samples from a Gumbel distribution with specified location (or mean)\n        and scale (or standard deviation).\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, it may be derived by\n        considering a Gaussian process of measurements, and generating the pdf for\n        the maximum values from that set of measurements (see examples).\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        See Also\n        --------\n        scipy.stats.gumbel : probability density function,\n            distribution or cumulative density function, etc.\n        weibull, scipy.stats.genextreme\n\n        Notes\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`\n        is the scale parameter.\n\n        The Gumbel (named for German mathematician Emil Julius Gumbel) was used\n        very early in the hydrology literature, for modeling the occurrence of\n        flood events. It is also used for modeling maximum wind speed and 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        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        .. [1] Gumbel, E.J. (1958). Statistics of Extremes. Columbia University\n               Press.\n        .. [2] Reiss, R.-D. and Thomas M. (2001), Statistical Analysis of Extreme\n               Values, from Insurance, Finance, Hydrology and Other Fields,\n               Birkhauser Verlag, Basel: Boston : Berlin.\n        .. [3] Wikipedia, \"Gumbel distribution\",\n               http://en.wikipedia.org/wiki/Gumbel_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, beta = 0, 0.1 # location and scale\n        >>> s = np.random.gumbel(mu, beta, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n        >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n        ...          * np.exp( -np.exp( -(bins - mu) /beta) ),\n        ...          linewidth=2, color=\'r\')\n        >>> plt.show()\n\n        Show how an extreme value distribution can arise from a Gaussian process\n        and compare to a Gaussian:\n\n        >>> means = []\n        >>> maxima = []\n        >>> for i in range(0,1000) :\n        ...    a = np.random.normal(mu, beta, 1000)\n        ...    means.append(a.mean())\n        ...    maxima.append(a.max())\n        >>> count, bins, ignored = plt.hist(maxima, 30, normed=True)\n        >>> beta = np.std(maxima)*np.pi/np.sqrt(6)\n        >>> mu = np.mean(maxima) - 0.57721*beta\n        >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n        ...          * np.exp(-np.exp(-(bins - mu)/beta)),\n        ...          linewidth=2, color=\'r\')\n        >>> plt.plot(bins, 1/(beta * np.sqrt(2 * np.pi))\n        ...          * np.exp(-(bins - mu)**2 / (2 * beta**2)),\n        ...          linewidth=2, color=\'g\')\n        >>> plt.show()\n\n        ";
-static PyObject *__pyx_f_6mtrand_11RandomState_gumbel(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1949
+ *         return cont2_array(self.internal_state, rk_laplace, size, oloc, oscale)
+ * 
+ *     def gumbel(self, loc=0.0, scale=1.0, size=None):             # <<<<<<<<<<<<<<
+ *         """
+ *         gumbel(loc=0.0, scale=1.0, size=None)
+ */
+
+static PyObject *__pyx_pf_6mtrand_11RandomState_gumbel(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_gumbel[] = "\n""        gumbel(loc=0.0, scale=1.0, size=None)\n""\n""        Gumbel distribution.\n""\n""        Draw samples from a Gumbel distribution with specified location (or mean)\n""        and scale (or standard deviation).\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, it may be derived by\n""        considering a Gaussian process of measurements, and generating the pdf for\n""        the maximum values from that set of measurements (see examples).\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""        See Also\n""        --------\n""        scipy.stats.gumbel : probability density function,\n""            distribution or cumulative density function, etc.\n""        weibull, scipy.stats.genextreme\n""\n""        Notes\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`\n""        is the scale parameter.\n""\n""        The Gumbel (named for German mathematician Emil Julius Gumbel) was used\n""        very early in the hydrology literature, for modeling the occurrence of\n""        flood events. It is also used for modeling maximum wind speed and 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""        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""        .. [1] Gumbel, E.J. (1958). Statistics of Extremes. Columbia University\n""               Press.\n""        .. [2] Reiss, R.-D. and Thomas M. (2001), Statistical Analysis of Extreme\n""               Values, from Insurance, Finance, Hydrology and Other Fields,\n""               Birkhauser Verlag, Basel: Boston : Berlin.\n""        .. [3] Wikipedia, \"Gumbel distribution\",\n""               http://en.wikipedia.org/wiki/Gumbel_distribution\n""\n""        Examples\n""        --------\n""        Draw samples from the distribution:\n""\n""        >>> mu, beta = 0, 0.1 # location and scale\n""        >>> s = np.random.gumbel(mu, beta, 1000)\n""\n""        Display the histogram of the samples, along with\n""        the probability density function:\n""\n""        >>> import matplotlib.pyplot as plt\n""        >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n""        >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n""        ...          * np.exp( -np.exp( -(bins - mu) /beta) ),\n""        ...          linewidth=2, color='r')\n""        >>> plt.show()\n""\n""        Show how an extreme value distribution can arise from a Gaussian process\n""        and compare to a Gaussian:\n""\n""        >>> means = []\n""        >>> maxima = []\n""        >>> for i in range(0,1000) :\n""        ...    a = np.random.normal(mu, beta, 1000)\n""        ...    means.append(a.mean())\n""        ...    maxima.append(a.max())\n""        >>> count, bins, ignored = plt.hist(maxima, 30, normed=True)\n""        >>> beta = np.std(maxima)*np.pi/np.sqrt(6)\n""        >>> mu = np.mean(maxima) - 0.57721*beta\n""        >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n""        ...          * np.exp(-np.exp(-(bins - mu)/beta)),\n""        ...          linewidth=2, color='r')\n""        >>> plt.plot(bins, 1/(beta * np.sqrt(2 * np.pi))\n""        ...          * np.exp(-(bins - mu)**2 / (2 * beta**2)),\n""        ...          linewidth=2, color='g')\n""        >>> plt.show()\n""\n""        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_gumbel(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
   PyObject *__pyx_v_loc = 0;
   PyObject *__pyx_v_scale = 0;
   PyObject *__pyx_v_size = 0;
@@ -5622,139 +9668,227 @@ static PyObject *__pyx_f_6mtrand_11RandomState_gumbel(PyObject *__pyx_v_self, Py
   PyObject *__pyx_4 = 0;
   PyObject *__pyx_5 = 0;
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-  __pyx_v_loc = __pyx_k37;
-  __pyx_v_scale = __pyx_k38;
-  __pyx_v_size = __pyx_k39;
-  if (!PyArg_ParseTupleAndKeywords(__pyx_args, __pyx_kwds, "|OOO", __pyx_argnames, &__pyx_v_loc, &__pyx_v_scale, &__pyx_v_size)) return 0;
-  Py_INCREF(__pyx_v_self);
-  Py_INCREF(__pyx_v_loc);
-  Py_INCREF(__pyx_v_scale);
-  Py_INCREF(__pyx_v_size);
+  __pyx_v_loc = __pyx_k_11;
+  __pyx_v_scale = __pyx_k_12;
+  __pyx_v_size = Py_None;
+  if (likely(!__pyx_kwds) && likely(0 <= PyTuple_GET_SIZE(__pyx_args)) && likely(PyTuple_GET_SIZE(__pyx_args) <= 3)) {
+    if (PyTuple_GET_SIZE(__pyx_args) > 0) {
+      __pyx_v_loc = PyTuple_GET_ITEM(__pyx_args, 0);
+      if (PyTuple_GET_SIZE(__pyx_args) > 1) {
+        __pyx_v_scale = PyTuple_GET_ITEM(__pyx_args, 1);
+        if (PyTuple_GET_SIZE(__pyx_args) > 2) {
+          __pyx_v_size = PyTuple_GET_ITEM(__pyx_args, 2);
+        }
+      }
+    }
+  }
+  else {
+    if (unlikely(!PyArg_ParseTupleAndKeywords(__pyx_args, __pyx_kwds, "|OOO", __pyx_argnames, &__pyx_v_loc, &__pyx_v_scale, &__pyx_v_size))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1949; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+  }
+  goto __pyx_L4;
+  __pyx_L3_error:;
+  __Pyx_AddTraceback("mtrand.RandomState.gumbel");
+  return NULL;
+  __pyx_L4:;
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   __pyx_v_oscale = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2059 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2059
+ *         cdef double floc, fscale
+ * 
+ *         floc = PyFloat_AsDouble(loc)             # <<<<<<<<<<<<<<
+ *         fscale = PyFloat_AsDouble(scale)
+ *         if not PyErr_Occurred():
+ */
   __pyx_v_floc = PyFloat_AsDouble(__pyx_v_loc);
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2060 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2060
+ * 
+ *         floc = PyFloat_AsDouble(loc)
+ *         fscale = PyFloat_AsDouble(scale)             # <<<<<<<<<<<<<<
+ *         if not PyErr_Occurred():
+ *             if fscale <= 0:
+ */
   __pyx_v_fscale = PyFloat_AsDouble(__pyx_v_scale);
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2061 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2061
+ *         floc = PyFloat_AsDouble(loc)
+ *         fscale = PyFloat_AsDouble(scale)
+ *         if not PyErr_Occurred():             # <<<<<<<<<<<<<<
+ *             if fscale <= 0:
+ *                 raise ValueError("scale <= 0")
+ */
   __pyx_1 = (!PyErr_Occurred());
   if (__pyx_1) {
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2062 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2062
+ *         fscale = PyFloat_AsDouble(scale)
+ *         if not PyErr_Occurred():
+ *             if fscale <= 0:             # <<<<<<<<<<<<<<
+ *                 raise ValueError("scale <= 0")
+ *             return cont2_array_sc(self.internal_state, rk_gumbel, size, floc, fscale)
+ */
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     if (__pyx_1) {
-      __pyx_2 = PyTuple_New(1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2063; goto __pyx_L1;}
-      Py_INCREF(__pyx_k116p);
-      PyTuple_SET_ITEM(__pyx_2, 0, __pyx_k116p);
-      __pyx_3 = PyObject_CallObject(PyExc_ValueError, __pyx_2); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2063; goto __pyx_L1;}
-      Py_DECREF(__pyx_2); __pyx_2 = 0;
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2063
+ *         if not PyErr_Occurred():
+ *             if fscale <= 0:
+ *                 raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
+ *             return cont2_array_sc(self.internal_state, rk_gumbel, size, floc, fscale)
+ * 
+ */
+      __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2063; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      Py_INCREF(__pyx_kp_75);
+      PyTuple_SET_ITEM(__pyx_2, 0, __pyx_kp_75);
+      __pyx_3 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2063; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0;
       __Pyx_Raise(__pyx_3, 0, 0);
       Py_DECREF(__pyx_3); __pyx_3 = 0;
-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2063; goto __pyx_L1;}
-      goto __pyx_L3;
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2063; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      goto __pyx_L6;
     }
-    __pyx_L3:;
+    __pyx_L6:;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2064 */
-    __pyx_2 = __pyx_f_6mtrand_cont2_array_sc(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state,rk_gumbel,__pyx_v_size,__pyx_v_floc,__pyx_v_fscale); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2064; goto __pyx_L1;}
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2064
+ *             if fscale <= 0:
+ *                 raise ValueError("scale <= 0")
+ *             return cont2_array_sc(self.internal_state, rk_gumbel, size, floc, fscale)             # <<<<<<<<<<<<<<
+ * 
+ *         PyErr_Clear()
+ */
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     __pyx_r = __pyx_2;
     __pyx_2 = 0;
     goto __pyx_L0;
-    goto __pyx_L2;
+    goto __pyx_L5;
   }
-  __pyx_L2:;
+  __pyx_L5:;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2066 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2066
+ *             return cont2_array_sc(self.internal_state, rk_gumbel, size, floc, fscale)
+ * 
+ *         PyErr_Clear()             # <<<<<<<<<<<<<<
+ *         oloc = PyArray_FROM_OTF(loc, NPY_DOUBLE, NPY_ALIGNED)
+ *         oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED)
+ */
   PyErr_Clear();
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2067 */
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-  if (!__Pyx_TypeTest(__pyx_3, __pyx_ptype_6mtrand_ndarray)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2067; goto __pyx_L1;}
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2067
+ * 
+ *         PyErr_Clear()
+ *         oloc = PyArray_FROM_OTF(loc, NPY_DOUBLE, NPY_ALIGNED)             # <<<<<<<<<<<<<<
+ *         oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED)
+ *         if np.any(np.less_equal(oscale, 0.0)):
+ */
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+  if (!(__Pyx_TypeTest(__pyx_3, __pyx_ptype_6mtrand_ndarray))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2067; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   Py_DECREF(((PyObject *)__pyx_v_oloc));
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   __pyx_3 = 0;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2068 */
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-  if (!__Pyx_TypeTest(__pyx_2, __pyx_ptype_6mtrand_ndarray)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2068; goto __pyx_L1;}
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2068
+ *         PyErr_Clear()
+ *         oloc = PyArray_FROM_OTF(loc, NPY_DOUBLE, NPY_ALIGNED)
+ *         oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED)             # <<<<<<<<<<<<<<
+ *         if np.any(np.less_equal(oscale, 0.0)):
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+  if (!(__Pyx_TypeTest(__pyx_2, __pyx_ptype_6mtrand_ndarray))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2068; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2069 */
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2069
+ *         oloc = PyArray_FROM_OTF(loc, NPY_DOUBLE, NPY_ALIGNED)
+ *         oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED)
+ *         if np.any(np.less_equal(oscale, 0.0)):             # <<<<<<<<<<<<<<
+ *             raise ValueError("scale <= 0")
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   Py_DECREF(__pyx_3); __pyx_3 = 0;
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+ * 
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+ *         if np.any(np.less_equal(oscale, 0.0)):
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+ * 
+ *     def logistic(self, loc=0.0, scale=1.0, size=None):
+ */
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 }
 
-static PyObject *__pyx_k118p;
-static PyObject *__pyx_k119p;
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-static char __pyx_k118[] = "scale <= 0";
-static char __pyx_k119[] = "scale <= 0";
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-static PyObject *__pyx_f_6mtrand_11RandomState_logistic(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_logistic[] = "\n        logistic(loc=0.0, scale=1.0, size=None)\n\n        Draw samples from a Logistic distribution.\n\n        Samples are drawn from a Logistic distribution with specified\n        parameters, loc (location or mean, also median), and scale (>0).\n\n        Parameters\n        ----------\n        loc : float\n\n        scale : float > 0.\n\n        size : {tuple, int}\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n                  where the values are all integers in  [0, n].\n\n        See Also\n        --------\n        scipy.stats.distributions.logistic : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Logistic distribution is\n\n        .. math:: P(x) = P(x) = \\frac{e^{-(x-\\mu)/s}}{s(1+e^{-(x-\\mu)/s})^2},\n\n        where :math:`\\mu` = location and :math:`s` = scale.\n\n        The Logistic distribution is used in Extreme Value problems where it\n        can act as a mixture of Gumbel distributions, in Epidemiology, and by\n        the World Chess Federation (FIDE) where it is used in the Elo ranking\n        system, assuming the performance of each player is a logistically\n        distributed random variable.\n\n        References\n        ----------\n        .. [1] Reiss, R.-D. and Thomas M. (2001), Statistical Analysis of Extreme\n               Values, from Insurance, Finance, Hydrology and Other Fields,\n               Birkhauser Verlag, Basel, pp 132-133.\n        .. [2] Weisstein, Eric W. \"Logistic Distribution.\" From\n               MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/LogisticDistribution.html\n        .. [3] Wikipedia, \"Logistic-distribution\",\n               http://en.wikipedia.org/wiki/Logistic-distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> loc, scale = 10, 1\n        >>> s = np.random.logistic(loc, scale, 10000)\n        >>> count, bins, ignored = plt.hist(s, bins=50)\n\n        #   plot against distribution\n\n        >>> def logist(x, loc, scale):\n        ...     return exp((loc-x)/scale)/(scale*(1+exp((loc-x)/scale))**2)\n        >>> plt.plot(bins, logist(bins, loc, scale)*count.max()/\\\n        ... logist(bins, loc, scale).max())\n        >>> plt.show()\n\n        ";
-static PyObject *__pyx_f_6mtrand_11RandomState_logistic(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2073
+ *         return cont2_array(self.internal_state, rk_gumbel, size, oloc, oscale)
+ * 
+ *     def logistic(self, loc=0.0, scale=1.0, size=None):             # <<<<<<<<<<<<<<
+ *         """
+ *         logistic(loc=0.0, scale=1.0, size=None)
+ */
+
+static PyObject *__pyx_pf_6mtrand_11RandomState_logistic(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_logistic[] = "\n""        logistic(loc=0.0, scale=1.0, size=None)\n""\n""        Draw samples from a Logistic distribution.\n""\n""        Samples are drawn from a Logistic distribution with specified\n""        parameters, loc (location or mean, also median), and scale (>0).\n""\n""        Parameters\n""        ----------\n""        loc : float\n""\n""        scale : float > 0.\n""\n""        size : {tuple, int}\n""            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n""            ``m * n * k`` samples are drawn.\n""\n""        Returns\n""        -------\n""        samples : {ndarray, scalar}\n""                  where the values are all integers in  [0, n].\n""\n""        See Also\n""        --------\n""        scipy.stats.distributions.logistic : probability density function,\n""            distribution or cumulative density function, etc.\n""\n""        Notes\n""        -----\n""        The probability density for the Logistic distribution is\n""\n""        .. math:: P(x) = P(x) = \\frac{e^{-(x-\\mu)/s}}{s(1+e^{-(x-\\mu)/s})^2},\n""\n""        where :math:`\\mu` = location and :math:`s` = scale.\n""\n""        The Logistic distribution is used in Extreme Value problems where it\n""        can act as a mixture of Gumbel distributions, in Epidemiology, and by\n""        the World Chess Federation (FIDE) where it is used in the Elo ranking\n""        system, assuming the performance of each player is a logistically\n""        distributed random variable.\n""\n""        References\n""        ----------\n""        .. [1] Reiss, R.-D. and Thomas M. (2001), Statistical Analysis of Extreme\n""               Values, from Insurance, Finance, Hydrology and Other Fields,\n""               Birkhauser Verlag, Basel, pp 132-133.\n""        .. [2] Weisstein, Eric W. \"Logistic Distribution.\" From\n""               MathWorld--A Wolfram Web Resource.\n""               http://mathworld.wolfram.com/LogisticDistribution.html\n""        .. [3] Wikipedia, \"Logistic-distribution\",\n""               http://en.wikipedia.org/wiki/Logistic-distribution\n""\n""        Examples\n""        --------\n""        Draw samples from the distribution:\n""\n""        >>> loc, scale = 10, 1\n""        >>> s = np.random.logistic(loc, scale, 10000)\n""        >>> count, bins, ignored = plt.hist(s, bins=50)\n""\n""        #   plot against distribution\n""\n""        >>> def logist(x, loc, scale):\n""        ...     return exp((loc-x)/scale)/(scale*(1+exp((loc-x)/scale))**2)\n""        >>> plt.plot(bins, logist(bins, loc, scale)*count.max()/\\\n""        ... logist(bins, loc, scale).max())\n""        >>> plt.show()\n""\n""        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_logistic(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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@@ -5769,139 +9903,227 @@ static PyObject *__pyx_f_6mtrand_11RandomState_logistic(PyObject *__pyx_v_self,
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-  Py_INCREF(__pyx_v_scale);
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+      if (PyTuple_GET_SIZE(__pyx_args) > 1) {
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2147 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2147
+ *         cdef double floc, fscale
+ * 
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+ *         fscale = PyFloat_AsDouble(scale)
+ *         if not PyErr_Occurred():
+ */
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2148 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2148
+ * 
+ *         floc = PyFloat_AsDouble(loc)
+ *         fscale = PyFloat_AsDouble(scale)             # <<<<<<<<<<<<<<
+ *         if not PyErr_Occurred():
+ *             if fscale <= 0:
+ */
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2149 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2149
+ *         floc = PyFloat_AsDouble(loc)
+ *         fscale = PyFloat_AsDouble(scale)
+ *         if not PyErr_Occurred():             # <<<<<<<<<<<<<<
+ *             if fscale <= 0:
+ *                 raise ValueError("scale <= 0")
+ */
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   if (__pyx_1) {
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2150 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2150
+ *         fscale = PyFloat_AsDouble(scale)
+ *         if not PyErr_Occurred():
+ *             if fscale <= 0:             # <<<<<<<<<<<<<<
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+ */
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-      Py_DECREF(__pyx_2); __pyx_2 = 0;
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2151
+ *         if not PyErr_Occurred():
+ *             if fscale <= 0:
+ *                 raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
+ *             return cont2_array_sc(self.internal_state, rk_logistic, size, floc, fscale)
+ * 
+ */
+      __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2151; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      Py_INCREF(__pyx_kp_77);
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+      Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0;
       __Pyx_Raise(__pyx_3, 0, 0);
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2151; goto __pyx_L1;}
-      goto __pyx_L3;
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+      goto __pyx_L6;
     }
-    __pyx_L3:;
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2152 */
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2152
+ *             if fscale <= 0:
+ *                 raise ValueError("scale <= 0")
+ *             return cont2_array_sc(self.internal_state, rk_logistic, size, floc, fscale)             # <<<<<<<<<<<<<<
+ * 
+ *         PyErr_Clear()
+ */
+    __pyx_2 = __pyx_f_6mtrand_cont2_array_sc(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_logistic, __pyx_v_size, __pyx_v_floc, __pyx_v_fscale); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2152; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     __pyx_r = __pyx_2;
     __pyx_2 = 0;
     goto __pyx_L0;
-    goto __pyx_L2;
+    goto __pyx_L5;
   }
-  __pyx_L2:;
+  __pyx_L5:;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2154 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2154
+ *             return cont2_array_sc(self.internal_state, rk_logistic, size, floc, fscale)
+ * 
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+ *         oloc = PyArray_FROM_OTF(loc, NPY_DOUBLE, NPY_ALIGNED)
+ *         oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED)
+ */
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+ * 
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2159
+ *         if np.any(np.less_equal(oscale, 0.0)):
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+ *         return cont2_array(self.internal_state, rk_logistic, size, oloc, oscale)             # <<<<<<<<<<<<<<
+ * 
+ *     def lognormal(self, mean=0.0, sigma=1.0, size=None):
+ */
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-static PyObject *__pyx_k120p;
-static PyObject *__pyx_k121p;
-
-static char __pyx_k120[] = "sigma <= 0";
-static char __pyx_k121[] = "sigma <= 0.0";
-
-static PyObject *__pyx_f_6mtrand_11RandomState_lognormal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_lognormal[] = "\n        lognormal(mean=0.0, sigma=1.0, size=None)\n\n        Return samples drawn from a log-normal distribution.\n\n        Draw samples from a log-normal distribution with specified mean, standard\n        deviation, and shape. Note that the mean and standard deviation are not the\n        values for the distribution itself, but of the underlying normal\n        distribution it is derived from.\n\n\n        Parameters\n        ----------\n        mean : float\n            Mean value of the underlying normal distribution\n        sigma : float, >0.\n            Standard deviation of the underlying normal distribution\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        See Also\n        --------\n        scipy.stats.lognorm : probability density function, distribution,\n            cumulative density function, etc.\n\n        Notes\n        -----\n        A variable `x` has a log-normal distribution if `log(x)` is normally\n        distributed.\n\n        The probability density function for the log-normal distribution is\n\n        .. math:: p(x) = \\frac{1}{\\sigma x \\sqrt{2\\pi}}\n                         e^{(-\\frac{(ln(x)-\\mu)^2}{2\\sigma^2})}\n\n        where :math:`\\mu` is the mean and :math:`\\sigma` is the standard deviation\n        of the normally distributed logarithm of the variable.\n\n        A log-normal distribution results if a random variable is the *product* of\n        a large number of independent, identically-distributed variables in the\n        same way that a normal distribution results if the variable is the *sum*\n        of a large number of independent, identically-distributed variables\n        (see the last example). It is one of the so-called \"fat-tailed\"\n        distributions.\n\n        The log-normal distribution is commonly used to model the lifespan of units\n        with fatigue-stress failure modes. Since this includes\n        most mechanical systems, the log-normal distribution has widespread\n        application.\n\n        It is also commonly used to model oil field sizes, species abundance, and\n        latent periods of infectious diseases.\n\n        References\n        ----------\n        .. [1] Eckhard Limpert, Werner A. Stahel, and Markus Abbt, \"Log-normal\n               Distributions across the Sciences: Keys and Clues\", May 2001\n               Vol. 51 No. 5 BioScience\n               http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf\n        .. [2] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme\n               Values, Birkhauser Verlag, Basel, pp 31-32.\n        .. [3] Wikipedia, \"Lognormal distribution\",\n               http://en.wikipedia.org/wiki/Lognormal_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, sigma = 3., 1. # mean and standard deviation\n        >>> s = np.random.lognormal(mu, sigma, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 100, normed=True, align=\'center\')\n\n        >>> x = np.linspace(min(bins), max(bins), 10000)\n        >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n        ...        / (x * sigma * np.sqrt(2 * np.pi)))\n\n        >>> plt.plot(x, pdf, linewidth=2, color=\'r\')\n        >>> plt.axis(\'tight\')\n        >>> plt.show()\n\n        Demonstrate that taking the products of random samples from a uniform\n        distribution can be fit well by a log-normal probability density function.\n\n        >>> # Generate a thousand samples: each is the product of 100 random\n        >>> # values, drawn from a normal distribution.\n        >>> b = []\n        >>> for i in range(1000):\n        ...    a = 10. + np.random.random(100)\n        ...    b.append(np.product(a))\n\n        >>> b = np.array(b) / np.min(b) # scale values to be positive\n\n        >>> count, bins, ignored = plt.hist(b, 100, normed=True, align=\'center\')\n\n        >>> sigma = np.std(np.log(b))\n        >>> mu = np.mean(np.log(b))\n\n        >>> x = np.linspace(min(bins), max(bins), 10000)\n        >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n        ...        / (x * sigma * np.sqrt(2 * np.pi)))\n\n        >>> plt.plot(x, pdf, color=\'r\', linewidth=2)\n        >>> plt.show()\n\n        ";
-static PyObject *__pyx_f_6mtrand_11RandomState_lognormal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2161
+ *         return cont2_array(self.internal_state, rk_logistic, size, oloc, oscale)
+ * 
+ *     def lognormal(self, mean=0.0, sigma=1.0, size=None):             # <<<<<<<<<<<<<<
+ *         """
+ *         lognormal(mean=0.0, sigma=1.0, size=None)
+ */
+
+static PyObject *__pyx_pf_6mtrand_11RandomState_lognormal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_lognormal[] = "\n""        lognormal(mean=0.0, sigma=1.0, size=None)\n""\n""        Return samples drawn from a log-normal distribution.\n""\n""        Draw samples from a log-normal distribution with specified mean, standard\n""        deviation, and shape. Note that the mean and standard deviation are not the\n""        values for the distribution itself, but of the underlying normal\n""        distribution it is derived from.\n""\n""\n""        Parameters\n""        ----------\n""        mean : float\n""            Mean value of the underlying normal distribution\n""        sigma : float, >0.\n""            Standard deviation of the underlying normal distribution\n""        size : tuple of ints\n""            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n""            ``m * n * k`` samples are drawn.\n""\n""        See Also\n""        --------\n""        scipy.stats.lognorm : probability density function, distribution,\n""            cumulative density function, etc.\n""\n""        Notes\n""        -----\n""        A variable `x` has a log-normal distribution if `log(x)` is normally\n""        distributed.\n""\n""        The probability density function for the log-normal distribution is\n""\n""        .. math:: p(x) = \\frac{1}{\\sigma x \\sqrt{2\\pi}}\n""                         e^{(-\\frac{(ln(x)-\\mu)^2}{2\\sigma^2})}\n""\n""        where :math:`\\mu` is the mean and :math:`\\sigma` is the standard deviation\n""        of the normally distributed logarithm of the variable.\n""\n""        A log-normal distribution results if a random variable is the *product* of\n""        a large number of independent, identically-distributed variables in the\n""        same way that a normal distribution results if the variable is the *sum*\n""        of a large number of independent, identically-distributed variables\n""        (see the last example). It is one of the so-called \"fat-tailed\"\n""        distributions.\n""\n""        The log-normal distribution is commonly used to model the lifespan of units\n""        with fatigue-stress failure modes. Since this includes\n""        most mechanical systems, the log-normal distribution has widespread\n""        application.\n""\n""        It is also commonly used to model oil field sizes, species abundance, and\n""        latent periods of infectious diseases.\n""\n""        References\n""        ----------\n""        .. [1] Eckhard Limpert, Werner A. Stahel, and Markus Abbt, \"Log-normal\n""               Distributions across the Sciences: Keys and Clues\", May 2001\n""               Vol. 51 No. 5 BioScience\n""               http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf\n""        .. [2] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme\n""               Values, Birkhauser Verlag, Basel, pp 31-32.\n""        .. [3] Wikipedia, \"Lognormal distribution\",\n""               http://en.wikipedia.org/wiki/Lognormal_distribution\n""\n""        Examples\n""        --------\n""        Draw samples from the distribution:\n""\n""        >>> mu, sigma = 3., 1. # mean and standard deviation\n""        >>> s = np.random.lognormal(mu, sigma, 1000)\n""\n""        Display the histogram of the samples, along with\n""        the probability density function:\n""\n""        >>> import matplotlib.pyplot as plt\n""        >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='center')\n""\n""        >>> x = np.linspace(min(bins), max(bins), 10000)\n""        >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n""        ...        / (x * sigma * np.sqrt(2 * np.pi)))\n""\n""        >>> plt.plot(x, pdf, linewidth=2, color='r')\n""        >>> plt.axis('tight')\n""        >>> plt.show()\n""\n""        Demonstrate that taking the products of random samples from a uniform\n""        distribution can be fit well by a log-normal probability density function.\n""\n""        >>> # Generate a thousand samples: each is the product of 100 random\n""        >>> # values, drawn from a normal distribution.\n""        >>> b = []\n""        >>> for i in range(1000):\n""        ...    a = 10. + np.random.random(100)\n""        ...    b.append(np.product(a))\n""\n""        >>> b = np.array(b) / np.min(b) # scale values to be positive\n""\n""        >>> count, bins, ignored = plt.hist(b, 100, normed=True, align='center')\n""\n""        >>> sigma = np.std(np.log(b))\n""        >>> mu = np.mean(np.log(b))\n""\n""        >>> x = np.linspace(min(bins), max(bins), 10000)\n""        >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n""        ...        / (x * sigma * np.sqrt(2 * np.pi)))\n""\n""        >>> plt.plot(x, pdf, color='r', linewidth=2)\n""        >>> plt.show()\n""\n""        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_lognormal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
   PyObject *__pyx_v_mean = 0;
   PyObject *__pyx_v_sigma = 0;
   PyObject *__pyx_v_size = 0;
@@ -5916,139 +10138,227 @@ static PyObject *__pyx_f_6mtrand_11RandomState_lognormal(PyObject *__pyx_v_self,
   PyObject *__pyx_4 = 0;
   PyObject *__pyx_5 = 0;
   static char *__pyx_argnames[] = {"mean","sigma","size",0};
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-  __pyx_v_sigma = __pyx_k44;
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-  Py_INCREF(__pyx_v_self);
-  Py_INCREF(__pyx_v_mean);
-  Py_INCREF(__pyx_v_sigma);
-  Py_INCREF(__pyx_v_size);
+  __pyx_v_mean = __pyx_k_15;
+  __pyx_v_sigma = __pyx_k_16;
+  __pyx_v_size = Py_None;
+  if (likely(!__pyx_kwds) && likely(0 <= PyTuple_GET_SIZE(__pyx_args)) && likely(PyTuple_GET_SIZE(__pyx_args) <= 3)) {
+    if (PyTuple_GET_SIZE(__pyx_args) > 0) {
+      __pyx_v_mean = PyTuple_GET_ITEM(__pyx_args, 0);
+      if (PyTuple_GET_SIZE(__pyx_args) > 1) {
+        __pyx_v_sigma = PyTuple_GET_ITEM(__pyx_args, 1);
+        if (PyTuple_GET_SIZE(__pyx_args) > 2) {
+          __pyx_v_size = PyTuple_GET_ITEM(__pyx_args, 2);
+        }
+      }
+    }
+  }
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+    if (unlikely(!PyArg_ParseTupleAndKeywords(__pyx_args, __pyx_kwds, "|OOO", __pyx_argnames, &__pyx_v_mean, &__pyx_v_sigma, &__pyx_v_size))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2161; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+  }
+  goto __pyx_L4;
+  __pyx_L3_error:;
+  __Pyx_AddTraceback("mtrand.RandomState.lognormal");
+  return NULL;
+  __pyx_L4:;
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   __pyx_v_osigma = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2276 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2276
+ *         cdef double fmean, fsigma
+ * 
+ *         fmean = PyFloat_AsDouble(mean)             # <<<<<<<<<<<<<<
+ *         fsigma = PyFloat_AsDouble(sigma)
+ * 
+ */
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2277 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2277
+ * 
+ *         fmean = PyFloat_AsDouble(mean)
+ *         fsigma = PyFloat_AsDouble(sigma)             # <<<<<<<<<<<<<<
+ * 
+ *         if not PyErr_Occurred():
+ */
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2279 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2279
+ *         fsigma = PyFloat_AsDouble(sigma)
+ * 
+ *         if not PyErr_Occurred():             # <<<<<<<<<<<<<<
+ *             if fsigma <= 0:
+ *                 raise ValueError("sigma <= 0")
+ */
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2280 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2280
+ * 
+ *         if not PyErr_Occurred():
+ *             if fsigma <= 0:             # <<<<<<<<<<<<<<
+ *                 raise ValueError("sigma <= 0")
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-      Py_DECREF(__pyx_2); __pyx_2 = 0;
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2281
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+ *                 raise ValueError("sigma <= 0")             # <<<<<<<<<<<<<<
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+ * 
+ */
+      __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2281; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      Py_INCREF(__pyx_kp_79);
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2281; goto __pyx_L1;}
-      goto __pyx_L3;
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2281; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2282
+ *             if fsigma <= 0:
+ *                 raise ValueError("sigma <= 0")
+ *             return cont2_array_sc(self.internal_state, rk_lognormal, size, fmean, fsigma)             # <<<<<<<<<<<<<<
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+ */
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     __pyx_2 = 0;
     goto __pyx_L0;
-    goto __pyx_L2;
+    goto __pyx_L5;
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+  __pyx_L5:;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2284 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2284
+ *             return cont2_array_sc(self.internal_state, rk_lognormal, size, fmean, fsigma)
+ * 
+ *         PyErr_Clear()             # <<<<<<<<<<<<<<
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2286
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+ *         osigma = PyArray_FROM_OTF(sigma, NPY_DOUBLE, NPY_ALIGNED)
+ *         if np.any(np.less_equal(osigma, 0.0)):
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2287
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+ *         omean = PyArray_FROM_OTF(mean, NPY_DOUBLE, NPY_ALIGNED)
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2288
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+  Py_DECREF(((PyObject *)__pyx_4)); __pyx_4 = 0;
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-    Py_DECREF(__pyx_3); __pyx_3 = 0;
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2289
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+    goto __pyx_L7;
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-  __pyx_L4:;
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2290
+ *         if np.any(np.less_equal(osigma, 0.0)):
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   __pyx_r = Py_None; Py_INCREF(Py_None);
   goto __pyx_L0;
-  __pyx_L1:;
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   Py_DECREF(__pyx_v_osigma);
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-static PyObject *__pyx_k123p;
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 static char __pyx_doc_6mtrand_11RandomState_rayleigh[] = "\n        rayleigh(scale=1.0, size=None)\n\n        Rayleigh distribution.\n\n        ";
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@@ -6060,128 +10370,199 @@ static PyObject *__pyx_f_6mtrand_11RandomState_rayleigh(PyObject *__pyx_v_self,
   PyObject *__pyx_4 = 0;
   PyObject *__pyx_5 = 0;
   static char *__pyx_argnames[] = {"scale","size",0};
-  __pyx_v_scale = __pyx_k46;
-  __pyx_v_size = __pyx_k47;
-  if (!PyArg_ParseTupleAndKeywords(__pyx_args, __pyx_kwds, "|OO", __pyx_argnames, &__pyx_v_scale, &__pyx_v_size)) return 0;
-  Py_INCREF(__pyx_v_self);
-  Py_INCREF(__pyx_v_scale);
-  Py_INCREF(__pyx_v_size);
+  __pyx_v_scale = __pyx_k_17;
+  __pyx_v_size = Py_None;
+  if (likely(!__pyx_kwds) && likely(0 <= PyTuple_GET_SIZE(__pyx_args)) && likely(PyTuple_GET_SIZE(__pyx_args) <= 2)) {
+    if (PyTuple_GET_SIZE(__pyx_args) > 0) {
+      __pyx_v_scale = PyTuple_GET_ITEM(__pyx_args, 0);
+      if (PyTuple_GET_SIZE(__pyx_args) > 1) {
+        __pyx_v_size = PyTuple_GET_ITEM(__pyx_args, 1);
+      }
+    }
+  }
+  else {
+    if (unlikely(!PyArg_ParseTupleAndKeywords(__pyx_args, __pyx_kwds, "|OO", __pyx_argnames, &__pyx_v_scale, &__pyx_v_size))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2292; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+  }
+  goto __pyx_L4;
+  __pyx_L3_error:;
+  __Pyx_AddTraceback("mtrand.RandomState.rayleigh");
+  return NULL;
+  __pyx_L4:;
   __pyx_v_oscale = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2302 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2302
+ *         cdef double fscale
+ * 
+ *         fscale = PyFloat_AsDouble(scale)             # <<<<<<<<<<<<<<
+ * 
+ *         if not PyErr_Occurred():
+ */
   __pyx_v_fscale = PyFloat_AsDouble(__pyx_v_scale);
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2304 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2304
+ *         fscale = PyFloat_AsDouble(scale)
+ * 
+ *         if not PyErr_Occurred():             # <<<<<<<<<<<<<<
+ *             if fscale <= 0:
+ *                 raise ValueError("scale <= 0")
+ */
   __pyx_1 = (!PyErr_Occurred());
   if (__pyx_1) {
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2305 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2305
+ * 
+ *         if not PyErr_Occurred():
+ *             if fscale <= 0:             # <<<<<<<<<<<<<<
+ *                 raise ValueError("scale <= 0")
+ *             return cont1_array_sc(self.internal_state, rk_rayleigh, size, fscale)
+ */
     __pyx_1 = (__pyx_v_fscale <= 0);
     if (__pyx_1) {
-      __pyx_2 = PyTuple_New(1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2306; goto __pyx_L1;}
-      Py_INCREF(__pyx_k122p);
-      PyTuple_SET_ITEM(__pyx_2, 0, __pyx_k122p);
-      __pyx_3 = PyObject_CallObject(PyExc_ValueError, __pyx_2); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2306; goto __pyx_L1;}
-      Py_DECREF(__pyx_2); __pyx_2 = 0;
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2306
+ *         if not PyErr_Occurred():
+ *             if fscale <= 0:
+ *                 raise ValueError("scale <= 0")             # <<<<<<<<<<<<<<
+ *             return cont1_array_sc(self.internal_state, rk_rayleigh, size, fscale)
+ * 
+ */
+      __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2306; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      Py_INCREF(__pyx_kp_81);
+      PyTuple_SET_ITEM(__pyx_2, 0, __pyx_kp_81);
+      __pyx_3 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2306; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0;
       __Pyx_Raise(__pyx_3, 0, 0);
       Py_DECREF(__pyx_3); __pyx_3 = 0;
-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2306; goto __pyx_L1;}
-      goto __pyx_L3;
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2306; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      goto __pyx_L6;
     }
-    __pyx_L3:;
+    __pyx_L6:;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2307 */
-    __pyx_2 = __pyx_f_6mtrand_cont1_array_sc(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state,rk_rayleigh,__pyx_v_size,__pyx_v_fscale); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2307; goto __pyx_L1;}
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2307
+ *             if fscale <= 0:
+ *                 raise ValueError("scale <= 0")
+ *             return cont1_array_sc(self.internal_state, rk_rayleigh, size, fscale)             # <<<<<<<<<<<<<<
+ * 
+ *         PyErr_Clear()
+ */
+    __pyx_2 = __pyx_f_6mtrand_cont1_array_sc(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_rayleigh, __pyx_v_size, __pyx_v_fscale); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2307; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     __pyx_r = __pyx_2;
     __pyx_2 = 0;
     goto __pyx_L0;
-    goto __pyx_L2;
+    goto __pyx_L5;
   }
-  __pyx_L2:;
+  __pyx_L5:;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2309 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2309
+ *             return cont1_array_sc(self.internal_state, rk_rayleigh, size, fscale)
+ * 
+ *         PyErr_Clear()             # <<<<<<<<<<<<<<
+ * 
+ *         oscale = <ndarray>PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED)
+ */
   PyErr_Clear();
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2311 */
-  __pyx_3 = PyArray_FROM_OTF(__pyx_v_scale,NPY_DOUBLE,NPY_ALIGNED); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2311; goto __pyx_L1;}
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2311
+ *         PyErr_Clear()
+ * 
+ *         oscale = <ndarray>PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED)             # <<<<<<<<<<<<<<
+ *         if np.any(np.less_equal(oscale, 0.0)):
+ *             raise ValueError("scale <= 0.0")
+ */
+  __pyx_3 = PyArray_FROM_OTF(__pyx_v_scale, NPY_DOUBLE, NPY_ALIGNED); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2311; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_3)));
   Py_DECREF(((PyObject *)__pyx_v_oscale));
   __pyx_v_oscale = ((PyArrayObject *)__pyx_3);
   Py_DECREF(__pyx_3); __pyx_3 = 0;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2312 */
-  __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n_np); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2312; goto __pyx_L1;}
-  __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_n_any); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2312; goto __pyx_L1;}
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2312
+ * 
+ *         oscale = <ndarray>PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED)
+ *         if np.any(np.less_equal(oscale, 0.0)):             # <<<<<<<<<<<<<<
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+ *         return cont1_array(self.internal_state, rk_rayleigh, size, oscale)
+ */
+  __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2312; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_kp_any); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2312; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   Py_DECREF(__pyx_2); __pyx_2 = 0;
-  __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n_np); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2312; goto __pyx_L1;}
-  __pyx_4 = PyObject_GetAttr(__pyx_2, __pyx_n_less_equal); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2312; goto __pyx_L1;}
+  __pyx_2 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2312; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_4 = PyObject_GetAttr(__pyx_2, __pyx_kp_less_equal); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2312; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  __pyx_2 = PyFloat_FromDouble(0.0); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2312; goto __pyx_L1;}
-  __pyx_5 = PyTuple_New(2); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2312; goto __pyx_L1;}
+  __pyx_2 = PyFloat_FromDouble(0.0); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2312; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_5 = PyTuple_New(2); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2312; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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   PyTuple_SET_ITEM(__pyx_5, 0, ((PyObject *)__pyx_v_oscale));
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-  __pyx_2 = PyObject_CallObject(__pyx_4, __pyx_5); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2312; goto __pyx_L1;}
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-  Py_DECREF(__pyx_5); __pyx_5 = 0;
-  __pyx_4 = PyTuple_New(1); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2312; goto __pyx_L1;}
+  Py_DECREF(((PyObject *)__pyx_5)); __pyx_5 = 0;
+  __pyx_4 = PyTuple_New(1); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2312; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   PyTuple_SET_ITEM(__pyx_4, 0, __pyx_2);
   __pyx_2 = 0;
-  __pyx_5 = PyObject_CallObject(__pyx_3, __pyx_4); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2312; goto __pyx_L1;}
+  __pyx_5 = PyObject_Call(__pyx_3, ((PyObject *)__pyx_4), NULL); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2312; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   Py_DECREF(__pyx_3); __pyx_3 = 0;
-  Py_DECREF(__pyx_4); __pyx_4 = 0;
-  __pyx_1 = PyObject_IsTrue(__pyx_5); if (__pyx_1 < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2312; goto __pyx_L1;}
+  Py_DECREF(((PyObject *)__pyx_4)); __pyx_4 = 0;
+  __pyx_1 = __Pyx_PyObject_IsTrue(__pyx_5); if (unlikely(__pyx_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2312; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   Py_DECREF(__pyx_5); __pyx_5 = 0;
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-    __pyx_2 = PyTuple_New(1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2313; goto __pyx_L1;}
-    Py_INCREF(__pyx_k123p);
-    PyTuple_SET_ITEM(__pyx_2, 0, __pyx_k123p);
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-    Py_DECREF(__pyx_2); __pyx_2 = 0;
+
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2313
+ *         oscale = <ndarray>PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_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_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2313; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    Py_INCREF(__pyx_kp_82);
+    PyTuple_SET_ITEM(__pyx_2, 0, __pyx_kp_82);
+    __pyx_3 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2313; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0;
     __Pyx_Raise(__pyx_3, 0, 0);
     Py_DECREF(__pyx_3); __pyx_3 = 0;
-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2313; goto __pyx_L1;}
-    goto __pyx_L4;
+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2313; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    goto __pyx_L7;
   }
-  __pyx_L4:;
+  __pyx_L7:;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2314 */
-  __pyx_4 = __pyx_f_6mtrand_cont1_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state,rk_rayleigh,__pyx_v_size,__pyx_v_oscale); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2314; goto __pyx_L1;}
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2314
+ *         if np.any(np.less_equal(oscale, 0.0)):
+ *             raise ValueError("scale <= 0.0")
+ *         return cont1_array(self.internal_state, rk_rayleigh, size, oscale)             # <<<<<<<<<<<<<<
+ * 
+ *     def wald(self, mean, scale, size=None):
+ */
+  __pyx_4 = __pyx_f_6mtrand_cont1_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_rayleigh, __pyx_v_size, __pyx_v_oscale); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2314; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __pyx_r = __pyx_4;
   __pyx_4 = 0;
   goto __pyx_L0;
 
   __pyx_r = Py_None; Py_INCREF(Py_None);
   goto __pyx_L0;
-  __pyx_L1:;
+  __pyx_L1_error:;
   Py_XDECREF(__pyx_2);
   Py_XDECREF(__pyx_3);
   Py_XDECREF(__pyx_4);
   Py_XDECREF(__pyx_5);
   __Pyx_AddTraceback("mtrand.RandomState.rayleigh");
-  __pyx_r = 0;
+  __pyx_r = NULL;
   __pyx_L0:;
   Py_DECREF(__pyx_v_oscale);
-  Py_DECREF(__pyx_v_self);
-  Py_DECREF(__pyx_v_scale);
-  Py_DECREF(__pyx_v_size);
   return __pyx_r;
 }
 
-static PyObject *__pyx_k124p;
-static PyObject *__pyx_k125p;
-static PyObject *__pyx_k126p;
-static PyObject *__pyx_k127p;
-
-static char __pyx_k124[] = "mean <= 0";
-static char __pyx_k125[] = "scale <= 0";
-static char __pyx_k126[] = "mean <= 0.0";
-static char __pyx_k127[] = "scale <= 0.0";
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2316
+ *         return cont1_array(self.internal_state, rk_rayleigh, size, oscale)
+ * 
+ *     def wald(self, mean, scale, size=None):             # <<<<<<<<<<<<<<
+ *         """
+ *         wald(mean, scale, size=None)
+ */
 
-static PyObject *__pyx_f_6mtrand_11RandomState_wald(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static PyObject *__pyx_pf_6mtrand_11RandomState_wald(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
 static char __pyx_doc_6mtrand_11RandomState_wald[] = "\n        wald(mean, scale, size=None)\n\n        Wald (inverse Gaussian) distribution.\n\n        ";
-static PyObject *__pyx_f_6mtrand_11RandomState_wald(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_wald(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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   PyObject *__pyx_v_scale = 0;
   PyObject *__pyx_v_size = 0;
@@ -6196,197 +10577,300 @@ static PyObject *__pyx_f_6mtrand_11RandomState_wald(PyObject *__pyx_v_self, PyOb
   PyObject *__pyx_4 = 0;
   PyObject *__pyx_5 = 0;
   static char *__pyx_argnames[] = {"mean","scale","size",0};
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-  if (!PyArg_ParseTupleAndKeywords(__pyx_args, __pyx_kwds, "OO|O", __pyx_argnames, &__pyx_v_mean, &__pyx_v_scale, &__pyx_v_size)) return 0;
-  Py_INCREF(__pyx_v_self);
-  Py_INCREF(__pyx_v_mean);
-  Py_INCREF(__pyx_v_scale);
-  Py_INCREF(__pyx_v_size);
+  __pyx_v_size = Py_None;
+  if (likely(!__pyx_kwds) && likely(2 <= PyTuple_GET_SIZE(__pyx_args)) && likely(PyTuple_GET_SIZE(__pyx_args) <= 3)) {
+    __pyx_v_mean = PyTuple_GET_ITEM(__pyx_args, 0);
+    __pyx_v_scale = PyTuple_GET_ITEM(__pyx_args, 1);
+    if (PyTuple_GET_SIZE(__pyx_args) > 2) {
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+    }
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+  }
+  goto __pyx_L4;
+  __pyx_L3_error:;
+  __Pyx_AddTraceback("mtrand.RandomState.wald");
+  return NULL;
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2326 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2326
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2327 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2327
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2328 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2328
+ *         fmean = PyFloat_AsDouble(mean)
+ *         fscale = PyFloat_AsDouble(scale)
+ *         if not PyErr_Occurred():             # <<<<<<<<<<<<<<
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2329 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2329
+ *         fscale = PyFloat_AsDouble(scale)
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+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2330
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+ *                 raise ValueError("mean <= 0")             # <<<<<<<<<<<<<<
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2331 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2331
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+      Py_INCREF(__pyx_kp_84);
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2335 */
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2336 */
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+  Py_DECREF(((PyObject *)__pyx_3)); __pyx_3 = 0;
+  __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2340; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  __pyx_1 = PyObject_IsTrue(__pyx_3); if (__pyx_1 < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2340; goto __pyx_L1;}
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-    Py_DECREF(__pyx_4); __pyx_4 = 0;
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2341
+ *             raise ValueError("mean <= 0.0")
+ *         elif np.any(np.less_equal(oscale,0.0)):
+ *             raise ValueError("scale <= 0.0")             # <<<<<<<<<<<<<<
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+ * 
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+    __pyx_4 = PyTuple_New(1); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2341; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    Py_INCREF(__pyx_kp_86);
+    PyTuple_SET_ITEM(__pyx_4, 0, __pyx_kp_86);
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+    Py_DECREF(((PyObject *)__pyx_4)); __pyx_4 = 0;
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-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2341; goto __pyx_L1;}
-    goto __pyx_L5;
+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2341; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    goto __pyx_L8;
   }
-  __pyx_L5:;
+  __pyx_L8:;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2342 */
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2342
+ *         elif np.any(np.less_equal(oscale,0.0)):
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+ * 
+ */
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   __pyx_r = Py_None; Py_INCREF(Py_None);
   goto __pyx_L0;
-  __pyx_L1:;
+  __pyx_L1_error:;
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   Py_XDECREF(__pyx_3);
   Py_XDECREF(__pyx_4);
   Py_XDECREF(__pyx_5);
   __Pyx_AddTraceback("mtrand.RandomState.wald");
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-  Py_DECREF(__pyx_v_scale);
-  Py_DECREF(__pyx_v_size);
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 }
 
-static PyObject *__pyx_n_greater;
-static PyObject *__pyx_n_equal;
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+ * 
+ * 
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-static PyObject *__pyx_k129p;
-static PyObject *__pyx_k130p;
-static PyObject *__pyx_k131p;
-static PyObject *__pyx_k132p;
-static PyObject *__pyx_k133p;
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-static char __pyx_k133[] = "left == right";
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-static PyObject *__pyx_f_6mtrand_11RandomState_triangular(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
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 static char __pyx_doc_6mtrand_11RandomState_triangular[] = "\n        triangular(left, mode, right, size=None)\n\n        Triangular distribution starting at left, peaking at mode, and\n        ending at right (left <= mode <= right).\n\n        ";
-static PyObject *__pyx_f_6mtrand_11RandomState_triangular(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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-  Py_INCREF(__pyx_v_mode);
-  Py_INCREF(__pyx_v_right);
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+  __pyx_v_size = Py_None;
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+    __pyx_v_mode = PyTuple_GET_ITEM(__pyx_args, 1);
+    __pyx_v_right = PyTuple_GET_ITEM(__pyx_args, 2);
+    if (PyTuple_GET_SIZE(__pyx_args) > 3) {
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+  goto __pyx_L4;
+  __pyx_L3_error:;
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+  return NULL;
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2357 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2357
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+ * 
+ *         fleft = PyFloat_AsDouble(left)             # <<<<<<<<<<<<<<
+ *         fright = PyFloat_AsDouble(right)
+ *         fmode = PyFloat_AsDouble(mode)
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2358 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2358
+ * 
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+ *         fright = PyFloat_AsDouble(right)             # <<<<<<<<<<<<<<
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+ */
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2359 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2359
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+ *         fright = PyFloat_AsDouble(right)
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2360 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2360
+ *         fright = PyFloat_AsDouble(right)
+ *         fmode = PyFloat_AsDouble(mode)
+ *         if not PyErr_Occurred():             # <<<<<<<<<<<<<<
+ *             if fleft > fmode:
+ *                 raise ValueError("left > mode")
+ */
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   if (__pyx_1) {
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2361 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2361
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-      Py_DECREF(__pyx_2); __pyx_2 = 0;
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2362
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+      PyTuple_SET_ITEM(__pyx_2, 0, __pyx_kp_87);
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2362; goto __pyx_L1;}
-      goto __pyx_L3;
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+      goto __pyx_L6;
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-    __pyx_L3:;
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2363 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2363
+ *             if fleft > fmode:
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+ *                 raise ValueError("mode > right")
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+ */
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-      Py_DECREF(__pyx_2); __pyx_2 = 0;
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2364
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+      Py_INCREF(__pyx_kp_88);
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+      Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0;
       __Pyx_Raise(__pyx_3, 0, 0);
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2364; goto __pyx_L1;}
-      goto __pyx_L4;
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2364; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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     }
-    __pyx_L4:;
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2365 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2365
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-      Py_DECREF(__pyx_2); __pyx_2 = 0;
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2366
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       __Pyx_Raise(__pyx_3, 0, 0);
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2366; goto __pyx_L1;}
-      goto __pyx_L5;
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2366; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      goto __pyx_L8;
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+    __pyx_L8:;
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2368
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     __pyx_r = __pyx_2;
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     goto __pyx_L0;
-    goto __pyx_L2;
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2370 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2370
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-static PyObject *__pyx_f_6mtrand_11RandomState_binomial(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_binomial[] = "\n        binomial(n, p, size=None)\n\n        Draw samples from a binomial distribution.\n\n        Samples are drawn from a Binomial distribution with specified\n        parameters, n trials and p probability of success where\n        n an integer > 0 and p is in the interval [0,1]. (n may be\n        input as a float, but it is truncated to an integer in use)\n\n        Parameters\n        ----------\n        n : float (but truncated to an integer)\n                parameter, > 0.\n        p : float\n                parameter, >= 0 and <=1.\n        size : {tuple, int}\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n                  where the values are all integers in  [0, n].\n\n        See Also\n        --------\n        scipy.stats.distributions.binom : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Binomial distribution is\n\n        .. math:: P(N) = \\binom{n}{N}p^N(1-p)^{n-N},\n\n        where :math:`n` is the number of trials, :math:`p` is the probability\n        of success, and :math:`N` is the number of successes.\n\n        When estimating the standard error of a proportion in a population by\n        using a random sample, the normal distribution works well unless the\n        product p*n <=5, where p = population proportion estimate, and n =\n        number of samples, in which case the binomial distribution is used\n        instead. For example, a sample of 15 people shows 4 who are left\n        handed, and 11 who are right handed. Then p = 4/15 = 27%. 0.27*15 = 4,\n        so the binomial distribution should be used in this case.\n\n        References\n        ----------\n        .. [1] Dalgaard, Peter, \"Introductory Statistics with R\",\n               Springer-Verlag, 2002.\n        .. [2] Glantz, Stanton A. \"Primer of Biostatistics.\", McGraw-Hill,\n               Fifth Edition, 2002.\n        .. [3] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n               and Quigley, 1972.\n        .. [4] Weisstein, Eric W. \"Binomial Distribution.\" From MathWorld--A\n               Wolfram Web Resource.\n               http://mathworld.wolfram.com/BinomialDistribution.html\n        .. [5] Wikipedia, \"Binomial-distribution\",\n               http://en.wikipedia.org/wiki/Binomial_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> n, p = 10, .5 # number of trials, probability of each trial\n        >>> s = np.random.binomial(n, p, 1000)\n        # result of flipping a coin 10 times, tested 1000 times.\n\n        A real world example. A company drills 9 wild-cat oil exploration\n        wells, each with an estimated probability of success of 0.1. All nine\n        wells fail. What is the probability of that happening?\n\n        Let\'s do 20,000 trials of the model, and count the number that\n        generate zero positive results.\n\n        >>> sum(np.random.binomial(9,0.1,20000)==0)/20000.\n        answer = 0.38885, or 38%.\n\n        ";
-static PyObject *__pyx_f_6mtrand_11RandomState_binomial(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2385
+ * 
+ *     # Complicated, discrete distributions:
+ *     def binomial(self, n, p, size=None):             # <<<<<<<<<<<<<<
+ *         """
+ *         binomial(n, p, size=None)
+ */
+
+static PyObject *__pyx_pf_6mtrand_11RandomState_binomial(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_binomial[] = "\n""        binomial(n, p, size=None)\n""\n""        Draw samples from a binomial distribution.\n""\n""        Samples are drawn from a Binomial distribution with specified\n""        parameters, n trials and p probability of success where\n""        n an integer > 0 and p is in the interval [0,1]. (n may be\n""        input as a float, but it is truncated to an integer in use)\n""\n""        Parameters\n""        ----------\n""        n : float (but truncated to an integer)\n""                parameter, > 0.\n""        p : float\n""                parameter, >= 0 and <=1.\n""        size : {tuple, int}\n""            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n""            ``m * n * k`` samples are drawn.\n""\n""        Returns\n""        -------\n""        samples : {ndarray, scalar}\n""                  where the values are all integers in  [0, n].\n""\n""        See Also\n""        --------\n""        scipy.stats.distributions.binom : probability density function,\n""            distribution or cumulative density function, etc.\n""\n""        Notes\n""        -----\n""        The probability density for the Binomial distribution is\n""\n""        .. math:: P(N) = \\binom{n}{N}p^N(1-p)^{n-N},\n""\n""        where :math:`n` is the number of trials, :math:`p` is the probability\n""        of success, and :math:`N` is the number of successes.\n""\n""        When estimating the standard error of a proportion in a population by\n""        using a random sample, the normal distribution works well unless the\n""        product p*n <=5, where p = population proportion estimate, and n =\n""        number of samples, in which case the binomial distribution is used\n""        instead. For example, a sample of 15 people shows 4 who are left\n""        handed, and 11 who are right handed. Then p = 4/15 = 27%. 0.27*15 = 4,\n""        so the binomial distribution should be used in this case.\n""\n""        References\n""        ----------\n""        .. [1] Dalgaard, Peter, \"Introductory Statistics with R\",\n""               Springer-Verlag, 2002.\n""        .. [2] Glantz, Stanton A. \"Primer of Biostatistics.\", McGraw-Hill,\n""               Fifth Edition, 2002.\n""        .. [3] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n""               and Quigley, 1972.\n""        .. [4] Weisstein, Eric W. \"Binomial Distribution.\" From MathWorld--A\n""               Wolfram Web Resource.\n""               http://mathworld.wolfram.com/BinomialDistribution.html\n""        .. [5] Wikipedia, \"Binomial-distribution\",\n""               http://en.wikipedia.org/wiki/Binomial_distribution\n""\n""        Examples\n""        --------\n""        Draw samples from the distribution:\n""\n""        >>> n, p = 10, .5 # number of trials, probability of each trial\n""        >>> s = np.random.binomial(n, p, 1000)\n""        # result of flipping a coin 10 times, tested 1000 times.\n""\n""        A real world example. A company drills 9 wild-cat oil exploration\n""        wells, each with an estimated probability of success of 0.1. All nine\n""        wells fail. What is the probability of that happening?\n""\n""        Let's do 20,000 trials of the model, and count the number that\n""        generate zero positive results.\n""\n""        >>> sum(np.random.binomial(9,0.1,20000)==0)/20000.\n""        answer = 0.38885, or 38%.\n""\n""        ";
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+ * 
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+ *         ln = PyInt_AsLong(n)
+ *         if not PyErr_Occurred():
+ */
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2471
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+ */
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+ */
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-      Py_DECREF(__pyx_2); __pyx_2 = 0;
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+ */
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+ */
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2476
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2545 */
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2547
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+ * 
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-  __pyx_L1:;
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-  __pyx_r = 0;
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 }
 
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-static PyObject *__pyx_f_6mtrand_11RandomState_zipf(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
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-static PyObject *__pyx_f_6mtrand_11RandomState_zipf(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2552
+ *         return discd_array(self.internal_state, rk_poisson, size, olam)
+ * 
+ *     def zipf(self, a, size=None):             # <<<<<<<<<<<<<<
+ *         """
+ *         zipf(a, size=None)
+ */
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+static PyObject *__pyx_pf_6mtrand_11RandomState_zipf(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_zipf[] = "\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 (a),\n""        where a > 1.\n""\n""        The zipf distribution (also known as the zeta\n""        distribution) is a continuous probability distribution that satisfies\n""        Zipf's law, where the frequency of an item is inversely proportional to\n""        its rank in a frequency table.\n""\n""        Parameters\n""        ----------\n""        a : float\n""            parameter, > 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""            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""        Named after the American linguist George Kingsley Zipf, who noted that\n""        the frequency of any word in a sample of a language is inversely\n""        proportional to its rank in the frequency table.\n""\n""\n""        References\n""        ----------\n""        .. [1] Weisstein, Eric W. \"Zipf Distribution.\" From MathWorld--A Wolfram\n""               Web Resource. http://mathworld.wolfram.com/ZipfDistribution.html\n""        .. [2] Wikipedia, \"Zeta distribution\",\n""               http://en.wikipedia.org/wiki/Zeta_distribution\n""        .. [3] Wikipedia, \"Zipf's Law\",\n""               http://en.wikipedia.org/wiki/Zipf%27s_law\n""        .. [4] Zipf, George Kingsley (1932): Selected Studies of the Principle\n""               of Relative Frequency in Language. Cambridge (Mass.).\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 = arange(1., 50.)\n""        >>> y = x**(-a)/sps.zetac(a)\n""        >>> plt.plot(x, y/max(y), linewidth=2, color='r')\n""        >>> plt.show()\n""\n""        ";
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-  Py_INCREF(__pyx_v_size);
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2631 */
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2632 */
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-      Py_DECREF(__pyx_2); __pyx_2 = 0;
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+      goto __pyx_L6;
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2635
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2637 */
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+  __pyx_4 = PyTuple_New(1); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2640; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    goto __pyx_L4;
+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2641; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  __pyx_L4:;
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+ *         return discd_array(self.internal_state, rk_zipf, size, oa)             # <<<<<<<<<<<<<<
+ * 
+ *     def geometric(self, p, size=None):
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-  __pyx_L1:;
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-  __pyx_r = 0;
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-  Py_DECREF(__pyx_v_self);
-  Py_DECREF(__pyx_v_a);
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 }
 
-static PyObject *__pyx_k150p;
-static PyObject *__pyx_k151p;
-static PyObject *__pyx_k152p;
-static PyObject *__pyx_k153p;
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2644
+ *         return discd_array(self.internal_state, rk_zipf, size, oa)
+ * 
+ *     def geometric(self, p, size=None):             # <<<<<<<<<<<<<<
+ *         """
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+ */
 
-static char __pyx_k150[] = "p < 0.0";
-static char __pyx_k151[] = "p > 1.0";
-static char __pyx_k152[] = "p < 0.0";
-static char __pyx_k153[] = "p > 1.0";
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-static PyObject *__pyx_f_6mtrand_11RandomState_geometric(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
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 static char __pyx_doc_6mtrand_11RandomState_geometric[] = "\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        ";
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-  Py_INCREF(__pyx_v_size);
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2692 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2692
+ *         cdef double fp
+ * 
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2693 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2693
+ * 
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2694 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2694
+ *         fp = PyFloat_AsDouble(p)
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+ */
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-      Py_DECREF(__pyx_2); __pyx_2 = 0;
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2695
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2695; goto __pyx_L1;}
-      goto __pyx_L3;
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+ *             if fp > 1.0:             # <<<<<<<<<<<<<<
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-      Py_DECREF(__pyx_2); __pyx_2 = 0;
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2697
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+ *             return discd_array_sc(self.internal_state, rk_geometric, size, fp)
+ * 
+ */
+      __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2697; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      Py_INCREF(__pyx_kp_110);
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2697; goto __pyx_L1;}
-      goto __pyx_L4;
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2697; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      goto __pyx_L7;
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-    __pyx_L4:;
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2698
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     goto __pyx_L0;
-    goto __pyx_L2;
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2700 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2700
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+ * 
+ * 
+ */
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2703
+ * 
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2704
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-static PyObject *__pyx_f_6mtrand_11RandomState_hypergeometric(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_hypergeometric[] = "\n        hypergeometric(ngood, nbad, nsample, size=None)\n\n        Draw samples from a Hypergeometric distribution.\n\n        Samples are drawn from a Hypergeometric distribution with specified\n        parameters, ngood (ways to make a good selection), nbad (ways to make\n        a bad selection), and nsample = number of items sampled, which is less\n        than or equal to the sum ngood + nbad.\n\n        Parameters\n        ----------\n        ngood : float (but truncated to an integer)\n                parameter, > 0.\n        nbad  : float\n                parameter, >= 0.\n        nsample  : float\n                   parameter, > 0 and <= ngood+nbad\n        size : {tuple, int}\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n                  where the values are all integers in  [0, n].\n\n        See Also\n        --------\n        scipy.stats.distributions.hypergeom : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Hypergeometric distribution is\n\n        .. math:: P(x) = \\frac{\\binom{m}{n}\\binom{N-m}{n-x}}{\\binom{N}{n}},\n\n        where :math:`0 \\le x \\le m` and :math:`n+m-N \\le x \\le n`\n\n        for P(x) the probability of x successes, n = ngood, m = nbad, and\n        N = number of samples.\n\n        Consider an urn with black and white marbles in it, ngood of them\n        black and nbad are white. If you draw nsample balls without\n        replacement, then the Hypergeometric distribution describes the\n        distribution of black balls in the drawn sample.\n\n        Note that this distribution is very similar to the Binomial\n        distribution, except that in this case, samples are drawn without\n        replacement, whereas in the Binomial case samples are drawn with\n        replacement (or the sample space is infinite). As the sample space\n        becomes large, this distribution approaches the Binomial.\n\n        References\n        ----------\n        .. [1] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n               and Quigley, 1972.\n        .. [2] Weisstein, Eric W. \"Hypergeometric Distribution.\" From\n               MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/HypergeometricDistribution.html\n        .. [3] Wikipedia, \"Hypergeometric-distribution\",\n               http://en.wikipedia.org/wiki/Hypergeometric-distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> ngood, nbad, nsamp = 100, 2, 10\n        # number of good, number of bad, and number of samples\n        >>> s = np.random.hypergeometric(ngood, nbad, nsamp, 1000)\n        >>> hist(s)\n        #   note that it is very unlikely to grab both bad items\n\n        Suppose you have an urn with 15 white and 15 black marbles.\n        If you pull 15 marbles at random, how likely is it that\n        12 or more of them are one color?\n\n        >>> s = np.random.hypergeometric(15, 15, 15, 100000)\n        >>> sum(s>=12)/100000. + sum(s<=3)/100000.\n        #   answer = 0.003 ... pretty unlikely!\n\n        ";
-static PyObject *__pyx_f_6mtrand_11RandomState_hypergeometric(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2710
+ *         return discd_array(self.internal_state, rk_geometric, size, op)
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+ */
+
+static PyObject *__pyx_pf_6mtrand_11RandomState_hypergeometric(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_hypergeometric[] = "\n""        hypergeometric(ngood, nbad, nsample, size=None)\n""\n""        Draw samples from a Hypergeometric distribution.\n""\n""        Samples are drawn from a Hypergeometric distribution with specified\n""        parameters, ngood (ways to make a good selection), nbad (ways to make\n""        a bad selection), and nsample = number of items sampled, which is less\n""        than or equal to the sum ngood + nbad.\n""\n""        Parameters\n""        ----------\n""        ngood : float (but truncated to an integer)\n""                parameter, > 0.\n""        nbad  : float\n""                parameter, >= 0.\n""        nsample  : float\n""                   parameter, > 0 and <= ngood+nbad\n""        size : {tuple, int}\n""            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n""            ``m * n * k`` samples are drawn.\n""\n""        Returns\n""        -------\n""        samples : {ndarray, scalar}\n""                  where the values are all integers in  [0, n].\n""\n""        See Also\n""        --------\n""        scipy.stats.distributions.hypergeom : probability density function,\n""            distribution or cumulative density function, etc.\n""\n""        Notes\n""        -----\n""        The probability density for the Hypergeometric distribution is\n""\n""        .. math:: P(x) = \\frac{\\binom{m}{n}\\binom{N-m}{n-x}}{\\binom{N}{n}},\n""\n""        where :math:`0 \\le x \\le m` and :math:`n+m-N \\le x \\le n`\n""\n""        for P(x) the probability of x successes, n = ngood, m = nbad, and\n""        N = number of samples.\n""\n""        Consider an urn with black and white marbles in it, ngood of them\n""        black and nbad are white. If you draw nsample balls without\n""        replacement, then the Hypergeometric distribution describes the\n""        distribution of black balls in the drawn sample.\n""\n""        Note that this distribution is very similar to the Binomial\n""        distribution, except that in this case, samples are drawn without\n""        replacement, whereas in the Binomial case samples are drawn with\n""        replacement (or the sample space is infinite). As the sample space\n""        becomes large, this distribution approaches the Binomial.\n""\n""        References\n""        ----------\n""        .. [1] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n""               and Quigley, 1972.\n""        .. [2] Weisstein, Eric W. \"Hypergeometric Distribution.\" From\n""               MathWorld--A Wolfram Web Resource.\n""               http://mathworld.wolfram.com/HypergeometricDistribution.html\n""        .. [3] Wikipedia, \"Hypergeometric-distribution\",\n""               http://en.wikipedia.org/wiki/Hypergeometric-distribution\n""\n""        Examples\n""        --------\n""        Draw samples from the distribution:\n""\n""        >>> ngood, nbad, nsamp = 100, 2, 10\n""        # number of good, number of bad, and number of samples\n""        >>> s = np.random.hypergeometric(ngood, nbad, nsamp, 1000)\n""        >>> hist(s)\n""        #   note that it is very unlikely to grab both bad items\n""\n""        Suppose you have an urn with 15 white and 15 black marbles.\n""        If you pull 15 marbles at random, how likely is it that\n""        12 or more of them are one color?\n""\n""        >>> s = np.random.hypergeometric(15, 15, 15, 100000)\n""        >>> sum(s>=12)/100000. + sum(s<=3)/100000.\n""        #   answer = 0.003 ... pretty unlikely!\n""\n""        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_hypergeometric(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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@@ -7634,336 +12760,504 @@ static PyObject *__pyx_f_6mtrand_11RandomState_hypergeometric(PyObject *__pyx_v_
   PyObject *__pyx_5 = 0;
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-  Py_INCREF(__pyx_v_nbad);
-  Py_INCREF(__pyx_v_nsample);
-  Py_INCREF(__pyx_v_size);
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+    __pyx_v_ngood = PyTuple_GET_ITEM(__pyx_args, 0);
+    __pyx_v_nbad = PyTuple_GET_ITEM(__pyx_args, 1);
+    __pyx_v_nsample = PyTuple_GET_ITEM(__pyx_args, 2);
+    if (PyTuple_GET_SIZE(__pyx_args) > 3) {
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+    }
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+  }
+  goto __pyx_L4;
+  __pyx_L3_error:;
+  __Pyx_AddTraceback("mtrand.RandomState.hypergeometric");
+  return NULL;
+  __pyx_L4:;
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2797 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2797
+ *         cdef long lngood, lnbad, lnsample
+ * 
+ *         lngood = PyInt_AsLong(ngood)             # <<<<<<<<<<<<<<
+ *         lnbad = PyInt_AsLong(nbad)
+ *         lnsample = PyInt_AsLong(nsample)
+ */
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2798 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2798
+ * 
+ *         lngood = PyInt_AsLong(ngood)
+ *         lnbad = PyInt_AsLong(nbad)             # <<<<<<<<<<<<<<
+ *         lnsample = PyInt_AsLong(nsample)
+ *         if not PyErr_Occurred():
+ */
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2799 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2799
+ *         lngood = PyInt_AsLong(ngood)
+ *         lnbad = PyInt_AsLong(nbad)
+ *         lnsample = PyInt_AsLong(nsample)             # <<<<<<<<<<<<<<
+ *         if not PyErr_Occurred():
+ *             if ngood < 1:
+ */
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2800 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2800
+ *         lnbad = PyInt_AsLong(nbad)
+ *         lnsample = PyInt_AsLong(nsample)
+ *         if not PyErr_Occurred():             # <<<<<<<<<<<<<<
+ *             if ngood < 1:
+ *                 raise ValueError("ngood < 1")
+ */
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2801 */
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-    __pyx_1 = __pyx_1 < 0;
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2801
+ *         lnsample = PyInt_AsLong(nsample)
+ *         if not PyErr_Occurred():
+ *             if ngood < 1:             # <<<<<<<<<<<<<<
+ *                 raise ValueError("ngood < 1")
+ *             if nbad < 1:
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+    __pyx_1 = __Pyx_PyObject_IsTrue(__pyx_2); if (unlikely(__pyx_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2801; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-      Py_DECREF(__pyx_2); __pyx_2 = 0;
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2802
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+ *             if ngood < 1:
+ *                 raise ValueError("ngood < 1")             # <<<<<<<<<<<<<<
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+      Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0;
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+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2802; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      goto __pyx_L6;
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-    __pyx_L3:;
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2803 */
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+ *             if ngood < 1:
+ *                 raise ValueError("ngood < 1")
+ *             if nbad < 1:             # <<<<<<<<<<<<<<
+ *                 raise ValueError("nbad < 1")
+ *             if nsample < 1:
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-      Py_DECREF(__pyx_3); __pyx_3 = 0;
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2804
+ *                 raise ValueError("ngood < 1")
+ *             if nbad < 1:
+ *                 raise ValueError("nbad < 1")             # <<<<<<<<<<<<<<
+ *             if nsample < 1:
+ *                 raise ValueError("nsample < 1")
+ */
+      __pyx_3 = PyTuple_New(1); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2804; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      Py_INCREF(__pyx_kp_114);
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+      Py_DECREF(((PyObject *)__pyx_3)); __pyx_3 = 0;
       __Pyx_Raise(__pyx_2, 0, 0);
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2804; goto __pyx_L1;}
-      goto __pyx_L4;
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-    __pyx_L4:;
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+ *             if nbad < 1:
+ *                 raise ValueError("nbad < 1")
+ *             if nsample < 1:             # <<<<<<<<<<<<<<
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+ */
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2806
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     Py_DECREF(__pyx_2); __pyx_2 = 0;
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+    Py_DECREF(__pyx_3); __pyx_3 = 0;
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2808
+ *                 raise ValueError("nsample < 1")
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+ *                 raise ValueError("ngood + nbad < nsample")             # <<<<<<<<<<<<<<
+ *             return discnmN_array_sc(self.internal_state, rk_hypergeometric, size,
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-static char __pyx_doc_6mtrand_11RandomState_logseries[] = "\n        logseries(p, size=None)\n\n        Draw samples from a Logarithmic Series distribution.\n\n        Samples are drawn from a Log Series distribution with specified\n        parameter, p (probability, 0 < p < 1).\n\n        Parameters\n        ----------\n        loc : float\n\n        scale : float > 0.\n\n        size : {tuple, int}\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n                  where the values are all integers in  [0, n].\n\n        See Also\n        --------\n        scipy.stats.distributions.logser : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Log Series distribution is\n\n        .. math:: P(k) = \\frac{-p^k}{k \\ln(1-p)},\n\n        where p = probability.\n\n        The Log Series distribution is frequently used to represent species\n        richness and occurrence, first proposed by Fisher, Corbet, and\n        Williams in 1943 [2].  It may also be used to model the numbers of\n        occupants seen in cars [3].\n\n        References\n        ----------\n        .. [1] Buzas, Martin A.; Culver, Stephen J.,  Understanding regional\n               species diversity through the log series distribution of\n               occurrences: BIODIVERSITY RESEARCH Diversity & Distributions,\n               Volume 5, Number 5, September 1999 , pp. 187-195(9).\n        .. [2] Fisher, R.A,, A.S. Corbet, and C.B. Williams. 1943. The\n               relation between the number of species and the number of\n               individuals in a random sample of an animal population.\n               Journal of Animal Ecology, 12:42-58.\n        .. [3] D. J. Hand, F. Daly, D. Lunn, E. Ostrowski, A Handbook of Small\n               Data Sets, CRC Press, 1994.\n        .. [4] Wikipedia, \"Logarithmic-distribution\",\n               http://en.wikipedia.org/wiki/Logarithmic-distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a = .6\n        >>> s = np.random.logseries(a, 10000)\n        >>> count, bins, ignored = plt.hist(s)\n\n        #   plot against distribution\n\n        >>> def logseries(k, p):\n        ...     return -p**k/(k*log(1-p))\n        >>> plt.plot(bins, logseries(bins, a)*count.max()/\\\n            logseries(bins, a).max(),\'r\')\n        >>> plt.show()\n\n        ";
-static PyObject *__pyx_f_6mtrand_11RandomState_logseries(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2829
+ *             ongood, onbad, onsample)
+ * 
+ *     def logseries(self, p, size=None):             # <<<<<<<<<<<<<<
+ *         """
+ *         logseries(p, size=None)
+ */
+
+static PyObject *__pyx_pf_6mtrand_11RandomState_logseries(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_logseries[] = "\n""        logseries(p, size=None)\n""\n""        Draw samples from a Logarithmic Series distribution.\n""\n""        Samples are drawn from a Log Series distribution with specified\n""        parameter, p (probability, 0 < p < 1).\n""\n""        Parameters\n""        ----------\n""        loc : float\n""\n""        scale : float > 0.\n""\n""        size : {tuple, int}\n""            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n""            ``m * n * k`` samples are drawn.\n""\n""        Returns\n""        -------\n""        samples : {ndarray, scalar}\n""                  where the values are all integers in  [0, n].\n""\n""        See Also\n""        --------\n""        scipy.stats.distributions.logser : probability density function,\n""            distribution or cumulative density function, etc.\n""\n""        Notes\n""        -----\n""        The probability density for the Log Series distribution is\n""\n""        .. math:: P(k) = \\frac{-p^k}{k \\ln(1-p)},\n""\n""        where p = probability.\n""\n""        The Log Series distribution is frequently used to represent species\n""        richness and occurrence, first proposed by Fisher, Corbet, and\n""        Williams in 1943 [2].  It may also be used to model the numbers of\n""        occupants seen in cars [3].\n""\n""        References\n""        ----------\n""        .. [1] Buzas, Martin A.; Culver, Stephen J.,  Understanding regional\n""               species diversity through the log series distribution of\n""               occurrences: BIODIVERSITY RESEARCH Diversity & Distributions,\n""               Volume 5, Number 5, September 1999 , pp. 187-195(9).\n""        .. [2] Fisher, R.A,, A.S. Corbet, and C.B. Williams. 1943. The\n""               relation between the number of species and the number of\n""               individuals in a random sample of an animal population.\n""               Journal of Animal Ecology, 12:42-58.\n""        .. [3] D. J. Hand, F. Daly, D. Lunn, E. Ostrowski, A Handbook of Small\n""               Data Sets, CRC Press, 1994.\n""        .. [4] Wikipedia, \"Logarithmic-distribution\",\n""               http://en.wikipedia.org/wiki/Logarithmic-distribution\n""\n""        Examples\n""        --------\n""        Draw samples from the distribution:\n""\n""        >>> a = .6\n""        >>> s = np.random.logseries(a, 10000)\n""        >>> count, bins, ignored = plt.hist(s)\n""\n""        #   plot against distribution\n""\n""        >>> def logseries(k, p):\n""        ...     return -p**k/(k*log(1-p))\n""        >>> plt.plot(bins, logseries(bins, a)*count.max()/\\\n""            logseries(bins, a).max(),'r')\n""        >>> plt.show()\n""\n""        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_logseries(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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   PyObject *__pyx_v_size = 0;
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@@ -7975,188 +13269,276 @@ static PyObject *__pyx_f_6mtrand_11RandomState_logseries(PyObject *__pyx_v_self,
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-  Py_INCREF(__pyx_v_self);
-  Py_INCREF(__pyx_v_p);
-  Py_INCREF(__pyx_v_size);
+  __pyx_v_size = Py_None;
+  if (likely(!__pyx_kwds) && likely(1 <= PyTuple_GET_SIZE(__pyx_args)) && likely(PyTuple_GET_SIZE(__pyx_args) <= 2)) {
+    __pyx_v_p = PyTuple_GET_ITEM(__pyx_args, 0);
+    if (PyTuple_GET_SIZE(__pyx_args) > 1) {
+      __pyx_v_size = PyTuple_GET_ITEM(__pyx_args, 1);
+    }
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+  }
+  goto __pyx_L4;
+  __pyx_L3_error:;
+  __Pyx_AddTraceback("mtrand.RandomState.logseries");
+  return NULL;
+  __pyx_L4:;
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2906 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2906
+ *         cdef double fp
+ * 
+ *         fp = PyFloat_AsDouble(p)             # <<<<<<<<<<<<<<
+ *         if not PyErr_Occurred():
+ *             if fp <= 0.0:
+ */
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2907 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2907
+ * 
+ *         fp = PyFloat_AsDouble(p)
+ *         if not PyErr_Occurred():             # <<<<<<<<<<<<<<
+ *             if fp <= 0.0:
+ *                 raise ValueError("p <= 0.0")
+ */
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   if (__pyx_1) {
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2908 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2908
+ *         fp = PyFloat_AsDouble(p)
+ *         if not PyErr_Occurred():
+ *             if fp <= 0.0:             # <<<<<<<<<<<<<<
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+ *             if fp >= 1.0:
+ */
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-      Py_DECREF(__pyx_2); __pyx_2 = 0;
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2909
+ *         if not PyErr_Occurred():
+ *             if fp <= 0.0:
+ *                 raise ValueError("p <= 0.0")             # <<<<<<<<<<<<<<
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+      Py_INCREF(__pyx_kp_121);
+      PyTuple_SET_ITEM(__pyx_2, 0, __pyx_kp_121);
+      __pyx_3 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2909; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0;
       __Pyx_Raise(__pyx_3, 0, 0);
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-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2909; goto __pyx_L1;}
-      goto __pyx_L3;
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2909; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      goto __pyx_L6;
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-    __pyx_L3:;
+    __pyx_L6:;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2910 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2910
+ *             if fp <= 0.0:
+ *                 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)
+ */
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-      Py_DECREF(__pyx_2); __pyx_2 = 0;
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2911
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+ *             if fp >= 1.0:
+ *                 raise ValueError("p >= 1.0")             # <<<<<<<<<<<<<<
+ *             return discd_array_sc(self.internal_state, rk_logseries, size, fp)
+ * 
+ */
+      __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2911; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      Py_INCREF(__pyx_kp_122);
+      PyTuple_SET_ITEM(__pyx_2, 0, __pyx_kp_122);
+      __pyx_3 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_2), NULL); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2911; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0;
       __Pyx_Raise(__pyx_3, 0, 0);
       Py_DECREF(__pyx_3); __pyx_3 = 0;
-      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2911; goto __pyx_L1;}
-      goto __pyx_L4;
+      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2911; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      goto __pyx_L7;
     }
-    __pyx_L4:;
+    __pyx_L7:;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2912 */
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2912
+ *             if fp >= 1.0:
+ *                 raise ValueError("p >= 1.0")
+ *             return discd_array_sc(self.internal_state, rk_logseries, size, fp)             # <<<<<<<<<<<<<<
+ * 
+ *         PyErr_Clear()
+ */
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     __pyx_r = __pyx_2;
     __pyx_2 = 0;
     goto __pyx_L0;
-    goto __pyx_L2;
+    goto __pyx_L5;
   }
-  __pyx_L2:;
+  __pyx_L5:;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2914 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2914
+ *             return discd_array_sc(self.internal_state, rk_logseries, size, fp)
+ * 
+ *         PyErr_Clear()             # <<<<<<<<<<<<<<
+ * 
+ *         op = <ndarray>PyArray_FROM_OTF(p, NPY_DOUBLE, NPY_ALIGNED)
+ */
   PyErr_Clear();
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2916 */
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2916
+ *         PyErr_Clear()
+ * 
+ *         op = <ndarray>PyArray_FROM_OTF(p, NPY_DOUBLE, NPY_ALIGNED)             # <<<<<<<<<<<<<<
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+ */
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2917
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-  __pyx_L5:;
+  __pyx_L8:;
 
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2919
+ *         if np.any(np.less_equal(op, 0.0)):
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+  Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0;
+  __pyx_3 = PyTuple_New(1); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2919; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  __pyx_2 = PyObject_CallObject(__pyx_5, __pyx_3); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2919; goto __pyx_L1;}
+  __pyx_2 = PyObject_Call(__pyx_5, ((PyObject *)__pyx_3), NULL); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2919; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  Py_DECREF(__pyx_3); __pyx_3 = 0;
-  __pyx_1 = PyObject_IsTrue(__pyx_2); if (__pyx_1 < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2919; goto __pyx_L1;}
+  Py_DECREF(((PyObject *)__pyx_3)); __pyx_3 = 0;
+  __pyx_1 = __Pyx_PyObject_IsTrue(__pyx_2); if (unlikely(__pyx_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2919; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    __pyx_4 = PyTuple_New(1); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2920; goto __pyx_L1;}
-    Py_INCREF(__pyx_k165p);
-    PyTuple_SET_ITEM(__pyx_4, 0, __pyx_k165p);
-    __pyx_5 = PyObject_CallObject(PyExc_ValueError, __pyx_4); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2920; goto __pyx_L1;}
-    Py_DECREF(__pyx_4); __pyx_4 = 0;
+
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2920
+ *             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_4 = PyTuple_New(1); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2920; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    Py_INCREF(__pyx_kp_124);
+    PyTuple_SET_ITEM(__pyx_4, 0, __pyx_kp_124);
+    __pyx_5 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_4), NULL); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2920; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    Py_DECREF(((PyObject *)__pyx_4)); __pyx_4 = 0;
     __Pyx_Raise(__pyx_5, 0, 0);
     Py_DECREF(__pyx_5); __pyx_5 = 0;
-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2920; goto __pyx_L1;}
-    goto __pyx_L6;
+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2920; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    goto __pyx_L9;
   }
-  __pyx_L6:;
+  __pyx_L9:;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2921 */
-  __pyx_3 = __pyx_f_6mtrand_discd_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state,rk_logseries,__pyx_v_size,__pyx_v_op); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2921; goto __pyx_L1;}
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2921
+ *         if np.any(np.greater_equal(op, 1.0)):
+ *             raise ValueError("p >= 1.0")
+ *         return discd_array(self.internal_state, rk_logseries, size, op)             # <<<<<<<<<<<<<<
+ * 
+ *     # Multivariate distributions:
+ */
+  __pyx_3 = __pyx_f_6mtrand_discd_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_logseries, __pyx_v_size, __pyx_v_op); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2921; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   __pyx_r = __pyx_3;
   __pyx_3 = 0;
   goto __pyx_L0;
 
   __pyx_r = Py_None; Py_INCREF(Py_None);
   goto __pyx_L0;
-  __pyx_L1:;
+  __pyx_L1_error:;
   Py_XDECREF(__pyx_2);
   Py_XDECREF(__pyx_3);
   Py_XDECREF(__pyx_4);
   Py_XDECREF(__pyx_5);
   __Pyx_AddTraceback("mtrand.RandomState.logseries");
-  __pyx_r = 0;
+  __pyx_r = NULL;
   __pyx_L0:;
   Py_DECREF(__pyx_v_op);
-  Py_DECREF(__pyx_v_self);
-  Py_DECREF(__pyx_v_p);
-  Py_DECREF(__pyx_v_size);
   return __pyx_r;
 }
 
-static PyObject *__pyx_n_array;
-static PyObject *__pyx_n_shape;
-static PyObject *__pyx_n_append;
-static PyObject *__pyx_n_multiply;
-static PyObject *__pyx_n_reduce;
-static PyObject *__pyx_n_svd;
-static PyObject *__pyx_n_dot;
-static PyObject *__pyx_n_sqrt;
-
-static PyObject *__pyx_k166p;
-static PyObject *__pyx_k167p;
-static PyObject *__pyx_k168p;
-static PyObject *__pyx_k169p;
-
-static char __pyx_k166[] = "mean must be 1 dimensional";
-static char __pyx_k167[] = "cov must be 2 dimensional and square";
-static char __pyx_k168[] = "mean and cov must have same length";
-static char __pyx_k169[] = "numpy.dual";
-
-static PyObject *__pyx_f_6mtrand_11RandomState_multivariate_normal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_multivariate_normal[] = "\n        multivariate_normal(mean, cov[, size])\n\n        Draw random samples from a multivariate normal distribution.\n\n        The multivariate normal, multinormal or Gaussian distribution is a\n        generalisation of the one-dimensional normal distribution to higher\n        dimensions.\n\n        Such a distribution is specified by its mean and covariance matrix,\n        which are analogous to the mean (average or \"centre\") and variance\n        (standard deviation squared or \"width\") of the one-dimensional normal\n        distribution.\n\n        Parameters\n        ----------\n        mean : (N,) ndarray\n            Mean of the N-dimensional distribution.\n        cov : (N,N) ndarray\n            Covariance matrix of the distribution.\n        size : tuple of ints, optional\n            Given a shape of, for example, (m,n,k), m*n*k samples are\n            generated, and packed in an m-by-n-by-k arrangement.  Because each\n            sample is N-dimensional, the output shape is (m,n,k,N).  If no\n            shape is specified, a single sample is returned.\n\n        Returns\n        -------\n        out : ndarray\n            The drawn samples, arranged according to `size`.  If the\n            shape given is (m,n,...), then the shape of `out` is is\n            (m,n,...,N).\n\n            In other words, each entry ``out[i,j,...,:]`` is an N-dimensional\n            value drawn from the distribution.\n\n        Notes\n        -----\n        The mean is a coordinate in N-dimensional space, which represents the\n        location where samples are most likely to be generated.  This is\n        analogous to the peak of the bell curve for the one-dimensional or\n        univariate normal distribution.\n\n        Covariance indicates the level to which two variables vary together.\n        From the multivariate normal distribution, we draw N-dimensional\n        samples, :math:`X = [x_1, x_2, ... x_N]`.  The covariance matrix\n        element :math:`C_{ij}` is the covariance of :math:`x_i` and :math:`x_j`.\n        The element :math:`C_{ii}` is the variance of :math:`x_i` (i.e. its\n        \"spread\").\n\n        Instead of specifying the full covariance matrix, popular\n        approximations include:\n\n          - Spherical covariance (`cov` is a multiple of the identity matrix)\n          - Diagonal covariance (`cov` has non-negative elements, and only on\n            the diagonal)\n\n        This geometrical property can be seen in two dimensions by plotting\n        generated data-points:\n\n        >>> mean = [0,0]\n        >>> cov = [[1,0],[0,100]] # diagonal covariance, points lie on x or y-axis\n\n        >>> import matplotlib.pyplot as plt\n        >>> x,y = np.random.multivariate_normal(mean,cov,5000).T\n        >>> plt.plot(x,y,\'x\'); plt.axis(\'equal\'); plt.show()\n\n        Note that the covariance matrix must be non-negative definite.\n\n        References\n        ----------\n        .. [1] A. Papoulis, \"Probability, Random Variables, and Stochastic\n               Processes,\" 3rd ed., McGraw-Hill Companies, 1991\n        .. [2] R.O. Duda, P.E. Hart, and D.G. Stork, \"Pattern Classification,\"\n               2nd ed., Wiley, 2001.\n\n        Examples\n        --------\n        >>> mean = (1,2)\n        >>> cov = [[1,0],[1,0]]\n        >>> x = np.random.multivariate_normal(mean,cov,(3,3))\n        >>> x.shape\n        (3, 3, 2)\n\n        The following is probably true, given that 0.6 is roughly twice the\n        standard deviation:\n\n        >>> print list( (x[0,0,:] - mean) < 0.6 )\n        [True, True]\n\n        ";
-static PyObject *__pyx_f_6mtrand_11RandomState_multivariate_normal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2924
+ * 
+ *     # Multivariate distributions:
+ *     def multivariate_normal(self, mean, cov, size=None):             # <<<<<<<<<<<<<<
+ *         """
+ *         multivariate_normal(mean, cov[, size])
+ */
+
+static PyObject *__pyx_pf_6mtrand_11RandomState_multivariate_normal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_multivariate_normal[] = "\n""        multivariate_normal(mean, cov[, size])\n""\n""        Draw random samples from a multivariate normal distribution.\n""\n""        The multivariate normal, multinormal or Gaussian distribution is a\n""        generalisation of the one-dimensional normal distribution to higher\n""        dimensions.\n""\n""        Such a distribution is specified by its mean and covariance matrix,\n""        which are analogous to the mean (average or \"centre\") and variance\n""        (standard deviation squared or \"width\") of the one-dimensional normal\n""        distribution.\n""\n""        Parameters\n""        ----------\n""        mean : (N,) ndarray\n""            Mean of the N-dimensional distribution.\n""        cov : (N,N) ndarray\n""            Covariance matrix of the distribution.\n""        size : tuple of ints, optional\n""            Given a shape of, for example, (m,n,k), m*n*k samples are\n""            generated, and packed in an m-by-n-by-k arrangement.  Because each\n""            sample is N-dimensional, the output shape is (m,n,k,N).  If no\n""            shape is specified, a single sample is returned.\n""\n""        Returns\n""        -------\n""        out : ndarray\n""            The drawn samples, arranged according to `size`.  If the\n""            shape given is (m,n,...), then the shape of `out` is is\n""            (m,n,...,N).\n""\n""            In other words, each entry ``out[i,j,...,:]`` is an N-dimensional\n""            value drawn from the distribution.\n""\n""        Notes\n""        -----\n""        The mean is a coordinate in N-dimensional space, which represents the\n""        location where samples are most likely to be generated.  This is\n""        analogous to the peak of the bell curve for the one-dimensional or\n""        univariate normal distribution.\n""\n""        Covariance indicates the level to which two variables vary together.\n""        From the multivariate normal distribution, we draw N-dimensional\n""        samples, :math:`X = [x_1, x_2, ... x_N]`.  The covariance matrix\n""        element :math:`C_{ij}` is the covariance of :math:`x_i` and :math:`x_j`.\n""        The element :math:`C_{ii}` is the variance of :math:`x_i` (i.e. its\n""        \"spread\").\n""\n""        Instead of specifying the full covariance matrix, popular\n""        approximations include:\n""\n""          - Spherical covariance (`cov` is a multiple of the identity matrix)\n""          - Diagonal covariance (`cov` has non-negative elements, and only on\n""            the diagonal)\n""\n""        This geometrical property can be seen in two dimensions by plotting\n""        generated data-points:\n""\n""        >>> mean = [0,0]\n""        >>> cov = [[1,0],[0,100]] # diagonal covariance, points lie on x or y-axis\n""\n""        >>> import matplotlib.pyplot as plt\n""        >>> x,y = np.random.multivariate_normal(mean,cov,5000).T\n""        >>> plt.plot(x,y,'x'); plt.axis('equal'); plt.show()\n""\n""        Note that the covariance matrix must be non-negative definite.\n""\n""        References\n""        ----------\n""        .. [1] A. Papoulis, \"Probability, Random Variables, and Stochastic\n""               Processes,\" 3rd ed., McGraw-Hill Companies, 1991\n""        .. [2] R.O. Duda, P.E. Hart, and D.G. Stork, \"Pattern Classification,\"\n""               2nd ed., Wiley, 2001.\n""\n""        Examples\n""        --------\n""        >>> mean = (1,2)\n""        >>> cov = [[1,0],[1,0]]\n""        >>> x = np.random.multivariate_normal(mean,cov,(3,3))\n""        >>> x.shape\n""        (3, 3, 2)\n""\n""        The following is probably true, given that 0.6 is roughly twice the\n""        standard deviation:\n""\n""        >>> print list( (x[0,0,:] - mean) < 0.6 )\n""        [True, True]\n""\n""        ";
+static PyObject *__pyx_pf_6mtrand_11RandomState_multivariate_normal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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   PyObject *__pyx_v_cov = 0;
   PyObject *__pyx_v_size = 0;
@@ -8172,15 +13554,27 @@ static PyObject *__pyx_f_6mtrand_11RandomState_multivariate_normal(PyObject *__p
   PyObject *__pyx_2 = 0;
   PyObject *__pyx_3 = 0;
   int __pyx_4;
-  Py_ssize_t __pyx_5;
+  Py_ssize_t __pyx_5 = 0;
   PyObject *__pyx_6 = 0;
   static char *__pyx_argnames[] = {"mean","cov","size",0};
-  __pyx_v_size = __pyx_k58;
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-  Py_INCREF(__pyx_v_self);
+  __pyx_v_size = Py_None;
+  if (likely(!__pyx_kwds) && likely(2 <= PyTuple_GET_SIZE(__pyx_args)) && likely(PyTuple_GET_SIZE(__pyx_args) <= 3)) {
+    __pyx_v_mean = PyTuple_GET_ITEM(__pyx_args, 0);
+    __pyx_v_cov = PyTuple_GET_ITEM(__pyx_args, 1);
+    if (PyTuple_GET_SIZE(__pyx_args) > 2) {
+      __pyx_v_size = PyTuple_GET_ITEM(__pyx_args, 2);
+    }
+  }
+  else {
+    if (unlikely(!PyArg_ParseTupleAndKeywords(__pyx_args, __pyx_kwds, "OO|O", __pyx_argnames, &__pyx_v_mean, &__pyx_v_cov, &__pyx_v_size))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2924; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
+  }
+  goto __pyx_L4;
+  __pyx_L3_error:;
+  __Pyx_AddTraceback("mtrand.RandomState.multivariate_normal");
+  return NULL;
+  __pyx_L4:;
   Py_INCREF(__pyx_v_mean);
   Py_INCREF(__pyx_v_cov);
-  Py_INCREF(__pyx_v_size);
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   __pyx_v_final_shape = Py_None; Py_INCREF(Py_None);
   __pyx_v_x = Py_None; Py_INCREF(Py_None);
@@ -8189,308 +13583,494 @@ static PyObject *__pyx_f_6mtrand_11RandomState_multivariate_normal(PyObject *__p
   __pyx_v_s = Py_None; Py_INCREF(Py_None);
   __pyx_v_v = Py_None; Py_INCREF(Py_None);
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3017 */
-  __pyx_1 = __Pyx_GetName(__pyx_m, __pyx_n_np); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3017; goto __pyx_L1;}
-  __pyx_2 = PyObject_GetAttr(__pyx_1, __pyx_n_array); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3017; goto __pyx_L1;}
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3017
+ *         """
+ *         # Check preconditions on arguments
+ *         mean = np.array(mean)             # <<<<<<<<<<<<<<
+ *         cov = np.array(cov)
+ *         if size is None:
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3049
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3116 */
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3116
+ *         cdef double Sum
+ * 
+ *         d = len(pvals)             # <<<<<<<<<<<<<<
+ *         parr = <ndarray>PyArray_ContiguousFromObject(pvals, NPY_DOUBLE, 1, 1)
+ *         pix = <double*>parr.data
+ */
+  __pyx_1 = PyObject_Length(__pyx_v_pvals); if (unlikely(__pyx_1 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3116; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3117 */
-  __pyx_2 = PyArray_ContiguousFromObject(__pyx_v_pvals,NPY_DOUBLE,1,1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3117; goto __pyx_L1;}
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3117
+ * 
+ *         d = len(pvals)
+ *         parr = <ndarray>PyArray_ContiguousFromObject(pvals, NPY_DOUBLE, 1, 1)             # <<<<<<<<<<<<<<
+ *         pix = <double*>parr.data
+ * 
+ */
+  __pyx_2 = PyArray_ContiguousFromObject(__pyx_v_pvals, NPY_DOUBLE, 1, 1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3117; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3118 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3118
+ *         d = len(pvals)
+ *         parr = <ndarray>PyArray_ContiguousFromObject(pvals, NPY_DOUBLE, 1, 1)
+ *         pix = <double*>parr.data             # <<<<<<<<<<<<<<
+ * 
+ *         if kahan_sum(pix, d-1) > (1.0 + 1e-12):
+ */
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3120 */
-  __pyx_3 = (__pyx_f_6mtrand_kahan_sum(__pyx_v_pix,(__pyx_v_d - 1)) > (1.0 + 1e-12));
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3120
+ *         pix = <double*>parr.data
+ * 
+ *         if kahan_sum(pix, d-1) > (1.0 + 1e-12):             # <<<<<<<<<<<<<<
+ *             raise ValueError("sum(pvals[:-1]) > 1.0")
+ * 
+ */
+  __pyx_3 = (__pyx_f_6mtrand_kahan_sum(__pyx_v_pix, (__pyx_v_d - 1)) > (1.0 + 1e-12));
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-    __pyx_2 = PyTuple_New(1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3121; goto __pyx_L1;}
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-    Py_DECREF(__pyx_2); __pyx_2 = 0;
+
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3121
+ * 
+ *         if kahan_sum(pix, d-1) > (1.0 + 1e-12):
+ *             raise ValueError("sum(pvals[:-1]) > 1.0")             # <<<<<<<<<<<<<<
+ * 
+ *         if size is None:
+ */
+    __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3121; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    Py_INCREF(__pyx_kp_129);
+    PyTuple_SET_ITEM(__pyx_2, 0, __pyx_kp_129);
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+    Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0;
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-    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3121; goto __pyx_L1;}
-    goto __pyx_L2;
+    {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3121; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    goto __pyx_L5;
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-  __pyx_L2:;
+  __pyx_L5:;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3123 */
-  __pyx_3 = __pyx_v_size == Py_None;
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3123
+ *             raise ValueError("sum(pvals[:-1]) > 1.0")
+ * 
+ *         if size is None:             # <<<<<<<<<<<<<<
+ *             shape = (d,)
+ *         elif type(size) is int:
+ */
+  __pyx_3 = (__pyx_v_size == Py_None);
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-    __pyx_2 = PyInt_FromLong(__pyx_v_d); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3124; goto __pyx_L1;}
-    __pyx_4 = PyTuple_New(1); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3124; goto __pyx_L1;}
+
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3124
+ * 
+ *         if size is None:
+ *             shape = (d,)             # <<<<<<<<<<<<<<
+ *         elif type(size) is int:
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+    __pyx_2 = PyInt_FromLong(__pyx_v_d); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3124; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_4 = PyTuple_New(1); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3124; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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     __pyx_2 = 0;
     Py_DECREF(__pyx_v_shape);
-    __pyx_v_shape = __pyx_4;
+    __pyx_v_shape = ((PyObject *)__pyx_4);
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-    goto __pyx_L3;
+    goto __pyx_L6;
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-  __pyx_2 = PyTuple_New(1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3125; goto __pyx_L1;}
+
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3125
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+ *         elif type(size) is int:             # <<<<<<<<<<<<<<
+ *             shape = (size, d)
+ *         else:
+ */
+  __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3125; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  Py_DECREF(__pyx_2); __pyx_2 = 0;
-  __pyx_3 = __pyx_4 == ((PyObject *)(&PyInt_Type));
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+  Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0;
+  __pyx_3 = (__pyx_4 == ((PyObject*)&PyInt_Type));
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-    __pyx_2 = PyInt_FromLong(__pyx_v_d); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3126; goto __pyx_L1;}
-    __pyx_4 = PyTuple_New(2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3126; goto __pyx_L1;}
+
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3126
+ *             shape = (d,)
+ *         elif type(size) is int:
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+ *             shape = size + (d,)
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+    __pyx_4 = PyTuple_New(2); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3126; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+    __pyx_v_shape = ((PyObject *)__pyx_4);
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+    goto __pyx_L6;
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-    __pyx_2 = PyInt_FromLong(__pyx_v_d); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3128; goto __pyx_L1;}
-    __pyx_4 = PyTuple_New(1); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3128; goto __pyx_L1;}
+
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3128
+ *             shape = (size, d)
+ *         else:
+ *             shape = size + (d,)             # <<<<<<<<<<<<<<
+ * 
+ *         multin = np.zeros(shape, int)
+ */
+    __pyx_2 = PyInt_FromLong(__pyx_v_d); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3128; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_4 = PyTuple_New(1); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3128; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    Py_DECREF(__pyx_4); __pyx_4 = 0;
+    __pyx_2 = PyNumber_Add(__pyx_v_size, ((PyObject *)__pyx_4)); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3128; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    Py_DECREF(((PyObject *)__pyx_4)); __pyx_4 = 0;
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-  __pyx_L3:;
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3130 */
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-  __pyx_2 = PyObject_GetAttr(__pyx_4, __pyx_n_zeros); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3130; goto __pyx_L1;}
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3130
+ *             shape = size + (d,)
+ * 
+ *         multin = np.zeros(shape, int)             # <<<<<<<<<<<<<<
+ *         mnarr = <ndarray>multin
+ *         mnix = <long*>mnarr.data
+ */
+  __pyx_4 = __Pyx_GetName(__pyx_m, __pyx_kp_np); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3130; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_2 = PyObject_GetAttr(__pyx_4, __pyx_kp_zeros); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3130; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   Py_DECREF(__pyx_4); __pyx_4 = 0;
-  __pyx_4 = PyTuple_New(2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3130; goto __pyx_L1;}
+  __pyx_4 = PyTuple_New(2); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3130; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  Py_INCREF(((PyObject *)(&PyInt_Type)));
-  PyTuple_SET_ITEM(__pyx_4, 1, ((PyObject *)(&PyInt_Type)));
-  __pyx_5 = PyObject_CallObject(__pyx_2, __pyx_4); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3130; goto __pyx_L1;}
+  Py_INCREF(((PyObject*)&PyInt_Type));
+  PyTuple_SET_ITEM(__pyx_4, 1, ((PyObject*)&PyInt_Type));
+  __pyx_5 = PyObject_Call(__pyx_2, ((PyObject *)__pyx_4), NULL); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3130; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   Py_DECREF(__pyx_2); __pyx_2 = 0;
-  Py_DECREF(__pyx_4); __pyx_4 = 0;
+  Py_DECREF(((PyObject *)__pyx_4)); __pyx_4 = 0;
   Py_DECREF(__pyx_v_multin);
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   __pyx_5 = 0;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3131 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3131
+ * 
+ *         multin = np.zeros(shape, int)
+ *         mnarr = <ndarray>multin             # <<<<<<<<<<<<<<
+ *         mnix = <long*>mnarr.data
+ *         i = 0
+ */
   Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_v_multin)));
   Py_DECREF(((PyObject *)arrayObject_mnarr));
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3132 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3132
+ *         multin = np.zeros(shape, int)
+ *         mnarr = <ndarray>multin
+ *         mnix = <long*>mnarr.data             # <<<<<<<<<<<<<<
+ *         i = 0
+ *         while i < PyArray_SIZE(mnarr):
+ */
   __pyx_v_mnix = ((long *)arrayObject_mnarr->data);
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3133 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3133
+ *         mnarr = <ndarray>multin
+ *         mnix = <long*>mnarr.data
+ *         i = 0             # <<<<<<<<<<<<<<
+ *         while i < PyArray_SIZE(mnarr):
+ *             Sum = 1.0
+ */
   __pyx_v_i = 0;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3134 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3134
+ *         mnix = <long*>mnarr.data
+ *         i = 0
+ *         while i < PyArray_SIZE(mnarr):             # <<<<<<<<<<<<<<
+ *             Sum = 1.0
+ *             dn = n
+ */
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     __pyx_3 = (__pyx_v_i < PyArray_SIZE(arrayObject_mnarr));
     if (!__pyx_3) break;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3135 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3135
+ *         i = 0
+ *         while i < PyArray_SIZE(mnarr):
+ *             Sum = 1.0             # <<<<<<<<<<<<<<
+ *             dn = n
+ *             for j from 0 <= j < d-1:
+ */
     __pyx_v_Sum = 1.0;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3136 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3136
+ *         while i < PyArray_SIZE(mnarr):
+ *             Sum = 1.0
+ *             dn = n             # <<<<<<<<<<<<<<
+ *             for j from 0 <= j < d-1:
+ *                 mnix[i+j] = rk_binomial(self.internal_state, dn, pix[j]/Sum)
+ */
     __pyx_v_dn = __pyx_v_n;
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3137 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3137
+ *             Sum = 1.0
+ *             dn = n
+ *             for j from 0 <= j < d-1:             # <<<<<<<<<<<<<<
+ *                 mnix[i+j] = rk_binomial(self.internal_state, dn, pix[j]/Sum)
+ *                 dn = dn - mnix[i+j]
+ */
     __pyx_6 = (__pyx_v_d - 1);
-    for (__pyx_v_j = 0; __pyx_v_j < __pyx_6; ++__pyx_v_j) {
-
-      /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3138 */
-      (__pyx_v_mnix[(__pyx_v_i + __pyx_v_j)]) = rk_binomial(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state,__pyx_v_dn,((__pyx_v_pix[__pyx_v_j]) / __pyx_v_Sum));
-
-      /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3139 */
+    for (__pyx_v_j = 0; __pyx_v_j < __pyx_6; __pyx_v_j++) {
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3138
+ *             dn = n
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+ *                 mnix[i+j] = rk_binomial(self.internal_state, dn, pix[j]/Sum)             # <<<<<<<<<<<<<<
+ *                 dn = dn - mnix[i+j]
+ *                 if dn <= 0:
+ */
+      (__pyx_v_mnix[(__pyx_v_i + __pyx_v_j)]) = rk_binomial(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, __pyx_v_dn, ((__pyx_v_pix[__pyx_v_j]) / __pyx_v_Sum));
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3139
+ *             for j from 0 <= j < d-1:
+ *                 mnix[i+j] = rk_binomial(self.internal_state, dn, pix[j]/Sum)
+ *                 dn = dn - mnix[i+j]             # <<<<<<<<<<<<<<
+ *                 if dn <= 0:
+ *                     break
+ */
       __pyx_v_dn = (__pyx_v_dn - (__pyx_v_mnix[(__pyx_v_i + __pyx_v_j)]));
 
-      /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3140 */
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3140
+ *                 mnix[i+j] = rk_binomial(self.internal_state, dn, pix[j]/Sum)
+ *                 dn = dn - mnix[i+j]
+ *                 if dn <= 0:             # <<<<<<<<<<<<<<
+ *                     break
+ *                 Sum = Sum - pix[j]
+ */
       __pyx_3 = (__pyx_v_dn <= 0);
       if (__pyx_3) {
-        goto __pyx_L7;
-        goto __pyx_L8;
-      }
-      __pyx_L8:;
 
-      /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3142 */
+        /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3141
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+        goto __pyx_L10;
+        goto __pyx_L11;
+      }
+      __pyx_L11:;
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3142
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+ */
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-    __pyx_L7:;
-
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3143 */
+    __pyx_L10:;
+
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3143
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+ *                 mnix[i+d-1] = dn
+ * 
+ */
     __pyx_3 = (__pyx_v_dn > 0);
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+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3144
+ *                 Sum = Sum - pix[j]
+ *             if dn > 0:
+ *                 mnix[i+d-1] = dn             # <<<<<<<<<<<<<<
+ * 
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+ */
       (__pyx_v_mnix[((__pyx_v_i + __pyx_v_d) - 1)]) = __pyx_v_dn;
-      goto __pyx_L9;
+      goto __pyx_L12;
     }
-    __pyx_L9:;
-
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3146 */
+    __pyx_L12:;
+
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3146
+ *                 mnix[i+d-1] = dn
+ * 
+ *             i = i + d             # <<<<<<<<<<<<<<
+ * 
+ *         return multin
+ */
     __pyx_v_i = (__pyx_v_i + __pyx_v_d);
   }
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3148 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3148
+ *             i = i + d
+ * 
+ *         return multin             # <<<<<<<<<<<<<<
+ * 
+ *     def dirichlet(self, object alpha, size=None):
+ */
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   __pyx_r = __pyx_v_multin;
   goto __pyx_L0;
 
   __pyx_r = Py_None; Py_INCREF(Py_None);
   goto __pyx_L0;
-  __pyx_L1:;
+  __pyx_L1_error:;
   Py_XDECREF(__pyx_2);
   Py_XDECREF(__pyx_4);
   Py_XDECREF(__pyx_5);
   __Pyx_AddTraceback("mtrand.RandomState.multinomial");
-  __pyx_r = 0;
+  __pyx_r = NULL;
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   Py_DECREF(arrayObject_mnarr);
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 }
 
-static PyObject *__pyx_f_6mtrand_11RandomState_dirichlet(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3150
+ *         return multin
+ * 
+ *     def dirichlet(self, object alpha, size=None):             # <<<<<<<<<<<<<<
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+ */
+
+static PyObject *__pyx_pf_6mtrand_11RandomState_dirichlet(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
 static char __pyx_doc_6mtrand_11RandomState_dirichlet[] = "\n        dirichlet(alpha, size=None)\n\n        Draw samples from the Dirichlet distribution.\n\n        Draw `size` samples of dimension k from a Dirichlet distribution. A\n        Dirichlet-distributed random variable can be seen as a multivariate\n        generalization of a Beta distribution. Dirichlet pdf is the conjugate\n        prior of a multinomial in Bayesian inference.\n\n        Parameters\n        ----------\n        alpha : array\n            Parameter of the distribution (k dimension for sample of\n            dimension k).\n        size : array\n            Number of samples to draw.\n\n        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\n        ";
-static PyObject *__pyx_f_6mtrand_11RandomState_dirichlet(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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   PyObject *__pyx_v_diric;
   PyObject *__pyx_r;
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-  Py_INCREF(__pyx_v_self);
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+ *         alpha_arr   = <ndarray>PyArray_ContiguousFromObject(alpha, NPY_DOUBLE, 1, 1)
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3215
+ * 
+ *         k           = len(alpha)
+ *         alpha_arr   = <ndarray>PyArray_ContiguousFromObject(alpha, NPY_DOUBLE, 1, 1)             # <<<<<<<<<<<<<<
+ *         alpha_data  = <double*>alpha_arr.data
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+ */
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3216 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3216
+ *         k           = len(alpha)
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+ * 
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+ */
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-  __pyx_3 = __pyx_v_size == Py_None;
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3218
+ *         alpha_data  = <double*>alpha_arr.data
+ * 
+ *         if size is None:             # <<<<<<<<<<<<<<
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+ *         elif type(size) is int:
+ */
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+
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3219
+ * 
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+    __pyx_4 = PyTuple_New(1); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3219; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    goto __pyx_L2;
+    goto __pyx_L5;
   }
-  __pyx_2 = PyTuple_New(1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3220; goto __pyx_L1;}
+
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3220
+ *         if size is None:
+ *             shape = (k,)
+ *         elif type(size) is int:             # <<<<<<<<<<<<<<
+ *             shape = (size, k)
+ *         else:
+ */
+  __pyx_2 = PyTuple_New(1); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3220; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  Py_DECREF(__pyx_2); __pyx_2 = 0;
-  __pyx_3 = __pyx_4 == ((PyObject *)(&PyInt_Type));
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+  Py_DECREF(((PyObject *)__pyx_2)); __pyx_2 = 0;
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-    __pyx_4 = PyTuple_New(2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3221; goto __pyx_L1;}
+
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3221
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-    __pyx_4 = PyTuple_New(1); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3223; goto __pyx_L1;}
+
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3223
+ *             shape = (size, k)
+ *         else:
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+ * 
+ *         diric   = np.zeros(shape, np.float64)
+ */
+    __pyx_2 = PyInt_FromLong(__pyx_v_k); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3223; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3225
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3226
+ * 
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3227 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3227
+ *         diric   = np.zeros(shape, np.float64)
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3229 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3229
+ *         val_data= <double*>val_arr.data
+ * 
+ *         i = 0             # <<<<<<<<<<<<<<
+ *         totsize = PyArray_SIZE(val_arr)
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   __pyx_v_i = 0;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3230 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3230
+ * 
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3231 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3231
+ *         i = 0
+ *         totsize = PyArray_SIZE(val_arr)
+ *         while i < totsize:             # <<<<<<<<<<<<<<
+ *             acc = 0.0
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3232 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3232
+ *         totsize = PyArray_SIZE(val_arr)
+ *         while i < totsize:
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+ *             for j from 0 <= j < k:
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+ */
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-      /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3234 */
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-
-      /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3235 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3233
+ *         while i < totsize:
+ *             acc = 0.0
+ *             for j from 0 <= j < k:             # <<<<<<<<<<<<<<
+ *                 val_data[i+j]   = rk_standard_gamma(self.internal_state, alpha_data[j])
+ *                 acc             = acc + val_data[i+j]
+ */
+    for (__pyx_v_j = 0; __pyx_v_j < __pyx_v_k; __pyx_v_j++) {
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3234
+ *             acc = 0.0
+ *             for j from 0 <= j < k:
+ *                 val_data[i+j]   = rk_standard_gamma(self.internal_state, alpha_data[j])             # <<<<<<<<<<<<<<
+ *                 acc             = acc + val_data[i+j]
+ *             invacc  = 1/acc
+ */
+      (__pyx_v_val_data[(__pyx_v_i + __pyx_v_j)]) = rk_standard_gamma(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, (__pyx_v_alpha_data[__pyx_v_j]));
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3235
+ *             for j from 0 <= j < k:
+ *                 val_data[i+j]   = rk_standard_gamma(self.internal_state, alpha_data[j])
+ *                 acc             = acc + val_data[i+j]             # <<<<<<<<<<<<<<
+ *             invacc  = 1/acc
+ *             for j from 0 <= j < k:
+ */
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3236 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3236
+ *                 val_data[i+j]   = rk_standard_gamma(self.internal_state, alpha_data[j])
+ *                 acc             = acc + val_data[i+j]
+ *             invacc  = 1/acc             # <<<<<<<<<<<<<<
+ *             for j from 0 <= j < k:
+ *                 val_data[i+j]   = val_data[i+j] * invacc
+ */
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3237 */
-    for (__pyx_v_j = 0; __pyx_v_j < __pyx_v_k; ++__pyx_v_j) {
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3237
+ *                 acc             = acc + val_data[i+j]
+ *             invacc  = 1/acc
+ *             for j from 0 <= j < k:             # <<<<<<<<<<<<<<
+ *                 val_data[i+j]   = val_data[i+j] * invacc
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+ */
+    for (__pyx_v_j = 0; __pyx_v_j < __pyx_v_k; __pyx_v_j++) {
+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3238
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+ *             for j from 0 <= j < k:
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+ *             i = i + k
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+ */
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3239 */
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3239
+ *             for j from 0 <= j < k:
+ *                 val_data[i+j]   = val_data[i+j] * invacc
+ *             i = i + k             # <<<<<<<<<<<<<<
+ * 
+ *         return diric
+ */
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3241 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3241
+ *             i = i + k
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+ *         return diric             # <<<<<<<<<<<<<<
+ * 
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+ */
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   __pyx_r = Py_None; Py_INCREF(Py_None);
   goto __pyx_L0;
-  __pyx_L1:;
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   Py_XDECREF(__pyx_4);
   Py_XDECREF(__pyx_5);
   __Pyx_AddTraceback("mtrand.RandomState.dirichlet");
-  __pyx_r = 0;
+  __pyx_r = NULL;
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-  Py_DECREF(__pyx_v_alpha);
-  Py_DECREF(__pyx_v_size);
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 }
 
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+/* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3244
+ * 
+ *     # Shuffling and permutations:
+ *     def shuffle(self, object x):             # <<<<<<<<<<<<<<
+ *         """
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+ */
 
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-static PyObject *__pyx_f_6mtrand_11RandomState_shuffle(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
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 static char __pyx_doc_6mtrand_11RandomState_shuffle[] = "\n        shuffle(x)\n\n        Modify a sequence in-place by shuffling its contents.\n\n        ";
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-  PyObject *__pyx_v_x = 0;
+static PyObject *__pyx_pf_6mtrand_11RandomState_shuffle(PyObject *__pyx_v_self, PyObject *__pyx_v_x) {
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   long __pyx_v_j;
   int __pyx_v_copy;
   PyObject *__pyx_r;
-  Py_ssize_t __pyx_1;
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+  int __pyx_6;
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3254
+ *         cdef int copy
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+ *         i = len(x) - 1             # <<<<<<<<<<<<<<
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+ *             j = len(x[0])
+ */
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3255 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3255
+ * 
+ *         i = len(x) - 1
+ *         try:             # <<<<<<<<<<<<<<
+ *             j = len(x[0])
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+ */
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-    __pyx_2 = __Pyx_GetItemInt(__pyx_v_x, 0); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3256; goto __pyx_L2;}
-    __pyx_1 = PyObject_Length(__pyx_2); if (__pyx_1 == -1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3256; goto __pyx_L2;}
+
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3256
+ *         i = len(x) - 1
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+ *             j = len(x[0])             # <<<<<<<<<<<<<<
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+    __pyx_1 = PyObject_Length(__pyx_2); if (unlikely(__pyx_1 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3256; __pyx_clineno = __LINE__; goto __pyx_L5_error;}
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-  goto __pyx_L3;
-  __pyx_L2:;
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+  __pyx_L5_error:;
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3257 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3257
+ *         try:
+ *             j = len(x[0])
+ *         except:             # <<<<<<<<<<<<<<
+ *             j = 0
+ * 
+ */
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+    if (__Pyx_GetException(&__pyx_2, &__pyx_3, &__pyx_4) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3257; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3258
+ *             j = len(x[0])
+ *         except:
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+ */
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     Py_DECREF(__pyx_3); __pyx_3 = 0;
     Py_DECREF(__pyx_4); __pyx_4 = 0;
-    goto __pyx_L3;
+    goto __pyx_L6;
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-  __pyx_L3:;
+  __pyx_L6:;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3260 */
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3260
+ *             j = 0
+ * 
+ *         if (j == 0):             # <<<<<<<<<<<<<<
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+ *             while i > 0:
+ */
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3262
+ *         if (j == 0):
+ *             # adaptation of random.shuffle()
+ *             while i > 0:             # <<<<<<<<<<<<<<
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+ */
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3263
+ *             # adaptation of random.shuffle()
+ *             while i > 0:
+ *                 j = rk_interval(i, self.internal_state)             # <<<<<<<<<<<<<<
+ *                 x[i], x[j] = x[j], x[i]
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+ */
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+
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3264
+ *             while i > 0:
+ *                 j = rk_interval(i, self.internal_state)
+ *                 x[i], x[j] = x[j], x[i]             # <<<<<<<<<<<<<<
+ *                 i = i - 1
+ *         else:
+ */
+      __pyx_2 = __Pyx_GetItemInt(__pyx_v_x, __pyx_v_j, 0); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3264; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      __pyx_3 = __Pyx_GetItemInt(__pyx_v_x, __pyx_v_i, 0); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3264; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+      if (__Pyx_SetItemInt(__pyx_v_x, __pyx_v_i, __pyx_2, 0) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3264; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+      if (__Pyx_SetItemInt(__pyx_v_x, __pyx_v_j, __pyx_3, 0) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3264; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-      /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3265 */
+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3265
+ *                 j = rk_interval(i, self.internal_state)
+ *                 x[i], x[j] = x[j], x[i]
+ *                 i = i - 1             # <<<<<<<<<<<<<<
+ *         else:
+ *             # make copies
+ */
       __pyx_v_i = (__pyx_v_i - 1);
     }
-    goto __pyx_L4;
+    goto __pyx_L7;
   }
   /*else*/ {
 
-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3268 */
-    __pyx_4 = __Pyx_GetItemInt(__pyx_v_x, 0); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3268; goto __pyx_L1;}
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+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3268
+ *         else:
+ *             # make copies
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+ *             if copy:
+ *                 while(i > 0):
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+    __pyx_4 = __Pyx_GetItemInt(__pyx_v_x, 0, 0); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3268; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+    __pyx_5 = PyObject_HasAttr(__pyx_4, __pyx_kp_130); if (unlikely(__pyx_5 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3268; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-    /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3269 */
-    __pyx_5 = __pyx_v_copy;
-    if (__pyx_5) {
+    /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3269
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+ *             if copy:             # <<<<<<<<<<<<<<
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+      /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3270
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+ *             if copy:
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+        /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3271
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+        /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3272
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@@ -9341,66 +15237,66 @@ static void __pyx_tp_dealloc_6mtrand_RandomState(PyObject *o) {
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+    ++Py_REFCNT(o);
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 }
 
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-  {"exponential", (PyCFunction)__pyx_f_6mtrand_11RandomState_exponential, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_exponential},
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-  {"f", (PyCFunction)__pyx_f_6mtrand_11RandomState_f, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_f},
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-  {"chisquare", (PyCFunction)__pyx_f_6mtrand_11RandomState_chisquare, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_chisquare},
-  {"noncentral_chisquare", (PyCFunction)__pyx_f_6mtrand_11RandomState_noncentral_chisquare, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_noncentral_chisquare},
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-  {"weibull", (PyCFunction)__pyx_f_6mtrand_11RandomState_weibull, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_weibull},
-  {"power", (PyCFunction)__pyx_f_6mtrand_11RandomState_power, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_power},
-  {"laplace", (PyCFunction)__pyx_f_6mtrand_11RandomState_laplace, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_laplace},
-  {"gumbel", (PyCFunction)__pyx_f_6mtrand_11RandomState_gumbel, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_gumbel},
-  {"logistic", (PyCFunction)__pyx_f_6mtrand_11RandomState_logistic, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_logistic},
-  {"lognormal", (PyCFunction)__pyx_f_6mtrand_11RandomState_lognormal, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_lognormal},
-  {"rayleigh", (PyCFunction)__pyx_f_6mtrand_11RandomState_rayleigh, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_rayleigh},
-  {"wald", (PyCFunction)__pyx_f_6mtrand_11RandomState_wald, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_wald},
-  {"triangular", (PyCFunction)__pyx_f_6mtrand_11RandomState_triangular, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_triangular},
-  {"binomial", (PyCFunction)__pyx_f_6mtrand_11RandomState_binomial, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_binomial},
-  {"negative_binomial", (PyCFunction)__pyx_f_6mtrand_11RandomState_negative_binomial, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_negative_binomial},
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-  {"hypergeometric", (PyCFunction)__pyx_f_6mtrand_11RandomState_hypergeometric, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_hypergeometric},
-  {"logseries", (PyCFunction)__pyx_f_6mtrand_11RandomState_logseries, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_logseries},
-  {"multivariate_normal", (PyCFunction)__pyx_f_6mtrand_11RandomState_multivariate_normal, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_multivariate_normal},
-  {"multinomial", (PyCFunction)__pyx_f_6mtrand_11RandomState_multinomial, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_multinomial},
-  {"dirichlet", (PyCFunction)__pyx_f_6mtrand_11RandomState_dirichlet, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_dirichlet},
-  {"shuffle", (PyCFunction)__pyx_f_6mtrand_11RandomState_shuffle, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_shuffle},
-  {"permutation", (PyCFunction)__pyx_f_6mtrand_11RandomState_permutation, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_permutation},
+  {"seed", (PyCFunction)__pyx_pf_6mtrand_11RandomState_seed, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_seed},
+  {"get_state", (PyCFunction)__pyx_pf_6mtrand_11RandomState_get_state, METH_NOARGS, __pyx_doc_6mtrand_11RandomState_get_state},
+  {"set_state", (PyCFunction)__pyx_pf_6mtrand_11RandomState_set_state, METH_O, __pyx_doc_6mtrand_11RandomState_set_state},
+  {"__getstate__", (PyCFunction)__pyx_pf_6mtrand_11RandomState___getstate__, METH_NOARGS, 0},
+  {"__setstate__", (PyCFunction)__pyx_pf_6mtrand_11RandomState___setstate__, METH_O, 0},
+  {"__reduce__", (PyCFunction)__pyx_pf_6mtrand_11RandomState___reduce__, METH_NOARGS, 0},
+  {"random_sample", (PyCFunction)__pyx_pf_6mtrand_11RandomState_random_sample, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_random_sample},
+  {"tomaxint", (PyCFunction)__pyx_pf_6mtrand_11RandomState_tomaxint, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_tomaxint},
+  {"randint", (PyCFunction)__pyx_pf_6mtrand_11RandomState_randint, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_randint},
+  {"bytes", (PyCFunction)__pyx_pf_6mtrand_11RandomState_bytes, METH_O, __pyx_doc_6mtrand_11RandomState_bytes},
+  {"uniform", (PyCFunction)__pyx_pf_6mtrand_11RandomState_uniform, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_uniform},
+  {"rand", (PyCFunction)__pyx_pf_6mtrand_11RandomState_rand, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_rand},
+  {"randn", (PyCFunction)__pyx_pf_6mtrand_11RandomState_randn, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_randn},
+  {"random_integers", (PyCFunction)__pyx_pf_6mtrand_11RandomState_random_integers, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_random_integers},
+  {"standard_normal", (PyCFunction)__pyx_pf_6mtrand_11RandomState_standard_normal, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_standard_normal},
+  {"normal", (PyCFunction)__pyx_pf_6mtrand_11RandomState_normal, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_normal},
+  {"beta", (PyCFunction)__pyx_pf_6mtrand_11RandomState_beta, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_beta},
+  {"exponential", (PyCFunction)__pyx_pf_6mtrand_11RandomState_exponential, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_exponential},
+  {"standard_exponential", (PyCFunction)__pyx_pf_6mtrand_11RandomState_standard_exponential, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_standard_exponential},
+  {"standard_gamma", (PyCFunction)__pyx_pf_6mtrand_11RandomState_standard_gamma, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_standard_gamma},
+  {"gamma", (PyCFunction)__pyx_pf_6mtrand_11RandomState_gamma, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_gamma},
+  {"f", (PyCFunction)__pyx_pf_6mtrand_11RandomState_f, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_f},
+  {"noncentral_f", (PyCFunction)__pyx_pf_6mtrand_11RandomState_noncentral_f, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_noncentral_f},
+  {"chisquare", (PyCFunction)__pyx_pf_6mtrand_11RandomState_chisquare, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_chisquare},
+  {"noncentral_chisquare", (PyCFunction)__pyx_pf_6mtrand_11RandomState_noncentral_chisquare, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_noncentral_chisquare},
+  {"standard_cauchy", (PyCFunction)__pyx_pf_6mtrand_11RandomState_standard_cauchy, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_standard_cauchy},
+  {"standard_t", (PyCFunction)__pyx_pf_6mtrand_11RandomState_standard_t, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_standard_t},
+  {"vonmises", (PyCFunction)__pyx_pf_6mtrand_11RandomState_vonmises, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_vonmises},
+  {"pareto", (PyCFunction)__pyx_pf_6mtrand_11RandomState_pareto, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_pareto},
+  {"weibull", (PyCFunction)__pyx_pf_6mtrand_11RandomState_weibull, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_weibull},
+  {"power", (PyCFunction)__pyx_pf_6mtrand_11RandomState_power, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_power},
+  {"laplace", (PyCFunction)__pyx_pf_6mtrand_11RandomState_laplace, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_laplace},
+  {"gumbel", (PyCFunction)__pyx_pf_6mtrand_11RandomState_gumbel, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_gumbel},
+  {"logistic", (PyCFunction)__pyx_pf_6mtrand_11RandomState_logistic, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_logistic},
+  {"lognormal", (PyCFunction)__pyx_pf_6mtrand_11RandomState_lognormal, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_lognormal},
+  {"rayleigh", (PyCFunction)__pyx_pf_6mtrand_11RandomState_rayleigh, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_rayleigh},
+  {"wald", (PyCFunction)__pyx_pf_6mtrand_11RandomState_wald, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_wald},
+  {"triangular", (PyCFunction)__pyx_pf_6mtrand_11RandomState_triangular, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_triangular},
+  {"binomial", (PyCFunction)__pyx_pf_6mtrand_11RandomState_binomial, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_binomial},
+  {"negative_binomial", (PyCFunction)__pyx_pf_6mtrand_11RandomState_negative_binomial, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_negative_binomial},
+  {"poisson", (PyCFunction)__pyx_pf_6mtrand_11RandomState_poisson, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_poisson},
+  {"zipf", (PyCFunction)__pyx_pf_6mtrand_11RandomState_zipf, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_zipf},
+  {"geometric", (PyCFunction)__pyx_pf_6mtrand_11RandomState_geometric, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_geometric},
+  {"hypergeometric", (PyCFunction)__pyx_pf_6mtrand_11RandomState_hypergeometric, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_hypergeometric},
+  {"logseries", (PyCFunction)__pyx_pf_6mtrand_11RandomState_logseries, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_logseries},
+  {"multivariate_normal", (PyCFunction)__pyx_pf_6mtrand_11RandomState_multivariate_normal, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_multivariate_normal},
+  {"multinomial", (PyCFunction)__pyx_pf_6mtrand_11RandomState_multinomial, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_multinomial},
+  {"dirichlet", (PyCFunction)__pyx_pf_6mtrand_11RandomState_dirichlet, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_dirichlet},
+  {"shuffle", (PyCFunction)__pyx_pf_6mtrand_11RandomState_shuffle, METH_O, __pyx_doc_6mtrand_11RandomState_shuffle},
+  {"permutation", (PyCFunction)__pyx_pf_6mtrand_11RandomState_permutation, METH_O, __pyx_doc_6mtrand_11RandomState_permutation},
   {0, 0, 0, 0}
 };
 
@@ -9408,7 +15304,9 @@ 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*/
@@ -9422,16 +15320,24 @@ static PyNumberMethods __pyx_tp_as_number_RandomState = {
   0, /*nb_and*/
   0, /*nb_xor*/
   0, /*nb_or*/
+  #if PY_MAJOR_VERSION < 3
   0, /*nb_coerce*/
+  #endif
   0, /*nb_int*/
   0, /*nb_long*/
   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*/
@@ -9443,7 +15349,7 @@ static PyNumberMethods __pyx_tp_as_number_RandomState = {
   0, /*nb_true_divide*/
   0, /*nb_inplace_floor_divide*/
   0, /*nb_inplace_true_divide*/
-  #if Py_TPFLAGS_DEFAULT & Py_TPFLAGS_HAVE_INDEX
+  #if (PY_MAJOR_VERSION >= 3) || (Py_TPFLAGS_DEFAULT & Py_TPFLAGS_HAVE_INDEX)
   0, /*nb_index*/
   #endif
 };
@@ -9468,15 +15374,28 @@ static PyMappingMethods __pyx_tp_as_mapping_RandomState = {
 };
 
 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_MAJOR_VERSION >= 3) || (Py_TPFLAGS_DEFAULT & Py_TPFLAGS_HAVE_NEWBUFFER)
+  0, /*bf_getbuffer*/
+  #endif
+  #if (PY_MAJOR_VERSION >= 3) || (Py_TPFLAGS_DEFAULT & Py_TPFLAGS_HAVE_NEWBUFFER)
+  0, /*bf_releasebuffer*/
+  #endif
 };
 
 PyTypeObject __pyx_type_6mtrand_RandomState = {
-  PyObject_HEAD_INIT(0)
-  0, /*ob_size*/
+  PyVarObject_HEAD_INIT(0, 0)
   "mtrand.RandomState", /*tp_name*/
   sizeof(struct __pyx_obj_6mtrand_RandomState), /*tp_basicsize*/
   0, /*tp_itemsize*/
@@ -9511,7 +15430,7 @@ PyTypeObject __pyx_type_6mtrand_RandomState = {
   0, /*tp_descr_get*/
   0, /*tp_descr_set*/
   0, /*tp_dictoffset*/
-  __pyx_f_6mtrand_11RandomState___init__, /*tp_init*/
+  __pyx_pf_6mtrand_11RandomState___init__, /*tp_init*/
   0, /*tp_alloc*/
   __pyx_tp_new_6mtrand_RandomState, /*tp_new*/
   0, /*tp_free*/
@@ -9529,8 +15448,244 @@ static struct PyMethodDef __pyx_methods[] = {
 
 static void __pyx_init_filenames(void); /*proto*/
 
+#if PY_MAJOR_VERSION >= 3
+static struct PyModuleDef __pyx_moduledef = {
+    PyModuleDef_HEAD_INIT,
+    "mtrand",
+    0, /* m_doc */
+    -1, /* m_size */
+    __pyx_methods /* m_methods */,
+    NULL, /* m_reload */
+    NULL, /* m_traverse */
+    NULL, /* m_clear */
+    NULL /* m_free */
+};
+#endif
+
+static __Pyx_StringTabEntry __pyx_string_tab[] = {
+  {&__pyx_kp___init__, __pyx_k___init__, sizeof(__pyx_k___init__), 0, 1, 1},
+  {&__pyx_kp___dealloc__, __pyx_k___dealloc__, sizeof(__pyx_k___dealloc__), 0, 1, 1},
+  {&__pyx_kp_seed, __pyx_k_seed, sizeof(__pyx_k_seed), 0, 1, 1},
+  {&__pyx_kp_get_state, __pyx_k_get_state, sizeof(__pyx_k_get_state), 0, 1, 1},
+  {&__pyx_kp_set_state, __pyx_k_set_state, sizeof(__pyx_k_set_state), 0, 1, 1},
+  {&__pyx_kp___getstate__, __pyx_k___getstate__, sizeof(__pyx_k___getstate__), 0, 1, 1},
+  {&__pyx_kp___setstate__, __pyx_k___setstate__, sizeof(__pyx_k___setstate__), 0, 1, 1},
+  {&__pyx_kp___reduce__, __pyx_k___reduce__, sizeof(__pyx_k___reduce__), 0, 1, 1},
+  {&__pyx_kp_random_sample, __pyx_k_random_sample, sizeof(__pyx_k_random_sample), 0, 1, 1},
+  {&__pyx_kp_tomaxint, __pyx_k_tomaxint, sizeof(__pyx_k_tomaxint), 0, 1, 1},
+  {&__pyx_kp_randint, __pyx_k_randint, sizeof(__pyx_k_randint), 0, 1, 1},
+  {&__pyx_kp_bytes, __pyx_k_bytes, sizeof(__pyx_k_bytes), 0, 1, 1},
+  {&__pyx_kp_uniform, __pyx_k_uniform, sizeof(__pyx_k_uniform), 0, 1, 1},
+  {&__pyx_kp_rand, __pyx_k_rand, sizeof(__pyx_k_rand), 0, 1, 1},
+  {&__pyx_kp_randn, __pyx_k_randn, sizeof(__pyx_k_randn), 0, 1, 1},
+  {&__pyx_kp_random_integers, __pyx_k_random_integers, sizeof(__pyx_k_random_integers), 0, 1, 1},
+  {&__pyx_kp_standard_normal, __pyx_k_standard_normal, sizeof(__pyx_k_standard_normal), 0, 1, 1},
+  {&__pyx_kp_normal, __pyx_k_normal, sizeof(__pyx_k_normal), 0, 1, 1},
+  {&__pyx_kp_beta, __pyx_k_beta, sizeof(__pyx_k_beta), 0, 1, 1},
+  {&__pyx_kp_exponential, __pyx_k_exponential, sizeof(__pyx_k_exponential), 0, 1, 1},
+  {&__pyx_kp_1, __pyx_k_1, sizeof(__pyx_k_1), 0, 1, 1},
+  {&__pyx_kp_standard_gamma, __pyx_k_standard_gamma, sizeof(__pyx_k_standard_gamma), 0, 1, 1},
+  {&__pyx_kp_gamma, __pyx_k_gamma, sizeof(__pyx_k_gamma), 0, 1, 1},
+  {&__pyx_kp_f, __pyx_k_f, sizeof(__pyx_k_f), 0, 1, 1},
+  {&__pyx_kp_noncentral_f, __pyx_k_noncentral_f, sizeof(__pyx_k_noncentral_f), 0, 1, 1},
+  {&__pyx_kp_chisquare, __pyx_k_chisquare, sizeof(__pyx_k_chisquare), 0, 1, 1},
+  {&__pyx_kp_2, __pyx_k_2, sizeof(__pyx_k_2), 0, 1, 1},
+  {&__pyx_kp_standard_cauchy, __pyx_k_standard_cauchy, sizeof(__pyx_k_standard_cauchy), 0, 1, 1},
+  {&__pyx_kp_standard_t, __pyx_k_standard_t, sizeof(__pyx_k_standard_t), 0, 1, 1},
+  {&__pyx_kp_vonmises, __pyx_k_vonmises, sizeof(__pyx_k_vonmises), 0, 1, 1},
+  {&__pyx_kp_pareto, __pyx_k_pareto, sizeof(__pyx_k_pareto), 0, 1, 1},
+  {&__pyx_kp_weibull, __pyx_k_weibull, sizeof(__pyx_k_weibull), 0, 1, 1},
+  {&__pyx_kp_power, __pyx_k_power, sizeof(__pyx_k_power), 0, 1, 1},
+  {&__pyx_kp_laplace, __pyx_k_laplace, sizeof(__pyx_k_laplace), 0, 1, 1},
+  {&__pyx_kp_gumbel, __pyx_k_gumbel, sizeof(__pyx_k_gumbel), 0, 1, 1},
+  {&__pyx_kp_logistic, __pyx_k_logistic, sizeof(__pyx_k_logistic), 0, 1, 1},
+  {&__pyx_kp_lognormal, __pyx_k_lognormal, sizeof(__pyx_k_lognormal), 0, 1, 1},
+  {&__pyx_kp_rayleigh, __pyx_k_rayleigh, sizeof(__pyx_k_rayleigh), 0, 1, 1},
+  {&__pyx_kp_wald, __pyx_k_wald, sizeof(__pyx_k_wald), 0, 1, 1},
+  {&__pyx_kp_triangular, __pyx_k_triangular, sizeof(__pyx_k_triangular), 0, 1, 1},
+  {&__pyx_kp_binomial, __pyx_k_binomial, sizeof(__pyx_k_binomial), 0, 1, 1},
+  {&__pyx_kp_negative_binomial, __pyx_k_negative_binomial, sizeof(__pyx_k_negative_binomial), 0, 1, 1},
+  {&__pyx_kp_poisson, __pyx_k_poisson, sizeof(__pyx_k_poisson), 0, 1, 1},
+  {&__pyx_kp_zipf, __pyx_k_zipf, sizeof(__pyx_k_zipf), 0, 1, 1},
+  {&__pyx_kp_geometric, __pyx_k_geometric, sizeof(__pyx_k_geometric), 0, 1, 1},
+  {&__pyx_kp_hypergeometric, __pyx_k_hypergeometric, sizeof(__pyx_k_hypergeometric), 0, 1, 1},
+  {&__pyx_kp_logseries, __pyx_k_logseries, sizeof(__pyx_k_logseries), 0, 1, 1},
+  {&__pyx_kp_multivariate_normal, __pyx_k_multivariate_normal, sizeof(__pyx_k_multivariate_normal), 0, 1, 1},
+  {&__pyx_kp_multinomial, __pyx_k_multinomial, sizeof(__pyx_k_multinomial), 0, 1, 1},
+  {&__pyx_kp_dirichlet, __pyx_k_dirichlet, sizeof(__pyx_k_dirichlet), 0, 1, 1},
+  {&__pyx_kp_shuffle, __pyx_k_shuffle, sizeof(__pyx_k_shuffle), 0, 1, 1},
+  {&__pyx_kp_permutation, __pyx_k_permutation, sizeof(__pyx_k_permutation), 0, 1, 1},
+  {&__pyx_kp_numpy, __pyx_k_numpy, sizeof(__pyx_k_numpy), 1, 1, 1},
+  {&__pyx_kp_np, __pyx_k_np, sizeof(__pyx_k_np), 0, 1, 1},
+  {&__pyx_kp__rand, __pyx_k__rand, sizeof(__pyx_k__rand), 1, 1, 1},
+  {&__pyx_kp_empty, __pyx_k_empty, sizeof(__pyx_k_empty), 1, 1, 1},
+  {&__pyx_kp_19, __pyx_k_19, sizeof(__pyx_k_19), 1, 1, 1},
+  {&__pyx_kp_ValueError, __pyx_k_ValueError, sizeof(__pyx_k_ValueError), 1, 1, 1},
+  {&__pyx_kp_integer, __pyx_k_integer, sizeof(__pyx_k_integer), 1, 1, 1},
+  {&__pyx_kp_uint, __pyx_k_uint, sizeof(__pyx_k_uint), 1, 1, 1},
+  {&__pyx_kp_asarray, __pyx_k_asarray, sizeof(__pyx_k_asarray), 1, 1, 1},
+  {&__pyx_kp_27, __pyx_k_27, sizeof(__pyx_k_27), 1, 1, 1},
+  {&__pyx_kp_28, __pyx_k_28, sizeof(__pyx_k_28), 0, 1, 0},
+  {&__pyx_kp_29, __pyx_k_29, sizeof(__pyx_k_29), 0, 1, 0},
+  {&__pyx_kp_TypeError, __pyx_k_TypeError, sizeof(__pyx_k_TypeError), 1, 1, 1},
+  {&__pyx_kp_random, __pyx_k_random, sizeof(__pyx_k_random), 1, 1, 1},
+  {&__pyx_kp___RandomState_ctor, __pyx_k___RandomState_ctor, sizeof(__pyx_k___RandomState_ctor), 1, 1, 1},
+  {&__pyx_kp_subtract, __pyx_k_subtract, sizeof(__pyx_k_subtract), 1, 1, 1},
+  {&__pyx_kp_size, __pyx_k_size, sizeof(__pyx_k_size), 1, 1, 1},
+  {&__pyx_kp_any, __pyx_k_any, sizeof(__pyx_k_any), 1, 1, 1},
+  {&__pyx_kp_less_equal, __pyx_k_less_equal, sizeof(__pyx_k_less_equal), 1, 1, 1},
+  {&__pyx_kp_less, __pyx_k_less, sizeof(__pyx_k_less), 1, 1, 1},
+  {&__pyx_kp_greater, __pyx_k_greater, sizeof(__pyx_k_greater), 1, 1, 1},
+  {&__pyx_kp_equal, __pyx_k_equal, sizeof(__pyx_k_equal), 1, 1, 1},
+  {&__pyx_kp_add, __pyx_k_add, sizeof(__pyx_k_add), 1, 1, 1},
+  {&__pyx_kp_greater_equal, __pyx_k_greater_equal, sizeof(__pyx_k_greater_equal), 1, 1, 1},
+  {&__pyx_kp_array, __pyx_k_array, sizeof(__pyx_k_array), 1, 1, 1},
+  {&__pyx_kp_shape, __pyx_k_shape, sizeof(__pyx_k_shape), 1, 1, 1},
+  {&__pyx_kp_append, __pyx_k_append, sizeof(__pyx_k_append), 1, 1, 1},
+  {&__pyx_kp_multiply, __pyx_k_multiply, sizeof(__pyx_k_multiply), 1, 1, 1},
+  {&__pyx_kp_reduce, __pyx_k_reduce, sizeof(__pyx_k_reduce), 1, 1, 1},
+  {&__pyx_kp_128, __pyx_k_128, sizeof(__pyx_k_128), 1, 1, 1},
+  {&__pyx_kp_svd, __pyx_k_svd, sizeof(__pyx_k_svd), 1, 1, 1},
+  {&__pyx_kp_dot, __pyx_k_dot, sizeof(__pyx_k_dot), 1, 1, 1},
+  {&__pyx_kp_sqrt, __pyx_k_sqrt, sizeof(__pyx_k_sqrt), 1, 1, 1},
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+}
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+}
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+#if PY_MAJOR_VERSION < 3
 PyMODINIT_FUNC initmtrand(void); /*proto*/
-PyMODINIT_FUNC initmtrand(void) {
+PyMODINIT_FUNC initmtrand(void)
+#else
+PyMODINIT_FUNC PyInit_mtrand(void); /*proto*/
+PyMODINIT_FUNC PyInit_mtrand(void)
+#endif
+{
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   PyObject *__pyx_2 = 0;
   PyObject *__pyx_3 = 0;
@@ -9549,575 +15704,800 @@ PyMODINIT_FUNC initmtrand(void) {
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   PyObject *__pyx_17 = 0;
   PyObject *__pyx_18 = 0;
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+  /*--- Libary function declarations ---*/
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+  /*--- Initialize various global constants etc. ---*/
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   __pyx_m = Py_InitModule4("mtrand", __pyx_methods, 0, 0, PYTHON_API_VERSION);
-  if (!__pyx_m) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 24; goto __pyx_L1;};
+  #else
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+  #endif
+  __pyx_b = PyImport_AddModule(__Pyx_BUILTIN_MODULE_NAME);
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+  if (PyObject_SetAttrString(__pyx_m, "__builtins__", __pyx_b) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
+  /*--- Builtin init code ---*/
+  if (unlikely(__Pyx_InitCachedBuiltins() < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_skip_dispatch = 0;
+  /*--- Global init code ---*/
+  /*--- Function export code ---*/
+  /*--- Type init code ---*/
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+  __pyx_ptype_6mtrand_broadcast = __Pyx_ImportType("numpy", "broadcast", sizeof(PyArrayMultiIterObject)); if (unlikely(!__pyx_ptype_6mtrand_broadcast)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 94; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyType_Ready(&__pyx_type_6mtrand_RandomState) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 519; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyObject_SetAttrString(__pyx_m, "RandomState", (PyObject *)&__pyx_type_6mtrand_RandomState) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 519; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":121 */
+  /*--- Type import code ---*/
+  /*--- Function import code ---*/
+  /*--- Execution code ---*/
+
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":121
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+ * # Initialize numpy
+ * import_array()             # <<<<<<<<<<<<<<
+ * 
+ * import numpy as np
+ */
   import_array();
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":123 */
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":123
+ * import_array()
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+  if (PyObject_SetAttr(__pyx_m, __pyx_kp_np, __pyx_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 123; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":790 */
-  __pyx_1 = PyFloat_FromDouble(0.0); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 790; goto __pyx_L1;}
-  __pyx_k8 = __pyx_1;
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":790
+ *         return bytestring
+ * 
+ *     def uniform(self, low=0.0, high=1.0, size=None):             # <<<<<<<<<<<<<<
+ *         """
+ *         uniform(low=0.0, high=1.0, size=1)
+ */
+  __pyx_1 = PyFloat_FromDouble(0.0); if (unlikely(!__pyx_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 790; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_k_3 = __pyx_1;
   __pyx_1 = 0;
-  __pyx_2 = PyFloat_FromDouble(1.0); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 790; goto __pyx_L1;}
-  __pyx_k9 = __pyx_2;
+  __pyx_2 = PyFloat_FromDouble(1.0); if (unlikely(!__pyx_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 790; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_k_4 = __pyx_2;
   __pyx_2 = 0;
-  Py_INCREF(Py_None);
-  __pyx_k10 = Py_None;
-
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":938 */
-  Py_INCREF(Py_None);
-  __pyx_k11 = Py_None;
-  Py_INCREF(Py_None);
-  __pyx_k12 = Py_None;
-
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":953 */
-  Py_INCREF(Py_None);
-  __pyx_k13 = Py_None;
-
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":974 */
-  __pyx_3 = PyFloat_FromDouble(0.0); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 974; goto __pyx_L1;}
-  __pyx_k14 = __pyx_3;
+
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":974
+ *         return cont0_array(self.internal_state, rk_gauss, size)
+ * 
+ *     def normal(self, loc=0.0, scale=1.0, size=None):             # <<<<<<<<<<<<<<
+ *         """
+ *         normal(loc=0.0, scale=1.0, size=None)
+ */
+  __pyx_3 = PyFloat_FromDouble(0.0); if (unlikely(!__pyx_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 974; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_k_5 = __pyx_3;
   __pyx_3 = 0;
-  __pyx_4 = PyFloat_FromDouble(1.0); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 974; goto __pyx_L1;}
-  __pyx_k15 = __pyx_4;
+  __pyx_4 = PyFloat_FromDouble(1.0); if (unlikely(!__pyx_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 974; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_k_6 = __pyx_4;
   __pyx_4 = 0;
-  Py_INCREF(Py_None);
-  __pyx_k16 = Py_None;
-
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1074 */
-  Py_INCREF(Py_None);
-  __pyx_k17 = Py_None;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1133 */
-  __pyx_5 = PyFloat_FromDouble(1.0); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1133; goto __pyx_L1;}
-  __pyx_k18 = __pyx_5;
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1133
+ *         return cont2_array(self.internal_state, rk_beta, size, oa, ob)
+ * 
+ *     def exponential(self, scale=1.0, size=None):             # <<<<<<<<<<<<<<
+ *         """
+ *         exponential(scale=1.0, size=None)
+ */
+  __pyx_5 = PyFloat_FromDouble(1.0); if (unlikely(!__pyx_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1133; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_k_7 = __pyx_5;
   __pyx_5 = 0;
-  Py_INCREF(Py_None);
-  __pyx_k19 = Py_None;
-
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1187 */
-  Py_INCREF(Py_None);
-  __pyx_k20 = Py_None;
-
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1196 */
-  Py_INCREF(Py_None);
-  __pyx_k21 = Py_None;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1218 */
-  __pyx_6 = PyFloat_FromDouble(1.0); if (!__pyx_6) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1218; goto __pyx_L1;}
-  __pyx_k22 = __pyx_6;
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1218
+ *         return cont1_array(self.internal_state, rk_standard_gamma, size, oshape)
+ * 
+ *     def gamma(self, shape, scale=1.0, size=None):             # <<<<<<<<<<<<<<
+ *         """
+ *         gamma(shape, scale=1.0, size=None)
+ */
+  __pyx_6 = PyFloat_FromDouble(1.0); if (unlikely(!__pyx_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1218; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_k_8 = __pyx_6;
   __pyx_6 = 0;
-  Py_INCREF(Py_None);
-  __pyx_k23 = Py_None;
-
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1309 */
-  Py_INCREF(Py_None);
-  __pyx_k24 = Py_None;
-
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1412 */
-  Py_INCREF(Py_None);
-  __pyx_k25 = Py_None;
-
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1450 */
-  Py_INCREF(Py_None);
-  __pyx_k26 = Py_None;
-
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1530 */
-  Py_INCREF(Py_None);
-  __pyx_k27 = Py_None;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1572 */
-  Py_INCREF(Py_None);
-  __pyx_k28 = Py_None;
-
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1581 */
-  Py_INCREF(Py_None);
-  __pyx_k29 = Py_None;
-
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1604 */
-  Py_INCREF(Py_None);
-  __pyx_k30 = Py_None;
-
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1699 */
-  Py_INCREF(Py_None);
-  __pyx_k31 = Py_None;
-
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1788 */
-  Py_INCREF(Py_None);
-  __pyx_k32 = Py_None;
-
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1887 */
-  Py_INCREF(Py_None);
-  __pyx_k33 = Py_None;
-
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1910 */
-  __pyx_7 = PyFloat_FromDouble(0.0); if (!__pyx_7) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1910; goto __pyx_L1;}
-  __pyx_k34 = __pyx_7;
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1910
+ *         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)
+ */
+  __pyx_7 = PyFloat_FromDouble(0.0); if (unlikely(!__pyx_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1910; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_k_9 = __pyx_7;
   __pyx_7 = 0;
-  __pyx_8 = PyFloat_FromDouble(1.0); if (!__pyx_8) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1910; goto __pyx_L1;}
-  __pyx_k35 = __pyx_8;
+  __pyx_8 = PyFloat_FromDouble(1.0); if (unlikely(!__pyx_8)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1910; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_k_10 = __pyx_8;
   __pyx_8 = 0;
-  Py_INCREF(Py_None);
-  __pyx_k36 = Py_None;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":1949 */
-  __pyx_9 = PyFloat_FromDouble(0.0); if (!__pyx_9) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1949; goto __pyx_L1;}
-  __pyx_k37 = __pyx_9;
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":1949
+ *         return cont2_array(self.internal_state, rk_laplace, size, oloc, oscale)
+ * 
+ *     def gumbel(self, loc=0.0, scale=1.0, size=None):             # <<<<<<<<<<<<<<
+ *         """
+ *         gumbel(loc=0.0, scale=1.0, size=None)
+ */
+  __pyx_9 = PyFloat_FromDouble(0.0); if (unlikely(!__pyx_9)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1949; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_k_11 = __pyx_9;
   __pyx_9 = 0;
-  __pyx_10 = PyFloat_FromDouble(1.0); if (!__pyx_10) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1949; goto __pyx_L1;}
-  __pyx_k38 = __pyx_10;
+  __pyx_10 = PyFloat_FromDouble(1.0); if (unlikely(!__pyx_10)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1949; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_k_12 = __pyx_10;
   __pyx_10 = 0;
-  Py_INCREF(Py_None);
-  __pyx_k39 = Py_None;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2073 */
-  __pyx_11 = PyFloat_FromDouble(0.0); if (!__pyx_11) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2073; goto __pyx_L1;}
-  __pyx_k40 = __pyx_11;
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2073
+ *         return cont2_array(self.internal_state, rk_gumbel, size, oloc, oscale)
+ * 
+ *     def logistic(self, loc=0.0, scale=1.0, size=None):             # <<<<<<<<<<<<<<
+ *         """
+ *         logistic(loc=0.0, scale=1.0, size=None)
+ */
+  __pyx_11 = PyFloat_FromDouble(0.0); if (unlikely(!__pyx_11)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2073; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_k_13 = __pyx_11;
   __pyx_11 = 0;
-  __pyx_12 = PyFloat_FromDouble(1.0); if (!__pyx_12) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2073; goto __pyx_L1;}
-  __pyx_k41 = __pyx_12;
+  __pyx_12 = PyFloat_FromDouble(1.0); if (unlikely(!__pyx_12)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2073; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_k_14 = __pyx_12;
   __pyx_12 = 0;
-  Py_INCREF(Py_None);
-  __pyx_k42 = Py_None;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2161 */
-  __pyx_13 = PyFloat_FromDouble(0.0); if (!__pyx_13) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2161; goto __pyx_L1;}
-  __pyx_k43 = __pyx_13;
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2161
+ *         return cont2_array(self.internal_state, rk_logistic, size, oloc, oscale)
+ * 
+ *     def lognormal(self, mean=0.0, sigma=1.0, size=None):             # <<<<<<<<<<<<<<
+ *         """
+ *         lognormal(mean=0.0, sigma=1.0, size=None)
+ */
+  __pyx_13 = PyFloat_FromDouble(0.0); if (unlikely(!__pyx_13)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2161; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_k_15 = __pyx_13;
   __pyx_13 = 0;
-  __pyx_14 = PyFloat_FromDouble(1.0); if (!__pyx_14) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2161; goto __pyx_L1;}
-  __pyx_k44 = __pyx_14;
+  __pyx_14 = PyFloat_FromDouble(1.0); if (unlikely(!__pyx_14)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2161; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_k_16 = __pyx_14;
   __pyx_14 = 0;
-  Py_INCREF(Py_None);
-  __pyx_k45 = Py_None;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2292 */
-  __pyx_15 = PyFloat_FromDouble(1.0); if (!__pyx_15) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2292; goto __pyx_L1;}
-  __pyx_k46 = __pyx_15;
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2292
+ *         return cont2_array(self.internal_state, rk_lognormal, size, omean, osigma)
+ * 
+ *     def rayleigh(self, scale=1.0, size=None):             # <<<<<<<<<<<<<<
+ *         """
+ *         rayleigh(scale=1.0, size=None)
+ */
+  __pyx_15 = PyFloat_FromDouble(1.0); if (unlikely(!__pyx_15)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2292; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_k_17 = __pyx_15;
   __pyx_15 = 0;
-  Py_INCREF(Py_None);
-  __pyx_k47 = Py_None;
-
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2316 */
-  Py_INCREF(Py_None);
-  __pyx_k48 = Py_None;
-
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2346 */
-  Py_INCREF(Py_None);
-  __pyx_k49 = Py_None;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2385 */
-  Py_INCREF(Py_None);
-  __pyx_k50 = Py_None;
-
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2493 */
-  Py_INCREF(Py_None);
-  __pyx_k51 = Py_None;
-
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2530 */
-  __pyx_16 = PyFloat_FromDouble(1.0); if (!__pyx_16) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2530; goto __pyx_L1;}
-  __pyx_k52 = __pyx_16;
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":2530
+ *                             on, op)
+ * 
+ *     def poisson(self, lam=1.0, size=None):             # <<<<<<<<<<<<<<
+ *         """
+ *         poisson(lam=1.0, size=None)
+ */
+  __pyx_16 = PyFloat_FromDouble(1.0); if (unlikely(!__pyx_16)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2530; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_k_18 = __pyx_16;
   __pyx_16 = 0;
-  Py_INCREF(Py_None);
-  __pyx_k53 = Py_None;
-
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2552 */
-  Py_INCREF(Py_None);
-  __pyx_k54 = Py_None;
-
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2644 */
-  Py_INCREF(Py_None);
-  __pyx_k55 = Py_None;
-
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2710 */
-  Py_INCREF(Py_None);
-  __pyx_k56 = Py_None;
-
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2829 */
-  Py_INCREF(Py_None);
-  __pyx_k57 = Py_None;
-
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":2924 */
-  Py_INCREF(Py_None);
-  __pyx_k58 = Py_None;
-
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3057 */
-  Py_INCREF(Py_None);
-  __pyx_k59 = Py_None;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3150 */
-  Py_INCREF(Py_None);
-  __pyx_k60 = Py_None;
-
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3314 */
-  __pyx_17 = PyObject_CallObject(((PyObject *)__pyx_ptype_6mtrand_RandomState), 0); if (!__pyx_17) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3314; goto __pyx_L1;}
-  if (PyObject_SetAttr(__pyx_m, __pyx_n__rand, __pyx_17) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3314; goto __pyx_L1;}
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3314
+ *         return arr
+ * 
+ * _rand = RandomState()             # <<<<<<<<<<<<<<
+ * seed = _rand.seed
+ * get_state = _rand.get_state
+ */
+  __pyx_17 = PyObject_Call(((PyObject*)__pyx_ptype_6mtrand_RandomState), ((PyObject *)__pyx_empty_tuple), NULL); if (unlikely(!__pyx_17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3314; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  if (PyObject_SetAttr(__pyx_m, __pyx_kp__rand, __pyx_17) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3314; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   Py_DECREF(__pyx_17); __pyx_17 = 0;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3315 */
-  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_n__rand); if (!__pyx_17) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3315; goto __pyx_L1;}
-  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_n_seed); if (!__pyx_18) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3315; goto __pyx_L1;}
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3315
+ * 
+ * _rand = RandomState()
+ * seed = _rand.seed             # <<<<<<<<<<<<<<
+ * get_state = _rand.get_state
+ * set_state = _rand.set_state
+ */
+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_kp__rand); if (unlikely(!__pyx_17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3315; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_kp_seed); if (unlikely(!__pyx_18)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3315; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   Py_DECREF(__pyx_17); __pyx_17 = 0;
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+  if (PyObject_SetAttr(__pyx_m, __pyx_kp_seed, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3315; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3328
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+  if (PyObject_SetAttr(__pyx_m, __pyx_kp_exponential, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3328; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3329
+ * beta = _rand.beta
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+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_kp__rand); if (unlikely(!__pyx_17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3329; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  if (PyObject_SetAttr(__pyx_m, __pyx_kp_1, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3329; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3330
+ * exponential = _rand.exponential
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+ * standard_gamma = _rand.standard_gamma             # <<<<<<<<<<<<<<
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+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_kp__rand); if (unlikely(!__pyx_17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3330; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_kp_standard_gamma); if (unlikely(!__pyx_18)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3330; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  if (PyObject_SetAttr(__pyx_m, __pyx_kp_standard_gamma, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3330; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3331
+ * standard_exponential = _rand.standard_exponential
+ * standard_gamma = _rand.standard_gamma
+ * gamma = _rand.gamma             # <<<<<<<<<<<<<<
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+ * noncentral_f = _rand.noncentral_f
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+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_kp__rand); if (unlikely(!__pyx_17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3331; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_kp_gamma); if (unlikely(!__pyx_18)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3331; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  if (PyObject_SetAttr(__pyx_m, __pyx_kp_gamma, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3331; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3332
+ * standard_gamma = _rand.standard_gamma
+ * gamma = _rand.gamma
+ * f = _rand.f             # <<<<<<<<<<<<<<
+ * noncentral_f = _rand.noncentral_f
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+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_kp__rand); if (unlikely(!__pyx_17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3332; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_kp_f); if (unlikely(!__pyx_18)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3332; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  if (PyObject_SetAttr(__pyx_m, __pyx_kp_f, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3332; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3333
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+ * f = _rand.f
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+  if (PyObject_SetAttr(__pyx_m, __pyx_kp_noncentral_f, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3333; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3334
+ * f = _rand.f
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+ * chisquare = _rand.chisquare             # <<<<<<<<<<<<<<
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+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_kp__rand); if (unlikely(!__pyx_17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3334; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_kp_chisquare); if (unlikely(!__pyx_18)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3334; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  if (PyObject_SetAttr(__pyx_m, __pyx_kp_chisquare, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3334; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3335
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+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_kp__rand); if (unlikely(!__pyx_17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3335; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  if (PyObject_SetAttr(__pyx_m, __pyx_kp_2, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3335; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3336
+ * chisquare = _rand.chisquare
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+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_kp__rand); if (unlikely(!__pyx_17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3336; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_kp_standard_cauchy); if (unlikely(!__pyx_18)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3336; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  if (PyObject_SetAttr(__pyx_m, __pyx_kp_standard_cauchy, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3336; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3337
+ * noncentral_chisquare = _rand.noncentral_chisquare
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+ * standard_t = _rand.standard_t             # <<<<<<<<<<<<<<
+ * vonmises = _rand.vonmises
+ * pareto = _rand.pareto
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+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_kp__rand); if (unlikely(!__pyx_17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3337; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_kp_standard_t); if (unlikely(!__pyx_18)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3337; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  if (PyObject_SetAttr(__pyx_m, __pyx_n_standard_t, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3337; goto __pyx_L1;}
+  if (PyObject_SetAttr(__pyx_m, __pyx_kp_standard_t, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3337; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3338
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+ * vonmises = _rand.vonmises             # <<<<<<<<<<<<<<
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+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_kp__rand); if (unlikely(!__pyx_17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3338; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_kp_vonmises); if (unlikely(!__pyx_18)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3338; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  if (PyObject_SetAttr(__pyx_m, __pyx_kp_vonmises, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3338; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3339
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+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_kp__rand); if (unlikely(!__pyx_17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3339; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_kp_pareto); if (unlikely(!__pyx_18)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3339; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  if (PyObject_SetAttr(__pyx_m, __pyx_kp_pareto, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3339; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3340
+ * vonmises = _rand.vonmises
+ * pareto = _rand.pareto
+ * weibull = _rand.weibull             # <<<<<<<<<<<<<<
+ * power = _rand.power
+ * laplace = _rand.laplace
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+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_kp__rand); if (unlikely(!__pyx_17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3340; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  if (PyObject_SetAttr(__pyx_m, __pyx_kp_weibull, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3340; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3341
+ * pareto = _rand.pareto
+ * weibull = _rand.weibull
+ * power = _rand.power             # <<<<<<<<<<<<<<
+ * laplace = _rand.laplace
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+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_kp__rand); if (unlikely(!__pyx_17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3341; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3342
+ * weibull = _rand.weibull
+ * power = _rand.power
+ * laplace = _rand.laplace             # <<<<<<<<<<<<<<
+ * gumbel = _rand.gumbel
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+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_kp__rand); if (unlikely(!__pyx_17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3342; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_kp_laplace); if (unlikely(!__pyx_18)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3342; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  if (PyObject_SetAttr(__pyx_m, __pyx_kp_laplace, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3342; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3343
+ * power = _rand.power
+ * laplace = _rand.laplace
+ * gumbel = _rand.gumbel             # <<<<<<<<<<<<<<
+ * logistic = _rand.logistic
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+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_kp__rand); if (unlikely(!__pyx_17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3343; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  if (PyObject_SetAttr(__pyx_m, __pyx_kp_gumbel, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3343; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3344
+ * laplace = _rand.laplace
+ * gumbel = _rand.gumbel
+ * logistic = _rand.logistic             # <<<<<<<<<<<<<<
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+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_kp__rand); if (unlikely(!__pyx_17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3344; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_kp_logistic); if (unlikely(!__pyx_18)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3344; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  if (PyObject_SetAttr(__pyx_m, __pyx_kp_logistic, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3344; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3345
+ * gumbel = _rand.gumbel
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+ * lognormal = _rand.lognormal             # <<<<<<<<<<<<<<
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+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_kp__rand); if (unlikely(!__pyx_17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3345; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_kp_lognormal); if (unlikely(!__pyx_18)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3345; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  if (PyObject_SetAttr(__pyx_m, __pyx_kp_lognormal, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3345; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3346
+ * logistic = _rand.logistic
+ * lognormal = _rand.lognormal
+ * rayleigh = _rand.rayleigh             # <<<<<<<<<<<<<<
+ * wald = _rand.wald
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+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_kp__rand); if (unlikely(!__pyx_17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3346; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_kp_rayleigh); if (unlikely(!__pyx_18)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3346; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  if (PyObject_SetAttr(__pyx_m, __pyx_kp_rayleigh, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3346; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3347
+ * lognormal = _rand.lognormal
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+ * triangular = _rand.triangular
+ * 
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+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_kp__rand); if (unlikely(!__pyx_17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3347; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_kp_wald); if (unlikely(!__pyx_18)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3347; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  if (PyObject_SetAttr(__pyx_m, __pyx_kp_wald, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3347; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3348
+ * rayleigh = _rand.rayleigh
+ * wald = _rand.wald
+ * triangular = _rand.triangular             # <<<<<<<<<<<<<<
+ * 
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+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_kp__rand); if (unlikely(!__pyx_17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3348; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_kp_triangular); if (unlikely(!__pyx_18)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3348; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  if (PyObject_SetAttr(__pyx_m, __pyx_kp_triangular, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3348; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3350
+ * triangular = _rand.triangular
+ * 
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+ * negative_binomial = _rand.negative_binomial
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+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_kp__rand); if (unlikely(!__pyx_17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3350; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_kp_binomial); if (unlikely(!__pyx_18)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3350; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  if (PyObject_SetAttr(__pyx_m, __pyx_n_binomial, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3350; goto __pyx_L1;}
+  if (PyObject_SetAttr(__pyx_m, __pyx_kp_binomial, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3350; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3351
+ * 
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+ * poisson = _rand.poisson
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+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_kp__rand); if (unlikely(!__pyx_17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3351; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_kp_negative_binomial); if (unlikely(!__pyx_18)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3351; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  if (PyObject_SetAttr(__pyx_m, __pyx_n_negative_binomial, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3351; goto __pyx_L1;}
+  if (PyObject_SetAttr(__pyx_m, __pyx_kp_negative_binomial, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3351; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3352
+ * binomial = _rand.binomial
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+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_kp__rand); if (unlikely(!__pyx_17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3352; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_kp_poisson); if (unlikely(!__pyx_18)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3352; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  if (PyObject_SetAttr(__pyx_m, __pyx_kp_poisson, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3352; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3353 */
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-  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_n_zipf); if (!__pyx_18) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3353; goto __pyx_L1;}
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3353
+ * negative_binomial = _rand.negative_binomial
+ * poisson = _rand.poisson
+ * zipf = _rand.zipf             # <<<<<<<<<<<<<<
+ * geometric = _rand.geometric
+ * hypergeometric = _rand.hypergeometric
+ */
+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_kp__rand); if (unlikely(!__pyx_17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3353; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_kp_zipf); if (unlikely(!__pyx_18)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3353; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  if (PyObject_SetAttr(__pyx_m, __pyx_n_zipf, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3353; goto __pyx_L1;}
+  if (PyObject_SetAttr(__pyx_m, __pyx_kp_zipf, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3353; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   Py_DECREF(__pyx_18); __pyx_18 = 0;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3354 */
-  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_n__rand); if (!__pyx_17) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3354; goto __pyx_L1;}
-  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_n_geometric); if (!__pyx_18) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3354; goto __pyx_L1;}
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3354
+ * poisson = _rand.poisson
+ * zipf = _rand.zipf
+ * geometric = _rand.geometric             # <<<<<<<<<<<<<<
+ * hypergeometric = _rand.hypergeometric
+ * logseries = _rand.logseries
+ */
+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_kp__rand); if (unlikely(!__pyx_17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3354; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_kp_geometric); if (unlikely(!__pyx_18)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3354; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  if (PyObject_SetAttr(__pyx_m, __pyx_n_geometric, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3354; goto __pyx_L1;}
+  if (PyObject_SetAttr(__pyx_m, __pyx_kp_geometric, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3354; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   Py_DECREF(__pyx_18); __pyx_18 = 0;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3355 */
-  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_n__rand); if (!__pyx_17) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3355; goto __pyx_L1;}
-  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_n_hypergeometric); if (!__pyx_18) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3355; goto __pyx_L1;}
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3355
+ * zipf = _rand.zipf
+ * geometric = _rand.geometric
+ * hypergeometric = _rand.hypergeometric             # <<<<<<<<<<<<<<
+ * logseries = _rand.logseries
+ * 
+ */
+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_kp__rand); if (unlikely(!__pyx_17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3355; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_kp_hypergeometric); if (unlikely(!__pyx_18)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3355; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   Py_DECREF(__pyx_17); __pyx_17 = 0;
-  if (PyObject_SetAttr(__pyx_m, __pyx_n_hypergeometric, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3355; goto __pyx_L1;}
+  if (PyObject_SetAttr(__pyx_m, __pyx_kp_hypergeometric, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3355; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   Py_DECREF(__pyx_18); __pyx_18 = 0;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3356 */
-  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_n__rand); if (!__pyx_17) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3356; goto __pyx_L1;}
-  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_n_logseries); if (!__pyx_18) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3356; goto __pyx_L1;}
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3356
+ * geometric = _rand.geometric
+ * hypergeometric = _rand.hypergeometric
+ * logseries = _rand.logseries             # <<<<<<<<<<<<<<
+ * 
+ * multivariate_normal = _rand.multivariate_normal
+ */
+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_kp__rand); if (unlikely(!__pyx_17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3356; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_kp_logseries); if (unlikely(!__pyx_18)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3356; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   Py_DECREF(__pyx_17); __pyx_17 = 0;
-  if (PyObject_SetAttr(__pyx_m, __pyx_n_logseries, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3356; goto __pyx_L1;}
+  if (PyObject_SetAttr(__pyx_m, __pyx_kp_logseries, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3356; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   Py_DECREF(__pyx_18); __pyx_18 = 0;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3358 */
-  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_n__rand); if (!__pyx_17) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3358; goto __pyx_L1;}
-  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_n_multivariate_normal); if (!__pyx_18) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3358; goto __pyx_L1;}
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3358
+ * logseries = _rand.logseries
+ * 
+ * multivariate_normal = _rand.multivariate_normal             # <<<<<<<<<<<<<<
+ * multinomial = _rand.multinomial
+ * dirichlet = _rand.dirichlet
+ */
+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_kp__rand); if (unlikely(!__pyx_17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3358; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_kp_multivariate_normal); if (unlikely(!__pyx_18)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3358; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   Py_DECREF(__pyx_17); __pyx_17 = 0;
-  if (PyObject_SetAttr(__pyx_m, __pyx_n_multivariate_normal, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3358; goto __pyx_L1;}
+  if (PyObject_SetAttr(__pyx_m, __pyx_kp_multivariate_normal, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3358; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   Py_DECREF(__pyx_18); __pyx_18 = 0;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3359 */
-  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_n__rand); if (!__pyx_17) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3359; goto __pyx_L1;}
-  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_n_multinomial); if (!__pyx_18) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3359; goto __pyx_L1;}
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3359
+ * 
+ * multivariate_normal = _rand.multivariate_normal
+ * multinomial = _rand.multinomial             # <<<<<<<<<<<<<<
+ * dirichlet = _rand.dirichlet
+ * 
+ */
+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_kp__rand); if (unlikely(!__pyx_17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3359; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_kp_multinomial); if (unlikely(!__pyx_18)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3359; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   Py_DECREF(__pyx_17); __pyx_17 = 0;
-  if (PyObject_SetAttr(__pyx_m, __pyx_n_multinomial, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3359; goto __pyx_L1;}
+  if (PyObject_SetAttr(__pyx_m, __pyx_kp_multinomial, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3359; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   Py_DECREF(__pyx_18); __pyx_18 = 0;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3360 */
-  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_n__rand); if (!__pyx_17) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3360; goto __pyx_L1;}
-  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_n_dirichlet); if (!__pyx_18) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3360; goto __pyx_L1;}
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3360
+ * multivariate_normal = _rand.multivariate_normal
+ * multinomial = _rand.multinomial
+ * dirichlet = _rand.dirichlet             # <<<<<<<<<<<<<<
+ * 
+ * shuffle = _rand.shuffle
+ */
+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_kp__rand); if (unlikely(!__pyx_17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3360; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_kp_dirichlet); if (unlikely(!__pyx_18)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3360; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   Py_DECREF(__pyx_17); __pyx_17 = 0;
-  if (PyObject_SetAttr(__pyx_m, __pyx_n_dirichlet, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3360; goto __pyx_L1;}
+  if (PyObject_SetAttr(__pyx_m, __pyx_kp_dirichlet, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3360; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   Py_DECREF(__pyx_18); __pyx_18 = 0;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3362 */
-  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_n__rand); if (!__pyx_17) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3362; goto __pyx_L1;}
-  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_n_shuffle); if (!__pyx_18) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3362; goto __pyx_L1;}
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3362
+ * dirichlet = _rand.dirichlet
+ * 
+ * shuffle = _rand.shuffle             # <<<<<<<<<<<<<<
+ * permutation = _rand.permutation
+ */
+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_kp__rand); if (unlikely(!__pyx_17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3362; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_kp_shuffle); if (unlikely(!__pyx_18)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3362; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   Py_DECREF(__pyx_17); __pyx_17 = 0;
-  if (PyObject_SetAttr(__pyx_m, __pyx_n_shuffle, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3362; goto __pyx_L1;}
+  if (PyObject_SetAttr(__pyx_m, __pyx_kp_shuffle, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3362; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   Py_DECREF(__pyx_18); __pyx_18 = 0;
 
-  /* "/home/pauli/koodi/proj/scipy/numpy.git/numpy/random/mtrand/mtrand.pyx":3363 */
-  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_n__rand); if (!__pyx_17) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3363; goto __pyx_L1;}
-  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_n_permutation); if (!__pyx_18) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3363; goto __pyx_L1;}
+  /* "/home/charris/Workspace/numpy.git/numpy/random/mtrand/mtrand.pyx":3363
+ * 
+ * shuffle = _rand.shuffle
+ * permutation = _rand.permutation             # <<<<<<<<<<<<<<
+ */
+  __pyx_17 = __Pyx_GetName(__pyx_m, __pyx_kp__rand); if (unlikely(!__pyx_17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3363; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_18 = PyObject_GetAttr(__pyx_17, __pyx_kp_permutation); if (unlikely(!__pyx_18)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3363; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   Py_DECREF(__pyx_17); __pyx_17 = 0;
-  if (PyObject_SetAttr(__pyx_m, __pyx_n_permutation, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3363; goto __pyx_L1;}
+  if (PyObject_SetAttr(__pyx_m, __pyx_kp_permutation, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3363; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
   Py_DECREF(__pyx_18); __pyx_18 = 0;
+  #if PY_MAJOR_VERSION < 3
   return;
-  __pyx_L1:;
+  #else
+  return __pyx_m;
+  #endif
+  __pyx_L1_error:;
   Py_XDECREF(__pyx_1);
   Py_XDECREF(__pyx_2);
   Py_XDECREF(__pyx_3);
@@ -10137,9 +16517,12 @@ PyMODINIT_FUNC initmtrand(void) {
   Py_XDECREF(__pyx_17);
   Py_XDECREF(__pyx_18);
   __Pyx_AddTraceback("mtrand");
+  #if PY_MAJOR_VERSION >= 3
+  return NULL;
+  #endif
 }
 
-static char *__pyx_filenames[] = {
+static const char *__pyx_filenames[] = {
   "mtrand.pyx",
   "numpy.pxi",
 };
@@ -10150,101 +16533,69 @@ static void __pyx_init_filenames(void) {
   __pyx_f = __pyx_filenames;
 }
 
-static int __Pyx_GetStarArgs(
+static INLINE void __Pyx_RaiseArgtupleTooLong(
+    Py_ssize_t num_expected,
+    Py_ssize_t num_found)
+{
+    const char* error_message =
+    #if PY_VERSION_HEX < 0x02050000
+        "function takes at most %d positional arguments (%d given)";
+    #else
+        "function takes at most %zd positional arguments (%zd given)";
+    #endif
+    PyErr_Format(PyExc_TypeError, error_message, num_expected, num_found);
+}
+
+static INLINE int __Pyx_SplitStarArg(
     PyObject **args, 
-    PyObject **kwds,
-    char *kwd_list[], 
     Py_ssize_t nargs,
-    PyObject **args2, 
-    PyObject **kwds2,
-    char rqd_kwds[])
+    PyObject **args2)
 {
-    PyObject *x = 0, *args1 = 0, *kwds1 = 0;
-    int i;
-    char **p;
-    
-    if (args2)
+    PyObject *args1 = 0;
+    args1 = PyTuple_GetSlice(*args, 0, nargs);
+    if (!args1) {
         *args2 = 0;
-    if (kwds2)
-        *kwds2 = 0;
-    
-    if (args2) {
-        args1 = PyTuple_GetSlice(*args, 0, nargs);
-        if (!args1)
-            goto bad;
-        *args2 = PyTuple_GetSlice(*args, nargs, PyTuple_GET_SIZE(*args));
-        if (!*args2)
-            goto bad;
-    }
-    else if (PyTuple_GET_SIZE(*args) > nargs) {
-        int m = nargs;
-        int n = PyTuple_GET_SIZE(*args);
-        PyErr_Format(PyExc_TypeError,
-            "function takes at most %d positional arguments (%d given)",
-                m, n);
-        goto bad;
-    }
-    else {
-        args1 = *args;
-        Py_INCREF(args1);
-    }
-    
-    if (rqd_kwds && !*kwds)
-            for (i = 0, p = kwd_list; *p; i++, p++)
-                if (rqd_kwds[i])
-                    goto missing_kwarg;
-    
-    if (kwds2) {
-        if (*kwds) {
-            kwds1 = PyDict_New();
-            if (!kwds1)
-                goto bad;
-            *kwds2 = PyDict_Copy(*kwds);
-            if (!*kwds2)
-                goto bad;
-            for (i = 0, p = kwd_list; *p; i++, p++) {
-                x = PyDict_GetItemString(*kwds, *p);
-                if (x) {
-                    if (PyDict_SetItemString(kwds1, *p, x) < 0)
-                        goto bad;
-                    if (PyDict_DelItemString(*kwds2, *p) < 0)
-                        goto bad;
-                }
-                else if (rqd_kwds && rqd_kwds[i])
-                    goto missing_kwarg;
-            }
-        }
-        else {
-            *kwds2 = PyDict_New();
-            if (!*kwds2)
-                goto bad;
-        }
+        return -1;
     }
-    else {
-        kwds1 = *kwds;
-        Py_XINCREF(kwds1);
-        if (rqd_kwds && *kwds)
-            for (i = 0, p = kwd_list; *p; i++, p++)
-                if (rqd_kwds[i] && !PyDict_GetItemString(*kwds, *p))
-                        goto missing_kwarg;
+    *args2 = PyTuple_GetSlice(*args, nargs, PyTuple_GET_SIZE(*args));
+    if (!*args2) {
+        Py_DECREF(args1);
+        return -1;
     }
-    
     *args = args1;
-    *kwds = kwds1;
     return 0;
-missing_kwarg:
-    PyErr_Format(PyExc_TypeError,
-        "required keyword argument '%s' is missing", *p);
-bad:
-    Py_XDECREF(args1);
-    Py_XDECREF(kwds1);
-    if (args2) {
-        Py_XDECREF(*args2);
+}
+
+static int __Pyx_CheckKeywordStrings(
+    PyObject *kwdict,
+    const char* function_name,
+    int kw_allowed)
+{
+    PyObject* key = 0;
+    Py_ssize_t pos = 0;
+    while (PyDict_Next(kwdict, &pos, &key, 0)) {
+        #if PY_MAJOR_VERSION < 3
+        if (unlikely(!PyString_Check(key))) {
+        #else
+        if (unlikely(!PyUnicode_Check(key))) {
+        #endif
+            PyErr_Format(PyExc_TypeError,
+                         "%s() keywords must be strings", function_name);
+            return 0;
+        }
     }
-    if (kwds2) {
-        Py_XDECREF(*kwds2);
+    if (unlikely(!kw_allowed) && unlikely(key)) {
+        PyErr_Format(PyExc_TypeError,
+        #if PY_MAJOR_VERSION < 3
+                     "'%s' is an invalid keyword argument for this function",
+                     PyString_AsString(key));
+        #else
+                     "'%U' is an invalid keyword argument for this function",
+                     key);
+        #endif
+        return 0;
     }
-    return -1;
+    return 1;
 }
 
 static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list) {
@@ -10288,36 +16639,6 @@ static PyObject *__Pyx_GetName(PyObject *dict, PyObject *name) {
     return result;
 }
 
-static int __Pyx_SetItemInt(PyObject *o, Py_ssize_t i, PyObject *v) {
-    PyTypeObject *t = o->ob_type;
-    int r;
-    if (t->tp_as_sequence && t->tp_as_sequence->sq_item)
-        r = PySequence_SetItem(o, i, v);
-    else {
-        PyObject *j = PyInt_FromLong(i);
-        if (!j)
-            return -1;
-        r = PyObject_SetItem(o, j, v);
-        Py_DECREF(j);
-    }
-    return r;
-}
-
-static PyObject *__Pyx_GetItemInt(PyObject *o, Py_ssize_t i) {
-    PyTypeObject *t = o->ob_type;
-    PyObject *r;
-    if (t->tp_as_sequence && t->tp_as_sequence->sq_item)
-        r = PySequence_GetItem(o, i);
-    else {
-        PyObject *j = PyInt_FromLong(i);
-        if (!j)
-            return 0;
-        r = PyObject_GetItem(o, j);
-        Py_DECREF(j);
-    }
-    return r;
-}
-
 static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb) {
     Py_XINCREF(type);
     Py_XINCREF(value);
@@ -10358,12 +16679,13 @@ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb) {
                 Py_INCREF(type);
             }
             else {
+                type = 0;
                 PyErr_SetString(PyExc_TypeError,
                     "raise: exception must be an old-style class or instance");
                 goto raise_error;
             }
         #else
-            type = (PyObject*) type->ob_type;
+            type = (PyObject*) Py_TYPE(type);
             Py_INCREF(type);
             if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) {
                 PyErr_SetString(PyExc_TypeError,
@@ -10381,15 +16703,18 @@ raise_error:
     return;
 }
 
-static void __Pyx_UnpackError(void) {
-    PyErr_SetString(PyExc_ValueError, "unpack sequence of wrong size");
-}
 
-static PyObject *__Pyx_UnpackItem(PyObject *iter) {
+static PyObject *__Pyx_UnpackItem(PyObject *iter, Py_ssize_t index) {
     PyObject *item;
     if (!(item = PyIter_Next(iter))) {
-        if (!PyErr_Occurred())
-            __Pyx_UnpackError();
+        if (!PyErr_Occurred()) {
+            PyErr_Format(PyExc_ValueError,
+                #if PY_VERSION_HEX < 0x02050000
+                    "need more than %d values to unpack", (int)index);
+                #else
+                    "need more than %zd values to unpack", index);
+                #endif
+        }
     }
     return item;
 }
@@ -10398,7 +16723,7 @@ static int __Pyx_EndUnpack(PyObject *iter) {
     PyObject *item;
     if ((item = PyIter_Next(iter))) {
         Py_DECREF(item);
-        __Pyx_UnpackError();
+        PyErr_SetString(PyExc_ValueError, "too many values to unpack");
         return -1;
     }
     else if (!PyErr_Occurred())
@@ -10408,6 +16733,7 @@ static int __Pyx_EndUnpack(PyObject *iter) {
 }
 
 static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) {
+    PyObject *tmp_type, *tmp_value, *tmp_tb;
     PyThreadState *tstate = PyThreadState_Get();
     PyErr_Fetch(type, value, tb);
     PyErr_NormalizeException(type, value, tb);
@@ -10416,12 +16742,17 @@ static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb)
     Py_INCREF(*type);
     Py_INCREF(*value);
     Py_INCREF(*tb);
-    Py_XDECREF(tstate->exc_type);
-    Py_XDECREF(tstate->exc_value);
-    Py_XDECREF(tstate->exc_traceback);
+    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;
+    /* Make sure tstate is in a consistent state when we XDECREF
+    these objects (XDECREF may run arbitrary code). */
+    Py_XDECREF(tmp_type);
+    Py_XDECREF(tmp_value);
+    Py_XDECREF(tmp_tb);
     return 0;
 bad:
     Py_XDECREF(*type);
@@ -10430,6 +16761,7 @@ bad:
     return -1;
 }
 
+
 static int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) {
     if (!type) {
         PyErr_Format(PyExc_SystemError, "Missing type object");
@@ -10438,38 +16770,28 @@ static int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) {
     if (obj == Py_None || PyObject_TypeCheck(obj, type))
         return 1;
     PyErr_Format(PyExc_TypeError, "Cannot convert %s to %s",
-        obj->ob_type->tp_name, type->tp_name);
+        Py_TYPE(obj)->tp_name, type->tp_name);
     return 0;
 }
 
-static int __Pyx_InternStrings(__Pyx_InternTabEntry *t) {
-    while (t->p) {
-        *t->p = PyString_InternFromString(t->s);
-        if (!*t->p)
-            return -1;
-        ++t;
-    }
-    return 0;
-}
-
-static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
-    while (t->p) {
-        *t->p = PyString_FromStringAndSize(t->s, t->n - 1);
-        if (!*t->p)
-            return -1;
-        ++t;
-    }
-    return 0;
-}
 
 #ifndef __PYX_HAVE_RT_ImportType
 #define __PYX_HAVE_RT_ImportType
-static PyTypeObject *__Pyx_ImportType(char *module_name, char *class_name, 
-    long size) 
+static PyTypeObject *__Pyx_ImportType(char *module_name, char *class_name,
+    long size)
 {
     PyObject *py_module = 0;
     PyObject *result = 0;
-    
+    PyObject *py_name = 0;
+
+    #if PY_MAJOR_VERSION < 3
+    py_name = PyString_FromString(module_name);
+    #else
+    py_name = PyUnicode_FromString(module_name);
+    #endif
+    if (!py_name)
+        goto bad;
+
     py_module = __Pyx_ImportModule(module_name);
     if (!py_module)
         goto bad;
@@ -10490,6 +16812,7 @@ static PyTypeObject *__Pyx_ImportType(char *module_name, char *class_name,
     }
     return (PyTypeObject *)result;
 bad:
+    Py_XDECREF(py_name);
     Py_XDECREF(result);
     return 0;
 }
@@ -10499,11 +16822,18 @@ bad:
 #define __PYX_HAVE_RT_ImportModule
 static PyObject *__Pyx_ImportModule(char *name) {
     PyObject *py_name = 0;
-    
+    PyObject *py_module = 0;
+
+    #if PY_MAJOR_VERSION < 3
     py_name = PyString_FromString(name);
+    #else
+    py_name = PyUnicode_FromString(name);
+    #endif
     if (!py_name)
         goto bad;
-    return PyImport_Import(py_name);
+    py_module = PyImport_Import(py_name);
+    Py_DECREF(py_name);
+    return py_module;
 bad:
     Py_XDECREF(py_name);
     return 0;
@@ -10514,36 +16844,57 @@ bad:
 #include "frameobject.h"
 #include "traceback.h"
 
-static void __Pyx_AddTraceback(char *funcname) {
+static void __Pyx_AddTraceback(const char *funcname) {
     PyObject *py_srcfile = 0;
     PyObject *py_funcname = 0;
     PyObject *py_globals = 0;
-    PyObject *empty_tuple = 0;
     PyObject *empty_string = 0;
     PyCodeObject *py_code = 0;
     PyFrameObject *py_frame = 0;
-    
+
+    #if PY_MAJOR_VERSION < 3
     py_srcfile = PyString_FromString(__pyx_filename);
+    #else
+    py_srcfile = PyUnicode_FromString(__pyx_filename);
+    #endif
     if (!py_srcfile) goto bad;
-    py_funcname = PyString_FromString(funcname);
+    if (__pyx_clineno) {
+        #if PY_MAJOR_VERSION < 3
+        py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, __pyx_clineno);
+        #else
+        py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, __pyx_clineno);
+        #endif
+    }
+    else {
+        #if PY_MAJOR_VERSION < 3
+        py_funcname = PyString_FromString(funcname);
+        #else
+        py_funcname = PyUnicode_FromString(funcname);
+        #endif
+    }
     if (!py_funcname) goto bad;
     py_globals = PyModule_GetDict(__pyx_m);
     if (!py_globals) goto bad;
-    empty_tuple = PyTuple_New(0);
-    if (!empty_tuple) goto bad;
-    empty_string = PyString_FromString("");
+    #if PY_MAJOR_VERSION < 3
+    empty_string = PyString_FromStringAndSize("", 0);
+    #else
+    empty_string = PyBytes_FromStringAndSize("", 0);
+    #endif
     if (!empty_string) goto bad;
     py_code = PyCode_New(
         0,            /*int argcount,*/
+        #if PY_MAJOR_VERSION >= 3
+        0,            /*int kwonlyargcount,*/
+        #endif
         0,            /*int nlocals,*/
         0,            /*int stacksize,*/
         0,            /*int flags,*/
         empty_string, /*PyObject *code,*/
-        empty_tuple,  /*PyObject *consts,*/
-        empty_tuple,  /*PyObject *names,*/
-        empty_tuple,  /*PyObject *varnames,*/
-        empty_tuple,  /*PyObject *freevars,*/
-        empty_tuple,  /*PyObject *cellvars,*/
+        __pyx_empty_tuple,  /*PyObject *consts,*/
+        __pyx_empty_tuple,  /*PyObject *names,*/
+        __pyx_empty_tuple,  /*PyObject *varnames,*/
+        __pyx_empty_tuple,  /*PyObject *freevars,*/
+        __pyx_empty_tuple,  /*PyObject *cellvars,*/
         py_srcfile,   /*PyObject *filename,*/
         py_funcname,  /*PyObject *name,*/
         __pyx_lineno,   /*int firstlineno,*/
@@ -10562,8 +16913,254 @@ static void __Pyx_AddTraceback(char *funcname) {
 bad:
     Py_XDECREF(py_srcfile);
     Py_XDECREF(py_funcname);
-    Py_XDECREF(empty_tuple);
     Py_XDECREF(empty_string);
     Py_XDECREF(py_code);
     Py_XDECREF(py_frame);
 }
+
+static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
+    while (t->p) {
+        #if PY_MAJOR_VERSION < 3
+        if (t->is_unicode && (!t->is_identifier)) {
+            *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL);
+        } else if (t->intern) {
+            *t->p = PyString_InternFromString(t->s);
+        } else {
+            *t->p = PyString_FromStringAndSize(t->s, t->n - 1);
+        }
+        #else  /* Python 3+ has unicode identifiers */
+        if (t->is_identifier || (t->is_unicode && t->intern)) {
+            *t->p = PyUnicode_InternFromString(t->s);
+        } else if (t->is_unicode) {
+            *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1);
+        } else {
+            *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1);
+        }
+        #endif
+        if (!*t->p)
+            return -1;
+        ++t;
+    }
+    return 0;
+}
+
+/* Type Conversion Functions */
+
+static INLINE Py_ssize_t __pyx_PyIndex_AsSsize_t(PyObject* b) {
+  Py_ssize_t ival;
+  PyObject* x = PyNumber_Index(b);
+  if (!x) return -1;
+  ival = PyInt_AsSsize_t(x);
+  Py_DECREF(x);
+  return ival;
+}
+
+static INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {
+   if (x == Py_True) return 1;
+   else if (x == Py_False) return 0;
+   else return PyObject_IsTrue(x);
+}
+
+static INLINE PY_LONG_LONG __pyx_PyInt_AsLongLong(PyObject* x) {
+    if (PyInt_CheckExact(x)) {
+        return PyInt_AS_LONG(x);
+    }
+    else if (PyLong_CheckExact(x)) {
+        return PyLong_AsLongLong(x);
+    }
+    else {
+        PY_LONG_LONG val;
+        PyObject* tmp = PyNumber_Int(x); if (!tmp) return (PY_LONG_LONG)-1;
+        val = __pyx_PyInt_AsLongLong(tmp);
+        Py_DECREF(tmp);
+        return val;
+    }
+}
+
+static INLINE unsigned PY_LONG_LONG __pyx_PyInt_AsUnsignedLongLong(PyObject* x) {
+    if (PyInt_CheckExact(x)) {
+        long val = PyInt_AS_LONG(x);
+        if (unlikely(val < 0)) {
+            PyErr_SetString(PyExc_TypeError, "Negative assignment to unsigned type.");
+            return (unsigned PY_LONG_LONG)-1;
+        }
+        return val;
+    }
+    else if (PyLong_CheckExact(x)) {
+        return PyLong_AsUnsignedLongLong(x);
+    }
+    else {
+        PY_LONG_LONG val;
+        PyObject* tmp = PyNumber_Int(x); if (!tmp) return (PY_LONG_LONG)-1;
+        val = __pyx_PyInt_AsUnsignedLongLong(tmp);
+        Py_DECREF(tmp);
+        return val;
+    }
+}
+
+
+static INLINE unsigned char __pyx_PyInt_unsigned_char(PyObject* x) {
+    if (sizeof(unsigned char) < sizeof(long)) {
+        long long_val = __pyx_PyInt_AsLong(x);
+        unsigned char val = (unsigned char)long_val;
+        if (unlikely((val != long_val)  || (long_val < 0))) {
+            PyErr_SetString(PyExc_OverflowError, "value too large to convert to unsigned char");
+            return (unsigned char)-1;
+        }
+        return val;
+    }
+    else {
+        return __pyx_PyInt_AsLong(x);
+    }
+}
+
+static INLINE unsigned short __pyx_PyInt_unsigned_short(PyObject* x) {
+    if (sizeof(unsigned short) < sizeof(long)) {
+        long long_val = __pyx_PyInt_AsLong(x);
+        unsigned short val = (unsigned short)long_val;
+        if (unlikely((val != long_val)  || (long_val < 0))) {
+            PyErr_SetString(PyExc_OverflowError, "value too large to convert to unsigned short");
+            return (unsigned short)-1;
+        }
+        return val;
+    }
+    else {
+        return __pyx_PyInt_AsLong(x);
+    }
+}
+
+static INLINE char __pyx_PyInt_char(PyObject* x) {
+    if (sizeof(char) < sizeof(long)) {
+        long long_val = __pyx_PyInt_AsLong(x);
+        char val = (char)long_val;
+        if (unlikely((val != long_val) )) {
+            PyErr_SetString(PyExc_OverflowError, "value too large to convert to char");
+            return (char)-1;
+        }
+        return val;
+    }
+    else {
+        return __pyx_PyInt_AsLong(x);
+    }
+}
+
+static INLINE short __pyx_PyInt_short(PyObject* x) {
+    if (sizeof(short) < sizeof(long)) {
+        long long_val = __pyx_PyInt_AsLong(x);
+        short val = (short)long_val;
+        if (unlikely((val != long_val) )) {
+            PyErr_SetString(PyExc_OverflowError, "value too large to convert to short");
+            return (short)-1;
+        }
+        return val;
+    }
+    else {
+        return __pyx_PyInt_AsLong(x);
+    }
+}
+
+static INLINE int __pyx_PyInt_int(PyObject* x) {
+    if (sizeof(int) < sizeof(long)) {
+        long long_val = __pyx_PyInt_AsLong(x);
+        int val = (int)long_val;
+        if (unlikely((val != long_val) )) {
+            PyErr_SetString(PyExc_OverflowError, "value too large to convert to int");
+            return (int)-1;
+        }
+        return val;
+    }
+    else {
+        return __pyx_PyInt_AsLong(x);
+    }
+}
+
+static INLINE long __pyx_PyInt_long(PyObject* x) {
+    if (sizeof(long) < sizeof(long)) {
+        long long_val = __pyx_PyInt_AsLong(x);
+        long val = (long)long_val;
+        if (unlikely((val != long_val) )) {
+            PyErr_SetString(PyExc_OverflowError, "value too large to convert to long");
+            return (long)-1;
+        }
+        return val;
+    }
+    else {
+        return __pyx_PyInt_AsLong(x);
+    }
+}
+
+static INLINE signed char __pyx_PyInt_signed_char(PyObject* x) {
+    if (sizeof(signed char) < sizeof(long)) {
+        long long_val = __pyx_PyInt_AsLong(x);
+        signed char val = (signed char)long_val;
+        if (unlikely((val != long_val) )) {
+            PyErr_SetString(PyExc_OverflowError, "value too large to convert to signed char");
+            return (signed char)-1;
+        }
+        return val;
+    }
+    else {
+        return __pyx_PyInt_AsLong(x);
+    }
+}
+
+static INLINE signed short __pyx_PyInt_signed_short(PyObject* x) {
+    if (sizeof(signed short) < sizeof(long)) {
+        long long_val = __pyx_PyInt_AsLong(x);
+        signed short val = (signed short)long_val;
+        if (unlikely((val != long_val) )) {
+            PyErr_SetString(PyExc_OverflowError, "value too large to convert to signed short");
+            return (signed short)-1;
+        }
+        return val;
+    }
+    else {
+        return __pyx_PyInt_AsLong(x);
+    }
+}
+
+static INLINE signed int __pyx_PyInt_signed_int(PyObject* x) {
+    if (sizeof(signed int) < sizeof(long)) {
+        long long_val = __pyx_PyInt_AsLong(x);
+        signed int val = (signed int)long_val;
+        if (unlikely((val != long_val) )) {
+            PyErr_SetString(PyExc_OverflowError, "value too large to convert to signed int");
+            return (signed int)-1;
+        }
+        return val;
+    }
+    else {
+        return __pyx_PyInt_AsLong(x);
+    }
+}
+
+static INLINE signed long __pyx_PyInt_signed_long(PyObject* x) {
+    if (sizeof(signed long) < sizeof(long)) {
+        long long_val = __pyx_PyInt_AsLong(x);
+        signed long val = (signed long)long_val;
+        if (unlikely((val != long_val) )) {
+            PyErr_SetString(PyExc_OverflowError, "value too large to convert to signed long");
+            return (signed long)-1;
+        }
+        return val;
+    }
+    else {
+        return __pyx_PyInt_AsLong(x);
+    }
+}
+
+static INLINE long double __pyx_PyInt_long_double(PyObject* x) {
+    if (sizeof(long double) < sizeof(long)) {
+        long long_val = __pyx_PyInt_AsLong(x);
+        long double val = (long double)long_val;
+        if (unlikely((val != long_val) )) {
+            PyErr_SetString(PyExc_OverflowError, "value too large to convert to long double");
+            return (long double)-1;
+        }
+        return val;
+    }
+    else {
+        return __pyx_PyInt_AsLong(x);
+    }
+}
+