GEN: regenerate mtrand.c to make doc changes show up.

1 files changed, 3025 insertions, 2226 deletions

diff --git a/numpy/random/mtrand/mtrand.c b/numpy/random/mtrand/mtrand.c
index 003df267a..d8357f477 100644
--- a/numpy/random/mtrand/mtrand.c
+++ b/numpy/random/mtrand/mtrand.c
@@ -1,11 +1,12 @@
-/* Generated by Cython 0.15.1 on Sun Apr  1 22:07:06 2012 */
+/* Generated by Cython 0.16 on Sat Jul  7 15:05:57 2012 */
 
 #define PY_SSIZE_T_CLEAN
 #include "Python.h"
 #ifndef Py_PYTHON_H
     #error Python headers needed to compile C extensions, please install development version of Python.
+#elif PY_VERSION_HEX < 0x02040000
+    #error Cython requires Python 2.4+.
 #else
-
 #include <stddef.h> /* For offsetof */
 #ifndef offsetof
 #define offsetof(type, member) ( (size_t) & ((type*)0) -> member )
@@ -34,10 +35,22 @@
   #define PY_LONG_LONG LONG_LONG
 #endif
 
-#if PY_VERSION_HEX < 0x02040000
-  #define METH_COEXIST 0
-  #define PyDict_CheckExact(op) (Py_TYPE(op) == &PyDict_Type)
-  #define PyDict_Contains(d,o)   PySequence_Contains(d,o)
+#ifndef Py_HUGE_VAL
+  #define Py_HUGE_VAL HUGE_VAL
+#endif
+
+#ifdef PYPY_VERSION
+#define CYTHON_COMPILING_IN_PYPY 1
+#define CYTHON_COMPILING_IN_CPYTHON 0
+#else
+#define CYTHON_COMPILING_IN_PYPY 0
+#define CYTHON_COMPILING_IN_CPYTHON 1
+#endif
+
+#if CYTHON_COMPILING_IN_PYPY
+  #define __Pyx_PyCFunction_Call PyObject_Call
+#else
+  #define __Pyx_PyCFunction_Call PyCFunction_Call
 #endif
 
 #if PY_VERSION_HEX < 0x02050000
@@ -50,6 +63,9 @@
   #define PyNumber_Index(o)    PyNumber_Int(o)
   #define PyIndex_Check(o)     PyNumber_Check(o)
   #define PyErr_WarnEx(category, message, stacklevel) PyErr_Warn(category, message)
+  #define __PYX_BUILD_PY_SSIZE_T "i"
+#else
+  #define __PYX_BUILD_PY_SSIZE_T "n"
 #endif
 
 #if PY_VERSION_HEX < 0x02060000
@@ -83,13 +99,25 @@
   #define PyBUF_F_CONTIGUOUS (0x0040 | PyBUF_STRIDES)
   #define PyBUF_ANY_CONTIGUOUS (0x0080 | PyBUF_STRIDES)
   #define PyBUF_INDIRECT (0x0100 | PyBUF_STRIDES)
+  #define PyBUF_RECORDS (PyBUF_STRIDES | PyBUF_FORMAT | PyBUF_WRITABLE)
+  #define PyBUF_FULL (PyBUF_INDIRECT | PyBUF_FORMAT | PyBUF_WRITABLE)
 
+  typedef int (*getbufferproc)(PyObject *, Py_buffer *, int);
+  typedef void (*releasebufferproc)(PyObject *, Py_buffer *);
 #endif
 
 #if PY_MAJOR_VERSION < 3
   #define __Pyx_BUILTIN_MODULE_NAME "__builtin__"
+  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \
+          PyCode_New(a, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
 #else
   #define __Pyx_BUILTIN_MODULE_NAME "builtins"
+  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \
+          PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
+#endif
+
+#if PY_MAJOR_VERSION < 3 && PY_MINOR_VERSION < 6
+  #define PyUnicode_FromString(s) PyUnicode_Decode(s, strlen(s), "UTF-8", "strict")
 #endif
 
 #if PY_MAJOR_VERSION >= 3
@@ -101,6 +129,17 @@
   #define Py_TPFLAGS_HAVE_NEWBUFFER 0
 #endif
 
+
+#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_GET_LENGTH)
+  #define CYTHON_PEP393_ENABLED 1
+  #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u)
+  #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i)
+#else
+  #define CYTHON_PEP393_ENABLED 0
+  #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u)
+  #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i]))
+#endif
+
 #if PY_MAJOR_VERSION >= 3
   #define PyBaseString_Type            PyUnicode_Type
   #define PyStringObject               PyUnicodeObject
@@ -168,15 +207,6 @@
   #define __Pyx_PyInt_AsHash_t   PyInt_AsSsize_t
 #endif
 
-
-#if PY_MAJOR_VERSION >= 3
-  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_TrueDivide(x,y)
-  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceTrueDivide(x,y)
-#else
-  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_Divide(x,y)
-  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceDivide(x,y)
-#endif
-
 #if (PY_MAJOR_VERSION < 3) || (PY_VERSION_HEX >= 0x03010300)
   #define __Pyx_PySequence_GetSlice(obj, a, b) PySequence_GetSlice(obj, a, b)
   #define __Pyx_PySequence_SetSlice(obj, a, b, value) PySequence_SetSlice(obj, a, b, value)
@@ -218,6 +248,14 @@
   #define __Pyx_DOCSTR(n)  (n)
 #endif
 
+#if PY_MAJOR_VERSION >= 3
+  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_TrueDivide(x,y)
+  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceTrueDivide(x,y)
+#else
+  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_Divide(x,y)
+  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceDivide(x,y)
+#endif
+
 #ifndef __PYX_EXTERN_C
   #ifdef __cplusplus
     #define __PYX_EXTERN_C extern "C"
@@ -270,7 +308,7 @@
 #   else
 #     define CYTHON_UNUSED
 #   endif
-# elif defined(__ICC) || defined(__INTEL_COMPILER)
+# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER))
 #   define CYTHON_UNUSED __attribute__ ((__unused__))
 # else
 #   define CYTHON_UNUSED
@@ -295,7 +333,7 @@ static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t);
 static CYTHON_INLINE size_t __Pyx_PyInt_AsSize_t(PyObject*);
 
 #define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x))
-
+#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x))
 
 #ifdef __GNUC__
   /* Test for GCC > 2.95 */
@@ -422,11 +460,9 @@ struct __pyx_obj_6mtrand_RandomState {
   rk_state *internal_state;
 };
 
-
 #ifndef CYTHON_REFNANNY
   #define CYTHON_REFNANNY 0
 #endif
-
 #if CYTHON_REFNANNY
   typedef struct {
     void (*INCREF)(void*, PyObject*, int);
@@ -439,8 +475,21 @@ struct __pyx_obj_6mtrand_RandomState {
   static __Pyx_RefNannyAPIStruct *__Pyx_RefNanny = NULL;
   static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname); /*proto*/
   #define __Pyx_RefNannyDeclarations void *__pyx_refnanny = NULL;
-  #define __Pyx_RefNannySetupContext(name)           __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__)
-  #define __Pyx_RefNannyFinishContext()           __Pyx_RefNanny->FinishContext(&__pyx_refnanny)
+#ifdef WITH_THREAD
+  #define __Pyx_RefNannySetupContext(name, acquire_gil) \
+          if (acquire_gil) { \
+              PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure(); \
+              __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__); \
+              PyGILState_Release(__pyx_gilstate_save); \
+          } else { \
+              __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__); \
+          }
+#else
+  #define __Pyx_RefNannySetupContext(name, acquire_gil) \
+          __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__)
+#endif
+  #define __Pyx_RefNannyFinishContext() \
+          __Pyx_RefNanny->FinishContext(&__pyx_refnanny)
   #define __Pyx_INCREF(r)  __Pyx_RefNanny->INCREF(__pyx_refnanny, (PyObject *)(r), __LINE__)
   #define __Pyx_DECREF(r)  __Pyx_RefNanny->DECREF(__pyx_refnanny, (PyObject *)(r), __LINE__)
   #define __Pyx_GOTREF(r)  __Pyx_RefNanny->GOTREF(__pyx_refnanny, (PyObject *)(r), __LINE__)
@@ -451,7 +500,7 @@ struct __pyx_obj_6mtrand_RandomState {
   #define __Pyx_XGIVEREF(r) do { if((r) != NULL) {__Pyx_GIVEREF(r);}} while(0)
 #else
   #define __Pyx_RefNannyDeclarations
-  #define __Pyx_RefNannySetupContext(name)
+  #define __Pyx_RefNannySetupContext(name, acquire_gil)
   #define __Pyx_RefNannyFinishContext()
   #define __Pyx_INCREF(r) Py_INCREF(r)
   #define __Pyx_DECREF(r) Py_DECREF(r)
@@ -462,6 +511,8 @@ struct __pyx_obj_6mtrand_RandomState {
   #define __Pyx_XGOTREF(r)
   #define __Pyx_XGIVEREF(r)
 #endif /* CYTHON_REFNANNY */
+#define __Pyx_CLEAR(r)    do { PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);} while(0)
+#define __Pyx_XCLEAR(r)   do { if((r) != NULL) {PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);}} while(0)
 
 static PyObject *__Pyx_GetName(PyObject *dict, PyObject *name); /*proto*/
 
@@ -470,15 +521,15 @@ static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyOb
 
 static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); /*proto*/
 
-static void __Pyx_RaiseDoubleKeywordsError(
-    const char* func_name, PyObject* kw_name); /*proto*/
+static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); /*proto*/
 
-static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[],     PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args,     const char* function_name); /*proto*/
+static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[], \
+    PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, \
+    const char* function_name); /*proto*/
 
 static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact,
     Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); /*proto*/
 
-
 static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) {
     PyObject *r;
     if (!j) return NULL;
@@ -486,12 +537,9 @@ static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j
     Py_DECREF(j);
     return r;
 }
-
-
 #define __Pyx_GetItemInt_List(o, i, size, to_py_func) (((size) <= sizeof(Py_ssize_t)) ? \
                                                     __Pyx_GetItemInt_List_Fast(o, i) : \
                                                     __Pyx_GetItemInt_Generic(o, to_py_func(i)))
-
 static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i) {
     if (likely(o != Py_None)) {
         if (likely((0 <= i) & (i < PyList_GET_SIZE(o)))) {
@@ -507,11 +555,9 @@ static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_
     }
     return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
 }
-
 #define __Pyx_GetItemInt_Tuple(o, i, size, to_py_func) (((size) <= sizeof(Py_ssize_t)) ? \
                                                     __Pyx_GetItemInt_Tuple_Fast(o, i) : \
                                                     __Pyx_GetItemInt_Generic(o, to_py_func(i)))
-
 static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i) {
     if (likely(o != Py_None)) {
         if (likely((0 <= i) & (i < PyTuple_GET_SIZE(o)))) {
@@ -527,29 +573,33 @@ static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize
     }
     return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
 }
-
-
 #define __Pyx_GetItemInt(o, i, size, to_py_func) (((size) <= sizeof(Py_ssize_t)) ? \
                                                     __Pyx_GetItemInt_Fast(o, i) : \
                                                     __Pyx_GetItemInt_Generic(o, to_py_func(i)))
-
 static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i) {
-    PyObject *r;
-    if (PyList_CheckExact(o) && ((0 <= i) & (i < PyList_GET_SIZE(o)))) {
-        r = PyList_GET_ITEM(o, i);
-        Py_INCREF(r);
-    }
-    else if (PyTuple_CheckExact(o) && ((0 <= i) & (i < PyTuple_GET_SIZE(o)))) {
-        r = PyTuple_GET_ITEM(o, i);
-        Py_INCREF(r);
+    if (PyList_CheckExact(o)) {
+        Py_ssize_t n = (likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);
+        if (likely((n >= 0) & (n < PyList_GET_SIZE(o)))) {
+            PyObject *r = PyList_GET_ITEM(o, n);
+            Py_INCREF(r);
+            return r;
+        }
     }
-    else if (Py_TYPE(o)->tp_as_sequence && Py_TYPE(o)->tp_as_sequence->sq_item && (likely(i >= 0))) {
-        r = PySequence_GetItem(o, i);
+    else if (PyTuple_CheckExact(o)) {
+        Py_ssize_t n = (likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o);
+        if (likely((n >= 0) & (n < PyTuple_GET_SIZE(o)))) {
+            PyObject *r = PyTuple_GET_ITEM(o, n);
+            Py_INCREF(r);
+            return r;
+        }
     }
-    else {
-        r = __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
+    else if (likely(i >= 0)) {
+        PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence;
+        if (likely(m && m->sq_item)) {
+            return m->sq_item(o, i);
+        }
     }
-    return r;
+    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
 }
 
 static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index);
@@ -560,17 +610,13 @@ static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected);
 
 static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); /*proto*/
 
-static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname);
-
-static CYTHON_INLINE int __Pyx_CheckKeywordStrings(PyObject *kwdict,
-    const char* function_name, int kw_allowed); /*proto*/
+static CYTHON_INLINE int __Pyx_CheckKeywordStrings(PyObject *kwdict, const char* function_name, int kw_allowed); /*proto*/
 
 static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); /*proto*/
 
 #define __Pyx_SetItemInt(o, i, v, size, to_py_func) (((size) <= sizeof(Py_ssize_t)) ? \
                                                     __Pyx_SetItemInt_Fast(o, i, v) : \
                                                     __Pyx_SetItemInt_Generic(o, to_py_func(i), v))
-
 static CYTHON_INLINE int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v) {
     int r;
     if (!j) return -1;
@@ -578,20 +624,24 @@ static CYTHON_INLINE int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyOb
     Py_DECREF(j);
     return r;
 }
-
 static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v) {
-    if (PyList_CheckExact(o) && ((0 <= i) & (i < PyList_GET_SIZE(o)))) {
-        Py_INCREF(v);
-        Py_DECREF(PyList_GET_ITEM(o, i));
-        PyList_SET_ITEM(o, i, v);
-        return 1;
+    if (PyList_CheckExact(o)) {
+        Py_ssize_t n = (likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);
+        if (likely((n >= 0) & (n < PyList_GET_SIZE(o)))) {
+            PyObject* old = PyList_GET_ITEM(o, n);
+            Py_INCREF(v);
+            PyList_SET_ITEM(o, n, v);
+            Py_DECREF(old);
+            return 1;
+        }
     }
-    else if (Py_TYPE(o)->tp_as_sequence && Py_TYPE(o)->tp_as_sequence->sq_ass_item && (likely(i >= 0)))
-        return PySequence_SetItem(o, i, v);
-    else {
-        PyObject *j = PyInt_FromSsize_t(i);
-        return __Pyx_SetItemInt_Generic(o, j, v);
+    else if (likely(i >= 0)) {
+        PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence;
+        if (likely(m && m->sq_ass_item)) {
+            return m->sq_ass_item(o, i, v);
+        }
     }
+    return __Pyx_SetItemInt_Generic(o, PyInt_FromSsize_t(i), v);
 }
 
 static CYTHON_INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb); /*proto*/
@@ -611,6 +661,8 @@ static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int
 #define __Pyx_PyString_Equals __Pyx_PyBytes_Equals
 #endif
 
+static CYTHON_INLINE void __Pyx_RaiseImportError(PyObject *name);
+
 static CYTHON_INLINE PyObject *__Pyx_PyInt_to_py_npy_intp(npy_intp);
 
 static CYTHON_INLINE unsigned char __Pyx_PyInt_AsUnsignedChar(PyObject *);
@@ -649,15 +701,38 @@ static CYTHON_INLINE npy_intp __Pyx_PyInt_from_py_npy_intp(PyObject *);
 
 static int __Pyx_check_binary_version(void);
 
+#if !defined(__Pyx_PyIdentifier_FromString)
+#if PY_MAJOR_VERSION < 3
+  #define __Pyx_PyIdentifier_FromString(s) PyString_FromString(s)
+#else
+  #define __Pyx_PyIdentifier_FromString(s) PyUnicode_FromString(s)
+#endif
+#endif
+
 static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict);  /*proto*/
 
 static PyObject *__Pyx_ImportModule(const char *name); /*proto*/
 
-static void __Pyx_AddTraceback(const char *funcname, int __pyx_clineno,
-                               int __pyx_lineno, const char *__pyx_filename); /*proto*/
+typedef struct {
+    int code_line;
+    PyCodeObject* code_object;
+} __Pyx_CodeObjectCacheEntry;
+struct __Pyx_CodeObjectCache {
+    int count;
+    int max_count;
+    __Pyx_CodeObjectCacheEntry* entries;
+};
+static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL};
+static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line);
+static PyCodeObject *__pyx_find_code_object(int code_line);
+static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object);
+
+static void __Pyx_AddTraceback(const char *funcname, int c_line,
+                               int py_line, const char *filename); /*proto*/
 
 static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); /*proto*/
 
+
 /* Module declarations from 'numpy' */
 
 /* Module declarations from 'mtrand' */
@@ -689,6 +764,59 @@ int __pyx_module_is_main_mtrand = 0;
 /* Implementation of 'mtrand' */
 static PyObject *__pyx_builtin_ValueError;
 static PyObject *__pyx_builtin_TypeError;
+static int __pyx_pf_6mtrand_11RandomState___init__(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_seed); /* proto */
+static void __pyx_pf_6mtrand_11RandomState_2__dealloc__(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_4seed(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_seed); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_6get_state(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_8set_state(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_state); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_10__getstate__(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_12__setstate__(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_state); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_14__reduce__(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_16random_sample(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_18tomaxint(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_20randint(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_low, PyObject *__pyx_v_high, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_22bytes(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, npy_intp __pyx_v_length); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_24choice(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_a, PyObject *__pyx_v_size, PyObject *__pyx_v_replace, PyObject *__pyx_v_p); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_26uniform(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_low, PyObject *__pyx_v_high, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_28rand(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_args); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_30randn(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_args); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_32random_integers(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_low, PyObject *__pyx_v_high, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_34standard_normal(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_36normal(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_loc, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_38beta(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_a, PyObject *__pyx_v_b, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_40exponential(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_42standard_exponential(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_44standard_gamma(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_shape, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_46gamma(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_shape, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_48f(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_dfnum, PyObject *__pyx_v_dfden, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_50noncentral_f(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_dfnum, PyObject *__pyx_v_dfden, PyObject *__pyx_v_nonc, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_52chisquare(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_df, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_54noncentral_chisquare(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_df, PyObject *__pyx_v_nonc, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_56standard_cauchy(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_58standard_t(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_df, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_60vonmises(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_mu, PyObject *__pyx_v_kappa, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_62pareto(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_a, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_64weibull(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_a, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_66power(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_a, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_68laplace(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_loc, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_70gumbel(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_loc, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_72logistic(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_loc, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_74lognormal(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_mean, PyObject *__pyx_v_sigma, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_76rayleigh(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_78wald(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_mean, PyObject *__pyx_v_scale, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_80triangular(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_left, PyObject *__pyx_v_mode, PyObject *__pyx_v_right, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_82binomial(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_n, PyObject *__pyx_v_p, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_84negative_binomial(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_n, PyObject *__pyx_v_p, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_86poisson(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_lam, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_88zipf(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_a, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_90geometric(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_p, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_92hypergeometric(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_ngood, PyObject *__pyx_v_nbad, PyObject *__pyx_v_nsample, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_94logseries(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_p, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_96multivariate_normal(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_mean, PyObject *__pyx_v_cov, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_98multinomial(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, npy_intp __pyx_v_n, PyObject *__pyx_v_pvals, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_100dirichlet(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_alpha, PyObject *__pyx_v_size); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_102shuffle(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_x); /* proto */
+static PyObject *__pyx_pf_6mtrand_11RandomState_104permutation(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_x); /* proto */
 static char __pyx_k_1[] = "size is not compatible with inputs";
 static char __pyx_k_9[] = "algorithm must be 'MT19937'";
 static char __pyx_k_11[] = "state must be 624 longs";
@@ -789,7 +917,7 @@ static char __pyx_k_226[] = "\n        chisquare(df, size=None)\n\n        Draw
 static char __pyx_k_227[] = "RandomState.noncentral_chisquare (line 2049)";
 static char __pyx_k_228[] = "\n        noncentral_chisquare(df, nonc, size=None)\n\n        Draw samples from a noncentral chi-square distribution.\n\n        The noncentral :math:`\\chi^2` distribution is a generalisation of\n        the :math:`\\chi^2` distribution.\n\n        Parameters\n        ----------\n        df : int\n            Degrees of freedom, should be >= 1.\n        nonc : float\n            Non-centrality, should be > 0.\n        size : int or tuple of ints\n            Shape of the output.\n\n        Notes\n        -----\n        The probability density function for the noncentral Chi-square distribution\n        is\n\n        .. math:: P(x;df,nonc) = \\sum^{\\infty}_{i=0}\n                               \\frac{e^{-nonc/2}(nonc/2)^{i}}{i!}P_{Y_{df+2i}}(x),\n\n        where :math:`Y_{q}` is the Chi-square with q degrees of freedom.\n\n        In Delhi (2007), it is noted that the noncentral chi-square is useful in\n        bombing and coverage problems, the probability of killing the point target\n        given by the noncentral chi-squared distribution.\n\n        References\n        ----------\n        .. [1] Delhi, M.S. Holla, \"On a noncentral chi-square distribution in the\n               analysis of weapon systems effectiveness\", Metrika, Volume 15,\n               Number 1 / December, 1970.\n        .. [2] Wikipedia, \"Noncentral chi-square distribution\"\n               http://en.wikipedia.org/wiki/Noncentral_chi-square_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram\n\n        >>> import matplotlib.pyplot as plt\n        >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),\n        ...                   bins=200, normed=True)\n        >>> plt.show()\n\n        Draw values from a noncentral chisquare with very small noncentrality,\n        and compare to a chisquare.\n\n        >>> plt.figure()\n        >>> values = plt.hist(np.random.noncentral_chisquare(3, .0000001, 100000),\n     ""   ...                   bins=np.arange(0., 25, .1), normed=True)\n        >>> values2 = plt.hist(np.random.chisquare(3, 100000),\n        ...                    bins=np.arange(0., 25, .1), normed=True)\n        >>> plt.plot(values[1][0:-1], values[0]-values2[0], 'ob')\n        >>> plt.show()\n\n        Demonstrate how large values of non-centrality lead to a more symmetric\n        distribution.\n\n        >>> plt.figure()\n        >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),\n        ...                   bins=200, normed=True)\n        >>> plt.show()\n\n        ";
 static char __pyx_k_229[] = "RandomState.standard_cauchy (line 2141)";
-static char __pyx_k_230[] = "\n        standard_cauchy(size=None)\n\n        Standard Cauchy distribution with mode = 0.\n\n        Also known as the Lorentz distribution.\n\n        Parameters\n        ----------\n        size : int or tuple of ints\n            Shape of the output.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            The drawn samples.\n\n        Notes\n        -----\n        The probability density function for the full Cauchy distribution is\n\n        .. math:: P(x; x_0, \\gamma) = \\frac{1}{\\pi \\gamma \\bigl[ 1+\n                  (\\frac{x-x_0}{\\gamma})^2 \\bigr] }\n\n        and the Standard Cauchy distribution just sets :math:`x_0=0` and\n        :math:`\\gamma=1`\n\n        The Cauchy distribution arises in the solution to the driven harmonic\n        oscillator problem, and also describes spectral line broadening. It\n        also describes the distribution of values at which a line tilted at\n        a random angle will cut the x axis.\n\n        When studying hypothesis tests that assume normality, seeing how the\n        tests perform on data from a Cauchy distribution is a good indicator of\n        their sensitivity to a heavy-tailed distribution, since the Cauchy looks\n        very much like a Gaussian distribution, but with heavier tails.\n\n        References\n        ----------\n        ..[1] NIST/SEMATECH e-Handbook of Statistical Methods, \"Cauchy\n              Distribution\",\n              http://www.itl.nist.gov/div898/handbook/eda/section3/eda3663.htm\n        ..[2] Weisstein, Eric W. \"Cauchy Distribution.\" From MathWorld--A\n              Wolfram Web Resource.\n              http://mathworld.wolfram.com/CauchyDistribution.html\n        ..[3] Wikipedia, \"Cauchy distribution\"\n              http://en.wikipedia.org/wiki/Cauchy_distribution\n\n        Examples\n        --------\n        Draw samples and plot the distribution:\n\n        >>> s = np.random.standard_cauchy(1000000)\n        >>> s = s[(s>-25) & (s<25)""]  # truncate distribution so it plots well\n        >>> plt.hist(s, bins=100)\n        >>> plt.show()\n\n        ";
+static char __pyx_k_230[] = "\n        standard_cauchy(size=None)\n\n        Standard Cauchy distribution with mode = 0.\n\n        Also known as the Lorentz distribution.\n\n        Parameters\n        ----------\n        size : int or tuple of ints\n            Shape of the output.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            The drawn samples.\n\n        Notes\n        -----\n        The probability density function for the full Cauchy distribution is\n\n        .. math:: P(x; x_0, \\gamma) = \\frac{1}{\\pi \\gamma \\bigl[ 1+\n                  (\\frac{x-x_0}{\\gamma})^2 \\bigr] }\n\n        and the Standard Cauchy distribution just sets :math:`x_0=0` and\n        :math:`\\gamma=1`\n\n        The Cauchy distribution arises in the solution to the driven harmonic\n        oscillator problem, and also describes spectral line broadening. It\n        also describes the distribution of values at which a line tilted at\n        a random angle will cut the x axis.\n\n        When studying hypothesis tests that assume normality, seeing how the\n        tests perform on data from a Cauchy distribution is a good indicator of\n        their sensitivity to a heavy-tailed distribution, since the Cauchy looks\n        very much like a Gaussian distribution, but with heavier tails.\n\n        References\n        ----------\n        .. [1] NIST/SEMATECH e-Handbook of Statistical Methods, \"Cauchy\n              Distribution\",\n              http://www.itl.nist.gov/div898/handbook/eda/section3/eda3663.htm\n        .. [2] Weisstein, Eric W. \"Cauchy Distribution.\" From MathWorld--A\n              Wolfram Web Resource.\n              http://mathworld.wolfram.com/CauchyDistribution.html\n        .. [3] Wikipedia, \"Cauchy distribution\"\n              http://en.wikipedia.org/wiki/Cauchy_distribution\n\n        Examples\n        --------\n        Draw samples and plot the distribution:\n\n        >>> s = np.random.standard_cauchy(1000000)\n        >>> s = s[(s>-25) & (s<""25)]  # truncate distribution so it plots well\n        >>> plt.hist(s, bins=100)\n        >>> plt.show()\n\n        ";
 static char __pyx_k_231[] = "RandomState.standard_t (line 2202)";
 static char __pyx_k_232[] = "\n        standard_t(df, size=None)\n\n        Standard Student's t distribution with df degrees of freedom.\n\n        A special case of the hyperbolic distribution.\n        As `df` gets large, the result resembles that of the standard normal\n        distribution (`standard_normal`).\n\n        Parameters\n        ----------\n        df : int\n            Degrees of freedom, should be > 0.\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single value is\n            returned.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            Drawn samples.\n\n        Notes\n        -----\n        The probability density function for the t distribution is\n\n        .. math:: P(x, df) = \\frac{\\Gamma(\\frac{df+1}{2})}{\\sqrt{\\pi df}\n                  \\Gamma(\\frac{df}{2})}\\Bigl( 1+\\frac{x^2}{df} \\Bigr)^{-(df+1)/2}\n\n        The t test is based on an assumption that the data come from a Normal\n        distribution. The t test provides a way to test whether the sample mean\n        (that is the mean calculated from the data) is a good estimate of the true\n        mean.\n\n        The derivation of the t-distribution was forst published in 1908 by William\n        Gisset while working for the Guinness Brewery in Dublin. Due to proprietary\n        issues, he had to publish under a pseudonym, and so he used the name\n        Student.\n\n        References\n        ----------\n        .. [1] Dalgaard, Peter, \"Introductory Statistics With R\",\n               Springer, 2002.\n        .. [2] Wikipedia, \"Student's t-distribution\"\n               http://en.wikipedia.org/wiki/Student's_t-distribution\n\n        Examples\n        --------\n        From Dalgaard page 83 [1]_, suppose the daily energy intake for 11\n        women in Kj is:\n\n        >>> intake = np.array([5260., 5470, 5640, 6180, 6390, 6515, 6805, 7515, \\\n        ...                    7515, 8230, 8770])\n\n        Doe""s their energy intake deviate systematically from the recommended\n        value of 7725 kJ?\n\n        We have 10 degrees of freedom, so is the sample mean within 95% of the\n        recommended value?\n\n        >>> s = np.random.standard_t(10, size=100000)\n        >>> np.mean(intake)\n        6753.636363636364\n        >>> intake.std(ddof=1)\n        1142.1232221373727\n\n        Calculate the t statistic, setting the ddof parameter to the unbiased\n        value so the divisor in the standard deviation will be degrees of\n        freedom, N-1.\n\n        >>> t = (np.mean(intake)-7725)/(intake.std(ddof=1)/np.sqrt(len(intake)))\n        >>> import matplotlib.pyplot as plt\n        >>> h = plt.hist(s, bins=100, normed=True)\n\n        For a one-sided t-test, how far out in the distribution does the t\n        statistic appear?\n\n        >>> >>> np.sum(s<t) / float(len(s))\n        0.0090699999999999999  #random\n\n        So the p-value is about 0.009, which says the null hypothesis has a\n        probability of about 99% of being true.\n\n        ";
 static char __pyx_k_233[] = "RandomState.vonmises (line 2303)";
@@ -797,7 +925,7 @@ static char __pyx_k_234[] = "\n        vonmises(mu, kappa, size=None)\n\n
 static char __pyx_k_235[] = "RandomState.pareto (line 2397)";
 static char __pyx_k_236[] = "\n        pareto(a, size=None)\n\n        Draw samples from a Pareto II or Lomax distribution with specified shape.\n\n        The Lomax or Pareto II distribution is a shifted Pareto distribution. The\n        classical Pareto distribution can be obtained from the Lomax distribution\n        by adding the location parameter m, see below. The smallest value of the\n        Lomax distribution is zero while for the classical Pareto distribution it\n        is m, where the standard Pareto distribution has location m=1.\n        Lomax can also be considered as a simplified version of the Generalized\n        Pareto distribution (available in SciPy), with the scale set to one and\n        the location set to zero.\n\n        The Pareto distribution must be greater than zero, and is unbounded above.\n        It is also known as the \"80-20 rule\".  In this distribution, 80 percent of\n        the weights are in the lowest 20 percent of the range, while the other 20\n        percent fill the remaining 80 percent of the range.\n\n        Parameters\n        ----------\n        shape : float, > 0.\n            Shape of the distribution.\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        See Also\n        --------\n        scipy.stats.distributions.lomax.pdf : probability density function,\n            distribution or cumulative density function, etc.\n        scipy.stats.distributions.genpareto.pdf : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Pareto distribution is\n\n        .. math:: p(x) = \\frac{am^a}{x^{a+1}}\n\n        where :math:`a` is the shape and :math:`m` the location\n\n        The Pareto distribution, named after the Italian economist Vilfredo Pareto,\n        is a power law probability distribution useful in many real world probl""ems.\n        Outside the field of economics it is generally referred to as the Bradford\n        distribution. Pareto developed the distribution to describe the\n        distribution of wealth in an economy.  It has also found use in insurance,\n        web page access statistics, oil field sizes, and many other problems,\n        including the download frequency for projects in Sourceforge [1].  It is\n        one of the so-called \"fat-tailed\" distributions.\n\n\n        References\n        ----------\n        .. [1] Francis Hunt and Paul Johnson, On the Pareto Distribution of\n               Sourceforge projects.\n        .. [2] Pareto, V. (1896). Course of Political Economy. Lausanne.\n        .. [3] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme\n               Values, Birkhauser Verlag, Basel, pp 23-30.\n        .. [4] Wikipedia, \"Pareto distribution\",\n               http://en.wikipedia.org/wiki/Pareto_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a, m = 3., 1. # shape and mode\n        >>> s = np.random.pareto(a, 1000) + m\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='center')\n        >>> fit = a*m**a/bins**(a+1)\n        >>> plt.plot(bins, max(count)*fit/max(fit),linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
 static char __pyx_k_237[] = "RandomState.weibull (line 2493)";
-static char __pyx_k_238[] = "\n        weibull(a, size=None)\n\n        Weibull distribution.\n\n        Draw samples from a 1-parameter Weibull distribution with the given\n        shape parameter `a`.\n\n        .. math:: X = (-ln(U))^{1/a}\n\n        Here, U is drawn from the uniform distribution over (0,1].\n\n        The more common 2-parameter Weibull, including a scale parameter\n        :math:`\\lambda` is just :math:`X = \\lambda(-ln(U))^{1/a}`.\n\n        Parameters\n        ----------\n        a : float\n            Shape of the distribution.\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        See Also\n        --------\n        scipy.stats.distributions.weibull : probability density function,\n            distribution or cumulative density function, etc.\n\n        gumbel, scipy.stats.distributions.genextreme\n\n        Notes\n        -----\n        The Weibull (or Type III asymptotic extreme value distribution for smallest\n        values, SEV Type III, or Rosin-Rammler distribution) is one of a class of\n        Generalized Extreme Value (GEV) distributions used in modeling extreme\n        value problems.  This class includes the Gumbel and Frechet distributions.\n\n        The probability density for the Weibull distribution is\n\n        .. math:: p(x) = \\frac{a}\n                         {\\lambda}(\\frac{x}{\\lambda})^{a-1}e^{-(x/\\lambda)^a},\n\n        where :math:`a` is the shape and :math:`\\lambda` the scale.\n\n        The function has its peak (the mode) at\n        :math:`\\lambda(\\frac{a-1}{a})^{1/a}`.\n\n        When ``a = 1``, the Weibull distribution reduces to the exponential\n        distribution.\n\n        References\n        ----------\n        .. [1] Waloddi Weibull, Professor, Royal Technical University, Stockholm,\n               1939 \"A Statistical Theory Of The Strength Of Materials\",\n               Ingeniorsvetenskapsakademiens Handlingar"" Nr 151, 1939,\n               Generalstabens Litografiska Anstalts Forlag, Stockholm.\n        .. [2] Waloddi Weibull, 1951 \"A Statistical Distribution Function of Wide\n               Applicability\",  Journal Of Applied Mechanics ASME Paper.\n        .. [3] Wikipedia, \"Weibull distribution\",\n               http://en.wikipedia.org/wiki/Weibull_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a = 5. # shape\n        >>> s = np.random.weibull(a, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> x = np.arange(1,100.)/50.\n        >>> def weib(x,n,a):\n        ...     return (a / n) * (x / n)**(a - 1) * np.exp(-(x / n)**a)\n\n        >>> count, bins, ignored = plt.hist(np.random.weibull(5.,1000))\n        >>> x = np.arange(1,100.)/50.\n        >>> scale = count.max()/weib(x, 1., 5.).max()\n        >>> plt.plot(x, weib(x, 1., 5.)*scale)\n        >>> plt.show()\n\n        ";
+static char __pyx_k_238[] = "\n        weibull(a, size=None)\n\n        Weibull distribution.\n\n        Draw samples from a 1-parameter Weibull distribution with the given\n        shape parameter `a`.\n\n        .. math:: X = (-ln(U))^{1/a}\n\n        Here, U is drawn from the uniform distribution over (0,1].\n\n        The more common 2-parameter Weibull, including a scale parameter\n        :math:`\\lambda` is just :math:`X = \\lambda(-ln(U))^{1/a}`.\n\n        Parameters\n        ----------\n        a : float\n            Shape of the distribution.\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        See Also\n        --------\n        scipy.stats.distributions.weibull_max\n        scipy.stats.distributions.weibull_min\n        scipy.stats.distributions.genextreme\n        gumbel\n\n        Notes\n        -----\n        The Weibull (or Type III asymptotic extreme value distribution for smallest\n        values, SEV Type III, or Rosin-Rammler distribution) is one of a class of\n        Generalized Extreme Value (GEV) distributions used in modeling extreme\n        value problems.  This class includes the Gumbel and Frechet distributions.\n\n        The probability density for the Weibull distribution is\n\n        .. math:: p(x) = \\frac{a}\n                         {\\lambda}(\\frac{x}{\\lambda})^{a-1}e^{-(x/\\lambda)^a},\n\n        where :math:`a` is the shape and :math:`\\lambda` the scale.\n\n        The function has its peak (the mode) at\n        :math:`\\lambda(\\frac{a-1}{a})^{1/a}`.\n\n        When ``a = 1``, the Weibull distribution reduces to the exponential\n        distribution.\n\n        References\n        ----------\n        .. [1] Waloddi Weibull, Professor, Royal Technical University, Stockholm,\n               1939 \"A Statistical Theory Of The Strength Of Materials\",\n               Ingeniorsvetenskapsakademiens Handlingar Nr 151, 1939,\n               General""stabens Litografiska Anstalts Forlag, Stockholm.\n        .. [2] Waloddi Weibull, 1951 \"A Statistical Distribution Function of Wide\n               Applicability\",  Journal Of Applied Mechanics ASME Paper.\n        .. [3] Wikipedia, \"Weibull distribution\",\n               http://en.wikipedia.org/wiki/Weibull_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a = 5. # shape\n        >>> s = np.random.weibull(a, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> x = np.arange(1,100.)/50.\n        >>> def weib(x,n,a):\n        ...     return (a / n) * (x / n)**(a - 1) * np.exp(-(x / n)**a)\n\n        >>> count, bins, ignored = plt.hist(np.random.weibull(5.,1000))\n        >>> x = np.arange(1,100.)/50.\n        >>> scale = count.max()/weib(x, 1., 5.).max()\n        >>> plt.plot(x, weib(x, 1., 5.)*scale)\n        >>> plt.show()\n\n        ";
 static char __pyx_k_239[] = "RandomState.power (line 2593)";
 static char __pyx_k_240[] = "\n        power(a, size=None)\n\n        Draws samples in [0, 1] from a power distribution with positive\n        exponent a - 1.\n\n        Also known as the power function distribution.\n\n        Parameters\n        ----------\n        a : float\n            parameter, > 0\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n                    ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n            The returned samples lie in [0, 1].\n\n        Raises\n        ------\n        ValueError\n            If a<1.\n\n        Notes\n        -----\n        The probability density function is\n\n        .. math:: P(x; a) = ax^{a-1}, 0 \\le x \\le 1, a>0.\n\n        The power function distribution is just the inverse of the Pareto\n        distribution. It may also be seen as a special case of the Beta\n        distribution.\n\n        It is used, for example, in modeling the over-reporting of insurance\n        claims.\n\n        References\n        ----------\n        .. [1] Christian Kleiber, Samuel Kotz, \"Statistical size distributions\n               in economics and actuarial sciences\", Wiley, 2003.\n        .. [2] Heckert, N. A. and Filliben, James J. (2003). NIST Handbook 148:\n               Dataplot Reference Manual, Volume 2: Let Subcommands and Library\n               Functions\", National Institute of Standards and Technology Handbook\n               Series, June 2003.\n               http://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/powpdf.pdf\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a = 5. # shape\n        >>> samples = 1000\n        >>> s = np.random.power(a, samples)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, bins=""30)\n        >>> x = np.linspace(0, 1, 100)\n        >>> y = a*x**(a-1.)\n        >>> normed_y = samples*np.diff(bins)[0]*y\n        >>> plt.plot(x, normed_y)\n        >>> plt.show()\n\n        Compare the power function distribution to the inverse of the Pareto.\n\n        >>> from scipy import stats\n        >>> rvs = np.random.power(5, 1000000)\n        >>> rvsp = np.random.pareto(5, 1000000)\n        >>> xx = np.linspace(0,1,100)\n        >>> powpdf = stats.powerlaw.pdf(xx,5)\n\n        >>> plt.figure()\n        >>> plt.hist(rvs, bins=50, normed=True)\n        >>> plt.plot(xx,powpdf,'r-')\n        >>> plt.title('np.random.power(5)')\n\n        >>> plt.figure()\n        >>> plt.hist(1./(1.+rvsp), bins=50, normed=True)\n        >>> plt.plot(xx,powpdf,'r-')\n        >>> plt.title('inverse of 1 + np.random.pareto(5)')\n\n        >>> plt.figure()\n        >>> plt.hist(1./(1.+rvsp), bins=50, normed=True)\n        >>> plt.plot(xx,powpdf,'r-')\n        >>> plt.title('inverse of stats.pareto(5)')\n\n        ";
 static char __pyx_k_241[] = "RandomState.laplace (line 2702)";
@@ -809,11 +937,11 @@ static char __pyx_k_246[] = "\n        logistic(loc=0.0, scale=1.0, size=None)\n
 static char __pyx_k_247[] = "RandomState.lognormal (line 3011)";
 static char __pyx_k_248[] = "\n        lognormal(mean=0.0, sigma=1.0, size=None)\n\n        Return samples drawn from a log-normal distribution.\n\n        Draw samples from a log-normal distribution with specified mean,\n        standard deviation, and array shape.  Note that the mean and standard\n        deviation are not the values for the distribution itself, but of the\n        underlying normal distribution it is derived from.\n\n        Parameters\n        ----------\n        mean : float\n            Mean value of the underlying normal distribution\n        sigma : float, > 0.\n            Standard deviation of the underlying normal distribution\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : ndarray or float\n            The desired samples. An array of the same shape as `size` if given,\n            if `size` is None a float is returned.\n\n        See Also\n        --------\n        scipy.stats.lognorm : probability density function, distribution,\n            cumulative density function, etc.\n\n        Notes\n        -----\n        A variable `x` has a log-normal distribution if `log(x)` is normally\n        distributed.  The probability density function for the log-normal\n        distribution is:\n\n        .. math:: p(x) = \\frac{1}{\\sigma x \\sqrt{2\\pi}}\n                         e^{(-\\frac{(ln(x)-\\mu)^2}{2\\sigma^2})}\n\n        where :math:`\\mu` is the mean and :math:`\\sigma` is the standard\n        deviation of the normally distributed logarithm of the variable.\n        A log-normal distribution results if a random variable is the *product*\n        of a large number of independent, identically-distributed variables in\n        the same way that a normal distribution results if the variable is the\n        *sum* of a large number of independent, identically-distributed\n        variables.\n\n        Reference""s\n        ----------\n        Limpert, E., Stahel, W. A., and Abbt, M., \"Log-normal Distributions\n        across the Sciences: Keys and Clues,\" *BioScience*, Vol. 51, No. 5,\n        May, 2001.  http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf\n\n        Reiss, R.D. and Thomas, M., *Statistical Analysis of Extreme Values*,\n        Basel: Birkhauser Verlag, 2001, pp. 31-32.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, sigma = 3., 1. # mean and standard deviation\n        >>> s = np.random.lognormal(mu, sigma, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='mid')\n\n        >>> x = np.linspace(min(bins), max(bins), 10000)\n        >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n        ...        / (x * sigma * np.sqrt(2 * np.pi)))\n\n        >>> plt.plot(x, pdf, linewidth=2, color='r')\n        >>> plt.axis('tight')\n        >>> plt.show()\n\n        Demonstrate that taking the products of random samples from a uniform\n        distribution can be fit well by a log-normal probability density function.\n\n        >>> # Generate a thousand samples: each is the product of 100 random\n        >>> # values, drawn from a normal distribution.\n        >>> b = []\n        >>> for i in range(1000):\n        ...    a = 10. + np.random.random(100)\n        ...    b.append(np.product(a))\n\n        >>> b = np.array(b) / np.min(b) # scale values to be positive\n        >>> count, bins, ignored = plt.hist(b, 100, normed=True, align='center')\n        >>> sigma = np.std(np.log(b))\n        >>> mu = np.mean(np.log(b))\n\n        >>> x = np.linspace(min(bins), max(bins), 10000)\n        >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n        ...        / (x * sigma * np.sqrt(2 * np.pi)))\n\n        >>> plt.plot(x, pdf, co""lor='r', linewidth=2)\n        >>> plt.show()\n\n        ";
 static char __pyx_k_249[] = "RandomState.rayleigh (line 3132)";
-static char __pyx_k_250[] = "\n        rayleigh(scale=1.0, size=None)\n\n        Draw samples from a Rayleigh distribution.\n\n        The :math:`\\chi` and Weibull distributions are generalizations of the\n        Rayleigh.\n\n        Parameters\n        ----------\n        scale : scalar\n            Scale, also equals the mode. Should be >= 0.\n        size : int or tuple of ints, optional\n            Shape of the output. Default is None, in which case a single\n            value is returned.\n\n        Notes\n        -----\n        The probability density function for the Rayleigh distribution is\n\n        .. math:: P(x;scale) = \\frac{x}{scale^2}e^{\\frac{-x^2}{2 \\cdotp scale^2}}\n\n        The Rayleigh distribution arises if the wind speed and wind direction are\n        both gaussian variables, then the vector wind velocity forms a Rayleigh\n        distribution. The Rayleigh distribution is used to model the expected\n        output from wind turbines.\n\n        References\n        ----------\n        ..[1] Brighton Webs Ltd., Rayleigh Distribution,\n              http://www.brighton-webs.co.uk/distributions/rayleigh.asp\n        ..[2] Wikipedia, \"Rayleigh distribution\"\n              http://en.wikipedia.org/wiki/Rayleigh_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram\n\n        >>> values = hist(np.random.rayleigh(3, 100000), bins=200, normed=True)\n\n        Wave heights tend to follow a Rayleigh distribution. If the mean wave\n        height is 1 meter, what fraction of waves are likely to be larger than 3\n        meters?\n\n        >>> meanvalue = 1\n        >>> modevalue = np.sqrt(2 / np.pi) * meanvalue\n        >>> s = np.random.rayleigh(modevalue, 1000000)\n\n        The percentage of waves larger than 3 meters is:\n\n        >>> 100.*sum(s>3)/1000000.\n        0.087300000000000003\n\n        ";
+static char __pyx_k_250[] = "\n        rayleigh(scale=1.0, size=None)\n\n        Draw samples from a Rayleigh distribution.\n\n        The :math:`\\chi` and Weibull distributions are generalizations of the\n        Rayleigh.\n\n        Parameters\n        ----------\n        scale : scalar\n            Scale, also equals the mode. Should be >= 0.\n        size : int or tuple of ints, optional\n            Shape of the output. Default is None, in which case a single\n            value is returned.\n\n        Notes\n        -----\n        The probability density function for the Rayleigh distribution is\n\n        .. math:: P(x;scale) = \\frac{x}{scale^2}e^{\\frac{-x^2}{2 \\cdotp scale^2}}\n\n        The Rayleigh distribution arises if the wind speed and wind direction are\n        both gaussian variables, then the vector wind velocity forms a Rayleigh\n        distribution. The Rayleigh distribution is used to model the expected\n        output from wind turbines.\n\n        References\n        ----------\n        .. [1] Brighton Webs Ltd., Rayleigh Distribution,\n              http://www.brighton-webs.co.uk/distributions/rayleigh.asp\n        .. [2] Wikipedia, \"Rayleigh distribution\"\n              http://en.wikipedia.org/wiki/Rayleigh_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram\n\n        >>> values = hist(np.random.rayleigh(3, 100000), bins=200, normed=True)\n\n        Wave heights tend to follow a Rayleigh distribution. If the mean wave\n        height is 1 meter, what fraction of waves are likely to be larger than 3\n        meters?\n\n        >>> meanvalue = 1\n        >>> modevalue = np.sqrt(2 / np.pi) * meanvalue\n        >>> s = np.random.rayleigh(modevalue, 1000000)\n\n        The percentage of waves larger than 3 meters is:\n\n        >>> 100.*sum(s>3)/1000000.\n        0.087300000000000003\n\n        ";
 static char __pyx_k_251[] = "RandomState.wald (line 3204)";
-static char __pyx_k_252[] = "\n        wald(mean, scale, size=None)\n\n        Draw samples from a Wald, or Inverse Gaussian, distribution.\n\n        As the scale approaches infinity, the distribution becomes more like a\n        Gaussian.\n\n        Some references claim that the Wald is an Inverse Gaussian with mean=1, but\n        this is by no means universal.\n\n        The Inverse Gaussian distribution was first studied in relationship to\n        Brownian motion. In 1956 M.C.K. Tweedie used the name Inverse Gaussian\n        because there is an inverse relationship between the time to cover a unit\n        distance and distance covered in unit time.\n\n        Parameters\n        ----------\n        mean : scalar\n            Distribution mean, should be > 0.\n        scale : scalar\n            Scale parameter, should be >= 0.\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single value is\n            returned.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            Drawn sample, all greater than zero.\n\n        Notes\n        -----\n        The probability density function for the Wald distribution is\n\n        .. math:: P(x;mean,scale) = \\sqrt{\\frac{scale}{2\\pi x^3}}e^\n                                    \\frac{-scale(x-mean)^2}{2\\cdotp mean^2x}\n\n        As noted above the Inverse Gaussian distribution first arise from attempts\n        to model Brownian Motion. It is also a competitor to the Weibull for use in\n        reliability modeling and modeling stock returns and interest rate\n        processes.\n\n        References\n        ----------\n        ..[1] Brighton Webs Ltd., Wald Distribution,\n              http://www.brighton-webs.co.uk/distributions/wald.asp\n        ..[2] Chhikara, Raj S., and Folks, J. Leroy, \"The Inverse Gaussian\n              Distribution: Theory : Methodology, and Applications\", CRC Press,\n              1988.\n        ..[3] Wikipedia, \"Wald distributio""n\"\n              http://en.wikipedia.org/wiki/Wald_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram:\n\n        >>> import matplotlib.pyplot as plt\n        >>> h = plt.hist(np.random.wald(3, 2, 100000), bins=200, normed=True)\n        >>> plt.show()\n\n        ";
+static char __pyx_k_252[] = "\n        wald(mean, scale, size=None)\n\n        Draw samples from a Wald, or Inverse Gaussian, distribution.\n\n        As the scale approaches infinity, the distribution becomes more like a\n        Gaussian.\n\n        Some references claim that the Wald is an Inverse Gaussian with mean=1, but\n        this is by no means universal.\n\n        The Inverse Gaussian distribution was first studied in relationship to\n        Brownian motion. In 1956 M.C.K. Tweedie used the name Inverse Gaussian\n        because there is an inverse relationship between the time to cover a unit\n        distance and distance covered in unit time.\n\n        Parameters\n        ----------\n        mean : scalar\n            Distribution mean, should be > 0.\n        scale : scalar\n            Scale parameter, should be >= 0.\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single value is\n            returned.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            Drawn sample, all greater than zero.\n\n        Notes\n        -----\n        The probability density function for the Wald distribution is\n\n        .. math:: P(x;mean,scale) = \\sqrt{\\frac{scale}{2\\pi x^3}}e^\n                                    \\frac{-scale(x-mean)^2}{2\\cdotp mean^2x}\n\n        As noted above the Inverse Gaussian distribution first arise from attempts\n        to model Brownian Motion. It is also a competitor to the Weibull for use in\n        reliability modeling and modeling stock returns and interest rate\n        processes.\n\n        References\n        ----------\n        .. [1] Brighton Webs Ltd., Wald Distribution,\n              http://www.brighton-webs.co.uk/distributions/wald.asp\n        .. [2] Chhikara, Raj S., and Folks, J. Leroy, \"The Inverse Gaussian\n              Distribution: Theory : Methodology, and Applications\", CRC Press,\n              1988.\n        .. [3] Wikipedia, \"Wald distribu""tion\"\n              http://en.wikipedia.org/wiki/Wald_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram:\n\n        >>> import matplotlib.pyplot as plt\n        >>> h = plt.hist(np.random.wald(3, 2, 100000), bins=200, normed=True)\n        >>> plt.show()\n\n        ";
 static char __pyx_k_253[] = "RandomState.triangular (line 3290)";
-static char __pyx_k_254[] = "\n        triangular(left, mode, right, size=None)\n\n        Draw samples from the triangular distribution.\n\n        The triangular distribution is a continuous probability distribution with\n        lower limit left, peak at mode, and upper limit right. Unlike the other\n        distributions, these parameters directly define the shape of the pdf.\n\n        Parameters\n        ----------\n        left : scalar\n            Lower limit.\n        mode : scalar\n            The value where the peak of the distribution occurs.\n            The value should fulfill the condition ``left <= mode <= right``.\n        right : scalar\n            Upper limit, should be larger than `left`.\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single value is\n            returned.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            The returned samples all lie in the interval [left, right].\n\n        Notes\n        -----\n        The probability density function for the Triangular distribution is\n\n        .. math:: P(x;l, m, r) = \\begin{cases}\n                  \\frac{2(x-l)}{(r-l)(m-l)}& \\text{for $l \\leq x \\leq m$},\\\\\n                  \\frac{2(m-x)}{(r-l)(r-m)}& \\text{for $m \\leq x \\leq r$},\\\\\n                  0& \\text{otherwise}.\n                  \\end{cases}\n\n        The triangular distribution is often used in ill-defined problems where the\n        underlying distribution is not known, but some knowledge of the limits and\n        mode exists. Often it is used in simulations.\n\n        References\n        ----------\n        ..[1] Wikipedia, \"Triangular distribution\"\n              http://en.wikipedia.org/wiki/Triangular_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram:\n\n        >>> import matplotlib.pyplot as plt\n        >>> h = plt.hist(np.random.triangular(-3, 0, 8, 100000), bins=2""00,\n        ...              normed=True)\n        >>> plt.show()\n\n        ";
+static char __pyx_k_254[] = "\n        triangular(left, mode, right, size=None)\n\n        Draw samples from the triangular distribution.\n\n        The triangular distribution is a continuous probability distribution with\n        lower limit left, peak at mode, and upper limit right. Unlike the other\n        distributions, these parameters directly define the shape of the pdf.\n\n        Parameters\n        ----------\n        left : scalar\n            Lower limit.\n        mode : scalar\n            The value where the peak of the distribution occurs.\n            The value should fulfill the condition ``left <= mode <= right``.\n        right : scalar\n            Upper limit, should be larger than `left`.\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single value is\n            returned.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            The returned samples all lie in the interval [left, right].\n\n        Notes\n        -----\n        The probability density function for the Triangular distribution is\n\n        .. math:: P(x;l, m, r) = \\begin{cases}\n                  \\frac{2(x-l)}{(r-l)(m-l)}& \\text{for $l \\leq x \\leq m$},\\\\\n                  \\frac{2(m-x)}{(r-l)(r-m)}& \\text{for $m \\leq x \\leq r$},\\\\\n                  0& \\text{otherwise}.\n                  \\end{cases}\n\n        The triangular distribution is often used in ill-defined problems where the\n        underlying distribution is not known, but some knowledge of the limits and\n        mode exists. Often it is used in simulations.\n\n        References\n        ----------\n        .. [1] Wikipedia, \"Triangular distribution\"\n              http://en.wikipedia.org/wiki/Triangular_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram:\n\n        >>> import matplotlib.pyplot as plt\n        >>> h = plt.hist(np.random.triangular(-3, 0, 8, 100000), bins=""200,\n        ...              normed=True)\n        >>> plt.show()\n\n        ";
 static char __pyx_k_255[] = "RandomState.binomial (line 3378)";
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 }
 
+/* Python wrapper */
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-static PyObject *__pyx_pf_6mtrand_11RandomState_3get_state(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_6get_state(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self) {
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   const char *__pyx_filename = NULL;
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  *         state = <ndarray>np.asarray(state, np.uint32)
  *         return ('MT19937', state, self.internal_state.pos,
  */
-  memcpy(((void *)PyArray_DATA(arrayObject_state)), ((void *)((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state->key), (624 * (sizeof(long))));
+  memcpy(((void *)PyArray_DATA(arrayObject_state)), ((void *)__pyx_v_self->internal_state->key), (624 * (sizeof(long))));
 
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  *         state = <ndarray>np.empty(624, np.uint)
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-  __Pyx_GOTREF(((PyObject *)__pyx_t_3));
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   PyTuple_SET_ITEM(__pyx_t_3, 0, ((PyObject *)arrayObject_state));
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  * 
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+  __pyx_t_2 = PyInt_FromLong(__pyx_v_self->internal_state->pos); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 637; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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@@ -5240,12 +5408,12 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_3get_state(PyObject *__pyx_v_sel
  * 
  *     def set_state(self, state):
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-  __pyx_t_3 = PyInt_FromLong(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state->has_gauss); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 638; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
+  __pyx_t_3 = PyInt_FromLong(__pyx_v_self->internal_state->has_gauss); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 638; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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+  __pyx_t_1 = PyFloat_FromDouble(__pyx_v_self->internal_state->gauss); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 638; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  __Pyx_GOTREF(((PyObject *)__pyx_t_4));
+  __Pyx_GOTREF(__pyx_t_4);
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   PyTuple_SET_ITEM(__pyx_t_4, 0, ((PyObject *)__pyx_n_s__MT19937));
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@@ -5281,6 +5449,18 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_3get_state(PyObject *__pyx_v_sel
   return __pyx_r;
 }
 
+/* Python wrapper */
+static PyObject *__pyx_pw_6mtrand_11RandomState_9set_state(PyObject *__pyx_v_self, PyObject *__pyx_v_state); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_8set_state[] = "\n        set_state(state)\n\n        Set the internal state of the generator from a tuple.\n\n        For use if one has reason to manually (re-)set the internal state of the\n        \"Mersenne Twister\"[1]_ pseudo-random number generating algorithm.\n\n        Parameters\n        ----------\n        state : tuple(str, ndarray of 624 uints, int, int, float)\n            The `state` tuple has the following items:\n\n            1. the string 'MT19937', specifying the Mersenne Twister algorithm.\n            2. a 1-D array of 624 unsigned integers ``keys``.\n            3. an integer ``pos``.\n            4. an integer ``has_gauss``.\n            5. a float ``cached_gaussian``.\n\n        Returns\n        -------\n        out : None\n            Returns 'None' on success.\n\n        See Also\n        --------\n        get_state\n\n        Notes\n        -----\n        `set_state` and `get_state` are not needed to work with any of the\n        random distributions in NumPy. If the internal state is manually altered,\n        the user should know exactly what he/she is doing.\n\n        For backwards compatibility, the form (str, array of 624 uints, int) is\n        also accepted although it is missing some information about the cached\n        Gaussian value: ``state = ('MT19937', keys, pos)``.\n\n        References\n        ----------\n        .. [1] M. Matsumoto and T. Nishimura, \"Mersenne Twister: A\n           623-dimensionally equidistributed uniform pseudorandom number\n           generator,\" *ACM Trans. on Modeling and Computer Simulation*,\n           Vol. 8, No. 1, pp. 3-30, Jan. 1998.\n\n        ";
+static PyObject *__pyx_pw_6mtrand_11RandomState_9set_state(PyObject *__pyx_v_self, PyObject *__pyx_v_state) {
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+  __Pyx_RefNannyDeclarations
+  __Pyx_RefNannySetupContext("set_state (wrapper)", 0);
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+  __Pyx_RefNannyFinishContext();
+  return __pyx_r;
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 /* "mtrand.pyx":640
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-static PyObject *__pyx_pf_6mtrand_11RandomState_4set_state(PyObject *__pyx_v_self, PyObject *__pyx_v_state); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_4set_state[] = "\n        set_state(state)\n\n        Set the internal state of the generator from a tuple.\n\n        For use if one has reason to manually (re-)set the internal state of the\n        \"Mersenne Twister\"[1]_ pseudo-random number generating algorithm.\n\n        Parameters\n        ----------\n        state : tuple(str, ndarray of 624 uints, int, int, float)\n            The `state` tuple has the following items:\n\n            1. the string 'MT19937', specifying the Mersenne Twister algorithm.\n            2. a 1-D array of 624 unsigned integers ``keys``.\n            3. an integer ``pos``.\n            4. an integer ``has_gauss``.\n            5. a float ``cached_gaussian``.\n\n        Returns\n        -------\n        out : None\n            Returns 'None' on success.\n\n        See Also\n        --------\n        get_state\n\n        Notes\n        -----\n        `set_state` and `get_state` are not needed to work with any of the\n        random distributions in NumPy. If the internal state is manually altered,\n        the user should know exactly what he/she is doing.\n\n        For backwards compatibility, the form (str, array of 624 uints, int) is\n        also accepted although it is missing some information about the cached\n        Gaussian value: ``state = ('MT19937', keys, pos)``.\n\n        References\n        ----------\n        .. [1] M. Matsumoto and T. Nishimura, \"Mersenne Twister: A\n           623-dimensionally equidistributed uniform pseudorandom number\n           generator,\" *ACM Trans. on Modeling and Computer Simulation*,\n           Vol. 8, No. 1, pp. 3-30, Jan. 1998.\n\n        ";
-static PyObject *__pyx_pf_6mtrand_11RandomState_4set_state(PyObject *__pyx_v_self, PyObject *__pyx_v_state) {
+static PyObject *__pyx_pf_6mtrand_11RandomState_8set_state(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_state) {
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   int __pyx_v_pos;
   PyObject *__pyx_v_algorithm_name = NULL;
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-    index = 1; __pyx_t_4 = __pyx_t_6(__pyx_t_5); if (unlikely(!__pyx_t_4)) goto __pyx_L6_unpacking_failed;
+    index = 1; __pyx_t_4 = __pyx_t_6(__pyx_t_5); if (unlikely(!__pyx_t_4)) goto __pyx_L4_unpacking_failed;
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-    goto __pyx_L7_unpacking_done;
-    __pyx_L6_unpacking_failed:;
+    goto __pyx_L5_unpacking_done;
+    __pyx_L4_unpacking_failed:;
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     if (PyErr_Occurred() && PyErr_ExceptionMatches(PyExc_StopIteration)) PyErr_Clear();
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-    __pyx_L7_unpacking_done:;
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@@ -5485,26 +5663,26 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_4set_state(PyObject *__pyx_v_sel
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+static PyObject *__pyx_pw_6mtrand_11RandomState_13__setstate__(PyObject *__pyx_v_self, PyObject *__pyx_v_state) {
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+/* Python wrapper */
+static PyObject *__pyx_pw_6mtrand_11RandomState_15__reduce__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/
+static PyObject *__pyx_pw_6mtrand_11RandomState_15__reduce__(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) {
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+static char __pyx_doc_6mtrand_11RandomState_18tomaxint[] = "\n        tomaxint(size=None)\n\n        Random integers between 0 and ``sys.maxint``, inclusive.\n\n        Return a sample of uniformly distributed random integers in the interval\n        [0, ``sys.maxint``].\n\n        Parameters\n        ----------\n        size : tuple of ints, int, optional\n            Shape of output.  If this is, for example, (m,n,k), m*n*k samples\n            are generated.  If no shape is specified, a single sample is\n            returned.\n\n        Returns\n        -------\n        out : ndarray\n            Drawn samples, with shape `size`.\n\n        See Also\n        --------\n        randint : Uniform sampling over a given half-open interval of integers.\n        random_integers : Uniform sampling over a given closed interval of\n            integers.\n\n        Examples\n        --------\n        >>> RS = np.random.mtrand.RandomState() # need a RandomState object\n        >>> RS.tomaxint((2,2,2))\n        array([[[1170048599, 1600360186],\n                [ 739731006, 1947757578]],\n               [[1871712945,  752307660],\n                [1601631370, 1479324245]]])\n        >>> import sys\n        >>> sys.maxint\n        2147483647\n        >>> RS.tomaxint((2,2,2)) < sys.maxint\n        array([[[ True,  True],\n                [ True,  True]],\n               [[ True,  True],\n                [ True,  True]]], dtype=bool)\n\n        ";
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 }
 
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-static PyObject *__pyx_pf_6mtrand_11RandomState_10randint(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+/* Python wrapper */
+static PyObject *__pyx_pw_6mtrand_11RandomState_21randint(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
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+static PyObject *__pyx_pw_6mtrand_11RandomState_21randint(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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-  long __pyx_v_hi;
-  long __pyx_v_rv;
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-  PyObject *__pyx_t_5 = NULL;
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+  __Pyx_RefNannyDeclarations
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+    /* "mtrand.pyx":811
+ *         return disc0_array(self.internal_state, rk_long, size)
+ * 
+ *     def randint(self, low, high=None, size=None):             # <<<<<<<<<<<<<<
+ *         """
+ *         randint(low, high=None, size=None)
+ */
     values[1] = ((PyObject *)Py_None);
     values[2] = ((PyObject *)Py_None);
     if (unlikely(__pyx_kwds)) {
       Py_ssize_t kw_args;
-      switch (PyTuple_GET_SIZE(__pyx_args)) {
+      const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args);
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         case  3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2);
         case  2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1);
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@@ -6066,7 +6281,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_10randint(PyObject *__pyx_v_self
         default: goto __pyx_L5_argtuple_error;
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-      switch (PyTuple_GET_SIZE(__pyx_args)) {
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         values[0] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__low);
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@@ -6083,7 +6298,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_10randint(PyObject *__pyx_v_self
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+  long __pyx_v_hi;
+  long __pyx_v_rv;
+  unsigned long __pyx_v_diff;
+  long *__pyx_v_array_data;
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+  __Pyx_RefNannyDeclarations
+  int __pyx_t_1;
+  long __pyx_t_2;
+  PyObject *__pyx_t_3 = NULL;
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+  PyObject *__pyx_t_5 = NULL;
+  npy_intp __pyx_t_6;
+  int __pyx_lineno = 0;
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   /* "mtrand.pyx":868
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     __pyx_v_hi = __pyx_t_2;
-    goto __pyx_L6;
+    goto __pyx_L3;
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@@ -6159,7 +6400,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_10randint(PyObject *__pyx_v_self
     __pyx_t_2 = __Pyx_PyInt_AsLong(__pyx_v_high); if (unlikely((__pyx_t_2 == (long)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 873; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
     __pyx_v_hi = __pyx_t_2;
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-  __pyx_L6:;
+  __pyx_L3:;
 
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@@ -6183,9 +6424,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_10randint(PyObject *__pyx_v_self
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     {__pyx_filename = __pyx_f[0]; __pyx_lineno = 876; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    goto __pyx_L7;
+    goto __pyx_L4;
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-  __pyx_L7:;
+  __pyx_L4:;
 
   /* "mtrand.pyx":878
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@@ -6213,7 +6454,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_10randint(PyObject *__pyx_v_self
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     __pyx_r = __pyx_t_3;
     __pyx_t_3 = 0;
     goto __pyx_L0;
-    goto __pyx_L8;
+    goto __pyx_L5;
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@@ -6245,7 +6486,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_10randint(PyObject *__pyx_v_self
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-    __Pyx_GOTREF(((PyObject *)__pyx_t_3));
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     __pyx_r = ((PyObject *)arrayObject);
     goto __pyx_L0;
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-  __pyx_L8:;
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@@ -6336,6 +6577,28 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_10randint(PyObject *__pyx_v_self
   return __pyx_r;
 }
 
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-      __Pyx_GOTREF(((PyObject *)__pyx_t_1));
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-static PyObject *__pyx_pf_6mtrand_11RandomState_13uniform(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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+static char __pyx_doc_6mtrand_11RandomState_28rand[] = "\n        rand(d0, d1, ..., dn)\n\n        Random values in a given shape.\n\n        Create an array of the given shape and propagate it with\n        random samples from a uniform distribution\n        over ``[0, 1)``.\n\n        Parameters\n        ----------\n        d0, d1, ..., dn : int, optional\n            The dimensions of the returned array, should all be positive.\n            If no argument is given a single Python float is returned.\n\n        Returns\n        -------\n        out : ndarray, shape ``(d0, d1, ..., dn)``\n            Random values.\n\n        See Also\n        --------\n        random\n\n        Notes\n        -----\n        This is a convenience function. If you want an interface that\n        takes a shape-tuple as the first argument, refer to\n        np.random.random_sample .\n\n        Examples\n        --------\n        >>> np.random.rand(3,2)\n        array([[ 0.14022471,  0.96360618],  #random\n               [ 0.37601032,  0.25528411],  #random\n               [ 0.49313049,  0.94909878]]) #random\n\n        ";
+static PyObject *__pyx_pw_6mtrand_11RandomState_29rand(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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+static char __pyx_doc_6mtrand_11RandomState_36normal[] = "\n        normal(loc=0.0, scale=1.0, size=None)\n\n        Draw random samples from a normal (Gaussian) distribution.\n\n        The probability density function of the normal distribution, first\n        derived by De Moivre and 200 years later by both Gauss and Laplace\n        independently [2]_, is often called the bell curve because of\n        its characteristic shape (see the example below).\n\n        The normal distributions occurs often in nature.  For example, it\n        describes the commonly occurring distribution of samples influenced\n        by a large number of tiny, random disturbances, each with its own\n        unique distribution [2]_.\n\n        Parameters\n        ----------\n        loc : float\n            Mean (\"centre\") of the distribution.\n        scale : float\n            Standard deviation (spread or \"width\") of the distribution.\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        See Also\n        --------\n        scipy.stats.distributions.norm : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Gaussian distribution is\n\n        .. math:: p(x) = \\frac{1}{\\sqrt{ 2 \\pi \\sigma^2 }}\n                         e^{ - \\frac{ (x - \\mu)^2 } {2 \\sigma^2} },\n\n        where :math:`\\mu` is the mean and :math:`\\sigma` the standard deviation.\n        The square of the standard deviation, :math:`\\sigma^2`, is called the\n        variance.\n\n        The function has its peak at the mean, and its \"spread\" increases with\n        the standard deviation (the function reaches 0.607 times its maximum at\n        :math:`x + \\sigma` and :math:`x - \\sigma` [2]_).  This implies that\n        `numpy.random.normal` is more likely to return samples lying close to the\n        mean, rather than those far away.\n""\n        References\n        ----------\n        .. [1] Wikipedia, \"Normal distribution\",\n               http://en.wikipedia.org/wiki/Normal_distribution\n        .. [2] P. R. Peebles Jr., \"Central Limit Theorem\" in \"Probability, Random\n               Variables and Random Signal Principles\", 4th ed., 2001,\n               pp. 51, 51, 125.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, sigma = 0, 0.1 # mean and standard deviation\n        >>> s = np.random.normal(mu, sigma, 1000)\n\n        Verify the mean and the variance:\n\n        >>> abs(mu - np.mean(s)) < 0.01\n        True\n\n        >>> abs(sigma - np.std(s, ddof=1)) < 0.01\n        True\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n        >>> plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *\n        ...                np.exp( - (bins - mu)**2 / (2 * sigma**2) ),\n        ...          linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
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 }
 
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+/* Python wrapper */
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+static char __pyx_doc_6mtrand_11RandomState_44standard_gamma[] = "\n        standard_gamma(shape, size=None)\n\n        Draw samples from a Standard Gamma distribution.\n\n        Samples are drawn from a Gamma distribution with specified parameters,\n        shape (sometimes designated \"k\") and scale=1.\n\n        Parameters\n        ----------\n        shape : float\n            Parameter, should be > 0.\n        size : int or tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            The drawn samples.\n\n        See Also\n        --------\n        scipy.stats.distributions.gamma : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Gamma distribution is\n\n        .. math:: p(x) = x^{k-1}\\frac{e^{-x/\\theta}}{\\theta^k\\Gamma(k)},\n\n        where :math:`k` is the shape and :math:`\\theta` the scale,\n        and :math:`\\Gamma` is the Gamma function.\n\n        The Gamma distribution is often used to model the times to failure of\n        electronic components, and arises naturally in processes for which the\n        waiting times between Poisson distributed events are relevant.\n\n        References\n        ----------\n        .. [1] Weisstein, Eric W. \"Gamma Distribution.\" From MathWorld--A\n               Wolfram Web Resource.\n               http://mathworld.wolfram.com/GammaDistribution.html\n        .. [2] Wikipedia, \"Gamma-distribution\",\n               http://en.wikipedia.org/wiki/Gamma-distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> shape, scale = 2., 1. # mean and width\n        >>> s = np.random.standard_gamma(shape, 1000000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt""\n        >>> import scipy.special as sps\n        >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n        >>> y = bins**(shape-1) * ((np.exp(-bins/scale))/ \\\n        ...                       (sps.gamma(shape) * scale**shape))\n        >>> plt.plot(bins, y, linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
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-static PyObject *__pyx_pf_6mtrand_11RandomState_25noncentral_f(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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 }
 
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-static char __pyx_doc_6mtrand_11RandomState_27noncentral_chisquare[] = "\n        noncentral_chisquare(df, nonc, size=None)\n\n        Draw samples from a noncentral chi-square distribution.\n\n        The noncentral :math:`\\chi^2` distribution is a generalisation of\n        the :math:`\\chi^2` distribution.\n\n        Parameters\n        ----------\n        df : int\n            Degrees of freedom, should be >= 1.\n        nonc : float\n            Non-centrality, should be > 0.\n        size : int or tuple of ints\n            Shape of the output.\n\n        Notes\n        -----\n        The probability density function for the noncentral Chi-square distribution\n        is\n\n        .. math:: P(x;df,nonc) = \\sum^{\\infty}_{i=0}\n                               \\frac{e^{-nonc/2}(nonc/2)^{i}}{i!}P_{Y_{df+2i}}(x),\n\n        where :math:`Y_{q}` is the Chi-square with q degrees of freedom.\n\n        In Delhi (2007), it is noted that the noncentral chi-square is useful in\n        bombing and coverage problems, the probability of killing the point target\n        given by the noncentral chi-squared distribution.\n\n        References\n        ----------\n        .. [1] Delhi, M.S. Holla, \"On a noncentral chi-square distribution in the\n               analysis of weapon systems effectiveness\", Metrika, Volume 15,\n               Number 1 / December, 1970.\n        .. [2] Wikipedia, \"Noncentral chi-square distribution\"\n               http://en.wikipedia.org/wiki/Noncentral_chi-square_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram\n\n        >>> import matplotlib.pyplot as plt\n        >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),\n        ...                   bins=200, normed=True)\n        >>> plt.show()\n\n        Draw values from a noncentral chisquare with very small noncentrality,\n        and compare to a chisquare.\n\n        >>> plt.figure()\n        >>> values = plt.hist(np.random.noncentral_chisquare(3, .0000001, 100000),\n     ""   ...                   bins=np.arange(0., 25, .1), normed=True)\n        >>> values2 = plt.hist(np.random.chisquare(3, 100000),\n        ...                    bins=np.arange(0., 25, .1), normed=True)\n        >>> plt.plot(values[1][0:-1], values[0]-values2[0], 'ob')\n        >>> plt.show()\n\n        Demonstrate how large values of non-centrality lead to a more symmetric\n        distribution.\n\n        >>> plt.figure()\n        >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),\n        ...                   bins=200, normed=True)\n        >>> plt.show()\n\n        ";
-static PyObject *__pyx_pf_6mtrand_11RandomState_27noncentral_chisquare(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+/* Python wrapper */
+static PyObject *__pyx_pw_6mtrand_11RandomState_55noncentral_chisquare(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_54noncentral_chisquare[] = "\n        noncentral_chisquare(df, nonc, size=None)\n\n        Draw samples from a noncentral chi-square distribution.\n\n        The noncentral :math:`\\chi^2` distribution is a generalisation of\n        the :math:`\\chi^2` distribution.\n\n        Parameters\n        ----------\n        df : int\n            Degrees of freedom, should be >= 1.\n        nonc : float\n            Non-centrality, should be > 0.\n        size : int or tuple of ints\n            Shape of the output.\n\n        Notes\n        -----\n        The probability density function for the noncentral Chi-square distribution\n        is\n\n        .. math:: P(x;df,nonc) = \\sum^{\\infty}_{i=0}\n                               \\frac{e^{-nonc/2}(nonc/2)^{i}}{i!}P_{Y_{df+2i}}(x),\n\n        where :math:`Y_{q}` is the Chi-square with q degrees of freedom.\n\n        In Delhi (2007), it is noted that the noncentral chi-square is useful in\n        bombing and coverage problems, the probability of killing the point target\n        given by the noncentral chi-squared distribution.\n\n        References\n        ----------\n        .. [1] Delhi, M.S. Holla, \"On a noncentral chi-square distribution in the\n               analysis of weapon systems effectiveness\", Metrika, Volume 15,\n               Number 1 / December, 1970.\n        .. [2] Wikipedia, \"Noncentral chi-square distribution\"\n               http://en.wikipedia.org/wiki/Noncentral_chi-square_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram\n\n        >>> import matplotlib.pyplot as plt\n        >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),\n        ...                   bins=200, normed=True)\n        >>> plt.show()\n\n        Draw values from a noncentral chisquare with very small noncentrality,\n        and compare to a chisquare.\n\n        >>> plt.figure()\n        >>> values = plt.hist(np.random.noncentral_chisquare(3, .0000001, 100000),\n     ""   ...                   bins=np.arange(0., 25, .1), normed=True)\n        >>> values2 = plt.hist(np.random.chisquare(3, 100000),\n        ...                    bins=np.arange(0., 25, .1), normed=True)\n        >>> plt.plot(values[1][0:-1], values[0]-values2[0], 'ob')\n        >>> plt.show()\n\n        Demonstrate how large values of non-centrality lead to a more symmetric\n        distribution.\n\n        >>> plt.figure()\n        >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),\n        ...                   bins=200, normed=True)\n        >>> plt.show()\n\n        ";
+static PyObject *__pyx_pw_6mtrand_11RandomState_55noncentral_chisquare(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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+ *         return cont1_array(self.internal_state, rk_chisquare, size, odf)
+ * 
+ *     def noncentral_chisquare(self, df, nonc, size=None):             # <<<<<<<<<<<<<<
+ *         """
+ *         noncentral_chisquare(df, nonc, size=None)
+ */
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-      switch (PyTuple_GET_SIZE(__pyx_args)) {
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-      goto __pyx_L7;
+      goto __pyx_L4;
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+    __pyx_L4:;
 
     /* "mtrand.pyx":2125
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     /* "mtrand.pyx":2127
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-    goto __pyx_L6;
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   /* "mtrand.pyx":2130
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@@ -11186,7 +11605,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_27noncentral_chisquare(PyObject
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+  __Pyx_GOTREF(__pyx_t_5);
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     {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2135; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    goto __pyx_L9;
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-  __pyx_L9:;
+  __pyx_L6:;
 
   /* "mtrand.pyx":2136
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@@ -11234,7 +11653,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_27noncentral_chisquare(PyObject
   __pyx_t_2 = PyFloat_FromDouble(0.0); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2136; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  __Pyx_GOTREF(((PyObject *)__pyx_t_4));
+  __Pyx_GOTREF(__pyx_t_4);
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@@ -11246,7 +11665,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_27noncentral_chisquare(PyObject
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-  __Pyx_GOTREF(((PyObject *)__pyx_t_4));
+  __Pyx_GOTREF(__pyx_t_4);
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-  __pyx_L10:;
+  __pyx_L7:;
 
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  * 
  *     def standard_cauchy(self, size=None):
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   return __pyx_r;
 }
 
-/* "mtrand.pyx":2141
+/* Python wrapper */
+static PyObject *__pyx_pw_6mtrand_11RandomState_57standard_cauchy(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_56standard_cauchy[] = "\n        standard_cauchy(size=None)\n\n        Standard Cauchy distribution with mode = 0.\n\n        Also known as the Lorentz distribution.\n\n        Parameters\n        ----------\n        size : int or tuple of ints\n            Shape of the output.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            The drawn samples.\n\n        Notes\n        -----\n        The probability density function for the full Cauchy distribution is\n\n        .. math:: P(x; x_0, \\gamma) = \\frac{1}{\\pi \\gamma \\bigl[ 1+\n                  (\\frac{x-x_0}{\\gamma})^2 \\bigr] }\n\n        and the Standard Cauchy distribution just sets :math:`x_0=0` and\n        :math:`\\gamma=1`\n\n        The Cauchy distribution arises in the solution to the driven harmonic\n        oscillator problem, and also describes spectral line broadening. It\n        also describes the distribution of values at which a line tilted at\n        a random angle will cut the x axis.\n\n        When studying hypothesis tests that assume normality, seeing how the\n        tests perform on data from a Cauchy distribution is a good indicator of\n        their sensitivity to a heavy-tailed distribution, since the Cauchy looks\n        very much like a Gaussian distribution, but with heavier tails.\n\n        References\n        ----------\n        .. [1] NIST/SEMATECH e-Handbook of Statistical Methods, \"Cauchy\n              Distribution\",\n              http://www.itl.nist.gov/div898/handbook/eda/section3/eda3663.htm\n        .. [2] Weisstein, Eric W. \"Cauchy Distribution.\" From MathWorld--A\n              Wolfram Web Resource.\n              http://mathworld.wolfram.com/CauchyDistribution.html\n        .. [3] Wikipedia, \"Cauchy distribution\"\n              http://en.wikipedia.org/wiki/Cauchy_distribution\n\n        Examples\n        --------\n        Draw samples and plot the distribution:\n\n        >>> s = np.random.standard_cauchy(1000000)\n        >>> s = s[(s>-25) & (s<""25)]  # truncate distribution so it plots well\n        >>> plt.hist(s, bins=100)\n        >>> plt.show()\n\n        ";
+static PyObject *__pyx_pw_6mtrand_11RandomState_57standard_cauchy(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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  * 
  *     def standard_cauchy(self, size=None):             # <<<<<<<<<<<<<<
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  *         standard_cauchy(size=None)
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-static char __pyx_doc_6mtrand_11RandomState_30vonmises[] = "\n        vonmises(mu, kappa, size=None)\n\n        Draw samples from a von Mises distribution.\n\n        Samples are drawn from a von Mises distribution with specified mode\n        (mu) and dispersion (kappa), on the interval [-pi, pi].\n\n        The von Mises distribution (also known as the circular normal\n        distribution) is a continuous probability distribution on the unit\n        circle.  It may be thought of as the circular analogue of the normal\n        distribution.\n\n        Parameters\n        ----------\n        mu : float\n            Mode (\"center\") of the distribution.\n        kappa : float\n            Dispersion of the distribution, has to be >=0.\n        size : int or tuple of int\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : scalar or ndarray\n            The returned samples, which are in the interval [-pi, pi].\n\n        See Also\n        --------\n        scipy.stats.distributions.vonmises : probability density function,\n            distribution, or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the von Mises distribution is\n\n        .. math:: p(x) = \\frac{e^{\\kappa cos(x-\\mu)}}{2\\pi I_0(\\kappa)},\n\n        where :math:`\\mu` is the mode and :math:`\\kappa` the dispersion,\n        and :math:`I_0(\\kappa)` is the modified Bessel function of order 0.\n\n        The von Mises is named for Richard Edler von Mises, who was born in\n        Austria-Hungary, in what is now the Ukraine.  He fled to the United\n        States in 1939 and became a professor at Harvard.  He worked in\n        probability theory, aerodynamics, fluid mechanics, and philosophy of\n        science.\n\n        References\n        ----------\n        Abramowitz, M. and Stegun, I. A. (ed.), *Handbook of Mathematical\n        Functions*, New York: Dover, 1965.\n\n      ""  von Mises, R., *Mathematical Theory of Probability and Statistics*,\n        New York: Academic Press, 1964.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, kappa = 0.0, 4.0 # mean and dispersion\n        >>> s = np.random.vonmises(mu, kappa, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> import scipy.special as sps\n        >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n        >>> x = np.arange(-np.pi, np.pi, 2*np.pi/50.)\n        >>> y = -np.exp(kappa*np.cos(x-mu))/(2*np.pi*sps.jn(0,kappa))\n        >>> plt.plot(x, y/max(y), linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
-static PyObject *__pyx_pf_6mtrand_11RandomState_30vonmises(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+/* Python wrapper */
+static PyObject *__pyx_pw_6mtrand_11RandomState_61vonmises(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_60vonmises[] = "\n        vonmises(mu, kappa, size=None)\n\n        Draw samples from a von Mises distribution.\n\n        Samples are drawn from a von Mises distribution with specified mode\n        (mu) and dispersion (kappa), on the interval [-pi, pi].\n\n        The von Mises distribution (also known as the circular normal\n        distribution) is a continuous probability distribution on the unit\n        circle.  It may be thought of as the circular analogue of the normal\n        distribution.\n\n        Parameters\n        ----------\n        mu : float\n            Mode (\"center\") of the distribution.\n        kappa : float\n            Dispersion of the distribution, has to be >=0.\n        size : int or tuple of int\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : scalar or ndarray\n            The returned samples, which are in the interval [-pi, pi].\n\n        See Also\n        --------\n        scipy.stats.distributions.vonmises : probability density function,\n            distribution, or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the von Mises distribution is\n\n        .. math:: p(x) = \\frac{e^{\\kappa cos(x-\\mu)}}{2\\pi I_0(\\kappa)},\n\n        where :math:`\\mu` is the mode and :math:`\\kappa` the dispersion,\n        and :math:`I_0(\\kappa)` is the modified Bessel function of order 0.\n\n        The von Mises is named for Richard Edler von Mises, who was born in\n        Austria-Hungary, in what is now the Ukraine.  He fled to the United\n        States in 1939 and became a professor at Harvard.  He worked in\n        probability theory, aerodynamics, fluid mechanics, and philosophy of\n        science.\n\n        References\n        ----------\n        Abramowitz, M. and Stegun, I. A. (ed.), *Handbook of Mathematical\n        Functions*, New York: Dover, 1965.\n\n      ""  von Mises, R., *Mathematical Theory of Probability and Statistics*,\n        New York: Academic Press, 1964.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, kappa = 0.0, 4.0 # mean and dispersion\n        >>> s = np.random.vonmises(mu, kappa, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> import scipy.special as sps\n        >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n        >>> x = np.arange(-np.pi, np.pi, 2*np.pi/50.)\n        >>> y = -np.exp(kappa*np.cos(x-mu))/(2*np.pi*sps.jn(0,kappa))\n        >>> plt.plot(x, y/max(y), linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
+static PyObject *__pyx_pw_6mtrand_11RandomState_61vonmises(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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+ * 
+ *     def vonmises(self, mu, kappa, size=None):             # <<<<<<<<<<<<<<
+ *         """
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-/* "mtrand.pyx":2397
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+static PyObject *__pyx_pw_6mtrand_11RandomState_63pareto(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_62pareto[] = "\n        pareto(a, size=None)\n\n        Draw samples from a Pareto II or Lomax distribution with specified shape.\n\n        The Lomax or Pareto II distribution is a shifted Pareto distribution. The\n        classical Pareto distribution can be obtained from the Lomax distribution\n        by adding the location parameter m, see below. The smallest value of the\n        Lomax distribution is zero while for the classical Pareto distribution it\n        is m, where the standard Pareto distribution has location m=1.\n        Lomax can also be considered as a simplified version of the Generalized\n        Pareto distribution (available in SciPy), with the scale set to one and\n        the location set to zero.\n\n        The Pareto distribution must be greater than zero, and is unbounded above.\n        It is also known as the \"80-20 rule\".  In this distribution, 80 percent of\n        the weights are in the lowest 20 percent of the range, while the other 20\n        percent fill the remaining 80 percent of the range.\n\n        Parameters\n        ----------\n        shape : float, > 0.\n            Shape of the distribution.\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        See Also\n        --------\n        scipy.stats.distributions.lomax.pdf : probability density function,\n            distribution or cumulative density function, etc.\n        scipy.stats.distributions.genpareto.pdf : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Pareto distribution is\n\n        .. math:: p(x) = \\frac{am^a}{x^{a+1}}\n\n        where :math:`a` is the shape and :math:`m` the location\n\n        The Pareto distribution, named after the Italian economist Vilfredo Pareto,\n        is a power law probability distribution useful in many real world probl""ems.\n        Outside the field of economics it is generally referred to as the Bradford\n        distribution. Pareto developed the distribution to describe the\n        distribution of wealth in an economy.  It has also found use in insurance,\n        web page access statistics, oil field sizes, and many other problems,\n        including the download frequency for projects in Sourceforge [1].  It is\n        one of the so-called \"fat-tailed\" distributions.\n\n\n        References\n        ----------\n        .. [1] Francis Hunt and Paul Johnson, On the Pareto Distribution of\n               Sourceforge projects.\n        .. [2] Pareto, V. (1896). Course of Political Economy. Lausanne.\n        .. [3] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme\n               Values, Birkhauser Verlag, Basel, pp 23-30.\n        .. [4] Wikipedia, \"Pareto distribution\",\n               http://en.wikipedia.org/wiki/Pareto_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a, m = 3., 1. # shape and mode\n        >>> s = np.random.pareto(a, 1000) + m\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='center')\n        >>> fit = a*m**a/bins**(a+1)\n        >>> plt.plot(bins, max(count)*fit/max(fit),linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
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-static char __pyx_doc_6mtrand_11RandomState_31pareto[] = "\n        pareto(a, size=None)\n\n        Draw samples from a Pareto II or Lomax distribution with specified shape.\n\n        The Lomax or Pareto II distribution is a shifted Pareto distribution. The\n        classical Pareto distribution can be obtained from the Lomax distribution\n        by adding the location parameter m, see below. The smallest value of the\n        Lomax distribution is zero while for the classical Pareto distribution it\n        is m, where the standard Pareto distribution has location m=1.\n        Lomax can also be considered as a simplified version of the Generalized\n        Pareto distribution (available in SciPy), with the scale set to one and\n        the location set to zero.\n\n        The Pareto distribution must be greater than zero, and is unbounded above.\n        It is also known as the \"80-20 rule\".  In this distribution, 80 percent of\n        the weights are in the lowest 20 percent of the range, while the other 20\n        percent fill the remaining 80 percent of the range.\n\n        Parameters\n        ----------\n        shape : float, > 0.\n            Shape of the distribution.\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        See Also\n        --------\n        scipy.stats.distributions.lomax.pdf : probability density function,\n            distribution or cumulative density function, etc.\n        scipy.stats.distributions.genpareto.pdf : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Pareto distribution is\n\n        .. math:: p(x) = \\frac{am^a}{x^{a+1}}\n\n        where :math:`a` is the shape and :math:`m` the location\n\n        The Pareto distribution, named after the Italian economist Vilfredo Pareto,\n        is a power law probability distribution useful in many real world probl""ems.\n        Outside the field of economics it is generally referred to as the Bradford\n        distribution. Pareto developed the distribution to describe the\n        distribution of wealth in an economy.  It has also found use in insurance,\n        web page access statistics, oil field sizes, and many other problems,\n        including the download frequency for projects in Sourceforge [1].  It is\n        one of the so-called \"fat-tailed\" distributions.\n\n\n        References\n        ----------\n        .. [1] Francis Hunt and Paul Johnson, On the Pareto Distribution of\n               Sourceforge projects.\n        .. [2] Pareto, V. (1896). Course of Political Economy. Lausanne.\n        .. [3] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme\n               Values, Birkhauser Verlag, Basel, pp 23-30.\n        .. [4] Wikipedia, \"Pareto distribution\",\n               http://en.wikipedia.org/wiki/Pareto_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a, m = 3., 1. # shape and mode\n        >>> s = np.random.pareto(a, 1000) + m\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='center')\n        >>> fit = a*m**a/bins**(a+1)\n        >>> plt.plot(bins, max(count)*fit/max(fit),linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
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 }
 
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+static PyObject *__pyx_pw_6mtrand_11RandomState_67power(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_66power[] = "\n        power(a, size=None)\n\n        Draws samples in [0, 1] from a power distribution with positive\n        exponent a - 1.\n\n        Also known as the power function distribution.\n\n        Parameters\n        ----------\n        a : float\n            parameter, > 0\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n                    ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n            The returned samples lie in [0, 1].\n\n        Raises\n        ------\n        ValueError\n            If a<1.\n\n        Notes\n        -----\n        The probability density function is\n\n        .. math:: P(x; a) = ax^{a-1}, 0 \\le x \\le 1, a>0.\n\n        The power function distribution is just the inverse of the Pareto\n        distribution. It may also be seen as a special case of the Beta\n        distribution.\n\n        It is used, for example, in modeling the over-reporting of insurance\n        claims.\n\n        References\n        ----------\n        .. [1] Christian Kleiber, Samuel Kotz, \"Statistical size distributions\n               in economics and actuarial sciences\", Wiley, 2003.\n        .. [2] Heckert, N. A. and Filliben, James J. (2003). NIST Handbook 148:\n               Dataplot Reference Manual, Volume 2: Let Subcommands and Library\n               Functions\", National Institute of Standards and Technology Handbook\n               Series, June 2003.\n               http://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/powpdf.pdf\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a = 5. # shape\n        >>> samples = 1000\n        >>> s = np.random.power(a, samples)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, bins=""30)\n        >>> x = np.linspace(0, 1, 100)\n        >>> y = a*x**(a-1.)\n        >>> normed_y = samples*np.diff(bins)[0]*y\n        >>> plt.plot(x, normed_y)\n        >>> plt.show()\n\n        Compare the power function distribution to the inverse of the Pareto.\n\n        >>> from scipy import stats\n        >>> rvs = np.random.power(5, 1000000)\n        >>> rvsp = np.random.pareto(5, 1000000)\n        >>> xx = np.linspace(0,1,100)\n        >>> powpdf = stats.powerlaw.pdf(xx,5)\n\n        >>> plt.figure()\n        >>> plt.hist(rvs, bins=50, normed=True)\n        >>> plt.plot(xx,powpdf,'r-')\n        >>> plt.title('np.random.power(5)')\n\n        >>> plt.figure()\n        >>> plt.hist(1./(1.+rvsp), bins=50, normed=True)\n        >>> plt.plot(xx,powpdf,'r-')\n        >>> plt.title('inverse of 1 + np.random.pareto(5)')\n\n        >>> plt.figure()\n        >>> plt.hist(1./(1.+rvsp), bins=50, normed=True)\n        >>> plt.plot(xx,powpdf,'r-')\n        >>> plt.title('inverse of stats.pareto(5)')\n\n        ";
+static PyObject *__pyx_pw_6mtrand_11RandomState_67power(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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-static char __pyx_doc_6mtrand_11RandomState_33power[] = "\n        power(a, size=None)\n\n        Draws samples in [0, 1] from a power distribution with positive\n        exponent a - 1.\n\n        Also known as the power function distribution.\n\n        Parameters\n        ----------\n        a : float\n            parameter, > 0\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n                    ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n            The returned samples lie in [0, 1].\n\n        Raises\n        ------\n        ValueError\n            If a<1.\n\n        Notes\n        -----\n        The probability density function is\n\n        .. math:: P(x; a) = ax^{a-1}, 0 \\le x \\le 1, a>0.\n\n        The power function distribution is just the inverse of the Pareto\n        distribution. It may also be seen as a special case of the Beta\n        distribution.\n\n        It is used, for example, in modeling the over-reporting of insurance\n        claims.\n\n        References\n        ----------\n        .. [1] Christian Kleiber, Samuel Kotz, \"Statistical size distributions\n               in economics and actuarial sciences\", Wiley, 2003.\n        .. [2] Heckert, N. A. and Filliben, James J. (2003). NIST Handbook 148:\n               Dataplot Reference Manual, Volume 2: Let Subcommands and Library\n               Functions\", National Institute of Standards and Technology Handbook\n               Series, June 2003.\n               http://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/powpdf.pdf\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a = 5. # shape\n        >>> samples = 1000\n        >>> s = np.random.power(a, samples)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, bins=""30)\n        >>> x = np.linspace(0, 1, 100)\n        >>> y = a*x**(a-1.)\n        >>> normed_y = samples*np.diff(bins)[0]*y\n        >>> plt.plot(x, normed_y)\n        >>> plt.show()\n\n        Compare the power function distribution to the inverse of the Pareto.\n\n        >>> from scipy import stats\n        >>> rvs = np.random.power(5, 1000000)\n        >>> rvsp = np.random.pareto(5, 1000000)\n        >>> xx = np.linspace(0,1,100)\n        >>> powpdf = stats.powerlaw.pdf(xx,5)\n\n        >>> plt.figure()\n        >>> plt.hist(rvs, bins=50, normed=True)\n        >>> plt.plot(xx,powpdf,'r-')\n        >>> plt.title('np.random.power(5)')\n\n        >>> plt.figure()\n        >>> plt.hist(1./(1.+rvsp), bins=50, normed=True)\n        >>> plt.plot(xx,powpdf,'r-')\n        >>> plt.title('inverse of 1 + np.random.pareto(5)')\n\n        >>> plt.figure()\n        >>> plt.hist(1./(1.+rvsp), bins=50, normed=True)\n        >>> plt.plot(xx,powpdf,'r-')\n        >>> plt.title('inverse of stats.pareto(5)')\n\n        ";
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+static char __pyx_doc_6mtrand_11RandomState_68laplace[] = "\n        laplace(loc=0.0, scale=1.0, size=None)\n\n        Draw samples from the Laplace or double exponential distribution with\n        specified location (or mean) and scale (decay).\n\n        The Laplace distribution is similar to the Gaussian/normal distribution,\n        but is sharper at the peak and has fatter tails. It represents the\n        difference between two independent, identically distributed exponential\n        random variables.\n\n        Parameters\n        ----------\n        loc : float\n            The position, :math:`\\mu`, of the distribution peak.\n        scale : float\n            :math:`\\lambda`, the exponential decay.\n\n        Notes\n        -----\n        It has the probability density function\n\n        .. math:: f(x; \\mu, \\lambda) = \\frac{1}{2\\lambda}\n                                       \\exp\\left(-\\frac{|x - \\mu|}{\\lambda}\\right).\n\n        The first law of Laplace, from 1774, states that the frequency of an error\n        can be expressed as an exponential function of the absolute magnitude of\n        the error, which leads to the Laplace distribution. For many problems in\n        Economics and Health sciences, this distribution seems to model the data\n        better than the standard Gaussian distribution\n\n\n        References\n        ----------\n        .. [1] Abramowitz, M. and Stegun, I. A. (Eds.). Handbook of Mathematical\n               Functions with Formulas, Graphs, and Mathematical Tables, 9th\n               printing.  New York: Dover, 1972.\n\n        .. [2] The Laplace distribution and generalizations\n               By Samuel Kotz, Tomasz J. Kozubowski, Krzysztof Podgorski,\n               Birkhauser, 2001.\n\n        .. [3] Weisstein, Eric W. \"Laplace Distribution.\"\n               From MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/LaplaceDistribution.html\n\n        .. [4] Wikipedia, \"Laplace distribution\",\n               http://en.wikipedia.org/wik""i/Laplace_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution\n\n        >>> loc, scale = 0., 1.\n        >>> s = np.random.laplace(loc, scale, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n        >>> x = np.arange(-8., 8., .01)\n        >>> pdf = np.exp(-abs(x-loc/scale))/(2.*scale)\n        >>> plt.plot(x, pdf)\n\n        Plot Gaussian for comparison:\n\n        >>> g = (1/(scale * np.sqrt(2 * np.pi)) * \n        ...      np.exp( - (x - loc)**2 / (2 * scale**2) ))\n        >>> plt.plot(x,g)\n\n        ";
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+ *     def laplace(self, loc=0.0, scale=1.0, size=None):             # <<<<<<<<<<<<<<
+ *         """
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- *     def gumbel(self, loc=0.0, scale=1.0, size=None):             # <<<<<<<<<<<<<<
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-static PyObject *__pyx_pf_6mtrand_11RandomState_35gumbel(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_35gumbel[] = "\n        gumbel(loc=0.0, scale=1.0, size=None)\n\n        Gumbel distribution.\n\n        Draw samples from a Gumbel distribution with specified location and scale.\n        For more information on the Gumbel distribution, see Notes and References\n        below.\n\n        Parameters\n        ----------\n        loc : float\n            The location of the mode of the distribution.\n        scale : float\n            The scale parameter of the distribution.\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray\n            The samples\n\n        See Also\n        --------\n        scipy.stats.gumbel_l\n        scipy.stats.gumbel_r\n        scipy.stats.genextreme\n            probability density function, distribution, or cumulative density\n            function, etc. for each of the above\n        weibull\n\n        Notes\n        -----\n        The Gumbel (or Smallest Extreme Value (SEV) or the Smallest Extreme Value\n        Type I) distribution is one of a class of Generalized Extreme Value (GEV)\n        distributions used in modeling extreme value problems.  The Gumbel is a\n        special case of the Extreme Value Type I distribution for maximums from\n        distributions with \"exponential-like\" tails.\n\n        The probability density for the Gumbel distribution is\n\n        .. math:: p(x) = \\frac{e^{-(x - \\mu)/ \\beta}}{\\beta} e^{ -e^{-(x - \\mu)/\n                  \\beta}},\n\n        where :math:`\\mu` is the mode, a location parameter, and :math:`\\beta` is\n        the scale parameter.\n\n        The Gumbel (named for German mathematician Emil Julius Gumbel) was used\n        very early in the hydrology literature, for modeling the occurrence of\n        flood events. It is also used for modeling maximum wind speed and rainfall\n        rates.  It is a \"fat-tailed\" distribution - the ""probability of an event in\n        the tail of the distribution is larger than if one used a Gaussian, hence\n        the surprisingly frequent occurrence of 100-year floods. Floods were\n        initially modeled as a Gaussian process, which underestimated the frequency\n        of extreme events.\n\n\n        It is one of a class of extreme value distributions, the Generalized\n        Extreme Value (GEV) distributions, which also includes the Weibull and\n        Frechet.\n\n        The function has a mean of :math:`\\mu + 0.57721\\beta` and a variance of\n        :math:`\\frac{\\pi^2}{6}\\beta^2`.\n\n        References\n        ----------\n        Gumbel, E. J., *Statistics of Extremes*, New York: Columbia University\n        Press, 1958.\n\n        Reiss, R.-D. and Thomas, M., *Statistical Analysis of Extreme Values from\n        Insurance, Finance, Hydrology and Other Fields*, Basel: Birkhauser Verlag,\n        2001.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, beta = 0, 0.1 # location and scale\n        >>> s = np.random.gumbel(mu, beta, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n        >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n        ...          * np.exp( -np.exp( -(bins - mu) /beta) ),\n        ...          linewidth=2, color='r')\n        >>> plt.show()\n\n        Show how an extreme value distribution can arise from a Gaussian process\n        and compare to a Gaussian:\n\n        >>> means = []\n        >>> maxima = []\n        >>> for i in range(0,1000) :\n        ...    a = np.random.normal(mu, beta, 1000)\n        ...    means.append(a.mean())\n        ...    maxima.append(a.max())\n        >>> count, bins, ignored = plt.hist(maxima, 30, normed=True)\n        >>> beta = np.std(maxima)*np.pi/np.sqrt(6)""\n        >>> mu = np.mean(maxima) - 0.57721*beta\n        >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n        ...          * np.exp(-np.exp(-(bins - mu)/beta)),\n        ...          linewidth=2, color='r')\n        >>> plt.plot(bins, 1/(beta * np.sqrt(2 * np.pi))\n        ...          * np.exp(-(bins - mu)**2 / (2 * beta**2)),\n        ...          linewidth=2, color='g')\n        >>> plt.show()\n\n        ";
-static PyObject *__pyx_pf_6mtrand_11RandomState_35gumbel(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+/* Python wrapper */
+static PyObject *__pyx_pw_6mtrand_11RandomState_71gumbel(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_70gumbel[] = "\n        gumbel(loc=0.0, scale=1.0, size=None)\n\n        Gumbel distribution.\n\n        Draw samples from a Gumbel distribution with specified location and scale.\n        For more information on the Gumbel distribution, see Notes and References\n        below.\n\n        Parameters\n        ----------\n        loc : float\n            The location of the mode of the distribution.\n        scale : float\n            The scale parameter of the distribution.\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray\n            The samples\n\n        See Also\n        --------\n        scipy.stats.gumbel_l\n        scipy.stats.gumbel_r\n        scipy.stats.genextreme\n            probability density function, distribution, or cumulative density\n            function, etc. for each of the above\n        weibull\n\n        Notes\n        -----\n        The Gumbel (or Smallest Extreme Value (SEV) or the Smallest Extreme Value\n        Type I) distribution is one of a class of Generalized Extreme Value (GEV)\n        distributions used in modeling extreme value problems.  The Gumbel is a\n        special case of the Extreme Value Type I distribution for maximums from\n        distributions with \"exponential-like\" tails.\n\n        The probability density for the Gumbel distribution is\n\n        .. math:: p(x) = \\frac{e^{-(x - \\mu)/ \\beta}}{\\beta} e^{ -e^{-(x - \\mu)/\n                  \\beta}},\n\n        where :math:`\\mu` is the mode, a location parameter, and :math:`\\beta` is\n        the scale parameter.\n\n        The Gumbel (named for German mathematician Emil Julius Gumbel) was used\n        very early in the hydrology literature, for modeling the occurrence of\n        flood events. It is also used for modeling maximum wind speed and rainfall\n        rates.  It is a \"fat-tailed\" distribution - the ""probability of an event in\n        the tail of the distribution is larger than if one used a Gaussian, hence\n        the surprisingly frequent occurrence of 100-year floods. Floods were\n        initially modeled as a Gaussian process, which underestimated the frequency\n        of extreme events.\n\n\n        It is one of a class of extreme value distributions, the Generalized\n        Extreme Value (GEV) distributions, which also includes the Weibull and\n        Frechet.\n\n        The function has a mean of :math:`\\mu + 0.57721\\beta` and a variance of\n        :math:`\\frac{\\pi^2}{6}\\beta^2`.\n\n        References\n        ----------\n        Gumbel, E. J., *Statistics of Extremes*, New York: Columbia University\n        Press, 1958.\n\n        Reiss, R.-D. and Thomas, M., *Statistical Analysis of Extreme Values from\n        Insurance, Finance, Hydrology and Other Fields*, Basel: Birkhauser Verlag,\n        2001.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, beta = 0, 0.1 # location and scale\n        >>> s = np.random.gumbel(mu, beta, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n        >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n        ...          * np.exp( -np.exp( -(bins - mu) /beta) ),\n        ...          linewidth=2, color='r')\n        >>> plt.show()\n\n        Show how an extreme value distribution can arise from a Gaussian process\n        and compare to a Gaussian:\n\n        >>> means = []\n        >>> maxima = []\n        >>> for i in range(0,1000) :\n        ...    a = np.random.normal(mu, beta, 1000)\n        ...    means.append(a.mean())\n        ...    maxima.append(a.max())\n        >>> count, bins, ignored = plt.hist(maxima, 30, normed=True)\n        >>> beta = np.std(maxima)*np.pi/np.sqrt(6)""\n        >>> mu = np.mean(maxima) - 0.57721*beta\n        >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n        ...          * np.exp(-np.exp(-(bins - mu)/beta)),\n        ...          linewidth=2, color='r')\n        >>> plt.plot(bins, 1/(beta * np.sqrt(2 * np.pi))\n        ...          * np.exp(-(bins - mu)**2 / (2 * beta**2)),\n        ...          linewidth=2, color='g')\n        >>> plt.show()\n\n        ";
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-  __Pyx_GOTREF(((PyObject *)__pyx_t_5));
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-    goto __pyx_L8;
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   /* "mtrand.pyx":3009
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@@ -13497,7 +14015,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_36logistic(PyObject *__pyx_v_sel
  *     def lognormal(self, mean=0.0, sigma=1.0, size=None):
  */
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+  __pyx_t_2 = __pyx_f_6mtrand_cont2_array(__pyx_v_self->internal_state, rk_logistic, __pyx_v_size, __pyx_v_oloc, __pyx_v_oscale); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3009; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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@@ -13520,44 +14038,34 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_36logistic(PyObject *__pyx_v_sel
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 }
 
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- * 
- *     def lognormal(self, mean=0.0, sigma=1.0, size=None):             # <<<<<<<<<<<<<<
- *         """
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- */
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-static char __pyx_doc_6mtrand_11RandomState_37lognormal[] = "\n        lognormal(mean=0.0, sigma=1.0, size=None)\n\n        Return samples drawn from a log-normal distribution.\n\n        Draw samples from a log-normal distribution with specified mean,\n        standard deviation, and array shape.  Note that the mean and standard\n        deviation are not the values for the distribution itself, but of the\n        underlying normal distribution it is derived from.\n\n        Parameters\n        ----------\n        mean : float\n            Mean value of the underlying normal distribution\n        sigma : float, > 0.\n            Standard deviation of the underlying normal distribution\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : ndarray or float\n            The desired samples. An array of the same shape as `size` if given,\n            if `size` is None a float is returned.\n\n        See Also\n        --------\n        scipy.stats.lognorm : probability density function, distribution,\n            cumulative density function, etc.\n\n        Notes\n        -----\n        A variable `x` has a log-normal distribution if `log(x)` is normally\n        distributed.  The probability density function for the log-normal\n        distribution is:\n\n        .. math:: p(x) = \\frac{1}{\\sigma x \\sqrt{2\\pi}}\n                         e^{(-\\frac{(ln(x)-\\mu)^2}{2\\sigma^2})}\n\n        where :math:`\\mu` is the mean and :math:`\\sigma` is the standard\n        deviation of the normally distributed logarithm of the variable.\n        A log-normal distribution results if a random variable is the *product*\n        of a large number of independent, identically-distributed variables in\n        the same way that a normal distribution results if the variable is the\n        *sum* of a large number of independent, identically-distributed\n        variables.\n\n        Reference""s\n        ----------\n        Limpert, E., Stahel, W. A., and Abbt, M., \"Log-normal Distributions\n        across the Sciences: Keys and Clues,\" *BioScience*, Vol. 51, No. 5,\n        May, 2001.  http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf\n\n        Reiss, R.D. and Thomas, M., *Statistical Analysis of Extreme Values*,\n        Basel: Birkhauser Verlag, 2001, pp. 31-32.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, sigma = 3., 1. # mean and standard deviation\n        >>> s = np.random.lognormal(mu, sigma, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='mid')\n\n        >>> x = np.linspace(min(bins), max(bins), 10000)\n        >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n        ...        / (x * sigma * np.sqrt(2 * np.pi)))\n\n        >>> plt.plot(x, pdf, linewidth=2, color='r')\n        >>> plt.axis('tight')\n        >>> plt.show()\n\n        Demonstrate that taking the products of random samples from a uniform\n        distribution can be fit well by a log-normal probability density function.\n\n        >>> # Generate a thousand samples: each is the product of 100 random\n        >>> # values, drawn from a normal distribution.\n        >>> b = []\n        >>> for i in range(1000):\n        ...    a = 10. + np.random.random(100)\n        ...    b.append(np.product(a))\n\n        >>> b = np.array(b) / np.min(b) # scale values to be positive\n        >>> count, bins, ignored = plt.hist(b, 100, normed=True, align='center')\n        >>> sigma = np.std(np.log(b))\n        >>> mu = np.mean(np.log(b))\n\n        >>> x = np.linspace(min(bins), max(bins), 10000)\n        >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n        ...        / (x * sigma * np.sqrt(2 * np.pi)))\n\n        >>> plt.plot(x, pdf, co""lor='r', linewidth=2)\n        >>> plt.show()\n\n        ";
-static PyObject *__pyx_pf_6mtrand_11RandomState_37lognormal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+/* Python wrapper */
+static PyObject *__pyx_pw_6mtrand_11RandomState_75lognormal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_74lognormal[] = "\n        lognormal(mean=0.0, sigma=1.0, size=None)\n\n        Return samples drawn from a log-normal distribution.\n\n        Draw samples from a log-normal distribution with specified mean,\n        standard deviation, and array shape.  Note that the mean and standard\n        deviation are not the values for the distribution itself, but of the\n        underlying normal distribution it is derived from.\n\n        Parameters\n        ----------\n        mean : float\n            Mean value of the underlying normal distribution\n        sigma : float, > 0.\n            Standard deviation of the underlying normal distribution\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : ndarray or float\n            The desired samples. An array of the same shape as `size` if given,\n            if `size` is None a float is returned.\n\n        See Also\n        --------\n        scipy.stats.lognorm : probability density function, distribution,\n            cumulative density function, etc.\n\n        Notes\n        -----\n        A variable `x` has a log-normal distribution if `log(x)` is normally\n        distributed.  The probability density function for the log-normal\n        distribution is:\n\n        .. math:: p(x) = \\frac{1}{\\sigma x \\sqrt{2\\pi}}\n                         e^{(-\\frac{(ln(x)-\\mu)^2}{2\\sigma^2})}\n\n        where :math:`\\mu` is the mean and :math:`\\sigma` is the standard\n        deviation of the normally distributed logarithm of the variable.\n        A log-normal distribution results if a random variable is the *product*\n        of a large number of independent, identically-distributed variables in\n        the same way that a normal distribution results if the variable is the\n        *sum* of a large number of independent, identically-distributed\n        variables.\n\n        Reference""s\n        ----------\n        Limpert, E., Stahel, W. A., and Abbt, M., \"Log-normal Distributions\n        across the Sciences: Keys and Clues,\" *BioScience*, Vol. 51, No. 5,\n        May, 2001.  http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf\n\n        Reiss, R.D. and Thomas, M., *Statistical Analysis of Extreme Values*,\n        Basel: Birkhauser Verlag, 2001, pp. 31-32.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, sigma = 3., 1. # mean and standard deviation\n        >>> s = np.random.lognormal(mu, sigma, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='mid')\n\n        >>> x = np.linspace(min(bins), max(bins), 10000)\n        >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n        ...        / (x * sigma * np.sqrt(2 * np.pi)))\n\n        >>> plt.plot(x, pdf, linewidth=2, color='r')\n        >>> plt.axis('tight')\n        >>> plt.show()\n\n        Demonstrate that taking the products of random samples from a uniform\n        distribution can be fit well by a log-normal probability density function.\n\n        >>> # Generate a thousand samples: each is the product of 100 random\n        >>> # values, drawn from a normal distribution.\n        >>> b = []\n        >>> for i in range(1000):\n        ...    a = 10. + np.random.random(100)\n        ...    b.append(np.product(a))\n\n        >>> b = np.array(b) / np.min(b) # scale values to be positive\n        >>> count, bins, ignored = plt.hist(b, 100, normed=True, align='center')\n        >>> sigma = np.std(np.log(b))\n        >>> mu = np.mean(np.log(b))\n\n        >>> x = np.linspace(min(bins), max(bins), 10000)\n        >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n        ...        / (x * sigma * np.sqrt(2 * np.pi)))\n\n        >>> plt.plot(x, pdf, co""lor='r', linewidth=2)\n        >>> plt.show()\n\n        ";
+static PyObject *__pyx_pw_6mtrand_11RandomState_75lognormal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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+
+    /* "mtrand.pyx":3011
+ *         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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+ */
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-      switch (PyTuple_GET_SIZE(__pyx_args)) {
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+  PyArrayObject *__pyx_v_osigma = 0;
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+static char __pyx_doc_6mtrand_11RandomState_76rayleigh[] = "\n        rayleigh(scale=1.0, size=None)\n\n        Draw samples from a Rayleigh distribution.\n\n        The :math:`\\chi` and Weibull distributions are generalizations of the\n        Rayleigh.\n\n        Parameters\n        ----------\n        scale : scalar\n            Scale, also equals the mode. Should be >= 0.\n        size : int or tuple of ints, optional\n            Shape of the output. Default is None, in which case a single\n            value is returned.\n\n        Notes\n        -----\n        The probability density function for the Rayleigh distribution is\n\n        .. math:: P(x;scale) = \\frac{x}{scale^2}e^{\\frac{-x^2}{2 \\cdotp scale^2}}\n\n        The Rayleigh distribution arises if the wind speed and wind direction are\n        both gaussian variables, then the vector wind velocity forms a Rayleigh\n        distribution. The Rayleigh distribution is used to model the expected\n        output from wind turbines.\n\n        References\n        ----------\n        .. [1] Brighton Webs Ltd., Rayleigh Distribution,\n              http://www.brighton-webs.co.uk/distributions/rayleigh.asp\n        .. [2] Wikipedia, \"Rayleigh distribution\"\n              http://en.wikipedia.org/wiki/Rayleigh_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram\n\n        >>> values = hist(np.random.rayleigh(3, 100000), bins=200, normed=True)\n\n        Wave heights tend to follow a Rayleigh distribution. If the mean wave\n        height is 1 meter, what fraction of waves are likely to be larger than 3\n        meters?\n\n        >>> meanvalue = 1\n        >>> modevalue = np.sqrt(2 / np.pi) * meanvalue\n        >>> s = np.random.rayleigh(modevalue, 1000000)\n\n        The percentage of waves larger than 3 meters is:\n\n        >>> 100.*sum(s>3)/1000000.\n        0.087300000000000003\n\n        ";
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 }
 
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-static PyObject *__pyx_pf_6mtrand_11RandomState_43poisson(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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-static char __pyx_doc_6mtrand_11RandomState_46hypergeometric[] = "\n        hypergeometric(ngood, nbad, nsample, size=None)\n\n        Draw samples from a Hypergeometric distribution.\n\n        Samples are drawn from a Hypergeometric distribution with specified\n        parameters, ngood (ways to make a good selection), nbad (ways to make\n        a bad selection), and nsample = number of items sampled, which is less\n        than or equal to the sum ngood + nbad.\n\n        Parameters\n        ----------\n        ngood : float (but truncated to an integer)\n                parameter, > 0.\n        nbad  : float\n                parameter, >= 0.\n        nsample  : float\n                   parameter, > 0 and <= ngood+nbad\n        size : {tuple, int}\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n                  where the values are all integers in  [0, n].\n\n        See Also\n        --------\n        scipy.stats.distributions.hypergeom : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Hypergeometric distribution is\n\n        .. math:: P(x) = \\frac{\\binom{m}{n}\\binom{N-m}{n-x}}{\\binom{N}{n}},\n\n        where :math:`0 \\le x \\le m` and :math:`n+m-N \\le x \\le n`\n\n        for P(x) the probability of x successes, n = ngood, m = nbad, and\n        N = number of samples.\n\n        Consider an urn with black and white marbles in it, ngood of them\n        black and nbad are white. If you draw nsample balls without\n        replacement, then the Hypergeometric distribution describes the\n        distribution of black balls in the drawn sample.\n\n        Note that this distribution is very similar to the Binomial\n        distribution, except that in this case, samples are drawn without\n        replacement, whereas in the Binomial case samples are drawn wit""h\n        replacement (or the sample space is infinite). As the sample space\n        becomes large, this distribution approaches the Binomial.\n\n        References\n        ----------\n        .. [1] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n               and Quigley, 1972.\n        .. [2] Weisstein, Eric W. \"Hypergeometric Distribution.\" From\n               MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/HypergeometricDistribution.html\n        .. [3] Wikipedia, \"Hypergeometric-distribution\",\n               http://en.wikipedia.org/wiki/Hypergeometric-distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> ngood, nbad, nsamp = 100, 2, 10\n        # number of good, number of bad, and number of samples\n        >>> s = np.random.hypergeometric(ngood, nbad, nsamp, 1000)\n        >>> hist(s)\n        #   note that it is very unlikely to grab both bad items\n\n        Suppose you have an urn with 15 white and 15 black marbles.\n        If you pull 15 marbles at random, how likely is it that\n        12 or more of them are one color?\n\n        >>> s = np.random.hypergeometric(15, 15, 15, 100000)\n        >>> sum(s>=12)/100000. + sum(s<=3)/100000.\n        #   answer = 0.003 ... pretty unlikely!\n\n        ";
-static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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+static char __pyx_doc_6mtrand_11RandomState_92hypergeometric[] = "\n        hypergeometric(ngood, nbad, nsample, size=None)\n\n        Draw samples from a Hypergeometric distribution.\n\n        Samples are drawn from a Hypergeometric distribution with specified\n        parameters, ngood (ways to make a good selection), nbad (ways to make\n        a bad selection), and nsample = number of items sampled, which is less\n        than or equal to the sum ngood + nbad.\n\n        Parameters\n        ----------\n        ngood : float (but truncated to an integer)\n                parameter, > 0.\n        nbad  : float\n                parameter, >= 0.\n        nsample  : float\n                   parameter, > 0 and <= ngood+nbad\n        size : {tuple, int}\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n                  where the values are all integers in  [0, n].\n\n        See Also\n        --------\n        scipy.stats.distributions.hypergeom : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Hypergeometric distribution is\n\n        .. math:: P(x) = \\frac{\\binom{m}{n}\\binom{N-m}{n-x}}{\\binom{N}{n}},\n\n        where :math:`0 \\le x \\le m` and :math:`n+m-N \\le x \\le n`\n\n        for P(x) the probability of x successes, n = ngood, m = nbad, and\n        N = number of samples.\n\n        Consider an urn with black and white marbles in it, ngood of them\n        black and nbad are white. If you draw nsample balls without\n        replacement, then the Hypergeometric distribution describes the\n        distribution of black balls in the drawn sample.\n\n        Note that this distribution is very similar to the Binomial\n        distribution, except that in this case, samples are drawn without\n        replacement, whereas in the Binomial case samples are drawn wit""h\n        replacement (or the sample space is infinite). As the sample space\n        becomes large, this distribution approaches the Binomial.\n\n        References\n        ----------\n        .. [1] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n               and Quigley, 1972.\n        .. [2] Weisstein, Eric W. \"Hypergeometric Distribution.\" From\n               MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/HypergeometricDistribution.html\n        .. [3] Wikipedia, \"Hypergeometric-distribution\",\n               http://en.wikipedia.org/wiki/Hypergeometric-distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> ngood, nbad, nsamp = 100, 2, 10\n        # number of good, number of bad, and number of samples\n        >>> s = np.random.hypergeometric(ngood, nbad, nsamp, 1000)\n        >>> hist(s)\n        #   note that it is very unlikely to grab both bad items\n\n        Suppose you have an urn with 15 white and 15 black marbles.\n        If you pull 15 marbles at random, how likely is it that\n        12 or more of them are one color?\n\n        >>> s = np.random.hypergeometric(15, 15, 15, 100000)\n        >>> sum(s>=12)/100000. + sum(s<=3)/100000.\n        #   answer = 0.003 ... pretty unlikely!\n\n        ";
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 }
 
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     {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4119; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    goto __pyx_L7;
+    goto __pyx_L4;
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-  __pyx_L7:;
+  __pyx_L4:;
 
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-    goto __pyx_L8;
+    goto __pyx_L5;
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-  __pyx_L8:;
+  __pyx_L5:;
 
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     {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4123; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-    goto __pyx_L9;
+    goto __pyx_L6;
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-  __pyx_L9:;
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-    __Pyx_GOTREF(((PyObject *)__pyx_t_2));
+    __Pyx_GOTREF(__pyx_t_2);
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-    goto __pyx_L10;
+    goto __pyx_L7;
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-  __pyx_L10:;
+  __pyx_L7:;
 
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@@ -18048,7 +18698,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_48multivariate_normal(PyObject *
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-  __Pyx_GOTREF(((PyObject *)__pyx_t_3));
+  __Pyx_GOTREF(__pyx_t_3);
   PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_2);
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@@ -18065,9 +18715,6 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_48multivariate_normal(PyObject *
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-  }
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@@ -18083,7 +18730,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_48multivariate_normal(PyObject *
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+  __pyx_t_3 = PyObject_GetAttr(((PyObject *)__pyx_v_self), __pyx_n_s__standard_normal); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4132; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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-  __Pyx_GOTREF(((PyObject *)__pyx_t_1));
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-  __Pyx_GOTREF(((PyObject *)__pyx_t_1));
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@@ -18129,14 +18776,11 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_48multivariate_normal(PyObject *
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-  __Pyx_GOTREF(((PyObject *)__pyx_t_3));
+  __Pyx_GOTREF(__pyx_t_3);
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   __Pyx_GIVEREF(((PyObject *)__pyx_t_1));
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@@ -18158,7 +18802,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_48multivariate_normal(PyObject *
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-  __Pyx_GOTREF(((PyObject *)__pyx_t_3));
+  __Pyx_GOTREF(__pyx_t_3);
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   PyTuple_SET_ITEM(__pyx_t_3, 1, __pyx_t_9);
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-  __Pyx_GOTREF(((PyObject *)__pyx_t_3));
+  __Pyx_GOTREF(__pyx_t_3);
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+  __pyx_t_3 = PyObject_GetAttr(__pyx_t_9, __pyx_n_s__svd);
+  if (__pyx_t_3 == NULL) {
+    if (PyErr_ExceptionMatches(PyExc_AttributeError)) __Pyx_RaiseImportError(__pyx_n_s__svd);
+    if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4142; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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@@ -18206,7 +18854,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_48multivariate_normal(PyObject *
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-  __Pyx_GOTREF(((PyObject *)__pyx_t_9));
+  __Pyx_GOTREF(__pyx_t_9);
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-    index = 0; __pyx_t_9 = __pyx_t_11(__pyx_t_10); if (unlikely(!__pyx_t_9)) goto __pyx_L11_unpacking_failed;
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-    index = 1; __pyx_t_1 = __pyx_t_11(__pyx_t_10); if (unlikely(!__pyx_t_1)) goto __pyx_L11_unpacking_failed;
+    index = 1; __pyx_t_1 = __pyx_t_11(__pyx_t_10); if (unlikely(!__pyx_t_1)) goto __pyx_L8_unpacking_failed;
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-    index = 2; __pyx_t_2 = __pyx_t_11(__pyx_t_10); if (unlikely(!__pyx_t_2)) goto __pyx_L11_unpacking_failed;
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-    goto __pyx_L12_unpacking_done;
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-    __pyx_L12_unpacking_done:;
+    __pyx_L9_unpacking_done:;
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@@ -18285,7 +18933,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_48multivariate_normal(PyObject *
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-  __Pyx_GOTREF(((PyObject *)__pyx_t_3));
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-  __Pyx_GOTREF(((PyObject *)__pyx_t_9));
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-  __Pyx_GOTREF(((PyObject *)__pyx_t_3));
+  __Pyx_GOTREF(__pyx_t_3);
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@@ -18393,50 +19038,32 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_48multivariate_normal(PyObject *
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 }
 
-/* "mtrand.pyx":4152
- *         return x
- * 
- *     def multinomial(self, npy_intp n, object pvals, size=None):             # <<<<<<<<<<<<<<
- *         """
- *         multinomial(n, pvals, size=None)
- */
-
-static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_49multinomial[] = "\n        multinomial(n, pvals, size=None)\n\n        Draw samples from a multinomial distribution.\n\n        The multinomial distribution is a multivariate generalisation of the\n        binomial distribution.  Take an experiment with one of ``p``\n        possible outcomes.  An example of such an experiment is throwing a dice,\n        where the outcome can be 1 through 6.  Each sample drawn from the\n        distribution represents `n` such experiments.  Its values,\n        ``X_i = [X_0, X_1, ..., X_p]``, represent the number of times the outcome\n        was ``i``.\n\n        Parameters\n        ----------\n        n : int\n            Number of experiments.\n        pvals : sequence of floats, length p\n            Probabilities of each of the ``p`` different outcomes.  These\n            should sum to 1 (however, the last element is always assumed to\n            account for the remaining probability, as long as\n            ``sum(pvals[:-1]) <= 1)``.\n        size : tuple of ints\n            Given a `size` of ``(M, N, K)``, then ``M*N*K`` samples are drawn,\n            and the output shape becomes ``(M, N, K, p)``, since each sample\n            has shape ``(p,)``.\n\n        Examples\n        --------\n        Throw a dice 20 times:\n\n        >>> np.random.multinomial(20, [1/6.]*6, size=1)\n        array([[4, 1, 7, 5, 2, 1]])\n\n        It landed 4 times on 1, once on 2, etc.\n\n        Now, throw the dice 20 times, and 20 times again:\n\n        >>> np.random.multinomial(20, [1/6.]*6, size=2)\n        array([[3, 4, 3, 3, 4, 3],\n               [2, 4, 3, 4, 0, 7]])\n\n        For the first run, we threw 3 times 1, 4 times 2, etc.  For the second,\n        we threw 2 times 1, 4 times 2, etc.\n\n        A loaded dice is more likely to land on number 6:\n\n        >>> np.random.multinomial(100, [1/7.]*5)\n        array([13, 16, 13, 16, 42])\n\n        ";
-static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
+/* Python wrapper */
+static PyObject *__pyx_pw_6mtrand_11RandomState_99multinomial(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
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@@ -18616,7 +19272,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_
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-  __pyx_L7:;
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@@ -18641,7 +19297,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_
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-  __Pyx_GOTREF(((PyObject *)__pyx_t_2));
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       /* "mtrand.pyx":4234
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-        goto __pyx_L11_break;
-        goto __pyx_L12;
+        goto __pyx_L8_break;
+        goto __pyx_L9;
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-    __pyx_L13:;
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 }
 
-/* "mtrand.pyx":4245
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+static PyObject *__pyx_pw_6mtrand_11RandomState_101dirichlet(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
+static char __pyx_doc_6mtrand_11RandomState_100dirichlet[] = "\n        dirichlet(alpha, size=None)\n\n        Draw samples from the Dirichlet distribution.\n\n        Draw `size` samples of dimension k from a Dirichlet distribution. A\n        Dirichlet-distributed random variable can be seen as a multivariate\n        generalization of a Beta distribution. Dirichlet pdf is the conjugate\n        prior of a multinomial in Bayesian inference.\n\n        Parameters\n        ----------\n        alpha : array\n            Parameter of the distribution (k dimension for sample of\n            dimension k).\n        size : array\n            Number of samples to draw.\n\n        Returns\n        -------\n        samples : ndarray,\n            The drawn samples, of shape (alpha.ndim, size).\n\n        Notes\n        -----\n        .. math:: X \\approx \\prod_{i=1}^{k}{x^{\\alpha_i-1}_i}\n\n        Uses the following property for computation: for each dimension,\n        draw a random sample y_i from a standard gamma generator of shape\n        `alpha_i`, then\n        :math:`X = \\frac{1}{\\sum_{i=1}^k{y_i}} (y_1, \\ldots, y_n)` is\n        Dirichlet distributed.\n\n        References\n        ----------\n        .. [1] David McKay, \"Information Theory, Inference and Learning\n               Algorithms,\" chapter 23,\n               http://www.inference.phy.cam.ac.uk/mackay/\n        .. [2] Wikipedia, \"Dirichlet distribution\",\n               http://en.wikipedia.org/wiki/Dirichlet_distribution\n\n        Examples\n        --------\n        Taking an example cited in Wikipedia, this distribution can be used if\n        one wanted to cut strings (each of initial length 1.0) into K pieces\n        with different lengths, where each piece had, on average, a designated\n        average length, but allowing some variation in the relative sizes of the\n        pieces.\n\n        >>> s = np.random.dirichlet((10, 5, 3), 20).transpose()\n\n        >>> plt.barh(range(20), s[0])\n        >>> plt.barh(range(20), s[1], left=s[0], color='g')""\n        >>> plt.barh(range(20), s[2], left=s[0]+s[1], color='r')\n        >>> plt.title(\"Lengths of Strings\")\n\n        ";
+static PyObject *__pyx_pw_6mtrand_11RandomState_101dirichlet(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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-    __Pyx_GOTREF(((PyObject *)__pyx_t_1));
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-    goto __pyx_L5;
+    goto __pyx_L3;
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@@ -19649,7 +20336,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_52permutation(PyObject *__pyx_v_
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-  __pyx_L5:;
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-  {__Pyx_NAMESTR("normal"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_18normal, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_18normal)},
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-  {__Pyx_NAMESTR("gamma"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_23gamma, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_23gamma)},
-  {__Pyx_NAMESTR("f"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_24f, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_24f)},
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-  {__Pyx_NAMESTR("chisquare"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_26chisquare, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_26chisquare)},
-  {__Pyx_NAMESTR("noncentral_chisquare"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_27noncentral_chisquare, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_27noncentral_chisquare)},
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-  {__Pyx_NAMESTR("standard_t"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_29standard_t, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_29standard_t)},
-  {__Pyx_NAMESTR("vonmises"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_30vonmises, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_30vonmises)},
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-  {__Pyx_NAMESTR("weibull"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_32weibull, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_32weibull)},
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-  {__Pyx_NAMESTR("gumbel"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_35gumbel, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_35gumbel)},
-  {__Pyx_NAMESTR("logistic"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_36logistic, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_36logistic)},
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-  {__Pyx_NAMESTR("rayleigh"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_38rayleigh, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_38rayleigh)},
-  {__Pyx_NAMESTR("wald"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_39wald, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_39wald)},
-  {__Pyx_NAMESTR("triangular"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_40triangular, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_40triangular)},
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-  {__Pyx_NAMESTR("dirichlet"), (PyCFunction)__pyx_pf_6mtrand_11RandomState_50dirichlet, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_50dirichlet)},
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+  {__Pyx_NAMESTR("seed"), (PyCFunction)__pyx_pw_6mtrand_11RandomState_5seed, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_4seed)},
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+  {__Pyx_NAMESTR("randint"), (PyCFunction)__pyx_pw_6mtrand_11RandomState_21randint, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_20randint)},
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+  {__Pyx_NAMESTR("randn"), (PyCFunction)__pyx_pw_6mtrand_11RandomState_31randn, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_30randn)},
+  {__Pyx_NAMESTR("random_integers"), (PyCFunction)__pyx_pw_6mtrand_11RandomState_33random_integers, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_32random_integers)},
+  {__Pyx_NAMESTR("standard_normal"), (PyCFunction)__pyx_pw_6mtrand_11RandomState_35standard_normal, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_34standard_normal)},
+  {__Pyx_NAMESTR("normal"), (PyCFunction)__pyx_pw_6mtrand_11RandomState_37normal, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_36normal)},
+  {__Pyx_NAMESTR("beta"), (PyCFunction)__pyx_pw_6mtrand_11RandomState_39beta, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_38beta)},
+  {__Pyx_NAMESTR("exponential"), (PyCFunction)__pyx_pw_6mtrand_11RandomState_41exponential, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_40exponential)},
+  {__Pyx_NAMESTR("standard_exponential"), (PyCFunction)__pyx_pw_6mtrand_11RandomState_43standard_exponential, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_42standard_exponential)},
+  {__Pyx_NAMESTR("standard_gamma"), (PyCFunction)__pyx_pw_6mtrand_11RandomState_45standard_gamma, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_44standard_gamma)},
+  {__Pyx_NAMESTR("gamma"), (PyCFunction)__pyx_pw_6mtrand_11RandomState_47gamma, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_46gamma)},
+  {__Pyx_NAMESTR("f"), (PyCFunction)__pyx_pw_6mtrand_11RandomState_49f, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_48f)},
+  {__Pyx_NAMESTR("noncentral_f"), (PyCFunction)__pyx_pw_6mtrand_11RandomState_51noncentral_f, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_50noncentral_f)},
+  {__Pyx_NAMESTR("chisquare"), (PyCFunction)__pyx_pw_6mtrand_11RandomState_53chisquare, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_52chisquare)},
+  {__Pyx_NAMESTR("noncentral_chisquare"), (PyCFunction)__pyx_pw_6mtrand_11RandomState_55noncentral_chisquare, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_54noncentral_chisquare)},
+  {__Pyx_NAMESTR("standard_cauchy"), (PyCFunction)__pyx_pw_6mtrand_11RandomState_57standard_cauchy, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_56standard_cauchy)},
+  {__Pyx_NAMESTR("standard_t"), (PyCFunction)__pyx_pw_6mtrand_11RandomState_59standard_t, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_58standard_t)},
+  {__Pyx_NAMESTR("vonmises"), (PyCFunction)__pyx_pw_6mtrand_11RandomState_61vonmises, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_60vonmises)},
+  {__Pyx_NAMESTR("pareto"), (PyCFunction)__pyx_pw_6mtrand_11RandomState_63pareto, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_62pareto)},
+  {__Pyx_NAMESTR("weibull"), (PyCFunction)__pyx_pw_6mtrand_11RandomState_65weibull, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_64weibull)},
+  {__Pyx_NAMESTR("power"), (PyCFunction)__pyx_pw_6mtrand_11RandomState_67power, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_66power)},
+  {__Pyx_NAMESTR("laplace"), (PyCFunction)__pyx_pw_6mtrand_11RandomState_69laplace, METH_VARARGS|METH_KEYWORDS, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_68laplace)},
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   __Pyx_GOTREF(__pyx_t_4);
   __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
   __pyx_t_1 = PyTuple_New(1); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 558; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
-  __Pyx_GOTREF(((PyObject *)__pyx_t_1));
+  __Pyx_GOTREF(__pyx_t_1);
   PyTuple_SET_ITEM(__pyx_t_1, 0, __pyx_t_4);
   __Pyx_GIVEREF(__pyx_t_4);
   __pyx_t_4 = 0;
@@ -23008,7 +23701,6 @@ PyMODINIT_FUNC PyInit_mtrand(void)
 }
 
 /* Runtime support code */
-
 #if CYTHON_REFNANNY
 static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname) {
     PyObject *m = NULL, *p = NULL;
@@ -23041,9 +23733,9 @@ static PyObject *__Pyx_GetName(PyObject *dict, PyObject *name) {
 }
 
 static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) {
+#if CYTHON_COMPILING_IN_CPYTHON
     PyObject *tmp_type, *tmp_value, *tmp_tb;
     PyThreadState *tstate = PyThreadState_GET();
-
     tmp_type = tstate->curexc_type;
     tmp_value = tstate->curexc_value;
     tmp_tb = tstate->curexc_traceback;
@@ -23053,27 +23745,30 @@ static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyOb
     Py_XDECREF(tmp_type);
     Py_XDECREF(tmp_value);
     Py_XDECREF(tmp_tb);
+#else
+    PyErr_Restore(type, value, tb);
+#endif
 }
-
 static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb) {
+#if CYTHON_COMPILING_IN_CPYTHON
     PyThreadState *tstate = PyThreadState_GET();
     *type = tstate->curexc_type;
     *value = tstate->curexc_value;
     *tb = tstate->curexc_traceback;
-
     tstate->curexc_type = 0;
     tstate->curexc_value = 0;
     tstate->curexc_traceback = 0;
+#else
+    PyErr_Fetch(type, value, tb);
+#endif
 }
 
-
 #if PY_MAJOR_VERSION < 3
-static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {
-    /* cause is unused */
+static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb,
+                        CYTHON_UNUSED PyObject *cause) {
     Py_XINCREF(type);
     Py_XINCREF(value);
     Py_XINCREF(tb);
-    /* First, check the traceback argument, replacing None with NULL. */
     if (tb == Py_None) {
         Py_DECREF(tb);
         tb = 0;
@@ -23083,7 +23778,6 @@ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject
             "raise: arg 3 must be a traceback or None");
         goto raise_error;
     }
-    /* Next, replace a missing value with None */
     if (value == NULL) {
         value = Py_None;
         Py_INCREF(value);
@@ -23094,13 +23788,11 @@ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject
     if (!PyType_Check(type))
     #endif
     {
-        /* Raising an instance.  The value should be a dummy. */
         if (value != Py_None) {
             PyErr_SetString(PyExc_TypeError,
                 "instance exception may not have a separate value");
             goto raise_error;
         }
-        /* Normalize to raise <class>, <instance> */
         Py_DECREF(value);
         value = type;
         #if PY_VERSION_HEX < 0x02050000
@@ -23124,7 +23816,6 @@ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject
             }
         #endif
     }
-
     __Pyx_ErrRestore(type, value, tb);
     return;
 raise_error:
@@ -23133,9 +23824,7 @@ raise_error:
     Py_XDECREF(tb);
     return;
 }
-
 #else /* Python 3+ */
-
 static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {
     if (tb == Py_None) {
         tb = 0;
@@ -23146,7 +23835,6 @@ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject
     }
     if (value == Py_None)
         value = 0;
-
     if (PyExceptionInstance_Check(type)) {
         if (value) {
             PyErr_SetString(PyExc_TypeError,
@@ -23160,7 +23848,6 @@ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject
             "raise: exception class must be a subclass of BaseException");
         goto bad;
     }
-
     if (cause) {
         PyObject *fixed_cause;
         if (PyExceptionClass_Check(cause)) {
@@ -23183,9 +23870,7 @@ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject
         }
         PyException_SetCause(value, fixed_cause);
     }
-
     PyErr_SetObject(type, value);
-
     if (tb) {
         PyThreadState *tstate = PyThreadState_GET();
         PyObject* tmp_tb = tstate->curexc_traceback;
@@ -23195,7 +23880,6 @@ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject
             Py_XDECREF(tmp_tb);
         }
     }
-
 bad:
     return;
 }
@@ -23226,7 +23910,6 @@ static int __Pyx_ParseOptionalKeywords(
     Py_ssize_t pos = 0;
     PyObject*** name;
     PyObject*** first_kw_arg = argnames + num_pos_args;
-
     while (PyDict_Next(kwds, &pos, &key, &value)) {
         name = first_kw_arg;
         while (*name && (**name != key)) name++;
@@ -23236,7 +23919,7 @@ static int __Pyx_ParseOptionalKeywords(
             #if PY_MAJOR_VERSION < 3
             if (unlikely(!PyString_CheckExact(key)) && unlikely(!PyString_Check(key))) {
             #else
-            if (unlikely(!PyUnicode_CheckExact(key)) && unlikely(!PyUnicode_Check(key))) {
+            if (unlikely(!PyUnicode_Check(key))) {
             #endif
                 goto invalid_keyword_type;
             } else {
@@ -23252,7 +23935,6 @@ static int __Pyx_ParseOptionalKeywords(
                 if (*name) {
                     values[name-argnames] = value;
                 } else {
-                    /* unexpected keyword found */
                     for (name=argnames; name != first_kw_arg; name++) {
                         if (**name == key) goto arg_passed_twice;
                         #if PY_MAJOR_VERSION >= 3
@@ -23302,7 +23984,6 @@ static void __Pyx_RaiseArgtupleInvalid(
 {
     Py_ssize_t num_expected;
     const char *more_or_less;
-
     if (num_found < num_min) {
         num_expected = num_min;
         more_or_less = "at least";
@@ -23320,6 +24001,7 @@ static void __Pyx_RaiseArgtupleInvalid(
 }
 
 
+
 static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {
     PyErr_Format(PyExc_ValueError,
                  "need more than %"PY_FORMAT_SIZE_T"d value%s to unpack",
@@ -23392,11 +24074,6 @@ bad:
     return -1;
 }
 
-
-static CYTHON_INLINE void __Pyx_RaiseUnboundLocalError(const char *varname) {
-    PyErr_Format(PyExc_UnboundLocalError, "local variable '%s' referenced before assignment", varname);
-}
-
 static CYTHON_INLINE int __Pyx_CheckKeywordStrings(
     PyObject *kwdict,
     const char* function_name,
@@ -23408,7 +24085,7 @@ static CYTHON_INLINE int __Pyx_CheckKeywordStrings(
         #if PY_MAJOR_VERSION < 3
         if (unlikely(!PyString_CheckExact(key)) && unlikely(!PyString_Check(key)))
         #else
-        if (unlikely(!PyUnicode_CheckExact(key)) && unlikely(!PyUnicode_Check(key)))
+        if (unlikely(!PyUnicode_Check(key)))
         #endif
             goto invalid_keyword_type;
     }
@@ -23444,6 +24121,7 @@ static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) {
 }
 
 
+
 static CYTHON_INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb) {
     PyThreadState *tstate = PyThreadState_GET();
     *type = tstate->exc_type;
@@ -23453,7 +24131,6 @@ static CYTHON_INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value,
     Py_XINCREF(*value);
     Py_XINCREF(*tb);
 }
-
 static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb) {
     PyObject *tmp_type, *tmp_value, *tmp_tb;
     PyThreadState *tstate = PyThreadState_GET();
@@ -23494,12 +24171,33 @@ static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, long level) {
         goto bad;
     #if PY_VERSION_HEX >= 0x02050000
     {
-        PyObject *py_level = PyInt_FromLong(level);
-        if (!py_level)
-            goto bad;
-        module = PyObject_CallFunctionObjArgs(py_import,
-            name, global_dict, empty_dict, list, py_level, NULL);
-        Py_DECREF(py_level);
+        #if PY_MAJOR_VERSION >= 3
+        if (level == -1) {
+            if (strchr(__Pyx_MODULE_NAME, '.')) {
+                /* try package relative import first */
+                PyObject *py_level = PyInt_FromLong(1);
+                if (!py_level)
+                    goto bad;
+                module = PyObject_CallFunctionObjArgs(py_import,
+                    name, global_dict, empty_dict, list, py_level, NULL);
+                Py_DECREF(py_level);
+                if (!module) {
+                    if (!PyErr_ExceptionMatches(PyExc_ImportError))
+                        goto bad;
+                    PyErr_Clear();
+                }
+            }
+            level = 0; /* try absolute import on failure */
+        }
+        #endif
+        if (!module) {
+            PyObject *py_level = PyInt_FromLong(level);
+            if (!py_level)
+                goto bad;
+            module = PyObject_CallFunctionObjArgs(py_import,
+                name, global_dict, empty_dict, list, py_level, NULL);
+            Py_DECREF(py_level);
+        }
     }
     #else
     if (level>0) {
@@ -23517,7 +24215,7 @@ bad:
 }
 
 static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) {
-    if (s1 == s2) {   /* as done by PyObject_RichCompareBool(); also catches the (interned) empty string */
+    if (s1 == s2) {
         return (equals == Py_EQ);
     } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) {
         if (PyBytes_GET_SIZE(s1) != PyBytes_GET_SIZE(s2)) {
@@ -23547,16 +24245,26 @@ static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int eq
 }
 
 static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) {
-    if (s1 == s2) {   /* as done by PyObject_RichCompareBool(); also catches the (interned) empty string */
+    if (s1 == s2) {
         return (equals == Py_EQ);
     } else if (PyUnicode_CheckExact(s1) & PyUnicode_CheckExact(s2)) {
+        #if CYTHON_PEP393_ENABLED
+        if ((PyUnicode_READY(s1) < 0) || (PyUnicode_READY(s2) < 0))
+            return -1;
+        if (PyUnicode_GET_LENGTH(s1) != PyUnicode_GET_LENGTH(s2)) {
+            return (equals == Py_NE);
+        } else if (PyUnicode_GET_LENGTH(s1) == 1) {
+            Py_UCS4 ch1 = PyUnicode_READ_CHAR(s1, 0);
+            Py_UCS4 ch2 = PyUnicode_READ_CHAR(s2, 0);
+            return (equals == Py_EQ) ? (ch1 == ch2) : (ch1 != ch2);
+        #else
         if (PyUnicode_GET_SIZE(s1) != PyUnicode_GET_SIZE(s2)) {
             return (equals == Py_NE);
         } else if (PyUnicode_GET_SIZE(s1) == 1) {
-            if (equals == Py_EQ)
-                return (PyUnicode_AS_UNICODE(s1)[0] == PyUnicode_AS_UNICODE(s2)[0]);
-            else
-                return (PyUnicode_AS_UNICODE(s1)[0] != PyUnicode_AS_UNICODE(s2)[0]);
+            Py_UNICODE ch1 = PyUnicode_AS_UNICODE(s1)[0];
+            Py_UNICODE ch2 = PyUnicode_AS_UNICODE(s2)[0];
+            return (equals == Py_EQ) ? (ch1 == ch2) : (ch1 != ch2);
+        #endif
         } else {
             int result = PyUnicode_Compare(s1, s2);
             if ((result == -1) && unlikely(PyErr_Occurred()))
@@ -23578,6 +24286,15 @@ static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int
     }
 }
 
+static CYTHON_INLINE void __Pyx_RaiseImportError(PyObject *name) {
+#if PY_MAJOR_VERSION < 3
+    PyErr_Format(PyExc_ImportError, "cannot import name %.230s",
+                 PyString_AsString(name));
+#else
+    PyErr_Format(PyExc_ImportError, "cannot import name %S", name);
+#endif
+}
+
 static CYTHON_INLINE PyObject *__Pyx_PyInt_to_py_npy_intp(npy_intp val) {
     const npy_intp neg_one = (npy_intp)-1, const_zero = (npy_intp)0;
     const int is_unsigned = const_zero < neg_one;
@@ -24083,15 +24800,10 @@ static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class
     PyObject *result = 0;
     PyObject *py_name = 0;
     char warning[200];
-
     py_module = __Pyx_ImportModule(module_name);
     if (!py_module)
         goto bad;
-    #if PY_MAJOR_VERSION < 3
-    py_name = PyString_FromString(class_name);
-    #else
-    py_name = PyUnicode_FromString(class_name);
-    #endif
+    py_name = __Pyx_PyIdentifier_FromString(class_name);
     if (!py_name)
         goto bad;
     result = PyObject_GetAttr(py_module, py_name);
@@ -24107,7 +24819,7 @@ static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class
             module_name, class_name);
         goto bad;
     }
-    if (!strict && ((PyTypeObject *)result)->tp_basicsize > (Py_ssize_t)size) {
+    if (!strict && (size_t)((PyTypeObject *)result)->tp_basicsize > size) {
         PyOS_snprintf(warning, sizeof(warning),
             "%s.%s size changed, may indicate binary incompatibility",
             module_name, class_name);
@@ -24117,7 +24829,7 @@ static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class
         if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad;
         #endif
     }
-    else if (((PyTypeObject *)result)->tp_basicsize != (Py_ssize_t)size) {
+    else if ((size_t)((PyTypeObject *)result)->tp_basicsize != size) {
         PyErr_Format(PyExc_ValueError,
             "%s.%s has the wrong size, try recompiling",
             module_name, class_name);
@@ -24136,12 +24848,7 @@ bad:
 static PyObject *__Pyx_ImportModule(const 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
+    py_name = __Pyx_PyIdentifier_FromString(name);
     if (!py_name)
         goto bad;
     py_module = PyImport_Import(py_name);
@@ -24153,29 +24860,105 @@ bad:
 }
 #endif
 
+static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) {
+    int start = 0, mid = 0, end = count - 1;
+    if (end >= 0 && code_line > entries[end].code_line) {
+        return count;
+    }
+    while (start < end) {
+        mid = (start + end) / 2;
+        if (code_line < entries[mid].code_line) {
+            end = mid;
+        } else if (code_line > entries[mid].code_line) {
+             start = mid + 1;
+        } else {
+            return mid;
+        }
+    }
+    if (code_line <= entries[mid].code_line) {
+        return mid;
+    } else {
+        return mid + 1;
+    }
+}
+static PyCodeObject *__pyx_find_code_object(int code_line) {
+    PyCodeObject* code_object;
+    int pos;
+    if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) {
+        return NULL;
+    }
+    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
+    if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) {
+        return NULL;
+    }
+    code_object = __pyx_code_cache.entries[pos].code_object;
+    Py_INCREF(code_object);
+    return code_object;
+}
+static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) {
+    int pos, i;
+    __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries;
+    if (unlikely(!code_line)) {
+        return;
+    }
+    if (unlikely(!entries)) {
+        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry));
+        if (likely(entries)) {
+            __pyx_code_cache.entries = entries;
+            __pyx_code_cache.max_count = 64;
+            __pyx_code_cache.count = 1;
+            entries[0].code_line = code_line;
+            entries[0].code_object = code_object;
+            Py_INCREF(code_object);
+        }
+        return;
+    }
+    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
+    if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) {
+        PyCodeObject* tmp = entries[pos].code_object;
+        entries[pos].code_object = code_object;
+        Py_DECREF(tmp);
+        return;
+    }
+    if (__pyx_code_cache.count == __pyx_code_cache.max_count) {
+        int new_max = __pyx_code_cache.max_count + 64;
+        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc(
+            __pyx_code_cache.entries, new_max*sizeof(__Pyx_CodeObjectCacheEntry));
+        if (unlikely(!entries)) {
+            return;
+        }
+        __pyx_code_cache.entries = entries;
+        __pyx_code_cache.max_count = new_max;
+    }
+    for (i=__pyx_code_cache.count; i>pos; i--) {
+        entries[i] = entries[i-1];
+    }
+    entries[pos].code_line = code_line;
+    entries[pos].code_object = code_object;
+    __pyx_code_cache.count++;
+    Py_INCREF(code_object);
+}
+
 #include "compile.h"
 #include "frameobject.h"
 #include "traceback.h"
-
-static void __Pyx_AddTraceback(const char *funcname, int __pyx_clineno,
-                               int __pyx_lineno, const char *__pyx_filename) {
+static PyCodeObject* __Pyx_CreateCodeObjectForTraceback(
+            const char *funcname, int c_line,
+            int py_line, const char *filename) {
+    PyCodeObject *py_code = 0;
     PyObject *py_srcfile = 0;
     PyObject *py_funcname = 0;
-    PyObject *py_globals = 0;
-    PyCodeObject *py_code = 0;
-    PyFrameObject *py_frame = 0;
-
     #if PY_MAJOR_VERSION < 3
-    py_srcfile = PyString_FromString(__pyx_filename);
+    py_srcfile = PyString_FromString(filename);
     #else
-    py_srcfile = PyUnicode_FromString(__pyx_filename);
+    py_srcfile = PyUnicode_FromString(filename);
     #endif
     if (!py_srcfile) goto bad;
-    if (__pyx_clineno) {
+    if (c_line) {
         #if PY_MAJOR_VERSION < 3
-        py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, __pyx_clineno);
+        py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
         #else
-        py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, __pyx_clineno);
+        py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
         #endif
     }
     else {
@@ -24186,28 +24969,45 @@ static void __Pyx_AddTraceback(const char *funcname, int __pyx_clineno,
         #endif
     }
     if (!py_funcname) goto bad;
-    py_globals = PyModule_GetDict(__pyx_m);
-    if (!py_globals) goto bad;
-    py_code = PyCode_New(
+    py_code = __Pyx_PyCode_New(
         0,            /*int argcount,*/
-        #if PY_MAJOR_VERSION >= 3
         0,            /*int kwonlyargcount,*/
-        #endif
         0,            /*int nlocals,*/
         0,            /*int stacksize,*/
         0,            /*int flags,*/
         __pyx_empty_bytes, /*PyObject *code,*/
-        __pyx_empty_tuple,  /*PyObject *consts,*/
-        __pyx_empty_tuple,  /*PyObject *names,*/
-        __pyx_empty_tuple,  /*PyObject *varnames,*/
-        __pyx_empty_tuple,  /*PyObject *freevars,*/
-        __pyx_empty_tuple,  /*PyObject *cellvars,*/
+        __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,*/
+        py_line,      /*int firstlineno,*/
         __pyx_empty_bytes  /*PyObject *lnotab*/
     );
-    if (!py_code) goto bad;
+    Py_DECREF(py_srcfile);
+    Py_DECREF(py_funcname);
+    return py_code;
+bad:
+    Py_XDECREF(py_srcfile);
+    Py_XDECREF(py_funcname);
+    return NULL;
+}
+static void __Pyx_AddTraceback(const char *funcname, int c_line,
+                               int py_line, const char *filename) {
+    PyCodeObject *py_code = 0;
+    PyObject *py_globals = 0;
+    PyFrameObject *py_frame = 0;
+    py_code = __pyx_find_code_object(c_line ? c_line : py_line);
+    if (!py_code) {
+        py_code = __Pyx_CreateCodeObjectForTraceback(
+            funcname, c_line, py_line, filename);
+        if (!py_code) goto bad;
+        __pyx_insert_code_object(c_line ? c_line : py_line, py_code);
+    }
+    py_globals = PyModule_GetDict(__pyx_m);
+    if (!py_globals) goto bad;
     py_frame = PyFrame_New(
         PyThreadState_GET(), /*PyThreadState *tstate,*/
         py_code,             /*PyCodeObject *code,*/
@@ -24215,11 +25015,9 @@ static void __Pyx_AddTraceback(const char *funcname, int __pyx_clineno,
         0                    /*PyObject *locals*/
     );
     if (!py_frame) goto bad;
-    py_frame->f_lineno = __pyx_lineno;
+    py_frame->f_lineno = py_line;
     PyTraceBack_Here(py_frame);
 bad:
-    Py_XDECREF(py_srcfile);
-    Py_XDECREF(py_funcname);
     Py_XDECREF(py_code);
     Py_XDECREF(py_frame);
 }
@@ -24254,6 +25052,7 @@ static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) {
     return 0;
 }
 
+
 /* Type Conversion Functions */
 
 static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {