From 727cbc4a71aa088d479dd4b3508a0f5cc16cbf7b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ond=C5=99ej=20=C4=8Cert=C3=ADk?= Date: Sun, 2 Sep 2012 09:47:35 -0700 Subject: [PATCH] FIX: Regenerate mtrand.c with Cython 0.17 Fixes #416 --- numpy/random/mtrand/mtrand.c | 15965 +++++++++++++++++---------------- 1 file changed, 8209 insertions(+), 7756 deletions(-) diff --git a/numpy/random/mtrand/mtrand.c b/numpy/random/mtrand/mtrand.c index 984acf8cbfd5..33c26ba13315 100644 --- a/numpy/random/mtrand/mtrand.c +++ b/numpy/random/mtrand/mtrand.c @@ -1,16 +1,16 @@ -/* Generated by Cython 0.14.1 on Thu Jul 12 12:48:58 2012 */ +/* Generated by Cython 0.17 on Sun Sep 2 09:46:21 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 /* For offsetof */ #ifndef offsetof #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) #endif - #if !defined(WIN32) && !defined(MS_WINDOWS) #ifndef __stdcall #define __stdcall @@ -22,36 +22,44 @@ #define __fastcall #endif #endif - #ifndef DL_IMPORT #define DL_IMPORT(t) t #endif #ifndef DL_EXPORT #define DL_EXPORT(t) t #endif - #ifndef PY_LONG_LONG #define PY_LONG_LONG LONG_LONG #endif - -#if PY_VERSION_HEX < 0x02040000 - #define METH_COEXIST 0 - #define PyDict_CheckExact(op) (Py_TYPE(op) == &PyDict_Type) - #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 PY_VERSION_HEX < 0x02050000 typedef int Py_ssize_t; #define PY_SSIZE_T_MAX INT_MAX #define PY_SSIZE_T_MIN INT_MIN #define PY_FORMAT_SIZE_T "" + #define CYTHON_FORMAT_SSIZE_T "" #define PyInt_FromSsize_t(z) PyInt_FromLong(z) - #define PyInt_AsSsize_t(o) PyInt_AsLong(o) - #define PyNumber_Index(o) PyNumber_Int(o) - #define PyIndex_Check(o) PyNumber_Check(o) + #define PyInt_AsSsize_t(o) __Pyx_PyInt_AsInt(o) + #define PyNumber_Index(o) ((PyNumber_Check(o) && !PyFloat_Check(o)) ? PyNumber_Int(o) : \ + (PyErr_Format(PyExc_TypeError, \ + "expected index value, got %.200s", Py_TYPE(o)->tp_name), \ + (PyObject*)0)) + #define PyIndex_Check(o) (PyNumber_Check(o) && !PyFloat_Check(o) && !PyComplex_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" + #define CYTHON_FORMAT_SSIZE_T "z" #endif - #if PY_VERSION_HEX < 0x02060000 #define Py_REFCNT(ob) (((PyObject*)(ob))->ob_refcnt) #define Py_TYPE(ob) (((PyObject*)(ob))->ob_type) @@ -59,7 +67,6 @@ #define PyVarObject_HEAD_INIT(type, size) \ PyObject_HEAD_INIT(type) size, #define PyType_Modified(t) - typedef struct { void *buf; PyObject *obj; @@ -73,7 +80,6 @@ Py_ssize_t *suboffsets; void *internal; } Py_buffer; - #define PyBUF_SIMPLE 0 #define PyBUF_WRITABLE 0x0001 #define PyBUF_FORMAT 0x0004 @@ -83,24 +89,44 @@ #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 #define Py_TPFLAGS_CHECKTYPES 0 #define Py_TPFLAGS_HAVE_INDEX 0 #endif - #if (PY_VERSION_HEX < 0x02060000) || (PY_MAJOR_VERSION >= 3) #define Py_TPFLAGS_HAVE_NEWBUFFER 0 #endif - +#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) + #define CYTHON_PEP393_ENABLED 1 + #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ? \ + 0 : _PyUnicode_Ready((PyObject *)(op))) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) + #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) +#else + #define CYTHON_PEP393_ENABLED 0 + #define __Pyx_PyUnicode_READY(op) (0) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) + #define __Pyx_PyUnicode_READ(k, d, i) ((k=k), (Py_UCS4)(((Py_UNICODE*)d)[i])) +#endif #if PY_MAJOR_VERSION >= 3 #define PyBaseString_Type PyUnicode_Type #define PyStringObject PyUnicodeObject @@ -108,7 +134,6 @@ #define PyString_Check PyUnicode_Check #define PyString_CheckExact PyUnicode_CheckExact #endif - #if PY_VERSION_HEX < 0x02060000 #define PyBytesObject PyStringObject #define PyBytes_Type PyString_Type @@ -127,7 +152,6 @@ #define PyBytes_Concat PyString_Concat #define PyBytes_ConcatAndDel PyString_ConcatAndDel #endif - #if PY_VERSION_HEX < 0x02060000 #define PySet_Check(obj) PyObject_TypeCheck(obj, &PySet_Type) #define PyFrozenSet_Check(obj) PyObject_TypeCheck(obj, &PyFrozenSet_Type) @@ -135,9 +159,7 @@ #ifndef PySet_CheckExact #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type) #endif - #define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) - #if PY_MAJOR_VERSION >= 3 #define PyIntObject PyLongObject #define PyInt_Type PyLong_Type @@ -154,20 +176,17 @@ #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask #endif - #if PY_MAJOR_VERSION >= 3 #define PyBoolObject PyLongObject #endif - - -#if PY_MAJOR_VERSION >= 3 - #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) - #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) +#if PY_VERSION_HEX < 0x03020000 + typedef long Py_hash_t; + #define __Pyx_PyInt_FromHash_t PyInt_FromLong + #define __Pyx_PyInt_AsHash_t PyInt_AsLong #else - #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) - #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) + #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t + #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t #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) @@ -186,11 +205,9 @@ (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_DelSlice(obj, a, b)) : \ (PyErr_Format(PyExc_TypeError, "'%.200s' object doesn't support slice deletion", (obj)->ob_type->tp_name), -1))) #endif - #if PY_MAJOR_VERSION >= 3 #define PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : PyInstanceMethod_New(func)) #endif - #if PY_VERSION_HEX < 0x02050000 #define __Pyx_GetAttrString(o,n) PyObject_GetAttrString((o),((char *)(n))) #define __Pyx_SetAttrString(o,n,a) PyObject_SetAttrString((o),((char *)(n)),(a)) @@ -200,7 +217,6 @@ #define __Pyx_SetAttrString(o,n,a) PyObject_SetAttrString((o),(n),(a)) #define __Pyx_DelAttrString(o,n) PyObject_DelAttrString((o),(n)) #endif - #if PY_VERSION_HEX < 0x02050000 #define __Pyx_NAMESTR(n) ((char *)(n)) #define __Pyx_DOCSTR(n) ((char *)(n)) @@ -209,16 +225,28 @@ #define __Pyx_DOCSTR(n) (n) #endif -#ifdef __cplusplus -#define __PYX_EXTERN_C extern "C" + +#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_EXTERN_C extern + #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) +#endif + +#ifndef __PYX_EXTERN_C + #ifdef __cplusplus + #define __PYX_EXTERN_C extern "C" + #else + #define __PYX_EXTERN_C extern + #endif #endif #if defined(WIN32) || defined(MS_WINDOWS) #define _USE_MATH_DEFINES #endif #include +#define __PYX_HAVE__mtrand #define __PYX_HAVE_API__mtrand #include "string.h" #include "math.h" @@ -228,6 +256,9 @@ #include "randomkit.h" #include "distributions.h" #include "initarray.h" +#ifdef _OPENMP +#include +#endif /* _OPENMP */ #ifdef PYREX_WITHOUT_ASSERTIONS #define CYTHON_WITHOUT_ASSERTIONS @@ -255,7 +286,7 @@ # 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 @@ -270,6 +301,7 @@ typedef struct {PyObject **p; char *s; const long n; const char* encoding; const #define __Pyx_PyBytes_FromUString(s) PyBytes_FromString((char*)s) #define __Pyx_PyBytes_AsUString(s) ((unsigned char*) PyBytes_AsString(s)) +#define __Pyx_Owned_Py_None(b) (Py_INCREF(Py_None), Py_None) #define __Pyx_PyBool_FromLong(b) ((b) ? (Py_INCREF(Py_True), Py_True) : (Py_INCREF(Py_False), Py_False)) static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x); @@ -278,21 +310,25 @@ static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); static CYTHON_INLINE size_t __Pyx_PyInt_AsSize_t(PyObject*); +#if CYTHON_COMPILING_IN_CPYTHON #define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) - +#else +#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) +#endif +#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) #ifdef __GNUC__ -/* Test for GCC > 2.95 */ -#if __GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)) -#define likely(x) __builtin_expect(!!(x), 1) -#define unlikely(x) __builtin_expect(!!(x), 0) -#else /* __GNUC__ > 2 ... */ -#define likely(x) (x) -#define unlikely(x) (x) -#endif /* __GNUC__ > 2 ... */ + /* Test for GCC > 2.95 */ + #if __GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95)) + #define likely(x) __builtin_expect(!!(x), 1) + #define unlikely(x) __builtin_expect(!!(x), 0) + #else /* __GNUC__ > 2 ... */ + #define likely(x) (x) + #define unlikely(x) (x) + #endif /* __GNUC__ > 2 ... */ #else /* __GNUC__ */ -#define likely(x) (x) -#define unlikely(x) (x) + #define likely(x) (x) + #define unlikely(x) (x) #endif /* __GNUC__ */ static PyObject *__pyx_m; @@ -310,24 +346,88 @@ static const char *__pyx_f[] = { "numpy.pxd", }; -/* Type declarations */ +/*--- Type declarations ---*/ +struct __pyx_obj_6mtrand_RandomState; +/* "mtrand.pyx":107 + * long rk_logseries(rk_state *state, double p) + * + * ctypedef double (* rk_cont0)(rk_state *state) # <<<<<<<<<<<<<< + * ctypedef double (* rk_cont1)(rk_state *state, double a) + * ctypedef double (* rk_cont2)(rk_state *state, double a, double b) + */ typedef double (*__pyx_t_6mtrand_rk_cont0)(rk_state *); +/* "mtrand.pyx":108 + * + * ctypedef double (* rk_cont0)(rk_state *state) + * ctypedef double (* rk_cont1)(rk_state *state, double a) # <<<<<<<<<<<<<< + * ctypedef double (* rk_cont2)(rk_state *state, double a, double b) + * ctypedef double (* rk_cont3)(rk_state *state, double a, double b, double c) + */ typedef double (*__pyx_t_6mtrand_rk_cont1)(rk_state *, double); +/* "mtrand.pyx":109 + * ctypedef double (* rk_cont0)(rk_state *state) + * ctypedef double (* rk_cont1)(rk_state *state, double a) + * ctypedef double (* rk_cont2)(rk_state *state, double a, double b) # <<<<<<<<<<<<<< + * ctypedef double (* rk_cont3)(rk_state *state, double a, double b, double c) + * + */ typedef double (*__pyx_t_6mtrand_rk_cont2)(rk_state *, double, double); +/* "mtrand.pyx":110 + * ctypedef double (* rk_cont1)(rk_state *state, double a) + * ctypedef double (* rk_cont2)(rk_state *state, double a, double b) + * ctypedef double (* rk_cont3)(rk_state *state, double a, double b, double c) # <<<<<<<<<<<<<< + * + * ctypedef long (* rk_disc0)(rk_state *state) + */ typedef double (*__pyx_t_6mtrand_rk_cont3)(rk_state *, double, double, double); +/* "mtrand.pyx":112 + * ctypedef double (* rk_cont3)(rk_state *state, double a, double b, double c) + * + * ctypedef long (* rk_disc0)(rk_state *state) # <<<<<<<<<<<<<< + * ctypedef long (* rk_discnp)(rk_state *state, long n, double p) + * ctypedef long (* rk_discdd)(rk_state *state, double n, double p) + */ typedef long (*__pyx_t_6mtrand_rk_disc0)(rk_state *); +/* "mtrand.pyx":113 + * + * ctypedef long (* rk_disc0)(rk_state *state) + * ctypedef long (* rk_discnp)(rk_state *state, long n, double p) # <<<<<<<<<<<<<< + * ctypedef long (* rk_discdd)(rk_state *state, double n, double p) + * ctypedef long (* rk_discnmN)(rk_state *state, long n, long m, long N) + */ typedef long (*__pyx_t_6mtrand_rk_discnp)(rk_state *, long, double); +/* "mtrand.pyx":114 + * ctypedef long (* rk_disc0)(rk_state *state) + * ctypedef long (* rk_discnp)(rk_state *state, long n, double p) + * ctypedef long (* rk_discdd)(rk_state *state, double n, double p) # <<<<<<<<<<<<<< + * ctypedef long (* rk_discnmN)(rk_state *state, long n, long m, long N) + * ctypedef long (* rk_discd)(rk_state *state, double a) + */ typedef long (*__pyx_t_6mtrand_rk_discdd)(rk_state *, double, double); +/* "mtrand.pyx":115 + * ctypedef long (* rk_discnp)(rk_state *state, long n, double p) + * ctypedef long (* rk_discdd)(rk_state *state, double n, double p) + * ctypedef long (* rk_discnmN)(rk_state *state, long n, long m, long N) # <<<<<<<<<<<<<< + * ctypedef long (* rk_discd)(rk_state *state, double a) + * + */ typedef long (*__pyx_t_6mtrand_rk_discnmN)(rk_state *, long, long, long); +/* "mtrand.pyx":116 + * ctypedef long (* rk_discdd)(rk_state *state, double n, double p) + * ctypedef long (* rk_discnmN)(rk_state *state, long n, long m, long N) + * ctypedef long (* rk_discd)(rk_state *state, double a) # <<<<<<<<<<<<<< + * + * + */ typedef long (*__pyx_t_6mtrand_rk_discd)(rk_state *, double); /* "mtrand.pyx":524 @@ -337,7 +437,6 @@ typedef long (*__pyx_t_6mtrand_rk_discd)(rk_state *, double); * """ * RandomState(seed=None) */ - struct __pyx_obj_6mtrand_RandomState { PyObject_HEAD rk_state *internal_state; @@ -346,7 +445,6 @@ struct __pyx_obj_6mtrand_RandomState { #ifndef CYTHON_REFNANNY #define CYTHON_REFNANNY 0 #endif - #if CYTHON_REFNANNY typedef struct { void (*INCREF)(void*, PyObject*, int); @@ -357,54 +455,63 @@ struct __pyx_obj_6mtrand_RandomState { void (*FinishContext)(void**); } __Pyx_RefNannyAPIStruct; static __Pyx_RefNannyAPIStruct *__Pyx_RefNanny = NULL; - static __Pyx_RefNannyAPIStruct * __Pyx_RefNannyImportAPI(const char *modname) { - PyObject *m = NULL, *p = NULL; - void *r = NULL; - m = PyImport_ImportModule((char *)modname); - if (!m) goto end; - p = PyObject_GetAttrString(m, (char *)"RefNannyAPI"); - if (!p) goto end; - r = PyLong_AsVoidPtr(p); - end: - Py_XDECREF(p); - Py_XDECREF(m); - return (__Pyx_RefNannyAPIStruct *)r; - } - #define __Pyx_RefNannySetupContext(name) void *__pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__) - #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__) + static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname); /*proto*/ + #define __Pyx_RefNannyDeclarations void *__pyx_refnanny = NULL; +#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__) #define __Pyx_GIVEREF(r) __Pyx_RefNanny->GIVEREF(__pyx_refnanny, (PyObject *)(r), __LINE__) - #define __Pyx_XDECREF(r) do { if((r) != NULL) {__Pyx_DECREF(r);} } while(0) + #define __Pyx_XINCREF(r) do { if((r) != NULL) {__Pyx_INCREF(r); }} while(0) + #define __Pyx_XDECREF(r) do { if((r) != NULL) {__Pyx_DECREF(r); }} while(0) + #define __Pyx_XGOTREF(r) do { if((r) != NULL) {__Pyx_GOTREF(r); }} while(0) + #define __Pyx_XGIVEREF(r) do { if((r) != NULL) {__Pyx_GIVEREF(r);}} while(0) #else - #define __Pyx_RefNannySetupContext(name) + #define __Pyx_RefNannyDeclarations + #define __Pyx_RefNannySetupContext(name, acquire_gil) #define __Pyx_RefNannyFinishContext() #define __Pyx_INCREF(r) Py_INCREF(r) #define __Pyx_DECREF(r) Py_DECREF(r) #define __Pyx_GOTREF(r) #define __Pyx_GIVEREF(r) + #define __Pyx_XINCREF(r) Py_XINCREF(r) #define __Pyx_XDECREF(r) Py_XDECREF(r) + #define __Pyx_XGOTREF(r) + #define __Pyx_XGIVEREF(r) #endif /* CYTHON_REFNANNY */ -#define __Pyx_XGIVEREF(r) do { if((r) != NULL) {__Pyx_GIVEREF(r);} } while(0) -#define __Pyx_XGOTREF(r) do { if((r) != NULL) {__Pyx_GOTREF(r);} } while(0) +#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*/ static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb); /*proto*/ static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb); /*proto*/ -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb); /*proto*/ +static void __Pyx_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; @@ -412,90 +519,102 @@ 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)))) { - PyObject *r = PyList_GET_ITEM(o, i); - Py_INCREF(r); - return r; - } - else if ((-PyList_GET_SIZE(o) <= i) & (i < 0)) { - PyObject *r = PyList_GET_ITEM(o, PyList_GET_SIZE(o) + i); - Py_INCREF(r); - return r; - } +#if CYTHON_COMPILING_IN_CPYTHON + if (likely((0 <= i) & (i < PyList_GET_SIZE(o)))) { + PyObject *r = PyList_GET_ITEM(o, i); + Py_INCREF(r); + return r; + } + else if ((-PyList_GET_SIZE(o) <= i) & (i < 0)) { + PyObject *r = PyList_GET_ITEM(o, PyList_GET_SIZE(o) + i); + Py_INCREF(r); + return r; } return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +#else + return PySequence_GetItem(o, i); +#endif } - #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)))) { - PyObject *r = PyTuple_GET_ITEM(o, i); - Py_INCREF(r); - return r; - } - else if ((-PyTuple_GET_SIZE(o) <= i) & (i < 0)) { - PyObject *r = PyTuple_GET_ITEM(o, PyTuple_GET_SIZE(o) + i); - Py_INCREF(r); - return r; - } +#if CYTHON_COMPILING_IN_CPYTHON + if (likely((0 <= i) & (i < PyTuple_GET_SIZE(o)))) { + PyObject *r = PyTuple_GET_ITEM(o, i); + Py_INCREF(r); + return r; + } + else if ((-PyTuple_GET_SIZE(o) <= i) & (i < 0)) { + PyObject *r = PyTuple_GET_ITEM(o, PyTuple_GET_SIZE(o) + i); + Py_INCREF(r); + return r; } return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +#else + return PySequence_GetItem(o, i); +#endif } - - #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 CYTHON_COMPILING_IN_CPYTHON + 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 { /* inlined PySequence_GetItem() */ + PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence; + if (likely(m && m->sq_item)) { + if (unlikely(i < 0) && likely(m->sq_length)) { + Py_ssize_t l = m->sq_length(o); + if (unlikely(l < 0)) return NULL; + i += l; + } + return m->sq_item(o, i); + } } - else { - r = __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); +#else + if (PySequence_Check(o)) { + return PySequence_GetItem(o, i); } - return r; +#endif + return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i)); } +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); + static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); -static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); +static CYTHON_INLINE int __Pyx_IterFinish(void); /*proto*/ -static PyObject *__Pyx_UnpackItem(PyObject *, Py_ssize_t index); /*proto*/ -static int __Pyx_EndUnpack(PyObject *, Py_ssize_t expected); /*proto*/ +static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected); /*proto*/ static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); /*proto*/ -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; @@ -503,26 +622,48 @@ 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 CYTHON_COMPILING_IN_CPYTHON + 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 { /* inlined PySequence_SetItem() */ + PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence; + if (likely(m && m->sq_ass_item)) { + if (unlikely(i < 0) && likely(m->sq_length)) { + Py_ssize_t l = m->sq_length(o); + if (unlikely(l < 0)) return -1; + i += l; + } + return m->sq_ass_item(o, i, v); + } } - else if (Py_TYPE(o)->tp_as_sequence && Py_TYPE(o)->tp_as_sequence->sq_ass_item && (likely(i >= 0))) +#else +#if CYTHON_COMPILING_IN_PYPY + if (PySequence_Check(o) && !PyDict_Check(o)) { +#else + if (PySequence_Check(o)) { +#endif return PySequence_SetItem(o, i, v); - else { - PyObject *j = PyInt_FromSsize_t(i); - return __Pyx_SetItemInt_Generic(o, j, v); } +#endif + return __Pyx_SetItemInt_Generic(o, PyInt_FromSsize_t(i), v); } static CYTHON_INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb); /*proto*/ static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb); /*proto*/ -static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list); /*proto*/ +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, long level); /*proto*/ + +static CYTHON_INLINE npy_intp __Pyx_PyInt_from_py_npy_intp(PyObject *); + +static CYTHON_INLINE void __Pyx_RaiseImportError(PyObject *name); static CYTHON_INLINE PyObject *__Pyx_PyInt_to_py_npy_intp(npy_intp); @@ -558,19 +699,43 @@ static CYTHON_INLINE signed long __Pyx_PyInt_AsSignedLong(PyObject *); static CYTHON_INLINE signed PY_LONG_LONG __Pyx_PyInt_AsSignedLongLong(PyObject *); -static CYTHON_INLINE npy_intp __Pyx_PyInt_from_py_npy_intp(PyObject *); +static int __Pyx_check_binary_version(void); -static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, long size, int strict); /*proto*/ +#if !defined(__Pyx_PyIdentifier_FromString) +#if PY_MAJOR_VERSION < 3 + #define __Pyx_PyIdentifier_FromString(s) PyString_FromString(s) +#else + #define __Pyx_PyIdentifier_FromString(s) PyUnicode_FromString(s) +#endif +#endif static PyObject *__Pyx_ImportModule(const char *name); /*proto*/ -static void __Pyx_AddTraceback(const char *funcname); /*proto*/ +static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict); /*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 */ +/* Module declarations from 'numpy' */ + +/* Module declarations from 'mtrand' */ static PyTypeObject *__pyx_ptype_6mtrand_dtype = 0; static PyTypeObject *__pyx_ptype_6mtrand_ndarray = 0; static PyTypeObject *__pyx_ptype_6mtrand_flatiter = 0; @@ -594,11 +759,64 @@ static PyObject *__pyx_f_6mtrand_discd_array_sc(rk_state *, __pyx_t_6mtrand_rk_d static PyObject *__pyx_f_6mtrand_discd_array(rk_state *, __pyx_t_6mtrand_rk_discd, PyObject *, PyArrayObject *); /*proto*/ static double __pyx_f_6mtrand_kahan_sum(double *, npy_intp); /*proto*/ #define __Pyx_MODULE_NAME "mtrand" -static int __pyx_module_is_main_mtrand = 0; +int __pyx_module_is_main_mtrand = 0; -/* Implementation of mtrand */ +/* 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"; @@ -750,19 +968,16 @@ static char __pyx_k_277[] = "RandomState.permutation (line 4419)"; static char __pyx_k_278[] = "\n permutation(x)\n\n Randomly permute a sequence, or return a permuted range.\n\n If `x` is a multi-dimensional array, it is only shuffled along its\n first index.\n\n Parameters\n ----------\n x : int or array_like\n If `x` is an integer, randomly permute ``np.arange(x)``.\n If `x` is an array, make a copy and shuffle the elements\n randomly.\n\n Returns\n -------\n out : ndarray\n Permuted sequence or array range.\n\n Examples\n --------\n >>> np.random.permutation(10)\n array([1, 7, 4, 3, 0, 9, 2, 5, 8, 6])\n\n >>> np.random.permutation([1, 4, 9, 12, 15])\n array([15, 1, 9, 4, 12])\n\n >>> arr = np.arange(9).reshape((3, 3))\n >>> np.random.permutation(arr)\n array([[6, 7, 8],\n [0, 1, 2],\n [3, 4, 5]])\n\n "; static char __pyx_k__df[] = "df"; static char __pyx_k__mu[] = "mu"; -static char __pyx_k__nd[] = "nd"; static char __pyx_k__np[] = "np"; static char __pyx_k__add[] = "add"; static char __pyx_k__any[] = "any"; static char __pyx_k__cov[] = "cov"; static char __pyx_k__dot[] = "dot"; static char __pyx_k__int[] = "int"; -static char __pyx_k__key[] = "key"; static char __pyx_k__lam[] = "lam"; static char __pyx_k__loc[] = "loc"; static char __pyx_k__low[] = "low"; static char __pyx_k__max[] = "max"; -static char __pyx_k__pos[] = "pos"; static char __pyx_k__sum[] = "sum"; static char __pyx_k__svd[] = "svd"; static char __pyx_k__beta[] = "beta"; @@ -795,7 +1010,6 @@ static char __pyx_k__dtype[] = "dtype"; static char __pyx_k__empty[] = "empty"; static char __pyx_k__equal[] = "equal"; static char __pyx_k__gamma[] = "gamma"; -static char __pyx_k__gauss[] = "gauss"; static char __pyx_k__iinfo[] = "iinfo"; static char __pyx_k__kappa[] = "kappa"; static char __pyx_k__ndmin[] = "ndmin"; @@ -809,7 +1023,6 @@ static char __pyx_k__scale[] = "scale"; static char __pyx_k__shape[] = "shape"; static char __pyx_k__sigma[] = "sigma"; static char __pyx_k__zeros[] = "zeros"; -static char __pyx_k__append[] = "append"; static char __pyx_k__arange[] = "arange"; static char __pyx_k__choice[] = "choice"; static char __pyx_k__cumsum[] = "cumsum"; @@ -823,7 +1036,6 @@ static char __pyx_k__uint32[] = "uint32"; static char __pyx_k__unique[] = "unique"; static char __pyx_k__MT19937[] = "MT19937"; static char __pyx_k__asarray[] = "asarray"; -static char __pyx_k__dataptr[] = "dataptr"; static char __pyx_k__float64[] = "float64"; static char __pyx_k__greater[] = "greater"; static char __pyx_k__integer[] = "integer"; @@ -850,12 +1062,10 @@ static char __pyx_k__chisquare[] = "chisquare"; static char __pyx_k__dirichlet[] = "dirichlet"; static char __pyx_k__geometric[] = "geometric"; static char __pyx_k__get_state[] = "get_state"; -static char __pyx_k__has_gauss[] = "has_gauss"; static char __pyx_k__lognormal[] = "lognormal"; static char __pyx_k__logseries[] = "logseries"; static char __pyx_k__set_state[] = "set_state"; static char __pyx_k__ValueError[] = "ValueError"; -static char __pyx_k__dimensions[] = "dimensions"; 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- PyArrayObject *arrayObject; + PyArrayObject *arrayObject = 0; npy_intp __pyx_v_length; npy_intp __pyx_v_i; PyObject *__pyx_r = NULL; + __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; npy_intp __pyx_t_5; - __Pyx_RefNannySetupContext("cont0_array"); - arrayObject = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None); + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; + __Pyx_RefNannySetupContext("cont0_array", 0); /* "mtrand.pyx":134 * cdef npy_intp i @@ -1360,7 +1563,7 @@ static PyObject *__pyx_f_6mtrand_cont0_array(rk_state *__pyx_v_state, __pyx_t_6 __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_2 = PyTuple_New(2); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 137; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_t_2)); + __Pyx_GOTREF(__pyx_t_2); __Pyx_INCREF(__pyx_v_size); PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_v_size); 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- PyArrayObject *arrayObject; + PyArrayObject *arrayObject = 0; npy_intp __pyx_v_length; npy_intp __pyx_v_i; PyObject *__pyx_r = NULL; + __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; npy_intp __pyx_t_5; - __Pyx_RefNannySetupContext("cont1_array_sc"); - arrayObject = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None); + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; + __Pyx_RefNannySetupContext("cont1_array_sc", 0); /* "mtrand.pyx":151 * cdef npy_intp i @@ -1510,7 +1715,7 @@ static PyObject *__pyx_f_6mtrand_cont1_array_sc(rk_state *__pyx_v_state, __pyx_ __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __pyx_t_2 = PyTuple_New(2); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 154; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_t_2)); + __Pyx_GOTREF(__pyx_t_2); __Pyx_INCREF(__pyx_v_size); PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_v_size); 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double *__pyx_v_oa_data; - PyArrayObject *arrayObject; + PyArrayObject *arrayObject = 0; npy_intp __pyx_v_length; npy_intp __pyx_v_i; - PyArrayIterObject *__pyx_v_itera; - PyArrayMultiIterObject *__pyx_v_multi; + PyArrayIterObject *__pyx_v_itera = 0; + PyArrayMultiIterObject *__pyx_v_multi = 0; PyObject *__pyx_r = NULL; + __Pyx_RefNannyDeclarations int __pyx_t_1; PyObject *__pyx_t_2 = NULL; - PyObject *__pyx_t_3 = NULL; + npy_intp __pyx_t_3; PyObject *__pyx_t_4 = NULL; - npy_intp __pyx_t_5; - __Pyx_RefNannySetupContext("cont1_array"); - arrayObject = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None); - __pyx_v_itera = ((PyArrayIterObject *)Py_None); __Pyx_INCREF(Py_None); - __pyx_v_multi = ((PyArrayMultiIterObject *)Py_None); __Pyx_INCREF(Py_None); + PyObject *__pyx_t_5 = NULL; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; + __Pyx_RefNannySetupContext("cont1_array", 0); /* "mtrand.pyx":170 * cdef broadcast multi @@ -1630,16 +1835,6 @@ static PyObject *__pyx_f_6mtrand_cont1_array(rk_state *__pyx_v_state, __pyx_t_6 __pyx_t_1 = (__pyx_v_size == Py_None); 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If the internal state is manually altered,\n the user should know exactly what he/she is doing.\n\n "; +static PyObject *__pyx_pw_6mtrand_11RandomState_7get_state(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { + PyObject *__pyx_r = 0; + __Pyx_RefNannyDeclarations + __Pyx_RefNannySetupContext("get_state (wrapper)", 0); + __pyx_r = __pyx_pf_6mtrand_11RandomState_6get_state(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)); + __Pyx_RefNannyFinishContext(); + return __pyx_r; +} + /* "mtrand.pyx":603 * PyArray_DIM(obj, 0)) * @@ -5091,17 +5303,18 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_2seed(PyObject *__pyx_v_self, Py * get_state() */ -static PyObject *__pyx_pf_6mtrand_11RandomState_3get_state(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused); /*proto*/ -static char __pyx_doc_6mtrand_11RandomState_3get_state[] = "\n get_state()\n\n Return a tuple representing the internal state of the generator.\n\n For more details, see `set_state`.\n\n Returns\n -------\n out : tuple(str, ndarray of 624 uints, int, int, float)\n The returned tuple has the following items:\n\n 1. the string 'MT19937'.\n 2. a 1-D array of 624 unsigned integer keys.\n 3. an integer ``pos``.\n 4. an integer ``has_gauss``.\n 5. a float ``cached_gaussian``.\n\n See Also\n --------\n set_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 "; -static PyObject *__pyx_pf_6mtrand_11RandomState_3get_state(PyObject *__pyx_v_self, CYTHON_UNUSED PyObject *unused) { - PyArrayObject *arrayObject_state; +static PyObject *__pyx_pf_6mtrand_11RandomState_6get_state(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self) { + PyArrayObject *arrayObject_state = 0; PyObject *__pyx_r = NULL; + __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; - __Pyx_RefNannySetupContext("get_state"); - arrayObject_state = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None); + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; + __Pyx_RefNannySetupContext("get_state", 0); /* "mtrand.pyx":634 * """ @@ -5121,7 +5334,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_3get_state(PyObject *__pyx_v_sel __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_t_1 = PyTuple_New(2); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 634; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_t_1)); + __Pyx_GOTREF(__pyx_t_1); __Pyx_INCREF(__pyx_int_624); PyTuple_SET_ITEM(__pyx_t_1, 0, __pyx_int_624); __Pyx_GIVEREF(__pyx_int_624); @@ -5133,7 +5346,6 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_3get_state(PyObject *__pyx_v_sel __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_1)); __pyx_t_1 = 0; __Pyx_INCREF(((PyObject *)((PyArrayObject *)__pyx_t_3))); - __Pyx_DECREF(((PyObject *)arrayObject_state)); arrayObject_state = ((PyArrayObject *)__pyx_t_3); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; @@ -5144,7 +5356,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_3get_state(PyObject *__pyx_v_sel * state = 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)))); /* "mtrand.pyx":636 * state = np.empty(624, np.uint) @@ -5164,7 +5376,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_3get_state(PyObject *__pyx_v_sel __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = PyTuple_New(2); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 636; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_t_3)); + __Pyx_GOTREF(__pyx_t_3); __Pyx_INCREF(((PyObject *)arrayObject_state)); PyTuple_SET_ITEM(__pyx_t_3, 0, ((PyObject *)arrayObject_state)); __Pyx_GIVEREF(((PyObject *)arrayObject_state)); @@ -5188,7 +5400,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_3get_state(PyObject *__pyx_v_sel * */ __Pyx_XDECREF(__pyx_r); - __pyx_t_2 = PyInt_FromLong(((struct __pyx_obj_6mtrand_RandomState *)__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;} + __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;} __Pyx_GOTREF(__pyx_t_2); /* "mtrand.pyx":638 @@ -5198,12 +5410,12 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_3get_state(PyObject *__pyx_v_sel * * def set_state(self, state): */ - __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;} __Pyx_GOTREF(__pyx_t_3); - __pyx_t_1 = PyFloat_FromDouble(((struct __pyx_obj_6mtrand_RandomState *)__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;} + __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;} __Pyx_GOTREF(__pyx_t_1); __pyx_t_4 = PyTuple_New(5); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 637; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_t_4)); + __Pyx_GOTREF(__pyx_t_4); __Pyx_INCREF(((PyObject *)__pyx_n_s__MT19937)); PyTuple_SET_ITEM(__pyx_t_4, 0, ((PyObject *)__pyx_n_s__MT19937)); __Pyx_GIVEREF(((PyObject *)__pyx_n_s__MT19937)); @@ -5230,15 +5442,27 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_3get_state(PyObject *__pyx_v_sel __Pyx_XDECREF(__pyx_t_2); __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); - __Pyx_AddTraceback("mtrand.RandomState.get_state"); + __Pyx_AddTraceback("mtrand.RandomState.get_state", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; - __Pyx_DECREF((PyObject *)arrayObject_state); + __Pyx_XDECREF((PyObject *)arrayObject_state); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); 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) { + PyObject *__pyx_r = 0; + __Pyx_RefNannyDeclarations + __Pyx_RefNannySetupContext("set_state (wrapper)", 0); + __pyx_r = __pyx_pf_6mtrand_11RandomState_8set_state(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self), ((PyObject *)__pyx_v_state)); + __Pyx_RefNannyFinishContext(); + return __pyx_r; +} + /* "mtrand.pyx":640 * self.internal_state.has_gauss, self.internal_state.gauss) * @@ -5247,30 +5471,31 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_3get_state(PyObject *__pyx_v_sel * set_state(state) */ -static PyObject *__pyx_pf_6mtrand_11RandomState_4set_state(PyObject *__pyx_v_self, PyObject *__pyx_v_state); /*proto*/ -static char __pyx_doc_6mtrand_11RandomState_4set_state[] = "\n set_state(state)\n\n Set the internal state of the generator from a tuple.\n\n For use if one has reason to manually (re-)set the internal state of the\n \"Mersenne Twister\"[1]_ pseudo-random number generating algorithm.\n\n Parameters\n ----------\n state : tuple(str, ndarray of 624 uints, int, int, float)\n The `state` tuple has the following items:\n\n 1. the string 'MT19937', specifying the Mersenne Twister algorithm.\n 2. a 1-D array of 624 unsigned integers ``keys``.\n 3. an integer ``pos``.\n 4. an integer ``has_gauss``.\n 5. a float ``cached_gaussian``.\n\n Returns\n -------\n out : None\n Returns 'None' on success.\n\n See Also\n --------\n get_state\n\n Notes\n -----\n `set_state` and `get_state` are not needed to work with any of the\n random distributions in NumPy. If the internal state is manually altered,\n the user should know exactly what he/she is doing.\n\n For backwards compatibility, the form (str, array of 624 uints, int) is\n also accepted although it is missing some information about the cached\n Gaussian value: ``state = ('MT19937', keys, pos)``.\n\n References\n ----------\n .. [1] M. Matsumoto and T. Nishimura, \"Mersenne Twister: A\n 623-dimensionally equidistributed uniform pseudorandom number\n generator,\" *ACM Trans. on Modeling and Computer Simulation*,\n Vol. 8, No. 1, pp. 3-30, Jan. 1998.\n\n "; -static PyObject *__pyx_pf_6mtrand_11RandomState_4set_state(PyObject *__pyx_v_self, PyObject *__pyx_v_state) { - PyArrayObject *arrayObject_obj; +static PyObject *__pyx_pf_6mtrand_11RandomState_8set_state(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_state) { + PyArrayObject *arrayObject_obj = 0; int __pyx_v_pos; - PyObject *__pyx_v_algorithm_name; - PyObject *__pyx_v_key; - PyObject *__pyx_v_has_gauss; - PyObject *__pyx_v_cached_gaussian; + PyObject *__pyx_v_algorithm_name = NULL; + PyObject *__pyx_v_key = NULL; + PyObject *__pyx_v_has_gauss = NULL; + PyObject *__pyx_v_cached_gaussian = NULL; PyObject *__pyx_r = NULL; + __Pyx_RefNannyDeclarations PyObject *__pyx_t_1 = NULL; int __pyx_t_2; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; - int __pyx_t_5; - PyObject *__pyx_t_6 = NULL; - Py_ssize_t __pyx_t_7; - double __pyx_t_8; - __Pyx_RefNannySetupContext("set_state"); - arrayObject_obj = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None); - __pyx_v_algorithm_name = Py_None; __Pyx_INCREF(Py_None); - __pyx_v_key = Py_None; __Pyx_INCREF(Py_None); - __pyx_v_has_gauss = Py_None; __Pyx_INCREF(Py_None); - __pyx_v_cached_gaussian = Py_None; __Pyx_INCREF(Py_None); + PyObject *__pyx_t_5 = NULL; + PyObject *(*__pyx_t_6)(PyObject *); + int __pyx_t_7; + Py_ssize_t __pyx_t_8; + PyObject *__pyx_t_9 = NULL; + PyObject *__pyx_t_10 = NULL; + PyObject *__pyx_t_11 = NULL; + double __pyx_t_12; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; + __Pyx_RefNannySetupContext("set_state", 0); /* "mtrand.pyx":689 * cdef ndarray obj "arrayObject_obj" @@ -5281,7 +5506,6 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_4set_state(PyObject *__pyx_v_sel */ __pyx_t_1 = __Pyx_GetItemInt(__pyx_v_state, 0, sizeof(long), PyInt_FromLong); if (!__pyx_t_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 689; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_1); - __Pyx_DECREF(__pyx_v_algorithm_name); __pyx_v_algorithm_name = __pyx_t_1; __pyx_t_1 = 0; @@ -5292,8 +5516,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_4set_state(PyObject *__pyx_v_sel * raise ValueError("algorithm must be 'MT19937'") * key, pos = state[1:3] */ - __pyx_t_1 = PyObject_RichCompare(__pyx_v_algorithm_name, ((PyObject *)__pyx_n_s__MT19937), Py_NE); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 690; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_1); + __pyx_t_1 = PyObject_RichCompare(__pyx_v_algorithm_name, ((PyObject *)__pyx_n_s__MT19937), Py_NE); __Pyx_XGOTREF(__pyx_t_1); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 690; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_t_1); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; 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__pyx_lineno = 692; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + } + #if CYTHON_COMPILING_IN_CPYTHON + if (likely(PyTuple_CheckExact(sequence))) { + __pyx_t_3 = PyTuple_GET_ITEM(sequence, 0); + __pyx_t_4 = PyTuple_GET_ITEM(sequence, 1); + } else { + __pyx_t_3 = PyList_GET_ITEM(sequence, 0); + __pyx_t_4 = PyList_GET_ITEM(sequence, 1); + } + __Pyx_INCREF(__pyx_t_3); + __Pyx_INCREF(__pyx_t_4); + #else + __pyx_t_3 = PySequence_ITEM(sequence, 0); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 692; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = PySequence_ITEM(sequence, 1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 692; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + #endif __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; - __Pyx_DECREF(__pyx_v_key); - __pyx_v_key = __pyx_t_3; - __pyx_t_3 = 0; - __pyx_v_pos = __pyx_t_5; - } else { - __pyx_t_6 = PyObject_GetIter(__pyx_t_1); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 692; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_6); + } else + { + Py_ssize_t index = -1; + __pyx_t_5 = PyObject_GetIter(__pyx_t_1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 692; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_5); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; - __pyx_t_3 = __Pyx_UnpackItem(__pyx_t_6, 0); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 692; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_6 = Py_TYPE(__pyx_t_5)->tp_iternext; + index = 0; __pyx_t_3 = __pyx_t_6(__pyx_t_5); if (unlikely(!__pyx_t_3)) goto __pyx_L4_unpacking_failed; __Pyx_GOTREF(__pyx_t_3); - __pyx_t_4 = __Pyx_UnpackItem(__pyx_t_6, 1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 692; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + index = 1; __pyx_t_4 = __pyx_t_6(__pyx_t_5); if (unlikely(!__pyx_t_4)) goto __pyx_L4_unpacking_failed; __Pyx_GOTREF(__pyx_t_4); 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goto __pyx_L1_error;} + __pyx_L5_unpacking_done:; } + __pyx_t_7 = __Pyx_PyInt_AsInt(__pyx_t_4); if (unlikely((__pyx_t_7 == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 692; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; + __pyx_v_key = __pyx_t_3; + __pyx_t_3 = 0; + __pyx_v_pos = __pyx_t_7; /* "mtrand.pyx":693 * raise ValueError("algorithm must be 'MT19937'") @@ -5359,11 +5608,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_4set_state(PyObject *__pyx_v_sel * has_gauss = 0 * cached_gaussian = 0.0 */ - __pyx_t_1 = __pyx_v_state; - __Pyx_INCREF(__pyx_t_1); - __pyx_t_7 = PyObject_Length(__pyx_t_1); if (unlikely(__pyx_t_7 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 693; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; - __pyx_t_2 = (__pyx_t_7 == 3); + __pyx_t_8 = PyObject_Length(__pyx_v_state); if (unlikely(__pyx_t_8 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 693; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = (__pyx_t_8 == 3); if (__pyx_t_2) { /* "mtrand.pyx":694 @@ -5374,7 +5620,6 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_4set_state(PyObject *__pyx_v_sel * else: */ __Pyx_INCREF(__pyx_int_0); - __Pyx_DECREF(__pyx_v_has_gauss); __pyx_v_has_gauss = __pyx_int_0; /* "mtrand.pyx":695 @@ -5386,7 +5631,6 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_4set_state(PyObject *__pyx_v_sel */ __pyx_t_1 = PyFloat_FromDouble(0.0); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 695; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_1); - __Pyx_DECREF(__pyx_v_cached_gaussian); __pyx_v_cached_gaussian = __pyx_t_1; __pyx_t_1 = 0; goto __pyx_L6; @@ -5402,34 +5646,59 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_4set_state(PyObject *__pyx_v_sel */ __pyx_t_1 = __Pyx_PySequence_GetSlice(__pyx_v_state, 3, 5); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 697; __pyx_clineno = __LINE__; 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/*proto*/ +static char __pyx_doc_6mtrand_11RandomState_16random_sample[] = "\n random_sample(size=None)\n\n Return random floats in the half-open interval [0.0, 1.0).\n\n Results are from the \"continuous uniform\" distribution over the\n stated interval. 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__pyx_r = NULL; __pyx_L0:; __Pyx_XGIVEREF(__pyx_r); @@ -5936,91 +6265,105 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_9tomaxint(PyObject *__pyx_v_self return __pyx_r; } -/* "mtrand.pyx":811 +/* Python wrapper */ +static PyObject *__pyx_pw_6mtrand_11RandomState_21randint(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ +static char __pyx_doc_6mtrand_11RandomState_20randint[] = "\n randint(low, high=None, size=None)\n\n Return random integers from `low` (inclusive) to `high` (exclusive).\n\n Return random integers from the \"discrete uniform\" distribution in the\n \"half-open\" interval [`low`, `high`). If `high` is None (the default),\n then results are from [0, `low`).\n\n Parameters\n ----------\n low : int\n Lowest (signed) integer to be drawn from the distribution (unless\n ``high=None``, in which case this parameter is the *highest* such\n integer).\n high : int, optional\n If provided, one above the largest (signed) integer to be drawn\n from the distribution (see above for behavior if ``high=None``).\n size : int or tuple of ints, optional\n Output shape. Default is None, in which case a single int is\n returned.\n\n Returns\n -------\n out : int or ndarray of ints\n `size`-shaped array of random integers from the appropriate\n distribution, or a single such random int if `size` not provided.\n\n See Also\n --------\n random.random_integers : similar to `randint`, only for the closed\n interval [`low`, `high`], and 1 is the lowest value if `high` is\n omitted. 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__pyx_lineno = 811; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + } + } else { + switch (PyTuple_GET_SIZE(__pyx_args)) { + case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); + case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); + case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); + break; + default: goto __pyx_L5_argtuple_error; } - } - if (unlikely(kw_args > 0)) { - if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "randint") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 811; __pyx_clineno = __LINE__; goto __pyx_L3_error;} } __pyx_v_low = values[0]; __pyx_v_high = values[1]; __pyx_v_size = values[2]; - } else { - __pyx_v_high = ((PyObject *)Py_None); - __pyx_v_size = ((PyObject *)Py_None); - switch (PyTuple_GET_SIZE(__pyx_args)) { - case 3: __pyx_v_size = PyTuple_GET_ITEM(__pyx_args, 2); - case 2: __pyx_v_high = PyTuple_GET_ITEM(__pyx_args, 1); - case 1: __pyx_v_low = PyTuple_GET_ITEM(__pyx_args, 0); - break; - default: goto __pyx_L5_argtuple_error; - } } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("randint", 0, 1, 3, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 811; __pyx_clineno = __LINE__; goto __pyx_L3_error;} __pyx_L3_error:; - __Pyx_AddTraceback("mtrand.RandomState.randint"); + __Pyx_AddTraceback("mtrand.RandomState.randint", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; - arrayObject = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None); + __pyx_r = __pyx_pf_6mtrand_11RandomState_20randint(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self), __pyx_v_low, __pyx_v_high, __pyx_v_size); + __Pyx_RefNannyFinishContext(); + return __pyx_r; +} + +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) { + long __pyx_v_lo; + long __pyx_v_hi; + long __pyx_v_rv; + unsigned long __pyx_v_diff; + long *__pyx_v_array_data; + PyArrayObject *arrayObject = 0; + npy_intp __pyx_v_length; + npy_intp __pyx_v_i; + PyObject *__pyx_r = NULL; + __Pyx_RefNannyDeclarations + int __pyx_t_1; + long __pyx_t_2; + PyObject *__pyx_t_3 = NULL; + PyObject *__pyx_t_4 = NULL; + PyObject *__pyx_t_5 = NULL; + npy_intp __pyx_t_6; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; + __Pyx_RefNannySetupContext("randint", 0); /* "mtrand.pyx":868 * cdef npy_intp i @@ -6050,7 +6393,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_10randint(PyObject *__pyx_v_self */ __pyx_t_2 = __Pyx_PyInt_AsLong(__pyx_v_low); if (unlikely((__pyx_t_2 == (long)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 870; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_v_hi = __pyx_t_2; - goto __pyx_L6; + goto __pyx_L3; } /*else*/ { @@ -6074,7 +6417,7 @@ 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; } - __pyx_L6:; + __pyx_L3:; /* "mtrand.pyx":875 * hi = high @@ -6095,12 +6438,12 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_10randint(PyObject *__pyx_v_self */ __pyx_t_3 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_14), NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 876; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); - __Pyx_Raise(__pyx_t_3, 0, 0); + __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 876; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - goto __pyx_L7; + goto __pyx_L4; } - __pyx_L7:; + __pyx_L4:; /* "mtrand.pyx":878 * raise ValueError("low >= high") @@ -6128,7 +6471,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_10randint(PyObject *__pyx_v_self * return rv * else: */ - __pyx_v_rv = (__pyx_v_lo + ((long)rk_interval(__pyx_v_diff, ((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state))); 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__pyx_t_3 = 0; __Pyx_INCREF(((PyObject *)((PyArrayObject *)__pyx_t_5))); - __Pyx_DECREF(((PyObject *)arrayObject)); arrayObject = ((PyArrayObject *)__pyx_t_5); __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; @@ -6211,7 +6553,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_10randint(PyObject *__pyx_v_self * array_data[i] = rv * return array */ - __pyx_v_rv = (__pyx_v_lo + ((long)rk_interval(__pyx_v_diff, ((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state))); + __pyx_v_rv = (__pyx_v_lo + ((long)rk_interval(__pyx_v_diff, __pyx_v_self->internal_state))); /* "mtrand.pyx":888 * for i from 0 <= i < length: @@ -6235,7 +6577,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_10randint(PyObject *__pyx_v_self __pyx_r = ((PyObject *)arrayObject); goto __pyx_L0; } - __pyx_L8:; + __pyx_L5:; __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; @@ -6243,15 +6585,37 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_10randint(PyObject *__pyx_v_self __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); 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__pyx_L1_error:; __Pyx_XDECREF(__pyx_t_1); - __Pyx_AddTraceback("mtrand.RandomState.bytes"); + __Pyx_AddTraceback("mtrand.RandomState.bytes", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; - __Pyx_DECREF(__pyx_v_bytestring); + __Pyx_XDECREF(__pyx_v_bytestring); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } -/* "mtrand.pyx":919 - * - * - * def choice(self, a, size=1, replace=True, p=None): # <<<<<<<<<<<<<< - * """ - * choice(a, size=1, replace=True, p=None) - */ - -static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ -static char __pyx_doc_6mtrand_11RandomState_12choice[] = "\n choice(a, size=1, replace=True, p=None)\n\n Generates a random sample from a given 1-D array\n\n .. versionadded:: 1.7.0\n\n Parameters\n -----------\n a : 1-D array-like or int\n If an ndarray, a random sample is generated from its elements.\n If an int, the random sample is generated as if a was np.arange(n)\n size : int\n Positive integer, the size of the sample.\n replace : boolean, optional\n Whether the sample is with or without replacement\n p : 1-D array-like, optional\n The probabilities associated with each entry in a.\n If not given the sample assumes a uniform distribtion over all\n entries in a.\n\n Returns\n --------\n samples : 1-D ndarray, shape (size,)\n The generated random samples\n\n Raises\n -------\n ValueError\n If a is an int and less than zero, if a or p are not 1-dimensional,\n if a is an array-like of size 0, if p is not a vector of\n probabilities, if a and p have different lengths, or if\n replace=False and the sample size is greater than the population\n size\n\n See Also\n ---------\n randint, shuffle, permutation\n\n Examples\n ---------\n Generate a uniform random sample from np.arange(5) of size 3:\n\n >>> np.random.choice(5, 3)\n array([0, 3, 4])\n >>> #This is equivalent to np.random.randint(0,5,3)\n\n Generate a non-uniform random sample from np.arange(5) of size 3:\n\n >>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])\n array([3, 3, 0])\n\n Generate a uniform random sample from np.arange(5) of size 3 without\n replacement:\n\n >>> np.random.choice(5, 3, replace=False)\n array([3,1,0])\n >>> #This is equivalent to np.random.shuffle(np.arange(5))[:3]\n\n "" Generate a non-uniform random sample from np.arange(5) of size\n 3 without replacement:\n\n >>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])\n array([2, 3, 0])\n\n Any of the above can be repeated with an arbitrary array-like\n instead of just integers. 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goto __pyx_L1_error;} + __pyx_t_5 = PyObject_Call(__pyx_t_3, ((PyObject *)__pyx_t_1), ((PyObject *)__pyx_t_4)); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1002; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_5); - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; - __Pyx_DECREF(((PyObject *)__pyx_t_2)); __pyx_t_2 = 0; + __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; + __Pyx_DECREF(((PyObject *)__pyx_t_1)); __pyx_t_1 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_4)); __pyx_t_4 = 0; __Pyx_DECREF(__pyx_v_a); __pyx_v_a = __pyx_t_5; @@ -6536,12 +6891,11 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, */ __pyx_t_5 = PyObject_GetAttr(__pyx_v_a, __pyx_n_s__ndim); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1003; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_5); - __pyx_t_4 = PyObject_RichCompare(__pyx_t_5, __pyx_int_1, Py_NE); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1003; 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__pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_4)); __pyx_t_4 = 0; - __Pyx_DECREF(((PyObject *)__pyx_t_2)); __pyx_t_2 = 0; + __Pyx_DECREF(((PyObject *)__pyx_t_1)); __pyx_t_1 = 0; __Pyx_DECREF(__pyx_v_p); __pyx_v_p = __pyx_t_6; __pyx_t_6 = 0; @@ -6659,12 +7011,11 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, */ __pyx_t_6 = PyObject_GetAttr(__pyx_v_p, __pyx_n_s__ndim); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1011; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_6); - __pyx_t_2 = PyObject_RichCompare(__pyx_t_6, __pyx_int_1, Py_NE); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1011; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); + __pyx_t_1 = PyObject_RichCompare(__pyx_t_6, __pyx_int_1, Py_NE); __Pyx_XGOTREF(__pyx_t_1); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1011; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; - __pyx_t_3 = __Pyx_PyObject_IsTrue(__pyx_t_2); if (unlikely(__pyx_t_3 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1011; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - if (__pyx_t_3) { + __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_t_1); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1011; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + if (__pyx_t_2) { /* "mtrand.pyx":1012 * p = np.array(p, dtype=np.double, ndmin=1, copy=0) @@ -6673,14 +7024,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, * if p.size != pop_size: * raise ValueError("a and p must have same size") */ - __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_23), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1012; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __Pyx_Raise(__pyx_t_2, 0, 0); - __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + __pyx_t_1 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_23), NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1012; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __Pyx_Raise(__pyx_t_1, 0, 0, 0); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1012; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - goto __pyx_L11; + goto __pyx_L8; } - __pyx_L11:; + __pyx_L8:; /* "mtrand.pyx":1013 * if p.ndim != 1: @@ -6689,14 +7040,13 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, * raise ValueError("a and p must have same size") * if np.any(p < 0): */ - __pyx_t_2 = PyObject_GetAttr(__pyx_v_p, __pyx_n_s__size); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1013; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __pyx_t_6 = PyObject_RichCompare(__pyx_t_2, __pyx_v_pop_size, Py_NE); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1013; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_6); - __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - __pyx_t_3 = __Pyx_PyObject_IsTrue(__pyx_t_6); if (unlikely(__pyx_t_3 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1013; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = PyObject_GetAttr(__pyx_v_p, __pyx_n_s__size); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1013; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __pyx_t_6 = PyObject_RichCompare(__pyx_t_1, __pyx_v_pop_size, Py_NE); __Pyx_XGOTREF(__pyx_t_6); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1013; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_t_6); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1013; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; - if (__pyx_t_3) { + if (__pyx_t_2) { /* "mtrand.pyx":1014 * raise ValueError("p must be 1-dimensional") @@ -6707,12 +7057,12 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, */ __pyx_t_6 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_25), NULL); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1014; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_6); - __Pyx_Raise(__pyx_t_6, 0, 0); + __Pyx_Raise(__pyx_t_6, 0, 0, 0); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1014; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - goto __pyx_L12; + goto __pyx_L9; } - __pyx_L12:; + __pyx_L9:; /* "mtrand.pyx":1015 * if p.size != pop_size: @@ -6723,23 +7073,22 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, */ __pyx_t_6 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1015; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_6); - __pyx_t_2 = PyObject_GetAttr(__pyx_t_6, __pyx_n_s__any); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1015; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); + __pyx_t_1 = PyObject_GetAttr(__pyx_t_6, __pyx_n_s__any); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1015; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; - __pyx_t_6 = PyObject_RichCompare(__pyx_v_p, __pyx_int_0, Py_LT); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1015; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_6); + __pyx_t_6 = PyObject_RichCompare(__pyx_v_p, __pyx_int_0, Py_LT); __Pyx_XGOTREF(__pyx_t_6); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1015; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1015; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_t_4)); + __Pyx_GOTREF(__pyx_t_4); PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_6); __Pyx_GIVEREF(__pyx_t_6); __pyx_t_6 = 0; - __pyx_t_6 = PyObject_Call(__pyx_t_2, ((PyObject *)__pyx_t_4), NULL); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1015; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_6 = PyObject_Call(__pyx_t_1, ((PyObject *)__pyx_t_4), NULL); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1015; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_6); - __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_4)); __pyx_t_4 = 0; - __pyx_t_3 = __Pyx_PyObject_IsTrue(__pyx_t_6); if (unlikely(__pyx_t_3 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1015; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_t_6); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1015; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; - if (__pyx_t_3) { + if (__pyx_t_2) { /* "mtrand.pyx":1016 * raise ValueError("a and p must have same size") @@ -6750,12 +7099,12 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, */ __pyx_t_6 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_27), NULL); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1016; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_6); - __Pyx_Raise(__pyx_t_6, 0, 0); + __Pyx_Raise(__pyx_t_6, 0, 0, 0); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1016; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - goto __pyx_L13; + goto __pyx_L10; } - __pyx_L13:; 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__pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_t_6)); - PyTuple_SET_ITEM(__pyx_t_6, 0, __pyx_t_2); - __Pyx_GIVEREF(__pyx_t_2); + __Pyx_GOTREF(__pyx_t_6); + PyTuple_SET_ITEM(__pyx_t_6, 0, __pyx_t_1); + __Pyx_GIVEREF(__pyx_t_1); __Pyx_INCREF(__pyx_int_1); PyTuple_SET_ITEM(__pyx_t_6, 1, __pyx_int_1); __Pyx_GIVEREF(__pyx_int_1); - __pyx_t_2 = 0; - __pyx_t_2 = PyObject_Call(__pyx_t_4, ((PyObject *)__pyx_t_6), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1017; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); + __pyx_t_1 = 0; + __pyx_t_1 = PyObject_Call(__pyx_t_4, ((PyObject *)__pyx_t_6), NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1017; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_6)); __pyx_t_6 = 0; - __pyx_t_3 = __Pyx_PyObject_IsTrue(__pyx_t_2); if (unlikely(__pyx_t_3 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1017; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - __pyx_t_7 = (!__pyx_t_3); + __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_t_1); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1017; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + __pyx_t_7 = (!__pyx_t_2); if (__pyx_t_7) { /* "mtrand.pyx":1018 @@ -6798,17 +7147,17 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, * * # Actual sampling */ - __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_29), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1018; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __Pyx_Raise(__pyx_t_2, 0, 0); - __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + __pyx_t_1 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_29), NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1018; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __Pyx_Raise(__pyx_t_1, 0, 0, 0); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1018; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - goto __pyx_L14; + goto __pyx_L11; } - __pyx_L14:; - goto __pyx_L10; + __pyx_L11:; + goto __pyx_L7; } - __pyx_L10:; + __pyx_L7:; /* "mtrand.pyx":1021 * @@ -6827,10 +7176,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, * cdf = p.cumsum() * cdf /= cdf[-1] */ - __pyx_t_2 = PyObject_RichCompare(Py_None, __pyx_v_p, Py_NE); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1022; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __pyx_t_7 = __Pyx_PyObject_IsTrue(__pyx_t_2); if (unlikely(__pyx_t_7 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1022; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + __pyx_t_1 = PyObject_RichCompare(Py_None, __pyx_v_p, Py_NE); __Pyx_XGOTREF(__pyx_t_1); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1022; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_7 = __Pyx_PyObject_IsTrue(__pyx_t_1); if (unlikely(__pyx_t_7 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1022; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; if (__pyx_t_7) { /* "mtrand.pyx":1023 @@ -6840,12 +7188,11 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, * cdf /= cdf[-1] * uniform_samples = np.random.random(size) */ - __pyx_t_2 = PyObject_GetAttr(__pyx_v_p, __pyx_n_s__cumsum); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1023; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __pyx_t_6 = PyObject_Call(__pyx_t_2, ((PyObject *)__pyx_empty_tuple), NULL); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1023; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = PyObject_GetAttr(__pyx_v_p, __pyx_n_s__cumsum); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1023; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __pyx_t_6 = PyObject_Call(__pyx_t_1, ((PyObject *)__pyx_empty_tuple), NULL); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1023; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_6); - __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - __Pyx_DECREF(__pyx_v_cdf); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __pyx_v_cdf = __pyx_t_6; __pyx_t_6 = 0; @@ -6858,12 +7205,12 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, */ __pyx_t_6 = __Pyx_GetItemInt(__pyx_v_cdf, -1, sizeof(long), PyInt_FromLong); if (!__pyx_t_6) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1024; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_6); - __pyx_t_2 = __Pyx_PyNumber_InPlaceDivide(__pyx_v_cdf, __pyx_t_6); 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if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1025; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1025; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __pyx_t_6 = PyObject_GetAttr(__pyx_t_1, __pyx_n_s__random); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1025; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_6); - __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; - __pyx_t_2 = PyObject_GetAttr(__pyx_t_6, __pyx_n_s__random); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1025; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + __pyx_t_1 = PyObject_GetAttr(__pyx_t_6, __pyx_n_s__random); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1025; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; __pyx_t_6 = PyTuple_New(1); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1025; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_t_6)); + __Pyx_GOTREF(__pyx_t_6); __Pyx_INCREF(__pyx_v_size); PyTuple_SET_ITEM(__pyx_t_6, 0, __pyx_v_size); __Pyx_GIVEREF(__pyx_v_size); - __pyx_t_4 = PyObject_Call(__pyx_t_2, ((PyObject *)__pyx_t_6), NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1025; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = PyObject_Call(__pyx_t_1, ((PyObject *)__pyx_t_6), NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1025; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_4); - __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_6)); __pyx_t_6 = 0; - __Pyx_DECREF(__pyx_v_uniform_samples); 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__pyx_t_6 = 0; - __Pyx_DECREF(((PyObject *)__pyx_t_2)); __pyx_t_2 = 0; - __Pyx_DECREF(__pyx_v_idx); + __Pyx_DECREF(((PyObject *)__pyx_t_1)); __pyx_t_1 = 0; __pyx_v_idx = __pyx_t_5; __pyx_t_5 = 0; - goto __pyx_L16; + goto __pyx_L13; } /*else*/ { @@ -6929,30 +7274,29 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, * else: * if size > pop_size: */ - __pyx_t_5 = PyObject_GetAttr(__pyx_v_self, __pyx_n_s__randint); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1028; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_5 = PyObject_GetAttr(((PyObject *)__pyx_v_self), __pyx_n_s__randint); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1028; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_5); - __pyx_t_2 = PyTuple_New(2); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1028; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_t_2)); + __pyx_t_1 = PyTuple_New(2); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1028; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); __Pyx_INCREF(__pyx_int_0); - PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_int_0); + PyTuple_SET_ITEM(__pyx_t_1, 0, __pyx_int_0); __Pyx_GIVEREF(__pyx_int_0); __Pyx_INCREF(__pyx_v_pop_size); - PyTuple_SET_ITEM(__pyx_t_2, 1, __pyx_v_pop_size); + PyTuple_SET_ITEM(__pyx_t_1, 1, __pyx_v_pop_size); __Pyx_GIVEREF(__pyx_v_pop_size); __pyx_t_6 = PyDict_New(); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1028; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_6)); if (PyDict_SetItem(__pyx_t_6, ((PyObject *)__pyx_n_s__size), __pyx_v_size) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1028; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_t_4 = PyEval_CallObjectWithKeywords(__pyx_t_5, ((PyObject *)__pyx_t_2), ((PyObject *)__pyx_t_6)); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1028; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = PyObject_Call(__pyx_t_5, ((PyObject *)__pyx_t_1), ((PyObject *)__pyx_t_6)); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1028; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; - __Pyx_DECREF(((PyObject *)__pyx_t_2)); __pyx_t_2 = 0; + __Pyx_DECREF(((PyObject *)__pyx_t_1)); __pyx_t_1 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_6)); __pyx_t_6 = 0; - __Pyx_DECREF(__pyx_v_idx); __pyx_v_idx = __pyx_t_4; __pyx_t_4 = 0; } - __pyx_L16:; - goto __pyx_L15; + __pyx_L13:; + goto __pyx_L12; } /*else*/ { @@ -6963,8 +7307,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, * raise ValueError(''.join(["Cannot take a larger sample than ", * "population when 'replace=False'"])) */ - __pyx_t_4 = PyObject_RichCompare(__pyx_v_size, __pyx_v_pop_size, Py_GT); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1030; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_4); + __pyx_t_4 = PyObject_RichCompare(__pyx_v_size, __pyx_v_pop_size, Py_GT); __Pyx_XGOTREF(__pyx_t_4); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1030; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_t_7 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_7 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1030; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; if (__pyx_t_7) { @@ -6979,36 +7322,36 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, __pyx_t_4 = PyObject_GetAttr(((PyObject *)__pyx_kp_s_30), __pyx_n_s__join); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1031; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_4); __pyx_t_6 = PyList_New(2); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1031; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_t_6)); + __Pyx_GOTREF(__pyx_t_6); __Pyx_INCREF(((PyObject *)__pyx_kp_s_31)); PyList_SET_ITEM(__pyx_t_6, 0, ((PyObject *)__pyx_kp_s_31)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_31)); __Pyx_INCREF(((PyObject *)__pyx_kp_s_32)); PyList_SET_ITEM(__pyx_t_6, 1, ((PyObject *)__pyx_kp_s_32)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_32)); - __pyx_t_2 = PyTuple_New(1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1031; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_t_2)); - PyTuple_SET_ITEM(__pyx_t_2, 0, ((PyObject *)__pyx_t_6)); + __pyx_t_1 = PyTuple_New(1); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1031; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + PyTuple_SET_ITEM(__pyx_t_1, 0, ((PyObject *)__pyx_t_6)); __Pyx_GIVEREF(((PyObject *)__pyx_t_6)); __pyx_t_6 = 0; - __pyx_t_6 = PyObject_Call(__pyx_t_4, ((PyObject *)__pyx_t_2), NULL); 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__pyx_t_1 = 0; + goto __pyx_L19; } - __pyx_L22:; + __pyx_L19:; /* "mtrand.pyx":1044 * if n_uniq > 0: @@ -7205,23 +7541,23 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, * cdf /= cdf[-1] * new = cdf.searchsorted(x, side='right') */ - __pyx_t_2 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1044; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); - __pyx_t_6 = PyObject_GetAttr(__pyx_t_2, __pyx_n_s__cumsum); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1044; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1044; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + __pyx_t_6 = PyObject_GetAttr(__pyx_t_1, __pyx_n_s__cumsum); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1044; 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__pyx_lineno = 1044; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_3); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; - __Pyx_DECREF(((PyObject *)__pyx_t_2)); __pyx_t_2 = 0; - __Pyx_DECREF(__pyx_v_cdf); - __pyx_v_cdf = __pyx_t_1; - __pyx_t_1 = 0; + __Pyx_DECREF(((PyObject *)__pyx_t_1)); __pyx_t_1 = 0; + __Pyx_XDECREF(__pyx_v_cdf); + __pyx_v_cdf = __pyx_t_3; + __pyx_t_3 = 0; /* "mtrand.pyx":1045 * p[found[0:n_uniq]] = 0 @@ -7230,14 +7566,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, * new = cdf.searchsorted(x, side='right') * new = np.unique(new) */ - __pyx_t_1 = __Pyx_GetItemInt(__pyx_v_cdf, -1, sizeof(long), PyInt_FromLong); if (!__pyx_t_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1045; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_3 = __Pyx_GetItemInt(__pyx_v_cdf, -1, sizeof(long), PyInt_FromLong); if (!__pyx_t_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1045; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_3); 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/* "mtrand.pyx":1048 * new = cdf.searchsorted(x, side='right') @@ -7298,11 +7634,11 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, * idx = found */ __pyx_t_8 = __Pyx_PyIndex_AsSsize_t(__pyx_v_n_uniq); if (unlikely((__pyx_t_8 == (Py_ssize_t)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1048; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_t_1 = PyObject_GetAttr(__pyx_v_new, __pyx_n_s__size); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1048; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_1); - __pyx_t_4 = PyNumber_Add(__pyx_v_n_uniq, __pyx_t_1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1048; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_3 = PyObject_GetAttr(__pyx_v_new, __pyx_n_s__size); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1048; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_3); 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__pyx_lineno = 1052; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_6); - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_4)); __pyx_t_4 = 0; __pyx_t_9 = __Pyx_PyIndex_AsSsize_t(__pyx_v_size); if (unlikely((__pyx_t_9 == (Py_ssize_t)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1052; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_t_4 = __Pyx_PySequence_GetSlice(__pyx_t_6, 0, __pyx_t_9); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1052; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_4); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; - __Pyx_DECREF(__pyx_v_idx); __pyx_v_idx = __pyx_t_4; __pyx_t_4 = 0; } - __pyx_L18:; + __pyx_L15:; } - __pyx_L15:; + __pyx_L12:; /* "mtrand.pyx":1055 * @@ -7375,13 +7709,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_12choice(PyObject *__pyx_v_self, * return idx * else: */ - __pyx_t_4 = __pyx_v_a; 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if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1188; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_3 = PyObject_GetAttr(((PyObject *)__pyx_v_self), __pyx_n_s__random_sample); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1188; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = PyObject_Call(__pyx_t_3, ((PyObject *)__pyx_empty_tuple), NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1188; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_4); @@ -7777,7 +8106,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_14rand(PyObject *__pyx_v_self, P __pyx_r = __pyx_t_4; __pyx_t_4 = 0; goto __pyx_L0; - goto __pyx_L5; + goto __pyx_L3; } /*else*/ { @@ -7789,12 +8118,12 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_14rand(PyObject *__pyx_v_self, P * def randn(self, *args): */ __Pyx_XDECREF(__pyx_r); - __pyx_t_4 = PyObject_GetAttr(__pyx_v_self, __pyx_n_s__random_sample); 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/*proto*/ +static char __pyx_doc_6mtrand_11RandomState_30randn[] = "\n randn(d0, d1, ..., dn)\n\n Return a sample (or samples) from the \"standard normal\" distribution.\n\n If positive, int_like or int-convertible arguments are provided,\n `randn` generates an array of shape ``(d0, d1, ..., dn)``, filled\n with random floats sampled from a univariate \"normal\" (Gaussian)\n distribution of mean 0 and variance 1 (if any of the :math:`d_i` are\n floats, they are first converted to integers by truncation). A single\n float randomly sampled from the distribution is returned if no\n argument is provided.\n\n This is a convenience function. If you want an interface that takes a\n tuple as the first argument, use `numpy.random.standard_normal` instead.\n\n Parameters\n ----------\n d0, d1, ..., dn : int, optional\n The dimensions of the returned array, should be all positive.\n If no argument is given a single Python float is returned.\n\n Returns\n -------\n Z : ndarray or float\n A ``(d0, d1, ..., dn)``-shaped array of floating-point samples from\n the standard normal distribution, or a single such float if\n no parameters were supplied.\n\n See Also\n --------\n random.standard_normal : Similar, but takes a tuple as its argument.\n\n Notes\n -----\n For random samples from :math:`N(\\mu, \\sigma^2)`, use:\n\n ``sigma * np.random.randn(...) + mu``\n\n Examples\n --------\n >>> np.random.randn()\n 2.1923875335537315 #random\n\n Two-by-four array of samples from N(3, 6.25):\n\n >>> 2.5 * np.random.randn(2, 4) + 3\n array([[-4.49401501, 4.00950034, -1.81814867, 7.29718677], #random\n [ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) #random\n\n "; +static PyObject *__pyx_pw_6mtrand_11RandomState_31randn(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { + PyObject *__pyx_v_args = 0; + PyObject *__pyx_r = 0; + __Pyx_RefNannyDeclarations + __Pyx_RefNannySetupContext("randn (wrapper)", 0); + if (unlikely(__pyx_kwds) && unlikely(PyDict_Size(__pyx_kwds) > 0) && unlikely(!__Pyx_CheckKeywordStrings(__pyx_kwds, "randn", 0))) return NULL; + __Pyx_INCREF(__pyx_args); + __pyx_v_args = __pyx_args; + __pyx_r = __pyx_pf_6mtrand_11RandomState_30randn(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self), __pyx_v_args); + __Pyx_XDECREF(__pyx_v_args); + __Pyx_RefNannyFinishContext(); + return __pyx_r; +} + /* "mtrand.pyx":1192 * return self.random_sample(size=args) * @@ -7827,20 +8172,18 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_14rand(PyObject *__pyx_v_self, P * randn(d0, d1, ..., dn) */ -static PyObject *__pyx_pf_6mtrand_11RandomState_15randn(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ -static char __pyx_doc_6mtrand_11RandomState_15randn[] = "\n randn(d0, d1, ..., dn)\n\n Return a sample (or samples) from the \"standard normal\" distribution.\n\n If positive, int_like or int-convertible arguments are provided,\n `randn` generates an array of shape ``(d0, d1, ..., dn)``, filled\n with random floats sampled from a univariate \"normal\" (Gaussian)\n distribution of mean 0 and variance 1 (if any of the :math:`d_i` are\n floats, they are first converted to integers by truncation). A single\n float randomly sampled from the distribution is returned if no\n argument is provided.\n\n This is a convenience function. If you want an interface that takes a\n tuple as the first argument, use `numpy.random.standard_normal` instead.\n\n Parameters\n ----------\n d0, d1, ..., dn : int, optional\n The dimensions of the returned array, should be all positive.\n If no argument is given a single Python float is returned.\n\n Returns\n -------\n Z : ndarray or float\n A ``(d0, d1, ..., dn)``-shaped array of floating-point samples from\n the standard normal distribution, or a single such float if\n no parameters were supplied.\n\n See Also\n --------\n random.standard_normal : Similar, but takes a tuple as its argument.\n\n Notes\n -----\n For random samples from :math:`N(\\mu, \\sigma^2)`, use:\n\n ``sigma * np.random.randn(...) + mu``\n\n Examples\n --------\n >>> np.random.randn()\n 2.1923875335537315 #random\n\n Two-by-four array of samples from N(3, 6.25):\n\n >>> 2.5 * np.random.randn(2, 4) + 3\n array([[-4.49401501, 4.00950034, -1.81814867, 7.29718677], #random\n [ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) #random\n\n "; 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{__pyx_filename = __pyx_f[0]; __pyx_lineno = 1244; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - } - __pyx_t_1 = PyTuple_GET_SIZE(((PyObject *)__pyx_v_args)); + __pyx_t_1 = PyTuple_GET_SIZE(((PyObject *)__pyx_v_args)); if (unlikely(__pyx_t_1 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1244; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_t_2 = (__pyx_t_1 == 0); if (__pyx_t_2) { @@ -7864,7 +8204,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_15randn(PyObject *__pyx_v_self, * return self.standard_normal(args) */ __Pyx_XDECREF(__pyx_r); - __pyx_t_3 = PyObject_GetAttr(__pyx_v_self, __pyx_n_s__standard_normal); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1245; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __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 = 1245; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); __pyx_t_4 = PyObject_Call(__pyx_t_3, ((PyObject *)__pyx_empty_tuple), NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1245; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_4); @@ -7872,7 +8212,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_15randn(PyObject *__pyx_v_self, __pyx_r = __pyx_t_4; __pyx_t_4 = 0; goto __pyx_L0; - goto __pyx_L5; + goto __pyx_L3; } /*else*/ { @@ -7884,10 +8224,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_15randn(PyObject *__pyx_v_self, * def random_integers(self, low, high=None, size=None): */ __Pyx_XDECREF(__pyx_r); - __pyx_t_4 = PyObject_GetAttr(__pyx_v_self, __pyx_n_s__standard_normal); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1247; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_4 = PyObject_GetAttr(((PyObject *)__pyx_v_self), __pyx_n_s__standard_normal); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1247; __pyx_clineno = __LINE__; 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If `high` is None (the default),\n then results are from [1, `low`].\n\n Parameters\n ----------\n low : int\n Lowest (signed) integer to be drawn from the distribution (unless\n ``high=None``, in which case this parameter is the *highest* such\n integer).\n high : int, optional\n If provided, the largest (signed) integer to be drawn from the\n distribution (see above for behavior if ``high=None``).\n size : int or tuple of ints, optional\n Output shape. Default is None, in which case a single int is returned.\n\n Returns\n -------\n out : int or ndarray of ints\n `size`-shaped array of random integers from the appropriate\n distribution, or a single such random int if `size` not provided.\n\n See Also\n --------\n random.randint : Similar to `random_integers`, only for the half-open\n interval [`low`, `high`), and 0 is the lowest value if `high` is\n omitted.\n\n Notes\n -----\n To sample from N evenly spaced floating-point numbers between a and b,\n use::\n\n a + (b - a) * (np.random.random_integers(N) - 1) / (N - 1.)\n\n Examples\n --------\n >>> np.random.random_integers(5)\n 4\n >>> type(np.random.random_integers(5))\n \n >>> np.random.random_integers(5, size=(3.,2.))\n array([[5, 4],\n [3, 3],\n [4, 5]])\n\n Choose five random numbers from the set of five evenly-spaced\n numbers between 0 and 2.5, inclusive (*i.e.*, from the set\n :math:`{0, 5/8, 10/8, 15/8, 20/8}`):\n""\n >>> 2.5 * (np.random.random_integers(5, size=(5,)) - 1) / 4.\n array([ 0.625, 1.25 , 0.625, 0.625, 2.5 ])\n\n Roll two six sided dice 1000 times and sum the results:\n\n >>> d1 = np.random.random_integers(1, 6, 1000)\n >>> d2 = np.random.random_integers(1, 6, 1000)\n >>> dsums = d1 + d2\n\n Display results as a histogram:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(dsums, 11, normed=True)\n >>> plt.show()\n\n "; +static PyObject *__pyx_pw_6mtrand_11RandomState_33random_integers(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { + PyObject *__pyx_v_low = 0; + PyObject *__pyx_v_high = 0; + PyObject *__pyx_v_size = 0; + PyObject *__pyx_r = 0; + __Pyx_RefNannyDeclarations + __Pyx_RefNannySetupContext("random_integers (wrapper)", 0); + { + static PyObject **__pyx_pyargnames[] = {&__pyx_n_s__low,&__pyx_n_s__high,&__pyx_n_s__size,0}; + PyObject* values[3] = {0,0,0}; + + /* "mtrand.pyx":1249 * return self.standard_normal(args) * * def random_integers(self, low, high=None, size=None): # <<<<<<<<<<<<<< * """ * random_integers(low, high=None, size=None) */ - -static PyObject *__pyx_pf_6mtrand_11RandomState_16random_integers(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ -static char __pyx_doc_6mtrand_11RandomState_16random_integers[] = "\n random_integers(low, high=None, size=None)\n\n Return random integers between `low` and `high`, inclusive.\n\n Return random integers from the \"discrete uniform\" distribution in the\n closed interval [`low`, `high`]. 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[1] Wikipedia, \"Normal distribution\",\n http://en.wikipedia.org/wiki/Normal_distribution\n .. [2] P. R. Peebles Jr., \"Central Limit Theorem\" in \"Probability, Random\n Variables and Random Signal Principles\", 4th ed., 2001,\n pp. 51, 51, 125.\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> mu, sigma = 0, 0.1 # mean and standard deviation\n >>> s = np.random.normal(mu, sigma, 1000)\n\n Verify the mean and the variance:\n\n >>> abs(mu - np.mean(s)) < 0.01\n True\n\n >>> abs(sigma - np.std(s, ddof=1)) < 0.01\n True\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n >>> plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *\n ... np.exp( - (bins - mu)**2 / (2 * sigma**2) ),\n ... linewidth=2, color='r')\n >>> plt.show()\n\n "; -static PyObject *__pyx_pf_6mtrand_11RandomState_18normal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { +/* Python wrapper */ +static PyObject *__pyx_pw_6mtrand_11RandomState_37normal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ +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. 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[3] \"Exponential Distribution, Wikipedia,\n http://en.wikipedia.org/wiki/Exponential_distribution\n\n "; +static PyObject *__pyx_pw_6mtrand_11RandomState_41exponential(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { + PyObject *__pyx_v_scale = 0; + PyObject *__pyx_v_size = 0; + PyObject *__pyx_r = 0; + __Pyx_RefNannyDeclarations + __Pyx_RefNannySetupContext("exponential (wrapper)", 0); + { + static PyObject **__pyx_pyargnames[] = {&__pyx_n_s__scale,&__pyx_n_s__size,0}; + PyObject* values[2] = {0,0}; + values[0] = __pyx_k_48; + + /* "mtrand.pyx":1518 * return cont2_array(self.internal_state, rk_beta, size, oa, ob) * * def exponential(self, scale=1.0, size=None): # <<<<<<<<<<<<<< * """ * exponential(scale=1.0, size=None) */ - -static PyObject *__pyx_pf_6mtrand_11RandomState_20exponential(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ -static char __pyx_doc_6mtrand_11RandomState_20exponential[] = "\n exponential(scale=1.0, size=None)\n\n Exponential distribution.\n\n Its probability density function is\n\n .. math:: f(x; \\frac{1}{\\beta}) = \\frac{1}{\\beta} \\exp(-\\frac{x}{\\beta}),\n\n for ``x > 0`` and 0 elsewhere. :math:`\\beta` is the scale parameter,\n which is the inverse of the rate parameter :math:`\\lambda = 1/\\beta`.\n The rate parameter is an alternative, widely used parameterization\n of the exponential distribution [3]_.\n\n The exponential distribution is a continuous analogue of the\n geometric distribution. 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if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1562; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_2); + __Pyx_Raise(__pyx_t_2, 0, 0, 0); + __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1562; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - goto __pyx_L7; + goto __pyx_L4; } - __pyx_L7:; + __pyx_L4:; /* "mtrand.pyx":1563 * if fscale <= 0: @@ -8977,18 +9330,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_20exponential(PyObject *__pyx_v_ * PyErr_Clear() */ __Pyx_XDECREF(__pyx_r); - __pyx_t_3 = ((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state; - __pyx_t_1 = __pyx_v_size; - __Pyx_INCREF(__pyx_t_1); - __pyx_t_4 = __pyx_f_6mtrand_cont1_array_sc(__pyx_t_3, rk_exponential, __pyx_t_1, __pyx_v_fscale); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1563; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_4); - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; 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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 .. 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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 .. 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Default is equal to 1.\n size : shape_tuple, optional\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n out : ndarray, float\n Returns one sample unless `size` parameter is specified.\n\n See Also\n --------\n scipy.stats.distributions.gamma : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Gamma distribution is\n\n .. math:: p(x) = x^{k-1}\\frac{e^{-x/\\theta}}{\\theta^k\\Gamma(k)},\n\n where :math:`k` is the shape and :math:`\\theta` the scale,\n and :math:`\\Gamma` is the Gamma function.\n\n The Gamma distribution is often used to model the times to failure of\n electronic components, and arises naturally in processes for which the\n waiting times between Poisson distributed events are relevant.\n\n References\n ----------\n .. [1] Weisstein, Eric W. \"Gamma Distribution.\" From MathWorld--A\n Wolfram Web Resource.\n http://mathworld.wolfram.com/GammaDistribution.html\n .. [2] Wikipedia, \"Gamma-distribution\",\n http://en.wikipedia.org/wiki/Gamma-distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> shape, scale = 2.,"" 2. # mean and dispersion\n >>> s = np.random.gamma(shape, scale, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> import scipy.special as sps\n >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n >>> y = bins**(shape-1)*(np.exp(-bins/scale) /\n ... (sps.gamma(shape)*scale**shape))\n >>> plt.plot(bins, y, linewidth=2, color='r')\n >>> plt.show()\n\n "; +static PyObject *__pyx_pw_6mtrand_11RandomState_47gamma(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { + PyObject *__pyx_v_shape = 0; + PyObject *__pyx_v_scale = 0; + PyObject *__pyx_v_size = 0; + PyObject *__pyx_r = 0; + __Pyx_RefNannyDeclarations + __Pyx_RefNannySetupContext("gamma (wrapper)", 0); + { + static PyObject **__pyx_pyargnames[] = {&__pyx_n_s__shape,&__pyx_n_s__scale,&__pyx_n_s__size,0}; + PyObject* values[3] = {0,0,0}; + values[1] = __pyx_k_54; + + /* "mtrand.pyx":1682 * return cont1_array(self.internal_state, rk_standard_gamma, size, oshape) * * def gamma(self, shape, scale=1.0, size=None): # <<<<<<<<<<<<<< * """ * gamma(shape, scale=1.0, size=None) */ - -static PyObject *__pyx_pf_6mtrand_11RandomState_23gamma(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ -static char __pyx_doc_6mtrand_11RandomState_23gamma[] = "\n gamma(shape, scale=1.0, size=None)\n\n Draw samples from a Gamma distribution.\n\n Samples are drawn from a Gamma distribution with specified parameters,\n `shape` (sometimes designated \"k\") and `scale` (sometimes designated\n \"theta\"), where both parameters are > 0.\n\n Parameters\n ----------\n shape : scalar > 0\n The shape of the gamma distribution.\n scale : scalar > 0, optional\n The scale of the gamma distribution. 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[1] Weisstein, Eric W. \"Gamma Distribution.\" From MathWorld--A\n Wolfram Web Resource.\n http://mathworld.wolfram.com/GammaDistribution.html\n .. [2] Wikipedia, \"Gamma-distribution\",\n http://en.wikipedia.org/wiki/Gamma-distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> shape, scale = 2.,"" 2. # mean and dispersion\n >>> s = np.random.gamma(shape, scale, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> import scipy.special as sps\n >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n >>> y = bins**(shape-1)*(np.exp(-bins/scale) /\n ... 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if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1759; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_2); + __Pyx_Raise(__pyx_t_2, 0, 0, 0); + __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1759; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - goto __pyx_L7; + goto __pyx_L4; } - __pyx_L7:; + __pyx_L4:; /* "mtrand.pyx":1760 * if fshape <= 0: @@ -9604,8 +9960,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_23gamma(PyObject *__pyx_v_self, * raise ValueError("scale <= 0") * return cont2_array_sc(self.internal_state, rk_gamma, size, fshape, fscale) */ - __pyx_t_2 = (__pyx_v_fscale <= 0.0); - if (__pyx_t_2) { + __pyx_t_1 = (__pyx_v_fscale <= 0.0); + if (__pyx_t_1) { /* "mtrand.pyx":1761 * raise ValueError("shape <= 0") @@ -9614,14 +9970,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_23gamma(PyObject *__pyx_v_self, * return cont2_array_sc(self.internal_state, rk_gamma, size, fshape, fscale) * */ - __pyx_t_1 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_56), NULL); 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{__pyx_filename = __pyx_f[0]; __pyx_lineno = 1773; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + } + case 2: + if (kw_args > 0) { + PyObject* value = PyDict_GetItem(__pyx_kwds, __pyx_n_s__size); + if (value) { values[2] = value; kw_args--; } + } + } + if (unlikely(kw_args > 0)) { + if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "f") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1773; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + } + } else { + switch (PyTuple_GET_SIZE(__pyx_args)) { + case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); + case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); + values[0] = PyTuple_GET_ITEM(__pyx_args, 0); + break; + default: goto __pyx_L5_argtuple_error; } - } - if (unlikely(kw_args > 0)) { - if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "f") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1773; __pyx_clineno = __LINE__; goto __pyx_L3_error;} } __pyx_v_dfnum = values[0]; 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goto __pyx_L1_error;} - goto __pyx_L7; + goto __pyx_L4; } - __pyx_L7:; + __pyx_L4:; /* "mtrand.pyx":1862 * if fdfnum <= 0: @@ -9993,8 +10337,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_24f(PyObject *__pyx_v_self, PyOb * raise ValueError("scale <= 0") * return cont2_array_sc(self.internal_state, rk_f, size, fdfnum, fdfden) */ - __pyx_t_2 = (__pyx_v_fdfden <= 0.0); - if (__pyx_t_2) { + __pyx_t_1 = (__pyx_v_fdfden <= 0.0); + if (__pyx_t_1) { /* "mtrand.pyx":1863 * raise ValueError("shape <= 0") @@ -10003,14 +10347,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_24f(PyObject *__pyx_v_self, PyOb * return cont2_array_sc(self.internal_state, rk_f, size, fdfnum, fdfden) * */ - __pyx_t_1 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_60), NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1863; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_1); - __Pyx_Raise(__pyx_t_1, 0, 0); - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; 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if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1870; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; - if (__pyx_t_2) { + __Pyx_DECREF(((PyObject *)__pyx_t_5)); __pyx_t_5 = 0; + __pyx_t_5 = PyTuple_New(1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1870; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_5); + PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_t_2); + __Pyx_GIVEREF(__pyx_t_2); + __pyx_t_2 = 0; + __pyx_t_2 = PyObject_Call(__pyx_t_3, ((PyObject *)__pyx_t_5), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1870; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_2); + __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; + __Pyx_DECREF(((PyObject *)__pyx_t_5)); __pyx_t_5 = 0; + __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_2); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1870; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + if (__pyx_t_1) { /* "mtrand.pyx":1871 * odfden = PyArray_FROM_OTF(dfden, NPY_DOUBLE, NPY_ARRAY_ALIGNED) @@ -10127,14 +10459,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_24f(PyObject *__pyx_v_self, PyOb * if np.any(np.less_equal(odfden, 0.0)): * raise ValueError("dfden <= 0") */ - __pyx_t_4 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_62), NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1871; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_4); - __Pyx_Raise(__pyx_t_4, 0, 0); - __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; + __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_62), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1871; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_2); + __Pyx_Raise(__pyx_t_2, 0, 0, 0); + __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; {__pyx_filename = __pyx_f[0]; 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- __pyx_v_dfnum = PyTuple_GET_ITEM(__pyx_args, 0); - break; - default: goto __pyx_L5_argtuple_error; - } } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("noncentral_f", 0, 3, 4, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1876; __pyx_clineno = __LINE__; goto __pyx_L3_error;} __pyx_L3_error:; - __Pyx_AddTraceback("mtrand.RandomState.noncentral_f"); + __Pyx_AddTraceback("mtrand.RandomState.noncentral_f", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; - __pyx_v_odfnum = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None); - __pyx_v_odfden = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None); - __pyx_v_ononc = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None); + __pyx_r = __pyx_pf_6mtrand_11RandomState_50noncentral_f(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self), __pyx_v_dfnum, __pyx_v_dfden, __pyx_v_nonc, __pyx_v_size); + __Pyx_RefNannyFinishContext(); + return __pyx_r; +} + +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) { + PyArrayObject *__pyx_v_odfnum = 0; + PyArrayObject *__pyx_v_odfden = 0; + PyArrayObject *__pyx_v_ononc = 0; + double __pyx_v_fdfnum; + double __pyx_v_fdfden; + double __pyx_v_fnonc; + PyObject *__pyx_r = NULL; + __Pyx_RefNannyDeclarations + int __pyx_t_1; + PyObject *__pyx_t_2 = NULL; + PyObject *__pyx_t_3 = NULL; + PyObject *__pyx_t_4 = NULL; + PyObject *__pyx_t_5 = NULL; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; + __Pyx_RefNannySetupContext("noncentral_f", 0); /* "mtrand.pyx":1943 * cdef double fdfnum, fdfden, fnonc @@ -10335,10 +10671,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_25noncentral_f(PyObject *__pyx_v * fdfden = PyFloat_AsDouble(dfden) * fnonc = PyFloat_AsDouble(nonc) */ - __pyx_t_1 = __pyx_v_dfnum; 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- if (__pyx_t_2) { + __pyx_t_1 = (!PyErr_Occurred()); + if (__pyx_t_1) { /* "mtrand.pyx":1947 * fnonc = PyFloat_AsDouble(nonc) @@ -10381,8 +10708,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_25noncentral_f(PyObject *__pyx_v * raise ValueError("dfnum <= 1") * if fdfden <= 0: */ - __pyx_t_2 = (__pyx_v_fdfnum <= 1.0); - if (__pyx_t_2) { + __pyx_t_1 = (__pyx_v_fdfnum <= 1.0); + if (__pyx_t_1) { /* "mtrand.pyx":1948 * if not PyErr_Occurred(): @@ -10391,14 +10718,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_25noncentral_f(PyObject *__pyx_v * if fdfden <= 0: * raise ValueError("dfden <= 0") */ - __pyx_t_1 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_66), NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1948; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_1); - __Pyx_Raise(__pyx_t_1, 0, 0); - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_66), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1948; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_2); + __Pyx_Raise(__pyx_t_2, 0, 0, 0); + __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1948; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - goto __pyx_L7; + goto __pyx_L4; } - __pyx_L7:; + __pyx_L4:; /* "mtrand.pyx":1949 * if fdfnum <= 1: @@ -10407,8 +10734,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_25noncentral_f(PyObject *__pyx_v * raise ValueError("dfden <= 0") * if fnonc < 0: */ - __pyx_t_2 = (__pyx_v_fdfden <= 0.0); - if (__pyx_t_2) { + __pyx_t_1 = (__pyx_v_fdfden <= 0.0); + if (__pyx_t_1) { /* "mtrand.pyx":1950 * raise ValueError("dfnum <= 1") @@ -10417,14 +10744,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_25noncentral_f(PyObject *__pyx_v * if fnonc < 0: * raise ValueError("nonc < 0") */ - __pyx_t_1 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_67), NULL); 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if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1953; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_2); + __pyx_r = __pyx_t_2; + __pyx_t_2 = 0; goto __pyx_L0; - goto __pyx_L6; + goto __pyx_L3; } - __pyx_L6:; + __pyx_L3:; /* "mtrand.pyx":1956 * fdfnum, fdfden, fnonc) @@ -10497,15 +10820,11 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_25noncentral_f(PyObject *__pyx_v * odfden = PyArray_FROM_OTF(dfden, NPY_DOUBLE, NPY_ARRAY_ALIGNED) * ononc = PyArray_FROM_OTF(nonc, NPY_DOUBLE, NPY_ARRAY_ALIGNED) */ - __pyx_t_4 = __pyx_v_dfnum; - __Pyx_INCREF(__pyx_t_4); - __pyx_t_1 = PyArray_FROM_OTF(__pyx_t_4, NPY_DOUBLE, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1958; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_1); - __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; - __Pyx_INCREF(((PyObject *)((PyArrayObject *)__pyx_t_1))); - __Pyx_DECREF(((PyObject *)__pyx_v_odfnum)); - __pyx_v_odfnum = ((PyArrayObject *)__pyx_t_1); 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[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) { + PyObject *__pyx_v_df = 0; + PyObject *__pyx_v_nonc = 0; + PyObject *__pyx_v_size = 0; + PyObject *__pyx_r = 0; + __Pyx_RefNannyDeclarations + __Pyx_RefNannySetupContext("noncentral_chisquare (wrapper)", 0); + { + static PyObject **__pyx_pyargnames[] = {&__pyx_n_s__df,&__pyx_n_s__nonc,&__pyx_n_s__size,0}; + PyObject* values[3] = {0,0,0}; + + /* "mtrand.pyx":2049 * return cont1_array(self.internal_state, rk_chisquare, size, odf) * * def noncentral_chisquare(self, df, nonc, size=None): # <<<<<<<<<<<<<< * """ * noncentral_chisquare(df, nonc, size=None) */ - -static PyObject *__pyx_pf_6mtrand_11RandomState_27noncentral_chisquare(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ -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 "; 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- __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + __pyx_v_fnonc = PyFloat_AsDouble(__pyx_v_nonc); /* "mtrand.pyx":2122 * fdf = PyFloat_AsDouble(df) @@ -11138,8 +11448,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_27noncentral_chisquare(PyObject * if fdf <= 1: * raise ValueError("df <= 0") */ - __pyx_t_2 = (!PyErr_Occurred()); - if (__pyx_t_2) { + __pyx_t_1 = (!PyErr_Occurred()); + if (__pyx_t_1) { /* "mtrand.pyx":2123 * fnonc = PyFloat_AsDouble(nonc) @@ -11148,8 +11458,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_27noncentral_chisquare(PyObject * raise ValueError("df <= 0") * if fnonc <= 0: */ - __pyx_t_2 = (__pyx_v_fdf <= 1.0); - if (__pyx_t_2) { + __pyx_t_1 = (__pyx_v_fdf <= 1.0); + if (__pyx_t_1) { /* "mtrand.pyx":2124 * if not PyErr_Occurred(): @@ -11158,14 +11468,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_27noncentral_chisquare(PyObject * if fnonc <= 0: * raise ValueError("nonc <= 0") */ - __pyx_t_1 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_76), NULL); 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if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2127; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_2); + __pyx_r = __pyx_t_2; + __pyx_t_2 = 0; goto __pyx_L0; - goto __pyx_L6; + goto __pyx_L3; } - __pyx_L6:; + __pyx_L3:; /* "mtrand.pyx":2130 * size, fdf, fnonc) @@ -11238,15 +11544,11 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_27noncentral_chisquare(PyObject * ononc = PyArray_FROM_OTF(nonc, NPY_DOUBLE, NPY_ARRAY_ALIGNED) * if np.any(np.less_equal(odf, 0.0)): */ - __pyx_t_4 = __pyx_v_df; - __Pyx_INCREF(__pyx_t_4); - __pyx_t_1 = PyArray_FROM_OTF(__pyx_t_4, NPY_DOUBLE, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2132; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_1); - __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; - __Pyx_INCREF(((PyObject *)((PyArrayObject *)__pyx_t_1))); - __Pyx_DECREF(((PyObject *)__pyx_v_odf)); - __pyx_v_odf = ((PyArrayObject *)__pyx_t_1); - __Pyx_DECREF(__pyx_t_1); 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/*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) { + PyObject *__pyx_v_size = 0; + PyObject *__pyx_r = 0; + __Pyx_RefNannyDeclarations + __Pyx_RefNannySetupContext("standard_cauchy (wrapper)", 0); + { + static PyObject **__pyx_pyargnames[] = {&__pyx_n_s__size,0}; + PyObject* values[1] = {0}; + + /* "mtrand.pyx":2141 * odf, ononc) * * def standard_cauchy(self, size=None): # <<<<<<<<<<<<<< * """ * standard_cauchy(size=None) */ - -static PyObject *__pyx_pf_6mtrand_11RandomState_28standard_cauchy(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ -static char __pyx_doc_6mtrand_11RandomState_28standard_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. 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} -/* "mtrand.pyx":2202 +/* Python wrapper */ +static PyObject *__pyx_pw_6mtrand_11RandomState_59standard_t(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ +static char __pyx_doc_6mtrand_11RandomState_58standard_t[] = "\n standard_t(df, size=None)\n\n Standard Student's t distribution with df degrees of freedom.\n\n A special case of the hyperbolic distribution.\n As `df` gets large, the result resembles that of the standard normal\n distribution (`standard_normal`).\n\n Parameters\n ----------\n df : int\n Degrees of freedom, should be > 0.\n size : int or tuple of ints, optional\n Output shape. Default is None, in which case a single value is\n returned.\n\n Returns\n -------\n samples : ndarray or scalar\n Drawn samples.\n\n Notes\n -----\n The probability density function for the t distribution is\n\n .. math:: P(x, df) = \\frac{\\Gamma(\\frac{df+1}{2})}{\\sqrt{\\pi df}\n \\Gamma(\\frac{df}{2})}\\Bigl( 1+\\frac{x^2}{df} \\Bigr)^{-(df+1)/2}\n\n The t test is based on an assumption that the data come from a Normal\n distribution. The t test provides a way to test whether the sample mean\n (that is the mean calculated from the data) is a good estimate of the true\n mean.\n\n The derivation of the t-distribution was forst published in 1908 by William\n Gisset while working for the Guinness Brewery in Dublin. Due to proprietary\n issues, he had to publish under a pseudonym, and so he used the name\n Student.\n\n References\n ----------\n .. [1] Dalgaard, Peter, \"Introductory Statistics With R\",\n Springer, 2002.\n .. [2] Wikipedia, \"Student's t-distribution\"\n http://en.wikipedia.org/wiki/Student's_t-distribution\n\n Examples\n --------\n From Dalgaard page 83 [1]_, suppose the daily energy intake for 11\n women in Kj is:\n\n >>> intake = np.array([5260., 5470, 5640, 6180, 6390, 6515, 6805, 7515, \\\n ... 7515, 8230, 8770])\n\n Doe""s their energy intake deviate systematically from the recommended\n value of 7725 kJ?\n\n We have 10 degrees of freedom, so is the sample mean within 95% of the\n recommended value?\n\n >>> s = np.random.standard_t(10, size=100000)\n >>> np.mean(intake)\n 6753.636363636364\n >>> intake.std(ddof=1)\n 1142.1232221373727\n\n Calculate the t statistic, setting the ddof parameter to the unbiased\n value so the divisor in the standard deviation will be degrees of\n freedom, N-1.\n\n >>> t = (np.mean(intake)-7725)/(intake.std(ddof=1)/np.sqrt(len(intake)))\n >>> import matplotlib.pyplot as plt\n >>> h = plt.hist(s, bins=100, normed=True)\n\n For a one-sided t-test, how far out in the distribution does the t\n statistic appear?\n\n >>> >>> np.sum(s>> 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 0) { - PyObject* value = PyDict_GetItem(__pyx_kwds, __pyx_n_s__size); 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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) { + PyObject *__pyx_v_mu = 0; + PyObject *__pyx_v_kappa = 0; + PyObject *__pyx_v_size = 0; + PyObject *__pyx_r = 0; + __Pyx_RefNannyDeclarations + __Pyx_RefNannySetupContext("vonmises (wrapper)", 0); + { + static PyObject **__pyx_pyargnames[] = {&__pyx_n_s__mu,&__pyx_n_s__kappa,&__pyx_n_s__size,0}; + PyObject* values[3] = {0,0,0}; + + /* "mtrand.pyx":2303 * return cont1_array(self.internal_state, rk_standard_t, size, odf) * * def vonmises(self, mu, kappa, size=None): # <<<<<<<<<<<<<< * """ * vonmises(mu, kappa, size=None) */ - -static PyObject *__pyx_pf_6mtrand_11RandomState_30vonmises(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ -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) { - PyObject *__pyx_v_mu = 0; - PyObject *__pyx_v_kappa = 0; - PyObject *__pyx_v_size = 0; - PyArrayObject *__pyx_v_omu; - PyArrayObject *__pyx_v_okappa; 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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. 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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. 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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 PyObject *__pyx_pw_6mtrand_11RandomState_65weibull(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { + PyObject *__pyx_v_a = 0; + PyObject *__pyx_v_size = 0; + PyObject *__pyx_r = 0; + __Pyx_RefNannyDeclarations + __Pyx_RefNannySetupContext("weibull (wrapper)", 0); + { + static PyObject **__pyx_pyargnames[] = {&__pyx_n_s__a,&__pyx_n_s__size,0}; + PyObject* values[2] = {0,0}; + + /* "mtrand.pyx":2493 * return cont1_array(self.internal_state, rk_pareto, size, oa) * * def weibull(self, a, size=None): # <<<<<<<<<<<<<< * """ * weibull(a, size=None) */ - -static PyObject *__pyx_pf_6mtrand_11RandomState_32weibull(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ -static char __pyx_doc_6mtrand_11RandomState_32weibull[] = "\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 .. 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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) { + PyObject *__pyx_v_a = 0; + PyObject *__pyx_v_size = 0; + PyObject *__pyx_r = 0; + __Pyx_RefNannyDeclarations + __Pyx_RefNannySetupContext("power (wrapper)", 0); + { + static PyObject **__pyx_pyargnames[] = {&__pyx_n_s__a,&__pyx_n_s__size,0}; + PyObject* values[2] = {0,0}; + + /* "mtrand.pyx":2593 * return cont1_array(self.internal_state, rk_weibull, size, oa) * * def power(self, a, size=None): # <<<<<<<<<<<<<< * """ * power(a, size=None) */ - -static PyObject *__pyx_pf_6mtrand_11RandomState_33power(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ -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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It represents the\n difference between two independent, identically distributed exponential\n random variables.\n\n Parameters\n ----------\n loc : float\n The position, :math:`\\mu`, of the distribution peak.\n scale : float\n :math:`\\lambda`, the exponential decay.\n\n Notes\n -----\n It has the probability density function\n\n .. math:: f(x; \\mu, \\lambda) = \\frac{1}{2\\lambda}\n \\exp\\left(-\\frac{|x - \\mu|}{\\lambda}\\right).\n\n The first law of Laplace, from 1774, states that the frequency of an error\n can be expressed as an exponential function of the absolute magnitude of\n the error, which leads to the Laplace distribution. For many problems in\n Economics and Health sciences, this distribution seems to model the data\n better than the standard Gaussian distribution\n\n\n References\n ----------\n .. [1] Abramowitz, M. and Stegun, I. A. (Eds.). Handbook of Mathematical\n Functions with Formulas, Graphs, and Mathematical Tables, 9th\n printing. New York: Dover, 1972.\n\n .. [2] The Laplace distribution and generalizations\n By Samuel Kotz, Tomasz J. Kozubowski, Krzysztof Podgorski,\n Birkhauser, 2001.\n\n .. [3] Weisstein, Eric W. \"Laplace Distribution.\"\n From MathWorld--A Wolfram Web Resource.\n http://mathworld.wolfram.com/LaplaceDistribution.html\n\n .. [4] Wikipedia, \"Laplace distribution\",\n http://en.wikipedia.org/wik""i/Laplace_distribution\n\n Examples\n --------\n Draw samples from the distribution\n\n >>> loc, scale = 0., 1.\n >>> s = np.random.laplace(loc, scale, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n >>> x = np.arange(-8., 8., .01)\n >>> pdf = np.exp(-abs(x-loc/scale))/(2.*scale)\n >>> plt.plot(x, pdf)\n\n Plot Gaussian for comparison:\n\n >>> g = (1/(scale * np.sqrt(2 * np.pi)) * \n ... np.exp( - (x - loc)**2 / (2 * scale**2) ))\n >>> plt.plot(x,g)\n\n "; -static PyObject *__pyx_pf_6mtrand_11RandomState_34laplace(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { +/* Python wrapper */ +static PyObject *__pyx_pw_6mtrand_11RandomState_69laplace(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ +static char __pyx_doc_6mtrand_11RandomState_68laplace[] = "\n laplace(loc=0.0, scale=1.0, size=None)\n\n Draw samples from the Laplace or double exponential distribution with\n specified location (or mean) and scale (decay).\n\n The Laplace distribution is similar to the Gaussian/normal distribution,\n but is sharper at the peak and has fatter tails. It represents the\n difference between two independent, identically distributed exponential\n random variables.\n\n Parameters\n ----------\n loc : float\n The position, :math:`\\mu`, of the distribution peak.\n scale : float\n :math:`\\lambda`, the exponential decay.\n\n Notes\n -----\n It has the probability density function\n\n .. math:: f(x; \\mu, \\lambda) = \\frac{1}{2\\lambda}\n \\exp\\left(-\\frac{|x - \\mu|}{\\lambda}\\right).\n\n The first law of Laplace, from 1774, states that the frequency of an error\n can be expressed as an exponential function of the absolute magnitude of\n the error, which leads to the Laplace distribution. For many problems in\n Economics and Health sciences, this distribution seems to model the data\n better than the standard Gaussian distribution\n\n\n References\n ----------\n .. [1] Abramowitz, M. and Stegun, I. A. (Eds.). Handbook of Mathematical\n Functions with Formulas, Graphs, and Mathematical Tables, 9th\n printing. New York: Dover, 1972.\n\n .. [2] The Laplace distribution and generalizations\n By Samuel Kotz, Tomasz J. Kozubowski, Krzysztof Podgorski,\n Birkhauser, 2001.\n\n .. [3] Weisstein, Eric W. \"Laplace Distribution.\"\n From MathWorld--A Wolfram Web Resource.\n http://mathworld.wolfram.com/LaplaceDistribution.html\n\n .. [4] Wikipedia, \"Laplace distribution\",\n http://en.wikipedia.org/wik""i/Laplace_distribution\n\n Examples\n --------\n Draw samples from the distribution\n\n >>> loc, scale = 0., 1.\n >>> s = np.random.laplace(loc, scale, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n >>> x = np.arange(-8., 8., .01)\n >>> pdf = np.exp(-abs(x-loc/scale))/(2.*scale)\n >>> plt.plot(x, pdf)\n\n Plot Gaussian for comparison:\n\n >>> g = (1/(scale * np.sqrt(2 * np.pi)) * \n ... np.exp( - (x - loc)**2 / (2 * scale**2) ))\n >>> plt.plot(x,g)\n\n "; +static PyObject *__pyx_pw_6mtrand_11RandomState_69laplace(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { PyObject *__pyx_v_loc = 0; PyObject *__pyx_v_scale = 0; PyObject *__pyx_v_size = 0; - PyArrayObject *__pyx_v_oloc; - PyArrayObject *__pyx_v_oscale; - double __pyx_v_floc; - double __pyx_v_fscale; 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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. 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If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : {ndarray, scalar}\n where the values are all integers in [0, n].\n\n See Also\n --------\n scipy.stats.distributions.logistic : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Logistic distribution is\n\n .. math:: P(x) = P(x) = \\frac{e^{-(x-\\mu)/s}}{s(1+e^{-(x-\\mu)/s})^2},\n\n where :math:`\\mu` = location and :math:`s` = scale.\n\n The Logistic distribution is used in Extreme Value problems where it\n can act as a mixture of Gumbel distributions, in Epidemiology, and by\n the World Chess Federation (FIDE) where it is used in the Elo ranking\n system, assuming the performance of each player is a logistically\n distributed random variable.\n\n References\n ----------\n .. [1] Reiss, R.-D. and Thomas M. (2001), Statistical Analysis of Extreme\n Values, from Insurance, Finance, Hydrology and Other Fields,\n Birkhauser Verlag, Basel, pp 132-133.\n .. [2] Weisstein, Eric W. \"Logistic Distribution.\" From\n MathWorld--A Wolfram Web Resource.\n http://mathworld.wolfram.com/LogisticDistribution.html\n .. [3] Wikipedia, \"Logistic-distribution\",\n http://en.wikipedia.org/wiki/Logistic-distribution\n\n Examples\n "" --------\n Draw samples from the distribution:\n\n >>> loc, scale = 10, 1\n >>> s = np.random.logistic(loc, scale, 10000)\n >>> count, bins, ignored = plt.hist(s, bins=50)\n\n # plot against distribution\n\n >>> def logist(x, loc, scale):\n ... return exp((loc-x)/scale)/(scale*(1+exp((loc-x)/scale))**2)\n >>> plt.plot(bins, logist(bins, loc, scale)*count.max()/\\\n ... logist(bins, loc, scale).max())\n >>> plt.show()\n\n "; -static PyObject *__pyx_pf_6mtrand_11RandomState_36logistic(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { +/* Python wrapper */ +static PyObject *__pyx_pw_6mtrand_11RandomState_73logistic(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ +static char __pyx_doc_6mtrand_11RandomState_72logistic[] = "\n logistic(loc=0.0, scale=1.0, size=None)\n\n Draw samples from a Logistic distribution.\n\n Samples are drawn from a Logistic distribution with specified\n parameters, loc (location or mean, also median), and scale (>0).\n\n Parameters\n ----------\n loc : float\n\n scale : float > 0.\n\n size : {tuple, int}\n Output shape. 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(2001), Statistical Analysis of Extreme\n Values, from Insurance, Finance, Hydrology and Other Fields,\n Birkhauser Verlag, Basel, pp 132-133.\n .. [2] Weisstein, Eric W. \"Logistic Distribution.\" From\n MathWorld--A Wolfram Web Resource.\n http://mathworld.wolfram.com/LogisticDistribution.html\n .. 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/*proto*/ -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 "; 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values[1] = __pyx_k_106; + + /* "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): # <<<<<<<<<<<<<< + * """ + * lognormal(mean=0.0, sigma=1.0, size=None) + */ values[2] = ((PyObject *)Py_None); - switch (PyTuple_GET_SIZE(__pyx_args)) { - case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); - case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); - case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); - case 0: break; - default: goto __pyx_L5_argtuple_error; - } - switch (PyTuple_GET_SIZE(__pyx_args)) { - case 0: - if (kw_args > 0) { - PyObject* value = PyDict_GetItem(__pyx_kwds, __pyx_n_s__mean); - if (value) { values[0] = value; kw_args--; } + if (unlikely(__pyx_kwds)) { + Py_ssize_t kw_args; + const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args); + switch (pos_args) { + case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); + case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); + case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); 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- __pyx_v_fsigma = PyFloat_AsDouble(__pyx_t_1); - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + __pyx_v_fsigma = PyFloat_AsDouble(__pyx_v_sigma); /* "mtrand.pyx":3119 * fsigma = PyFloat_AsDouble(sigma) @@ -13881,8 +14145,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_37lognormal(PyObject *__pyx_v_se * if fsigma <= 0: * raise ValueError("sigma <= 0") */ - __pyx_t_2 = (!PyErr_Occurred()); - if (__pyx_t_2) { + __pyx_t_1 = (!PyErr_Occurred()); + if (__pyx_t_1) { /* "mtrand.pyx":3120 * @@ -13891,8 +14155,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_37lognormal(PyObject *__pyx_v_se * raise ValueError("sigma <= 0") * return cont2_array_sc(self.internal_state, rk_lognormal, size, fmean, fsigma) */ - __pyx_t_2 = (__pyx_v_fsigma <= 0.0); - if (__pyx_t_2) { + __pyx_t_1 = (__pyx_v_fsigma <= 0.0); + if (__pyx_t_1) { /* "mtrand.pyx":3121 * if not PyErr_Occurred(): @@ -13901,14 +14165,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_37lognormal(PyObject *__pyx_v_se * return cont2_array_sc(self.internal_state, rk_lognormal, size, fmean, fsigma) * */ - __pyx_t_1 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_108), NULL); 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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 PyObject *__pyx_pw_6mtrand_11RandomState_79wald(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { + PyObject *__pyx_v_mean = 0; + PyObject *__pyx_v_scale = 0; + PyObject *__pyx_v_size = 0; + PyObject *__pyx_r = 0; + __Pyx_RefNannyDeclarations + __Pyx_RefNannySetupContext("wald (wrapper)", 0); + { + static PyObject **__pyx_pyargnames[] = {&__pyx_n_s__mean,&__pyx_n_s__scale,&__pyx_n_s__size,0}; + PyObject* values[3] = {0,0,0}; + + /* "mtrand.pyx":3204 * return cont1_array(self.internal_state, rk_rayleigh, size, oscale) * * def wald(self, mean, scale, size=None): # <<<<<<<<<<<<<< * """ * wald(mean, scale, size=None) */ - -static PyObject *__pyx_pf_6mtrand_11RandomState_39wald(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ -static char __pyx_doc_6mtrand_11RandomState_39wald[] = "\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 PyObject *__pyx_pf_6mtrand_11RandomState_39wald(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { - PyObject *__pyx_v_mean = 0; - PyObject *__pyx_v_scale = 0; - PyObject *__pyx_v_size = 0; - PyArrayObject *__pyx_v_omean; - PyArrayObject *__pyx_v_oscale; - double __pyx_v_fmean; - double __pyx_v_fscale; - PyObject *__pyx_r = NULL; - PyObject *__pyx_t_1 = NULL; - int __pyx_t_2; - rk_state *__pyx_t_3; - PyObject *__pyx_t_4 = NULL; - PyObject *__pyx_t_5 = NULL; - PyObject *__pyx_t_6 = NULL; - static PyObject **__pyx_pyargnames[] = {&__pyx_n_s__mean,&__pyx_n_s__scale,&__pyx_n_s__size,0}; - __Pyx_RefNannySetupContext("wald"); - if (unlikely(__pyx_kwds)) { - Py_ssize_t kw_args = PyDict_Size(__pyx_kwds); 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__pyx_lineno = 3204; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + } + } else { + switch (PyTuple_GET_SIZE(__pyx_args)) { + case 3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2); + case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); + values[0] = PyTuple_GET_ITEM(__pyx_args, 0); + break; + default: goto __pyx_L5_argtuple_error; } - } - if (unlikely(kw_args > 0)) { - if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "wald") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3204; __pyx_clineno = __LINE__; goto __pyx_L3_error;} } __pyx_v_mean = values[0]; __pyx_v_scale = values[1]; __pyx_v_size = values[2]; - } else { - __pyx_v_size = ((PyObject *)Py_None); - switch (PyTuple_GET_SIZE(__pyx_args)) { - case 3: - __pyx_v_size = PyTuple_GET_ITEM(__pyx_args, 2); - case 2: - __pyx_v_scale = PyTuple_GET_ITEM(__pyx_args, 1); - __pyx_v_mean = PyTuple_GET_ITEM(__pyx_args, 0); - break; - default: goto __pyx_L5_argtuple_error; - } } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; __Pyx_RaiseArgtupleInvalid("wald", 0, 2, 3, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3204; __pyx_clineno = __LINE__; goto __pyx_L3_error;} __pyx_L3_error:; - __Pyx_AddTraceback("mtrand.RandomState.wald"); + __Pyx_AddTraceback("mtrand.RandomState.wald", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; - __pyx_v_omean = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None); - __pyx_v_oscale = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None); + __pyx_r = __pyx_pf_6mtrand_11RandomState_78wald(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self), __pyx_v_mean, __pyx_v_scale, __pyx_v_size); + __Pyx_RefNannyFinishContext(); + return __pyx_r; +} + +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) { + PyArrayObject *__pyx_v_omean = 0; + PyArrayObject *__pyx_v_oscale = 0; + double __pyx_v_fmean; + double __pyx_v_fscale; + PyObject *__pyx_r = NULL; + __Pyx_RefNannyDeclarations + int __pyx_t_1; + PyObject *__pyx_t_2 = NULL; + PyObject *__pyx_t_3 = NULL; + PyObject *__pyx_t_4 = NULL; + PyObject *__pyx_t_5 = NULL; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; + __Pyx_RefNannySetupContext("wald", 0); /* "mtrand.pyx":3270 * cdef double fmean, fscale @@ -14421,10 +14678,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_39wald(PyObject *__pyx_v_self, P * fscale = PyFloat_AsDouble(scale) * if not PyErr_Occurred(): */ - __pyx_t_1 = __pyx_v_mean; - __Pyx_INCREF(__pyx_t_1); - __pyx_v_fmean = PyFloat_AsDouble(__pyx_t_1); - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + __pyx_v_fmean = PyFloat_AsDouble(__pyx_v_mean); /* "mtrand.pyx":3271 * @@ -14433,10 +14687,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_39wald(PyObject *__pyx_v_self, P * if not PyErr_Occurred(): * if fmean <= 0: */ - __pyx_t_1 = __pyx_v_scale; - __Pyx_INCREF(__pyx_t_1); - __pyx_v_fscale = PyFloat_AsDouble(__pyx_t_1); - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + __pyx_v_fscale = PyFloat_AsDouble(__pyx_v_scale); /* "mtrand.pyx":3272 * fmean = PyFloat_AsDouble(mean) @@ -14445,8 +14696,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_39wald(PyObject *__pyx_v_self, P * if fmean <= 0: * raise ValueError("mean <= 0") */ - __pyx_t_2 = (!PyErr_Occurred()); - if (__pyx_t_2) { + __pyx_t_1 = (!PyErr_Occurred()); + if (__pyx_t_1) { /* "mtrand.pyx":3273 * fscale = PyFloat_AsDouble(scale) @@ -14455,8 +14706,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_39wald(PyObject *__pyx_v_self, P * raise ValueError("mean <= 0") * if fscale <= 0: */ - __pyx_t_2 = (__pyx_v_fmean <= 0.0); - if (__pyx_t_2) { + __pyx_t_1 = (__pyx_v_fmean <= 0.0); + if (__pyx_t_1) { /* "mtrand.pyx":3274 * if not PyErr_Occurred(): @@ -14465,14 +14716,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_39wald(PyObject *__pyx_v_self, P * if fscale <= 0: * raise ValueError("scale <= 0") */ - __pyx_t_1 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_116), NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3274; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_1); - __Pyx_Raise(__pyx_t_1, 0, 0); - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_116), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3274; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_2); + __Pyx_Raise(__pyx_t_2, 0, 0, 0); + __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3274; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - goto __pyx_L7; + goto __pyx_L4; } - __pyx_L7:; + __pyx_L4:; /* "mtrand.pyx":3275 * if fmean <= 0: @@ -14481,8 +14732,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_39wald(PyObject *__pyx_v_self, P * raise ValueError("scale <= 0") * return cont2_array_sc(self.internal_state, rk_wald, size, fmean, fscale) */ - __pyx_t_2 = (__pyx_v_fscale <= 0.0); - if (__pyx_t_2) { + __pyx_t_1 = (__pyx_v_fscale <= 0.0); + if (__pyx_t_1) { /* "mtrand.pyx":3276 * raise ValueError("mean <= 0") @@ -14491,14 +14742,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_39wald(PyObject *__pyx_v_self, P * return cont2_array_sc(self.internal_state, rk_wald, size, fmean, fscale) * */ - __pyx_t_1 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_117), NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3276; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_1); - __Pyx_Raise(__pyx_t_1, 0, 0); - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_117), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3276; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_2); + __Pyx_Raise(__pyx_t_2, 0, 0, 0); + __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3276; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - goto __pyx_L8; + goto __pyx_L5; } - __pyx_L8:; + __pyx_L5:; /* "mtrand.pyx":3277 * if fscale <= 0: @@ -14508,18 +14759,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_39wald(PyObject *__pyx_v_self, P * PyErr_Clear() */ __Pyx_XDECREF(__pyx_r); - __pyx_t_3 = ((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state; - __pyx_t_1 = __pyx_v_size; - __Pyx_INCREF(__pyx_t_1); - __pyx_t_4 = __pyx_f_6mtrand_cont2_array_sc(__pyx_t_3, rk_wald, __pyx_t_1, __pyx_v_fmean, __pyx_v_fscale); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3277; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_4); - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; - __pyx_r = __pyx_t_4; - __pyx_t_4 = 0; + __pyx_t_2 = __pyx_f_6mtrand_cont2_array_sc(__pyx_v_self->internal_state, rk_wald, __pyx_v_size, __pyx_v_fmean, __pyx_v_fscale); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3277; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_2); + __pyx_r = __pyx_t_2; + __pyx_t_2 = 0; goto __pyx_L0; - goto __pyx_L6; + goto __pyx_L3; } - __pyx_L6:; + __pyx_L3:; /* "mtrand.pyx":3279 * return cont2_array_sc(self.internal_state, rk_wald, size, fmean, fscale) @@ -14537,15 +14784,11 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_39wald(PyObject *__pyx_v_self, P * oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ARRAY_ALIGNED) * if np.any(np.less_equal(omean,0.0)): */ - __pyx_t_4 = __pyx_v_mean; - __Pyx_INCREF(__pyx_t_4); - __pyx_t_1 = PyArray_FROM_OTF(__pyx_t_4, NPY_DOUBLE, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3280; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_1); - __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; - if (!(likely(((__pyx_t_1) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_1, __pyx_ptype_6mtrand_ndarray))))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3280; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_DECREF(((PyObject *)__pyx_v_omean)); - __pyx_v_omean = ((PyArrayObject *)__pyx_t_1); - __pyx_t_1 = 0; + __pyx_t_2 = PyArray_FROM_OTF(__pyx_v_mean, NPY_DOUBLE, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3280; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_2); + if (!(likely(((__pyx_t_2) == Py_None) || likely(__Pyx_TypeTest(__pyx_t_2, __pyx_ptype_6mtrand_ndarray))))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3280; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_v_omean = ((PyArrayObject *)__pyx_t_2); + __pyx_t_2 = 0; /* "mtrand.pyx":3281 * PyErr_Clear() @@ -14554,15 +14797,11 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_39wald(PyObject *__pyx_v_self, P * if np.any(np.less_equal(omean,0.0)): * raise ValueError("mean <= 0.0") */ - __pyx_t_1 = __pyx_v_scale; - __Pyx_INCREF(__pyx_t_1); - __pyx_t_4 = PyArray_FROM_OTF(__pyx_t_1, NPY_DOUBLE, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3281; __pyx_clineno = __LINE__; 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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 PyObject *__pyx_pw_6mtrand_11RandomState_81triangular(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { + PyObject *__pyx_v_left = 0; + PyObject *__pyx_v_mode = 0; + PyObject *__pyx_v_right = 0; + PyObject *__pyx_v_size = 0; + PyObject *__pyx_r = 0; + __Pyx_RefNannyDeclarations + __Pyx_RefNannySetupContext("triangular (wrapper)", 0); + { + static PyObject **__pyx_pyargnames[] = {&__pyx_n_s__left,&__pyx_n_s__mode,&__pyx_n_s__right,&__pyx_n_s__size,0}; + PyObject* values[4] = {0,0,0,0}; + + /* "mtrand.pyx":3290 * * * def triangular(self, left, mode, right, size=None): # <<<<<<<<<<<<<< * """ * triangular(left, mode, right, size=None) */ - -static PyObject *__pyx_pf_6mtrand_11RandomState_40triangular(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ -static char __pyx_doc_6mtrand_11RandomState_40triangular[] = "\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. 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{__pyx_filename = __pyx_f[0]; __pyx_lineno = 3290; __pyx_clineno = __LINE__; goto __pyx_L3_error;} __pyx_L3_error:; - __Pyx_AddTraceback("mtrand.RandomState.triangular"); + __Pyx_AddTraceback("mtrand.RandomState.triangular", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; - __pyx_v_oleft = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None); - __pyx_v_omode = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None); - __pyx_v_oright = ((PyArrayObject *)Py_None); __Pyx_INCREF(Py_None); + __pyx_r = __pyx_pf_6mtrand_11RandomState_80triangular(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self), __pyx_v_left, __pyx_v_mode, __pyx_v_right, __pyx_v_size); + __Pyx_RefNannyFinishContext(); + return __pyx_r; +} + +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) { + PyArrayObject *__pyx_v_oleft = 0; + PyArrayObject *__pyx_v_omode = 0; + PyArrayObject *__pyx_v_oright = 0; + double __pyx_v_fleft; + double __pyx_v_fmode; + double __pyx_v_fright; + PyObject *__pyx_r = NULL; + __Pyx_RefNannyDeclarations + int __pyx_t_1; + PyObject *__pyx_t_2 = NULL; + PyObject *__pyx_t_3 = NULL; + PyObject *__pyx_t_4 = NULL; + PyObject *__pyx_t_5 = NULL; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; + __Pyx_RefNannySetupContext("triangular", 0); /* "mtrand.pyx":3350 * cdef double fleft, fmode, fright @@ -14822,10 +15065,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_40triangular(PyObject *__pyx_v_s * fright = PyFloat_AsDouble(right) * fmode = PyFloat_AsDouble(mode) */ - __pyx_t_1 = __pyx_v_left; - __Pyx_INCREF(__pyx_t_1); - __pyx_v_fleft = PyFloat_AsDouble(__pyx_t_1); - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + __pyx_v_fleft = PyFloat_AsDouble(__pyx_v_left); /* "mtrand.pyx":3351 * @@ -14834,10 +15074,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_40triangular(PyObject *__pyx_v_s * fmode = PyFloat_AsDouble(mode) * if not PyErr_Occurred(): */ - __pyx_t_1 = __pyx_v_right; - __Pyx_INCREF(__pyx_t_1); - __pyx_v_fright = PyFloat_AsDouble(__pyx_t_1); - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + __pyx_v_fright = PyFloat_AsDouble(__pyx_v_right); /* "mtrand.pyx":3352 * fleft = PyFloat_AsDouble(left) @@ -14846,10 +15083,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_40triangular(PyObject *__pyx_v_s * if not PyErr_Occurred(): * if fleft > fmode: */ - __pyx_t_1 = __pyx_v_mode; - __Pyx_INCREF(__pyx_t_1); - __pyx_v_fmode = PyFloat_AsDouble(__pyx_t_1); - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + __pyx_v_fmode = PyFloat_AsDouble(__pyx_v_mode); /* "mtrand.pyx":3353 * fright = PyFloat_AsDouble(right) @@ -14858,8 +15092,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_40triangular(PyObject *__pyx_v_s * if fleft > fmode: * raise ValueError("left > mode") */ - __pyx_t_2 = (!PyErr_Occurred()); - if (__pyx_t_2) { + __pyx_t_1 = (!PyErr_Occurred()); + if (__pyx_t_1) { /* "mtrand.pyx":3354 * fmode = PyFloat_AsDouble(mode) @@ -14868,8 +15102,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_40triangular(PyObject *__pyx_v_s * raise ValueError("left > mode") * if fmode > fright: */ - __pyx_t_2 = (__pyx_v_fleft > __pyx_v_fmode); - if (__pyx_t_2) { + __pyx_t_1 = (__pyx_v_fleft > __pyx_v_fmode); + if (__pyx_t_1) { /* "mtrand.pyx":3355 * if not PyErr_Occurred(): @@ -14878,14 +15112,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_40triangular(PyObject *__pyx_v_s * if fmode > fright: * raise ValueError("mode > right") */ - __pyx_t_1 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_122), NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3355; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_1); - __Pyx_Raise(__pyx_t_1, 0, 0); - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_122), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3355; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_2); + __Pyx_Raise(__pyx_t_2, 0, 0, 0); + __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3355; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - goto __pyx_L7; + goto __pyx_L4; } - __pyx_L7:; + __pyx_L4:; /* "mtrand.pyx":3356 * if fleft > fmode: @@ -14894,8 +15128,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_40triangular(PyObject *__pyx_v_s * raise ValueError("mode > right") * if fleft == fright: */ - __pyx_t_2 = (__pyx_v_fmode > __pyx_v_fright); - if (__pyx_t_2) { + __pyx_t_1 = (__pyx_v_fmode > __pyx_v_fright); + if (__pyx_t_1) { /* "mtrand.pyx":3357 * raise ValueError("left > mode") @@ -14904,14 +15138,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_40triangular(PyObject *__pyx_v_s * if fleft == fright: * raise ValueError("left == right") */ - __pyx_t_1 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_124), NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3357; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_1); - __Pyx_Raise(__pyx_t_1, 0, 0); - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_124), NULL); 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(n may be\n input as a float, but it is truncated to an integer in use)\n\n Parameters\n ----------\n n : float (but truncated to an integer)\n parameter, > 0.\n p : float\n parameter, >= 0 and <=1.\n size : {tuple, int}\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : {ndarray, scalar}\n where the values are all integers in [0, n].\n\n See Also\n --------\n scipy.stats.distributions.binom : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Binomial distribution is\n\n .. math:: P(N) = \\binom{n}{N}p^N(1-p)^{n-N},\n\n where :math:`n` is the number of trials, :math:`p` is the probability\n of success, and :math:`N` is the number of successes.\n\n When estimating the standard error of a proportion in a population by\n using a random sample, the normal distribution works well unless the\n product p*n <=5, where p = population proportion estimate, and n =\n number of samples, in which case the binomial distribution is used\n instead. For example, a sample of 15 people shows 4 who are left\n handed, and 11 who are right handed. Then p = 4/15 = 27%. 0.27*15 = 4,\n so the binomial distribution should be used in this case.\n\n References\n ----------\n .. [1] Dalgaard, Peter, \"Introductory Statistics with R\",\n Springer-Verlag, 2002.\n "" .. [2] Glantz, Stanton A. \"Primer of Biostatistics.\", McGraw-Hill,\n Fifth Edition, 2002.\n .. [3] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n and Quigley, 1972.\n .. [4] Weisstein, Eric W. \"Binomial Distribution.\" From MathWorld--A\n Wolfram Web Resource.\n http://mathworld.wolfram.com/BinomialDistribution.html\n .. [5] Wikipedia, \"Binomial-distribution\",\n http://en.wikipedia.org/wiki/Binomial_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> n, p = 10, .5 # number of trials, probability of each trial\n >>> s = np.random.binomial(n, p, 1000)\n # result of flipping a coin 10 times, tested 1000 times.\n\n A real world example. 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What is the probability of that happening?\n\n Let's do 20,000 trials of the model, and count the number that\n generate zero positive results.\n\n >>> sum(np.random.binomial(9,0.1,20000)==0)/20000.\n answer = 0.38885, or 38%.\n\n "; +static PyObject *__pyx_pw_6mtrand_11RandomState_83binomial(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { + PyObject *__pyx_v_n = 0; + PyObject *__pyx_v_p = 0; + PyObject *__pyx_v_size = 0; + PyObject *__pyx_r = 0; + __Pyx_RefNannyDeclarations + __Pyx_RefNannySetupContext("binomial (wrapper)", 0); + { + static PyObject **__pyx_pyargnames[] = {&__pyx_n_s__n,&__pyx_n_s__p,&__pyx_n_s__size,0}; + PyObject* values[3] = {0,0,0}; + + /* "mtrand.pyx":3378 * * # Complicated, discrete distributions: * def binomial(self, n, p, size=None): # <<<<<<<<<<<<<< * """ * binomial(n, p, size=None) */ - -static PyObject *__pyx_pf_6mtrand_11RandomState_41binomial(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ -static char __pyx_doc_6mtrand_11RandomState_41binomial[] = "\n binomial(n, p, size=None)\n\n Draw samples from a binomial distribution.\n\n Samples are drawn from a Binomial distribution with specified\n parameters, n trials and p probability of success where\n n an integer > 0 and p is in the interval [0,1]. (n may be\n input as a float, but it is truncated to an integer in use)\n\n Parameters\n ----------\n n : float (but truncated to an integer)\n parameter, > 0.\n p : float\n parameter, >= 0 and <=1.\n size : {tuple, int}\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : {ndarray, scalar}\n where the values are all integers in [0, n].\n\n See Also\n --------\n scipy.stats.distributions.binom : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Binomial distribution is\n\n .. math:: P(N) = \\binom{n}{N}p^N(1-p)^{n-N},\n\n where :math:`n` is the number of trials, :math:`p` is the probability\n of success, and :math:`N` is the number of successes.\n\n When estimating the standard error of a proportion in a population by\n using a random sample, the normal distribution works well unless the\n product p*n <=5, where p = population proportion estimate, and n =\n number of samples, in which case the binomial distribution is used\n instead. For example, a sample of 15 people shows 4 who are left\n handed, and 11 who are right handed. Then p = 4/15 = 27%. 0.27*15 = 4,\n so the binomial distribution should be used in this case.\n\n References\n ----------\n .. [1] Dalgaard, Peter, \"Introductory Statistics with R\",\n Springer-Verlag, 2002.\n "" .. [2] Glantz, Stanton A. \"Primer of Biostatistics.\", McGraw-Hill,\n Fifth Edition, 2002.\n .. [3] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n and Quigley, 1972.\n .. [4] Weisstein, Eric W. \"Binomial Distribution.\" From MathWorld--A\n Wolfram Web Resource.\n http://mathworld.wolfram.com/BinomialDistribution.html\n .. [5] Wikipedia, \"Binomial-distribution\",\n http://en.wikipedia.org/wiki/Binomial_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> n, p = 10, .5 # number of trials, probability of each trial\n >>> s = np.random.binomial(n, p, 1000)\n # result of flipping a coin 10 times, tested 1000 times.\n\n A real world example. A company drills 9 wild-cat oil exploration\n wells, each with an estimated probability of success of 0.1. All nine\n wells fail. 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__pyx_t_5 = 0; + __pyx_t_5 = PyTuple_New(1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3480; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_5); + PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_t_4); + __Pyx_GIVEREF(__pyx_t_4); + __pyx_t_4 = 0; + __pyx_t_4 = PyObject_Call(__pyx_t_2, ((PyObject *)__pyx_t_5), NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3480; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_4); + __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + __Pyx_DECREF(((PyObject *)__pyx_t_5)); __pyx_t_5 = 0; + __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_4); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3480; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; - __Pyx_DECREF(((PyObject *)__pyx_t_6)); __pyx_t_6 = 0; - __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_t_5); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3480; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; - if (__pyx_t_2) { + if (__pyx_t_1) { /* "mtrand.pyx":3481 * raise ValueError("n <= 0") @@ -15612,14 +15818,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_41binomial(PyObject *__pyx_v_sel * if np.any(np.greater(p, 1)): * raise ValueError("p > 1") */ - __pyx_t_5 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_137), NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3481; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_5); - __Pyx_Raise(__pyx_t_5, 0, 0); - __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0; + __pyx_t_4 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_137), NULL); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3481; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_4); + __Pyx_Raise(__pyx_t_4, 0, 0, 0); + __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; {__pyx_filename = __pyx_f[0]; 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If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : int or ndarray of ints\n Drawn samples.\n\n Notes\n -----\n The probability density for the Negative Binomial distribution is\n\n .. math:: P(N;n,p) = \\binom{N+n-1}{n-1}p^{n}(1-p)^{N},\n\n where :math:`n-1` is the number of successes, :math:`p` is the probability\n of success, and :math:`N+n-1` is the number of trials.\n\n The negative binomial distribution gives the probability of n-1 successes\n and N failures in N+n-1 trials, and success on the (N+n)th trial.\n\n If one throws a die repeatedly until the third time a \"1\" appears, then the\n probability distribution of the number of non-\"1\"s that appear before the\n third \"1\" is a negative binomial distribution.\n\n References\n ----------\n .. [1] Weisstein, Eric W. \"Negative Binomial Distribution.\" From\n MathWorld--A Wolfram Web Resource.\n http://mathworld.wolfram.com/NegativeBinomialDistribution.html\n .. [2] Wikipedia, \"Negative binomial distribution\",\n http://en.wikipedia.org/wiki/Negative_binomial_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n A real world example. A company drills wild-cat oil exploration well""s, each\n with an estimated probability of success of 0.1. 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If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Notes\n -----\n The Poisson distribution\n\n .. math:: f(k; \\lambda)=\\frac{\\lambda^k e^{-\\lambda}}{k!}\n\n For events with an expected separation :math:`\\lambda` the Poisson\n distribution :math:`f(k; \\lambda)` describes the probability of\n :math:`k` events occurring within the observed interval :math:`\\lambda`.\n\n Because the output is limited to the range of the C long type, a\n ValueError is raised when `lam` is within 10 sigma of the maximum\n representable value.\n\n References\n ----------\n .. [1] Weisstein, Eric W. \"Poisson Distribution.\" From MathWorld--A Wolfram\n Web Resource. http://mathworld.wolfram.com/PoissonDistribution.html\n .. [2] Wikipedia, \"Poisson distribution\",\n http://en.wikipedia.org/wiki/Poisson_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> import numpy as np\n >>> s = np.random.poisson(5, 10000)\n\n Display histogram of the sample:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, 14, normed=True)\n >>> plt.show()\n\n "; +static PyObject *__pyx_pw_6mtrand_11RandomState_87poisson(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { + PyObject *__pyx_v_lam = 0; + PyObject *__pyx_v_size = 0; + PyObject *__pyx_r = 0; + __Pyx_RefNannyDeclarations + __Pyx_RefNannySetupContext("poisson (wrapper)", 0); + { + static PyObject **__pyx_pyargnames[] = {&__pyx_n_s__lam,&__pyx_n_s__size,0}; + PyObject* values[2] = {0,0}; + values[0] = __pyx_k_145; + + /* "mtrand.pyx":3581 * on, op) * * def poisson(self, lam=1.0, size=None): # <<<<<<<<<<<<<< * """ * poisson(lam=1.0, size=None) */ - -static PyObject *__pyx_pf_6mtrand_11RandomState_43poisson(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ -static char __pyx_doc_6mtrand_11RandomState_43poisson[] = "\n poisson(lam=1.0, size=None)\n\n Draw samples from a Poisson distribution.\n\n The Poisson distribution is the limit of the Binomial\n distribution for large N.\n\n Parameters\n ----------\n lam : float\n Expectation of interval, should be >= 0.\n size : int or tuple of ints, optional\n Output shape. 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+ if (value) { values[0] = value; kw_args--; } + } + case 1: + if (kw_args > 0) { + PyObject* value = PyDict_GetItem(__pyx_kwds, __pyx_n_s__size); + if (value) { values[1] = value; kw_args--; } + } + } + if (unlikely(kw_args > 0)) { + if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "poisson") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3581; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + } + } else { + switch (PyTuple_GET_SIZE(__pyx_args)) { + case 2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1); + case 1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0); + case 0: break; + default: goto __pyx_L5_argtuple_error; } - } - if (unlikely(kw_args > 0)) { - if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "poisson") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3581; __pyx_clineno = __LINE__; goto __pyx_L3_error;} } __pyx_v_lam = values[0]; __pyx_v_size = values[1]; - } else { - __pyx_v_lam = __pyx_k_145; 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- if (__pyx_t_2) { + __pyx_t_1 = (!PyErr_Occurred()); + if (__pyx_t_1) { /* "mtrand.pyx":3637 * flam = PyFloat_AsDouble(lam) @@ -16299,11 +16500,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_43poisson(PyObject *__pyx_v_self * raise ValueError("lam < 0") * if lam > self.poisson_lam_max: */ - __pyx_t_1 = PyObject_RichCompare(__pyx_v_lam, __pyx_int_0, Py_LT); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3637; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_1); - __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_t_1); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3637; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; - if (__pyx_t_2) { + __pyx_t_2 = PyObject_RichCompare(__pyx_v_lam, __pyx_int_0, Py_LT); __Pyx_XGOTREF(__pyx_t_2); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3637; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_2); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3637; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + if (__pyx_t_1) { /* "mtrand.pyx":3638 * if not PyErr_Occurred(): @@ -16312,14 +16512,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_43poisson(PyObject *__pyx_v_self * if lam > self.poisson_lam_max: * raise ValueError("lam value too large") */ - __pyx_t_1 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_147), NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3638; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_1); - __Pyx_Raise(__pyx_t_1, 0, 0); - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_147), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3638; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_2); + __Pyx_Raise(__pyx_t_2, 0, 0, 0); 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__pyx_lineno = 3639; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyObject_GetAttr(((PyObject *)__pyx_v_self), __pyx_n_s__poisson_lam_max); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3639; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_2); + __pyx_t_3 = PyObject_RichCompare(__pyx_v_lam, __pyx_t_2, Py_GT); __Pyx_XGOTREF(__pyx_t_3); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3639; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3639; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - if (__pyx_t_2) { + if (__pyx_t_1) { /* "mtrand.pyx":3640 * raise ValueError("lam < 0") @@ -16346,12 +16545,12 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_43poisson(PyObject *__pyx_v_self */ __pyx_t_3 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_149), NULL); 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If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn; a single integer is equivalent in\n its result to providing a mono-tuple, i.e., a 1-D array of length\n *size* is returned. The default is None, in which case a single\n scalar is returned.\n\n Returns\n -------\n samples : scalar or ndarray\n The returned samples are greater than or equal to one.\n\n See Also\n --------\n scipy.stats.distributions.zipf : probability density function,\n distribution, or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Zipf distribution is\n\n .. math:: p(x) = \\frac{x^{-a}}{\\zeta(a)},\n\n where :math:`\\zeta` is the Riemann Zeta function.\n\n It is named for the American linguist George Kingsley Zipf, who noted\n that the frequency of any word in a sample of a language is inversely\n proportional to its rank in the frequency table.\n\n References\n ----------\n Zipf, G. K., *Selected Studies of the Principle of Relative Frequency\n in Language*, Cambridge, MA: Harvard Univ. 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The geometric distribution models the number of trials\n that must be run in order to achieve success. It is therefore\n supported on the positive integers, ``k = 1, 2, ...``.\n\n The probability mass function of the geometric distribution is\n\n .. math:: f(k) = (1 - p)^{k - 1} p\n\n where `p` is the probability of success of an individual trial.\n\n Parameters\n ----------\n p : float\n The probability of success of an individual trial.\n size : tuple of ints\n Number of values to draw from the distribution. 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__pyx_lineno = 3791; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_2); + __Pyx_Raise(__pyx_t_2, 0, 0, 0); + __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3791; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - goto __pyx_L7; + goto __pyx_L4; } - __pyx_L7:; + __pyx_L4:; /* "mtrand.pyx":3792 * if fp < 0.0: @@ -16934,8 +17131,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_45geometric(PyObject *__pyx_v_se * raise ValueError("p > 1.0") * return discd_array_sc(self.internal_state, rk_geometric, size, fp) */ - __pyx_t_2 = (__pyx_v_fp > 1.0); - if (__pyx_t_2) { + __pyx_t_1 = (__pyx_v_fp > 1.0); + if (__pyx_t_1) { /* "mtrand.pyx":3793 * raise ValueError("p < 0.0") @@ -16944,14 +17141,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_45geometric(PyObject *__pyx_v_se * return discd_array_sc(self.internal_state, rk_geometric, size, fp) * */ - __pyx_t_1 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_159), NULL); 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{__pyx_filename = __pyx_f[0]; __pyx_lineno = 3803; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - goto __pyx_L10; + goto __pyx_L7; } - __pyx_L10:; + __pyx_L7:; /* "mtrand.pyx":3804 * if np.any(np.greater(op, 1.0)): @@ -17128,129 +17317,133 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_45geometric(PyObject *__pyx_v_se * def hypergeometric(self, ngood, nbad, nsample, size=None): */ __Pyx_XDECREF(__pyx_r); - __pyx_t_3 = ((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state; - __pyx_t_1 = __pyx_v_size; - __Pyx_INCREF(__pyx_t_1); - __pyx_t_5 = __pyx_f_6mtrand_discd_array(__pyx_t_3, rk_geometric, __pyx_t_1, __pyx_v_op); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3804; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_5); - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; - __pyx_r = __pyx_t_5; - __pyx_t_5 = 0; + __pyx_t_2 = __pyx_f_6mtrand_discd_array(__pyx_v_self->internal_state, rk_geometric, __pyx_v_size, __pyx_v_op); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3804; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_2); + __pyx_r = __pyx_t_2; + __pyx_t_2 = 0; goto __pyx_L0; __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; - __Pyx_XDECREF(__pyx_t_1); + __Pyx_XDECREF(__pyx_t_2); + __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_5); - __Pyx_XDECREF(__pyx_t_6); - __Pyx_AddTraceback("mtrand.RandomState.geometric"); + __Pyx_AddTraceback("mtrand.RandomState.geometric", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; - __Pyx_DECREF((PyObject *)__pyx_v_op); + __Pyx_XDECREF((PyObject *)__pyx_v_op); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } -/* "mtrand.pyx":3806 - * return discd_array(self.internal_state, rk_geometric, size, op) - * - * def hypergeometric(self, ngood, nbad, nsample, size=None): # <<<<<<<<<<<<<< - * """ - * hypergeometric(ngood, nbad, nsample, size=None) - */ - -static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ -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) { +/* Python wrapper */ +static PyObject *__pyx_pw_6mtrand_11RandomState_93hypergeometric(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ +static char __pyx_doc_6mtrand_11RandomState_92hypergeometric[] = "\n hypergeometric(ngood, nbad, nsample, size=None)\n\n Draw samples from a Hypergeometric distribution.\n\n Samples are drawn from a Hypergeometric distribution with specified\n parameters, ngood (ways to make a good selection), nbad (ways to make\n a bad selection), and nsample = number of items sampled, which is less\n than or equal to the sum ngood + nbad.\n\n Parameters\n ----------\n ngood : 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_pw_6mtrand_11RandomState_93hypergeometric(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { PyObject *__pyx_v_ngood = 0; PyObject *__pyx_v_nbad = 0; PyObject *__pyx_v_nsample = 0; - PyObject *__pyx_v_size = 0; - PyArrayObject *__pyx_v_ongood; - PyArrayObject *__pyx_v_onbad; 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if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3898; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_1); - __Pyx_Raise(__pyx_t_1, 0, 0); - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_163), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3898; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_2); + __Pyx_Raise(__pyx_t_2, 0, 0, 0); + __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3898; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - goto __pyx_L7; + goto __pyx_L4; } - __pyx_L7:; + __pyx_L4:; /* "mtrand.pyx":3899 * if ngood < 1: @@ -17334,11 +17517,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx * raise ValueError("nbad < 1") * if nsample < 1: */ - __pyx_t_1 = PyObject_RichCompare(__pyx_v_nbad, __pyx_int_1, Py_LT); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3899; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_1); - __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_t_1); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3899; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; - if (__pyx_t_2) { + __pyx_t_2 = PyObject_RichCompare(__pyx_v_nbad, __pyx_int_1, Py_LT); __Pyx_XGOTREF(__pyx_t_2); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3899; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_2); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3899; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + if (__pyx_t_1) { /* "mtrand.pyx":3900 * raise ValueError("ngood < 1") @@ -17347,14 +17529,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx * if nsample < 1: * raise ValueError("nsample < 1") */ - __pyx_t_1 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_165), NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3900; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_1); - __Pyx_Raise(__pyx_t_1, 0, 0); - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_165), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3900; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_2); + __Pyx_Raise(__pyx_t_2, 0, 0, 0); + __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3900; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - goto __pyx_L8; + goto __pyx_L5; } - __pyx_L8:; + __pyx_L5:; /* "mtrand.pyx":3901 * if nbad < 1: @@ -17363,11 +17545,10 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx * raise ValueError("nsample < 1") * if ngood + nbad < nsample: */ - __pyx_t_1 = PyObject_RichCompare(__pyx_v_nsample, __pyx_int_1, Py_LT); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3901; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_1); - __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_t_1); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3901; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; - if (__pyx_t_2) { + __pyx_t_2 = PyObject_RichCompare(__pyx_v_nsample, __pyx_int_1, Py_LT); __Pyx_XGOTREF(__pyx_t_2); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3901; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_2); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3901; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + if (__pyx_t_1) { /* "mtrand.pyx":3902 * raise ValueError("nbad < 1") @@ -17376,14 +17557,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx * if ngood + nbad < nsample: * raise ValueError("ngood + nbad < nsample") */ - __pyx_t_1 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_167), NULL); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3902; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_1); - __Pyx_Raise(__pyx_t_1, 0, 0); - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_167), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3902; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_2); + __Pyx_Raise(__pyx_t_2, 0, 0, 0); + __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3902; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - goto __pyx_L9; + goto __pyx_L6; } - __pyx_L9:; + __pyx_L6:; /* "mtrand.pyx":3903 * if nsample < 1: @@ -17392,14 +17573,13 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx * raise ValueError("ngood + nbad < nsample") * return discnmN_array_sc(self.internal_state, rk_hypergeometric, size, */ - __pyx_t_1 = PyNumber_Add(__pyx_v_ngood, __pyx_v_nbad); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3903; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_1); - __pyx_t_3 = PyObject_RichCompare(__pyx_t_1, __pyx_v_nsample, Py_LT); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3903; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_3); - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; - __pyx_t_2 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_2 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3903; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PyNumber_Add(__pyx_v_ngood, __pyx_v_nbad); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3903; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_2); + __pyx_t_3 = PyObject_RichCompare(__pyx_t_2, __pyx_v_nsample, Py_LT); __Pyx_XGOTREF(__pyx_t_3); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3903; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; + __pyx_t_1 = __Pyx_PyObject_IsTrue(__pyx_t_3); if (unlikely(__pyx_t_1 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3903; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - if (__pyx_t_2) { + if (__pyx_t_1) { /* "mtrand.pyx":3904 * raise ValueError("nsample < 1") @@ -17410,12 +17590,12 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx */ __pyx_t_3 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_169), NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3904; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); - __Pyx_Raise(__pyx_t_3, 0, 0); + __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3904; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - goto __pyx_L10; + goto __pyx_L7; } - __pyx_L10:; + __pyx_L7:; /* "mtrand.pyx":3905 * if ngood + nbad < nsample: @@ -17425,9 +17605,6 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx * */ __Pyx_XDECREF(__pyx_r); - __pyx_t_4 = ((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state; - __pyx_t_3 = __pyx_v_size; - __Pyx_INCREF(__pyx_t_3); /* "mtrand.pyx":3906 * raise ValueError("ngood + nbad < nsample") @@ -17436,15 +17613,14 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx * * */ - __pyx_t_1 = __pyx_f_6mtrand_discnmN_array_sc(__pyx_t_4, rk_hypergeometric, __pyx_t_3, __pyx_v_lngood, __pyx_v_lnbad, __pyx_v_lnsample); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3905; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_1); - __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - __pyx_r = __pyx_t_1; - __pyx_t_1 = 0; + __pyx_t_3 = __pyx_f_6mtrand_discnmN_array_sc(__pyx_v_self->internal_state, rk_hypergeometric, __pyx_v_size, __pyx_v_lngood, __pyx_v_lnbad, __pyx_v_lnsample); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3905; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_3); + __pyx_r = __pyx_t_3; + __pyx_t_3 = 0; goto __pyx_L0; - goto __pyx_L6; + goto __pyx_L3; } - __pyx_L6:; + __pyx_L3:; /* "mtrand.pyx":3909 * @@ -17462,13 +17638,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx * onbad = PyArray_FROM_OTF(nbad, NPY_LONG, NPY_ARRAY_ALIGNED) * onsample = PyArray_FROM_OTF(nsample, NPY_LONG, NPY_ARRAY_ALIGNED) */ - __pyx_t_1 = __pyx_v_ngood; - __Pyx_INCREF(__pyx_t_1); - __pyx_t_3 = PyArray_FROM_OTF(__pyx_t_1, NPY_LONG, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3911; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_3 = PyArray_FROM_OTF(__pyx_v_ngood, NPY_LONG, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3911; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_INCREF(((PyObject *)((PyArrayObject *)__pyx_t_3))); - __Pyx_DECREF(((PyObject *)__pyx_v_ongood)); __pyx_v_ongood = ((PyArrayObject *)__pyx_t_3); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; @@ -17479,15 +17651,11 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx * onsample = PyArray_FROM_OTF(nsample, NPY_LONG, NPY_ARRAY_ALIGNED) * if np.any(np.less(ongood, 1)): */ - __pyx_t_3 = __pyx_v_nbad; - __Pyx_INCREF(__pyx_t_3); - __pyx_t_1 = PyArray_FROM_OTF(__pyx_t_3, NPY_LONG, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3912; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_1); + __pyx_t_3 = PyArray_FROM_OTF(__pyx_v_nbad, NPY_LONG, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3912; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_3); + __Pyx_INCREF(((PyObject *)((PyArrayObject *)__pyx_t_3))); + __pyx_v_onbad = ((PyArrayObject *)__pyx_t_3); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - __Pyx_INCREF(((PyObject *)((PyArrayObject *)__pyx_t_1))); - __Pyx_DECREF(((PyObject *)__pyx_v_onbad)); - __pyx_v_onbad = ((PyArrayObject *)__pyx_t_1); - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "mtrand.pyx":3913 * ongood = PyArray_FROM_OTF(ngood, NPY_LONG, NPY_ARRAY_ALIGNED) @@ -17496,13 +17664,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx * if np.any(np.less(ongood, 1)): * raise ValueError("ngood < 1") */ - __pyx_t_1 = __pyx_v_nsample; - __Pyx_INCREF(__pyx_t_1); - __pyx_t_3 = PyArray_FROM_OTF(__pyx_t_1, NPY_LONG, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3913; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_3 = PyArray_FROM_OTF(__pyx_v_nsample, NPY_LONG, NPY_ARRAY_ALIGNED); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3913; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_INCREF(((PyObject *)((PyArrayObject *)__pyx_t_3))); - __Pyx_DECREF(((PyObject *)__pyx_v_onsample)); __pyx_v_onsample = ((PyArrayObject *)__pyx_t_3); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; @@ -17515,38 +17679,38 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_46hypergeometric(PyObject *__pyx */ __pyx_t_3 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3914; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); - __pyx_t_1 = PyObject_GetAttr(__pyx_t_3, __pyx_n_s__any); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3914; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_1); + __pyx_t_2 = PyObject_GetAttr(__pyx_t_3, __pyx_n_s__any); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3914; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_2); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3914; __pyx_clineno = __LINE__; 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goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_5); __Pyx_INCREF(((PyObject *)__pyx_v_onbad)); - PyTuple_SET_ITEM(__pyx_t_6, 0, ((PyObject *)__pyx_v_onbad)); + PyTuple_SET_ITEM(__pyx_t_5, 0, ((PyObject *)__pyx_v_onbad)); __Pyx_GIVEREF(((PyObject *)__pyx_v_onbad)); __Pyx_INCREF(__pyx_int_1); - PyTuple_SET_ITEM(__pyx_t_6, 1, __pyx_int_1); + PyTuple_SET_ITEM(__pyx_t_5, 1, __pyx_int_1); __Pyx_GIVEREF(__pyx_int_1); - __pyx_t_5 = PyObject_Call(__pyx_t_1, ((PyObject *)__pyx_t_6), NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3916; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_5); - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; - __Pyx_DECREF(((PyObject *)__pyx_t_6)); __pyx_t_6 = 0; - __pyx_t_6 = PyTuple_New(1); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3916; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_t_6)); - PyTuple_SET_ITEM(__pyx_t_6, 0, __pyx_t_5); - __Pyx_GIVEREF(__pyx_t_5); 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If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : {ndarray, scalar}\n where the values are all integers in [0, n].\n\n See Also\n --------\n scipy.stats.distributions.logser : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Log Series distribution is\n\n .. math:: P(k) = \\frac{-p^k}{k \\ln(1-p)},\n\n where p = probability.\n\n The Log Series distribution is frequently used to represent species\n richness and occurrence, first proposed by Fisher, Corbet, and\n Williams in 1943 [2]. It may also be used to model the numbers of\n occupants seen in cars [3].\n\n References\n ----------\n .. [1] Buzas, Martin A.; Culver, Stephen J., Understanding regional\n species diversity through the log series distribution of\n occurrences: BIODIVERSITY RESEARCH Diversity & Distributions,\n Volume 5, Number 5, September 1999 , pp. 187-195(9).\n .. [2] Fisher, R.A,, A.S. Corbet, and C.B. Williams. 1943. The\n relation between the number of species and the number of\n individuals in a random sample of an animal population.\n Journal of Animal Ecology, 12:42-58.\n .. [3] D. J. Hand, F. Daly, D. Lunn, E. Ostrowski, A Handbook of Small\n Data Sets, CRC Press, 1994.\n .. [4] Wikipedia, \"Log""arithmic-distribution\",\n http://en.wikipedia.org/wiki/Logarithmic-distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> a = .6\n >>> s = np.random.logseries(a, 10000)\n >>> count, bins, ignored = plt.hist(s)\n\n # plot against distribution\n\n >>> def logseries(k, p):\n ... return -p**k/(k*log(1-p))\n >>> plt.plot(bins, logseries(bins, a)*count.max()/\n logseries(bins, a).max(), 'r')\n >>> plt.show()\n\n "; +static PyObject *__pyx_pw_6mtrand_11RandomState_95logseries(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { + PyObject *__pyx_v_p = 0; + PyObject *__pyx_v_size = 0; + PyObject *__pyx_r = 0; + __Pyx_RefNannyDeclarations + __Pyx_RefNannySetupContext("logseries (wrapper)", 0); + { + static PyObject **__pyx_pyargnames[] = {&__pyx_n_s__p,&__pyx_n_s__size,0}; + PyObject* values[2] = {0,0}; + + /* "mtrand.pyx":3925 * ongood, onbad, onsample) * * def logseries(self, p, size=None): # <<<<<<<<<<<<<< * """ * logseries(p, size=None) */ - -static PyObject *__pyx_pf_6mtrand_11RandomState_47logseries(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ -static char __pyx_doc_6mtrand_11RandomState_47logseries[] = "\n logseries(p, size=None)\n\n Draw samples from a Logarithmic Series distribution.\n\n Samples are drawn from a Log Series distribution with specified\n parameter, p (probability, 0 < p < 1).\n\n Parameters\n ----------\n loc : float\n\n scale : float > 0.\n\n size : {tuple, int}\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : {ndarray, scalar}\n where the values are all integers in [0, n].\n\n See Also\n --------\n scipy.stats.distributions.logser : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Log Series distribution is\n\n .. math:: P(k) = \\frac{-p^k}{k \\ln(1-p)},\n\n where p = probability.\n\n The Log Series distribution is frequently used to represent species\n richness and occurrence, first proposed by Fisher, Corbet, and\n Williams in 1943 [2]. It may also be used to model the numbers of\n occupants seen in cars [3].\n\n References\n ----------\n .. [1] Buzas, Martin A.; Culver, Stephen J., Understanding regional\n species diversity through the log series distribution of\n occurrences: BIODIVERSITY RESEARCH Diversity & Distributions,\n Volume 5, Number 5, September 1999 , pp. 187-195(9).\n .. [2] Fisher, R.A,, A.S. Corbet, and C.B. Williams. 1943. The\n relation between the number of species and the number of\n individuals in a random sample of an animal population.\n Journal of Animal Ecology, 12:42-58.\n .. [3] D. J. Hand, F. Daly, D. Lunn, E. Ostrowski, A Handbook of Small\n Data Sets, CRC Press, 1994.\n .. 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__pyx_lineno = 4017; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_2); + __pyx_r = __pyx_t_2; + __pyx_t_2 = 0; goto __pyx_L0; __pyx_r = Py_None; __Pyx_INCREF(Py_None); goto __pyx_L0; __pyx_L1_error:; - __Pyx_XDECREF(__pyx_t_1); + __Pyx_XDECREF(__pyx_t_2); + __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); __Pyx_XDECREF(__pyx_t_5); - __Pyx_XDECREF(__pyx_t_6); - __Pyx_AddTraceback("mtrand.RandomState.logseries"); + __Pyx_AddTraceback("mtrand.RandomState.logseries", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; __pyx_L0:; - __Pyx_DECREF((PyObject *)__pyx_v_op); + __Pyx_XDECREF((PyObject *)__pyx_v_op); __Pyx_XGIVEREF(__pyx_r); __Pyx_RefNannyFinishContext(); return __pyx_r; } -/* "mtrand.pyx":4020 +/* Python wrapper */ +static PyObject *__pyx_pw_6mtrand_11RandomState_97multivariate_normal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ +static char __pyx_doc_6mtrand_11RandomState_96multivariate_normal[] = "\n multivariate_normal(mean, cov[, size])\n\n Draw random samples from a multivariate normal distribution.\n\n The multivariate normal, multinormal or Gaussian distribution is a\n generalization of the one-dimensional normal distribution to higher\n dimensions. Such a distribution is specified by its mean and\n covariance matrix. These parameters are analogous to the mean\n (average or \"center\") and variance (standard deviation, or \"width,\"\n squared) of the one-dimensional normal distribution.\n\n Parameters\n ----------\n mean : 1-D array_like, of length N\n Mean of the N-dimensional distribution.\n cov : 2-D array_like, of shape (N, N)\n Covariance matrix of the distribution. Must be symmetric and\n positive semi-definite for \"physically meaningful\" results.\n size : tuple of ints, optional\n Given a shape of, for example, ``(m,n,k)``, ``m*n*k`` samples are\n generated, and packed in an `m`-by-`n`-by-`k` arrangement. Because\n each sample is `N`-dimensional, the output shape is ``(m,n,k,N)``.\n If no shape is specified, a single (`N`-D) sample is returned.\n\n Returns\n -------\n out : ndarray\n The drawn samples, of shape *size*, if that was provided. If not,\n the shape is ``(N,)``.\n\n In other words, each entry ``out[i,j,...,:]`` is an N-dimensional\n value drawn from the distribution.\n\n Notes\n -----\n The mean is a coordinate in N-dimensional space, which represents the\n location where samples are most likely to be generated. This is\n analogous to the peak of the bell curve for the one-dimensional or\n univariate normal distribution.\n\n Covariance indicates the level to which two variables vary together.\n From the multivariate normal distribution, we draw ""N-dimensional\n samples, :math:`X = [x_1, x_2, ... x_N]`. The covariance matrix\n element :math:`C_{ij}` is the covariance of :math:`x_i` and :math:`x_j`.\n The element :math:`C_{ii}` is the variance of :math:`x_i` (i.e. its\n \"spread\").\n\n Instead of specifying the full covariance matrix, popular\n approximations include:\n\n - Spherical covariance (*cov* is a multiple of the identity matrix)\n - Diagonal covariance (*cov* has non-negative elements, and only on\n the diagonal)\n\n This geometrical property can be seen in two dimensions by plotting\n generated data-points:\n\n >>> mean = [0,0]\n >>> cov = [[1,0],[0,100]] # diagonal covariance, points lie on x or y-axis\n\n >>> import matplotlib.pyplot as plt\n >>> x,y = np.random.multivariate_normal(mean,cov,5000).T\n >>> plt.plot(x,y,'x'); plt.axis('equal'); plt.show()\n\n Note that the covariance matrix must be non-negative definite.\n\n References\n ----------\n Papoulis, A., *Probability, Random Variables, and Stochastic Processes*,\n 3rd ed., New York: McGraw-Hill, 1991.\n\n Duda, R. O., Hart, P. E., and Stork, D. G., *Pattern Classification*,\n 2nd ed., New York: Wiley, 2001.\n\n Examples\n --------\n >>> mean = (1,2)\n >>> cov = [[1,0],[1,0]]\n >>> x = np.random.multivariate_normal(mean,cov,(3,3))\n >>> x.shape\n (3, 3, 2)\n\n The following is probably true, given that 0.6 is roughly twice the\n standard deviation:\n\n >>> print list( (x[0,0,:] - mean) < 0.6 )\n [True, True]\n\n "; +static PyObject *__pyx_pw_6mtrand_11RandomState_97multivariate_normal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { + PyObject *__pyx_v_mean = 0; + PyObject *__pyx_v_cov = 0; + PyObject *__pyx_v_size = 0; + PyObject *__pyx_r = 0; + __Pyx_RefNannyDeclarations + __Pyx_RefNannySetupContext("multivariate_normal (wrapper)", 0); + { + static PyObject **__pyx_pyargnames[] = {&__pyx_n_s__mean,&__pyx_n_s__cov,&__pyx_n_s__size,0}; + PyObject* values[3] = {0,0,0}; + + /* "mtrand.pyx":4020 * * # Multivariate distributions: * def multivariate_normal(self, mean, cov, size=None): # <<<<<<<<<<<<<< * """ * multivariate_normal(mean, cov[, size]) */ - -static PyObject *__pyx_pf_6mtrand_11RandomState_48multivariate_normal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ -static char __pyx_doc_6mtrand_11RandomState_48multivariate_normal[] = "\n multivariate_normal(mean, cov[, size])\n\n Draw random samples from a multivariate normal distribution.\n\n The multivariate normal, multinormal or Gaussian distribution is a\n generalization of the one-dimensional normal distribution to higher\n dimensions. Such a distribution is specified by its mean and\n covariance matrix. These parameters are analogous to the mean\n (average or \"center\") and variance (standard deviation, or \"width,\"\n squared) of the one-dimensional normal distribution.\n\n Parameters\n ----------\n mean : 1-D array_like, of length N\n Mean of the N-dimensional distribution.\n cov : 2-D array_like, of shape (N, N)\n Covariance matrix of the distribution. Must be symmetric and\n positive semi-definite for \"physically meaningful\" results.\n size : tuple of ints, optional\n Given a shape of, for example, ``(m,n,k)``, ``m*n*k`` samples are\n generated, and packed in an `m`-by-`n`-by-`k` arrangement. Because\n each sample is `N`-dimensional, the output shape is ``(m,n,k,N)``.\n If no shape is specified, a single (`N`-D) sample is returned.\n\n Returns\n -------\n out : ndarray\n The drawn samples, of shape *size*, if that was provided. If not,\n the shape is ``(N,)``.\n\n In other words, each entry ``out[i,j,...,:]`` is an N-dimensional\n value drawn from the distribution.\n\n Notes\n -----\n The mean is a coordinate in N-dimensional space, which represents the\n location where samples are most likely to be generated. This is\n analogous to the peak of the bell curve for the one-dimensional or\n univariate normal distribution.\n\n Covariance indicates the level to which two variables vary together.\n From the multivariate normal distribution, we draw ""N-dimensional\n samples, :math:`X = [x_1, x_2, ... x_N]`. The covariance matrix\n element :math:`C_{ij}` is the covariance of :math:`x_i` and :math:`x_j`.\n The element :math:`C_{ii}` is the variance of :math:`x_i` (i.e. its\n \"spread\").\n\n Instead of specifying the full covariance matrix, popular\n approximations include:\n\n - Spherical covariance (*cov* is a multiple of the identity matrix)\n - Diagonal covariance (*cov* has non-negative elements, and only on\n the diagonal)\n\n This geometrical property can be seen in two dimensions by plotting\n generated data-points:\n\n >>> mean = [0,0]\n >>> cov = [[1,0],[0,100]] # diagonal covariance, points lie on x or y-axis\n\n >>> import matplotlib.pyplot as plt\n >>> x,y = np.random.multivariate_normal(mean,cov,5000).T\n >>> plt.plot(x,y,'x'); plt.axis('equal'); plt.show()\n\n Note that the covariance matrix must be non-negative definite.\n\n References\n ----------\n Papoulis, A., *Probability, Random Variables, and Stochastic Processes*,\n 3rd ed., New York: McGraw-Hill, 1991.\n\n Duda, R. 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- __Pyx_DECREF(__pyx_v_shape); __pyx_v_shape = __pyx_v_size; } - __pyx_L6:; + __pyx_L3:; /* "mtrand.pyx":4118 * else: @@ -18354,12 +18518,12 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_48multivariate_normal(PyObject * */ __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_181), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4119; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); - __Pyx_Raise(__pyx_t_2, 0, 0); + __Pyx_Raise(__pyx_t_2, 0, 0, 0); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4119; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - goto __pyx_L7; + goto __pyx_L4; } - __pyx_L7:; + __pyx_L4:; /* "mtrand.pyx":4120 * if len(mean.shape) != 1: @@ -18384,8 +18548,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_48multivariate_normal(PyObject * __pyx_t_1 = __Pyx_GetItemInt(__pyx_t_2, 1, sizeof(long), PyInt_FromLong); if (!__pyx_t_1) {__pyx_filename = __pyx_f[0]; 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__pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_2); + __pyx_t_2 = PyObject_RichCompare(__pyx_t_1, __pyx_t_3, Py_NE); __Pyx_XGOTREF(__pyx_t_2); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4122; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_7 = __Pyx_PyObject_IsTrue(__pyx_t_2); if (unlikely(__pyx_t_7 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4122; __pyx_clineno = __LINE__; goto __pyx_L1_error;} @@ -18446,12 +18608,12 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_48multivariate_normal(PyObject * */ __pyx_t_2 = PyObject_Call(__pyx_builtin_ValueError, ((PyObject *)__pyx_k_tuple_185), NULL); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4123; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); - __Pyx_Raise(__pyx_t_2, 0, 0); + __Pyx_Raise(__pyx_t_2, 0, 0, 0); __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4123; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - goto __pyx_L9; + goto __pyx_L6; } - __pyx_L9:; + __pyx_L6:; /* "mtrand.pyx":4125 * raise ValueError("mean and cov must have same length") @@ -18474,16 +18636,16 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_48multivariate_normal(PyObject * * final_shape.append(mean.shape[0]) */ __pyx_t_2 = PyList_New(1); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4126; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_t_2)); + __Pyx_GOTREF(__pyx_t_2); __Pyx_INCREF(__pyx_v_shape); PyList_SET_ITEM(__pyx_t_2, 0, __pyx_v_shape); __Pyx_GIVEREF(__pyx_v_shape); __Pyx_DECREF(__pyx_v_shape); __pyx_v_shape = ((PyObject *)__pyx_t_2); __pyx_t_2 = 0; - goto __pyx_L10; + goto __pyx_L7; } - __pyx_L10:; + __pyx_L7:; /* "mtrand.pyx":4127 * if isinstance(shape, int): @@ -18495,14 +18657,13 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_48multivariate_normal(PyObject * __pyx_t_2 = __Pyx_PySequence_GetSlice(__pyx_v_shape, 0, PY_SSIZE_T_MAX); 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__pyx_lineno = 4132; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __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;} __Pyx_GOTREF(__pyx_t_3); __pyx_t_2 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4132; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); @@ -18542,7 +18700,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_48multivariate_normal(PyObject * __Pyx_GOTREF(__pyx_t_2); __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 = 4132; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_t_1)); + __Pyx_GOTREF(__pyx_t_1); __Pyx_INCREF(((PyObject *)__pyx_v_final_shape)); PyTuple_SET_ITEM(__pyx_t_1, 0, ((PyObject *)__pyx_v_final_shape)); __Pyx_GIVEREF(((PyObject *)__pyx_v_final_shape)); @@ -18551,7 +18709,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_48multivariate_normal(PyObject * __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0; __Pyx_DECREF(((PyObject *)__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 = 4132; __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_9); __Pyx_GIVEREF(__pyx_t_9); __pyx_t_9 = 0; @@ -18559,7 +18717,6 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_48multivariate_normal(PyObject * __Pyx_GOTREF(__pyx_t_9); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_DECREF(((PyObject *)__pyx_t_1)); __pyx_t_1 = 0; - __Pyx_DECREF(__pyx_v_x); __pyx_v_x = __pyx_t_9; __pyx_t_9 = 0; @@ -18578,14 +18735,11 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_48multivariate_normal(PyObject * __pyx_t_9 = PyObject_GetAttr(__pyx_t_1, __pyx_n_s__reduce); if (unlikely(!__pyx_t_9)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4133; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_9); __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; - if (unlikely(__pyx_v_final_shape == Py_None)) { - PyErr_SetString(PyExc_TypeError, "object of type 'NoneType' has no len()"); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4133; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - } - __pyx_t_5 = PyList_GET_SIZE(((PyObject *)__pyx_v_final_shape)); + __pyx_t_5 = PyList_GET_SIZE(((PyObject *)__pyx_v_final_shape)); if (unlikely(__pyx_t_5 == -1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4133; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_t_1 = __Pyx_PySequence_GetSlice(((PyObject *)__pyx_v_final_shape), 0, (__pyx_t_5 - 1)); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4133; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(((PyObject *)__pyx_t_1)); __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4133; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_t_3)); + __Pyx_GOTREF(__pyx_t_3); PyTuple_SET_ITEM(__pyx_t_3, 0, ((PyObject *)__pyx_t_1)); __Pyx_GIVEREF(((PyObject *)__pyx_t_1)); __pyx_t_1 = 0; @@ -18607,7 +18761,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_48multivariate_normal(PyObject * __Pyx_GOTREF(__pyx_t_9); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __pyx_t_3 = PyTuple_New(2); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4133; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_t_3)); + __Pyx_GOTREF(__pyx_t_3); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_1); __Pyx_GIVEREF(__pyx_t_1); PyTuple_SET_ITEM(__pyx_t_3, 1, __pyx_t_9); @@ -18633,17 +18787,20 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_48multivariate_normal(PyObject * * (u,s,v) = svd(cov) */ __pyx_t_3 = PyList_New(1); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4142; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_t_3)); + __Pyx_GOTREF(__pyx_t_3); __Pyx_INCREF(((PyObject *)__pyx_n_s__svd)); PyList_SET_ITEM(__pyx_t_3, 0, ((PyObject *)__pyx_n_s__svd)); __Pyx_GIVEREF(((PyObject *)__pyx_n_s__svd)); - __pyx_t_9 = __Pyx_Import(((PyObject *)__pyx_n_s_186), ((PyObject *)__pyx_t_3)); if (unlikely(!__pyx_t_9)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4142; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_9 = __Pyx_Import(((PyObject *)__pyx_n_s_186), ((PyObject *)__pyx_t_3), -1); if (unlikely(!__pyx_t_9)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4142; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_9); __Pyx_DECREF(((PyObject *)__pyx_t_3)); __pyx_t_3 = 0; - __pyx_t_3 = PyObject_GetAttr(__pyx_t_9, __pyx_n_s__svd); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4142; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __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); 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if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4144; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = PySequence_ITEM(sequence, 2); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4144; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + #endif __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - __Pyx_DECREF(__pyx_v_u); - __pyx_v_u = __pyx_t_9; - __pyx_t_9 = 0; - __Pyx_DECREF(__pyx_v_s); - __pyx_v_s = __pyx_t_1; - __pyx_t_1 = 0; - __Pyx_DECREF(__pyx_v_v); - __pyx_v_v = __pyx_t_2; - __pyx_t_2 = 0; - } else { + } else + { + Py_ssize_t index = -1; __pyx_t_10 = PyObject_GetIter(__pyx_t_3); if (unlikely(!__pyx_t_10)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4144; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_10); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - __pyx_t_9 = __Pyx_UnpackItem(__pyx_t_10, 0); if (unlikely(!__pyx_t_9)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4144; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_11 = Py_TYPE(__pyx_t_10)->tp_iternext; 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/*proto*/ +static char __pyx_doc_6mtrand_11RandomState_98multinomial[] = "\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. 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__pyx_lineno = 4221; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_1); - __pyx_t_3 = PyTuple_New(2); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4221; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_t_3)); + __pyx_t_4 = __Pyx_PyInt_to_py_npy_intp(__pyx_v_d); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4221; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_4); + __pyx_t_2 = PyTuple_New(2); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4221; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_2); __Pyx_INCREF(__pyx_v_size); - PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_v_size); + PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_v_size); __Pyx_GIVEREF(__pyx_v_size); - PyTuple_SET_ITEM(__pyx_t_3, 1, __pyx_t_1); - __Pyx_GIVEREF(__pyx_t_1); - __pyx_t_1 = 0; - __Pyx_DECREF(__pyx_v_shape); - __pyx_v_shape = ((PyObject *)__pyx_t_3); - __pyx_t_3 = 0; 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goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_3); - __Pyx_DECREF(((PyObject *)__pyx_t_1)); __pyx_t_1 = 0; - __Pyx_DECREF(__pyx_v_shape); - __pyx_v_shape = __pyx_t_3; - __pyx_t_3 = 0; + __pyx_t_2 = __Pyx_PyInt_to_py_npy_intp(__pyx_v_d); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4223; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_2); + __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4223; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_4); + PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_2); + __Pyx_GIVEREF(__pyx_t_2); + __pyx_t_2 = 0; + __pyx_t_2 = PyNumber_Add(__pyx_v_size, ((PyObject *)__pyx_t_4)); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4223; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_2); + __Pyx_DECREF(((PyObject *)__pyx_t_4)); __pyx_t_4 = 0; + __pyx_v_shape = __pyx_t_2; + __pyx_t_2 = 0; } - __pyx_L7:; + __pyx_L4:; /* "mtrand.pyx":4225 * shape = size + (d,) @@ -19079,24 +19256,23 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_ * mnarr = multin * mnix = PyArray_DATA(mnarr) */ - __pyx_t_3 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4225; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_3); - __pyx_t_1 = PyObject_GetAttr(__pyx_t_3, __pyx_n_s__zeros); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4225; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(__pyx_t_1); - __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - __pyx_t_3 = PyTuple_New(2); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4225; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_t_3)); + __pyx_t_2 = __Pyx_GetName(__pyx_m, __pyx_n_s__np); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4225; __pyx_clineno = __LINE__; 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__pyx_lineno = 4225; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_5 = PyObject_Call(__pyx_t_4, ((PyObject *)__pyx_t_2), NULL); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4225; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_5); - __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; - __Pyx_DECREF(((PyObject *)__pyx_t_3)); __pyx_t_3 = 0; - __Pyx_DECREF(__pyx_v_multin); + __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0; + __Pyx_DECREF(((PyObject *)__pyx_t_2)); __pyx_t_2 = 0; __pyx_v_multin = __pyx_t_5; __pyx_t_5 = 0; @@ -19108,7 +19284,6 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_ * i = 0 */ __Pyx_INCREF(((PyObject *)((PyArrayObject *)__pyx_v_multin))); - __Pyx_DECREF(((PyObject *)arrayObject_mnarr)); arrayObject_mnarr = ((PyArrayObject *)__pyx_v_multin); /* "mtrand.pyx":4227 @@ -19137,8 +19312,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_ * dn = n */ while (1) { - __pyx_t_4 = (__pyx_v_i < PyArray_SIZE(arrayObject_mnarr)); 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- goto __pyx_L12; + goto __pyx_L8_break; + goto __pyx_L9; } - __pyx_L12:; + __pyx_L9:; /* "mtrand.pyx":4237 * if dn <= 0: @@ -19221,7 +19396,7 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_ */ __pyx_v_Sum = (__pyx_v_Sum - (__pyx_v_pix[__pyx_v_j])); } - __pyx_L11_break:; + __pyx_L8_break:; /* "mtrand.pyx":4238 * break @@ -19230,8 +19405,8 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_ * mnix[i+d-1] = dn * */ - __pyx_t_4 = (__pyx_v_dn > 0); - if (__pyx_t_4) { + __pyx_t_3 = (__pyx_v_dn > 0); + if (__pyx_t_3) { /* "mtrand.pyx":4239 * Sum = Sum - pix[j] @@ -19241,9 +19416,9 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_ * i = i + d */ (__pyx_v_mnix[((__pyx_v_i + __pyx_v_d) - 1)]) = __pyx_v_dn; - goto __pyx_L13; + goto __pyx_L10; } - __pyx_L13:; + __pyx_L10:; /* "mtrand.pyx":4241 * mnix[i+d-1] = dn @@ -19270,102 +19445,114 @@ static PyObject *__pyx_pf_6mtrand_11RandomState_49multinomial(PyObject *__pyx_v_ __pyx_r = Py_None; 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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) { + PyObject *__pyx_v_alpha = 0; + PyObject *__pyx_v_size = 0; + PyObject *__pyx_r = 0; + __Pyx_RefNannyDeclarations + __Pyx_RefNannySetupContext("dirichlet (wrapper)", 0); + { + static PyObject **__pyx_pyargnames[] = {&__pyx_n_s__alpha,&__pyx_n_s__size,0}; + PyObject* values[2] = {0,0}; + + /* "mtrand.pyx":4245 * return multin * * def dirichlet(self, object alpha, size=None): # <<<<<<<<<<<<<< * """ * dirichlet(alpha, size=None) */ - -static PyObject *__pyx_pf_6mtrand_11RandomState_50dirichlet(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ -static char __pyx_doc_6mtrand_11RandomState_50dirichlet[] = "\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 .. 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{__Pyx_NAMESTR("permutation"), (PyCFunction)__pyx_pw_6mtrand_11RandomState_105permutation, METH_O, __Pyx_DOCSTR(__pyx_doc_6mtrand_11RandomState_104permutation)}, {0, 0, 0, 0} }; @@ -20287,7 +20475,7 @@ static PyTypeObject __pyx_type_6mtrand_RandomState = { 0, /*tp_descr_get*/ 0, /*tp_descr_set*/ 0, /*tp_dictoffset*/ - __pyx_pf_6mtrand_11RandomState___init__, /*tp_init*/ + __pyx_pw_6mtrand_11RandomState_1__init__, /*tp_init*/ 0, /*tp_alloc*/ __pyx_tp_new_6mtrand_RandomState, /*tp_new*/ 0, /*tp_free*/ @@ -20477,7 +20665,6 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = { {&__pyx_n_s__allclose, __pyx_k__allclose, sizeof(__pyx_k__allclose), 0, 0, 1, 1}, {&__pyx_n_s__alpha, __pyx_k__alpha, sizeof(__pyx_k__alpha), 0, 0, 1, 1}, {&__pyx_n_s__any, __pyx_k__any, sizeof(__pyx_k__any), 0, 0, 1, 1}, - {&__pyx_n_s__append, __pyx_k__append, sizeof(__pyx_k__append), 0, 0, 1, 1}, {&__pyx_n_s__arange, __pyx_k__arange, sizeof(__pyx_k__arange), 0, 0, 1, 1}, {&__pyx_n_s__array, __pyx_k__array, 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{&__pyx_n_s__n, __pyx_k__n, sizeof(__pyx_k__n), 0, 0, 1, 1}, {&__pyx_n_s__nbad, __pyx_k__nbad, sizeof(__pyx_k__nbad), 0, 0, 1, 1}, - {&__pyx_n_s__nd, __pyx_k__nd, sizeof(__pyx_k__nd), 0, 0, 1, 1}, {&__pyx_n_s__ndarray, __pyx_k__ndarray, sizeof(__pyx_k__ndarray), 0, 0, 1, 1}, {&__pyx_n_s__ndim, __pyx_k__ndim, sizeof(__pyx_k__ndim), 0, 0, 1, 1}, {&__pyx_n_s__ndmin, __pyx_k__ndmin, sizeof(__pyx_k__ndmin), 0, 0, 1, 1}, @@ -20558,7 +20738,6 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = { {&__pyx_n_s__permutation, __pyx_k__permutation, sizeof(__pyx_k__permutation), 0, 0, 1, 1}, {&__pyx_n_s__poisson, __pyx_k__poisson, sizeof(__pyx_k__poisson), 0, 0, 1, 1}, {&__pyx_n_s__poisson_lam_max, __pyx_k__poisson_lam_max, sizeof(__pyx_k__poisson_lam_max), 0, 0, 1, 1}, - {&__pyx_n_s__pos, __pyx_k__pos, sizeof(__pyx_k__pos), 0, 0, 1, 1}, {&__pyx_n_s__power, __pyx_k__power, sizeof(__pyx_k__power), 0, 0, 1, 1}, {&__pyx_n_s__pvals, __pyx_k__pvals, sizeof(__pyx_k__pvals), 0, 0, 1, 1}, {&__pyx_n_s__rand, __pyx_k__rand, sizeof(__pyx_k__rand), 0, 0, 1, 1}, @@ -20610,7 +20789,8 @@ static int __Pyx_InitCachedBuiltins(void) { } static int __Pyx_InitCachedConstants(void) { - __Pyx_RefNannySetupContext("__Pyx_InitCachedConstants"); + __Pyx_RefNannyDeclarations + __Pyx_RefNannySetupContext("__Pyx_InitCachedConstants", 0); /* "mtrand.pyx":185 * oa) @@ -20620,7 +20800,7 @@ static int __Pyx_InitCachedConstants(void) { * oa_data = PyArray_MultiIter_DATA(multi, 1) */ __pyx_k_tuple_2 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 185; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_2)); + __Pyx_GOTREF(__pyx_k_tuple_2); __Pyx_INCREF(((PyObject *)__pyx_kp_s_1)); PyTuple_SET_ITEM(__pyx_k_tuple_2, 0, ((PyObject *)__pyx_kp_s_1)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_1)); @@ -20634,7 +20814,7 @@ static int __Pyx_InitCachedConstants(void) { * oa_data = PyArray_MultiIter_DATA(multi, 1) */ __pyx_k_tuple_3 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 234; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_3)); + __Pyx_GOTREF(__pyx_k_tuple_3); __Pyx_INCREF(((PyObject *)__pyx_kp_s_1)); PyTuple_SET_ITEM(__pyx_k_tuple_3, 0, ((PyObject *)__pyx_kp_s_1)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_1)); @@ -20648,7 +20828,7 @@ static int __Pyx_InitCachedConstants(void) { * oa_data = PyArray_MultiIter_DATA(multi, 1) */ __pyx_k_tuple_4 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 289; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_4)); + __Pyx_GOTREF(__pyx_k_tuple_4); __Pyx_INCREF(((PyObject *)__pyx_kp_s_1)); PyTuple_SET_ITEM(__pyx_k_tuple_4, 0, ((PyObject *)__pyx_kp_s_1)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_1)); @@ -20662,7 +20842,7 @@ static int __Pyx_InitCachedConstants(void) { * on_data = PyArray_MultiIter_DATA(multi, 1) */ __pyx_k_tuple_5 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 353; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_5)); + __Pyx_GOTREF(__pyx_k_tuple_5); __Pyx_INCREF(((PyObject *)__pyx_kp_s_1)); PyTuple_SET_ITEM(__pyx_k_tuple_5, 0, ((PyObject *)__pyx_kp_s_1)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_1)); @@ -20676,7 +20856,7 @@ static int __Pyx_InitCachedConstants(void) { * on_data = PyArray_MultiIter_DATA(multi, 1) */ __pyx_k_tuple_6 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_6)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 402; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_6)); + __Pyx_GOTREF(__pyx_k_tuple_6); __Pyx_INCREF(((PyObject *)__pyx_kp_s_1)); PyTuple_SET_ITEM(__pyx_k_tuple_6, 0, ((PyObject *)__pyx_kp_s_1)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_1)); @@ -20690,7 +20870,7 @@ static int __Pyx_InitCachedConstants(void) { * on_data = PyArray_MultiIter_DATA(multi, 1) */ __pyx_k_tuple_7 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 456; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_7)); + __Pyx_GOTREF(__pyx_k_tuple_7); __Pyx_INCREF(((PyObject *)__pyx_kp_s_1)); PyTuple_SET_ITEM(__pyx_k_tuple_7, 0, ((PyObject *)__pyx_kp_s_1)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_1)); @@ -20704,7 +20884,7 @@ static int __Pyx_InitCachedConstants(void) { * oa_data = PyArray_MultiIter_DATA(multi, 1) */ __pyx_k_tuple_8 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_8)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 505; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_8)); + __Pyx_GOTREF(__pyx_k_tuple_8); __Pyx_INCREF(((PyObject *)__pyx_kp_s_1)); PyTuple_SET_ITEM(__pyx_k_tuple_8, 0, ((PyObject *)__pyx_kp_s_1)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_1)); @@ -20718,7 +20898,7 @@ static int __Pyx_InitCachedConstants(void) { * if len(state) == 3: */ __pyx_k_tuple_10 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_10)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 691; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_10)); + __Pyx_GOTREF(__pyx_k_tuple_10); __Pyx_INCREF(((PyObject *)__pyx_kp_s_9)); PyTuple_SET_ITEM(__pyx_k_tuple_10, 0, ((PyObject *)__pyx_kp_s_9)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_9)); @@ -20732,7 +20912,7 @@ static int __Pyx_InitCachedConstants(void) { * self.internal_state.pos = pos */ __pyx_k_tuple_12 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_12)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 704; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_12)); + __Pyx_GOTREF(__pyx_k_tuple_12); __Pyx_INCREF(((PyObject *)__pyx_kp_s_11)); PyTuple_SET_ITEM(__pyx_k_tuple_12, 0, ((PyObject *)__pyx_kp_s_11)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_11)); @@ -20746,7 +20926,7 @@ static int __Pyx_InitCachedConstants(void) { * diff = hi - lo - 1UL */ __pyx_k_tuple_14 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_14)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 876; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_14)); + __Pyx_GOTREF(__pyx_k_tuple_14); __Pyx_INCREF(((PyObject *)__pyx_kp_s_13)); PyTuple_SET_ITEM(__pyx_k_tuple_14, 0, ((PyObject *)__pyx_kp_s_13)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_13)); @@ -20760,7 +20940,7 @@ static int __Pyx_InitCachedConstants(void) { * a = np.array(a, ndmin=1, copy=0) */ __pyx_k_tuple_17 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_17)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1000; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_17)); + __Pyx_GOTREF(__pyx_k_tuple_17); __Pyx_INCREF(((PyObject *)__pyx_kp_s_16)); PyTuple_SET_ITEM(__pyx_k_tuple_17, 0, ((PyObject *)__pyx_kp_s_16)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_16)); @@ -20774,7 +20954,7 @@ static int __Pyx_InitCachedConstants(void) { * if pop_size is 0: */ __pyx_k_tuple_19 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_19)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1004; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_19)); + __Pyx_GOTREF(__pyx_k_tuple_19); __Pyx_INCREF(((PyObject *)__pyx_kp_s_18)); PyTuple_SET_ITEM(__pyx_k_tuple_19, 0, ((PyObject *)__pyx_kp_s_18)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_18)); @@ -20788,7 +20968,7 @@ static int __Pyx_InitCachedConstants(void) { * if None != p: */ __pyx_k_tuple_21 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_21)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1007; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_21)); + __Pyx_GOTREF(__pyx_k_tuple_21); __Pyx_INCREF(((PyObject *)__pyx_kp_s_20)); PyTuple_SET_ITEM(__pyx_k_tuple_21, 0, ((PyObject *)__pyx_kp_s_20)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_20)); @@ -20802,7 +20982,7 @@ static int __Pyx_InitCachedConstants(void) { * raise ValueError("a and p must have same size") */ __pyx_k_tuple_23 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_23)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1012; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_23)); + __Pyx_GOTREF(__pyx_k_tuple_23); __Pyx_INCREF(((PyObject *)__pyx_kp_s_22)); PyTuple_SET_ITEM(__pyx_k_tuple_23, 0, ((PyObject *)__pyx_kp_s_22)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_22)); @@ -20816,7 +20996,7 @@ static int __Pyx_InitCachedConstants(void) { * raise ValueError("probabilities are not non-negative") */ __pyx_k_tuple_25 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_25)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1014; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_25)); + __Pyx_GOTREF(__pyx_k_tuple_25); __Pyx_INCREF(((PyObject *)__pyx_kp_s_24)); PyTuple_SET_ITEM(__pyx_k_tuple_25, 0, ((PyObject *)__pyx_kp_s_24)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_24)); @@ -20830,7 +21010,7 @@ static int __Pyx_InitCachedConstants(void) { * raise ValueError("probabilities do not sum to 1") */ __pyx_k_tuple_27 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_27)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1016; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_27)); + __Pyx_GOTREF(__pyx_k_tuple_27); __Pyx_INCREF(((PyObject *)__pyx_kp_s_26)); PyTuple_SET_ITEM(__pyx_k_tuple_27, 0, ((PyObject *)__pyx_kp_s_26)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_26)); @@ -20844,7 +21024,7 @@ static int __Pyx_InitCachedConstants(void) { * # Actual sampling */ __pyx_k_tuple_29 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_29)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1018; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_29)); + __Pyx_GOTREF(__pyx_k_tuple_29); __Pyx_INCREF(((PyObject *)__pyx_kp_s_28)); PyTuple_SET_ITEM(__pyx_k_tuple_29, 0, ((PyObject *)__pyx_kp_s_28)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_28)); @@ -20858,7 +21038,7 @@ static int __Pyx_InitCachedConstants(void) { * p = p.copy() */ __pyx_k_tuple_34 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_34)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1036; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_34)); + __Pyx_GOTREF(__pyx_k_tuple_34); __Pyx_INCREF(((PyObject *)__pyx_kp_s_33)); PyTuple_SET_ITEM(__pyx_k_tuple_34, 0, ((PyObject *)__pyx_kp_s_33)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_33)); @@ -20872,7 +21052,7 @@ static int __Pyx_InitCachedConstants(void) { * */ __pyx_k_tuple_40 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_40)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1448; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_40)); + __Pyx_GOTREF(__pyx_k_tuple_40); __Pyx_INCREF(((PyObject *)__pyx_kp_s_39)); PyTuple_SET_ITEM(__pyx_k_tuple_40, 0, ((PyObject *)__pyx_kp_s_39)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_39)); @@ -20886,7 +21066,7 @@ static int __Pyx_InitCachedConstants(void) { * */ __pyx_k_tuple_41 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_41)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1456; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_41)); + __Pyx_GOTREF(__pyx_k_tuple_41); __Pyx_INCREF(((PyObject *)__pyx_kp_s_39)); PyTuple_SET_ITEM(__pyx_k_tuple_41, 0, ((PyObject *)__pyx_kp_s_39)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_39)); @@ -20900,7 +21080,7 @@ static int __Pyx_InitCachedConstants(void) { * raise ValueError("b <= 0") */ __pyx_k_tuple_43 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_43)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1503; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_43)); + __Pyx_GOTREF(__pyx_k_tuple_43); __Pyx_INCREF(((PyObject *)__pyx_kp_s_42)); PyTuple_SET_ITEM(__pyx_k_tuple_43, 0, ((PyObject *)__pyx_kp_s_42)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_42)); @@ -20914,7 +21094,7 @@ static int __Pyx_InitCachedConstants(void) { * */ __pyx_k_tuple_45 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_45)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1505; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_45)); + __Pyx_GOTREF(__pyx_k_tuple_45); __Pyx_INCREF(((PyObject *)__pyx_kp_s_44)); PyTuple_SET_ITEM(__pyx_k_tuple_45, 0, ((PyObject *)__pyx_kp_s_44)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_44)); @@ -20928,7 +21108,7 @@ static int __Pyx_InitCachedConstants(void) { * raise ValueError("b <= 0") */ __pyx_k_tuple_46 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_46)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1513; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_46)); + __Pyx_GOTREF(__pyx_k_tuple_46); __Pyx_INCREF(((PyObject *)__pyx_kp_s_42)); PyTuple_SET_ITEM(__pyx_k_tuple_46, 0, ((PyObject *)__pyx_kp_s_42)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_42)); @@ -20942,7 +21122,7 @@ static int __Pyx_InitCachedConstants(void) { * */ __pyx_k_tuple_47 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_47)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1515; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_47)); + __Pyx_GOTREF(__pyx_k_tuple_47); __Pyx_INCREF(((PyObject *)__pyx_kp_s_44)); PyTuple_SET_ITEM(__pyx_k_tuple_47, 0, ((PyObject *)__pyx_kp_s_44)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_44)); @@ -20956,7 +21136,7 @@ static int __Pyx_InitCachedConstants(void) { * */ __pyx_k_tuple_49 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_49)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1562; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_49)); + __Pyx_GOTREF(__pyx_k_tuple_49); __Pyx_INCREF(((PyObject *)__pyx_kp_s_39)); PyTuple_SET_ITEM(__pyx_k_tuple_49, 0, ((PyObject *)__pyx_kp_s_39)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_39)); @@ -20970,7 +21150,7 @@ static int __Pyx_InitCachedConstants(void) { * */ __pyx_k_tuple_50 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_50)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1569; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_50)); + __Pyx_GOTREF(__pyx_k_tuple_50); __Pyx_INCREF(((PyObject *)__pyx_kp_s_39)); PyTuple_SET_ITEM(__pyx_k_tuple_50, 0, ((PyObject *)__pyx_kp_s_39)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_39)); @@ -20984,7 +21164,7 @@ static int __Pyx_InitCachedConstants(void) { * */ __pyx_k_tuple_52 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_52)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1673; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_52)); + __Pyx_GOTREF(__pyx_k_tuple_52); __Pyx_INCREF(((PyObject *)__pyx_kp_s_51)); PyTuple_SET_ITEM(__pyx_k_tuple_52, 0, ((PyObject *)__pyx_kp_s_51)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_51)); @@ -20998,7 +21178,7 @@ static int __Pyx_InitCachedConstants(void) { * */ __pyx_k_tuple_53 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_53)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1679; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_53)); + __Pyx_GOTREF(__pyx_k_tuple_53); __Pyx_INCREF(((PyObject *)__pyx_kp_s_51)); PyTuple_SET_ITEM(__pyx_k_tuple_53, 0, ((PyObject *)__pyx_kp_s_51)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_51)); @@ -21012,7 +21192,7 @@ static int __Pyx_InitCachedConstants(void) { * raise ValueError("scale <= 0") */ __pyx_k_tuple_55 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_55)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1759; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_55)); + __Pyx_GOTREF(__pyx_k_tuple_55); __Pyx_INCREF(((PyObject *)__pyx_kp_s_51)); PyTuple_SET_ITEM(__pyx_k_tuple_55, 0, ((PyObject *)__pyx_kp_s_51)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_51)); @@ -21026,7 +21206,7 @@ static int __Pyx_InitCachedConstants(void) { * */ __pyx_k_tuple_56 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_56)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1761; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_56)); + __Pyx_GOTREF(__pyx_k_tuple_56); __Pyx_INCREF(((PyObject *)__pyx_kp_s_39)); PyTuple_SET_ITEM(__pyx_k_tuple_56, 0, ((PyObject *)__pyx_kp_s_39)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_39)); @@ -21040,7 +21220,7 @@ static int __Pyx_InitCachedConstants(void) { * raise ValueError("scale <= 0") */ __pyx_k_tuple_57 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_57)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1768; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_57)); + __Pyx_GOTREF(__pyx_k_tuple_57); __Pyx_INCREF(((PyObject *)__pyx_kp_s_51)); PyTuple_SET_ITEM(__pyx_k_tuple_57, 0, ((PyObject *)__pyx_kp_s_51)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_51)); @@ -21054,7 +21234,7 @@ static int __Pyx_InitCachedConstants(void) { * */ __pyx_k_tuple_58 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_58)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1770; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_58)); + __Pyx_GOTREF(__pyx_k_tuple_58); __Pyx_INCREF(((PyObject *)__pyx_kp_s_39)); PyTuple_SET_ITEM(__pyx_k_tuple_58, 0, ((PyObject *)__pyx_kp_s_39)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_39)); @@ -21068,7 +21248,7 @@ static int __Pyx_InitCachedConstants(void) { * raise ValueError("scale <= 0") */ __pyx_k_tuple_59 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_59)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1861; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_59)); + __Pyx_GOTREF(__pyx_k_tuple_59); __Pyx_INCREF(((PyObject *)__pyx_kp_s_51)); PyTuple_SET_ITEM(__pyx_k_tuple_59, 0, ((PyObject *)__pyx_kp_s_51)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_51)); @@ -21082,7 +21262,7 @@ static int __Pyx_InitCachedConstants(void) { * */ __pyx_k_tuple_60 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_60)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1863; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_60)); + __Pyx_GOTREF(__pyx_k_tuple_60); __Pyx_INCREF(((PyObject *)__pyx_kp_s_39)); PyTuple_SET_ITEM(__pyx_k_tuple_60, 0, ((PyObject *)__pyx_kp_s_39)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_39)); @@ -21096,7 +21276,7 @@ static int __Pyx_InitCachedConstants(void) { * raise ValueError("dfden <= 0") */ __pyx_k_tuple_62 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_62)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1871; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_62)); + __Pyx_GOTREF(__pyx_k_tuple_62); __Pyx_INCREF(((PyObject *)__pyx_kp_s_61)); PyTuple_SET_ITEM(__pyx_k_tuple_62, 0, ((PyObject *)__pyx_kp_s_61)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_61)); @@ -21110,7 +21290,7 @@ static int 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{__pyx_filename = __pyx_f[0]; __pyx_lineno = 2590; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_90)); + __Pyx_GOTREF(__pyx_k_tuple_90); __Pyx_INCREF(((PyObject *)__pyx_kp_s_42)); PyTuple_SET_ITEM(__pyx_k_tuple_90, 0, ((PyObject *)__pyx_kp_s_42)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_42)); @@ -21404,7 +21584,7 @@ static int __Pyx_InitCachedConstants(void) { * */ __pyx_k_tuple_91 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_91)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2692; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_91)); + __Pyx_GOTREF(__pyx_k_tuple_91); __Pyx_INCREF(((PyObject *)__pyx_kp_s_42)); PyTuple_SET_ITEM(__pyx_k_tuple_91, 0, ((PyObject *)__pyx_kp_s_42)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_42)); @@ -21418,7 +21598,7 @@ static int __Pyx_InitCachedConstants(void) { * */ __pyx_k_tuple_92 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_92)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2699; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_92)); + __Pyx_GOTREF(__pyx_k_tuple_92); __Pyx_INCREF(((PyObject *)__pyx_kp_s_42)); PyTuple_SET_ITEM(__pyx_k_tuple_92, 0, ((PyObject *)__pyx_kp_s_42)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_42)); @@ -21432,7 +21612,7 @@ static int __Pyx_InitCachedConstants(void) { * */ __pyx_k_tuple_95 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_95)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2782; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_95)); + __Pyx_GOTREF(__pyx_k_tuple_95); __Pyx_INCREF(((PyObject *)__pyx_kp_s_39)); PyTuple_SET_ITEM(__pyx_k_tuple_95, 0, ((PyObject *)__pyx_kp_s_39)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_39)); @@ -21446,7 +21626,7 @@ static int __Pyx_InitCachedConstants(void) { * */ __pyx_k_tuple_96 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_96)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2789; __pyx_clineno = __LINE__; goto 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+ __Pyx_GOTREF(__pyx_k_tuple_117); __Pyx_INCREF(((PyObject *)__pyx_kp_s_39)); PyTuple_SET_ITEM(__pyx_k_tuple_117, 0, ((PyObject *)__pyx_kp_s_39)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_39)); @@ -21600,7 +21780,7 @@ static int __Pyx_InitCachedConstants(void) { * raise ValueError("scale <= 0.0") */ __pyx_k_tuple_119 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_119)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3283; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_119)); + __Pyx_GOTREF(__pyx_k_tuple_119); __Pyx_INCREF(((PyObject *)__pyx_kp_s_118)); PyTuple_SET_ITEM(__pyx_k_tuple_119, 0, ((PyObject *)__pyx_kp_s_118)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_118)); @@ -21614,7 +21794,7 @@ static int __Pyx_InitCachedConstants(void) { * */ __pyx_k_tuple_120 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_120)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3285; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_120)); + __Pyx_GOTREF(__pyx_k_tuple_120); __Pyx_INCREF(((PyObject *)__pyx_kp_s_113)); PyTuple_SET_ITEM(__pyx_k_tuple_120, 0, ((PyObject *)__pyx_kp_s_113)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_113)); @@ -21628,7 +21808,7 @@ static int __Pyx_InitCachedConstants(void) { * raise ValueError("mode > right") */ __pyx_k_tuple_122 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_122)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3355; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_122)); + __Pyx_GOTREF(__pyx_k_tuple_122); __Pyx_INCREF(((PyObject *)__pyx_kp_s_121)); PyTuple_SET_ITEM(__pyx_k_tuple_122, 0, ((PyObject *)__pyx_kp_s_121)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_121)); @@ -21642,7 +21822,7 @@ static int __Pyx_InitCachedConstants(void) { * raise ValueError("left == right") */ __pyx_k_tuple_124 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_124)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3357; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_124)); + __Pyx_GOTREF(__pyx_k_tuple_124); __Pyx_INCREF(((PyObject *)__pyx_kp_s_123)); PyTuple_SET_ITEM(__pyx_k_tuple_124, 0, ((PyObject *)__pyx_kp_s_123)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_123)); @@ -21656,7 +21836,7 @@ static int __Pyx_InitCachedConstants(void) { * fmode, fright) */ __pyx_k_tuple_126 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_126)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3359; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_126)); + __Pyx_GOTREF(__pyx_k_tuple_126); __Pyx_INCREF(((PyObject *)__pyx_kp_s_125)); PyTuple_SET_ITEM(__pyx_k_tuple_126, 0, ((PyObject *)__pyx_kp_s_125)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_125)); @@ -21670,7 +21850,7 @@ static int __Pyx_InitCachedConstants(void) { * raise ValueError("mode > right") */ __pyx_k_tuple_127 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_127)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3369; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_127)); + __Pyx_GOTREF(__pyx_k_tuple_127); __Pyx_INCREF(((PyObject *)__pyx_kp_s_121)); PyTuple_SET_ITEM(__pyx_k_tuple_127, 0, ((PyObject *)__pyx_kp_s_121)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_121)); @@ -21684,7 +21864,7 @@ static int __Pyx_InitCachedConstants(void) { * raise ValueError("left == right") */ __pyx_k_tuple_128 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_128)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3371; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_128)); + __Pyx_GOTREF(__pyx_k_tuple_128); __Pyx_INCREF(((PyObject *)__pyx_kp_s_123)); PyTuple_SET_ITEM(__pyx_k_tuple_128, 0, ((PyObject *)__pyx_kp_s_123)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_123)); @@ -21698,7 +21878,7 @@ static int __Pyx_InitCachedConstants(void) { * omode, oright) */ __pyx_k_tuple_129 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_129)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3373; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_129)); + __Pyx_GOTREF(__pyx_k_tuple_129); __Pyx_INCREF(((PyObject *)__pyx_kp_s_125)); PyTuple_SET_ITEM(__pyx_k_tuple_129, 0, ((PyObject *)__pyx_kp_s_125)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_125)); @@ -21712,7 +21892,7 @@ static int __Pyx_InitCachedConstants(void) { * raise ValueError("p < 0") */ __pyx_k_tuple_131 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_131)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3467; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_131)); + __Pyx_GOTREF(__pyx_k_tuple_131); __Pyx_INCREF(((PyObject *)__pyx_kp_s_130)); PyTuple_SET_ITEM(__pyx_k_tuple_131, 0, ((PyObject *)__pyx_kp_s_130)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_130)); @@ -21726,7 +21906,7 @@ static int __Pyx_InitCachedConstants(void) { * raise ValueError("p > 1") */ __pyx_k_tuple_133 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_133)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3469; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_133)); + __Pyx_GOTREF(__pyx_k_tuple_133); __Pyx_INCREF(((PyObject *)__pyx_kp_s_132)); PyTuple_SET_ITEM(__pyx_k_tuple_133, 0, ((PyObject *)__pyx_kp_s_132)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_132)); @@ -21740,7 +21920,7 @@ static int __Pyx_InitCachedConstants(void) { * */ __pyx_k_tuple_135 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_135)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3471; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_135)); + __Pyx_GOTREF(__pyx_k_tuple_135); __Pyx_INCREF(((PyObject *)__pyx_kp_s_134)); PyTuple_SET_ITEM(__pyx_k_tuple_135, 0, ((PyObject *)__pyx_kp_s_134)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_134)); @@ -21754,7 +21934,7 @@ static int __Pyx_InitCachedConstants(void) { * raise ValueError("p < 0") */ __pyx_k_tuple_136 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_136)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3479; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_136)); + __Pyx_GOTREF(__pyx_k_tuple_136); __Pyx_INCREF(((PyObject *)__pyx_kp_s_130)); PyTuple_SET_ITEM(__pyx_k_tuple_136, 0, ((PyObject *)__pyx_kp_s_130)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_130)); @@ -21768,7 +21948,7 @@ static int __Pyx_InitCachedConstants(void) { * raise ValueError("p > 1") */ __pyx_k_tuple_137 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_137)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3481; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_137)); + __Pyx_GOTREF(__pyx_k_tuple_137); __Pyx_INCREF(((PyObject *)__pyx_kp_s_132)); PyTuple_SET_ITEM(__pyx_k_tuple_137, 0, ((PyObject *)__pyx_kp_s_132)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_132)); @@ -21782,7 +21962,7 @@ static int __Pyx_InitCachedConstants(void) { * */ __pyx_k_tuple_138 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_138)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3483; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_138)); + __Pyx_GOTREF(__pyx_k_tuple_138); __Pyx_INCREF(((PyObject *)__pyx_kp_s_134)); PyTuple_SET_ITEM(__pyx_k_tuple_138, 0, ((PyObject *)__pyx_kp_s_134)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_134)); @@ -21796,7 +21976,7 @@ static int __Pyx_InitCachedConstants(void) { * raise ValueError("p < 0") */ __pyx_k_tuple_139 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_139)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3560; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_139)); + __Pyx_GOTREF(__pyx_k_tuple_139); __Pyx_INCREF(((PyObject *)__pyx_kp_s_130)); PyTuple_SET_ITEM(__pyx_k_tuple_139, 0, ((PyObject *)__pyx_kp_s_130)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_130)); @@ -21810,7 +21990,7 @@ static int __Pyx_InitCachedConstants(void) { * raise ValueError("p > 1") */ __pyx_k_tuple_140 = PyTuple_New(1); if 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@@ static int __Pyx_InitCachedConstants(void) { * */ __pyx_k_tuple_152 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_152)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3649; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_152)); + __Pyx_GOTREF(__pyx_k_tuple_152); __Pyx_INCREF(((PyObject *)__pyx_kp_s_151)); PyTuple_SET_ITEM(__pyx_k_tuple_152, 0, ((PyObject *)__pyx_kp_s_151)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_151)); @@ -21936,7 +22116,7 @@ static int __Pyx_InitCachedConstants(void) { * */ __pyx_k_tuple_154 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_154)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3730; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_154)); + __Pyx_GOTREF(__pyx_k_tuple_154); __Pyx_INCREF(((PyObject *)__pyx_kp_s_153)); PyTuple_SET_ITEM(__pyx_k_tuple_154, 0, ((PyObject *)__pyx_kp_s_153)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_153)); @@ -21950,7 +22130,7 @@ static int 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__Pyx_GOTREF(__pyx_k_tuple_181); __Pyx_INCREF(((PyObject *)__pyx_kp_s_180)); PyTuple_SET_ITEM(__pyx_k_tuple_181, 0, ((PyObject *)__pyx_kp_s_180)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_180)); @@ -22202,7 +22382,7 @@ static int __Pyx_InitCachedConstants(void) { * raise ValueError("mean and cov must have same length") */ __pyx_k_tuple_183 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_183)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4121; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_183)); + __Pyx_GOTREF(__pyx_k_tuple_183); __Pyx_INCREF(((PyObject *)__pyx_kp_s_182)); PyTuple_SET_ITEM(__pyx_k_tuple_183, 0, ((PyObject *)__pyx_kp_s_182)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_182)); @@ -22216,7 +22396,7 @@ static int __Pyx_InitCachedConstants(void) { * if isinstance(shape, int): */ __pyx_k_tuple_185 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_185)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4123; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_185)); + __Pyx_GOTREF(__pyx_k_tuple_185); __Pyx_INCREF(((PyObject *)__pyx_kp_s_184)); PyTuple_SET_ITEM(__pyx_k_tuple_185, 0, ((PyObject *)__pyx_kp_s_184)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_184)); @@ -22230,7 +22410,7 @@ static int __Pyx_InitCachedConstants(void) { * if size is None: */ __pyx_k_tuple_188 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_188)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4216; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_188)); + __Pyx_GOTREF(__pyx_k_tuple_188); __Pyx_INCREF(((PyObject *)__pyx_kp_s_187)); PyTuple_SET_ITEM(__pyx_k_tuple_188, 0, ((PyObject *)__pyx_kp_s_187)); __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_187)); @@ -22244,13 +22424,13 @@ static int __Pyx_InitCachedConstants(void) { * def __init__(self, seed=None): */ __pyx_k_tuple_189 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_189)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_189)); + __Pyx_GOTREF(__pyx_k_tuple_189); __Pyx_INCREF(((PyObject *)__pyx_n_s__l)); PyTuple_SET_ITEM(__pyx_k_tuple_189, 0, ((PyObject *)__pyx_n_s__l)); __Pyx_GIVEREF(((PyObject *)__pyx_n_s__l)); __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_189)); __pyx_k_tuple_190 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_190)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 558; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_190)); + __Pyx_GOTREF(__pyx_k_tuple_190); __Pyx_INCREF(((PyObject *)__pyx_n_s__l)); PyTuple_SET_ITEM(__pyx_k_tuple_190, 0, ((PyObject *)__pyx_n_s__l)); __Pyx_GIVEREF(((PyObject *)__pyx_n_s__l)); @@ -22285,8 +22465,8 @@ PyMODINIT_FUNC PyInit_mtrand(void) PyObject *__pyx_t_2 = NULL; PyObject *__pyx_t_3 = NULL; PyObject *__pyx_t_4 = NULL; + __Pyx_RefNannyDeclarations #if CYTHON_REFNANNY - void* __pyx_refnanny = NULL; __Pyx_RefNanny = __Pyx_RefNannyImportAPI("refnanny"); if (!__Pyx_RefNanny) { PyErr_Clear(); @@ -22294,12 +22474,19 @@ PyMODINIT_FUNC PyInit_mtrand(void) if (!__Pyx_RefNanny) Py_FatalError("failed to import 'refnanny' module"); } - __pyx_refnanny = __Pyx_RefNanny->SetupContext("PyMODINIT_FUNC PyInit_mtrand(void)", __LINE__, __FILE__); #endif + __Pyx_RefNannySetupContext("PyMODINIT_FUNC PyInit_mtrand(void)", 0); + if ( __Pyx_check_binary_version() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_empty_tuple = PyTuple_New(0); if (unlikely(!__pyx_empty_tuple)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_empty_bytes = PyBytes_FromStringAndSize("", 0); if (unlikely(!__pyx_empty_bytes)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - #ifdef __pyx_binding_PyCFunctionType_USED - if (__pyx_binding_PyCFunctionType_init() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + #ifdef __Pyx_CyFunction_USED + if (__Pyx_CyFunction_init() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + #endif + #ifdef __Pyx_FusedFunction_USED + if (__pyx_FusedFunction_init() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + #endif + #ifdef __Pyx_Generator_USED + if (__pyx_Generator_init() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} #endif /*--- Library function declarations ---*/ /*--- Threads initialization code ---*/ @@ -22310,16 +22497,15 @@ PyMODINIT_FUNC PyInit_mtrand(void) #endif /*--- Module creation code ---*/ #if PY_MAJOR_VERSION < 3 - __pyx_m = Py_InitModule4(__Pyx_NAMESTR("mtrand"), __pyx_methods, 0, 0, PYTHON_API_VERSION); + __pyx_m = Py_InitModule4(__Pyx_NAMESTR("mtrand"), __pyx_methods, 0, 0, PYTHON_API_VERSION); Py_XINCREF(__pyx_m); #else __pyx_m = PyModule_Create(&__pyx_moduledef); #endif - if (!__pyx_m) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}; - #if PY_MAJOR_VERSION < 3 - Py_INCREF(__pyx_m); + if (unlikely(!__pyx_m)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_b = PyImport_AddModule(__Pyx_NAMESTR(__Pyx_BUILTIN_MODULE_NAME)); if (unlikely(!__pyx_b)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + #if CYTHON_COMPILING_IN_PYPY + Py_INCREF(__pyx_b); #endif - __pyx_b = PyImport_AddModule(__Pyx_NAMESTR(__Pyx_BUILTIN_MODULE_NAME)); - if (!__pyx_b) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}; if (__Pyx_SetAttrString(__pyx_m, "__builtins__", __pyx_b) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}; /*--- Initialize various global constants etc. ---*/ if (unlikely(__Pyx_InitGlobals() < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} @@ -22331,6 +22517,7 @@ PyMODINIT_FUNC PyInit_mtrand(void) /*--- Constants init code ---*/ if (unlikely(__Pyx_InitCachedConstants() < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} /*--- Global init code ---*/ + /*--- Variable export code ---*/ /*--- Function export code ---*/ /*--- Type init code ---*/ __pyx_ptype_6mtrand_dtype = __Pyx_ImportType("numpy", "dtype", sizeof(PyArray_Descr), 0); if (unlikely(!__pyx_ptype_6mtrand_dtype)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 76; __pyx_clineno = __LINE__; goto __pyx_L1_error;} @@ -22341,6 +22528,7 @@ PyMODINIT_FUNC PyInit_mtrand(void) if (__Pyx_SetAttrString(__pyx_m, "RandomState", (PyObject *)&__pyx_type_6mtrand_RandomState) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 524; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_ptype_6mtrand_RandomState = &__pyx_type_6mtrand_RandomState; /*--- Type import code ---*/ + /*--- Variable import code ---*/ /*--- Function import code ---*/ /*--- Execution code ---*/ @@ -22360,7 +22548,7 @@ PyMODINIT_FUNC PyInit_mtrand(void) * * cdef object cont0_array(rk_state *state, rk_cont0 func, object size): */ - __pyx_t_1 = __Pyx_Import(((PyObject *)__pyx_n_s__numpy), 0); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 126; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = __Pyx_Import(((PyObject *)__pyx_n_s__numpy), 0, -1); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 126; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_1); if (PyObject_SetAttr(__pyx_m, __pyx_n_s__np, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 126; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; @@ -22400,7 +22588,7 @@ PyMODINIT_FUNC PyInit_mtrand(void) __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; @@ -23365,7 +23553,7 @@ PyMODINIT_FUNC PyInit_mtrand(void) __Pyx_XDECREF(__pyx_t_3); __Pyx_XDECREF(__pyx_t_4); if (__pyx_m) { - __Pyx_AddTraceback("init mtrand"); + __Pyx_AddTraceback("init mtrand", __pyx_clineno, __pyx_lineno, __pyx_filename); Py_DECREF(__pyx_m); __pyx_m = 0; } else if (!PyErr_Occurred()) { PyErr_SetString(PyExc_ImportError, "init mtrand"); @@ -23380,19 +23568,41 @@ PyMODINIT_FUNC PyInit_mtrand(void) } /* Runtime support code */ +#if CYTHON_REFNANNY +static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname) { + PyObject *m = NULL, *p = NULL; + void *r = NULL; + m = PyImport_ImportModule((char *)modname); + if (!m) goto end; + p = PyObject_GetAttrString(m, (char *)"RefNannyAPI"); + if (!p) goto end; + r = PyLong_AsVoidPtr(p); +end: + Py_XDECREF(p); + Py_XDECREF(m); + return (__Pyx_RefNannyAPIStruct *)r; +} +#endif /* CYTHON_REFNANNY */ static PyObject *__Pyx_GetName(PyObject *dict, PyObject *name) { PyObject *result; result = PyObject_GetAttr(dict, name); - if (!result) - PyErr_SetObject(PyExc_NameError, name); + if (!result) { + if (dict != __pyx_b) { + PyErr_Clear(); + result = PyObject_GetAttr(__pyx_b, name); + } + if (!result) { + PyErr_SetObject(PyExc_NameError, name); + } + } return result; } 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; @@ -23402,54 +23612,60 @@ 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) { +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; - } - else if (tb != NULL && !PyTraceBack_Check(tb)) { - PyErr_SetString(PyExc_TypeError, - "raise: arg 3 must be a traceback or None"); - goto raise_error; - } - /* Next, replace a missing value with None */ - if (value == NULL) { - value = Py_None; + if (!value || value == Py_None) + value = NULL; + else Py_INCREF(value); + if (!tb || tb == Py_None) + tb = NULL; + else { + Py_INCREF(tb); + if (!PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto raise_error; + } } #if PY_VERSION_HEX < 0x02050000 - if (!PyClass_Check(type)) + if (PyClass_Check(type)) { #else - if (!PyType_Check(type)) + if (PyType_Check(type)) { #endif - { - /* Raising an instance. The value should be a dummy. */ - if (value != Py_None) { +#if CYTHON_COMPILING_IN_PYPY + if (!value) { + Py_INCREF(Py_None); + value = Py_None; + } +#endif + PyErr_NormalizeException(&type, &value, &tb); + } else { + if (value) { PyErr_SetString(PyExc_TypeError, "instance exception may not have a separate value"); goto raise_error; } - /* Normalize to raise , */ - Py_DECREF(value); value = type; #if PY_VERSION_HEX < 0x02050000 if (PyInstance_Check(type)) { @@ -23472,7 +23688,6 @@ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb) { } #endif } - __Pyx_ErrRestore(type, value, tb); return; raise_error: @@ -23481,10 +23696,9 @@ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb) { Py_XDECREF(tb); return; } - #else /* Python 3+ */ - -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb) { +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { + PyObject* owned_instance = NULL; if (tb == Py_None) { tb = 0; } else if (tb && !PyTraceBack_Check(tb)) { @@ -23494,7 +23708,6 @@ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb) { } if (value == Py_None) value = 0; - if (PyExceptionInstance_Check(type)) { if (value) { PyErr_SetString(PyExc_TypeError, @@ -23503,14 +23716,55 @@ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb) { } value = type; type = (PyObject*) Py_TYPE(value); - } else if (!PyExceptionClass_Check(type)) { + } else if (PyExceptionClass_Check(type)) { + PyObject *args; + if (!value) + args = PyTuple_New(0); + else if (PyTuple_Check(value)) { + Py_INCREF(value); + args = value; + } + else + args = PyTuple_Pack(1, value); + if (!args) + goto bad; + owned_instance = PyEval_CallObject(type, args); + Py_DECREF(args); + if (!owned_instance) + goto bad; + value = owned_instance; + if (!PyExceptionInstance_Check(value)) { + PyErr_Format(PyExc_TypeError, + "calling %R should have returned an instance of " + "BaseException, not %R", + type, Py_TYPE(value)); + goto bad; + } + } else { PyErr_SetString(PyExc_TypeError, "raise: exception class must be a subclass of BaseException"); goto bad; } - + if (cause && cause != Py_None) { + PyObject *fixed_cause; + if (PyExceptionClass_Check(cause)) { + fixed_cause = PyObject_CallObject(cause, NULL); + if (fixed_cause == NULL) + goto bad; + } + else if (PyExceptionInstance_Check(cause)) { + fixed_cause = cause; + Py_INCREF(fixed_cause); + } + else { + PyErr_SetString(PyExc_TypeError, + "exception causes must derive from " + "BaseException"); + goto bad; + } + PyException_SetCause(value, fixed_cause); + } PyErr_SetObject(type, value); - if (tb) { PyThreadState *tstate = PyThreadState_GET(); PyObject* tmp_tb = tstate->curexc_traceback; @@ -23520,8 +23774,8 @@ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb) { Py_XDECREF(tmp_tb); } } - bad: + Py_XDECREF(owned_instance); return; } #endif @@ -23535,7 +23789,7 @@ static void __Pyx_RaiseDoubleKeywordsError( "%s() got multiple values for keyword argument '%U'", func_name, kw_name); #else "%s() got multiple values for keyword argument '%s'", func_name, - PyString_AS_STRING(kw_name)); + PyString_AsString(kw_name)); #endif } @@ -23551,55 +23805,77 @@ 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++; if (*name) { values[name-argnames] = value; - } else { - #if PY_MAJOR_VERSION < 3 - if (unlikely(!PyString_CheckExact(key)) && unlikely(!PyString_Check(key))) { - #else - if (unlikely(!PyUnicode_CheckExact(key)) && unlikely(!PyUnicode_Check(key))) { - #endif - goto invalid_keyword_type; - } else { - for (name = first_kw_arg; *name; name++) { - #if PY_MAJOR_VERSION >= 3 - if (PyUnicode_GET_SIZE(**name) == PyUnicode_GET_SIZE(key) && - PyUnicode_Compare(**name, key) == 0) break; - #else - if (PyString_GET_SIZE(**name) == PyString_GET_SIZE(key) && - _PyString_Eq(**name, key)) break; - #endif - } - if (*name) { + continue; + } + name = first_kw_arg; + #if PY_MAJOR_VERSION < 3 + if (likely(PyString_CheckExact(key)) || likely(PyString_Check(key))) { + while (*name) { + if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) + && _PyString_Eq(**name, key)) { 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 - if (PyUnicode_GET_SIZE(**name) == PyUnicode_GET_SIZE(key) && - PyUnicode_Compare(**name, key) == 0) goto arg_passed_twice; - #else - if (PyString_GET_SIZE(**name) == PyString_GET_SIZE(key) && - _PyString_Eq(**name, key)) goto arg_passed_twice; - #endif - } - if (kwds2) { - if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; - } else { - goto invalid_keyword; + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + if ((**argname == key) || ( + (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) + && _PyString_Eq(**argname, key))) { + goto arg_passed_twice; } + argname++; + } + } + } else + #endif + if (likely(PyUnicode_Check(key))) { + while (*name) { + int cmp = (**name == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 : + #endif + PyUnicode_Compare(**name, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) { + values[name-argnames] = value; + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + int cmp = (**argname == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 : + #endif + PyUnicode_Compare(**argname, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) goto arg_passed_twice; + argname++; } } + } else + goto invalid_keyword_type; + if (kwds2) { + if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; + } else { + goto invalid_keyword; } } return 0; arg_passed_twice: - __Pyx_RaiseDoubleKeywordsError(function_name, **name); + __Pyx_RaiseDoubleKeywordsError(function_name, key); goto bad; invalid_keyword_type: PyErr_Format(PyExc_TypeError, @@ -23626,8 +23902,7 @@ static void __Pyx_RaiseArgtupleInvalid( Py_ssize_t num_found) { Py_ssize_t num_expected; - const char *number, *more_or_less; - + const char *more_or_less; if (num_found < num_min) { num_expected = num_min; more_or_less = "at least"; @@ -23638,61 +23913,71 @@ static void __Pyx_RaiseArgtupleInvalid( if (exact) { more_or_less = "exactly"; } - number = (num_expected == 1) ? "" : "s"; PyErr_Format(PyExc_TypeError, - #if PY_VERSION_HEX < 0x02050000 - "%s() takes %s %d positional argument%s (%d given)", - #else - "%s() takes %s %zd positional argument%s (%zd given)", - #endif - func_name, more_or_less, num_expected, number, num_found); + "%s() takes %s %" CYTHON_FORMAT_SSIZE_T "d positional argument%s (%" CYTHON_FORMAT_SSIZE_T "d given)", + func_name, more_or_less, num_expected, + (num_expected == 1) ? "" : "s", num_found); } - -static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { PyErr_Format(PyExc_ValueError, - #if PY_VERSION_HEX < 0x02050000 - "need more than %d value%s to unpack", (int)index, - #else - "need more than %zd value%s to unpack", index, - #endif - (index == 1) ? "" : "s"); + "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); } -static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { PyErr_Format(PyExc_ValueError, - #if PY_VERSION_HEX < 0x02050000 - "too many values to unpack (expected %d)", (int)expected); - #else - "too many values to unpack (expected %zd)", expected); - #endif + "need more than %" CYTHON_FORMAT_SSIZE_T "d value%s to unpack", + index, (index == 1) ? "" : "s"); } -static PyObject *__Pyx_UnpackItem(PyObject *iter, Py_ssize_t index) { - PyObject *item; - if (!(item = PyIter_Next(iter))) { - if (!PyErr_Occurred()) { - __Pyx_RaiseNeedMoreValuesError(index); +static CYTHON_INLINE int __Pyx_IterFinish(void) { +#if CYTHON_COMPILING_IN_CPYTHON + PyThreadState *tstate = PyThreadState_GET(); + PyObject* exc_type = tstate->curexc_type; + if (unlikely(exc_type)) { + if (likely(exc_type == PyExc_StopIteration) || PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration)) { + PyObject *exc_value, *exc_tb; + exc_value = tstate->curexc_value; + exc_tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; + Py_DECREF(exc_type); + Py_XDECREF(exc_value); + Py_XDECREF(exc_tb); + return 0; + } else { + return -1; + } + } + return 0; +#else + if (unlikely(PyErr_Occurred())) { + if (likely(PyErr_ExceptionMatches(PyExc_StopIteration))) { + PyErr_Clear(); + return 0; + } else { + return -1; } } - return item; + return 0; +#endif } -static int __Pyx_EndUnpack(PyObject *iter, Py_ssize_t expected) { - PyObject *item; - if ((item = PyIter_Next(iter))) { - Py_DECREF(item); +static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected) { + if (unlikely(retval)) { + Py_DECREF(retval); __Pyx_RaiseTooManyValuesError(expected); return -1; + } else { + return __Pyx_IterFinish(); } - else if (!PyErr_Occurred()) - return 0; - else - return -1; + return 0; } static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) { PyObject *local_type, *local_value, *local_tb; +#if CYTHON_COMPILING_IN_CPYTHON PyObject *tmp_type, *tmp_value, *tmp_tb; PyThreadState *tstate = PyThreadState_GET(); local_type = tstate->curexc_type; @@ -23701,19 +23986,27 @@ static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) tstate->curexc_type = 0; tstate->curexc_value = 0; tstate->curexc_traceback = 0; +#else + PyErr_Fetch(&local_type, &local_value, &local_tb); +#endif PyErr_NormalizeException(&local_type, &local_value, &local_tb); +#if CYTHON_COMPILING_IN_CPYTHON if (unlikely(tstate->curexc_type)) +#else + if (unlikely(PyErr_Occurred())) +#endif goto bad; #if PY_MAJOR_VERSION >= 3 if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) goto bad; #endif - *type = local_type; - *value = local_value; - *tb = local_tb; Py_INCREF(local_type); Py_INCREF(local_value); Py_INCREF(local_tb); + *type = local_type; + *value = local_value; + *tb = local_tb; +#if CYTHON_COMPILING_IN_CPYTHON tmp_type = tstate->exc_type; tmp_value = tstate->exc_value; tmp_tb = tstate->exc_traceback; @@ -23721,10 +24014,13 @@ static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) tstate->exc_value = local_value; tstate->exc_traceback = local_tb; /* Make sure tstate is in a consistent state when we XDECREF - these objects (XDECREF may run arbitrary code). */ + these objects (DECREF may run arbitrary code). */ Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); +#else + PyErr_SetExcInfo(local_type, local_value, local_tb); +#endif return 0; bad: *type = 0; @@ -23736,7 +24032,6 @@ static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) return -1; } - static CYTHON_INLINE int __Pyx_CheckKeywordStrings( PyObject *kwdict, const char* function_name, @@ -23744,13 +24039,17 @@ static CYTHON_INLINE int __Pyx_CheckKeywordStrings( { PyObject* key = 0; Py_ssize_t pos = 0; +#if CPYTHON_COMPILING_IN_PYPY + if (!kw_allowed && PyDict_Next(kwdict, &pos, &key, 0)) + goto invalid_keyword; + return 1; +#else while (PyDict_Next(kwdict, &pos, &key, 0)) { #if PY_MAJOR_VERSION < 3 if (unlikely(!PyString_CheckExact(key)) && unlikely(!PyString_Check(key))) - #else - if (unlikely(!PyUnicode_CheckExact(key)) && unlikely(!PyUnicode_Check(key))) #endif - goto invalid_keyword_type; + if (unlikely(!PyUnicode_Check(key))) + goto invalid_keyword_type; } if ((!kw_allowed) && unlikely(key)) goto invalid_keyword; @@ -23759,6 +24058,7 @@ static CYTHON_INLINE int __Pyx_CheckKeywordStrings( PyErr_Format(PyExc_TypeError, "%s() keywords must be strings", function_name); return 0; +#endif invalid_keyword: PyErr_Format(PyExc_TypeError, #if PY_MAJOR_VERSION < 3 @@ -23783,8 +24083,8 @@ static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { return 0; } - static CYTHON_INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb) { +#if CYTHON_COMPILING_IN_CPYTHON PyThreadState *tstate = PyThreadState_GET(); *type = tstate->exc_type; *value = tstate->exc_value; @@ -23792,9 +24092,12 @@ static CYTHON_INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, Py_XINCREF(*type); Py_XINCREF(*value); Py_XINCREF(*tb); +#else + PyErr_GetExcInfo(type, value, tb); +#endif } - static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb) { +#if CYTHON_COMPILING_IN_CPYTHON PyObject *tmp_type, *tmp_value, *tmp_tb; PyThreadState *tstate = PyThreadState_GET(); tmp_type = tstate->exc_type; @@ -23806,9 +24109,12 @@ static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb) Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); +#else + PyErr_SetExcInfo(type, value, tb); +#endif } -static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list) { +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, long level) { PyObject *py_import = 0; PyObject *empty_list = 0; PyObject *module = 0; @@ -23832,8 +24138,44 @@ static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list) { empty_dict = PyDict_New(); if (!empty_dict) goto bad; + #if PY_VERSION_HEX >= 0x02050000 + { + #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) { + PyErr_SetString(PyExc_RuntimeError, "Relative import is not supported for Python <=2.4."); + goto bad; + } module = PyObject_CallFunctionObjArgs(py_import, name, global_dict, empty_dict, list, NULL); + #endif bad: Py_XDECREF(empty_list); Py_XDECREF(py_import); @@ -23841,6 +24183,67 @@ static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list) { return module; } +static CYTHON_INLINE npy_intp __Pyx_PyInt_from_py_npy_intp(PyObject* x) { + const npy_intp neg_one = (npy_intp)-1, const_zero = (npy_intp)0; + const int is_unsigned = const_zero < neg_one; + if (sizeof(npy_intp) == sizeof(char)) { + if (is_unsigned) + return (npy_intp)__Pyx_PyInt_AsUnsignedChar(x); + else + return (npy_intp)__Pyx_PyInt_AsSignedChar(x); + } else if (sizeof(npy_intp) == sizeof(short)) { + if (is_unsigned) + return (npy_intp)__Pyx_PyInt_AsUnsignedShort(x); + else + return (npy_intp)__Pyx_PyInt_AsSignedShort(x); + } else if (sizeof(npy_intp) == sizeof(int)) { + if (is_unsigned) + return (npy_intp)__Pyx_PyInt_AsUnsignedInt(x); + else + return (npy_intp)__Pyx_PyInt_AsSignedInt(x); + } else if (sizeof(npy_intp) == sizeof(long)) { + if (is_unsigned) + return (npy_intp)__Pyx_PyInt_AsUnsignedLong(x); + else + return (npy_intp)__Pyx_PyInt_AsSignedLong(x); + } else if (sizeof(npy_intp) == sizeof(PY_LONG_LONG)) { + if (is_unsigned) + return (npy_intp)__Pyx_PyInt_AsUnsignedLongLong(x); + else + return (npy_intp)__Pyx_PyInt_AsSignedLongLong(x); + } else { + npy_intp val; + PyObject *v = __Pyx_PyNumber_Int(x); + #if PY_VERSION_HEX < 0x03000000 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } + return (npy_intp)-1; + } +} + +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; @@ -23861,7 +24264,7 @@ static CYTHON_INLINE PyObject *__Pyx_PyInt_to_py_npy_intp(npy_intp val) { } else { int one = 1; int little = (int)*(unsigned char *)&one; unsigned char *bytes = (unsigned char *)&val; - return _PyLong_FromByteArray(bytes, sizeof(npy_intp), + return _PyLong_FromByteArray(bytes, sizeof(npy_intp), little, !is_unsigned); } } @@ -24077,9 +24480,9 @@ static CYTHON_INLINE unsigned long __Pyx_PyInt_AsUnsignedLong(PyObject* x) { "can't convert negative value to unsigned long"); return (unsigned long)-1; } - return PyLong_AsUnsignedLong(x); + return (unsigned long)PyLong_AsUnsignedLong(x); } else { - return PyLong_AsLong(x); + return (unsigned long)PyLong_AsLong(x); } } else { unsigned long val; @@ -24112,9 +24515,9 @@ static CYTHON_INLINE unsigned PY_LONG_LONG __Pyx_PyInt_AsUnsignedLongLong(PyObje "can't convert negative value to unsigned PY_LONG_LONG"); return (unsigned PY_LONG_LONG)-1; } - return PyLong_AsUnsignedLongLong(x); + return (unsigned PY_LONG_LONG)PyLong_AsUnsignedLongLong(x); } else { - return PyLong_AsLongLong(x); + return (unsigned PY_LONG_LONG)PyLong_AsLongLong(x); } } else { unsigned PY_LONG_LONG val; @@ -24147,9 +24550,9 @@ static CYTHON_INLINE long __Pyx_PyInt_AsLong(PyObject* x) { "can't convert negative value to long"); return (long)-1; } - return PyLong_AsUnsignedLong(x); + return (long)PyLong_AsUnsignedLong(x); } else { - return PyLong_AsLong(x); + return (long)PyLong_AsLong(x); } } else { long val; @@ -24182,9 +24585,9 @@ static CYTHON_INLINE PY_LONG_LONG __Pyx_PyInt_AsLongLong(PyObject* x) { "can't convert negative value to PY_LONG_LONG"); return (PY_LONG_LONG)-1; } - return PyLong_AsUnsignedLongLong(x); + return (PY_LONG_LONG)PyLong_AsUnsignedLongLong(x); } else { - return PyLong_AsLongLong(x); + return (PY_LONG_LONG)PyLong_AsLongLong(x); } } else { PY_LONG_LONG val; @@ -24217,9 +24620,9 @@ static CYTHON_INLINE signed long __Pyx_PyInt_AsSignedLong(PyObject* x) { "can't convert negative value to signed long"); return (signed long)-1; } - return PyLong_AsUnsignedLong(x); + return (signed long)PyLong_AsUnsignedLong(x); } else { - return PyLong_AsLong(x); + return (signed long)PyLong_AsLong(x); } } else { signed long val; @@ -24252,9 +24655,9 @@ static CYTHON_INLINE signed PY_LONG_LONG __Pyx_PyInt_AsSignedLongLong(PyObject* "can't convert negative value to signed PY_LONG_LONG"); return (signed PY_LONG_LONG)-1; } - return PyLong_AsUnsignedLongLong(x); + return (signed PY_LONG_LONG)PyLong_AsUnsignedLongLong(x); } else { - return PyLong_AsLongLong(x); + return (signed PY_LONG_LONG)PyLong_AsLongLong(x); } } else { signed PY_LONG_LONG val; @@ -24266,76 +24669,55 @@ static CYTHON_INLINE signed PY_LONG_LONG __Pyx_PyInt_AsSignedLongLong(PyObject* } } -static CYTHON_INLINE npy_intp __Pyx_PyInt_from_py_npy_intp(PyObject* x) { - const npy_intp neg_one = (npy_intp)-1, const_zero = (npy_intp)0; - const int is_unsigned = const_zero < neg_one; - if (sizeof(npy_intp) == sizeof(char)) { - if (is_unsigned) - return (npy_intp)__Pyx_PyInt_AsUnsignedChar(x); - else - return (npy_intp)__Pyx_PyInt_AsSignedChar(x); - } else if (sizeof(npy_intp) == sizeof(short)) { - if (is_unsigned) - return (npy_intp)__Pyx_PyInt_AsUnsignedShort(x); - else - return (npy_intp)__Pyx_PyInt_AsSignedShort(x); - } else if (sizeof(npy_intp) == sizeof(int)) { - if (is_unsigned) - return (npy_intp)__Pyx_PyInt_AsUnsignedInt(x); - else - return (npy_intp)__Pyx_PyInt_AsSignedInt(x); - } else if (sizeof(npy_intp) == sizeof(long)) { - if (is_unsigned) - return (npy_intp)__Pyx_PyInt_AsUnsignedLong(x); - else - return (npy_intp)__Pyx_PyInt_AsSignedLong(x); - } else if (sizeof(npy_intp) == sizeof(PY_LONG_LONG)) { - if (is_unsigned) - return (npy_intp)__Pyx_PyInt_AsUnsignedLongLong(x); - else - return (npy_intp)__Pyx_PyInt_AsSignedLongLong(x); - } else { - npy_intp val; - PyObject *v = __Pyx_PyNumber_Int(x); - #if PY_VERSION_HEX < 0x03000000 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } +static int __Pyx_check_binary_version(void) { + char ctversion[4], rtversion[4]; + PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); + PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); + if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { + char message[200]; + PyOS_snprintf(message, sizeof(message), + "compiletime version %s of module '%.100s' " + "does not match runtime version %s", + ctversion, __Pyx_MODULE_NAME, rtversion); + #if PY_VERSION_HEX < 0x02050000 + return PyErr_Warn(NULL, message); + #else + return PyErr_WarnEx(NULL, message, 1); #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } - return (npy_intp)-1; } + return 0; +} + +#ifndef __PYX_HAVE_RT_ImportModule +#define __PYX_HAVE_RT_ImportModule +static PyObject *__Pyx_ImportModule(const char *name) { + PyObject *py_name = 0; + PyObject *py_module = 0; + py_name = __Pyx_PyIdentifier_FromString(name); + if (!py_name) + goto bad; + py_module = PyImport_Import(py_name); + Py_DECREF(py_name); + return py_module; +bad: + Py_XDECREF(py_name); + return 0; } +#endif #ifndef __PYX_HAVE_RT_ImportType #define __PYX_HAVE_RT_ImportType static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, - long size, int strict) + size_t size, int strict) { PyObject *py_module = 0; 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); @@ -24351,17 +24733,17 @@ static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class module_name, class_name); goto bad; } - if (!strict && ((PyTypeObject *)result)->tp_basicsize > 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); #if PY_VERSION_HEX < 0x02050000 - PyErr_Warn(NULL, warning); + if (PyErr_Warn(NULL, warning) < 0) goto bad; #else - PyErr_WarnEx(NULL, warning, 0); + if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; #endif } - else if (((PyTypeObject *)result)->tp_basicsize != 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); @@ -24371,54 +24753,109 @@ static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class bad: Py_XDECREF(py_module); Py_XDECREF(result); - return 0; + return NULL; } #endif -#ifndef __PYX_HAVE_RT_ImportModule -#define __PYX_HAVE_RT_ImportModule -static PyObject *__Pyx_ImportModule(const char *name) { - PyObject *py_name = 0; - PyObject *py_module = 0; - - #if PY_MAJOR_VERSION < 3 - py_name = PyString_FromString(name); - #else - py_name = PyUnicode_FromString(name); - #endif - if (!py_name) - goto bad; - py_module = PyImport_Import(py_name); - Py_DECREF(py_name); - return py_module; -bad: - Py_XDECREF(py_name); - return 0; +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); } -#endif #include "compile.h" #include "frameobject.h" #include "traceback.h" - -static void __Pyx_AddTraceback(const char *funcname) { +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 { @@ -24429,28 +24866,45 @@ static void __Pyx_AddTraceback(const char *funcname) { #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,*/ @@ -24458,11 +24912,9 @@ static void __Pyx_AddTraceback(const char *funcname) { 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); } @@ -24497,6 +24949,7 @@ static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { return 0; } + /* Type Conversion Functions */ static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {