Ticket #38438: patch-numpy-upgrade-1.7.0.diff

File patch-numpy-upgrade-1.7.0.diff, 777.1 KB (added by seanfarley (Sean Farley), 11 years ago)

upgrade numpy to 1.7.0

  • dports/python/py-numpy/Portfile 

    # HG changeset patch
# User Sean Farley <sean.michael.farley@gmail.com>
# Date 1364092472 18000
#      Sat Mar 23 21:34:32 2013 -0500
# Node ID 75efd583378d2b69379db8e07a7efd01008dd3fc
# Parent  58f2aa0d00ae6eb03f33a59e8dde04eef1b8570b
py-numpy: update to 1.7.0

diff --git a/dports/python/py-numpy/Portfile b/dports/python/py-numpy/Portfile
    a b  
    33
    44PortSystem              1.0
    55PortGroup               python 1.0
    66PortGroup               github 1.0
    77
    8 github.setup            numpy numpy 1.6.2 v
     8github.setup            numpy numpy 1.7.0 v
    99name                    py-numpy
    10 revision                1
     10revision                0
    1111dist_subdir             ${name}/${version}_1
    1212
    1313categories-append       math
    1414license                 BSD
    1515platforms               darwin
    1616maintainers             dh openmaintainer
    1717description             The core utilities for the scientific library scipy for Python
    1818long_description        ${description}
    1919
    20 checksums               rmd160  9643c04a2e8fbb99cdb047281eedbbfb99423553 \
    21                         sha256  0992d326147d0ed83bd059519897e7a8ee52dea5ee66bbe04c0ea1c502cd8618
     20checksums               rmd160  f64215a07b35b7791a20ca236cdce45d4747473e \
     21                        sha256  757b91f89e2b9946a325ea4ae6955a7629c65a68e10ed751b98a50e21c13ff5e
    2222
    2323python.versions         24 25 26 27 31 32 33
    2424
    2525if {$subport != $name} {
    2626    patchfiles              patch-f2py_setup.py.diff \
    2727                            patch-fcompiler_g95.diff
    2828
    29     if {${python.version} == 33} {
    30         patchfiles-append   patch-python33-unicode.diff \
    31                             patch-python33-mtrand.diff \
    32                             patch-python33-shape.diff \
    33                             patch-python33-methods.diff
    34     }
    35 
    3629    depends_lib-append      port:fftw-3 \
    3730                            port:py${python.version}-nose
    3831
    3932    if {[variant_isset universal]} {
    4033        build.env-append        ARCHFLAGS="${configure.universal_ldflags}"
    … …  
    184177
    185178    livecheck.type        none
    186179} else {
    187180    livecheck.regex       archive/[join ${github.tag_prefix} ""](\[\\d+(?:\\.\\d+)*"\]+)${extract.suffix}"
    188181}
    189 

  • dports/python/py-numpy/files/patch-f2py_setup.py.diff 

    diff --git a/dports/python/py-numpy/files/patch-f2py_setup.py.diff b/dports/python/py-numpy/files/patch-f2py_setup.py.diff
    a b  
    1 --- numpy/f2py/setup.py 2011-02-27 23:40:29.000000000 -0600
    2 +++ numpy/f2py/setup.py 2011-05-15 09:18:40.000000000 -0500
     1--- numpy/f2py/setup.py.orig    2013-02-10 00:51:36.000000000 +0400
     2+++ numpy/f2py/setup.py 2013-03-19 15:27:15.000000000 +0400
     3@@ -41,7 +41,7 @@
     4     config.make_svn_version_py()
     5 
     6     def generate_f2py_py(build_dir):
     7-        f2py_exe = 'f2py'+os.path.basename(sys.executable)[6:]
     8+        f2py_exe = 'f2py'
     9         if f2py_exe[-4:]=='.exe':
     10             f2py_exe = f2py_exe[:-4] + '.py'
     11         if 'bdist_wininst' in sys.argv and f2py_exe[-3:] != '.py':
    312@@ -51,7 +51,7 @@
    413             log.info('Creating %s', target)
    514             f = open(target,'w')
    615             f.write('''\
    716-#!/usr/bin/env %s

  • dports/python/py-numpy/files/patch-fcompiler_g95.diff 

    diff --git a/dports/python/py-numpy/files/patch-fcompiler_g95.diff b/dports/python/py-numpy/files/patch-fcompiler_g95.diff
    a b  
    1 --- numpy/distutils/fcompiler/__init__.py       2011-03-15 00:22:25.000000000 -0500
    2 +++ numpy/distutils/fcompiler/__init__.py       2011-05-15 09:21:14.000000000 -0500
    3 @@ -698,7 +698,7 @@
     1--- numpy/distutils/fcompiler/__init__.py.orig  2013-03-19 13:35:03.000000000 +0400
     2+++ numpy/distutils/fcompiler/__init__.py       2013-03-19 13:35:27.000000000 +0400
     3@@ -708,7 +708,7 @@
    44     ('cygwin.*', ('gnu','intelv','absoft','compaqv','intelev','gnu95','g95')),
    5      ('linux.*', ('gnu','intel','lahey','pg','absoft','nag','vast','compaq',
    6                  'intele','intelem','gnu95','g95','pathf95')),
    7 -    ('darwin.*', ('nag', 'absoft', 'ibm', 'intel', 'gnu', 'gnu95', 'g95', 'pg')),
    8 +    ('darwin.*', ('nag', 'absoft', 'ibm', 'intel', 'gnu', 'gnu95', 'pg')),
     5     ('linux.*', ('gnu95','intel','lahey','pg','absoft','nag','vast','compaq',
     6                 'intele','intelem','gnu','g95','pathf95')),
     7-    ('darwin.*', ('gnu95', 'nag', 'absoft', 'ibm', 'intel', 'gnu', 'g95', 'pg')),
     8+    ('darwin.*', ('gnu95', 'nag', 'absoft', 'ibm', 'intel', 'gnu', 'pg')),
    99     ('sunos.*', ('sun','gnu','gnu95','g95')),
    1010     ('irix.*', ('mips','gnu','gnu95',)),
    1111     ('aix.*', ('ibm','gnu','gnu95',)),

  • deleted file dports/python/py-numpy/files/patch-python33-methods.diff 

    diff --git a/dports/python/py-numpy/files/patch-python33-methods.diff b/dports/python/py-numpy/files/patch-python33-methods.diff
deleted file mode 100644
    + -  
    1 --- numpy/core/src/multiarray/methods.old.c     2012-12-06 16:49:23.000000000 -0600
    2 +++ numpy/core/src/multiarray/methods.c 2012-12-06 16:48:07.000000000 -0600
    3 @@ -1476,7 +1476,7 @@
    4      if (!PyDataType_FLAGCHK(typecode, NPY_LIST_PICKLE)) {
    5          int swap=!PyArray_ISNOTSWAPPED(self);
    6          self->data = datastr;
    7 -        if (!_IsAligned(self) || swap) {
    8 +        if (!_IsAligned(self) || swap || (len <= 1000)) {
    9              intp num = PyArray_NBYTES(self);
    10              self->data = PyDataMem_NEW(num);
    11              if (self->data == NULL) {

  • deleted file dports/python/py-numpy/files/patch-python33-mtrand.diff 

    diff --git a/dports/python/py-numpy/files/patch-python33-mtrand.diff b/dports/python/py-numpy/files/patch-python33-mtrand.diff
deleted file mode 100644
    + -  
    1 --- numpy/random/mtrand/mtrand.old.c    2012-05-19 08:41:41.000000000 -0500
    2 +++ numpy/random/mtrand/mtrand.c        2012-12-06 15:02:43.000000000 -0600
    3 @@ -1,16 +1,16 @@
    4 -/* Generated by Cython 0.15.1 on Tue May  1 15:44:50 2012 */
    5 +/* Generated by Cython 0.17.1 on Thu Dec  6 15:02:42 2012 */
    6  
    7  #define PY_SSIZE_T_CLEAN
    8  #include "Python.h"
    9  #ifndef Py_PYTHON_H
    10      #error Python headers needed to compile C extensions, please install development version of Python.
    11 +#elif PY_VERSION_HEX < 0x02040000
    12 +    #error Cython requires Python 2.4+.
    13  #else
    14 -
    15  #include <stddef.h> /* For offsetof */
    16  #ifndef offsetof
    17  #define offsetof(type, member) ( (size_t) & ((type*)0) -> member )
    18  #endif
    19 -
    20  #if !defined(WIN32) && !defined(MS_WINDOWS)
    21    #ifndef __stdcall
    22      #define __stdcall
    23 @@ -22,36 +22,44 @@
    24      #define __fastcall
    25    #endif
    26  #endif
    27 -
    28  #ifndef DL_IMPORT
    29    #define DL_IMPORT(t) t
    30  #endif
    31  #ifndef DL_EXPORT
    32    #define DL_EXPORT(t) t
    33  #endif
    34 -
    35  #ifndef PY_LONG_LONG
    36    #define PY_LONG_LONG LONG_LONG
    37  #endif
    38 -
    39 -#if PY_VERSION_HEX < 0x02040000
    40 -  #define METH_COEXIST 0
    41 -  #define PyDict_CheckExact(op) (Py_TYPE(op) == &PyDict_Type)
    42 -  #define PyDict_Contains(d,o)   PySequence_Contains(d,o)
    43 +#ifndef Py_HUGE_VAL
    44 +  #define Py_HUGE_VAL HUGE_VAL
    45 +#endif
    46 +#ifdef PYPY_VERSION
    47 +#define CYTHON_COMPILING_IN_PYPY 1
    48 +#define CYTHON_COMPILING_IN_CPYTHON 0
    49 +#else
    50 +#define CYTHON_COMPILING_IN_PYPY 0
    51 +#define CYTHON_COMPILING_IN_CPYTHON 1
    52  #endif
    53 -
    54  #if PY_VERSION_HEX < 0x02050000
    55    typedef int Py_ssize_t;
    56    #define PY_SSIZE_T_MAX INT_MAX
    57    #define PY_SSIZE_T_MIN INT_MIN
    58    #define PY_FORMAT_SIZE_T ""
    59 +  #define CYTHON_FORMAT_SSIZE_T ""
    60    #define PyInt_FromSsize_t(z) PyInt_FromLong(z)
    61    #define PyInt_AsSsize_t(o)   __Pyx_PyInt_AsInt(o)
    62 -  #define PyNumber_Index(o)    PyNumber_Int(o)
    63 -  #define PyIndex_Check(o)     PyNumber_Check(o)
    64 +  #define PyNumber_Index(o)    ((PyNumber_Check(o) && !PyFloat_Check(o)) ? PyNumber_Int(o) : \
    65 +                                (PyErr_Format(PyExc_TypeError, \
    66 +                                              "expected index value, got %.200s", Py_TYPE(o)->tp_name), \
    67 +                                 (PyObject*)0))
    68 +  #define PyIndex_Check(o)     (PyNumber_Check(o) && !PyFloat_Check(o) && !PyComplex_Check(o))
    69    #define PyErr_WarnEx(category, message, stacklevel) PyErr_Warn(category, message)
    70 +  #define __PYX_BUILD_PY_SSIZE_T "i"
    71 +#else
    72 +  #define __PYX_BUILD_PY_SSIZE_T "n"
    73 +  #define CYTHON_FORMAT_SSIZE_T "z"
    74  #endif
    75 -
    76  #if PY_VERSION_HEX < 0x02060000
    77    #define Py_REFCNT(ob) (((PyObject*)(ob))->ob_refcnt)
    78    #define Py_TYPE(ob)   (((PyObject*)(ob))->ob_type)
    79 @@ -59,7 +67,6 @@
    80    #define PyVarObject_HEAD_INIT(type, size) \
    81            PyObject_HEAD_INIT(type) size,
    82    #define PyType_Modified(t)
    83 -
    84    typedef struct {
    85       void *buf;
    86       PyObject *obj;
    87 @@ -73,7 +80,6 @@
    88       Py_ssize_t *suboffsets;
    89       void *internal;
    90    } Py_buffer;
    91 -
    92    #define PyBUF_SIMPLE 0
    93    #define PyBUF_WRITABLE 0x0001
    94    #define PyBUF_FORMAT 0x0004
    95 @@ -83,24 +89,44 @@
    96    #define PyBUF_F_CONTIGUOUS (0x0040 | PyBUF_STRIDES)
    97    #define PyBUF_ANY_CONTIGUOUS (0x0080 | PyBUF_STRIDES)
    98    #define PyBUF_INDIRECT (0x0100 | PyBUF_STRIDES)
    99 -
    100 +  #define PyBUF_RECORDS (PyBUF_STRIDES | PyBUF_FORMAT | PyBUF_WRITABLE)
    101 +  #define PyBUF_FULL (PyBUF_INDIRECT | PyBUF_FORMAT | PyBUF_WRITABLE)
    102 +  typedef int (*getbufferproc)(PyObject *, Py_buffer *, int);
    103 +  typedef void (*releasebufferproc)(PyObject *, Py_buffer *);
    104  #endif
    105 -
    106  #if PY_MAJOR_VERSION < 3
    107    #define __Pyx_BUILTIN_MODULE_NAME "__builtin__"
    108 +  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \
    109 +          PyCode_New(a, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
    110  #else
    111    #define __Pyx_BUILTIN_MODULE_NAME "builtins"
    112 +  #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) \
    113 +          PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)
    114 +#endif
    115 +#if PY_MAJOR_VERSION < 3 && PY_MINOR_VERSION < 6
    116 +  #define PyUnicode_FromString(s) PyUnicode_Decode(s, strlen(s), "UTF-8", "strict")
    117  #endif
    118 -
    119  #if PY_MAJOR_VERSION >= 3
    120    #define Py_TPFLAGS_CHECKTYPES 0
    121    #define Py_TPFLAGS_HAVE_INDEX 0
    122  #endif
    123 -
    124  #if (PY_VERSION_HEX < 0x02060000) || (PY_MAJOR_VERSION >= 3)
    125    #define Py_TPFLAGS_HAVE_NEWBUFFER 0
    126  #endif
    127 -
    128 +#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND)
    129 +  #define CYTHON_PEP393_ENABLED 1
    130 +  #define __Pyx_PyUnicode_READY(op)       (likely(PyUnicode_IS_READY(op)) ? \
    131 +                                              0 : _PyUnicode_Ready((PyObject *)(op)))
    132 +  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_LENGTH(u)
    133 +  #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i)
    134 +  #define __Pyx_PyUnicode_READ(k, d, i)   PyUnicode_READ(k, d, i)
    135 +#else
    136 +  #define CYTHON_PEP393_ENABLED 0
    137 +  #define __Pyx_PyUnicode_READY(op)       (0)
    138 +  #define __Pyx_PyUnicode_GET_LENGTH(u)   PyUnicode_GET_SIZE(u)
    139 +  #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i]))
    140 +  #define __Pyx_PyUnicode_READ(k, d, i)   ((k=k), (Py_UCS4)(((Py_UNICODE*)d)[i]))
    141 +#endif
    142  #if PY_MAJOR_VERSION >= 3
    143    #define PyBaseString_Type            PyUnicode_Type
    144    #define PyStringObject               PyUnicodeObject
    145 @@ -108,7 +134,6 @@
    146    #define PyString_Check               PyUnicode_Check
    147    #define PyString_CheckExact          PyUnicode_CheckExact
    148  #endif
    149 -
    150  #if PY_VERSION_HEX < 0x02060000
    151    #define PyBytesObject                PyStringObject
    152    #define PyBytes_Type                 PyString_Type
    153 @@ -127,7 +152,6 @@
    154    #define PyBytes_Concat               PyString_Concat
    155    #define PyBytes_ConcatAndDel         PyString_ConcatAndDel
    156  #endif
    157 -
    158  #if PY_VERSION_HEX < 0x02060000
    159    #define PySet_Check(obj)             PyObject_TypeCheck(obj, &PySet_Type)
    160    #define PyFrozenSet_Check(obj)       PyObject_TypeCheck(obj, &PyFrozenSet_Type)
    161 @@ -135,9 +159,7 @@
    162  #ifndef PySet_CheckExact
    163    #define PySet_CheckExact(obj)        (Py_TYPE(obj) == &PySet_Type)
    164  #endif
    165 -
    166  #define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type)
    167 -
    168  #if PY_MAJOR_VERSION >= 3
    169    #define PyIntObject                  PyLongObject
    170    #define PyInt_Type                   PyLong_Type
    171 @@ -154,11 +176,9 @@
    172    #define PyInt_AsUnsignedLongMask     PyLong_AsUnsignedLongMask
    173    #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask
    174  #endif
    175 -
    176  #if PY_MAJOR_VERSION >= 3
    177    #define PyBoolObject                 PyLongObject
    178  #endif
    179 -
    180  #if PY_VERSION_HEX < 0x03020000
    181    typedef long Py_hash_t;
    182    #define __Pyx_PyInt_FromHash_t PyInt_FromLong
    183 @@ -167,16 +187,6 @@
    184    #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t
    185    #define __Pyx_PyInt_AsHash_t   PyInt_AsSsize_t
    186  #endif
    187 -
    188 -
    189 -#if PY_MAJOR_VERSION >= 3
    190 -  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_TrueDivide(x,y)
    191 -  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceTrueDivide(x,y)
    192 -#else
    193 -  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_Divide(x,y)
    194 -  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceDivide(x,y)
    195 -#endif
    196 -
    197  #if (PY_MAJOR_VERSION < 3) || (PY_VERSION_HEX >= 0x03010300)
    198    #define __Pyx_PySequence_GetSlice(obj, a, b) PySequence_GetSlice(obj, a, b)
    199    #define __Pyx_PySequence_SetSlice(obj, a, b, value) PySequence_SetSlice(obj, a, b, value)
    200 @@ -195,11 +205,9 @@
    201          (likely((obj)->ob_type->tp_as_mapping) ? (PySequence_DelSlice(obj, a, b)) : \
    202              (PyErr_Format(PyExc_TypeError, "'%.200s' object doesn't support slice deletion", (obj)->ob_type->tp_name), -1)))
    203  #endif
    204 -
    205  #if PY_MAJOR_VERSION >= 3
    206    #define PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : PyInstanceMethod_New(func))
    207  #endif
    208 -
    209  #if PY_VERSION_HEX < 0x02050000
    210    #define __Pyx_GetAttrString(o,n)   PyObject_GetAttrString((o),((char *)(n)))
    211    #define __Pyx_SetAttrString(o,n,a) PyObject_SetAttrString((o),((char *)(n)),(a))
    212 @@ -209,7 +217,6 @@
    213    #define __Pyx_SetAttrString(o,n,a) PyObject_SetAttrString((o),(n),(a))
    214    #define __Pyx_DelAttrString(o,n)   PyObject_DelAttrString((o),(n))
    215  #endif
    216 -
    217  #if PY_VERSION_HEX < 0x02050000
    218    #define __Pyx_NAMESTR(n) ((char *)(n))
    219    #define __Pyx_DOCSTR(n)  ((char *)(n))
    220 @@ -218,6 +225,15 @@
    221    #define __Pyx_DOCSTR(n)  (n)
    222  #endif
    223  
    224 +
    225 +#if PY_MAJOR_VERSION >= 3
    226 +  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_TrueDivide(x,y)
    227 +  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceTrueDivide(x,y)
    228 +#else
    229 +  #define __Pyx_PyNumber_Divide(x,y)         PyNumber_Divide(x,y)
    230 +  #define __Pyx_PyNumber_InPlaceDivide(x,y)  PyNumber_InPlaceDivide(x,y)
    231 +#endif
    232 +
    233  #ifndef __PYX_EXTERN_C
    234    #ifdef __cplusplus
    235      #define __PYX_EXTERN_C extern "C"
    236 @@ -269,7 +285,7 @@
    237  #   else
    238  #     define CYTHON_UNUSED
    239  #   endif
    240 -# elif defined(__ICC) || defined(__INTEL_COMPILER)
    241 +# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER))
    242  #   define CYTHON_UNUSED __attribute__ ((__unused__))
    243  # else
    244  #   define CYTHON_UNUSED
    245 @@ -293,8 +309,12 @@
    246  static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t);
    247  static CYTHON_INLINE size_t __Pyx_PyInt_AsSize_t(PyObject*);
    248  
    249 +#if CYTHON_COMPILING_IN_CPYTHON
    250  #define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x))
    251 -
    252 +#else
    253 +#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x)
    254 +#endif
    255 +#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x))
    256  
    257  #ifdef __GNUC__
    258    /* Test for GCC > 2.95 */
    259 @@ -421,11 +441,9 @@
    260    rk_state *internal_state;
    261  };
    262  
    263 -
    264  #ifndef CYTHON_REFNANNY
    265    #define CYTHON_REFNANNY 0
    266  #endif
    267 -
    268  #if CYTHON_REFNANNY
    269    typedef struct {
    270      void (*INCREF)(void*, PyObject*, int);
    271 @@ -438,8 +456,21 @@
    272    static __Pyx_RefNannyAPIStruct *__Pyx_RefNanny = NULL;
    273    static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname); /*proto*/
    274    #define __Pyx_RefNannyDeclarations void *__pyx_refnanny = NULL;
    275 -  #define __Pyx_RefNannySetupContext(name)           __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__)
    276 -  #define __Pyx_RefNannyFinishContext()           __Pyx_RefNanny->FinishContext(&__pyx_refnanny)
    277 +#ifdef WITH_THREAD
    278 +  #define __Pyx_RefNannySetupContext(name, acquire_gil) \
    279 +          if (acquire_gil) { \
    280 +              PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure(); \
    281 +              __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__); \
    282 +              PyGILState_Release(__pyx_gilstate_save); \
    283 +          } else { \
    284 +              __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__); \
    285 +          }
    286 +#else
    287 +  #define __Pyx_RefNannySetupContext(name, acquire_gil) \
    288 +          __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__)
    289 +#endif
    290 +  #define __Pyx_RefNannyFinishContext() \
    291 +          __Pyx_RefNanny->FinishContext(&__pyx_refnanny)
    292    #define __Pyx_INCREF(r)  __Pyx_RefNanny->INCREF(__pyx_refnanny, (PyObject *)(r), __LINE__)
    293    #define __Pyx_DECREF(r)  __Pyx_RefNanny->DECREF(__pyx_refnanny, (PyObject *)(r), __LINE__)
    294    #define __Pyx_GOTREF(r)  __Pyx_RefNanny->GOTREF(__pyx_refnanny, (PyObject *)(r), __LINE__)
    295 @@ -450,7 +481,7 @@
    296    #define __Pyx_XGIVEREF(r) do { if((r) != NULL) {__Pyx_GIVEREF(r);}} while(0)
    297  #else
    298    #define __Pyx_RefNannyDeclarations
    299 -  #define __Pyx_RefNannySetupContext(name)
    300 +  #define __Pyx_RefNannySetupContext(name, acquire_gil)
    301    #define __Pyx_RefNannyFinishContext()
    302    #define __Pyx_INCREF(r) Py_INCREF(r)
    303    #define __Pyx_DECREF(r) Py_DECREF(r)
    304 @@ -461,6 +492,8 @@
    305    #define __Pyx_XGOTREF(r)
    306    #define __Pyx_XGIVEREF(r)
    307  #endif /* CYTHON_REFNANNY */
    308 +#define __Pyx_CLEAR(r)    do { PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);} while(0)
    309 +#define __Pyx_XCLEAR(r)   do { if((r) != NULL) {PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);}} while(0)
    310  
    311  static PyObject *__Pyx_GetName(PyObject *dict, PyObject *name); /*proto*/
    312  
    313 @@ -469,15 +502,15 @@
    314  
    315  static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); /*proto*/
    316  
    317 -static void __Pyx_RaiseDoubleKeywordsError(
    318 -    const char* func_name, PyObject* kw_name); /*proto*/
    319 +static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); /*proto*/
    320  
    321 -static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[],     PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args,     const char* function_name); /*proto*/
    322 +static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[], \
    323 +    PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args, \
    324 +    const char* function_name); /*proto*/
    325  
    326  static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact,
    327      Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); /*proto*/
    328  
    329 -
    330  static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Generic(PyObject *o, PyObject* j) {
    331      PyObject *r;
    332      if (!j) return NULL;
    333 @@ -485,89 +518,102 @@
    334      Py_DECREF(j);
    335      return r;
    336  }
    337 -
    338 -
    339  #define __Pyx_GetItemInt_List(o, i, size, to_py_func) (((size) <= sizeof(Py_ssize_t)) ? \
    340                                                      __Pyx_GetItemInt_List_Fast(o, i) : \
    341                                                      __Pyx_GetItemInt_Generic(o, to_py_func(i)))
    342 -
    343  static CYTHON_INLINE PyObject *__Pyx_GetItemInt_List_Fast(PyObject *o, Py_ssize_t i) {
    344 -    if (likely(o != Py_None)) {
    345 -        if (likely((0 <= i) & (i < PyList_GET_SIZE(o)))) {
    346 -            PyObject *r = PyList_GET_ITEM(o, i);
    347 -            Py_INCREF(r);
    348 -            return r;
    349 -        }
    350 -        else if ((-PyList_GET_SIZE(o) <= i) & (i < 0)) {
    351 -            PyObject *r = PyList_GET_ITEM(o, PyList_GET_SIZE(o) + i);
    352 -            Py_INCREF(r);
    353 -            return r;
    354 -        }
    355 +#if CYTHON_COMPILING_IN_CPYTHON
    356 +    if (likely((0 <= i) & (i < PyList_GET_SIZE(o)))) {
    357 +        PyObject *r = PyList_GET_ITEM(o, i);
    358 +        Py_INCREF(r);
    359 +        return r;
    360 +    }
    361 +    else if ((-PyList_GET_SIZE(o) <= i) & (i < 0)) {
    362 +        PyObject *r = PyList_GET_ITEM(o, PyList_GET_SIZE(o) + i);
    363 +        Py_INCREF(r);
    364 +        return r;
    365      }
    366      return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
    367 +#else
    368 +    return PySequence_GetItem(o, i);
    369 +#endif
    370  }
    371 -
    372  #define __Pyx_GetItemInt_Tuple(o, i, size, to_py_func) (((size) <= sizeof(Py_ssize_t)) ? \
    373                                                      __Pyx_GetItemInt_Tuple_Fast(o, i) : \
    374                                                      __Pyx_GetItemInt_Generic(o, to_py_func(i)))
    375 -
    376  static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Tuple_Fast(PyObject *o, Py_ssize_t i) {
    377 -    if (likely(o != Py_None)) {
    378 -        if (likely((0 <= i) & (i < PyTuple_GET_SIZE(o)))) {
    379 -            PyObject *r = PyTuple_GET_ITEM(o, i);
    380 -            Py_INCREF(r);
    381 -            return r;
    382 -        }
    383 -        else if ((-PyTuple_GET_SIZE(o) <= i) & (i < 0)) {
    384 -            PyObject *r = PyTuple_GET_ITEM(o, PyTuple_GET_SIZE(o) + i);
    385 -            Py_INCREF(r);
    386 -            return r;
    387 -        }
    388 +#if CYTHON_COMPILING_IN_CPYTHON
    389 +    if (likely((0 <= i) & (i < PyTuple_GET_SIZE(o)))) {
    390 +        PyObject *r = PyTuple_GET_ITEM(o, i);
    391 +        Py_INCREF(r);
    392 +        return r;
    393 +    }
    394 +    else if ((-PyTuple_GET_SIZE(o) <= i) & (i < 0)) {
    395 +        PyObject *r = PyTuple_GET_ITEM(o, PyTuple_GET_SIZE(o) + i);
    396 +        Py_INCREF(r);
    397 +        return r;
    398      }
    399      return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
    400 +#else
    401 +    return PySequence_GetItem(o, i);
    402 +#endif
    403  }
    404 -
    405 -
    406  #define __Pyx_GetItemInt(o, i, size, to_py_func) (((size) <= sizeof(Py_ssize_t)) ? \
    407                                                      __Pyx_GetItemInt_Fast(o, i) : \
    408                                                      __Pyx_GetItemInt_Generic(o, to_py_func(i)))
    409 -
    410  static CYTHON_INLINE PyObject *__Pyx_GetItemInt_Fast(PyObject *o, Py_ssize_t i) {
    411 -    PyObject *r;
    412 -    if (PyList_CheckExact(o) && ((0 <= i) & (i < PyList_GET_SIZE(o)))) {
    413 -        r = PyList_GET_ITEM(o, i);
    414 -        Py_INCREF(r);
    415 -    }
    416 -    else if (PyTuple_CheckExact(o) && ((0 <= i) & (i < PyTuple_GET_SIZE(o)))) {
    417 -        r = PyTuple_GET_ITEM(o, i);
    418 -        Py_INCREF(r);
    419 +#if CYTHON_COMPILING_IN_CPYTHON
    420 +    if (PyList_CheckExact(o)) {
    421 +        Py_ssize_t n = (likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);
    422 +        if (likely((n >= 0) & (n < PyList_GET_SIZE(o)))) {
    423 +            PyObject *r = PyList_GET_ITEM(o, n);
    424 +            Py_INCREF(r);
    425 +            return r;
    426 +        }
    427      }
    428 -    else if (Py_TYPE(o)->tp_as_sequence && Py_TYPE(o)->tp_as_sequence->sq_item && (likely(i >= 0))) {
    429 -        r = PySequence_GetItem(o, i);
    430 +    else if (PyTuple_CheckExact(o)) {
    431 +        Py_ssize_t n = (likely(i >= 0)) ? i : i + PyTuple_GET_SIZE(o);
    432 +        if (likely((n >= 0) & (n < PyTuple_GET_SIZE(o)))) {
    433 +            PyObject *r = PyTuple_GET_ITEM(o, n);
    434 +            Py_INCREF(r);
    435 +            return r;
    436 +        }
    437 +    } else {  /* inlined PySequence_GetItem() */
    438 +        PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence;
    439 +        if (likely(m && m->sq_item)) {
    440 +            if (unlikely(i < 0) && likely(m->sq_length)) {
    441 +                Py_ssize_t l = m->sq_length(o);
    442 +                if (unlikely(l < 0)) return NULL;
    443 +                i += l;
    444 +            }
    445 +            return m->sq_item(o, i);
    446 +        }
    447      }
    448 -    else {
    449 -        r = __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
    450 +#else
    451 +    if (PySequence_Check(o)) {
    452 +        return PySequence_GetItem(o, i);
    453      }
    454 -    return r;
    455 +#endif
    456 +    return __Pyx_GetItemInt_Generic(o, PyInt_FromSsize_t(i));
    457  }
    458  
    459 +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected);
    460 +
    461  static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index);
    462  
    463 -static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected);
    464 +static CYTHON_INLINE int __Pyx_IterFinish(void); /*proto*/
    465  
    466  static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected); /*proto*/
    467  
    468  static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); /*proto*/
    469  
    470 -static CYTHON_INLINE int __Pyx_CheckKeywordStrings(PyObject *kwdict,
    471 -    const char* function_name, int kw_allowed); /*proto*/
    472 +static CYTHON_INLINE int __Pyx_CheckKeywordStrings(PyObject *kwdict, const char* function_name, int kw_allowed); /*proto*/
    473  
    474  static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); /*proto*/
    475  
    476  #define __Pyx_SetItemInt(o, i, v, size, to_py_func) (((size) <= sizeof(Py_ssize_t)) ? \
    477                                                      __Pyx_SetItemInt_Fast(o, i, v) : \
    478                                                      __Pyx_SetItemInt_Generic(o, to_py_func(i), v))
    479 -
    480  static CYTHON_INLINE int __Pyx_SetItemInt_Generic(PyObject *o, PyObject *j, PyObject *v) {
    481      int r;
    482      if (!j) return -1;
    483 @@ -575,20 +621,38 @@
    484      Py_DECREF(j);
    485      return r;
    486  }
    487 -
    488  static CYTHON_INLINE int __Pyx_SetItemInt_Fast(PyObject *o, Py_ssize_t i, PyObject *v) {
    489 -    if (PyList_CheckExact(o) && ((0 <= i) & (i < PyList_GET_SIZE(o)))) {
    490 -        Py_INCREF(v);
    491 -        Py_DECREF(PyList_GET_ITEM(o, i));
    492 -        PyList_SET_ITEM(o, i, v);
    493 -        return 1;
    494 +#if CYTHON_COMPILING_IN_CPYTHON
    495 +    if (PyList_CheckExact(o)) {
    496 +        Py_ssize_t n = (likely(i >= 0)) ? i : i + PyList_GET_SIZE(o);
    497 +        if (likely((n >= 0) & (n < PyList_GET_SIZE(o)))) {
    498 +            PyObject* old = PyList_GET_ITEM(o, n);
    499 +            Py_INCREF(v);
    500 +            PyList_SET_ITEM(o, n, v);
    501 +            Py_DECREF(old);
    502 +            return 1;
    503 +        }
    504 +    } else {  /* inlined PySequence_SetItem() */
    505 +        PySequenceMethods *m = Py_TYPE(o)->tp_as_sequence;
    506 +        if (likely(m && m->sq_ass_item)) {
    507 +            if (unlikely(i < 0) && likely(m->sq_length)) {
    508 +                Py_ssize_t l = m->sq_length(o);
    509 +                if (unlikely(l < 0)) return -1;
    510 +                i += l;
    511 +            }
    512 +            return m->sq_ass_item(o, i, v);
    513 +        }
    514      }
    515 -    else if (Py_TYPE(o)->tp_as_sequence && Py_TYPE(o)->tp_as_sequence->sq_ass_item && (likely(i >= 0)))
    516 +#else
    517 +#if CYTHON_COMPILING_IN_PYPY
    518 +    if (PySequence_Check(o) && !PyDict_Check(o)) {
    519 +#else
    520 +    if (PySequence_Check(o)) {
    521 +#endif
    522          return PySequence_SetItem(o, i, v);
    523 -    else {
    524 -        PyObject *j = PyInt_FromSsize_t(i);
    525 -        return __Pyx_SetItemInt_Generic(o, j, v);
    526      }
    527 +#endif
    528 +    return __Pyx_SetItemInt_Generic(o, PyInt_FromSsize_t(i), v);
    529  }
    530  
    531  static CYTHON_INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb); /*proto*/
    532 @@ -596,17 +660,9 @@
    533  
    534  static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, long level); /*proto*/
    535  
    536 -#include <string.h>
    537 -
    538 -static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals); /*proto*/
    539 -
    540 -static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals); /*proto*/
    541 +static CYTHON_INLINE npy_intp __Pyx_PyInt_from_py_npy_intp(PyObject *);
    542  
    543 -#if PY_MAJOR_VERSION >= 3
    544 -#define __Pyx_PyString_Equals __Pyx_PyUnicode_Equals
    545 -#else
    546 -#define __Pyx_PyString_Equals __Pyx_PyBytes_Equals
    547 -#endif
    548 +static CYTHON_INLINE void __Pyx_RaiseImportError(PyObject *name);
    549  
    550  static CYTHON_INLINE PyObject *__Pyx_PyInt_to_py_npy_intp(npy_intp);
    551  
    552 @@ -642,19 +698,40 @@
    553  
    554  static CYTHON_INLINE signed PY_LONG_LONG __Pyx_PyInt_AsSignedLongLong(PyObject *);
    555  
    556 -static CYTHON_INLINE npy_intp __Pyx_PyInt_from_py_npy_intp(PyObject *);
    557 -
    558  static int __Pyx_check_binary_version(void);
    559  
    560 -static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict);  /*proto*/
    561 +#if !defined(__Pyx_PyIdentifier_FromString)
    562 +#if PY_MAJOR_VERSION < 3
    563 +  #define __Pyx_PyIdentifier_FromString(s) PyString_FromString(s)
    564 +#else
    565 +  #define __Pyx_PyIdentifier_FromString(s) PyUnicode_FromString(s)
    566 +#endif
    567 +#endif
    568  
    569  static PyObject *__Pyx_ImportModule(const char *name); /*proto*/
    570  
    571 -static void __Pyx_AddTraceback(const char *funcname, int __pyx_clineno,
    572 -                               int __pyx_lineno, const char *__pyx_filename); /*proto*/
    573 +static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict);  /*proto*/
    574 +
    575 +typedef struct {
    576 +    int code_line;
    577 +    PyCodeObject* code_object;
    578 +} __Pyx_CodeObjectCacheEntry;
    579 +struct __Pyx_CodeObjectCache {
    580 +    int count;
    581 +    int max_count;
    582 +    __Pyx_CodeObjectCacheEntry* entries;
    583 +};
    584 +static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL};
    585 +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line);
    586 +static PyCodeObject *__pyx_find_code_object(int code_line);
    587 +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object);
    588 +
    589 +static void __Pyx_AddTraceback(const char *funcname, int c_line,
    590 +                               int py_line, const char *filename); /*proto*/
    591  
    592  static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); /*proto*/
    593  
    594 +
    595  /* Module declarations from 'numpy' */
    596  
    597  /* Module declarations from 'mtrand' */
    598 @@ -686,6 +763,58 @@
    599  /* Implementation of 'mtrand' */
    600  static PyObject *__pyx_builtin_ValueError;
    601  static PyObject *__pyx_builtin_TypeError;
    602 +static int __pyx_pf_6mtrand_11RandomState___init__(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_seed); /* proto */
    603 +static void __pyx_pf_6mtrand_11RandomState_2__dealloc__(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self); /* proto */
    604 +static PyObject *__pyx_pf_6mtrand_11RandomState_4seed(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_seed); /* proto */
    605 +static PyObject *__pyx_pf_6mtrand_11RandomState_6get_state(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self); /* proto */
    606 +static PyObject *__pyx_pf_6mtrand_11RandomState_8set_state(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_state); /* proto */
    607 +static PyObject *__pyx_pf_6mtrand_11RandomState_10__getstate__(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self); /* proto */
    608 +static PyObject *__pyx_pf_6mtrand_11RandomState_12__setstate__(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_state); /* proto */
    609 +static PyObject *__pyx_pf_6mtrand_11RandomState_14__reduce__(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self); /* proto */
    610 +static PyObject *__pyx_pf_6mtrand_11RandomState_16random_sample(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_size); /* proto */
    611 +static PyObject *__pyx_pf_6mtrand_11RandomState_18tomaxint(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_size); /* proto */
    612 +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 */
    613 +static PyObject *__pyx_pf_6mtrand_11RandomState_22bytes(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, npy_intp __pyx_v_length); /* proto */
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    1204 +  PyObject *__pyx_r = NULL;
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    1222 -    goto __pyx_L6;
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    1327   *         state = <ndarray>np.empty(624, np.uint)
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    1369 +static PyObject *__pyx_pw_6mtrand_11RandomState_9set_state(PyObject *__pyx_v_self, PyObject *__pyx_v_state) {
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    1380   *
    1381 @@ -5221,9 +5402,7 @@
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    1383   */
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    1395    int __pyx_clineno = 0;
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    1400   *         cdef ndarray obj "arrayObject_obj"
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    1403   *         key, pos = state[1:3]
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    1411      /* "mtrand.pyx":689
    1412 @@ -5283,9 +5464,9 @@
    1413      __Pyx_Raise(__pyx_t_1, 0, 0, 0);
    1414      __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
    1415      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 689; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    1416 -    goto __pyx_L5;
    1417 +    goto __pyx_L3;
    1418    }
    1419 -  __pyx_L5:;
    1420 +  __pyx_L3:;
    1421  
    1422    /* "mtrand.pyx":690
    1423   *         if algorithm_name != 'MT19937':
    1424 @@ -5298,45 +5479,52 @@
    1425    __Pyx_GOTREF(__pyx_t_1);
    1426    if ((likely(PyTuple_CheckExact(__pyx_t_1))) || (PyList_CheckExact(__pyx_t_1))) {
    1427      PyObject* sequence = __pyx_t_1;
    1428 +    #if CYTHON_COMPILING_IN_CPYTHON
    1429 +    Py_ssize_t size = Py_SIZE(sequence);
    1430 +    #else
    1431 +    Py_ssize_t size = PySequence_Size(sequence);
    1432 +    #endif
    1433 +    if (unlikely(size != 2)) {
    1434 +      if (size > 2) __Pyx_RaiseTooManyValuesError(2);
    1435 +      else if (size >= 0) __Pyx_RaiseNeedMoreValuesError(size);
    1436 +      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 690; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    1437 +    }
    1438 +    #if CYTHON_COMPILING_IN_CPYTHON
    1439      if (likely(PyTuple_CheckExact(sequence))) {
    1440 -      if (unlikely(PyTuple_GET_SIZE(sequence) != 2)) {
    1441 -        if (PyTuple_GET_SIZE(sequence) > 2) __Pyx_RaiseTooManyValuesError(2);
    1442 -        else __Pyx_RaiseNeedMoreValuesError(PyTuple_GET_SIZE(sequence));
    1443 -        {__pyx_filename = __pyx_f[0]; __pyx_lineno = 690; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    1444 -      }
    1445        __pyx_t_3 = PyTuple_GET_ITEM(sequence, 0);
    1446        __pyx_t_4 = PyTuple_GET_ITEM(sequence, 1);
    1447      } else {
    1448 -      if (unlikely(PyList_GET_SIZE(sequence) != 2)) {
    1449 -        if (PyList_GET_SIZE(sequence) > 2) __Pyx_RaiseTooManyValuesError(2);
    1450 -        else __Pyx_RaiseNeedMoreValuesError(PyList_GET_SIZE(sequence));
    1451 -        {__pyx_filename = __pyx_f[0]; __pyx_lineno = 690; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    1452 -      }
    1453        __pyx_t_3 = PyList_GET_ITEM(sequence, 0);
    1454        __pyx_t_4 = PyList_GET_ITEM(sequence, 1);
    1455      }
    1456      __Pyx_INCREF(__pyx_t_3);
    1457      __Pyx_INCREF(__pyx_t_4);
    1458 +    #else
    1459 +    __pyx_t_3 = PySequence_ITEM(sequence, 0); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 690; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    1460 +    __pyx_t_4 = PySequence_ITEM(sequence, 1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 690; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    1461 +    #endif
    1462      __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
    1463 -  } else {
    1464 +  } else
    1465 +  {
    1466      Py_ssize_t index = -1;
    1467      __pyx_t_5 = PyObject_GetIter(__pyx_t_1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 690; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    1468      __Pyx_GOTREF(__pyx_t_5);
    1469      __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
    1470      __pyx_t_6 = Py_TYPE(__pyx_t_5)->tp_iternext;
    1471 -    index = 0; __pyx_t_3 = __pyx_t_6(__pyx_t_5); if (unlikely(!__pyx_t_3)) goto __pyx_L6_unpacking_failed;
    1472 +    index = 0; __pyx_t_3 = __pyx_t_6(__pyx_t_5); if (unlikely(!__pyx_t_3)) goto __pyx_L4_unpacking_failed;
    1473      __Pyx_GOTREF(__pyx_t_3);
    1474 -    index = 1; __pyx_t_4 = __pyx_t_6(__pyx_t_5); if (unlikely(!__pyx_t_4)) goto __pyx_L6_unpacking_failed;
    1475 +    index = 1; __pyx_t_4 = __pyx_t_6(__pyx_t_5); if (unlikely(!__pyx_t_4)) goto __pyx_L4_unpacking_failed;
    1476      __Pyx_GOTREF(__pyx_t_4);
    1477      if (__Pyx_IternextUnpackEndCheck(__pyx_t_6(__pyx_t_5), 2) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 690; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    1478 +    __pyx_t_6 = NULL;
    1479      __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
    1480 -    goto __pyx_L7_unpacking_done;
    1481 -    __pyx_L6_unpacking_failed:;
    1482 +    goto __pyx_L5_unpacking_done;
    1483 +    __pyx_L4_unpacking_failed:;
    1484      __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
    1485 -    if (PyErr_Occurred() && PyErr_ExceptionMatches(PyExc_StopIteration)) PyErr_Clear();
    1486 -    if (!PyErr_Occurred()) __Pyx_RaiseNeedMoreValuesError(index);
    1487 +    __pyx_t_6 = NULL;
    1488 +    if (__Pyx_IterFinish() == 0) __Pyx_RaiseNeedMoreValuesError(index);
    1489      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 690; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    1490 -    __pyx_L7_unpacking_done:;
    1491 +    __pyx_L5_unpacking_done:;
    1492    }
    1493    __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 = 690; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    1494    __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
    1495 @@ -5376,7 +5564,7 @@
    1496      __Pyx_GOTREF(__pyx_t_1);
    1497      __pyx_v_cached_gaussian = __pyx_t_1;
    1498      __pyx_t_1 = 0;
    1499 -    goto __pyx_L8;
    1500 +    goto __pyx_L6;
    1501    }
    1502    /*else*/ {
    1503  
    1504 @@ -5391,52 +5579,59 @@
    1505      __Pyx_GOTREF(__pyx_t_1);
    1506      if ((likely(PyTuple_CheckExact(__pyx_t_1))) || (PyList_CheckExact(__pyx_t_1))) {
    1507        PyObject* sequence = __pyx_t_1;
    1508 +      #if CYTHON_COMPILING_IN_CPYTHON
    1509 +      Py_ssize_t size = Py_SIZE(sequence);
    1510 +      #else
    1511 +      Py_ssize_t size = PySequence_Size(sequence);
    1512 +      #endif
    1513 +      if (unlikely(size != 2)) {
    1514 +        if (size > 2) __Pyx_RaiseTooManyValuesError(2);
    1515 +        else if (size >= 0) __Pyx_RaiseNeedMoreValuesError(size);
    1516 +        {__pyx_filename = __pyx_f[0]; __pyx_lineno = 695; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    1517 +      }
    1518 +      #if CYTHON_COMPILING_IN_CPYTHON
    1519        if (likely(PyTuple_CheckExact(sequence))) {
    1520 -        if (unlikely(PyTuple_GET_SIZE(sequence) != 2)) {
    1521 -          if (PyTuple_GET_SIZE(sequence) > 2) __Pyx_RaiseTooManyValuesError(2);
    1522 -          else __Pyx_RaiseNeedMoreValuesError(PyTuple_GET_SIZE(sequence));
    1523 -          {__pyx_filename = __pyx_f[0]; __pyx_lineno = 695; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    1524 -        }
    1525          __pyx_t_4 = PyTuple_GET_ITEM(sequence, 0);
    1526          __pyx_t_3 = PyTuple_GET_ITEM(sequence, 1);
    1527        } else {
    1528 -        if (unlikely(PyList_GET_SIZE(sequence) != 2)) {
    1529 -          if (PyList_GET_SIZE(sequence) > 2) __Pyx_RaiseTooManyValuesError(2);
    1530 -          else __Pyx_RaiseNeedMoreValuesError(PyList_GET_SIZE(sequence));
    1531 -          {__pyx_filename = __pyx_f[0]; __pyx_lineno = 695; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    1532 -        }
    1533          __pyx_t_4 = PyList_GET_ITEM(sequence, 0);
    1534          __pyx_t_3 = PyList_GET_ITEM(sequence, 1);
    1535        }
    1536        __Pyx_INCREF(__pyx_t_4);
    1537        __Pyx_INCREF(__pyx_t_3);
    1538 +      #else
    1539 +      __pyx_t_4 = PySequence_ITEM(sequence, 0); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 695; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    1540 +      __pyx_t_3 = PySequence_ITEM(sequence, 1); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 695; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    1541 +      #endif
    1542        __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
    1543 -    } else {
    1544 +    } else
    1545 +    {
    1546        Py_ssize_t index = -1;
    1547        __pyx_t_5 = PyObject_GetIter(__pyx_t_1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 695; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    1548        __Pyx_GOTREF(__pyx_t_5);
    1549        __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
    1550        __pyx_t_6 = Py_TYPE(__pyx_t_5)->tp_iternext;
    1551 -      index = 0; __pyx_t_4 = __pyx_t_6(__pyx_t_5); if (unlikely(!__pyx_t_4)) goto __pyx_L9_unpacking_failed;
    1552 +      index = 0; __pyx_t_4 = __pyx_t_6(__pyx_t_5); if (unlikely(!__pyx_t_4)) goto __pyx_L7_unpacking_failed;
    1553        __Pyx_GOTREF(__pyx_t_4);
    1554 -      index = 1; __pyx_t_3 = __pyx_t_6(__pyx_t_5); if (unlikely(!__pyx_t_3)) goto __pyx_L9_unpacking_failed;
    1555 +      index = 1; __pyx_t_3 = __pyx_t_6(__pyx_t_5); if (unlikely(!__pyx_t_3)) goto __pyx_L7_unpacking_failed;
    1556        __Pyx_GOTREF(__pyx_t_3);
    1557        if (__Pyx_IternextUnpackEndCheck(__pyx_t_6(__pyx_t_5), 2) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 695; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    1558 +      __pyx_t_6 = NULL;
    1559        __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
    1560 -      goto __pyx_L10_unpacking_done;
    1561 -      __pyx_L9_unpacking_failed:;
    1562 +      goto __pyx_L8_unpacking_done;
    1563 +      __pyx_L7_unpacking_failed:;
    1564        __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
    1565 -      if (PyErr_Occurred() && PyErr_ExceptionMatches(PyExc_StopIteration)) PyErr_Clear();
    1566 -      if (!PyErr_Occurred()) __Pyx_RaiseNeedMoreValuesError(index);
    1567 +      __pyx_t_6 = NULL;
    1568 +      if (__Pyx_IterFinish() == 0) __Pyx_RaiseNeedMoreValuesError(index);
    1569        {__pyx_filename = __pyx_f[0]; __pyx_lineno = 695; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    1570 -      __pyx_L10_unpacking_done:;
    1571 +      __pyx_L8_unpacking_done:;
    1572      }
    1573      __pyx_v_has_gauss = __pyx_t_4;
    1574      __pyx_t_4 = 0;
    1575      __pyx_v_cached_gaussian = __pyx_t_3;
    1576      __pyx_t_3 = 0;
    1577    }
    1578 -  __pyx_L8:;
    1579 +  __pyx_L6:;
    1580  
    1581    /* "mtrand.pyx":696
    1582   *         else:
    1583 @@ -5459,7 +5654,7 @@
    1584   *         except TypeError:
    1585   *             # compatibility -- could be an older pickle
    1586   */
    1587 -      __pyx_t_1 = PyArray_ContiguousFromObject(__pyx_v_key, NPY_ULONG, 1, 1); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 697; __pyx_clineno = __LINE__; goto __pyx_L11_error;}
    1588 +      __pyx_t_1 = PyArray_ContiguousFromObject(__pyx_v_key, NPY_ULONG, 1, 1); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 697; __pyx_clineno = __LINE__; goto __pyx_L9_error;}
    1589        __Pyx_GOTREF(__pyx_t_1);
    1590        __Pyx_INCREF(((PyObject *)((PyArrayObject *)__pyx_t_1)));
    1591        arrayObject_obj = ((PyArrayObject *)__pyx_t_1);
    1592 @@ -5468,8 +5663,8 @@
    1593      __Pyx_XDECREF(__pyx_t_9); __pyx_t_9 = 0;
    1594      __Pyx_XDECREF(__pyx_t_10); __pyx_t_10 = 0;
    1595      __Pyx_XDECREF(__pyx_t_11); __pyx_t_11 = 0;
    1596 -    goto __pyx_L18_try_end;
    1597 -    __pyx_L11_error:;
    1598 +    goto __pyx_L16_try_end;
    1599 +    __pyx_L9_error:;
    1600      __Pyx_XDECREF(__pyx_t_5); __pyx_t_5 = 0;
    1601      __Pyx_XDECREF(__pyx_t_4); __pyx_t_4 = 0;
    1602      __Pyx_XDECREF(__pyx_t_3); __pyx_t_3 = 0;
    1603 @@ -5485,7 +5680,7 @@
    1604      __pyx_t_7 = PyErr_ExceptionMatches(__pyx_builtin_TypeError);
    1605      if (__pyx_t_7) {
    1606        __Pyx_AddTraceback("mtrand.RandomState.set_state", __pyx_clineno, __pyx_lineno, __pyx_filename);
    1607 -      if (__Pyx_GetException(&__pyx_t_1, &__pyx_t_3, &__pyx_t_4) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 698; __pyx_clineno = __LINE__; goto __pyx_L13_except_error;}
    1608 +      if (__Pyx_GetException(&__pyx_t_1, &__pyx_t_3, &__pyx_t_4) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 698; __pyx_clineno = __LINE__; goto __pyx_L11_except_error;}
    1609        __Pyx_GOTREF(__pyx_t_1);
    1610        __Pyx_GOTREF(__pyx_t_3);
    1611        __Pyx_GOTREF(__pyx_t_4);
    1612 @@ -5497,7 +5692,7 @@
    1613   *         if obj.dimensions[0] != 624:
    1614   *             raise ValueError("state must be 624 longs")
    1615   */
    1616 -      __pyx_t_5 = PyArray_ContiguousFromObject(__pyx_v_key, NPY_LONG, 1, 1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 700; __pyx_clineno = __LINE__; goto __pyx_L13_except_error;}
    1617 +      __pyx_t_5 = PyArray_ContiguousFromObject(__pyx_v_key, NPY_LONG, 1, 1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 700; __pyx_clineno = __LINE__; goto __pyx_L11_except_error;}
    1618        __Pyx_GOTREF(__pyx_t_5);
    1619        __Pyx_INCREF(((PyObject *)((PyArrayObject *)__pyx_t_5)));
    1620        __Pyx_XDECREF(((PyObject *)arrayObject_obj));
    1621 @@ -5506,20 +5701,20 @@
    1622        __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
    1623        __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
    1624        __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
    1625 -      goto __pyx_L12_exception_handled;
    1626 +      goto __pyx_L10_exception_handled;
    1627      }
    1628 -    __pyx_L13_except_error:;
    1629 +    __pyx_L11_except_error:;
    1630      __Pyx_XGIVEREF(__pyx_t_9);
    1631      __Pyx_XGIVEREF(__pyx_t_10);
    1632      __Pyx_XGIVEREF(__pyx_t_11);
    1633      __Pyx_ExceptionReset(__pyx_t_9, __pyx_t_10, __pyx_t_11);
    1634      goto __pyx_L1_error;
    1635 -    __pyx_L12_exception_handled:;
    1636 +    __pyx_L10_exception_handled:;
    1637      __Pyx_XGIVEREF(__pyx_t_9);
    1638      __Pyx_XGIVEREF(__pyx_t_10);
    1639      __Pyx_XGIVEREF(__pyx_t_11);
    1640      __Pyx_ExceptionReset(__pyx_t_9, __pyx_t_10, __pyx_t_11);
    1641 -    __pyx_L18_try_end:;
    1642 +    __pyx_L16_try_end:;
    1643    }
    1644  
    1645    /* "mtrand.pyx":701
    1646 @@ -5544,9 +5739,9 @@
    1647      __Pyx_Raise(__pyx_t_4, 0, 0, 0);
    1648      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
    1649      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 702; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    1650 -    goto __pyx_L21;
    1651 +    goto __pyx_L19;
    1652    }
    1653 -  __pyx_L21:;
    1654 +  __pyx_L19:;
    1655  
    1656    /* "mtrand.pyx":703
    1657   *         if obj.dimensions[0] != 624:
    1658 @@ -5555,7 +5750,7 @@
    1659   *         self.internal_state.pos = pos
    1660   *         self.internal_state.has_gauss = has_gauss
    1661   */
    1662 -  memcpy(((void *)((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state->key), ((void *)arrayObject_obj->data), (624 * (sizeof(long))));
    1663 +  memcpy(((void *)__pyx_v_self->internal_state->key), ((void *)arrayObject_obj->data), (624 * (sizeof(long))));
    1664  
    1665    /* "mtrand.pyx":704
    1666   *             raise ValueError("state must be 624 longs")
    1667 @@ -5564,7 +5759,7 @@
    1668   *         self.internal_state.has_gauss = has_gauss
    1669   *         self.internal_state.gauss = cached_gaussian
    1670   */
    1671 -  ((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state->pos = __pyx_v_pos;
    1672 +  __pyx_v_self->internal_state->pos = __pyx_v_pos;
    1673  
    1674    /* "mtrand.pyx":705
    1675   *         memcpy(<void*>(self.internal_state.key), <void*>(obj.data), 624*sizeof(long))
    1676 @@ -5574,7 +5769,7 @@
    1677   *
    1678   */
    1679    __pyx_t_7 = __Pyx_PyInt_AsInt(__pyx_v_has_gauss); if (unlikely((__pyx_t_7 == (int)-1) && PyErr_Occurred())) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 705; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    1680 -  ((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state->has_gauss = __pyx_t_7;
    1681 +  __pyx_v_self->internal_state->has_gauss = __pyx_t_7;
    1682  
    1683    /* "mtrand.pyx":706
    1684   *         self.internal_state.pos = pos
    1685 @@ -5584,7 +5779,7 @@
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    1943 +static char __pyx_doc_6mtrand_11RandomState_18tomaxint[] = "\n        tomaxint(size=None)\n\n        Random integers between 0 and ``sys.maxint``, inclusive.\n\n        Return a sample of uniformly distributed random integers in the interval\n        [0, ``sys.maxint``].\n\n        Parameters\n        ----------\n        size : tuple of ints, int, optional\n            Shape of output.  If this is, for example, (m,n,k), m*n*k samples\n            are generated.  If no shape is specified, a single sample is\n            returned.\n\n        Returns\n        -------\n        out : ndarray\n            Drawn samples, with shape `size`.\n\n        See Also\n        --------\n        randint : Uniform sampling over a given half-open interval of integers.\n        random_integers : Uniform sampling over a given closed interval of\n            integers.\n\n        Examples\n        --------\n        >>> RS = np.random.mtrand.RandomState() # need a RandomState object\n        >>> RS.tomaxint((2,2,2))\n        array([[[1170048599, 1600360186],\n                [ 739731006, 1947757578]],\n               [[1871712945,  752307660],\n                [1601631370, 1479324245]]])\n        >>> import sys\n        >>> sys.maxint\n        2147483647\n        >>> RS.tomaxint((2,2,2)) < sys.maxint\n        array([[[ True,  True],\n                [ True,  True]],\n               [[ True,  True],\n                [ True,  True]]], dtype=bool)\n\n        ";
    1944 +static PyObject *__pyx_pw_6mtrand_11RandomState_19tomaxint(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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    1954   *         return cont0_array(self.internal_state, rk_double, size)
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    1957   *         """
    1958   *         tomaxint(size=None)
    1959   */
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    1978 -      switch (PyTuple_GET_SIZE(__pyx_args)) {
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    1985        kw_args = PyDict_Size(__pyx_kwds);
    1986 -      switch (PyTuple_GET_SIZE(__pyx_args)) {
    1987 +      switch (pos_args) {
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    2079 + *         return disc0_array(self.internal_state, rk_long, size)
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    2172 -    goto __pyx_L7;
    2173 +    goto __pyx_L4;
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    2175 -  __pyx_L7:;
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    2177  
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    2179   *             raise ValueError("low >= high")
    2180 @@ -6145,7 +6402,7 @@
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    2182   *         else:
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    2185 +    __pyx_v_rv = (__pyx_v_lo + ((long)rk_interval(__pyx_v_diff, __pyx_v_self->internal_state)));
    2186  
    2187      /* "mtrand.pyx":879
    2188   *         if size is None:
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    2190      __pyx_r = __pyx_t_3;
    2191      __pyx_t_3 = 0;
    2192      goto __pyx_L0;
    2193 -    goto __pyx_L8;
    2194 +    goto __pyx_L5;
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    2199      __Pyx_GOTREF(__pyx_t_4);
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    2201      __pyx_t_3 = PyTuple_New(2); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 881; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    2202 -    __Pyx_GOTREF(((PyObject *)__pyx_t_3));
    2203 +    __Pyx_GOTREF(__pyx_t_3);
    2204      __Pyx_INCREF(__pyx_v_size);
    2205      PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_v_size);
    2206      __Pyx_GIVEREF(__pyx_v_size);
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    2212 +      __pyx_v_rv = (__pyx_v_lo + ((long)rk_interval(__pyx_v_diff, __pyx_v_self->internal_state)));
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    2218      goto __pyx_L0;
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    2220 -  __pyx_L8:;
    2221 +  __pyx_L5:;
    2222  
    2223    __pyx_r = Py_None; __Pyx_INCREF(Py_None);
    2224    goto __pyx_L0;
    2225 @@ -6268,6 +6525,28 @@
    2226    return __pyx_r;
    2227  }
    2228  
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    2232 +static PyObject *__pyx_pw_6mtrand_11RandomState_23bytes(PyObject *__pyx_v_self, PyObject *__pyx_arg_length) {
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    2240 +  goto __pyx_L4_argument_unpacking_done;
    2241 +  __pyx_L3_error:;
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    2243 +  __Pyx_RefNannyFinishContext();
    2244 +  return NULL;
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    2248 +  return __pyx_r;
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    2254 @@ -6276,10 +6555,7 @@
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    2260 -static PyObject *__pyx_pf_6mtrand_11RandomState_11bytes(PyObject *__pyx_v_self, PyObject *__pyx_arg_length) {
    2261 -  npy_intp __pyx_v_length;
    2262 +static PyObject *__pyx_pf_6mtrand_11RandomState_22bytes(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, npy_intp __pyx_v_length) {
    2263    void *__pyx_v_bytes;
    2264    PyObject *__pyx_v_bytestring = NULL;
    2265    PyObject *__pyx_r = NULL;
    2266 @@ -6288,16 +6564,7 @@
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    2268    const char *__pyx_filename = NULL;
    2269    int __pyx_clineno = 0;
    2270 -  __Pyx_RefNannySetupContext("bytes");
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    2274 -  goto __pyx_L4_argument_unpacking_done;
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    2286   *
    2287   */
    2288 -  rk_fill(__pyx_v_bytes, __pyx_v_length, ((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state);
    2289 +  rk_fill(__pyx_v_bytes, __pyx_v_length, __pyx_v_self->internal_state);
    2290  
    2291    /* "mtrand.pyx":914
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    2293 @@ -6345,45 +6612,34 @@
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    2298 - *         return bytestring
    2299 - *
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    2305 -static PyObject *__pyx_pf_6mtrand_11RandomState_12uniform(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
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    2307 -static PyObject *__pyx_pf_6mtrand_11RandomState_12uniform(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
    2308 +/* Python wrapper */
    2309 +static PyObject *__pyx_pw_6mtrand_11RandomState_25uniform(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    2310 +static char __pyx_doc_6mtrand_11RandomState_24uniform[] = "\n        uniform(low=0.0, high=1.0, size=1)\n\n        Draw samples from a uniform distribution.\n\n        Samples are uniformly distributed over the half-open interval\n        ``[low, high)`` (includes low, but excludes high).  In other words,\n        any value within the given interval is equally likely to be drawn\n        by `uniform`.\n\n        Parameters\n        ----------\n        low : float, optional\n            Lower boundary of the output interval.  All values generated will be\n            greater than or equal to low.  The default value is 0.\n        high : float\n            Upper boundary of the output interval.  All values generated will be\n            less than high.  The default value is 1.0.\n        size : int or tuple of ints, optional\n            Shape of output.  If the given size is, for example, (m,n,k),\n            m*n*k samples are generated.  If no shape is specified, a single sample\n            is returned.\n\n        Returns\n        -------\n        out : ndarray\n            Drawn samples, with shape `size`.\n\n        See Also\n        --------\n        randint : Discrete uniform distribution, yielding integers.\n        random_integers : Discrete uniform distribution over the closed\n                          interval ``[low, high]``.\n        random_sample : Floats uniformly distributed over ``[0, 1)``.\n        random : Alias for `random_sample`.\n        rand : Convenience function that accepts dimensions as input, e.g.,\n               ``rand(2,2)`` would generate a 2-by-2 array of floats,\n               uniformly distributed over ``[0, 1)``.\n\n        Notes\n        -----\n        The probability density function of the uniform distribution is\n\n        .. math:: p(x) = \\frac{1}{b - a}\n\n        anywhere within the interval ``[a, b)``, and zero elsewhere.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> s = np.random.uniform(-1,0,1000)\n\n        All values are w""ithin the given interval:\n\n        >>> np.all(s >= -1)\n        True\n        >>> np.all(s < 0)\n        True\n\n        Display the histogram of the samples, along with the\n        probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 15, normed=True)\n        >>> plt.plot(bins, np.ones_like(bins), linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
    2311 +static PyObject *__pyx_pw_6mtrand_11RandomState_25uniform(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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    2313    PyObject *__pyx_v_high = 0;
    2314    PyObject *__pyx_v_size = 0;
    2315 -  PyArrayObject *__pyx_v_olow = 0;
    2316 -  PyArrayObject *__pyx_v_ohigh = 0;
    2317 -  PyArrayObject *__pyx_v_odiff = 0;
    2318 -  double __pyx_v_flow;
    2319 -  double __pyx_v_fhigh;
    2320 -  PyObject *__pyx_v_temp = 0;
    2321 -  PyObject *__pyx_r = NULL;
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    2328 -  int __pyx_lineno = 0;
    2329 -  const char *__pyx_filename = NULL;
    2330 -  int __pyx_clineno = 0;
    2331 -  static PyObject **__pyx_pyargnames[] = {&__pyx_n_s__low,&__pyx_n_s__high,&__pyx_n_s__size,0};
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    2336      PyObject* values[3] = {0,0,0};
    2337      values[0] = __pyx_k_15;
    2338      values[1] = __pyx_k_16;
    2339 +
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    2341 + *         return bytestring
    2342 + *
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    2344 + *         """
    2345 + *         uniform(low=0.0, high=1.0, size=1)
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    2347      values[2] = ((PyObject *)Py_None);
    2348      if (unlikely(__pyx_kwds)) {
    2349        Py_ssize_t kw_args;
    2350 -      switch (PyTuple_GET_SIZE(__pyx_args)) {
    2351 +      const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args);
    2352 +      switch (pos_args) {
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    2354          case  2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1);
    2355          case  1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0);
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    2360 -      switch (PyTuple_GET_SIZE(__pyx_args)) {
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    2388 +  double __pyx_v_fhigh;
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    2396 +  int __pyx_lineno = 0;
    2397 +  const char *__pyx_filename = NULL;
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    2412      goto __pyx_L0;
    2413 -    goto __pyx_L6;
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    2570   *
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    2621 +    goto __pyx_L3;
    2622    }
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    2625 @@ -6762,10 +7055,10 @@
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    2627   */
    2628      __Pyx_XDECREF(__pyx_r);
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    2633 -    __Pyx_GOTREF(((PyObject *)__pyx_t_3));
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    2640      goto __pyx_L0;
    2641    }
    2642 -  __pyx_L5:;
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    2647 @@ -6788,44 +7081,38 @@
    2648    __Pyx_AddTraceback("mtrand.RandomState.randn", __pyx_clineno, __pyx_lineno, __pyx_filename);
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    2657 -/* "mtrand.pyx":1102
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    2659 - *
    2660 - *     def random_integers(self, low, high=None, size=None):             # <<<<<<<<<<<<<<
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    2662 - *         random_integers(low, high=None, size=None)
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    2665 -static PyObject *__pyx_pf_6mtrand_11RandomState_15random_integers(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    2666 -static char __pyx_doc_6mtrand_11RandomState_15random_integers[] = "\n        random_integers(low, high=None, size=None)\n\n        Return random integers between `low` and `high`, inclusive.\n\n        Return random integers from the \"discrete uniform\" distribution in the\n        closed interval [`low`, `high`].  If `high` is None (the default),\n        then results are from [1, `low`].\n\n        Parameters\n        ----------\n        low : int\n            Lowest (signed) integer to be drawn from the distribution (unless\n            ``high=None``, in which case this parameter is the *highest* such\n            integer).\n        high : int, optional\n            If provided, the largest (signed) integer to be drawn from the\n            distribution (see above for behavior if ``high=None``).\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single int is returned.\n\n        Returns\n        -------\n        out : int or ndarray of ints\n            `size`-shaped array of random integers from the appropriate\n            distribution, or a single such random int if `size` not provided.\n\n        See Also\n        --------\n        random.randint : Similar to `random_integers`, only for the half-open\n            interval [`low`, `high`), and 0 is the lowest value if `high` is\n            omitted.\n\n        Notes\n        -----\n        To sample from N evenly spaced floating-point numbers between a and b,\n        use::\n\n          a + (b - a) * (np.random.random_integers(N) - 1) / (N - 1.)\n\n        Examples\n        --------\n        >>> np.random.random_integers(5)\n        4\n        >>> type(np.random.random_integers(5))\n        <type 'int'>\n        >>> np.random.random_integers(5, size=(3.,2.))\n        array([[5, 4],\n               [3, 3],\n               [4, 5]])\n\n        Choose five random numbers from the set of five evenly-spaced\n        numbers between 0 and 2.5, inclusive (*i.e.*, from the set\n        :math:`{0, 5/8, 10/8, 15/8, 20/8}`):\n""\n        >>> 2.5 * (np.random.random_integers(5, size=(5,)) - 1) / 4.\n        array([ 0.625,  1.25 ,  0.625,  0.625,  2.5  ])\n\n        Roll two six sided dice 1000 times and sum the results:\n\n        >>> d1 = np.random.random_integers(1, 6, 1000)\n        >>> d2 = np.random.random_integers(1, 6, 1000)\n        >>> dsums = d1 + d2\n\n        Display results as a histogram:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(dsums, 11, normed=True)\n        >>> plt.show()\n\n        ";
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    2668 +/* Python wrapper */
    2669 +static PyObject *__pyx_pw_6mtrand_11RandomState_31random_integers(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    2670 +static char __pyx_doc_6mtrand_11RandomState_30random_integers[] = "\n        random_integers(low, high=None, size=None)\n\n        Return random integers between `low` and `high`, inclusive.\n\n        Return random integers from the \"discrete uniform\" distribution in the\n        closed interval [`low`, `high`].  If `high` is None (the default),\n        then results are from [1, `low`].\n\n        Parameters\n        ----------\n        low : int\n            Lowest (signed) integer to be drawn from the distribution (unless\n            ``high=None``, in which case this parameter is the *highest* such\n            integer).\n        high : int, optional\n            If provided, the largest (signed) integer to be drawn from the\n            distribution (see above for behavior if ``high=None``).\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single int is returned.\n\n        Returns\n        -------\n        out : int or ndarray of ints\n            `size`-shaped array of random integers from the appropriate\n            distribution, or a single such random int if `size` not provided.\n\n        See Also\n        --------\n        random.randint : Similar to `random_integers`, only for the half-open\n            interval [`low`, `high`), and 0 is the lowest value if `high` is\n            omitted.\n\n        Notes\n        -----\n        To sample from N evenly spaced floating-point numbers between a and b,\n        use::\n\n          a + (b - a) * (np.random.random_integers(N) - 1) / (N - 1.)\n\n        Examples\n        --------\n        >>> np.random.random_integers(5)\n        4\n        >>> type(np.random.random_integers(5))\n        <type 'int'>\n        >>> np.random.random_integers(5, size=(3.,2.))\n        array([[5, 4],\n               [3, 3],\n               [4, 5]])\n\n        Choose five random numbers from the set of five evenly-spaced\n        numbers between 0 and 2.5, inclusive (*i.e.*, from the set\n        :math:`{0, 5/8, 10/8, 15/8, 20/8}`):\n""\n        >>> 2.5 * (np.random.random_integers(5, size=(5,)) - 1) / 4.\n        array([ 0.625,  1.25 ,  0.625,  0.625,  2.5  ])\n\n        Roll two six sided dice 1000 times and sum the results:\n\n        >>> d1 = np.random.random_integers(1, 6, 1000)\n        >>> d2 = np.random.random_integers(1, 6, 1000)\n        >>> dsums = d1 + d2\n\n        Display results as a histogram:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(dsums, 11, normed=True)\n        >>> plt.show()\n\n        ";
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    2693 + *             return self.standard_normal(args)
    2694 + *
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    2697 + *         random_integers(low, high=None, size=None)
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    2799   *
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    2801   *     def standard_normal(self, size=None):             # <<<<<<<<<<<<<<
    2802   *         """
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    2826          case  1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0);
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    2832 +      switch (pos_args) {
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    2887 -static char __pyx_doc_6mtrand_11RandomState_17normal[] = "\n        normal(loc=0.0, scale=1.0, size=None)\n\n        Draw random samples from a normal (Gaussian) distribution.\n\n        The probability density function of the normal distribution, first\n        derived by De Moivre and 200 years later by both Gauss and Laplace\n        independently [2]_, is often called the bell curve because of\n        its characteristic shape (see the example below).\n\n        The normal distributions occurs often in nature.  For example, it\n        describes the commonly occurring distribution of samples influenced\n        by a large number of tiny, random disturbances, each with its own\n        unique distribution [2]_.\n\n        Parameters\n        ----------\n        loc : float\n            Mean (\"centre\") of the distribution.\n        scale : float\n            Standard deviation (spread or \"width\") of the distribution.\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        See Also\n        --------\n        scipy.stats.distributions.norm : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Gaussian distribution is\n\n        .. math:: p(x) = \\frac{1}{\\sqrt{ 2 \\pi \\sigma^2 }}\n                         e^{ - \\frac{ (x - \\mu)^2 } {2 \\sigma^2} },\n\n        where :math:`\\mu` is the mean and :math:`\\sigma` the standard deviation.\n        The square of the standard deviation, :math:`\\sigma^2`, is called the\n        variance.\n\n        The function has its peak at the mean, and its \"spread\" increases with\n        the standard deviation (the function reaches 0.607 times its maximum at\n        :math:`x + \\sigma` and :math:`x - \\sigma` [2]_).  This implies that\n        `numpy.random.normal` is more likely to return samples lying close to the\n        mean, rather than those far away.\n""\n        References\n        ----------\n        .. [1] Wikipedia, \"Normal distribution\",\n               http://en.wikipedia.org/wiki/Normal_distribution\n        .. [2] P. R. Peebles Jr., \"Central Limit Theorem\" in \"Probability, Random\n               Variables and Random Signal Principles\", 4th ed., 2001,\n               pp. 51, 51, 125.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, sigma = 0, 0.1 # mean and standard deviation\n        >>> s = np.random.normal(mu, sigma, 1000)\n\n        Verify the mean and the variance:\n\n        >>> abs(mu - np.mean(s)) < 0.01\n        True\n\n        >>> abs(sigma - np.std(s, ddof=1)) < 0.01\n        True\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n        >>> plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *\n        ...                np.exp( - (bins - mu)**2 / (2 * sigma**2) ),\n        ...          linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
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    2891 +static char __pyx_doc_6mtrand_11RandomState_34normal[] = "\n        normal(loc=0.0, scale=1.0, size=None)\n\n        Draw random samples from a normal (Gaussian) distribution.\n\n        The probability density function of the normal distribution, first\n        derived by De Moivre and 200 years later by both Gauss and Laplace\n        independently [2]_, is often called the bell curve because of\n        its characteristic shape (see the example below).\n\n        The normal distributions occurs often in nature.  For example, it\n        describes the commonly occurring distribution of samples influenced\n        by a large number of tiny, random disturbances, each with its own\n        unique distribution [2]_.\n\n        Parameters\n        ----------\n        loc : float\n            Mean (\"centre\") of the distribution.\n        scale : float\n            Standard deviation (spread or \"width\") of the distribution.\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        See Also\n        --------\n        scipy.stats.distributions.norm : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Gaussian distribution is\n\n        .. math:: p(x) = \\frac{1}{\\sqrt{ 2 \\pi \\sigma^2 }}\n                         e^{ - \\frac{ (x - \\mu)^2 } {2 \\sigma^2} },\n\n        where :math:`\\mu` is the mean and :math:`\\sigma` the standard deviation.\n        The square of the standard deviation, :math:`\\sigma^2`, is called the\n        variance.\n\n        The function has its peak at the mean, and its \"spread\" increases with\n        the standard deviation (the function reaches 0.607 times its maximum at\n        :math:`x + \\sigma` and :math:`x - \\sigma` [2]_).  This implies that\n        `numpy.random.normal` is more likely to return samples lying close to the\n        mean, rather than those far away.\n""\n        References\n        ----------\n        .. [1] Wikipedia, \"Normal distribution\",\n               http://en.wikipedia.org/wiki/Normal_distribution\n        .. [2] P. R. Peebles Jr., \"Central Limit Theorem\" in \"Probability, Random\n               Variables and Random Signal Principles\", 4th ed., 2001,\n               pp. 51, 51, 125.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, sigma = 0, 0.1 # mean and standard deviation\n        >>> s = np.random.normal(mu, sigma, 1000)\n\n        Verify the mean and the variance:\n\n        >>> abs(mu - np.mean(s)) < 0.01\n        True\n\n        >>> abs(sigma - np.std(s, ddof=1)) < 0.01\n        True\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n        >>> plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *\n        ...                np.exp( - (bins - mu)**2 / (2 * sigma**2) ),\n        ...          linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
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    2894    PyObject *__pyx_v_scale = 0;
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    2921 + *         return cont0_array(self.internal_state, rk_gauss, size)
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    2929        Py_ssize_t kw_args;
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    3056 - *         return cont2_array(self.internal_state, rk_normal, size, oloc, oscale)
    3057 - *
    3058 - *     def beta(self, a, b, size=None):             # <<<<<<<<<<<<<<
    3059 - *         """
    3060 - *         beta(a, b, size=None)
    3061 - */
    3062 -
    3063 -static PyObject *__pyx_pf_6mtrand_11RandomState_18beta(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    3064 -static char __pyx_doc_6mtrand_11RandomState_18beta[] = "\n        beta(a, b, size=None)\n\n        The Beta distribution over ``[0, 1]``.\n\n        The Beta distribution is a special case of the Dirichlet distribution,\n        and is related to the Gamma distribution.  It has the probability\n        distribution function\n\n        .. math:: f(x; a,b) = \\frac{1}{B(\\alpha, \\beta)} x^{\\alpha - 1}\n                                                         (1 - x)^{\\beta - 1},\n\n        where the normalisation, B, is the beta function,\n\n        .. math:: B(\\alpha, \\beta) = \\int_0^1 t^{\\alpha - 1}\n                                     (1 - t)^{\\beta - 1} dt.\n\n        It is often seen in Bayesian inference and order statistics.\n\n        Parameters\n        ----------\n        a : float\n            Alpha, non-negative.\n        b : float\n            Beta, non-negative.\n        size : tuple of ints, optional\n            The number of samples to draw.  The ouput is packed according to\n            the size given.\n\n        Returns\n        -------\n        out : ndarray\n            Array of the given shape, containing values drawn from a\n            Beta distribution.\n\n        ";
    3065 -static PyObject *__pyx_pf_6mtrand_11RandomState_18beta(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
    3066 +/* Python wrapper */
    3067 +static PyObject *__pyx_pw_6mtrand_11RandomState_37beta(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    3068 +static char __pyx_doc_6mtrand_11RandomState_36beta[] = "\n        beta(a, b, size=None)\n\n        The Beta distribution over ``[0, 1]``.\n\n        The Beta distribution is a special case of the Dirichlet distribution,\n        and is related to the Gamma distribution.  It has the probability\n        distribution function\n\n        .. math:: f(x; a,b) = \\frac{1}{B(\\alpha, \\beta)} x^{\\alpha - 1}\n                                                         (1 - x)^{\\beta - 1},\n\n        where the normalisation, B, is the beta function,\n\n        .. math:: B(\\alpha, \\beta) = \\int_0^1 t^{\\alpha - 1}\n                                     (1 - t)^{\\beta - 1} dt.\n\n        It is often seen in Bayesian inference and order statistics.\n\n        Parameters\n        ----------\n        a : float\n            Alpha, non-negative.\n        b : float\n            Beta, non-negative.\n        size : tuple of ints, optional\n            The number of samples to draw.  The ouput is packed according to\n            the size given.\n\n        Returns\n        -------\n        out : ndarray\n            Array of the given shape, containing values drawn from a\n            Beta distribution.\n\n        ";
    3069 +static PyObject *__pyx_pw_6mtrand_11RandomState_37beta(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
    3070    PyObject *__pyx_v_a = 0;
    3071    PyObject *__pyx_v_b = 0;
    3072    PyObject *__pyx_v_size = 0;
    3073 -  PyArrayObject *__pyx_v_oa = 0;
    3074 -  PyArrayObject *__pyx_v_ob = 0;
    3075 -  double __pyx_v_fa;
    3076 -  double __pyx_v_fb;
    3077 -  PyObject *__pyx_r = NULL;
    3078 +  PyObject *__pyx_r = 0;
    3079    __Pyx_RefNannyDeclarations
    3080 -  int __pyx_t_1;
    3081 -  PyObject *__pyx_t_2 = NULL;
    3082 -  PyObject *__pyx_t_3 = NULL;
    3083 -  PyObject *__pyx_t_4 = NULL;
    3084 -  PyObject *__pyx_t_5 = NULL;
    3085 -  int __pyx_lineno = 0;
    3086 -  const char *__pyx_filename = NULL;
    3087 -  int __pyx_clineno = 0;
    3088 -  static PyObject **__pyx_pyargnames[] = {&__pyx_n_s__a,&__pyx_n_s__b,&__pyx_n_s__size,0};
    3089 -  __Pyx_RefNannySetupContext("beta");
    3090 +  __Pyx_RefNannySetupContext("beta (wrapper)", 0);
    3091    {
    3092 +    static PyObject **__pyx_pyargnames[] = {&__pyx_n_s__a,&__pyx_n_s__b,&__pyx_n_s__size,0};
    3093      PyObject* values[3] = {0,0,0};
    3094 +
    3095 +    /* "mtrand.pyx":1312
    3096 + *         return cont2_array(self.internal_state, rk_normal, size, oloc, oscale)
    3097 + *
    3098 + *     def beta(self, a, b, size=None):             # <<<<<<<<<<<<<<
    3099 + *         """
    3100 + *         beta(a, b, size=None)
    3101 + */
    3102      values[2] = ((PyObject *)Py_None);
    3103      if (unlikely(__pyx_kwds)) {
    3104        Py_ssize_t kw_args;
    3105 -      switch (PyTuple_GET_SIZE(__pyx_args)) {
    3106 +      const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args);
    3107 +      switch (pos_args) {
    3108          case  3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2);
    3109          case  2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1);
    3110          case  1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0);
    3111 @@ -7368,14 +7682,12 @@
    3112          default: goto __pyx_L5_argtuple_error;
    3113        }
    3114        kw_args = PyDict_Size(__pyx_kwds);
    3115 -      switch (PyTuple_GET_SIZE(__pyx_args)) {
    3116 +      switch (pos_args) {
    3117          case  0:
    3118 -        values[0] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__a);
    3119 -        if (likely(values[0])) kw_args--;
    3120 +        if (likely((values[0] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__a)) != 0)) kw_args--;
    3121          else goto __pyx_L5_argtuple_error;
    3122          case  1:
    3123 -        values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__b);
    3124 -        if (likely(values[1])) kw_args--;
    3125 +        if (likely((values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__b)) != 0)) kw_args--;
    3126          else {
    3127            __Pyx_RaiseArgtupleInvalid("beta", 0, 2, 3, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1312; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
    3128          }
    3129 @@ -7386,7 +7698,7 @@
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    3131        }
    3132        if (unlikely(kw_args > 0)) {
    3133 -        if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "beta") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1312; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
    3134 +        if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "beta") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1312; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
    3135        }
    3136      } else {
    3137        switch (PyTuple_GET_SIZE(__pyx_args)) {
    3138 @@ -7409,6 +7721,27 @@
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    3140    return NULL;
    3141    __pyx_L4_argument_unpacking_done:;
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    3143 +  __Pyx_RefNannyFinishContext();
    3144 +  return __pyx_r;
    3145 +}
    3146 +
    3147 +static PyObject *__pyx_pf_6mtrand_11RandomState_36beta(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_a, PyObject *__pyx_v_b, PyObject *__pyx_v_size) {
    3148 +  PyArrayObject *__pyx_v_oa = 0;
    3149 +  PyArrayObject *__pyx_v_ob = 0;
    3150 +  double __pyx_v_fa;
    3151 +  double __pyx_v_fb;
    3152 +  PyObject *__pyx_r = NULL;
    3153 +  __Pyx_RefNannyDeclarations
    3154 +  int __pyx_t_1;
    3155 +  PyObject *__pyx_t_2 = NULL;
    3156 +  PyObject *__pyx_t_3 = NULL;
    3157 +  PyObject *__pyx_t_4 = NULL;
    3158 +  PyObject *__pyx_t_5 = NULL;
    3159 +  int __pyx_lineno = 0;
    3160 +  const char *__pyx_filename = NULL;
    3161 +  int __pyx_clineno = 0;
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    3163  
    3164    /* "mtrand.pyx":1352
    3165   *         cdef double fa, fb
    3166 @@ -7460,9 +7793,9 @@
    3167        __Pyx_Raise(__pyx_t_2, 0, 0, 0);
    3168        __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
    3169        {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1356; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    3170 -      goto __pyx_L7;
    3171 +      goto __pyx_L4;
    3172      }
    3173 -    __pyx_L7:;
    3174 +    __pyx_L4:;
    3175  
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    3178 @@ -7486,9 +7819,9 @@
    3179        __Pyx_Raise(__pyx_t_2, 0, 0, 0);
    3180        __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
    3181        {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1358; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    3182 -      goto __pyx_L8;
    3183 +      goto __pyx_L5;
    3184      }
    3185 -    __pyx_L8:;
    3186 +    __pyx_L5:;
    3187  
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    3189   *             if fb <= 0:
    3190 @@ -7498,14 +7831,14 @@
    3191   *         PyErr_Clear()
    3192   */
    3193      __Pyx_XDECREF(__pyx_r);
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    3195 +    __pyx_t_2 = __pyx_f_6mtrand_cont2_array_sc(__pyx_v_self->internal_state, rk_beta, __pyx_v_size, __pyx_v_fa, __pyx_v_fb); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1359; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    3196      __Pyx_GOTREF(__pyx_t_2);
    3197      __pyx_r = __pyx_t_2;
    3198      __pyx_t_2 = 0;
    3199      goto __pyx_L0;
    3200 -    goto __pyx_L6;
    3201 +    goto __pyx_L3;
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    3203 -  __pyx_L6:;
    3204 +  __pyx_L3:;
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    3207   *             return cont2_array_sc(self.internal_state, rk_beta, size, fa, fb)
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    3209    __Pyx_GOTREF(__pyx_t_4);
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    3211    __pyx_t_2 = PyTuple_New(2); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1365; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    3212 -  __Pyx_GOTREF(((PyObject *)__pyx_t_2));
    3213 +  __Pyx_GOTREF(__pyx_t_2);
    3214    __Pyx_INCREF(((PyObject *)__pyx_v_oa));
    3215    PyTuple_SET_ITEM(__pyx_t_2, 0, ((PyObject *)__pyx_v_oa));
    3216    __Pyx_GIVEREF(((PyObject *)__pyx_v_oa));
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    3221 -  __Pyx_GOTREF(((PyObject *)__pyx_t_2));
    3222 +  __Pyx_GOTREF(__pyx_t_2);
    3223    PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_t_5);
    3224    __Pyx_GIVEREF(__pyx_t_5);
    3225    __pyx_t_5 = 0;
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    3227      __Pyx_Raise(__pyx_t_5, 0, 0, 0);
    3228      __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
    3229      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1366; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    3230 -    goto __pyx_L9;
    3231 +    goto __pyx_L6;
    3232    }
    3233 -  __pyx_L9:;
    3234 +  __pyx_L6:;
    3235  
    3236    /* "mtrand.pyx":1367
    3237   *         if np.any(np.less_equal(oa, 0)):
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    3242 -  __Pyx_GOTREF(((PyObject *)__pyx_t_5));
    3243 +  __Pyx_GOTREF(__pyx_t_5);
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    3246    __Pyx_GIVEREF(((PyObject *)__pyx_v_ob));
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    3250    __pyx_t_5 = PyTuple_New(1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1367; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    3251 -  __Pyx_GOTREF(((PyObject *)__pyx_t_5));
    3252 +  __Pyx_GOTREF(__pyx_t_5);
    3253    PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_t_4);
    3254    __Pyx_GIVEREF(__pyx_t_4);
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    3260 -    goto __pyx_L10;
    3261 +    goto __pyx_L7;
    3262    }
    3263 -  __pyx_L10:;
    3264 +  __pyx_L7:;
    3265  
    3266    /* "mtrand.pyx":1369
    3267   *         if np.any(np.less_equal(ob, 0)):
    3268 @@ -7666,7 +7999,7 @@
    3269   *     def exponential(self, scale=1.0, size=None):
    3270   */
    3271    __Pyx_XDECREF(__pyx_r);
    3272 -  __pyx_t_4 = __pyx_f_6mtrand_cont2_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_beta, __pyx_v_size, __pyx_v_oa, __pyx_v_ob); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1369; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    3273 +  __pyx_t_4 = __pyx_f_6mtrand_cont2_array(__pyx_v_self->internal_state, rk_beta, __pyx_v_size, __pyx_v_oa, __pyx_v_ob); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1369; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    3274    __Pyx_GOTREF(__pyx_t_4);
    3275    __pyx_r = __pyx_t_4;
    3276    __pyx_t_4 = 0;
    3277 @@ -7689,47 +8022,39 @@
    3278    return __pyx_r;
    3279  }
    3280  
    3281 -/* "mtrand.pyx":1371
    3282 - *         return cont2_array(self.internal_state, rk_beta, size, oa, ob)
    3283 - *
    3284 - *     def exponential(self, scale=1.0, size=None):             # <<<<<<<<<<<<<<
    3285 - *         """
    3286 - *         exponential(scale=1.0, size=None)
    3287 - */
    3288 -
    3289 -static PyObject *__pyx_pf_6mtrand_11RandomState_19exponential(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    3290 -static char __pyx_doc_6mtrand_11RandomState_19exponential[] = "\n        exponential(scale=1.0, size=None)\n\n        Exponential distribution.\n\n        Its probability density function is\n\n        .. math:: f(x; \\frac{1}{\\beta}) = \\frac{1}{\\beta} \\exp(-\\frac{x}{\\beta}),\n\n        for ``x > 0`` and 0 elsewhere. :math:`\\beta` is the scale parameter,\n        which is the inverse of the rate parameter :math:`\\lambda = 1/\\beta`.\n        The rate parameter is an alternative, widely used parameterization\n        of the exponential distribution [3]_.\n\n        The exponential distribution is a continuous analogue of the\n        geometric distribution.  It describes many common situations, such as\n        the size of raindrops measured over many rainstorms [1]_, or the time\n        between page requests to Wikipedia [2]_.\n\n        Parameters\n        ----------\n        scale : float\n            The scale parameter, :math:`\\beta = 1/\\lambda`.\n        size : tuple of ints\n            Number of samples to draw.  The output is shaped\n            according to `size`.\n\n        References\n        ----------\n        .. [1] Peyton Z. Peebles Jr., \"Probability, Random Variables and\n               Random Signal Principles\", 4th ed, 2001, p. 57.\n        .. [2] \"Poisson Process\", Wikipedia,\n               http://en.wikipedia.org/wiki/Poisson_process\n        .. [3] \"Exponential Distribution, Wikipedia,\n               http://en.wikipedia.org/wiki/Exponential_distribution\n\n        ";
    3291 -static PyObject *__pyx_pf_6mtrand_11RandomState_19exponential(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
    3292 +/* Python wrapper */
    3293 +static PyObject *__pyx_pw_6mtrand_11RandomState_39exponential(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    3294 +static char __pyx_doc_6mtrand_11RandomState_38exponential[] = "\n        exponential(scale=1.0, size=None)\n\n        Exponential distribution.\n\n        Its probability density function is\n\n        .. math:: f(x; \\frac{1}{\\beta}) = \\frac{1}{\\beta} \\exp(-\\frac{x}{\\beta}),\n\n        for ``x > 0`` and 0 elsewhere. :math:`\\beta` is the scale parameter,\n        which is the inverse of the rate parameter :math:`\\lambda = 1/\\beta`.\n        The rate parameter is an alternative, widely used parameterization\n        of the exponential distribution [3]_.\n\n        The exponential distribution is a continuous analogue of the\n        geometric distribution.  It describes many common situations, such as\n        the size of raindrops measured over many rainstorms [1]_, or the time\n        between page requests to Wikipedia [2]_.\n\n        Parameters\n        ----------\n        scale : float\n            The scale parameter, :math:`\\beta = 1/\\lambda`.\n        size : tuple of ints\n            Number of samples to draw.  The output is shaped\n            according to `size`.\n\n        References\n        ----------\n        .. [1] Peyton Z. Peebles Jr., \"Probability, Random Variables and\n               Random Signal Principles\", 4th ed, 2001, p. 57.\n        .. [2] \"Poisson Process\", Wikipedia,\n               http://en.wikipedia.org/wiki/Poisson_process\n        .. [3] \"Exponential Distribution, Wikipedia,\n               http://en.wikipedia.org/wiki/Exponential_distribution\n\n        ";
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    3297    PyObject *__pyx_v_size = 0;
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    3320 + *         return cont2_array(self.internal_state, rk_beta, size, oa, ob)
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    3549 +static char __pyx_doc_6mtrand_11RandomState_42standard_gamma[] = "\n        standard_gamma(shape, size=None)\n\n        Draw samples from a Standard Gamma distribution.\n\n        Samples are drawn from a Gamma distribution with specified parameters,\n        shape (sometimes designated \"k\") and scale=1.\n\n        Parameters\n        ----------\n        shape : float\n            Parameter, should be > 0.\n        size : int or tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            The drawn samples.\n\n        See Also\n        --------\n        scipy.stats.distributions.gamma : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Gamma distribution is\n\n        .. math:: p(x) = x^{k-1}\\frac{e^{-x/\\theta}}{\\theta^k\\Gamma(k)},\n\n        where :math:`k` is the shape and :math:`\\theta` the scale,\n        and :math:`\\Gamma` is the Gamma function.\n\n        The Gamma distribution is often used to model the times to failure of\n        electronic components, and arises naturally in processes for which the\n        waiting times between Poisson distributed events are relevant.\n\n        References\n        ----------\n        .. [1] Weisstein, Eric W. \"Gamma Distribution.\" From MathWorld--A\n               Wolfram Web Resource.\n               http://mathworld.wolfram.com/GammaDistribution.html\n        .. [2] Wikipedia, \"Gamma-distribution\",\n               http://en.wikipedia.org/wiki/Gamma-distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> shape, scale = 2., 1. # mean and width\n        >>> s = np.random.standard_gamma(shape, 1000000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt""\n        >>> import scipy.special as sps\n        >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n        >>> y = bins**(shape-1) * ((np.exp(-bins/scale))/ \\\n        ...                       (sps.gamma(shape) * scale**shape))\n        >>> plt.plot(bins, y, linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
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    3561   *         return cont0_array(self.internal_state, rk_standard_exponential, size)
    3562   *
    3563   *     def standard_gamma(self, shape, size=None):             # <<<<<<<<<<<<<<
    3564   *         """
    3565   *         standard_gamma(shape, size=None)
    3566   */
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    3569 -static char __pyx_doc_6mtrand_11RandomState_21standard_gamma[] = "\n        standard_gamma(shape, size=None)\n\n        Draw samples from a Standard Gamma distribution.\n\n        Samples are drawn from a Gamma distribution with specified parameters,\n        shape (sometimes designated \"k\") and scale=1.\n\n        Parameters\n        ----------\n        shape : float\n            Parameter, should be > 0.\n        size : int or tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            The drawn samples.\n\n        See Also\n        --------\n        scipy.stats.distributions.gamma : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Gamma distribution is\n\n        .. math:: p(x) = x^{k-1}\\frac{e^{-x/\\theta}}{\\theta^k\\Gamma(k)},\n\n        where :math:`k` is the shape and :math:`\\theta` the scale,\n        and :math:`\\Gamma` is the Gamma function.\n\n        The Gamma distribution is often used to model the times to failure of\n        electronic components, and arises naturally in processes for which the\n        waiting times between Poisson distributed events are relevant.\n\n        References\n        ----------\n        .. [1] Weisstein, Eric W. \"Gamma Distribution.\" From MathWorld--A\n               Wolfram Web Resource.\n               http://mathworld.wolfram.com/GammaDistribution.html\n        .. [2] Wikipedia, \"Gamma-distribution\",\n               http://en.wikipedia.org/wiki/Gamma-distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> shape, scale = 2., 1. # mean and width\n        >>> s = np.random.standard_gamma(shape, 1000000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt""\n        >>> import scipy.special as sps\n        >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n        >>> y = bins**(shape-1) * ((np.exp(-bins/scale))/ \\\n        ...                       (sps.gamma(shape) * scale**shape))\n        >>> plt.plot(bins, y, linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
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    3722 - *         """
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    3727 -static char __pyx_doc_6mtrand_11RandomState_22gamma[] = "\n        gamma(shape, scale=1.0, size=None)\n\n        Draw samples from a Gamma distribution.\n\n        Samples are drawn from a Gamma distribution with specified parameters,\n        `shape` (sometimes designated \"k\") and `scale` (sometimes designated\n        \"theta\"), where both parameters are > 0.\n\n        Parameters\n        ----------\n        shape : scalar > 0\n            The shape of the gamma distribution.\n        scale : scalar > 0, optional\n            The scale of the gamma distribution.  Default is equal to 1.\n        size : shape_tuple, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray, float\n            Returns one sample unless `size` parameter is specified.\n\n        See Also\n        --------\n        scipy.stats.distributions.gamma : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Gamma distribution is\n\n        .. math:: p(x) = x^{k-1}\\frac{e^{-x/\\theta}}{\\theta^k\\Gamma(k)},\n\n        where :math:`k` is the shape and :math:`\\theta` the scale,\n        and :math:`\\Gamma` is the Gamma function.\n\n        The Gamma distribution is often used to model the times to failure of\n        electronic components, and arises naturally in processes for which the\n        waiting times between Poisson distributed events are relevant.\n\n        References\n        ----------\n        .. [1] Weisstein, Eric W. \"Gamma Distribution.\" From MathWorld--A\n               Wolfram Web Resource.\n               http://mathworld.wolfram.com/GammaDistribution.html\n        .. [2] Wikipedia, \"Gamma-distribution\",\n               http://en.wikipedia.org/wiki/Gamma-distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> shape, scale = 2.,"" 2. # mean and dispersion\n        >>> s = np.random.gamma(shape, scale, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> import scipy.special as sps\n        >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n        >>> y = bins**(shape-1)*(np.exp(-bins/scale) /\n        ...                      (sps.gamma(shape)*scale**shape))\n        >>> plt.plot(bins, y, linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
    3728 -static PyObject *__pyx_pf_6mtrand_11RandomState_22gamma(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
    3729 +/* Python wrapper */
    3730 +static PyObject *__pyx_pw_6mtrand_11RandomState_45gamma(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    3731 +static char __pyx_doc_6mtrand_11RandomState_44gamma[] = "\n        gamma(shape, scale=1.0, size=None)\n\n        Draw samples from a Gamma distribution.\n\n        Samples are drawn from a Gamma distribution with specified parameters,\n        `shape` (sometimes designated \"k\") and `scale` (sometimes designated\n        \"theta\"), where both parameters are > 0.\n\n        Parameters\n        ----------\n        shape : scalar > 0\n            The shape of the gamma distribution.\n        scale : scalar > 0, optional\n            The scale of the gamma distribution.  Default is equal to 1.\n        size : shape_tuple, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray, float\n            Returns one sample unless `size` parameter is specified.\n\n        See Also\n        --------\n        scipy.stats.distributions.gamma : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Gamma distribution is\n\n        .. math:: p(x) = x^{k-1}\\frac{e^{-x/\\theta}}{\\theta^k\\Gamma(k)},\n\n        where :math:`k` is the shape and :math:`\\theta` the scale,\n        and :math:`\\Gamma` is the Gamma function.\n\n        The Gamma distribution is often used to model the times to failure of\n        electronic components, and arises naturally in processes for which the\n        waiting times between Poisson distributed events are relevant.\n\n        References\n        ----------\n        .. [1] Weisstein, Eric W. \"Gamma Distribution.\" From MathWorld--A\n               Wolfram Web Resource.\n               http://mathworld.wolfram.com/GammaDistribution.html\n        .. [2] Wikipedia, \"Gamma-distribution\",\n               http://en.wikipedia.org/wiki/Gamma-distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> shape, scale = 2.,"" 2. # mean and dispersion\n        >>> s = np.random.gamma(shape, scale, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> import scipy.special as sps\n        >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n        >>> y = bins**(shape-1)*(np.exp(-bins/scale) /\n        ...                      (sps.gamma(shape)*scale**shape))\n        >>> plt.plot(bins, y, linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
    3732 +static PyObject *__pyx_pw_6mtrand_11RandomState_45gamma(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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    3734    PyObject *__pyx_v_scale = 0;
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    3737 -  PyArrayObject *__pyx_v_oscale = 0;
    3738 -  double __pyx_v_fshape;
    3739 -  double __pyx_v_fscale;
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    3756      PyObject* values[3] = {0,0,0};
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    3760 + *         return cont1_array(self.internal_state, rk_standard_gamma, size, oshape)
    3761 + *
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    3763 + *         """
    3764 + *         gamma(shape, scale=1.0, size=None)
    3765 + */
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    3769 -      switch (PyTuple_GET_SIZE(__pyx_args)) {
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    3941 - *         return cont2_array(self.internal_state, rk_gamma, size, oshape, oscale)
    3942 - *
    3943 - *     def f(self, dfnum, dfden, size=None):             # <<<<<<<<<<<<<<
    3944 - *         """
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    3949 -static char __pyx_doc_6mtrand_11RandomState_23f[] = "\n        f(dfnum, dfden, size=None)\n\n        Draw samples from a F distribution.\n\n        Samples are drawn from an F distribution with specified parameters,\n        `dfnum` (degrees of freedom in numerator) and `dfden` (degrees of freedom\n        in denominator), where both parameters should be greater than zero.\n\n        The random variate of the F distribution (also known as the\n        Fisher distribution) is a continuous probability distribution\n        that arises in ANOVA tests, and is the ratio of two chi-square\n        variates.\n\n        Parameters\n        ----------\n        dfnum : float\n            Degrees of freedom in numerator. Should be greater than zero.\n        dfden : float\n            Degrees of freedom in denominator. Should be greater than zero.\n        size : {tuple, int}, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``,\n            then ``m * n * k`` samples are drawn. By default only one sample\n            is returned.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n            Samples from the Fisher distribution.\n\n        See Also\n        --------\n        scipy.stats.distributions.f : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n\n        The F statistic is used to compare in-group variances to between-group\n        variances. Calculating the distribution depends on the sampling, and\n        so it is a function of the respective degrees of freedom in the\n        problem.  The variable `dfnum` is the number of samples minus one, the\n        between-groups degrees of freedom, while `dfden` is the within-groups\n        degrees of freedom, the sum of the number of samples in each group\n        minus the number of groups.\n\n        References\n        ----------\n        .. [1] Glantz, Stanton A. \"Primer of Biostatistics.\", McGraw-Hill,\n               Fifth Edition, 2002.""\n        .. [2] Wikipedia, \"F-distribution\",\n               http://en.wikipedia.org/wiki/F-distribution\n\n        Examples\n        --------\n        An example from Glantz[1], pp 47-40.\n        Two groups, children of diabetics (25 people) and children from people\n        without diabetes (25 controls). Fasting blood glucose was measured,\n        case group had a mean value of 86.1, controls had a mean value of\n        82.2. Standard deviations were 2.09 and 2.49 respectively. Are these\n        data consistent with the null hypothesis that the parents diabetic\n        status does not affect their children's blood glucose levels?\n        Calculating the F statistic from the data gives a value of 36.01.\n\n        Draw samples from the distribution:\n\n        >>> dfnum = 1. # between group degrees of freedom\n        >>> dfden = 48. # within groups degrees of freedom\n        >>> s = np.random.f(dfnum, dfden, 1000)\n\n        The lower bound for the top 1% of the samples is :\n\n        >>> sort(s)[-10]\n        7.61988120985\n\n        So there is about a 1% chance that the F statistic will exceed 7.62,\n        the measured value is 36, so the null hypothesis is rejected at the 1%\n        level.\n\n        ";
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    3951 +/* Python wrapper */
    3952 +static PyObject *__pyx_pw_6mtrand_11RandomState_47f(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    3953 +static char __pyx_doc_6mtrand_11RandomState_46f[] = "\n        f(dfnum, dfden, size=None)\n\n        Draw samples from a F distribution.\n\n        Samples are drawn from an F distribution with specified parameters,\n        `dfnum` (degrees of freedom in numerator) and `dfden` (degrees of freedom\n        in denominator), where both parameters should be greater than zero.\n\n        The random variate of the F distribution (also known as the\n        Fisher distribution) is a continuous probability distribution\n        that arises in ANOVA tests, and is the ratio of two chi-square\n        variates.\n\n        Parameters\n        ----------\n        dfnum : float\n            Degrees of freedom in numerator. Should be greater than zero.\n        dfden : float\n            Degrees of freedom in denominator. Should be greater than zero.\n        size : {tuple, int}, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``,\n            then ``m * n * k`` samples are drawn. By default only one sample\n            is returned.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n            Samples from the Fisher distribution.\n\n        See Also\n        --------\n        scipy.stats.distributions.f : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n\n        The F statistic is used to compare in-group variances to between-group\n        variances. Calculating the distribution depends on the sampling, and\n        so it is a function of the respective degrees of freedom in the\n        problem.  The variable `dfnum` is the number of samples minus one, the\n        between-groups degrees of freedom, while `dfden` is the within-groups\n        degrees of freedom, the sum of the number of samples in each group\n        minus the number of groups.\n\n        References\n        ----------\n        .. [1] Glantz, Stanton A. \"Primer of Biostatistics.\", McGraw-Hill,\n               Fifth Edition, 2002.""\n        .. [2] Wikipedia, \"F-distribution\",\n               http://en.wikipedia.org/wiki/F-distribution\n\n        Examples\n        --------\n        An example from Glantz[1], pp 47-40.\n        Two groups, children of diabetics (25 people) and children from people\n        without diabetes (25 controls). Fasting blood glucose was measured,\n        case group had a mean value of 86.1, controls had a mean value of\n        82.2. Standard deviations were 2.09 and 2.49 respectively. Are these\n        data consistent with the null hypothesis that the parents diabetic\n        status does not affect their children's blood glucose levels?\n        Calculating the F statistic from the data gives a value of 36.01.\n\n        Draw samples from the distribution:\n\n        >>> dfnum = 1. # between group degrees of freedom\n        >>> dfden = 48. # within groups degrees of freedom\n        >>> s = np.random.f(dfnum, dfden, 1000)\n\n        The lower bound for the top 1% of the samples is :\n\n        >>> sort(s)[-10]\n        7.61988120985\n\n        So there is about a 1% chance that the F statistic will exceed 7.62,\n        the measured value is 36, so the null hypothesis is rejected at the 1%\n        level.\n\n        ";
    3954 +static PyObject *__pyx_pw_6mtrand_11RandomState_47f(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
    3955    PyObject *__pyx_v_dfnum = 0;
    3956    PyObject *__pyx_v_dfden = 0;
    3957    PyObject *__pyx_v_size = 0;
    3958 -  PyArrayObject *__pyx_v_odfnum = 0;
    3959 -  PyArrayObject *__pyx_v_odfden = 0;
    3960 -  double __pyx_v_fdfnum;
    3961 -  double __pyx_v_fdfden;
    3962 -  PyObject *__pyx_r = NULL;
    3963 +  PyObject *__pyx_r = 0;
    3964    __Pyx_RefNannyDeclarations
    3965 -  int __pyx_t_1;
    3966 -  PyObject *__pyx_t_2 = NULL;
    3967 -  PyObject *__pyx_t_3 = NULL;
    3968 -  PyObject *__pyx_t_4 = NULL;
    3969 -  PyObject *__pyx_t_5 = NULL;
    3970 -  int __pyx_lineno = 0;
    3971 -  const char *__pyx_filename = NULL;
    3972 -  int __pyx_clineno = 0;
    3973 -  static PyObject **__pyx_pyargnames[] = {&__pyx_n_s__dfnum,&__pyx_n_s__dfden,&__pyx_n_s__size,0};
    3974 -  __Pyx_RefNannySetupContext("f");
    3975 +  __Pyx_RefNannySetupContext("f (wrapper)", 0);
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    3977 +    static PyObject **__pyx_pyargnames[] = {&__pyx_n_s__dfnum,&__pyx_n_s__dfden,&__pyx_n_s__size,0};
    3978      PyObject* values[3] = {0,0,0};
    3979 +
    3980 +    /* "mtrand.pyx":1626
    3981 + *         return cont2_array(self.internal_state, rk_gamma, size, oshape, oscale)
    3982 + *
    3983 + *     def f(self, dfnum, dfden, size=None):             # <<<<<<<<<<<<<<
    3984 + *         """
    3985 + *         f(dfnum, dfden, size=None)
    3986 + */
    3987      values[2] = ((PyObject *)Py_None);
    3988      if (unlikely(__pyx_kwds)) {
    3989        Py_ssize_t kw_args;
    3990 -      switch (PyTuple_GET_SIZE(__pyx_args)) {
    3991 +      const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args);
    3992 +      switch (pos_args) {
    3993          case  3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2);
    3994          case  2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1);
    3995          case  1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0);
    3996 @@ -8681,14 +9046,12 @@
    3997          default: goto __pyx_L5_argtuple_error;
    3998        }
    3999        kw_args = PyDict_Size(__pyx_kwds);
    4000 -      switch (PyTuple_GET_SIZE(__pyx_args)) {
    4001 +      switch (pos_args) {
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    4003 -        values[0] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__dfnum);
    4004 -        if (likely(values[0])) kw_args--;
    4005 +        if (likely((values[0] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__dfnum)) != 0)) kw_args--;
    4006          else goto __pyx_L5_argtuple_error;
    4007          case  1:
    4008 -        values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__dfden);
    4009 -        if (likely(values[1])) kw_args--;
    4010 +        if (likely((values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__dfden)) != 0)) kw_args--;
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    4012            __Pyx_RaiseArgtupleInvalid("f", 0, 2, 3, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1626; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
    4013          }
    4014 @@ -8699,7 +9062,7 @@
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    4017        if (unlikely(kw_args > 0)) {
    4018 -        if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "f") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1626; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
    4019 +        if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "f") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1626; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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    4028 +  __Pyx_RefNannyFinishContext();
    4029 +  return __pyx_r;
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    4031 +
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    4033 +  PyArrayObject *__pyx_v_odfnum = 0;
    4034 +  PyArrayObject *__pyx_v_odfden = 0;
    4035 +  double __pyx_v_fdfnum;
    4036 +  double __pyx_v_fdfden;
    4037 +  PyObject *__pyx_r = NULL;
    4038 +  __Pyx_RefNannyDeclarations
    4039 +  int __pyx_t_1;
    4040 +  PyObject *__pyx_t_2 = NULL;
    4041 +  PyObject *__pyx_t_3 = NULL;
    4042 +  PyObject *__pyx_t_4 = NULL;
    4043 +  PyObject *__pyx_t_5 = NULL;
    4044 +  int __pyx_lineno = 0;
    4045 +  const char *__pyx_filename = NULL;
    4046 +  int __pyx_clineno = 0;
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    4050   *         cdef double fdfnum, fdfden
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    4052        __Pyx_Raise(__pyx_t_2, 0, 0, 0);
    4053        __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
    4054        {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1714; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    4055 -      goto __pyx_L7;
    4056 +      goto __pyx_L4;
    4057      }
    4058 -    __pyx_L7:;
    4059 +    __pyx_L4:;
    4060  
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    4062   *             if fdfnum <= 0:
    4063 @@ -8799,9 +9183,9 @@
    4064        __Pyx_Raise(__pyx_t_2, 0, 0, 0);
    4065        __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
    4066        {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1716; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    4067 -      goto __pyx_L8;
    4068 +      goto __pyx_L5;
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    4070 -    __pyx_L8:;
    4071 +    __pyx_L5:;
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    4074   *             if fdfden <= 0:
    4075 @@ -8811,14 +9195,14 @@
    4076   *         PyErr_Clear()
    4077   */
    4078      __Pyx_XDECREF(__pyx_r);
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    4080 +    __pyx_t_2 = __pyx_f_6mtrand_cont2_array_sc(__pyx_v_self->internal_state, rk_f, __pyx_v_size, __pyx_v_fdfnum, __pyx_v_fdfden); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1717; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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    4083      __pyx_t_2 = 0;
    4084      goto __pyx_L0;
    4085 -    goto __pyx_L6;
    4086 +    goto __pyx_L3;
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    4088 -  __pyx_L6:;
    4089 +  __pyx_L3:;
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    4092   *             return cont2_array_sc(self.internal_state, rk_f, size, fdfnum, fdfden)
    4093 @@ -8875,7 +9259,7 @@
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    4095    __Pyx_GOTREF(__pyx_t_2);
    4096    __pyx_t_5 = PyTuple_New(2); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1723; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    4097 -  __Pyx_GOTREF(((PyObject *)__pyx_t_5));
    4098 +  __Pyx_GOTREF(__pyx_t_5);
    4099    __Pyx_INCREF(((PyObject *)__pyx_v_odfnum));
    4100    PyTuple_SET_ITEM(__pyx_t_5, 0, ((PyObject *)__pyx_v_odfnum));
    4101    __Pyx_GIVEREF(((PyObject *)__pyx_v_odfnum));
    4102 @@ -8887,7 +9271,7 @@
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    4105    __pyx_t_5 = PyTuple_New(1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1723; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    4106 -  __Pyx_GOTREF(((PyObject *)__pyx_t_5));
    4107 +  __Pyx_GOTREF(__pyx_t_5);
    4108    PyTuple_SET_ITEM(__pyx_t_5, 0, __pyx_t_2);
    4109    __Pyx_GIVEREF(__pyx_t_2);
    4110    __pyx_t_2 = 0;
    4111 @@ -8911,9 +9295,9 @@
    4112      __Pyx_Raise(__pyx_t_2, 0, 0, 0);
    4113      __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
    4114      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1724; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    4115 -    goto __pyx_L9;
    4116 +    goto __pyx_L6;
    4117    }
    4118 -  __pyx_L9:;
    4119 +  __pyx_L6:;
    4120  
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    4122   *         if np.any(np.less_equal(odfnum, 0.0)):
    4123 @@ -8935,7 +9319,7 @@
    4124    __pyx_t_2 = PyFloat_FromDouble(0.0); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1725; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    4125    __Pyx_GOTREF(__pyx_t_2);
    4126    __pyx_t_4 = PyTuple_New(2); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1725; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    4127 -  __Pyx_GOTREF(((PyObject *)__pyx_t_4));
    4128 +  __Pyx_GOTREF(__pyx_t_4);
    4129    __Pyx_INCREF(((PyObject *)__pyx_v_odfden));
    4130    PyTuple_SET_ITEM(__pyx_t_4, 0, ((PyObject *)__pyx_v_odfden));
    4131    __Pyx_GIVEREF(((PyObject *)__pyx_v_odfden));
    4132 @@ -8947,7 +9331,7 @@
    4133    __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
    4134    __Pyx_DECREF(((PyObject *)__pyx_t_4)); __pyx_t_4 = 0;
    4135    __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1725; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    4136 -  __Pyx_GOTREF(((PyObject *)__pyx_t_4));
    4137 +  __Pyx_GOTREF(__pyx_t_4);
    4138    PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_2);
    4139    __Pyx_GIVEREF(__pyx_t_2);
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    4142      __Pyx_Raise(__pyx_t_2, 0, 0, 0);
    4143      __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
    4144      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1726; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    4145 -    goto __pyx_L10;
    4146 +    goto __pyx_L7;
    4147    }
    4148 -  __pyx_L10:;
    4149 +  __pyx_L7:;
    4150  
    4151    /* "mtrand.pyx":1727
    4152   *         if np.any(np.less_equal(odfden, 0.0)):
    4153 @@ -8983,7 +9367,7 @@
    4154   *     def noncentral_f(self, dfnum, dfden, nonc, size=None):
    4155   */
    4156    __Pyx_XDECREF(__pyx_r);
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    4158 +  __pyx_t_2 = __pyx_f_6mtrand_cont2_array(__pyx_v_self->internal_state, rk_f, __pyx_v_size, __pyx_v_odfnum, __pyx_v_odfden); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1727; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    4159    __Pyx_GOTREF(__pyx_t_2);
    4160    __pyx_r = __pyx_t_2;
    4161    __pyx_t_2 = 0;
    4162 @@ -9006,45 +9390,33 @@
    4163    return __pyx_r;
    4164  }
    4165  
    4166 -/* "mtrand.pyx":1729
    4167 +/* Python wrapper */
    4168 +static PyObject *__pyx_pw_6mtrand_11RandomState_49noncentral_f(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    4169 +static char __pyx_doc_6mtrand_11RandomState_48noncentral_f[] = "\n        noncentral_f(dfnum, dfden, nonc, size=None)\n\n        Draw samples from the noncentral F distribution.\n\n        Samples are drawn from an F distribution with specified parameters,\n        `dfnum` (degrees of freedom in numerator) and `dfden` (degrees of\n        freedom in denominator), where both parameters > 1.\n        `nonc` is the non-centrality parameter.\n\n        Parameters\n        ----------\n        dfnum : int\n            Parameter, should be > 1.\n        dfden : int\n            Parameter, should be > 1.\n        nonc : float\n            Parameter, should be >= 0.\n        size : int or tuple of ints\n            Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : scalar or ndarray\n            Drawn samples.\n\n        Notes\n        -----\n        When calculating the power of an experiment (power = probability of\n        rejecting the null hypothesis when a specific alternative is true) the\n        non-central F statistic becomes important.  When the null hypothesis is\n        true, the F statistic follows a central F distribution. When the null\n        hypothesis is not true, then it follows a non-central F statistic.\n\n        References\n        ----------\n        Weisstein, Eric W. \"Noncentral F-Distribution.\" From MathWorld--A Wolfram\n        Web Resource.  http://mathworld.wolfram.com/NoncentralF-Distribution.html\n\n        Wikipedia, \"Noncentral F distribution\",\n        http://en.wikipedia.org/wiki/Noncentral_F-distribution\n\n        Examples\n        --------\n        In a study, testing for a specific alternative to the null hypothesis\n        requires use of the Noncentral F distribution. We need to calculate the\n        area in the tail of the distribution that exceeds the value of the F\n        distribution for the null hypothesis.  We'll plot the two probability\n        distributions for comp""arison.\n\n        >>> dfnum = 3 # between group deg of freedom\n        >>> dfden = 20 # within groups degrees of freedom\n        >>> nonc = 3.0\n        >>> nc_vals = np.random.noncentral_f(dfnum, dfden, nonc, 1000000)\n        >>> NF = np.histogram(nc_vals, bins=50, normed=True)\n        >>> c_vals = np.random.f(dfnum, dfden, 1000000)\n        >>> F = np.histogram(c_vals, bins=50, normed=True)\n        >>> plt.plot(F[1][1:], F[0])\n        >>> plt.plot(NF[1][1:], NF[0])\n        >>> plt.show()\n\n        ";
    4170 +static PyObject *__pyx_pw_6mtrand_11RandomState_49noncentral_f(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
    4171 +  PyObject *__pyx_v_dfnum = 0;
    4172 +  PyObject *__pyx_v_dfden = 0;
    4173 +  PyObject *__pyx_v_nonc = 0;
    4174 +  PyObject *__pyx_v_size = 0;
    4175 +  PyObject *__pyx_r = 0;
    4176 +  __Pyx_RefNannyDeclarations
    4177 +  __Pyx_RefNannySetupContext("noncentral_f (wrapper)", 0);
    4178 +  {
    4179 +    static PyObject **__pyx_pyargnames[] = {&__pyx_n_s__dfnum,&__pyx_n_s__dfden,&__pyx_n_s__nonc,&__pyx_n_s__size,0};
    4180 +    PyObject* values[4] = {0,0,0,0};
    4181 +
    4182 +    /* "mtrand.pyx":1729
    4183   *         return cont2_array(self.internal_state, rk_f, size, odfnum, odfden)
    4184   *
    4185   *     def noncentral_f(self, dfnum, dfden, nonc, size=None):             # <<<<<<<<<<<<<<
    4186   *         """
    4187   *         noncentral_f(dfnum, dfden, nonc, size=None)
    4188   */
    4189 -
    4190 -static PyObject *__pyx_pf_6mtrand_11RandomState_24noncentral_f(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    4191 -static char __pyx_doc_6mtrand_11RandomState_24noncentral_f[] = "\n        noncentral_f(dfnum, dfden, nonc, size=None)\n\n        Draw samples from the noncentral F distribution.\n\n        Samples are drawn from an F distribution with specified parameters,\n        `dfnum` (degrees of freedom in numerator) and `dfden` (degrees of\n        freedom in denominator), where both parameters > 1.\n        `nonc` is the non-centrality parameter.\n\n        Parameters\n        ----------\n        dfnum : int\n            Parameter, should be > 1.\n        dfden : int\n            Parameter, should be > 1.\n        nonc : float\n            Parameter, should be >= 0.\n        size : int or tuple of ints\n            Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : scalar or ndarray\n            Drawn samples.\n\n        Notes\n        -----\n        When calculating the power of an experiment (power = probability of\n        rejecting the null hypothesis when a specific alternative is true) the\n        non-central F statistic becomes important.  When the null hypothesis is\n        true, the F statistic follows a central F distribution. When the null\n        hypothesis is not true, then it follows a non-central F statistic.\n\n        References\n        ----------\n        Weisstein, Eric W. \"Noncentral F-Distribution.\" From MathWorld--A Wolfram\n        Web Resource.  http://mathworld.wolfram.com/NoncentralF-Distribution.html\n\n        Wikipedia, \"Noncentral F distribution\",\n        http://en.wikipedia.org/wiki/Noncentral_F-distribution\n\n        Examples\n        --------\n        In a study, testing for a specific alternative to the null hypothesis\n        requires use of the Noncentral F distribution. We need to calculate the\n        area in the tail of the distribution that exceeds the value of the F\n        distribution for the null hypothesis.  We'll plot the two probability\n        distributions for comp""arison.\n\n        >>> dfnum = 3 # between group deg of freedom\n        >>> dfden = 20 # within groups degrees of freedom\n        >>> nonc = 3.0\n        >>> nc_vals = np.random.noncentral_f(dfnum, dfden, nonc, 1000000)\n        >>> NF = np.histogram(nc_vals, bins=50, normed=True)\n        >>> c_vals = np.random.f(dfnum, dfden, 1000000)\n        >>> F = np.histogram(c_vals, bins=50, normed=True)\n        >>> plt.plot(F[1][1:], F[0])\n        >>> plt.plot(NF[1][1:], NF[0])\n        >>> plt.show()\n\n        ";
    4192 -static PyObject *__pyx_pf_6mtrand_11RandomState_24noncentral_f(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
    4193 -  PyObject *__pyx_v_dfnum = 0;
    4194 -  PyObject *__pyx_v_dfden = 0;
    4195 -  PyObject *__pyx_v_nonc = 0;
    4196 -  PyObject *__pyx_v_size = 0;
    4197 -  PyArrayObject *__pyx_v_odfnum = 0;
    4198 -  PyArrayObject *__pyx_v_odfden = 0;
    4199 -  PyArrayObject *__pyx_v_ononc = 0;
    4200 -  double __pyx_v_fdfnum;
    4201 -  double __pyx_v_fdfden;
    4202 -  double __pyx_v_fnonc;
    4203 -  PyObject *__pyx_r = NULL;
    4204 -  __Pyx_RefNannyDeclarations
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    4206 -  PyObject *__pyx_t_2 = NULL;
    4207 -  PyObject *__pyx_t_3 = NULL;
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    4209 -  PyObject *__pyx_t_5 = NULL;
    4210 -  int __pyx_lineno = 0;
    4211 -  const char *__pyx_filename = NULL;
    4212 -  int __pyx_clineno = 0;
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    4218      if (unlikely(__pyx_kwds)) {
    4219        Py_ssize_t kw_args;
    4220 -      switch (PyTuple_GET_SIZE(__pyx_args)) {
    4221 +      const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args);
    4222 +      switch (pos_args) {
    4223          case  4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3);
    4224          case  3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2);
    4225          case  2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1);
    4226 @@ -9053,20 +9425,17 @@
    4227          default: goto __pyx_L5_argtuple_error;
    4228        }
    4229        kw_args = PyDict_Size(__pyx_kwds);
    4230 -      switch (PyTuple_GET_SIZE(__pyx_args)) {
    4231 +      switch (pos_args) {
    4232          case  0:
    4233 -        values[0] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__dfnum);
    4234 -        if (likely(values[0])) kw_args--;
    4235 +        if (likely((values[0] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__dfnum)) != 0)) kw_args--;
    4236          else goto __pyx_L5_argtuple_error;
    4237          case  1:
    4238 -        values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__dfden);
    4239 -        if (likely(values[1])) kw_args--;
    4240 +        if (likely((values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__dfden)) != 0)) kw_args--;
    4241          else {
    4242            __Pyx_RaiseArgtupleInvalid("noncentral_f", 0, 3, 4, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1729; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
    4243          }
    4244          case  2:
    4245 -        values[2] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__nonc);
    4246 -        if (likely(values[2])) kw_args--;
    4247 +        if (likely((values[2] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__nonc)) != 0)) kw_args--;
    4248          else {
    4249            __Pyx_RaiseArgtupleInvalid("noncentral_f", 0, 3, 4, 2); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1729; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
    4250          }
    4251 @@ -9077,7 +9446,7 @@
    4252          }
    4253        }
    4254        if (unlikely(kw_args > 0)) {
    4255 -        if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, PyTuple_GET_SIZE(__pyx_args), "noncentral_f") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1729; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
    4256 +        if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "noncentral_f") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1729; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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    4259        switch (PyTuple_GET_SIZE(__pyx_args)) {
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    4265 +  __Pyx_RefNannyFinishContext();
    4266 +  return __pyx_r;
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    4271 +  PyArrayObject *__pyx_v_odfden = 0;
    4272 +  PyArrayObject *__pyx_v_ononc = 0;
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    4274 +  double __pyx_v_fdfden;
    4275 +  double __pyx_v_fnonc;
    4276 +  PyObject *__pyx_r = NULL;
    4277 +  __Pyx_RefNannyDeclarations
    4278 +  int __pyx_t_1;
    4279 +  PyObject *__pyx_t_2 = NULL;
    4280 +  PyObject *__pyx_t_3 = NULL;
    4281 +  PyObject *__pyx_t_4 = NULL;
    4282 +  PyObject *__pyx_t_5 = NULL;
    4283 +  int __pyx_lineno = 0;
    4284 +  const char *__pyx_filename = NULL;
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    4294 -      goto __pyx_L7;
    4295 +      goto __pyx_L4;
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    4297 -    __pyx_L7:;
    4298 +    __pyx_L4:;
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    4302 @@ -9188,9 +9580,9 @@
    4303        __Pyx_Raise(__pyx_t_2, 0, 0, 0);
    4304        __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
    4305        {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1803; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    4306 -      goto __pyx_L8;
    4307 +      goto __pyx_L5;
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    4309 -    __pyx_L8:;
    4310 +    __pyx_L5:;
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    4315        __Pyx_Raise(__pyx_t_2, 0, 0, 0);
    4316        __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
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    4318 -      goto __pyx_L9;
    4319 +      goto __pyx_L6;
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    4321 -    __pyx_L9:;
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    4328   *         PyErr_Clear()
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    4333      __pyx_r = __pyx_t_2;
    4334      __pyx_t_2 = 0;
    4335      goto __pyx_L0;
    4336 -    goto __pyx_L6;
    4337 +    goto __pyx_L3;
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    4339 -  __pyx_L6:;
    4340 +  __pyx_L3:;
    4341  
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    4343   *                                   fdfnum, fdfden, fnonc)
    4344 @@ -9311,7 +9703,7 @@
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    4346    __Pyx_GOTREF(__pyx_t_2);
    4347    __pyx_t_5 = PyTuple_New(2); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1815; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    4348 -  __Pyx_GOTREF(((PyObject *)__pyx_t_5));
    4349 +  __Pyx_GOTREF(__pyx_t_5);
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    4351    PyTuple_SET_ITEM(__pyx_t_5, 0, ((PyObject *)__pyx_v_odfnum));
    4352    __Pyx_GIVEREF(((PyObject *)__pyx_v_odfnum));
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    4355    __Pyx_DECREF(((PyObject *)__pyx_t_5)); __pyx_t_5 = 0;
    4356    __pyx_t_5 = PyTuple_New(1); if (unlikely(!__pyx_t_5)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1815; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    4357 -  __Pyx_GOTREF(((PyObject *)__pyx_t_5));
    4358 +  __Pyx_GOTREF(__pyx_t_5);
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    4360    __Pyx_GIVEREF(__pyx_t_2);
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    4365      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1816; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    4366 -    goto __pyx_L10;
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    4378 -  __Pyx_GOTREF(((PyObject *)__pyx_t_4));
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    4381    PyTuple_SET_ITEM(__pyx_t_4, 0, ((PyObject *)__pyx_v_odfden));
    4382    __Pyx_GIVEREF(((PyObject *)__pyx_v_odfden));
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    4387 -  __Pyx_GOTREF(((PyObject *)__pyx_t_4));
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    4390    __Pyx_GIVEREF(__pyx_t_2);
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    4396 -    goto __pyx_L11;
    4397 +    goto __pyx_L8;
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    4400 +  __pyx_L8:;
    4401  
    4402    /* "mtrand.pyx":1819
    4403   *         if np.any(np.less_equal(odfden, 0.0)):
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    4408 -  __Pyx_GOTREF(((PyObject *)__pyx_t_3));
    4409 +  __Pyx_GOTREF(__pyx_t_3);
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    4411    PyTuple_SET_ITEM(__pyx_t_3, 0, ((PyObject *)__pyx_v_ononc));
    4412    __Pyx_GIVEREF(((PyObject *)__pyx_v_ononc));
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    4414    __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
    4415    __Pyx_DECREF(((PyObject *)__pyx_t_3)); __pyx_t_3 = 0;
    4416    __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1819; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    4417 -  __Pyx_GOTREF(((PyObject *)__pyx_t_3));
    4418 +  __Pyx_GOTREF(__pyx_t_3);
    4419    PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_2);
    4420    __Pyx_GIVEREF(__pyx_t_2);
    4421    __pyx_t_2 = 0;
    4422 @@ -9467,9 +9859,9 @@
    4423      __Pyx_Raise(__pyx_t_2, 0, 0, 0);
    4424      __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
    4425      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1820; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    4426 -    goto __pyx_L12;
    4427 +    goto __pyx_L9;
    4428    }
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    4430 +  __pyx_L9:;
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    4433   *         if np.any(np.less(ononc, 0.0)):
    4434 @@ -9487,7 +9879,7 @@
    4435   *
    4436   *     def chisquare(self, df, size=None):
    4437   */
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    4439 +  __pyx_t_2 = __pyx_f_6mtrand_cont3_array(__pyx_v_self->internal_state, rk_noncentral_f, __pyx_v_size, __pyx_v_odfnum, __pyx_v_odfden, __pyx_v_ononc); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1821; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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    4442    __pyx_t_2 = 0;
    4443 @@ -9511,49 +9903,40 @@
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    4445  }
    4446  
    4447 -/* "mtrand.pyx":1824
    4448 +/* Python wrapper */
    4449 +static PyObject *__pyx_pw_6mtrand_11RandomState_51chisquare(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    4450 +static char __pyx_doc_6mtrand_11RandomState_50chisquare[] = "\n        chisquare(df, size=None)\n\n        Draw samples from a chi-square distribution.\n\n        When `df` independent random variables, each with standard normal\n        distributions (mean 0, variance 1), are squared and summed, the\n        resulting distribution is chi-square (see Notes).  This distribution\n        is often used in hypothesis testing.\n\n        Parameters\n        ----------\n        df : int\n             Number of degrees of freedom.\n        size : tuple of ints, int, optional\n             Size of the returned array.  By default, a scalar is\n             returned.\n\n        Returns\n        -------\n        output : ndarray\n            Samples drawn from the distribution, packed in a `size`-shaped\n            array.\n\n        Raises\n        ------\n        ValueError\n            When `df` <= 0 or when an inappropriate `size` (e.g. ``size=-1``)\n            is given.\n\n        Notes\n        -----\n        The variable obtained by summing the squares of `df` independent,\n        standard normally distributed random variables:\n\n        .. math:: Q = \\sum_{i=0}^{\\mathtt{df}} X^2_i\n\n        is chi-square distributed, denoted\n\n        .. math:: Q \\sim \\chi^2_k.\n\n        The probability density function of the chi-squared distribution is\n\n        .. math:: p(x) = \\frac{(1/2)^{k/2}}{\\Gamma(k/2)}\n                         x^{k/2 - 1} e^{-x/2},\n\n        where :math:`\\Gamma` is the gamma function,\n\n        .. math:: \\Gamma(x) = \\int_0^{-\\infty} t^{x - 1} e^{-t} dt.\n\n        References\n        ----------\n        `NIST/SEMATECH e-Handbook of Statistical Methods\n        <http://www.itl.nist.gov/div898/handbook/eda/section3/eda3666.htm>`_\n\n        Examples\n        --------\n        >>> np.random.chisquare(2,4)\n        array([ 1.89920014,  9.00867716,  3.13710533,  5.62318272])\n\n        ";
    4451 +static PyObject *__pyx_pw_6mtrand_11RandomState_51chisquare(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
    4452 +  PyObject *__pyx_v_df = 0;
    4453 +  PyObject *__pyx_v_size = 0;
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    4455 +  __Pyx_RefNannyDeclarations
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    4459 +    PyObject* values[2] = {0,0};
    4460 +
    4461 +    /* "mtrand.pyx":1824
    4462   *             odfden, ononc)
    4463   *
    4464   *     def chisquare(self, df, size=None):             # <<<<<<<<<<<<<<
    4465   *         """
    4466   *         chisquare(df, size=None)
    4467   */
    4468 -
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    4470 -static char __pyx_doc_6mtrand_11RandomState_25chisquare[] = "\n        chisquare(df, size=None)\n\n        Draw samples from a chi-square distribution.\n\n        When `df` independent random variables, each with standard normal\n        distributions (mean 0, variance 1), are squared and summed, the\n        resulting distribution is chi-square (see Notes).  This distribution\n        is often used in hypothesis testing.\n\n        Parameters\n        ----------\n        df : int\n             Number of degrees of freedom.\n        size : tuple of ints, int, optional\n             Size of the returned array.  By default, a scalar is\n             returned.\n\n        Returns\n        -------\n        output : ndarray\n            Samples drawn from the distribution, packed in a `size`-shaped\n            array.\n\n        Raises\n        ------\n        ValueError\n            When `df` <= 0 or when an inappropriate `size` (e.g. ``size=-1``)\n            is given.\n\n        Notes\n        -----\n        The variable obtained by summing the squares of `df` independent,\n        standard normally distributed random variables:\n\n        .. math:: Q = \\sum_{i=0}^{\\mathtt{df}} X^2_i\n\n        is chi-square distributed, denoted\n\n        .. math:: Q \\sim \\chi^2_k.\n\n        The probability density function of the chi-squared distribution is\n\n        .. math:: p(x) = \\frac{(1/2)^{k/2}}{\\Gamma(k/2)}\n                         x^{k/2 - 1} e^{-x/2},\n\n        where :math:`\\Gamma` is the gamma function,\n\n        .. math:: \\Gamma(x) = \\int_0^{-\\infty} t^{x - 1} e^{-t} dt.\n\n        References\n        ----------\n        `NIST/SEMATECH e-Handbook of Statistical Methods\n        <http://www.itl.nist.gov/div898/handbook/eda/section3/eda3666.htm>`_\n\n        Examples\n        --------\n        >>> np.random.chisquare(2,4)\n        array([ 1.89920014,  9.00867716,  3.13710533,  5.62318272])\n\n        ";
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    4493 -      switch (PyTuple_GET_SIZE(__pyx_args)) {
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    4495 +      switch (pos_args) {
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    4502 -      switch (PyTuple_GET_SIZE(__pyx_args)) {
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    4605   *         if np.any(np.less_equal(odf, 0.0)):
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    4607   *     def noncentral_chisquare(self, df, nonc, size=None):
    4608   */
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    4611 +  __pyx_t_2 = __pyx_f_6mtrand_cont1_array(__pyx_v_self->internal_state, rk_chisquare, __pyx_v_size, __pyx_v_odf); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1900; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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    4618  
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    4620 - *         return cont1_array(self.internal_state, rk_chisquare, size, odf)
    4621 - *
    4622 - *     def noncentral_chisquare(self, df, nonc, size=None):             # <<<<<<<<<<<<<<
    4623 - *         """
    4624 - *         noncentral_chisquare(df, nonc, size=None)
    4625 - */
    4626 -
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    4628 -static char __pyx_doc_6mtrand_11RandomState_26noncentral_chisquare[] = "\n        noncentral_chisquare(df, nonc, size=None)\n\n        Draw samples from a noncentral chi-square distribution.\n\n        The noncentral :math:`\\chi^2` distribution is a generalisation of\n        the :math:`\\chi^2` distribution.\n\n        Parameters\n        ----------\n        df : int\n            Degrees of freedom, should be >= 1.\n        nonc : float\n            Non-centrality, should be > 0.\n        size : int or tuple of ints\n            Shape of the output.\n\n        Notes\n        -----\n        The probability density function for the noncentral Chi-square distribution\n        is\n\n        .. math:: P(x;df,nonc) = \\sum^{\\infty}_{i=0}\n                               \\frac{e^{-nonc/2}(nonc/2)^{i}}{i!}P_{Y_{df+2i}}(x),\n\n        where :math:`Y_{q}` is the Chi-square with q degrees of freedom.\n\n        In Delhi (2007), it is noted that the noncentral chi-square is useful in\n        bombing and coverage problems, the probability of killing the point target\n        given by the noncentral chi-squared distribution.\n\n        References\n        ----------\n        .. [1] Delhi, M.S. Holla, \"On a noncentral chi-square distribution in the\n               analysis of weapon systems effectiveness\", Metrika, Volume 15,\n               Number 1 / December, 1970.\n        .. [2] Wikipedia, \"Noncentral chi-square distribution\"\n               http://en.wikipedia.org/wiki/Noncentral_chi-square_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram\n\n        >>> import matplotlib.pyplot as plt\n        >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),\n        ...                   bins=200, normed=True)\n        >>> plt.show()\n\n        Draw values from a noncentral chisquare with very small noncentrality,\n        and compare to a chisquare.\n\n        >>> plt.figure()\n        >>> values = plt.hist(np.random.noncentral_chisquare(3, .0000001, 100000),\n     ""   ...                   bins=np.arange(0., 25, .1), normed=True)\n        >>> values2 = plt.hist(np.random.chisquare(3, 100000),\n        ...                    bins=np.arange(0., 25, .1), normed=True)\n        >>> plt.plot(values[1][0:-1], values[0]-values2[0], 'ob')\n        >>> plt.show()\n\n        Demonstrate how large values of non-centrality lead to a more symmetric\n        distribution.\n\n        >>> plt.figure()\n        >>> values = plt.hist(np.random.noncentral_chisquare(3, 20, 100000),\n        ...                   bins=200, normed=True)\n        >>> plt.show()\n\n        ";
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    4630 +/* Python wrapper */
    4631 +static PyObject *__pyx_pw_6mtrand_11RandomState_53noncentral_chisquare(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    4632 +static char __pyx_doc_6mtrand_11RandomState_52noncentral_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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    4639 -  double __pyx_v_fdf;
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    4657      PyObject* values[3] = {0,0,0};
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    4660 + *         return cont1_array(self.internal_state, rk_chisquare, size, odf)
    4661 + *
    4662 + *     def noncentral_chisquare(self, df, nonc, size=None):             # <<<<<<<<<<<<<<
    4663 + *         """
    4664 + *         noncentral_chisquare(df, nonc, size=None)
    4665 + */
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    4669 -      switch (PyTuple_GET_SIZE(__pyx_args)) {
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    4683 -        if (likely(values[0])) kw_args--;
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    4831   *         if np.any(np.less_equal(ononc, 0.0)):
    4832 @@ -10119,7 +10530,7 @@
    4833   *
    4834   *     def standard_cauchy(self, size=None):
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    4843  }
    4844  
    4845 -/* "mtrand.pyx":1994
    4846 +/* Python wrapper */
    4847 +static PyObject *__pyx_pw_6mtrand_11RandomState_55standard_cauchy(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    4848 +static char __pyx_doc_6mtrand_11RandomState_54standard_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        ";
    4849 +static PyObject *__pyx_pw_6mtrand_11RandomState_55standard_cauchy(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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    4852 +  __Pyx_RefNannyDeclarations
    4853 +  __Pyx_RefNannySetupContext("standard_cauchy (wrapper)", 0);
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    4857 +
    4858 +    /* "mtrand.pyx":1994
    4859   *             odf, ononc)
    4860   *
    4861   *     def standard_cauchy(self, size=None):             # <<<<<<<<<<<<<<
    4862   *         """
    4863   *         standard_cauchy(size=None)
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    4941 +static char __pyx_doc_6mtrand_11RandomState_56standard_t[] = "\n        standard_t(df, size=None)\n\n        Standard Student's t distribution with df degrees of freedom.\n\n        A special case of the hyperbolic distribution.\n        As `df` gets large, the result resembles that of the standard normal\n        distribution (`standard_normal`).\n\n        Parameters\n        ----------\n        df : int\n            Degrees of freedom, should be > 0.\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single value is\n            returned.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            Drawn samples.\n\n        Notes\n        -----\n        The probability density function for the t distribution is\n\n        .. math:: P(x, df) = \\frac{\\Gamma(\\frac{df+1}{2})}{\\sqrt{\\pi df}\n                  \\Gamma(\\frac{df}{2})}\\Bigl( 1+\\frac{x^2}{df} \\Bigr)^{-(df+1)/2}\n\n        The t test is based on an assumption that the data come from a Normal\n        distribution. The t test provides a way to test whether the sample mean\n        (that is the mean calculated from the data) is a good estimate of the true\n        mean.\n\n        The derivation of the t-distribution was forst published in 1908 by William\n        Gisset while working for the Guinness Brewery in Dublin. Due to proprietary\n        issues, he had to publish under a pseudonym, and so he used the name\n        Student.\n\n        References\n        ----------\n        .. [1] Dalgaard, Peter, \"Introductory Statistics With R\",\n               Springer, 2002.\n        .. [2] Wikipedia, \"Student's t-distribution\"\n               http://en.wikipedia.org/wiki/Student's_t-distribution\n\n        Examples\n        --------\n        From Dalgaard page 83 [1]_, suppose the daily energy intake for 11\n        women in Kj is:\n\n        >>> intake = np.array([5260., 5470, 5640, 6180, 6390, 6515, 6805, 7515, \\\n        ...                    7515, 8230, 8770])\n\n        Doe""s their energy intake deviate systematically from the recommended\n        value of 7725 kJ?\n\n        We have 10 degrees of freedom, so is the sample mean within 95% of the\n        recommended value?\n\n        >>> s = np.random.standard_t(10, size=100000)\n        >>> np.mean(intake)\n        6753.636363636364\n        >>> intake.std(ddof=1)\n        1142.1232221373727\n\n        Calculate the t statistic, setting the ddof parameter to the unbiased\n        value so the divisor in the standard deviation will be degrees of\n        freedom, N-1.\n\n        >>> t = (np.mean(intake)-7725)/(intake.std(ddof=1)/np.sqrt(len(intake)))\n        >>> import matplotlib.pyplot as plt\n        >>> h = plt.hist(s, bins=100, normed=True)\n\n        For a one-sided t-test, how far out in the distribution does the t\n        statistic appear?\n\n        >>> >>> np.sum(s<t) / float(len(s))\n        0.0090699999999999999  #random\n\n        So the p-value is about 0.009, which says the null hypothesis has a\n        probability of about 99% of being true.\n\n        ";
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    5098   *     def vonmises(self, mu, kappa, size=None):
    5099   */
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    5111 - *         return cont1_array(self.internal_state, rk_standard_t, size, odf)
    5112 - *
    5113 - *     def vonmises(self, mu, kappa, size=None):             # <<<<<<<<<<<<<<
    5114 - *         """
    5115 - *         vonmises(mu, kappa, size=None)
    5116 - */
    5117 -
    5118 -static PyObject *__pyx_pf_6mtrand_11RandomState_29vonmises(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    5119 -static char __pyx_doc_6mtrand_11RandomState_29vonmises[] = "\n        vonmises(mu, kappa, size=None)\n\n        Draw samples from a von Mises distribution.\n\n        Samples are drawn from a von Mises distribution with specified mode\n        (mu) and dispersion (kappa), on the interval [-pi, pi].\n\n        The von Mises distribution (also known as the circular normal\n        distribution) is a continuous probability distribution on the unit\n        circle.  It may be thought of as the circular analogue of the normal\n        distribution.\n\n        Parameters\n        ----------\n        mu : float\n            Mode (\"center\") of the distribution.\n        kappa : float\n            Dispersion of the distribution, has to be >=0.\n        size : int or tuple of int\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : scalar or ndarray\n            The returned samples, which are in the interval [-pi, pi].\n\n        See Also\n        --------\n        scipy.stats.distributions.vonmises : probability density function,\n            distribution, or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the von Mises distribution is\n\n        .. math:: p(x) = \\frac{e^{\\kappa cos(x-\\mu)}}{2\\pi I_0(\\kappa)},\n\n        where :math:`\\mu` is the mode and :math:`\\kappa` the dispersion,\n        and :math:`I_0(\\kappa)` is the modified Bessel function of order 0.\n\n        The von Mises is named for Richard Edler von Mises, who was born in\n        Austria-Hungary, in what is now the Ukraine.  He fled to the United\n        States in 1939 and became a professor at Harvard.  He worked in\n        probability theory, aerodynamics, fluid mechanics, and philosophy of\n        science.\n\n        References\n        ----------\n        Abramowitz, M. and Stegun, I. A. (ed.), *Handbook of Mathematical\n        Functions*, New York: Dover, 1965.\n\n      ""  von Mises, R., *Mathematical Theory of Probability and Statistics*,\n        New York: Academic Press, 1964.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, kappa = 0.0, 4.0 # mean and dispersion\n        >>> s = np.random.vonmises(mu, kappa, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> import scipy.special as sps\n        >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n        >>> x = np.arange(-np.pi, np.pi, 2*np.pi/50.)\n        >>> y = -np.exp(kappa*np.cos(x-mu))/(2*np.pi*sps.jn(0,kappa))\n        >>> plt.plot(x, y/max(y), linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
    5120 -static PyObject *__pyx_pf_6mtrand_11RandomState_29vonmises(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
    5121 +/* Python wrapper */
    5122 +static PyObject *__pyx_pw_6mtrand_11RandomState_59vonmises(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    5123 +static char __pyx_doc_6mtrand_11RandomState_58vonmises[] = "\n        vonmises(mu, kappa, size=None)\n\n        Draw samples from a von Mises distribution.\n\n        Samples are drawn from a von Mises distribution with specified mode\n        (mu) and dispersion (kappa), on the interval [-pi, pi].\n\n        The von Mises distribution (also known as the circular normal\n        distribution) is a continuous probability distribution on the unit\n        circle.  It may be thought of as the circular analogue of the normal\n        distribution.\n\n        Parameters\n        ----------\n        mu : float\n            Mode (\"center\") of the distribution.\n        kappa : float\n            Dispersion of the distribution, has to be >=0.\n        size : int or tuple of int\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : scalar or ndarray\n            The returned samples, which are in the interval [-pi, pi].\n\n        See Also\n        --------\n        scipy.stats.distributions.vonmises : probability density function,\n            distribution, or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the von Mises distribution is\n\n        .. math:: p(x) = \\frac{e^{\\kappa cos(x-\\mu)}}{2\\pi I_0(\\kappa)},\n\n        where :math:`\\mu` is the mode and :math:`\\kappa` the dispersion,\n        and :math:`I_0(\\kappa)` is the modified Bessel function of order 0.\n\n        The von Mises is named for Richard Edler von Mises, who was born in\n        Austria-Hungary, in what is now the Ukraine.  He fled to the United\n        States in 1939 and became a professor at Harvard.  He worked in\n        probability theory, aerodynamics, fluid mechanics, and philosophy of\n        science.\n\n        References\n        ----------\n        Abramowitz, M. and Stegun, I. A. (ed.), *Handbook of Mathematical\n        Functions*, New York: Dover, 1965.\n\n      ""  von Mises, R., *Mathematical Theory of Probability and Statistics*,\n        New York: Academic Press, 1964.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, kappa = 0.0, 4.0 # mean and dispersion\n        >>> s = np.random.vonmises(mu, kappa, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> import scipy.special as sps\n        >>> count, bins, ignored = plt.hist(s, 50, normed=True)\n        >>> x = np.arange(-np.pi, np.pi, 2*np.pi/50.)\n        >>> y = -np.exp(kappa*np.cos(x-mu))/(2*np.pi*sps.jn(0,kappa))\n        >>> plt.plot(x, y/max(y), linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
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    5129 -  PyArrayObject *__pyx_v_okappa = 0;
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    5131 -  double __pyx_v_fkappa;
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    5148      PyObject* values[3] = {0,0,0};
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    5151 + *         return cont1_array(self.internal_state, rk_standard_t, size, odf)
    5152 + *
    5153 + *     def vonmises(self, mu, kappa, size=None):             # <<<<<<<<<<<<<<
    5154 + *         """
    5155 + *         vonmises(mu, kappa, size=None)
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    5159        Py_ssize_t kw_args;
    5160 -      switch (PyTuple_GET_SIZE(__pyx_args)) {
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    5162 +      switch (pos_args) {
    5163          case  3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2);
    5164          case  2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1);
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    5170 -      switch (PyTuple_GET_SIZE(__pyx_args)) {
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    5176          else goto __pyx_L5_argtuple_error;
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    5204 +  PyArrayObject *__pyx_v_okappa = 0;
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    5213 +  PyObject *__pyx_t_5 = NULL;
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    5286 +  __pyx_t_2 = __pyx_f_6mtrand_cont2_array(__pyx_v_self->internal_state, rk_vonmises, __pyx_v_size, __pyx_v_omu, __pyx_v_okappa); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2248; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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    5295 +/* Python wrapper */
    5296 +static PyObject *__pyx_pw_6mtrand_11RandomState_61pareto(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    5297 +static char __pyx_doc_6mtrand_11RandomState_60pareto[] = "\n        pareto(a, size=None)\n\n        Draw samples from a Pareto II or Lomax distribution with specified shape.\n\n        The Lomax or Pareto II distribution is a shifted Pareto distribution. The\n        classical Pareto distribution can be obtained from the Lomax distribution\n        by adding the location parameter m, see below. The smallest value of the\n        Lomax distribution is zero while for the classical Pareto distribution it\n        is m, where the standard Pareto distribution has location m=1.\n        Lomax can also be considered as a simplified version of the Generalized\n        Pareto distribution (available in SciPy), with the scale set to one and\n        the location set to zero.\n\n        The Pareto distribution must be greater than zero, and is unbounded above.\n        It is also known as the \"80-20 rule\".  In this distribution, 80 percent of\n        the weights are in the lowest 20 percent of the range, while the other 20\n        percent fill the remaining 80 percent of the range.\n\n        Parameters\n        ----------\n        shape : float, > 0.\n            Shape of the distribution.\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        See Also\n        --------\n        scipy.stats.distributions.lomax.pdf : probability density function,\n            distribution or cumulative density function, etc.\n        scipy.stats.distributions.genpareto.pdf : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Pareto distribution is\n\n        .. math:: p(x) = \\frac{am^a}{x^{a+1}}\n\n        where :math:`a` is the shape and :math:`m` the location\n\n        The Pareto distribution, named after the Italian economist Vilfredo Pareto,\n        is a power law probability distribution useful in many real world probl""ems.\n        Outside the field of economics it is generally referred to as the Bradford\n        distribution. Pareto developed the distribution to describe the\n        distribution of wealth in an economy.  It has also found use in insurance,\n        web page access statistics, oil field sizes, and many other problems,\n        including the download frequency for projects in Sourceforge [1].  It is\n        one of the so-called \"fat-tailed\" distributions.\n\n\n        References\n        ----------\n        .. [1] Francis Hunt and Paul Johnson, On the Pareto Distribution of\n               Sourceforge projects.\n        .. [2] Pareto, V. (1896). Course of Political Economy. Lausanne.\n        .. [3] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme\n               Values, Birkhauser Verlag, Basel, pp 23-30.\n        .. [4] Wikipedia, \"Pareto distribution\",\n               http://en.wikipedia.org/wiki/Pareto_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a, m = 3., 1. # shape and mode\n        >>> s = np.random.pareto(a, 1000) + m\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='center')\n        >>> fit = a*m**a/bins**(a+1)\n        >>> plt.plot(bins, max(count)*fit/max(fit),linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
    5298 +static PyObject *__pyx_pw_6mtrand_11RandomState_61pareto(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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    5309   *         return cont2_array(self.internal_state, rk_vonmises, size, omu, okappa)
    5310   *
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    5313   *         pareto(a, size=None)
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    5317 -static char __pyx_doc_6mtrand_11RandomState_30pareto[] = "\n        pareto(a, size=None)\n\n        Draw samples from a Pareto II or Lomax distribution with specified shape.\n\n        The Lomax or Pareto II distribution is a shifted Pareto distribution. The\n        classical Pareto distribution can be obtained from the Lomax distribution\n        by adding the location parameter m, see below. The smallest value of the\n        Lomax distribution is zero while for the classical Pareto distribution it\n        is m, where the standard Pareto distribution has location m=1.\n        Lomax can also be considered as a simplified version of the Generalized\n        Pareto distribution (available in SciPy), with the scale set to one and\n        the location set to zero.\n\n        The Pareto distribution must be greater than zero, and is unbounded above.\n        It is also known as the \"80-20 rule\".  In this distribution, 80 percent of\n        the weights are in the lowest 20 percent of the range, while the other 20\n        percent fill the remaining 80 percent of the range.\n\n        Parameters\n        ----------\n        shape : float, > 0.\n            Shape of the distribution.\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        See Also\n        --------\n        scipy.stats.distributions.lomax.pdf : probability density function,\n            distribution or cumulative density function, etc.\n        scipy.stats.distributions.genpareto.pdf : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Pareto distribution is\n\n        .. math:: p(x) = \\frac{am^a}{x^{a+1}}\n\n        where :math:`a` is the shape and :math:`m` the location\n\n        The Pareto distribution, named after the Italian economist Vilfredo Pareto,\n        is a power law probability distribution useful in many real world probl""ems.\n        Outside the field of economics it is generally referred to as the Bradford\n        distribution. Pareto developed the distribution to describe the\n        distribution of wealth in an economy.  It has also found use in insurance,\n        web page access statistics, oil field sizes, and many other problems,\n        including the download frequency for projects in Sourceforge [1].  It is\n        one of the so-called \"fat-tailed\" distributions.\n\n\n        References\n        ----------\n        .. [1] Francis Hunt and Paul Johnson, On the Pareto Distribution of\n               Sourceforge projects.\n        .. [2] Pareto, V. (1896). Course of Political Economy. Lausanne.\n        .. [3] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme\n               Values, Birkhauser Verlag, Basel, pp 23-30.\n        .. [4] Wikipedia, \"Pareto distribution\",\n               http://en.wikipedia.org/wiki/Pareto_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a, m = 3., 1. # shape and mode\n        >>> s = np.random.pareto(a, 1000) + m\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='center')\n        >>> fit = a*m**a/bins**(a+1)\n        >>> plt.plot(bins, max(count)*fit/max(fit),linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
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    5469 +static char __pyx_doc_6mtrand_11RandomState_62weibull[] = "\n        weibull(a, size=None)\n\n        Weibull distribution.\n\n        Draw samples from a 1-parameter Weibull distribution with the given\n        shape parameter `a`.\n\n        .. math:: X = (-ln(U))^{1/a}\n\n        Here, U is drawn from the uniform distribution over (0,1].\n\n        The more common 2-parameter Weibull, including a scale parameter\n        :math:`\\lambda` is just :math:`X = \\lambda(-ln(U))^{1/a}`.\n\n        Parameters\n        ----------\n        a : float\n            Shape of the distribution.\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        See Also\n        --------\n        scipy.stats.distributions.weibull : probability density function,\n            distribution or cumulative density function, etc.\n\n        gumbel, scipy.stats.distributions.genextreme\n\n        Notes\n        -----\n        The Weibull (or Type III asymptotic extreme value distribution for smallest\n        values, SEV Type III, or Rosin-Rammler distribution) is one of a class of\n        Generalized Extreme Value (GEV) distributions used in modeling extreme\n        value problems.  This class includes the Gumbel and Frechet distributions.\n\n        The probability density for the Weibull distribution is\n\n        .. math:: p(x) = \\frac{a}\n                         {\\lambda}(\\frac{x}{\\lambda})^{a-1}e^{-(x/\\lambda)^a},\n\n        where :math:`a` is the shape and :math:`\\lambda` the scale.\n\n        The function has its peak (the mode) at\n        :math:`\\lambda(\\frac{a-1}{a})^{1/a}`.\n\n        When ``a = 1``, the Weibull distribution reduces to the exponential\n        distribution.\n\n        References\n        ----------\n        .. [1] Waloddi Weibull, Professor, Royal Technical University, Stockholm,\n               1939 \"A Statistical Theory Of The Strength Of Materials\",\n               Ingeniorsvetenskapsakademiens Handlingar"" Nr 151, 1939,\n               Generalstabens Litografiska Anstalts Forlag, Stockholm.\n        .. [2] Waloddi Weibull, 1951 \"A Statistical Distribution Function of Wide\n               Applicability\",  Journal Of Applied Mechanics ASME Paper.\n        .. [3] Wikipedia, \"Weibull distribution\",\n               http://en.wikipedia.org/wiki/Weibull_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a = 5. # shape\n        >>> s = np.random.weibull(a, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> x = np.arange(1,100.)/50.\n        >>> def weib(x,n,a):\n        ...     return (a / n) * (x / n)**(a - 1) * np.exp(-(x / n)**a)\n\n        >>> count, bins, ignored = plt.hist(np.random.weibull(5.,1000))\n        >>> x = np.arange(1,100.)/50.\n        >>> scale = count.max()/weib(x, 1., 5.).max()\n        >>> plt.plot(x, weib(x, 1., 5.)*scale)\n        >>> plt.show()\n\n        ";
    5470 +static PyObject *__pyx_pw_6mtrand_11RandomState_63weibull(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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    5481   *         return cont1_array(self.internal_state, rk_pareto, size, oa)
    5482   *
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    5485   *         weibull(a, size=None)
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    5489 -static char __pyx_doc_6mtrand_11RandomState_31weibull[] = "\n        weibull(a, size=None)\n\n        Weibull distribution.\n\n        Draw samples from a 1-parameter Weibull distribution with the given\n        shape parameter `a`.\n\n        .. math:: X = (-ln(U))^{1/a}\n\n        Here, U is drawn from the uniform distribution over (0,1].\n\n        The more common 2-parameter Weibull, including a scale parameter\n        :math:`\\lambda` is just :math:`X = \\lambda(-ln(U))^{1/a}`.\n\n        Parameters\n        ----------\n        a : float\n            Shape of the distribution.\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        See Also\n        --------\n        scipy.stats.distributions.weibull : probability density function,\n            distribution or cumulative density function, etc.\n\n        gumbel, scipy.stats.distributions.genextreme\n\n        Notes\n        -----\n        The Weibull (or Type III asymptotic extreme value distribution for smallest\n        values, SEV Type III, or Rosin-Rammler distribution) is one of a class of\n        Generalized Extreme Value (GEV) distributions used in modeling extreme\n        value problems.  This class includes the Gumbel and Frechet distributions.\n\n        The probability density for the Weibull distribution is\n\n        .. math:: p(x) = \\frac{a}\n                         {\\lambda}(\\frac{x}{\\lambda})^{a-1}e^{-(x/\\lambda)^a},\n\n        where :math:`a` is the shape and :math:`\\lambda` the scale.\n\n        The function has its peak (the mode) at\n        :math:`\\lambda(\\frac{a-1}{a})^{1/a}`.\n\n        When ``a = 1``, the Weibull distribution reduces to the exponential\n        distribution.\n\n        References\n        ----------\n        .. [1] Waloddi Weibull, Professor, Royal Technical University, Stockholm,\n               1939 \"A Statistical Theory Of The Strength Of Materials\",\n               Ingeniorsvetenskapsakademiens Handlingar"" Nr 151, 1939,\n               Generalstabens Litografiska Anstalts Forlag, Stockholm.\n        .. [2] Waloddi Weibull, 1951 \"A Statistical Distribution Function of Wide\n               Applicability\",  Journal Of Applied Mechanics ASME Paper.\n        .. [3] Wikipedia, \"Weibull distribution\",\n               http://en.wikipedia.org/wiki/Weibull_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a = 5. # shape\n        >>> s = np.random.weibull(a, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> x = np.arange(1,100.)/50.\n        >>> def weib(x,n,a):\n        ...     return (a / n) * (x / n)**(a - 1) * np.exp(-(x / n)**a)\n\n        >>> count, bins, ignored = plt.hist(np.random.weibull(5.,1000))\n        >>> x = np.arange(1,100.)/50.\n        >>> scale = count.max()/weib(x, 1., 5.).max()\n        >>> plt.plot(x, weib(x, 1., 5.)*scale)\n        >>> plt.show()\n\n        ";
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    5641 +static char __pyx_doc_6mtrand_11RandomState_64power[] = "\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        ";
    5642 +static PyObject *__pyx_pw_6mtrand_11RandomState_65power(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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    5650 +    PyObject* values[2] = {0,0};
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    5653   *         return cont1_array(self.internal_state, rk_weibull, size, oa)
    5654   *
    5655   *     def power(self, a, size=None):             # <<<<<<<<<<<<<<
    5656   *         """
    5657   *         power(a, size=None)
    5658   */
    5659 -
    5660 -static PyObject *__pyx_pf_6mtrand_11RandomState_32power(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    5661 -static char __pyx_doc_6mtrand_11RandomState_32power[] = "\n        power(a, size=None)\n\n        Draws samples in [0, 1] from a power distribution with positive\n        exponent a - 1.\n\n        Also known as the power function distribution.\n\n        Parameters\n        ----------\n        a : float\n            parameter, > 0\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n                    ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n            The returned samples lie in [0, 1].\n\n        Raises\n        ------\n        ValueError\n            If a<1.\n\n        Notes\n        -----\n        The probability density function is\n\n        .. math:: P(x; a) = ax^{a-1}, 0 \\le x \\le 1, a>0.\n\n        The power function distribution is just the inverse of the Pareto\n        distribution. It may also be seen as a special case of the Beta\n        distribution.\n\n        It is used, for example, in modeling the over-reporting of insurance\n        claims.\n\n        References\n        ----------\n        .. [1] Christian Kleiber, Samuel Kotz, \"Statistical size distributions\n               in economics and actuarial sciences\", Wiley, 2003.\n        .. [2] Heckert, N. A. and Filliben, James J. (2003). NIST Handbook 148:\n               Dataplot Reference Manual, Volume 2: Let Subcommands and Library\n               Functions\", National Institute of Standards and Technology Handbook\n               Series, June 2003.\n               http://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/powpdf.pdf\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a = 5. # shape\n        >>> samples = 1000\n        >>> s = np.random.power(a, samples)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, bins=""30)\n        >>> x = np.linspace(0, 1, 100)\n        >>> y = a*x**(a-1.)\n        >>> normed_y = samples*np.diff(bins)[0]*y\n        >>> plt.plot(x, normed_y)\n        >>> plt.show()\n\n        Compare the power function distribution to the inverse of the Pareto.\n\n        >>> from scipy import stats\n        >>> rvs = np.random.power(5, 1000000)\n        >>> rvsp = np.random.pareto(5, 1000000)\n        >>> xx = np.linspace(0,1,100)\n        >>> powpdf = stats.powerlaw.pdf(xx,5)\n\n        >>> plt.figure()\n        >>> plt.hist(rvs, bins=50, normed=True)\n        >>> plt.plot(xx,powpdf,'r-')\n        >>> plt.title('np.random.power(5)')\n\n        >>> plt.figure()\n        >>> plt.hist(1./(1.+rvsp), bins=50, normed=True)\n        >>> plt.plot(xx,powpdf,'r-')\n        >>> plt.title('inverse of 1 + np.random.pareto(5)')\n\n        >>> plt.figure()\n        >>> plt.hist(1./(1.+rvsp), bins=50, normed=True)\n        >>> plt.plot(xx,powpdf,'r-')\n        >>> plt.title('inverse of stats.pareto(5)')\n\n        ";
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    5802 +  __pyx_t_2 = __pyx_f_6mtrand_cont1_array(__pyx_v_self->internal_state, rk_power, __pyx_v_size, __pyx_v_oa); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2553; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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    5819 -static char __pyx_doc_6mtrand_11RandomState_33laplace[] = "\n        laplace(loc=0.0, scale=1.0, size=None)\n\n        Draw samples from the Laplace or double exponential distribution with\n        specified location (or mean) and scale (decay).\n\n        The Laplace distribution is similar to the Gaussian/normal distribution,\n        but is sharper at the peak and has fatter tails. It represents the\n        difference between two independent, identically distributed exponential\n        random variables.\n\n        Parameters\n        ----------\n        loc : float\n            The position, :math:`\\mu`, of the distribution peak.\n        scale : float\n            :math:`\\lambda`, the exponential decay.\n\n        Notes\n        -----\n        It has the probability density function\n\n        .. math:: f(x; \\mu, \\lambda) = \\frac{1}{2\\lambda}\n                                       \\exp\\left(-\\frac{|x - \\mu|}{\\lambda}\\right).\n\n        The first law of Laplace, from 1774, states that the frequency of an error\n        can be expressed as an exponential function of the absolute magnitude of\n        the error, which leads to the Laplace distribution. For many problems in\n        Economics and Health sciences, this distribution seems to model the data\n        better than the standard Gaussian distribution\n\n\n        References\n        ----------\n        .. [1] Abramowitz, M. and Stegun, I. A. (Eds.). Handbook of Mathematical\n               Functions with Formulas, Graphs, and Mathematical Tables, 9th\n               printing.  New York: Dover, 1972.\n\n        .. [2] The Laplace distribution and generalizations\n               By Samuel Kotz, Tomasz J. Kozubowski, Krzysztof Podgorski,\n               Birkhauser, 2001.\n\n        .. [3] Weisstein, Eric W. \"Laplace Distribution.\"\n               From MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/LaplaceDistribution.html\n\n        .. [4] Wikipedia, \"Laplace distribution\",\n               http://en.wikipedia.org/wik""i/Laplace_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution\n\n        >>> loc, scale = 0., 1.\n        >>> s = np.random.laplace(loc, scale, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n        >>> x = np.arange(-8., 8., .01)\n        >>> pdf = np.exp(-abs(x-loc/scale))/(2.*scale)\n        >>> plt.plot(x, pdf)\n\n        Plot Gaussian for comparison:\n\n        >>> g = (1/(scale * np.sqrt(2 * np.pi)) * \n        ...      np.exp( - (x - loc)**2 / (2 * scale**2) ))\n        >>> plt.plot(x,g)\n\n        ";
    5820 -static PyObject *__pyx_pf_6mtrand_11RandomState_33laplace(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
    5821 +/* Python wrapper */
    5822 +static PyObject *__pyx_pw_6mtrand_11RandomState_67laplace(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    5823 +static char __pyx_doc_6mtrand_11RandomState_66laplace[] = "\n        laplace(loc=0.0, scale=1.0, size=None)\n\n        Draw samples from the Laplace or double exponential distribution with\n        specified location (or mean) and scale (decay).\n\n        The Laplace distribution is similar to the Gaussian/normal distribution,\n        but is sharper at the peak and has fatter tails. It represents the\n        difference between two independent, identically distributed exponential\n        random variables.\n\n        Parameters\n        ----------\n        loc : float\n            The position, :math:`\\mu`, of the distribution peak.\n        scale : float\n            :math:`\\lambda`, the exponential decay.\n\n        Notes\n        -----\n        It has the probability density function\n\n        .. math:: f(x; \\mu, \\lambda) = \\frac{1}{2\\lambda}\n                                       \\exp\\left(-\\frac{|x - \\mu|}{\\lambda}\\right).\n\n        The first law of Laplace, from 1774, states that the frequency of an error\n        can be expressed as an exponential function of the absolute magnitude of\n        the error, which leads to the Laplace distribution. For many problems in\n        Economics and Health sciences, this distribution seems to model the data\n        better than the standard Gaussian distribution\n\n\n        References\n        ----------\n        .. [1] Abramowitz, M. and Stegun, I. A. (Eds.). Handbook of Mathematical\n               Functions with Formulas, Graphs, and Mathematical Tables, 9th\n               printing.  New York: Dover, 1972.\n\n        .. [2] The Laplace distribution and generalizations\n               By Samuel Kotz, Tomasz J. Kozubowski, Krzysztof Podgorski,\n               Birkhauser, 2001.\n\n        .. [3] Weisstein, Eric W. \"Laplace Distribution.\"\n               From MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/LaplaceDistribution.html\n\n        .. [4] Wikipedia, \"Laplace distribution\",\n               http://en.wikipedia.org/wik""i/Laplace_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution\n\n        >>> loc, scale = 0., 1.\n        >>> s = np.random.laplace(loc, scale, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n        >>> x = np.arange(-8., 8., .01)\n        >>> pdf = np.exp(-abs(x-loc/scale))/(2.*scale)\n        >>> plt.plot(x, pdf)\n\n        Plot Gaussian for comparison:\n\n        >>> g = (1/(scale * np.sqrt(2 * np.pi)) * \n        ...      np.exp( - (x - loc)**2 / (2 * scale**2) ))\n        >>> plt.plot(x,g)\n\n        ";
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    5853 + *         return cont1_array(self.internal_state, rk_power, size, oa)
    5854 + *
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    5856 + *         """
    5857 + *         laplace(loc=0.0, scale=1.0, size=None)
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    5861        Py_ssize_t kw_args;
    5862 -      switch (PyTuple_GET_SIZE(__pyx_args)) {
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    5864 +      switch (pos_args) {
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    5866          case  2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1);
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    5906 +  PyObject *__pyx_t_5 = NULL;
    5907 +  int __pyx_lineno = 0;
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    5973   *         if np.any(np.less_equal(oscale, 0.0)):
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    5979 +  __pyx_t_2 = __pyx_f_6mtrand_cont2_array(__pyx_v_self->internal_state, rk_laplace, __pyx_v_size, __pyx_v_oloc, __pyx_v_oscale); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2643; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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    5988 - *         return cont2_array(self.internal_state, rk_laplace, size, oloc, oscale)
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    5994 -
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    5996 -static char __pyx_doc_6mtrand_11RandomState_34gumbel[] = "\n        gumbel(loc=0.0, scale=1.0, size=None)\n\n        Gumbel distribution.\n\n        Draw samples from a Gumbel distribution with specified location and scale.\n        For more information on the Gumbel distribution, see Notes and References\n        below.\n\n        Parameters\n        ----------\n        loc : float\n            The location of the mode of the distribution.\n        scale : float\n            The scale parameter of the distribution.\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray\n            The samples\n\n        See Also\n        --------\n        scipy.stats.gumbel_l\n        scipy.stats.gumbel_r\n        scipy.stats.genextreme\n            probability density function, distribution, or cumulative density\n            function, etc. for each of the above\n        weibull\n\n        Notes\n        -----\n        The Gumbel (or Smallest Extreme Value (SEV) or the Smallest Extreme Value\n        Type I) distribution is one of a class of Generalized Extreme Value (GEV)\n        distributions used in modeling extreme value problems.  The Gumbel is a\n        special case of the Extreme Value Type I distribution for maximums from\n        distributions with \"exponential-like\" tails.\n\n        The probability density for the Gumbel distribution is\n\n        .. math:: p(x) = \\frac{e^{-(x - \\mu)/ \\beta}}{\\beta} e^{ -e^{-(x - \\mu)/\n                  \\beta}},\n\n        where :math:`\\mu` is the mode, a location parameter, and :math:`\\beta` is\n        the scale parameter.\n\n        The Gumbel (named for German mathematician Emil Julius Gumbel) was used\n        very early in the hydrology literature, for modeling the occurrence of\n        flood events. It is also used for modeling maximum wind speed and rainfall\n        rates.  It is a \"fat-tailed\" distribution - the ""probability of an event in\n        the tail of the distribution is larger than if one used a Gaussian, hence\n        the surprisingly frequent occurrence of 100-year floods. Floods were\n        initially modeled as a Gaussian process, which underestimated the frequency\n        of extreme events.\n\n\n        It is one of a class of extreme value distributions, the Generalized\n        Extreme Value (GEV) distributions, which also includes the Weibull and\n        Frechet.\n\n        The function has a mean of :math:`\\mu + 0.57721\\beta` and a variance of\n        :math:`\\frac{\\pi^2}{6}\\beta^2`.\n\n        References\n        ----------\n        Gumbel, E. J., *Statistics of Extremes*, New York: Columbia University\n        Press, 1958.\n\n        Reiss, R.-D. and Thomas, M., *Statistical Analysis of Extreme Values from\n        Insurance, Finance, Hydrology and Other Fields*, Basel: Birkhauser Verlag,\n        2001.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, beta = 0, 0.1 # location and scale\n        >>> s = np.random.gumbel(mu, beta, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n        >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n        ...          * np.exp( -np.exp( -(bins - mu) /beta) ),\n        ...          linewidth=2, color='r')\n        >>> plt.show()\n\n        Show how an extreme value distribution can arise from a Gaussian process\n        and compare to a Gaussian:\n\n        >>> means = []\n        >>> maxima = []\n        >>> for i in range(0,1000) :\n        ...    a = np.random.normal(mu, beta, 1000)\n        ...    means.append(a.mean())\n        ...    maxima.append(a.max())\n        >>> count, bins, ignored = plt.hist(maxima, 30, normed=True)\n        >>> beta = np.std(maxima)*np.pi/np.sqrt(6)""\n        >>> mu = np.mean(maxima) - 0.57721*beta\n        >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n        ...          * np.exp(-np.exp(-(bins - mu)/beta)),\n        ...          linewidth=2, color='r')\n        >>> plt.plot(bins, 1/(beta * np.sqrt(2 * np.pi))\n        ...          * np.exp(-(bins - mu)**2 / (2 * beta**2)),\n        ...          linewidth=2, color='g')\n        >>> plt.show()\n\n        ";
    5997 -static PyObject *__pyx_pf_6mtrand_11RandomState_34gumbel(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
    5998 +/* Python wrapper */
    5999 +static PyObject *__pyx_pw_6mtrand_11RandomState_69gumbel(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    6000 +static char __pyx_doc_6mtrand_11RandomState_68gumbel[] = "\n        gumbel(loc=0.0, scale=1.0, size=None)\n\n        Gumbel distribution.\n\n        Draw samples from a Gumbel distribution with specified location and scale.\n        For more information on the Gumbel distribution, see Notes and References\n        below.\n\n        Parameters\n        ----------\n        loc : float\n            The location of the mode of the distribution.\n        scale : float\n            The scale parameter of the distribution.\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        out : ndarray\n            The samples\n\n        See Also\n        --------\n        scipy.stats.gumbel_l\n        scipy.stats.gumbel_r\n        scipy.stats.genextreme\n            probability density function, distribution, or cumulative density\n            function, etc. for each of the above\n        weibull\n\n        Notes\n        -----\n        The Gumbel (or Smallest Extreme Value (SEV) or the Smallest Extreme Value\n        Type I) distribution is one of a class of Generalized Extreme Value (GEV)\n        distributions used in modeling extreme value problems.  The Gumbel is a\n        special case of the Extreme Value Type I distribution for maximums from\n        distributions with \"exponential-like\" tails.\n\n        The probability density for the Gumbel distribution is\n\n        .. math:: p(x) = \\frac{e^{-(x - \\mu)/ \\beta}}{\\beta} e^{ -e^{-(x - \\mu)/\n                  \\beta}},\n\n        where :math:`\\mu` is the mode, a location parameter, and :math:`\\beta` is\n        the scale parameter.\n\n        The Gumbel (named for German mathematician Emil Julius Gumbel) was used\n        very early in the hydrology literature, for modeling the occurrence of\n        flood events. It is also used for modeling maximum wind speed and rainfall\n        rates.  It is a \"fat-tailed\" distribution - the ""probability of an event in\n        the tail of the distribution is larger than if one used a Gaussian, hence\n        the surprisingly frequent occurrence of 100-year floods. Floods were\n        initially modeled as a Gaussian process, which underestimated the frequency\n        of extreme events.\n\n\n        It is one of a class of extreme value distributions, the Generalized\n        Extreme Value (GEV) distributions, which also includes the Weibull and\n        Frechet.\n\n        The function has a mean of :math:`\\mu + 0.57721\\beta` and a variance of\n        :math:`\\frac{\\pi^2}{6}\\beta^2`.\n\n        References\n        ----------\n        Gumbel, E. J., *Statistics of Extremes*, New York: Columbia University\n        Press, 1958.\n\n        Reiss, R.-D. and Thomas, M., *Statistical Analysis of Extreme Values from\n        Insurance, Finance, Hydrology and Other Fields*, Basel: Birkhauser Verlag,\n        2001.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, beta = 0, 0.1 # location and scale\n        >>> s = np.random.gumbel(mu, beta, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n        >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n        ...          * np.exp( -np.exp( -(bins - mu) /beta) ),\n        ...          linewidth=2, color='r')\n        >>> plt.show()\n\n        Show how an extreme value distribution can arise from a Gaussian process\n        and compare to a Gaussian:\n\n        >>> means = []\n        >>> maxima = []\n        >>> for i in range(0,1000) :\n        ...    a = np.random.normal(mu, beta, 1000)\n        ...    means.append(a.mean())\n        ...    maxima.append(a.max())\n        >>> count, bins, ignored = plt.hist(maxima, 30, normed=True)\n        >>> beta = np.std(maxima)*np.pi/np.sqrt(6)""\n        >>> mu = np.mean(maxima) - 0.57721*beta\n        >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n        ...          * np.exp(-np.exp(-(bins - mu)/beta)),\n        ...          linewidth=2, color='r')\n        >>> plt.plot(bins, 1/(beta * np.sqrt(2 * np.pi))\n        ...          * np.exp(-(bins - mu)**2 / (2 * beta**2)),\n        ...          linewidth=2, color='g')\n        >>> plt.show()\n\n        ";
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    6026      values[0] = __pyx_k_77;
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    6030 + *         return cont2_array(self.internal_state, rk_laplace, size, oloc, oscale)
    6031 + *
    6032 + *     def gumbel(self, loc=0.0, scale=1.0, size=None):             # <<<<<<<<<<<<<<
    6033 + *         """
    6034 + *         gumbel(loc=0.0, scale=1.0, size=None)
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    6036      values[2] = ((PyObject *)Py_None);
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    6038        Py_ssize_t kw_args;
    6039 -      switch (PyTuple_GET_SIZE(__pyx_args)) {
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    6041 +      switch (pos_args) {
    6042          case  3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2);
    6043          case  2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1);
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    6074 +  PyArrayObject *__pyx_v_oscale = 0;
    6075 +  double __pyx_v_floc;
    6076 +  double __pyx_v_fscale;
    6077 +  PyObject *__pyx_r = NULL;
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    6080 +  PyObject *__pyx_t_2 = NULL;
    6081 +  PyObject *__pyx_t_3 = NULL;
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    6083 +  PyObject *__pyx_t_5 = NULL;
    6084 +  int __pyx_lineno = 0;
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    6104   *         PyErr_Clear()
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    6150   *         if np.any(np.less_equal(oscale, 0.0)):
    6151 @@ -12042,7 +12535,7 @@
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    6156 +  __pyx_t_2 = __pyx_f_6mtrand_cont2_array(__pyx_v_self->internal_state, rk_gumbel, __pyx_v_size, __pyx_v_oloc, __pyx_v_oscale); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2774; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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    6165 - *         return cont2_array(self.internal_state, rk_gumbel, size, oloc, oscale)
    6166 - *
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    6169 - *         logistic(loc=0.0, scale=1.0, size=None)
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    6171 -
    6172 -static PyObject *__pyx_pf_6mtrand_11RandomState_35logistic(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    6173 -static char __pyx_doc_6mtrand_11RandomState_35logistic[] = "\n        logistic(loc=0.0, scale=1.0, size=None)\n\n        Draw samples from a Logistic distribution.\n\n        Samples are drawn from a Logistic distribution with specified\n        parameters, loc (location or mean, also median), and scale (>0).\n\n        Parameters\n        ----------\n        loc : float\n\n        scale : float > 0.\n\n        size : {tuple, int}\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n                  where the values are all integers in  [0, n].\n\n        See Also\n        --------\n        scipy.stats.distributions.logistic : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Logistic distribution is\n\n        .. math:: P(x) = P(x) = \\frac{e^{-(x-\\mu)/s}}{s(1+e^{-(x-\\mu)/s})^2},\n\n        where :math:`\\mu` = location and :math:`s` = scale.\n\n        The Logistic distribution is used in Extreme Value problems where it\n        can act as a mixture of Gumbel distributions, in Epidemiology, and by\n        the World Chess Federation (FIDE) where it is used in the Elo ranking\n        system, assuming the performance of each player is a logistically\n        distributed random variable.\n\n        References\n        ----------\n        .. [1] Reiss, R.-D. and Thomas M. (2001), Statistical Analysis of Extreme\n               Values, from Insurance, Finance, Hydrology and Other Fields,\n               Birkhauser Verlag, Basel, pp 132-133.\n        .. [2] Weisstein, Eric W. \"Logistic Distribution.\" From\n               MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/LogisticDistribution.html\n        .. [3] Wikipedia, \"Logistic-distribution\",\n               http://en.wikipedia.org/wiki/Logistic-distribution\n\n        Examples\n   ""     --------\n        Draw samples from the distribution:\n\n        >>> loc, scale = 10, 1\n        >>> s = np.random.logistic(loc, scale, 10000)\n        >>> count, bins, ignored = plt.hist(s, bins=50)\n\n        #   plot against distribution\n\n        >>> def logist(x, loc, scale):\n        ...     return exp((loc-x)/scale)/(scale*(1+exp((loc-x)/scale))**2)\n        >>> plt.plot(bins, logist(bins, loc, scale)*count.max()/\\\n        ... logist(bins, loc, scale).max())\n        >>> plt.show()\n\n        ";
    6174 -static PyObject *__pyx_pf_6mtrand_11RandomState_35logistic(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
    6175 +/* Python wrapper */
    6176 +static PyObject *__pyx_pw_6mtrand_11RandomState_71logistic(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    6177 +static char __pyx_doc_6mtrand_11RandomState_70logistic[] = "\n        logistic(loc=0.0, scale=1.0, size=None)\n\n        Draw samples from a Logistic distribution.\n\n        Samples are drawn from a Logistic distribution with specified\n        parameters, loc (location or mean, also median), and scale (>0).\n\n        Parameters\n        ----------\n        loc : float\n\n        scale : float > 0.\n\n        size : {tuple, int}\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n                  where the values are all integers in  [0, n].\n\n        See Also\n        --------\n        scipy.stats.distributions.logistic : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Logistic distribution is\n\n        .. math:: P(x) = P(x) = \\frac{e^{-(x-\\mu)/s}}{s(1+e^{-(x-\\mu)/s})^2},\n\n        where :math:`\\mu` = location and :math:`s` = scale.\n\n        The Logistic distribution is used in Extreme Value problems where it\n        can act as a mixture of Gumbel distributions, in Epidemiology, and by\n        the World Chess Federation (FIDE) where it is used in the Elo ranking\n        system, assuming the performance of each player is a logistically\n        distributed random variable.\n\n        References\n        ----------\n        .. [1] Reiss, R.-D. and Thomas M. (2001), Statistical Analysis of Extreme\n               Values, from Insurance, Finance, Hydrology and Other Fields,\n               Birkhauser Verlag, Basel, pp 132-133.\n        .. [2] Weisstein, Eric W. \"Logistic Distribution.\" From\n               MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/LogisticDistribution.html\n        .. [3] Wikipedia, \"Logistic-distribution\",\n               http://en.wikipedia.org/wiki/Logistic-distribution\n\n        Examples\n   ""     --------\n        Draw samples from the distribution:\n\n        >>> loc, scale = 10, 1\n        >>> s = np.random.logistic(loc, scale, 10000)\n        >>> count, bins, ignored = plt.hist(s, bins=50)\n\n        #   plot against distribution\n\n        >>> def logist(x, loc, scale):\n        ...     return exp((loc-x)/scale)/(scale*(1+exp((loc-x)/scale))**2)\n        >>> plt.plot(bins, logist(bins, loc, scale)*count.max()/\\\n        ... logist(bins, loc, scale).max())\n        >>> plt.show()\n\n        ";
    6178 +static PyObject *__pyx_pw_6mtrand_11RandomState_71logistic(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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    6180    PyObject *__pyx_v_scale = 0;
    6181    PyObject *__pyx_v_size = 0;
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    6185 -  double __pyx_v_fscale;
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    6194 -  int __pyx_lineno = 0;
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    6204      values[1] = __pyx_k_82;
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    6207 + *         return cont2_array(self.internal_state, rk_gumbel, size, oloc, oscale)
    6208 + *
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    6210 + *         """
    6211 + *         logistic(loc=0.0, scale=1.0, size=None)
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    6215        Py_ssize_t kw_args;
    6216 -      switch (PyTuple_GET_SIZE(__pyx_args)) {
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    6218 +      switch (pos_args) {
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    6220          case  2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1);
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    6251 +  PyArrayObject *__pyx_v_oscale = 0;
    6252 +  double __pyx_v_floc;
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    6260 +  PyObject *__pyx_t_5 = NULL;
    6261 +  int __pyx_lineno = 0;
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    6327   *         if np.any(np.less_equal(oscale, 0.0)):
    6328 @@ -12326,7 +12830,7 @@
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    6330   */
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    6342 - *         return cont2_array(self.internal_state, rk_logistic, size, oloc, oscale)
    6343 - *
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    6347 - */
    6348 -
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    6350 -static char __pyx_doc_6mtrand_11RandomState_36lognormal[] = "\n        lognormal(mean=0.0, sigma=1.0, size=None)\n\n        Return samples drawn from a log-normal distribution.\n\n        Draw samples from a log-normal distribution with specified mean, standard\n        deviation, and shape. Note that the mean and standard deviation are not the\n        values for the distribution itself, but of the underlying normal\n        distribution it is derived from.\n\n\n        Parameters\n        ----------\n        mean : float\n            Mean value of the underlying normal distribution\n        sigma : float, >0.\n            Standard deviation of the underlying normal distribution\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        See Also\n        --------\n        scipy.stats.lognorm : probability density function, distribution,\n            cumulative density function, etc.\n\n        Notes\n        -----\n        A variable `x` has a log-normal distribution if `log(x)` is normally\n        distributed.\n\n        The probability density function for the log-normal distribution is\n\n        .. math:: p(x) = \\frac{1}{\\sigma x \\sqrt{2\\pi}}\n                         e^{(-\\frac{(ln(x)-\\mu)^2}{2\\sigma^2})}\n\n        where :math:`\\mu` is the mean and :math:`\\sigma` is the standard deviation\n        of the normally distributed logarithm of the variable.\n\n        A log-normal distribution results if a random variable is the *product* of\n        a large number of independent, identically-distributed variables in the\n        same way that a normal distribution results if the variable is the *sum*\n        of a large number of independent, identically-distributed variables\n        (see the last example). It is one of the so-called \"fat-tailed\"\n        distributions.\n\n        The log-normal distribution is commonly used to model the lifespan of units\n        with fatigue-stress failure modes. Since thi""s includes\n        most mechanical systems, the log-normal distribution has widespread\n        application.\n\n        It is also commonly used to model oil field sizes, species abundance, and\n        latent periods of infectious diseases.\n\n        References\n        ----------\n        .. [1] Eckhard Limpert, Werner A. Stahel, and Markus Abbt, \"Log-normal\n               Distributions across the Sciences: Keys and Clues\", May 2001\n               Vol. 51 No. 5 BioScience\n               http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf\n        .. [2] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme\n               Values, Birkhauser Verlag, Basel, pp 31-32.\n        .. [3] Wikipedia, \"Lognormal distribution\",\n               http://en.wikipedia.org/wiki/Lognormal_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, sigma = 3., 1. # mean and standard deviation\n        >>> s = np.random.lognormal(mu, sigma, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='mid')\n\n        >>> x = np.linspace(min(bins), max(bins), 10000)\n        >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n        ...        / (x * sigma * np.sqrt(2 * np.pi)))\n\n        >>> plt.plot(x, pdf, linewidth=2, color='r')\n        >>> plt.axis('tight')\n        >>> plt.show()\n\n        Demonstrate that taking the products of random samples from a uniform\n        distribution can be fit well by a log-normal probability density function.\n\n        >>> # Generate a thousand samples: each is the product of 100 random\n        >>> # values, drawn from a normal distribution.\n        >>> b = []\n        >>> for i in range(1000):\n        ...    a = 10. + np.random.random(100)\n        ...    b.append(np.product(a))\n\n        >>> b"" = np.array(b) / np.min(b) # scale values to be positive\n\n        >>> count, bins, ignored = plt.hist(b, 100, normed=True, align='center')\n\n        >>> sigma = np.std(np.log(b))\n        >>> mu = np.mean(np.log(b))\n\n        >>> x = np.linspace(min(bins), max(bins), 10000)\n        >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n        ...        / (x * sigma * np.sqrt(2 * np.pi)))\n\n        >>> plt.plot(x, pdf, color='r', linewidth=2)\n        >>> plt.show()\n\n        ";
    6351 -static PyObject *__pyx_pf_6mtrand_11RandomState_36lognormal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
    6352 +/* Python wrapper */
    6353 +static PyObject *__pyx_pw_6mtrand_11RandomState_73lognormal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    6354 +static char __pyx_doc_6mtrand_11RandomState_72lognormal[] = "\n        lognormal(mean=0.0, sigma=1.0, size=None)\n\n        Return samples drawn from a log-normal distribution.\n\n        Draw samples from a log-normal distribution with specified mean, standard\n        deviation, and shape. Note that the mean and standard deviation are not the\n        values for the distribution itself, but of the underlying normal\n        distribution it is derived from.\n\n\n        Parameters\n        ----------\n        mean : float\n            Mean value of the underlying normal distribution\n        sigma : float, >0.\n            Standard deviation of the underlying normal distribution\n        size : tuple of ints\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        See Also\n        --------\n        scipy.stats.lognorm : probability density function, distribution,\n            cumulative density function, etc.\n\n        Notes\n        -----\n        A variable `x` has a log-normal distribution if `log(x)` is normally\n        distributed.\n\n        The probability density function for the log-normal distribution is\n\n        .. math:: p(x) = \\frac{1}{\\sigma x \\sqrt{2\\pi}}\n                         e^{(-\\frac{(ln(x)-\\mu)^2}{2\\sigma^2})}\n\n        where :math:`\\mu` is the mean and :math:`\\sigma` is the standard deviation\n        of the normally distributed logarithm of the variable.\n\n        A log-normal distribution results if a random variable is the *product* of\n        a large number of independent, identically-distributed variables in the\n        same way that a normal distribution results if the variable is the *sum*\n        of a large number of independent, identically-distributed variables\n        (see the last example). It is one of the so-called \"fat-tailed\"\n        distributions.\n\n        The log-normal distribution is commonly used to model the lifespan of units\n        with fatigue-stress failure modes. Since thi""s includes\n        most mechanical systems, the log-normal distribution has widespread\n        application.\n\n        It is also commonly used to model oil field sizes, species abundance, and\n        latent periods of infectious diseases.\n\n        References\n        ----------\n        .. [1] Eckhard Limpert, Werner A. Stahel, and Markus Abbt, \"Log-normal\n               Distributions across the Sciences: Keys and Clues\", May 2001\n               Vol. 51 No. 5 BioScience\n               http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf\n        .. [2] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme\n               Values, Birkhauser Verlag, Basel, pp 31-32.\n        .. [3] Wikipedia, \"Lognormal distribution\",\n               http://en.wikipedia.org/wiki/Lognormal_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> mu, sigma = 3., 1. # mean and standard deviation\n        >>> s = np.random.lognormal(mu, sigma, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 100, normed=True, align='mid')\n\n        >>> x = np.linspace(min(bins), max(bins), 10000)\n        >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n        ...        / (x * sigma * np.sqrt(2 * np.pi)))\n\n        >>> plt.plot(x, pdf, linewidth=2, color='r')\n        >>> plt.axis('tight')\n        >>> plt.show()\n\n        Demonstrate that taking the products of random samples from a uniform\n        distribution can be fit well by a log-normal probability density function.\n\n        >>> # Generate a thousand samples: each is the product of 100 random\n        >>> # values, drawn from a normal distribution.\n        >>> b = []\n        >>> for i in range(1000):\n        ...    a = 10. + np.random.random(100)\n        ...    b.append(np.product(a))\n\n        >>> b"" = np.array(b) / np.min(b) # scale values to be positive\n\n        >>> count, bins, ignored = plt.hist(b, 100, normed=True, align='center')\n\n        >>> sigma = np.std(np.log(b))\n        >>> mu = np.mean(np.log(b))\n\n        >>> x = np.linspace(min(bins), max(bins), 10000)\n        >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n        ...        / (x * sigma * np.sqrt(2 * np.pi)))\n\n        >>> plt.plot(x, pdf, color='r', linewidth=2)\n        >>> plt.show()\n\n        ";
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    6362 -  double __pyx_v_fsigma;
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    6380      values[0] = __pyx_k_85;
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    6384 + *         return cont2_array(self.internal_state, rk_logistic, size, oloc, oscale)
    6385 + *
    6386 + *     def lognormal(self, mean=0.0, sigma=1.0, size=None):             # <<<<<<<<<<<<<<
    6387 + *         """
    6388 + *         lognormal(mean=0.0, sigma=1.0, size=None)
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    6390      values[2] = ((PyObject *)Py_None);
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    6392        Py_ssize_t kw_args;
    6393 -      switch (PyTuple_GET_SIZE(__pyx_args)) {
    6394 +      const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args);
    6395 +      switch (pos_args) {
    6396          case  3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2);
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    6403 -      switch (PyTuple_GET_SIZE(__pyx_args)) {
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    6427 +  PyArrayObject *__pyx_v_omean = 0;
    6428 +  PyArrayObject *__pyx_v_osigma = 0;
    6429 +  double __pyx_v_fmean;
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    6431 +  PyObject *__pyx_r = NULL;
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    6504   *         if np.any(np.less_equal(osigma, 0.0)):
    6505 @@ -12610,7 +13125,7 @@
    6506   *     def rayleigh(self, scale=1.0, size=None):
    6507   */
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    6519 - *         return cont2_array(self.internal_state, rk_lognormal, size, omean, osigma)
    6520 - *
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    6524 - */
    6525 -
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    6527 -static char __pyx_doc_6mtrand_11RandomState_37rayleigh[] = "\n        rayleigh(scale=1.0, size=None)\n\n        Draw samples from a Rayleigh distribution.\n\n        The :math:`\\chi` and Weibull distributions are generalizations of the\n        Rayleigh.\n\n        Parameters\n        ----------\n        scale : scalar\n            Scale, also equals the mode. Should be >= 0.\n        size : int or tuple of ints, optional\n            Shape of the output. Default is None, in which case a single\n            value is returned.\n\n        Notes\n        -----\n        The probability density function for the Rayleigh distribution is\n\n        .. math:: P(x;scale) = \\frac{x}{scale^2}e^{\\frac{-x^2}{2 \\cdotp scale^2}}\n\n        The Rayleigh distribution arises if the wind speed and wind direction are\n        both gaussian variables, then the vector wind velocity forms a Rayleigh\n        distribution. The Rayleigh distribution is used to model the expected\n        output from wind turbines.\n\n        References\n        ----------\n        ..[1] Brighton Webs Ltd., Rayleigh Distribution,\n              http://www.brighton-webs.co.uk/distributions/rayleigh.asp\n        ..[2] Wikipedia, \"Rayleigh distribution\"\n              http://en.wikipedia.org/wiki/Rayleigh_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram\n\n        >>> values = hist(np.random.rayleigh(3, 100000), bins=200, normed=True)\n\n        Wave heights tend to follow a Rayleigh distribution. If the mean wave\n        height is 1 meter, what fraction of waves are likely to be larger than 3\n        meters?\n\n        >>> meanvalue = 1\n        >>> modevalue = np.sqrt(2 / np.pi) * meanvalue\n        >>> s = np.random.rayleigh(modevalue, 1000000)\n\n        The percentage of waves larger than 3 meters is:\n\n        >>> 100.*sum(s>3)/1000000.\n        0.087300000000000003\n\n        ";
    6528 -static PyObject *__pyx_pf_6mtrand_11RandomState_37rayleigh(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
    6529 +/* Python wrapper */
    6530 +static PyObject *__pyx_pw_6mtrand_11RandomState_75rayleigh(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    6531 +static char __pyx_doc_6mtrand_11RandomState_74rayleigh[] = "\n        rayleigh(scale=1.0, size=None)\n\n        Draw samples from a Rayleigh distribution.\n\n        The :math:`\\chi` and Weibull distributions are generalizations of the\n        Rayleigh.\n\n        Parameters\n        ----------\n        scale : scalar\n            Scale, also equals the mode. Should be >= 0.\n        size : int or tuple of ints, optional\n            Shape of the output. Default is None, in which case a single\n            value is returned.\n\n        Notes\n        -----\n        The probability density function for the Rayleigh distribution is\n\n        .. math:: P(x;scale) = \\frac{x}{scale^2}e^{\\frac{-x^2}{2 \\cdotp scale^2}}\n\n        The Rayleigh distribution arises if the wind speed and wind direction are\n        both gaussian variables, then the vector wind velocity forms a Rayleigh\n        distribution. The Rayleigh distribution is used to model the expected\n        output from wind turbines.\n\n        References\n        ----------\n        ..[1] Brighton Webs Ltd., Rayleigh Distribution,\n              http://www.brighton-webs.co.uk/distributions/rayleigh.asp\n        ..[2] Wikipedia, \"Rayleigh distribution\"\n              http://en.wikipedia.org/wiki/Rayleigh_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram\n\n        >>> values = hist(np.random.rayleigh(3, 100000), bins=200, normed=True)\n\n        Wave heights tend to follow a Rayleigh distribution. If the mean wave\n        height is 1 meter, what fraction of waves are likely to be larger than 3\n        meters?\n\n        >>> meanvalue = 1\n        >>> modevalue = np.sqrt(2 / np.pi) * meanvalue\n        >>> s = np.random.rayleigh(modevalue, 1000000)\n\n        The percentage of waves larger than 3 meters is:\n\n        >>> 100.*sum(s>3)/1000000.\n        0.087300000000000003\n\n        ";
    6532 +static PyObject *__pyx_pw_6mtrand_11RandomState_75rayleigh(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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    6534    PyObject *__pyx_v_size = 0;
    6535 -  PyArrayObject *__pyx_v_oscale = 0;
    6536 -  double __pyx_v_fscale;
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    6553      PyObject* values[2] = {0,0};
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    6557 + *         return cont2_array(self.internal_state, rk_lognormal, size, omean, osigma)
    6558 + *
    6559 + *     def rayleigh(self, scale=1.0, size=None):             # <<<<<<<<<<<<<<
    6560 + *         """
    6561 + *         rayleigh(scale=1.0, size=None)
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    6674   *         if np.any(np.less_equal(oscale, 0.0)):
    6675 @@ -12860,7 +13386,7 @@
    6676   *     def wald(self, mean, scale, size=None):
    6677   */
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    6689 - *         return cont1_array(self.internal_state, rk_rayleigh, size, oscale)
    6690 - *
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    6692 - *         """
    6693 - *         wald(mean, scale, size=None)
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    6695 -
    6696 -static PyObject *__pyx_pf_6mtrand_11RandomState_38wald(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    6697 -static char __pyx_doc_6mtrand_11RandomState_38wald[] = "\n        wald(mean, scale, size=None)\n\n        Draw samples from a Wald, or Inverse Gaussian, distribution.\n\n        As the scale approaches infinity, the distribution becomes more like a\n        Gaussian.\n\n        Some references claim that the Wald is an Inverse Gaussian with mean=1, but\n        this is by no means universal.\n\n        The Inverse Gaussian distribution was first studied in relationship to\n        Brownian motion. In 1956 M.C.K. Tweedie used the name Inverse Gaussian\n        because there is an inverse relationship between the time to cover a unit\n        distance and distance covered in unit time.\n\n        Parameters\n        ----------\n        mean : scalar\n            Distribution mean, should be > 0.\n        scale : scalar\n            Scale parameter, should be >= 0.\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single value is\n            returned.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            Drawn sample, all greater than zero.\n\n        Notes\n        -----\n        The probability density function for the Wald distribution is\n\n        .. math:: P(x;mean,scale) = \\sqrt{\\frac{scale}{2\\pi x^3}}e^\n                                    \\frac{-scale(x-mean)^2}{2\\cdotp mean^2x}\n\n        As noted above the Inverse Gaussian distribution first arise from attempts\n        to model Brownian Motion. It is also a competitor to the Weibull for use in\n        reliability modeling and modeling stock returns and interest rate\n        processes.\n\n        References\n        ----------\n        ..[1] Brighton Webs Ltd., Wald Distribution,\n              http://www.brighton-webs.co.uk/distributions/wald.asp\n        ..[2] Chhikara, Raj S., and Folks, J. Leroy, \"The Inverse Gaussian\n              Distribution: Theory : Methodology, and Applications\", CRC Press,\n              1988.\n        ..[3] Wikipedia, \"Wald distributio""n\"\n              http://en.wikipedia.org/wiki/Wald_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram:\n\n        >>> import matplotlib.pyplot as plt\n        >>> h = plt.hist(np.random.wald(3, 2, 100000), bins=200, normed=True)\n        >>> plt.show()\n\n        ";
    6698 -static PyObject *__pyx_pf_6mtrand_11RandomState_38wald(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
    6699 +/* Python wrapper */
    6700 +static PyObject *__pyx_pw_6mtrand_11RandomState_77wald(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    6701 +static char __pyx_doc_6mtrand_11RandomState_76wald[] = "\n        wald(mean, scale, size=None)\n\n        Draw samples from a Wald, or Inverse Gaussian, distribution.\n\n        As the scale approaches infinity, the distribution becomes more like a\n        Gaussian.\n\n        Some references claim that the Wald is an Inverse Gaussian with mean=1, but\n        this is by no means universal.\n\n        The Inverse Gaussian distribution was first studied in relationship to\n        Brownian motion. In 1956 M.C.K. Tweedie used the name Inverse Gaussian\n        because there is an inverse relationship between the time to cover a unit\n        distance and distance covered in unit time.\n\n        Parameters\n        ----------\n        mean : scalar\n            Distribution mean, should be > 0.\n        scale : scalar\n            Scale parameter, should be >= 0.\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single value is\n            returned.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            Drawn sample, all greater than zero.\n\n        Notes\n        -----\n        The probability density function for the Wald distribution is\n\n        .. math:: P(x;mean,scale) = \\sqrt{\\frac{scale}{2\\pi x^3}}e^\n                                    \\frac{-scale(x-mean)^2}{2\\cdotp mean^2x}\n\n        As noted above the Inverse Gaussian distribution first arise from attempts\n        to model Brownian Motion. It is also a competitor to the Weibull for use in\n        reliability modeling and modeling stock returns and interest rate\n        processes.\n\n        References\n        ----------\n        ..[1] Brighton Webs Ltd., Wald Distribution,\n              http://www.brighton-webs.co.uk/distributions/wald.asp\n        ..[2] Chhikara, Raj S., and Folks, J. Leroy, \"The Inverse Gaussian\n              Distribution: Theory : Methodology, and Applications\", CRC Press,\n              1988.\n        ..[3] Wikipedia, \"Wald distributio""n\"\n              http://en.wikipedia.org/wiki/Wald_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram:\n\n        >>> import matplotlib.pyplot as plt\n        >>> h = plt.hist(np.random.wald(3, 2, 100000), bins=200, normed=True)\n        >>> plt.show()\n\n        ";
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    6730 + *
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    6737        Py_ssize_t kw_args;
    6738 -      switch (PyTuple_GET_SIZE(__pyx_args)) {
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    6740 +      switch (pos_args) {
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    6742          case  2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1);
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    6748 -      switch (PyTuple_GET_SIZE(__pyx_args)) {
    6749 +      switch (pos_args) {
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    6752 -        if (likely(values[0])) kw_args--;
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    6757 -        if (likely(values[1])) kw_args--;
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    6791 +  PyObject *__pyx_t_5 = NULL;
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    6801        __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
    6802        {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3137; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    6803 -      goto __pyx_L7;
    6804 +      goto __pyx_L4;
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    6806 -    __pyx_L7:;
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    6912 - *
    6913 - *
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    6920 -static char __pyx_doc_6mtrand_11RandomState_39triangular[] = "\n        triangular(left, mode, right, size=None)\n\n        Draw samples from the triangular distribution.\n\n        The triangular distribution is a continuous probability distribution with\n        lower limit left, peak at mode, and upper limit right. Unlike the other\n        distributions, these parameters directly define the shape of the pdf.\n\n        Parameters\n        ----------\n        left : scalar\n            Lower limit.\n        mode : scalar\n            The value where the peak of the distribution occurs.\n            The value should fulfill the condition ``left <= mode <= right``.\n        right : scalar\n            Upper limit, should be larger than `left`.\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single value is\n            returned.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            The returned samples all lie in the interval [left, right].\n\n        Notes\n        -----\n        The probability density function for the Triangular distribution is\n\n        .. math:: P(x;l, m, r) = \\begin{cases}\n                  \\frac{2(x-l)}{(r-l)(m-l)}& \\text{for $l \\leq x \\leq m$},\\\\\n                  \\frac{2(m-x)}{(r-l)(r-m)}& \\text{for $m \\leq x \\leq r$},\\\\\n                  0& \\text{otherwise}.\n                  \\end{cases}\n\n        The triangular distribution is often used in ill-defined problems where the\n        underlying distribution is not known, but some knowledge of the limits and\n        mode exists. Often it is used in simulations.\n\n        References\n        ----------\n        ..[1] Wikipedia, \"Triangular distribution\"\n              http://en.wikipedia.org/wiki/Triangular_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram:\n\n        >>> import matplotlib.pyplot as plt\n        >>> h = plt.hist(np.random.triangular(-3, 0, 8, 100000), bins=2""00,\n        ...              normed=True)\n        >>> plt.show()\n\n        ";
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    6922 +/* Python wrapper */
    6923 +static PyObject *__pyx_pw_6mtrand_11RandomState_79triangular(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    6924 +static char __pyx_doc_6mtrand_11RandomState_78triangular[] = "\n        triangular(left, mode, right, size=None)\n\n        Draw samples from the triangular distribution.\n\n        The triangular distribution is a continuous probability distribution with\n        lower limit left, peak at mode, and upper limit right. Unlike the other\n        distributions, these parameters directly define the shape of the pdf.\n\n        Parameters\n        ----------\n        left : scalar\n            Lower limit.\n        mode : scalar\n            The value where the peak of the distribution occurs.\n            The value should fulfill the condition ``left <= mode <= right``.\n        right : scalar\n            Upper limit, should be larger than `left`.\n        size : int or tuple of ints, optional\n            Output shape. Default is None, in which case a single value is\n            returned.\n\n        Returns\n        -------\n        samples : ndarray or scalar\n            The returned samples all lie in the interval [left, right].\n\n        Notes\n        -----\n        The probability density function for the Triangular distribution is\n\n        .. math:: P(x;l, m, r) = \\begin{cases}\n                  \\frac{2(x-l)}{(r-l)(m-l)}& \\text{for $l \\leq x \\leq m$},\\\\\n                  \\frac{2(m-x)}{(r-l)(r-m)}& \\text{for $m \\leq x \\leq r$},\\\\\n                  0& \\text{otherwise}.\n                  \\end{cases}\n\n        The triangular distribution is often used in ill-defined problems where the\n        underlying distribution is not known, but some knowledge of the limits and\n        mode exists. Often it is used in simulations.\n\n        References\n        ----------\n        ..[1] Wikipedia, \"Triangular distribution\"\n              http://en.wikipedia.org/wiki/Triangular_distribution\n\n        Examples\n        --------\n        Draw values from the distribution and plot the histogram:\n\n        >>> import matplotlib.pyplot as plt\n        >>> h = plt.hist(np.random.triangular(-3, 0, 8, 100000), bins=2""00,\n        ...              normed=True)\n        >>> plt.show()\n\n        ";
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    6927    PyObject *__pyx_v_mode = 0;
    6928    PyObject *__pyx_v_right = 0;
    6929    PyObject *__pyx_v_size = 0;
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    6934 -  double __pyx_v_fmode;
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    6968          case  3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2);
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    6974 -      switch (PyTuple_GET_SIZE(__pyx_args)) {
    6975 +      switch (pos_args) {
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    6978 -        if (likely(values[0])) kw_args--;
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    6984 +        if (likely((values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__mode)) != 0)) kw_args--;
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    6986            __Pyx_RaiseArgtupleInvalid("triangular", 0, 3, 4, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3153; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
    6987          }
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    6990 -        if (likely(values[2])) kw_args--;
    6991 +        if (likely((values[2] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__right)) != 0)) kw_args--;
    6992          else {
    6993            __Pyx_RaiseArgtupleInvalid("triangular", 0, 3, 4, 2); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3153; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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    7000 +        if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "triangular") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3153; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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    7015 +  PyArrayObject *__pyx_v_omode = 0;
    7016 +  PyArrayObject *__pyx_v_oright = 0;
    7017 +  double __pyx_v_fleft;
    7018 +  double __pyx_v_fmode;
    7019 +  double __pyx_v_fright;
    7020 +  PyObject *__pyx_r = NULL;
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    7023 +  PyObject *__pyx_t_2 = NULL;
    7024 +  PyObject *__pyx_t_3 = NULL;
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    7026 +  PyObject *__pyx_t_5 = NULL;
    7027 +  int __pyx_lineno = 0;
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    7033   *         cdef double fleft, fmode, fright
    7034 @@ -13405,9 +13948,9 @@
    7035        __Pyx_Raise(__pyx_t_2, 0, 0, 0);
    7036        __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
    7037        {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3218; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    7038 -      goto __pyx_L7;
    7039 +      goto __pyx_L4;
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    7041 -    __pyx_L7:;
    7042 +    __pyx_L4:;
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    7044      /* "mtrand.pyx":3219
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    7047        __Pyx_Raise(__pyx_t_2, 0, 0, 0);
    7048        __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
    7049        {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3220; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    7050 -      goto __pyx_L8;
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    7053 -    __pyx_L8:;
    7054 +    __pyx_L5:;
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    7092 -  __Pyx_GOTREF(((PyObject *)__pyx_t_2));
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    7096    __Pyx_GIVEREF(((PyObject *)__pyx_v_oleft));
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    7101 -  __Pyx_GOTREF(((PyObject *)__pyx_t_2));
    7102 +  __Pyx_GOTREF(__pyx_t_2);
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    7108      __Pyx_DECREF(__pyx_t_5); __pyx_t_5 = 0;
    7109      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3232; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    7110 -    goto __pyx_L10;
    7111 +    goto __pyx_L7;
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    7122 -  __Pyx_GOTREF(((PyObject *)__pyx_t_5));
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    7131 -  __Pyx_GOTREF(((PyObject *)__pyx_t_5));
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    7137      __Pyx_Raise(__pyx_t_4, 0, 0, 0);
    7138      __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
    7139      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3234; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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    7147   *         if np.any(np.greater(omode, oright)):
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    7149    __Pyx_GOTREF(__pyx_t_2);
    7150    __Pyx_DECREF(__pyx_t_4); __pyx_t_4 = 0;
    7151    __pyx_t_4 = PyTuple_New(2); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3235; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    7152 -  __Pyx_GOTREF(((PyObject *)__pyx_t_4));
    7153 +  __Pyx_GOTREF(__pyx_t_4);
    7154    __Pyx_INCREF(((PyObject *)__pyx_v_oleft));
    7155    PyTuple_SET_ITEM(__pyx_t_4, 0, ((PyObject *)__pyx_v_oleft));
    7156    __Pyx_GIVEREF(((PyObject *)__pyx_v_oleft));
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    7160    __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3235; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    7161 -  __Pyx_GOTREF(((PyObject *)__pyx_t_4));
    7162 +  __Pyx_GOTREF(__pyx_t_4);
    7163    PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_3);
    7164    __Pyx_GIVEREF(__pyx_t_3);
    7165    __pyx_t_3 = 0;
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    7167      __Pyx_Raise(__pyx_t_3, 0, 0, 0);
    7168      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
    7169      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3236; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    7170 -    goto __pyx_L12;
    7171 +    goto __pyx_L9;
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    7173 -  __pyx_L12:;
    7174 +  __pyx_L9:;
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    7177   *         if np.any(np.equal(oleft, oright)):
    7178 @@ -13724,7 +14267,7 @@
    7179   *
    7180   *     # Complicated, discrete distributions:
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    7190  
    7191 -/* "mtrand.pyx":3241
    7192 - *
    7193 - *     # Complicated, discrete distributions:
    7194 - *     def binomial(self, n, p, size=None):             # <<<<<<<<<<<<<<
    7195 - *         """
    7196 - *         binomial(n, p, size=None)
    7197 - */
    7198 -
    7199 -static PyObject *__pyx_pf_6mtrand_11RandomState_40binomial(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    7200 -static char __pyx_doc_6mtrand_11RandomState_40binomial[] = "\n        binomial(n, p, size=None)\n\n        Draw samples from a binomial distribution.\n\n        Samples are drawn from a Binomial distribution with specified\n        parameters, n trials and p probability of success where\n        n an integer > 0 and p is in the interval [0,1]. (n may be\n        input as a float, but it is truncated to an integer in use)\n\n        Parameters\n        ----------\n        n : float (but truncated to an integer)\n                parameter, > 0.\n        p : float\n                parameter, >= 0 and <=1.\n        size : {tuple, int}\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n                  where the values are all integers in  [0, n].\n\n        See Also\n        --------\n        scipy.stats.distributions.binom : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Binomial distribution is\n\n        .. math:: P(N) = \\binom{n}{N}p^N(1-p)^{n-N},\n\n        where :math:`n` is the number of trials, :math:`p` is the probability\n        of success, and :math:`N` is the number of successes.\n\n        When estimating the standard error of a proportion in a population by\n        using a random sample, the normal distribution works well unless the\n        product p*n <=5, where p = population proportion estimate, and n =\n        number of samples, in which case the binomial distribution is used\n        instead. For example, a sample of 15 people shows 4 who are left\n        handed, and 11 who are right handed. Then p = 4/15 = 27%. 0.27*15 = 4,\n        so the binomial distribution should be used in this case.\n\n        References\n        ----------\n        .. [1] Dalgaard, Peter, \"Introductory Statistics with R\",\n               Springer-Verlag, 2002.\n    ""    .. [2] Glantz, Stanton A. \"Primer of Biostatistics.\", McGraw-Hill,\n               Fifth Edition, 2002.\n        .. [3] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n               and Quigley, 1972.\n        .. [4] Weisstein, Eric W. \"Binomial Distribution.\" From MathWorld--A\n               Wolfram Web Resource.\n               http://mathworld.wolfram.com/BinomialDistribution.html\n        .. [5] Wikipedia, \"Binomial-distribution\",\n               http://en.wikipedia.org/wiki/Binomial_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> n, p = 10, .5 # number of trials, probability of each trial\n        >>> s = np.random.binomial(n, p, 1000)\n        # result of flipping a coin 10 times, tested 1000 times.\n\n        A real world example. A company drills 9 wild-cat oil exploration\n        wells, each with an estimated probability of success of 0.1. All nine\n        wells fail. What is the probability of that happening?\n\n        Let's do 20,000 trials of the model, and count the number that\n        generate zero positive results.\n\n        >>> sum(np.random.binomial(9,0.1,20000)==0)/20000.\n        answer = 0.38885, or 38%.\n\n        ";
    7201 -static PyObject *__pyx_pf_6mtrand_11RandomState_40binomial(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
    7202 +/* Python wrapper */
    7203 +static PyObject *__pyx_pw_6mtrand_11RandomState_81binomial(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    7204 +static char __pyx_doc_6mtrand_11RandomState_80binomial[] = "\n        binomial(n, p, size=None)\n\n        Draw samples from a binomial distribution.\n\n        Samples are drawn from a Binomial distribution with specified\n        parameters, n trials and p probability of success where\n        n an integer > 0 and p is in the interval [0,1]. (n may be\n        input as a float, but it is truncated to an integer in use)\n\n        Parameters\n        ----------\n        n : float (but truncated to an integer)\n                parameter, > 0.\n        p : float\n                parameter, >= 0 and <=1.\n        size : {tuple, int}\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n                  where the values are all integers in  [0, n].\n\n        See Also\n        --------\n        scipy.stats.distributions.binom : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Binomial distribution is\n\n        .. math:: P(N) = \\binom{n}{N}p^N(1-p)^{n-N},\n\n        where :math:`n` is the number of trials, :math:`p` is the probability\n        of success, and :math:`N` is the number of successes.\n\n        When estimating the standard error of a proportion in a population by\n        using a random sample, the normal distribution works well unless the\n        product p*n <=5, where p = population proportion estimate, and n =\n        number of samples, in which case the binomial distribution is used\n        instead. For example, a sample of 15 people shows 4 who are left\n        handed, and 11 who are right handed. Then p = 4/15 = 27%. 0.27*15 = 4,\n        so the binomial distribution should be used in this case.\n\n        References\n        ----------\n        .. [1] Dalgaard, Peter, \"Introductory Statistics with R\",\n               Springer-Verlag, 2002.\n    ""    .. [2] Glantz, Stanton A. \"Primer of Biostatistics.\", McGraw-Hill,\n               Fifth Edition, 2002.\n        .. [3] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n               and Quigley, 1972.\n        .. [4] Weisstein, Eric W. \"Binomial Distribution.\" From MathWorld--A\n               Wolfram Web Resource.\n               http://mathworld.wolfram.com/BinomialDistribution.html\n        .. [5] Wikipedia, \"Binomial-distribution\",\n               http://en.wikipedia.org/wiki/Binomial_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> n, p = 10, .5 # number of trials, probability of each trial\n        >>> s = np.random.binomial(n, p, 1000)\n        # result of flipping a coin 10 times, tested 1000 times.\n\n        A real world example. A company drills 9 wild-cat oil exploration\n        wells, each with an estimated probability of success of 0.1. All nine\n        wells fail. What is the probability of that happening?\n\n        Let's do 20,000 trials of the model, and count the number that\n        generate zero positive results.\n\n        >>> sum(np.random.binomial(9,0.1,20000)==0)/20000.\n        answer = 0.38885, or 38%.\n\n        ";
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    7464 -static PyObject *__pyx_pf_6mtrand_11RandomState_41negative_binomial(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    7465 -static char __pyx_doc_6mtrand_11RandomState_41negative_binomial[] = "\n        negative_binomial(n, p, size=None)\n\n        Draw samples from a negative_binomial distribution.\n\n        Samples are drawn from a negative_Binomial distribution with specified\n        parameters, `n` trials and `p` probability of success where `n` is an\n        integer > 0 and `p` is in the interval [0, 1].\n\n        Parameters\n        ----------\n        n : int\n            Parameter, > 0.\n        p : float\n            Parameter, >= 0 and <=1.\n        size : int or tuple of ints\n            Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : int or ndarray of ints\n            Drawn samples.\n\n        Notes\n        -----\n        The probability density for the Negative Binomial distribution is\n\n        .. math:: P(N;n,p) = \\binom{N+n-1}{n-1}p^{n}(1-p)^{N},\n\n        where :math:`n-1` is the number of successes, :math:`p` is the probability\n        of success, and :math:`N+n-1` is the number of trials.\n\n        The negative binomial distribution gives the probability of n-1 successes\n        and N failures in N+n-1 trials, and success on the (N+n)th trial.\n\n        If one throws a die repeatedly until the third time a \"1\" appears, then the\n        probability distribution of the number of non-\"1\"s that appear before the\n        third \"1\" is a negative binomial distribution.\n\n        References\n        ----------\n        .. [1] Weisstein, Eric W. \"Negative Binomial Distribution.\" From\n               MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/NegativeBinomialDistribution.html\n        .. [2] Wikipedia, \"Negative binomial distribution\",\n               http://en.wikipedia.org/wiki/Negative_binomial_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        A real world example. A company drills wild-cat oil exploration well""s, each\n        with an estimated probability of success of 0.1.  What is the probability\n        of having one success for each successive well, that is what is the\n        probability of a single success after drilling 5 wells, after 6 wells,\n        etc.?\n\n        >>> s = np.random.negative_binomial(1, 0.1, 100000)\n        >>> for i in range(1, 11):\n        ...    probability = sum(s<i) / 100000.\n        ...    print i, \"wells drilled, probability of one success =\", probability\n\n        ";
    7466 -static PyObject *__pyx_pf_6mtrand_11RandomState_41negative_binomial(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
    7467 +/* Python wrapper */
    7468 +static PyObject *__pyx_pw_6mtrand_11RandomState_83negative_binomial(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    7469 +static char __pyx_doc_6mtrand_11RandomState_82negative_binomial[] = "\n        negative_binomial(n, p, size=None)\n\n        Draw samples from a negative_binomial distribution.\n\n        Samples are drawn from a negative_Binomial distribution with specified\n        parameters, `n` trials and `p` probability of success where `n` is an\n        integer > 0 and `p` is in the interval [0, 1].\n\n        Parameters\n        ----------\n        n : int\n            Parameter, > 0.\n        p : float\n            Parameter, >= 0 and <=1.\n        size : int or tuple of ints\n            Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : int or ndarray of ints\n            Drawn samples.\n\n        Notes\n        -----\n        The probability density for the Negative Binomial distribution is\n\n        .. math:: P(N;n,p) = \\binom{N+n-1}{n-1}p^{n}(1-p)^{N},\n\n        where :math:`n-1` is the number of successes, :math:`p` is the probability\n        of success, and :math:`N+n-1` is the number of trials.\n\n        The negative binomial distribution gives the probability of n-1 successes\n        and N failures in N+n-1 trials, and success on the (N+n)th trial.\n\n        If one throws a die repeatedly until the third time a \"1\" appears, then the\n        probability distribution of the number of non-\"1\"s that appear before the\n        third \"1\" is a negative binomial distribution.\n\n        References\n        ----------\n        .. [1] Weisstein, Eric W. \"Negative Binomial Distribution.\" From\n               MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/NegativeBinomialDistribution.html\n        .. [2] Wikipedia, \"Negative binomial distribution\",\n               http://en.wikipedia.org/wiki/Negative_binomial_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        A real world example. A company drills wild-cat oil exploration well""s, each\n        with an estimated probability of success of 0.1.  What is the probability\n        of having one success for each successive well, that is what is the\n        probability of a single success after drilling 5 wells, after 6 wells,\n        etc.?\n\n        >>> s = np.random.negative_binomial(1, 0.1, 100000)\n        >>> for i in range(1, 11):\n        ...    probability = sum(s<i) / 100000.\n        ...    print i, \"wells drilled, probability of one success =\", probability\n\n        ";
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    7701      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3440; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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    7709   *         if np.any(np.greater(p, 1)):
    7710 @@ -14635,7 +15196,7 @@
    7711   *
    7712   *     def poisson(self, lam=1.0, size=None):
    7713   */
    7714 -  __pyx_t_3 = __pyx_f_6mtrand_discdd_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_negative_binomial, __pyx_v_size, __pyx_v_on, __pyx_v_op); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3441; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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    7724 - *                             on, op)
    7725 - *
    7726 - *     def poisson(self, lam=1.0, size=None):             # <<<<<<<<<<<<<<
    7727 - *         """
    7728 - *         poisson(lam=1.0, size=None)
    7729 - */
    7730 -
    7731 -static PyObject *__pyx_pf_6mtrand_11RandomState_42poisson(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    7732 -static char __pyx_doc_6mtrand_11RandomState_42poisson[] = "\n        poisson(lam=1.0, size=None)\n\n        Draw samples from a Poisson distribution.\n\n        The Poisson distribution is the limit of the Binomial\n        distribution for large N.\n\n        Parameters\n        ----------\n        lam : float\n            Expectation of interval, should be >= 0.\n        size : int or tuple of ints, optional\n            Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Notes\n        -----\n        The Poisson distribution\n\n        .. math:: f(k; \\lambda)=\\frac{\\lambda^k e^{-\\lambda}}{k!}\n\n        For events with an expected separation :math:`\\lambda` the Poisson\n        distribution :math:`f(k; \\lambda)` describes the probability of\n        :math:`k` events occurring within the observed interval :math:`\\lambda`.\n\n        Because the output is limited to the range of the C long type, a\n        ValueError is raised when `lam` is within 10 sigma of the maximum\n        representable value.\n\n        References\n        ----------\n        .. [1] Weisstein, Eric W. \"Poisson Distribution.\" From MathWorld--A Wolfram\n               Web Resource. http://mathworld.wolfram.com/PoissonDistribution.html\n        .. [2] Wikipedia, \"Poisson distribution\",\n           http://en.wikipedia.org/wiki/Poisson_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> import numpy as np\n        >>> s = np.random.poisson(5, 10000)\n\n        Display histogram of the sample:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 14, normed=True)\n        >>> plt.show()\n\n        ";
    7733 -static PyObject *__pyx_pf_6mtrand_11RandomState_42poisson(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
    7734 +/* Python wrapper */
    7735 +static PyObject *__pyx_pw_6mtrand_11RandomState_85poisson(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    7736 +static char __pyx_doc_6mtrand_11RandomState_84poisson[] = "\n        poisson(lam=1.0, size=None)\n\n        Draw samples from a Poisson distribution.\n\n        The Poisson distribution is the limit of the Binomial\n        distribution for large N.\n\n        Parameters\n        ----------\n        lam : float\n            Expectation of interval, should be >= 0.\n        size : int or tuple of ints, optional\n            Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Notes\n        -----\n        The Poisson distribution\n\n        .. math:: f(k; \\lambda)=\\frac{\\lambda^k e^{-\\lambda}}{k!}\n\n        For events with an expected separation :math:`\\lambda` the Poisson\n        distribution :math:`f(k; \\lambda)` describes the probability of\n        :math:`k` events occurring within the observed interval :math:`\\lambda`.\n\n        Because the output is limited to the range of the C long type, a\n        ValueError is raised when `lam` is within 10 sigma of the maximum\n        representable value.\n\n        References\n        ----------\n        .. [1] Weisstein, Eric W. \"Poisson Distribution.\" From MathWorld--A Wolfram\n               Web Resource. http://mathworld.wolfram.com/PoissonDistribution.html\n        .. [2] Wikipedia, \"Poisson distribution\",\n           http://en.wikipedia.org/wiki/Poisson_distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> import numpy as np\n        >>> s = np.random.poisson(5, 10000)\n\n        Display histogram of the sample:\n\n        >>> import matplotlib.pyplot as plt\n        >>> count, bins, ignored = plt.hist(s, 14, normed=True)\n        >>> plt.show()\n\n        ";
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    7739    PyObject *__pyx_v_size = 0;
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    7750 -  int __pyx_lineno = 0;
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    7758      PyObject* values[2] = {0,0};
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    7765 + *         """
    7766 + *         poisson(lam=1.0, size=None)
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    7771 -      switch (PyTuple_GET_SIZE(__pyx_args)) {
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    7773 +      switch (pos_args) {
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    7777          default: goto __pyx_L5_argtuple_error;
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    7834 -      goto __pyx_L7;
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    7963 +/* Python wrapper */
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    7965 +static char __pyx_doc_6mtrand_11RandomState_86zipf[] = "\n        zipf(a, size=None)\n\n        Draw samples from a Zipf distribution.\n\n        Samples are drawn from a Zipf distribution with specified parameter\n        `a` > 1.\n\n        The Zipf distribution (also known as the zeta distribution) is a\n        continuous probability distribution that satisfies Zipf's law: the\n        frequency of an item is inversely proportional to its rank in a\n        frequency table.\n\n        Parameters\n        ----------\n        a : float > 1\n            Distribution parameter.\n        size : int or tuple of int, optional\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn; a single integer is equivalent in\n            its result to providing a mono-tuple, i.e., a 1-D array of length\n            *size* is returned.  The default is None, in which case a single\n            scalar is returned.\n\n        Returns\n        -------\n        samples : scalar or ndarray\n            The returned samples are greater than or equal to one.\n\n        See Also\n        --------\n        scipy.stats.distributions.zipf : probability density function,\n            distribution, or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Zipf distribution is\n\n        .. math:: p(x) = \\frac{x^{-a}}{\\zeta(a)},\n\n        where :math:`\\zeta` is the Riemann Zeta function.\n\n        It is named for the American linguist George Kingsley Zipf, who noted\n        that the frequency of any word in a sample of a language is inversely\n        proportional to its rank in the frequency table.\n\n        References\n        ----------\n        Zipf, G. K., *Selected Studies of the Principle of Relative Frequency\n        in Language*, Cambridge, MA: Harvard Univ. Press, 1932.\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a = 2. # parameter\n        >>> s = np.random.zipf""(a, 1000)\n\n        Display the histogram of the samples, along with\n        the probability density function:\n\n        >>> import matplotlib.pyplot as plt\n        >>> import scipy.special as sps\n        Truncate s values at 50 so plot is interesting\n        >>> count, bins, ignored = plt.hist(s[s<50], 50, normed=True)\n        >>> x = np.arange(1., 50.)\n        >>> y = x**(-a)/sps.zetac(a)\n        >>> plt.plot(x, y/max(y), linewidth=2, color='r')\n        >>> plt.show()\n\n        ";
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    7977   *         return discd_array(self.internal_state, rk_poisson, size, olam)
    7978   *
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    8149   *         return discd_array(self.internal_state, rk_zipf, size, oa)
    8150   *
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    8152   *         """
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    8154   */
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    8158 -static PyObject *__pyx_pf_6mtrand_11RandomState_44geometric(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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    8161 -  PyArrayObject *__pyx_v_op = 0;
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    8170 -  int __pyx_lineno = 0;
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    8179        Py_ssize_t kw_args;
    8180 -      switch (PyTuple_GET_SIZE(__pyx_args)) {
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    8182 +      switch (pos_args) {
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    8189 -      switch (PyTuple_GET_SIZE(__pyx_args)) {
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    8348 -/* "mtrand.pyx":3669
    8349 - *         return discd_array(self.internal_state, rk_geometric, size, op)
    8350 - *
    8351 - *     def hypergeometric(self, ngood, nbad, nsample, size=None):             # <<<<<<<<<<<<<<
    8352 - *         """
    8353 - *         hypergeometric(ngood, nbad, nsample, size=None)
    8354 - */
    8355 -
    8356 -static PyObject *__pyx_pf_6mtrand_11RandomState_45hypergeometric(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    8357 -static char __pyx_doc_6mtrand_11RandomState_45hypergeometric[] = "\n        hypergeometric(ngood, nbad, nsample, size=None)\n\n        Draw samples from a Hypergeometric distribution.\n\n        Samples are drawn from a Hypergeometric distribution with specified\n        parameters, ngood (ways to make a good selection), nbad (ways to make\n        a bad selection), and nsample = number of items sampled, which is less\n        than or equal to the sum ngood + nbad.\n\n        Parameters\n        ----------\n        ngood : float (but truncated to an integer)\n                parameter, > 0.\n        nbad  : float\n                parameter, >= 0.\n        nsample  : float\n                   parameter, > 0 and <= ngood+nbad\n        size : {tuple, int}\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n                  where the values are all integers in  [0, n].\n\n        See Also\n        --------\n        scipy.stats.distributions.hypergeom : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Hypergeometric distribution is\n\n        .. math:: P(x) = \\frac{\\binom{m}{n}\\binom{N-m}{n-x}}{\\binom{N}{n}},\n\n        where :math:`0 \\le x \\le m` and :math:`n+m-N \\le x \\le n`\n\n        for P(x) the probability of x successes, n = ngood, m = nbad, and\n        N = number of samples.\n\n        Consider an urn with black and white marbles in it, ngood of them\n        black and nbad are white. If you draw nsample balls without\n        replacement, then the Hypergeometric distribution describes the\n        distribution of black balls in the drawn sample.\n\n        Note that this distribution is very similar to the Binomial\n        distribution, except that in this case, samples are drawn without\n        replacement, whereas in the Binomial case samples are drawn wit""h\n        replacement (or the sample space is infinite). As the sample space\n        becomes large, this distribution approaches the Binomial.\n\n        References\n        ----------\n        .. [1] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n               and Quigley, 1972.\n        .. [2] Weisstein, Eric W. \"Hypergeometric Distribution.\" From\n               MathWorld--A Wolfram Web Resource.\n               http://mathworld.wolfram.com/HypergeometricDistribution.html\n        .. [3] Wikipedia, \"Hypergeometric-distribution\",\n               http://en.wikipedia.org/wiki/Hypergeometric-distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> ngood, nbad, nsamp = 100, 2, 10\n        # number of good, number of bad, and number of samples\n        >>> s = np.random.hypergeometric(ngood, nbad, nsamp, 1000)\n        >>> hist(s)\n        #   note that it is very unlikely to grab both bad items\n\n        Suppose you have an urn with 15 white and 15 black marbles.\n        If you pull 15 marbles at random, how likely is it that\n        12 or more of them are one color?\n\n        >>> s = np.random.hypergeometric(15, 15, 15, 100000)\n        >>> sum(s>=12)/100000. + sum(s<=3)/100000.\n        #   answer = 0.003 ... pretty unlikely!\n\n        ";
    8358 -static PyObject *__pyx_pf_6mtrand_11RandomState_45hypergeometric(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
    8359 +/* Python wrapper */
    8360 +static PyObject *__pyx_pw_6mtrand_11RandomState_91hypergeometric(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    8361 +static char __pyx_doc_6mtrand_11RandomState_90hypergeometric[] = "\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        ";
    8362 +static PyObject *__pyx_pw_6mtrand_11RandomState_91hypergeometric(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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    8364    PyObject *__pyx_v_nbad = 0;
    8365    PyObject *__pyx_v_nsample = 0;
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    8368 -  PyArrayObject *__pyx_v_onbad = 0;
    8369 -  PyArrayObject *__pyx_v_onsample = 0;
    8370 -  long __pyx_v_lngood;
    8371 -  long __pyx_v_lnbad;
    8372 -  long __pyx_v_lnsample;
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    8390      PyObject* values[4] = {0,0,0,0};
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    8392 +    /* "mtrand.pyx":3669
    8393 + *         return discd_array(self.internal_state, rk_geometric, size, op)
    8394 + *
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    8396 + *         """
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    8398 + */
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    8401        Py_ssize_t kw_args;
    8402 -      switch (PyTuple_GET_SIZE(__pyx_args)) {
    8403 +      const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args);
    8404 +      switch (pos_args) {
    8405          case  4: values[3] = PyTuple_GET_ITEM(__pyx_args, 3);
    8406          case  3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2);
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    8412 -      switch (PyTuple_GET_SIZE(__pyx_args)) {
    8413 +      switch (pos_args) {
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    8416 -        if (likely(values[0])) kw_args--;
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    8453 +  PyArrayObject *__pyx_v_onbad = 0;
    8454 +  PyArrayObject *__pyx_v_onsample = 0;
    8455 +  long __pyx_v_lngood;
    8456 +  long __pyx_v_lnbad;
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    8465 +  PyObject *__pyx_t_6 = NULL;
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    8497   *             if nsample < 1:
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    8506        __Pyx_Raise(__pyx_t_2, 0, 0, 0);
    8507        __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
    8508        {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3763; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    8509 -      goto __pyx_L8;
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    8513 +    __pyx_L5:;
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    8516   *             if nbad < 1:
    8517 @@ -15780,8 +16376,7 @@
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    8519   *             if ngood + nbad < nsample:
    8520   */
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    8522 -    __Pyx_GOTREF(__pyx_t_2);
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    8531 -      goto __pyx_L9;
    8532 +      goto __pyx_L6;
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    8571 -    goto __pyx_L6;
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    8707   *         if np.any(np.less(np.add(ongood, onbad),onsample)):
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    8722 +/* Python wrapper */
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    8724 +static char __pyx_doc_6mtrand_11RandomState_92logseries[] = "\n        logseries(p, size=None)\n\n        Draw samples from a Logarithmic Series distribution.\n\n        Samples are drawn from a Log Series distribution with specified\n        parameter, p (probability, 0 < p < 1).\n\n        Parameters\n        ----------\n        loc : float\n\n        scale : float > 0.\n\n        size : {tuple, int}\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n                  where the values are all integers in  [0, n].\n\n        See Also\n        --------\n        scipy.stats.distributions.logser : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Log Series distribution is\n\n        .. math:: P(k) = \\frac{-p^k}{k \\ln(1-p)},\n\n        where p = probability.\n\n        The Log Series distribution is frequently used to represent species\n        richness and occurrence, first proposed by Fisher, Corbet, and\n        Williams in 1943 [2].  It may also be used to model the numbers of\n        occupants seen in cars [3].\n\n        References\n        ----------\n        .. [1] Buzas, Martin A.; Culver, Stephen J.,  Understanding regional\n               species diversity through the log series distribution of\n               occurrences: BIODIVERSITY RESEARCH Diversity & Distributions,\n               Volume 5, Number 5, September 1999 , pp. 187-195(9).\n        .. [2] Fisher, R.A,, A.S. Corbet, and C.B. Williams. 1943. The\n               relation between the number of species and the number of\n               individuals in a random sample of an animal population.\n               Journal of Animal Ecology, 12:42-58.\n        .. [3] D. J. Hand, F. Daly, D. Lunn, E. Ostrowski, A Handbook of Small\n               Data Sets, CRC Press, 1994.\n        .. [4] Wikipedia, \"Log""arithmic-distribution\",\n               http://en.wikipedia.org/wiki/Logarithmic-distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a = .6\n        >>> s = np.random.logseries(a, 10000)\n        >>> count, bins, ignored = plt.hist(s)\n\n        #   plot against distribution\n\n        >>> def logseries(k, p):\n        ...     return -p**k/(k*log(1-p))\n        >>> plt.plot(bins, logseries(bins, a)*count.max()/\n                     logseries(bins, a).max(), 'r')\n        >>> plt.show()\n\n        ";
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    8737   *
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    8739   *         """
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    8744 -static char __pyx_doc_6mtrand_11RandomState_46logseries[] = "\n        logseries(p, size=None)\n\n        Draw samples from a Logarithmic Series distribution.\n\n        Samples are drawn from a Log Series distribution with specified\n        parameter, p (probability, 0 < p < 1).\n\n        Parameters\n        ----------\n        loc : float\n\n        scale : float > 0.\n\n        size : {tuple, int}\n            Output shape.  If the given shape is, e.g., ``(m, n, k)``, then\n            ``m * n * k`` samples are drawn.\n\n        Returns\n        -------\n        samples : {ndarray, scalar}\n                  where the values are all integers in  [0, n].\n\n        See Also\n        --------\n        scipy.stats.distributions.logser : probability density function,\n            distribution or cumulative density function, etc.\n\n        Notes\n        -----\n        The probability density for the Log Series distribution is\n\n        .. math:: P(k) = \\frac{-p^k}{k \\ln(1-p)},\n\n        where p = probability.\n\n        The Log Series distribution is frequently used to represent species\n        richness and occurrence, first proposed by Fisher, Corbet, and\n        Williams in 1943 [2].  It may also be used to model the numbers of\n        occupants seen in cars [3].\n\n        References\n        ----------\n        .. [1] Buzas, Martin A.; Culver, Stephen J.,  Understanding regional\n               species diversity through the log series distribution of\n               occurrences: BIODIVERSITY RESEARCH Diversity & Distributions,\n               Volume 5, Number 5, September 1999 , pp. 187-195(9).\n        .. [2] Fisher, R.A,, A.S. Corbet, and C.B. Williams. 1943. The\n               relation between the number of species and the number of\n               individuals in a random sample of an animal population.\n               Journal of Animal Ecology, 12:42-58.\n        .. [3] D. J. Hand, F. Daly, D. Lunn, E. Ostrowski, A Handbook of Small\n               Data Sets, CRC Press, 1994.\n        .. [4] Wikipedia, \"Log""arithmic-distribution\",\n               http://en.wikipedia.org/wiki/Logarithmic-distribution\n\n        Examples\n        --------\n        Draw samples from the distribution:\n\n        >>> a = .6\n        >>> s = np.random.logseries(a, 10000)\n        >>> count, bins, ignored = plt.hist(s)\n\n        #   plot against distribution\n\n        >>> def logseries(k, p):\n        ...     return -p**k/(k*log(1-p))\n        >>> plt.plot(bins, logseries(bins, a)*count.max()/\n                     logseries(bins, a).max(), 'r')\n        >>> plt.show()\n\n        ";
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    8818    /* "mtrand.pyx":3865
    8819   *         cdef double fp
    8820 @@ -16311,9 +16915,9 @@
    8821        __Pyx_Raise(__pyx_t_2, 0, 0, 0);
    8822        __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
    8823        {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3868; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    8824 -      goto __pyx_L7;
    8825 +      goto __pyx_L4;
    8826      }
    8827 -    __pyx_L7:;
    8828 +    __pyx_L4:;
    8829  
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    8831   *             if fp <= 0.0:
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    8833        __Pyx_Raise(__pyx_t_2, 0, 0, 0);
    8834        __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
    8835        {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3870; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    8836 -      goto __pyx_L8;
    8837 +      goto __pyx_L5;
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    8839 -    __pyx_L8:;
    8840 +    __pyx_L5:;
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    8843   *             if fp >= 1.0:
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    8852      __pyx_t_2 = 0;
    8853      goto __pyx_L0;
    8854 -    goto __pyx_L6;
    8855 +    goto __pyx_L3;
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    8858 +  __pyx_L3:;
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    8894    __Pyx_GOTREF(__pyx_t_2);
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    8896 -  __Pyx_GOTREF(((PyObject *)__pyx_t_4));
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    8898    __Pyx_INCREF(((PyObject *)__pyx_v_op));
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    8900    __Pyx_GIVEREF(((PyObject *)__pyx_v_op));
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    8905 -  __Pyx_GOTREF(((PyObject *)__pyx_t_4));
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    8913      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3879; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    8914 -    goto __pyx_L10;
    8915 +    goto __pyx_L7;
    8916    }
    8917 -  __pyx_L10:;
    8918 +  __pyx_L7:;
    8919  
    8920    /* "mtrand.pyx":3880
    8921   *         if np.any(np.greater_equal(op, 1.0)):
    8922 @@ -16508,7 +17112,7 @@
    8923   *     # Multivariate distributions:
    8924   */
    8925    __Pyx_XDECREF(__pyx_r);
    8926 -  __pyx_t_2 = __pyx_f_6mtrand_discd_array(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state, rk_logseries, __pyx_v_size, __pyx_v_op); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3880; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    8927 +  __pyx_t_2 = __pyx_f_6mtrand_discd_array(__pyx_v_self->internal_state, rk_logseries, __pyx_v_size, __pyx_v_op); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3880; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    8928    __Pyx_GOTREF(__pyx_t_2);
    8929    __pyx_r = __pyx_t_2;
    8930    __pyx_t_2 = 0;
    8931 @@ -16530,51 +17134,32 @@
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    8933  }
    8934  
    8935 -/* "mtrand.pyx":3883
    8936 - *
    8937 - *     # Multivariate distributions:
    8938 - *     def multivariate_normal(self, mean, cov, size=None):             # <<<<<<<<<<<<<<
    8939 - *         """
    8940 - *         multivariate_normal(mean, cov[, size])
    8941 - */
    8942 -
    8943 -static PyObject *__pyx_pf_6mtrand_11RandomState_47multivariate_normal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    8944 -static char __pyx_doc_6mtrand_11RandomState_47multivariate_normal[] = "\n        multivariate_normal(mean, cov[, size])\n\n        Draw random samples from a multivariate normal distribution.\n\n        The multivariate normal, multinormal or Gaussian distribution is a\n        generalization of the one-dimensional normal distribution to higher\n        dimensions.  Such a distribution is specified by its mean and\n        covariance matrix.  These parameters are analogous to the mean\n        (average or \"center\") and variance (standard deviation, or \"width,\"\n        squared) of the one-dimensional normal distribution.\n\n        Parameters\n        ----------\n        mean : 1-D array_like, of length N\n            Mean of the N-dimensional distribution.\n        cov : 2-D array_like, of shape (N, N)\n            Covariance matrix of the distribution.  Must be symmetric and\n            positive semi-definite for \"physically meaningful\" results.\n        size : tuple of ints, optional\n            Given a shape of, for example, ``(m,n,k)``, ``m*n*k`` samples are\n            generated, and packed in an `m`-by-`n`-by-`k` arrangement.  Because\n            each sample is `N`-dimensional, the output shape is ``(m,n,k,N)``.\n            If no shape is specified, a single (`N`-D) sample is returned.\n\n        Returns\n        -------\n        out : ndarray\n            The drawn samples, of shape *size*, if that was provided.  If not,\n            the shape is ``(N,)``.\n\n            In other words, each entry ``out[i,j,...,:]`` is an N-dimensional\n            value drawn from the distribution.\n\n        Notes\n        -----\n        The mean is a coordinate in N-dimensional space, which represents the\n        location where samples are most likely to be generated.  This is\n        analogous to the peak of the bell curve for the one-dimensional or\n        univariate normal distribution.\n\n        Covariance indicates the level to which two variables vary together.\n        From the multivariate normal distribution, we draw ""N-dimensional\n        samples, :math:`X = [x_1, x_2, ... x_N]`.  The covariance matrix\n        element :math:`C_{ij}` is the covariance of :math:`x_i` and :math:`x_j`.\n        The element :math:`C_{ii}` is the variance of :math:`x_i` (i.e. its\n        \"spread\").\n\n        Instead of specifying the full covariance matrix, popular\n        approximations include:\n\n          - Spherical covariance (*cov* is a multiple of the identity matrix)\n          - Diagonal covariance (*cov* has non-negative elements, and only on\n            the diagonal)\n\n        This geometrical property can be seen in two dimensions by plotting\n        generated data-points:\n\n        >>> mean = [0,0]\n        >>> cov = [[1,0],[0,100]] # diagonal covariance, points lie on x or y-axis\n\n        >>> import matplotlib.pyplot as plt\n        >>> x,y = np.random.multivariate_normal(mean,cov,5000).T\n        >>> plt.plot(x,y,'x'); plt.axis('equal'); plt.show()\n\n        Note that the covariance matrix must be non-negative definite.\n\n        References\n        ----------\n        Papoulis, A., *Probability, Random Variables, and Stochastic Processes*,\n        3rd ed., New York: McGraw-Hill, 1991.\n\n        Duda, R. O., Hart, P. E., and Stork, D. G., *Pattern Classification*,\n        2nd ed., New York: Wiley, 2001.\n\n        Examples\n        --------\n        >>> mean = (1,2)\n        >>> cov = [[1,0],[1,0]]\n        >>> x = np.random.multivariate_normal(mean,cov,(3,3))\n        >>> x.shape\n        (3, 3, 2)\n\n        The following is probably true, given that 0.6 is roughly twice the\n        standard deviation:\n\n        >>> print list( (x[0,0,:] - mean) < 0.6 )\n        [True, True]\n\n        ";
    8945 -static PyObject *__pyx_pf_6mtrand_11RandomState_47multivariate_normal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
    8946 +/* Python wrapper */
    8947 +static PyObject *__pyx_pw_6mtrand_11RandomState_95multivariate_normal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    8948 +static char __pyx_doc_6mtrand_11RandomState_94multivariate_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        ";
    8949 +static PyObject *__pyx_pw_6mtrand_11RandomState_95multivariate_normal(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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    8951    PyObject *__pyx_v_cov = 0;
    8952    PyObject *__pyx_v_size = 0;
    8953 -  PyObject *__pyx_v_shape = NULL;
    8954 -  PyObject *__pyx_v_final_shape = NULL;
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    8956 -  PyObject *__pyx_v_svd = NULL;
    8957 -  PyObject *__pyx_v_u = NULL;
    8958 -  PyObject *__pyx_v_s = NULL;
    8959 -  PyObject *__pyx_v_v = NULL;
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    8966 -  int __pyx_t_4;
    8967 -  Py_ssize_t __pyx_t_5;
    8968 -  int __pyx_t_6;
    8969 -  int __pyx_t_7;
    8970 -  int __pyx_t_8;
    8971 -  PyObject *__pyx_t_9 = NULL;
    8972 -  PyObject *__pyx_t_10 = NULL;
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    8974 -  int __pyx_lineno = 0;
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    8982      PyObject* values[3] = {0,0,0};
    8983 +
    8984 +    /* "mtrand.pyx":3883
    8985 + *
    8986 + *     # Multivariate distributions:
    8987 + *     def multivariate_normal(self, mean, cov, size=None):             # <<<<<<<<<<<<<<
    8988 + *         """
    8989 + *         multivariate_normal(mean, cov[, size])
    8990 + */
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    8992      if (unlikely(__pyx_kwds)) {
    8993        Py_ssize_t kw_args;
    8994 -      switch (PyTuple_GET_SIZE(__pyx_args)) {
    8995 +      const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args);
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    8998          case  2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1);
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    9001          default: goto __pyx_L5_argtuple_error;
    9002        }
    9003        kw_args = PyDict_Size(__pyx_kwds);
    9004 -      switch (PyTuple_GET_SIZE(__pyx_args)) {
    9005 +      switch (pos_args) {
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    9007 -        values[0] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__mean);
    9008 -        if (likely(values[0])) kw_args--;
    9009 +        if (likely((values[0] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__mean)) != 0)) kw_args--;
    9010          else goto __pyx_L5_argtuple_error;
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    9013 -        if (likely(values[1])) kw_args--;
    9014 +        if (likely((values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__cov)) != 0)) kw_args--;
    9015          else {
    9016            __Pyx_RaiseArgtupleInvalid("multivariate_normal", 0, 2, 3, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3883; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
    9017          }
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    9019          }
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    9212 -  __Pyx_GOTREF(((PyObject *)__pyx_t_1));
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    9277 -  __Pyx_GOTREF(((PyObject *)__pyx_t_9));
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    9285      PyObject* sequence = __pyx_t_3;
    9286 +    #if CYTHON_COMPILING_IN_CPYTHON
    9287 +    Py_ssize_t size = Py_SIZE(sequence);
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    9291 +    if (unlikely(size != 3)) {
    9292 +      if (size > 3) __Pyx_RaiseTooManyValuesError(3);
    9293 +      else if (size >= 0) __Pyx_RaiseNeedMoreValuesError(size);
    9294 +      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4007; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    9295 +    }
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    9298 -      if (unlikely(PyTuple_GET_SIZE(sequence) != 3)) {
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    9301 -        {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4007; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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    9304        __pyx_t_1 = PyTuple_GET_ITEM(sequence, 1);
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    9310 -        {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4007; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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    9313        __pyx_t_1 = PyList_GET_ITEM(sequence, 1);
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    9316      __Pyx_INCREF(__pyx_t_9);
    9317      __Pyx_INCREF(__pyx_t_1);
    9318      __Pyx_INCREF(__pyx_t_2);
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    9321 +    __pyx_t_1 = PySequence_ITEM(sequence, 1); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4007; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    9322 +    __pyx_t_2 = PySequence_ITEM(sequence, 2); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4007; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
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    9324      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
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    9330      __Pyx_GOTREF(__pyx_t_10);
    9331      __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0;
    9332      __pyx_t_11 = Py_TYPE(__pyx_t_10)->tp_iternext;
    9333 -    index = 0; __pyx_t_9 = __pyx_t_11(__pyx_t_10); if (unlikely(!__pyx_t_9)) goto __pyx_L11_unpacking_failed;
    9334 +    index = 0; __pyx_t_9 = __pyx_t_11(__pyx_t_10); if (unlikely(!__pyx_t_9)) goto __pyx_L8_unpacking_failed;
    9335      __Pyx_GOTREF(__pyx_t_9);
    9336 -    index = 1; __pyx_t_1 = __pyx_t_11(__pyx_t_10); if (unlikely(!__pyx_t_1)) goto __pyx_L11_unpacking_failed;
    9337 +    index = 1; __pyx_t_1 = __pyx_t_11(__pyx_t_10); if (unlikely(!__pyx_t_1)) goto __pyx_L8_unpacking_failed;
    9338      __Pyx_GOTREF(__pyx_t_1);
    9339 -    index = 2; __pyx_t_2 = __pyx_t_11(__pyx_t_10); if (unlikely(!__pyx_t_2)) goto __pyx_L11_unpacking_failed;
    9340 +    index = 2; __pyx_t_2 = __pyx_t_11(__pyx_t_10); if (unlikely(!__pyx_t_2)) goto __pyx_L8_unpacking_failed;
    9341      __Pyx_GOTREF(__pyx_t_2);
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    9345 -    goto __pyx_L12_unpacking_done;
    9346 -    __pyx_L11_unpacking_failed:;
    9347 +    goto __pyx_L9_unpacking_done;
    9348 +    __pyx_L8_unpacking_failed:;
    9349      __Pyx_DECREF(__pyx_t_10); __pyx_t_10 = 0;
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    9351 -    if (!PyErr_Occurred()) __Pyx_RaiseNeedMoreValuesError(index);
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    9353 +    if (__Pyx_IterFinish() == 0) __Pyx_RaiseNeedMoreValuesError(index);
    9354      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4007; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    9355 -    __pyx_L12_unpacking_done:;
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    9364 -  __Pyx_GOTREF(((PyObject *)__pyx_t_3));
    9365 +  __Pyx_GOTREF(__pyx_t_3);
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    9373 -  __Pyx_GOTREF(((PyObject *)__pyx_t_9));
    9374 +  __Pyx_GOTREF(__pyx_t_9);
    9375    PyTuple_SET_ITEM(__pyx_t_9, 0, __pyx_t_3);
    9376    __Pyx_GIVEREF(__pyx_t_3);
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    9382 -  __Pyx_GOTREF(((PyObject *)__pyx_t_3));
    9383 +  __Pyx_GOTREF(__pyx_t_3);
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    9400  
    9401 -/* "mtrand.pyx":4015
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    9404 +static char __pyx_doc_6mtrand_11RandomState_96multinomial[] = "\n        multinomial(n, pvals, size=None)\n\n        Draw samples from a multinomial distribution.\n\n        The multinomial distribution is a multivariate generalisation of the\n        binomial distribution.  Take an experiment with one of ``p``\n        possible outcomes.  An example of such an experiment is throwing a dice,\n        where the outcome can be 1 through 6.  Each sample drawn from the\n        distribution represents `n` such experiments.  Its values,\n        ``X_i = [X_0, X_1, ..., X_p]``, represent the number of times the outcome\n        was ``i``.\n\n        Parameters\n        ----------\n        n : int\n            Number of experiments.\n        pvals : sequence of floats, length p\n            Probabilities of each of the ``p`` different outcomes.  These\n            should sum to 1 (however, the last element is always assumed to\n            account for the remaining probability, as long as\n            ``sum(pvals[:-1]) <= 1)``.\n        size : tuple of ints\n            Given a `size` of ``(M, N, K)``, then ``M*N*K`` samples are drawn,\n            and the output shape becomes ``(M, N, K, p)``, since each sample\n            has shape ``(p,)``.\n\n        Examples\n        --------\n        Throw a dice 20 times:\n\n        >>> np.random.multinomial(20, [1/6.]*6, size=1)\n        array([[4, 1, 7, 5, 2, 1]])\n\n        It landed 4 times on 1, once on 2, etc.\n\n        Now, throw the dice 20 times, and 20 times again:\n\n        >>> np.random.multinomial(20, [1/6.]*6, size=2)\n        array([[3, 4, 3, 3, 4, 3],\n               [2, 4, 3, 4, 0, 7]])\n\n        For the first run, we threw 3 times 1, 4 times 2, etc.  For the second,\n        we threw 2 times 1, 4 times 2, etc.\n\n        A loaded dice is more likely to land on number 6:\n\n        >>> np.random.multinomial(100, [1/7.]*5)\n        array([13, 16, 13, 16, 42])\n\n        ";
    9405 +static PyObject *__pyx_pw_6mtrand_11RandomState_97multinomial(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
    9406 +  npy_intp __pyx_v_n;
    9407 +  PyObject *__pyx_v_pvals = 0;
    9408 +  PyObject *__pyx_v_size = 0;
    9409 +  PyObject *__pyx_r = 0;
    9410 +  __Pyx_RefNannyDeclarations
    9411 +  __Pyx_RefNannySetupContext("multinomial (wrapper)", 0);
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    9414 +    PyObject* values[3] = {0,0,0};
    9415 +
    9416 +    /* "mtrand.pyx":4015
    9417   *         return x
    9418   *
    9419   *     def multinomial(self, npy_intp n, object pvals, size=None):             # <<<<<<<<<<<<<<
    9420   *         """
    9421   *         multinomial(n, pvals, size=None)
    9422   */
    9423 -
    9424 -static PyObject *__pyx_pf_6mtrand_11RandomState_48multinomial(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    9425 -static char __pyx_doc_6mtrand_11RandomState_48multinomial[] = "\n        multinomial(n, pvals, size=None)\n\n        Draw samples from a multinomial distribution.\n\n        The multinomial distribution is a multivariate generalisation of the\n        binomial distribution.  Take an experiment with one of ``p``\n        possible outcomes.  An example of such an experiment is throwing a dice,\n        where the outcome can be 1 through 6.  Each sample drawn from the\n        distribution represents `n` such experiments.  Its values,\n        ``X_i = [X_0, X_1, ..., X_p]``, represent the number of times the outcome\n        was ``i``.\n\n        Parameters\n        ----------\n        n : int\n            Number of experiments.\n        pvals : sequence of floats, length p\n            Probabilities of each of the ``p`` different outcomes.  These\n            should sum to 1 (however, the last element is always assumed to\n            account for the remaining probability, as long as\n            ``sum(pvals[:-1]) <= 1)``.\n        size : tuple of ints\n            Given a `size` of ``(M, N, K)``, then ``M*N*K`` samples are drawn,\n            and the output shape becomes ``(M, N, K, p)``, since each sample\n            has shape ``(p,)``.\n\n        Examples\n        --------\n        Throw a dice 20 times:\n\n        >>> np.random.multinomial(20, [1/6.]*6, size=1)\n        array([[4, 1, 7, 5, 2, 1]])\n\n        It landed 4 times on 1, once on 2, etc.\n\n        Now, throw the dice 20 times, and 20 times again:\n\n        >>> np.random.multinomial(20, [1/6.]*6, size=2)\n        array([[3, 4, 3, 3, 4, 3],\n               [2, 4, 3, 4, 0, 7]])\n\n        For the first run, we threw 3 times 1, 4 times 2, etc.  For the second,\n        we threw 2 times 1, 4 times 2, etc.\n\n        A loaded dice is more likely to land on number 6:\n\n        >>> np.random.multinomial(100, [1/7.]*5)\n        array([13, 16, 13, 16, 42])\n\n        ";
    9426 -static PyObject *__pyx_pf_6mtrand_11RandomState_48multinomial(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
    9427 -  npy_intp __pyx_v_n;
    9428 -  PyObject *__pyx_v_pvals = 0;
    9429 -  PyObject *__pyx_v_size = 0;
    9430 -  npy_intp __pyx_v_d;
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    9432 -  PyArrayObject *arrayObject_mnarr = 0;
    9433 -  double *__pyx_v_pix;
    9434 -  long *__pyx_v_mnix;
    9435 -  npy_intp __pyx_v_i;
    9436 -  npy_intp __pyx_v_j;
    9437 -  npy_intp __pyx_v_dn;
    9438 -  double __pyx_v_Sum;
    9439 -  PyObject *__pyx_v_shape = NULL;
    9440 -  PyObject *__pyx_v_multin = NULL;
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    9446 -  PyObject *__pyx_t_4 = NULL;
    9447 -  PyObject *__pyx_t_5 = NULL;
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    9456      values[2] = ((PyObject *)Py_None);
    9457      if (unlikely(__pyx_kwds)) {
    9458        Py_ssize_t kw_args;
    9459 -      switch (PyTuple_GET_SIZE(__pyx_args)) {
    9460 +      const Py_ssize_t pos_args = PyTuple_GET_SIZE(__pyx_args);
    9461 +      switch (pos_args) {
    9462          case  3: values[2] = PyTuple_GET_ITEM(__pyx_args, 2);
    9463          case  2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1);
    9464          case  1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0);
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    9466          default: goto __pyx_L5_argtuple_error;
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    9468        kw_args = PyDict_Size(__pyx_kwds);
    9469 -      switch (PyTuple_GET_SIZE(__pyx_args)) {
    9470 +      switch (pos_args) {
    9471          case  0:
    9472 -        values[0] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__n);
    9473 -        if (likely(values[0])) kw_args--;
    9474 +        if (likely((values[0] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__n)) != 0)) kw_args--;
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    9478 -        if (likely(values[1])) kw_args--;
    9479 +        if (likely((values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s__pvals)) != 0)) kw_args--;
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    9482          }
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    9488 +        if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "multinomial") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4015; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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    9504 +  PyArrayObject *arrayObject_mnarr = 0;
    9505 +  double *__pyx_v_pix;
    9506 +  long *__pyx_v_mnix;
    9507 +  npy_intp __pyx_v_i;
    9508 +  npy_intp __pyx_v_j;
    9509 +  npy_intp __pyx_v_dn;
    9510 +  double __pyx_v_Sum;
    9511 +  PyObject *__pyx_v_shape = NULL;
    9512 +  PyObject *__pyx_v_multin = NULL;
    9513 +  PyObject *__pyx_r = NULL;
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    9516 +  PyObject *__pyx_t_2 = NULL;
    9517 +  int __pyx_t_3;
    9518 +  PyObject *__pyx_t_4 = NULL;
    9519 +  PyObject *__pyx_t_5 = NULL;
    9520 +  long __pyx_t_6;
    9521 +  int __pyx_lineno = 0;
    9522 +  const char *__pyx_filename = NULL;
    9523 +  int __pyx_clineno = 0;
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    9527   *         cdef double Sum
    9528 @@ -17369,9 +17992,9 @@
    9529      __Pyx_Raise(__pyx_t_2, 0, 0, 0);
    9530      __Pyx_DECREF(__pyx_t_2); __pyx_t_2 = 0;
    9531      {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4079; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    9532 -    goto __pyx_L6;
    9533 +    goto __pyx_L3;
    9534    }
    9535 -  __pyx_L6:;
    9536 +  __pyx_L3:;
    9537  
    9538    /* "mtrand.pyx":4081
    9539   *             raise ValueError("sum(pvals[:-1]) > 1.0")
    9540 @@ -17393,13 +18016,13 @@
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    9542      __Pyx_GOTREF(__pyx_t_2);
    9543      __pyx_t_4 = PyTuple_New(1); if (unlikely(!__pyx_t_4)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4082; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    9544 -    __Pyx_GOTREF(((PyObject *)__pyx_t_4));
    9545 +    __Pyx_GOTREF(__pyx_t_4);
    9546      PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_2);
    9547      __Pyx_GIVEREF(__pyx_t_2);
    9548      __pyx_t_2 = 0;
    9549      __pyx_v_shape = ((PyObject *)__pyx_t_4);
    9550      __pyx_t_4 = 0;
    9551 -    goto __pyx_L7;
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    9556 @@ -17422,7 +18045,7 @@
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    9560 -    __Pyx_GOTREF(((PyObject *)__pyx_t_2));
    9561 +    __Pyx_GOTREF(__pyx_t_2);
    9562      __Pyx_INCREF(__pyx_v_size);
    9563      PyTuple_SET_ITEM(__pyx_t_2, 0, __pyx_v_size);
    9564      __Pyx_GIVEREF(__pyx_v_size);
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    9578 -    __Pyx_GOTREF(((PyObject *)__pyx_t_4));
    9579 +    __Pyx_GOTREF(__pyx_t_4);
    9580      PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_2);
    9581      __Pyx_GIVEREF(__pyx_t_2);
    9582      __pyx_t_2 = 0;
    9583 @@ -17455,7 +18078,7 @@
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    9588 +  __pyx_L4:;
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    9604        }
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    9610 @@ -17590,10 +18213,10 @@
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    9614 -        goto __pyx_L11_break;
    9615 -        goto __pyx_L12;
    9616 +        goto __pyx_L8_break;
    9617 +        goto __pyx_L9;
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    9619 -      __pyx_L12:;
    9620 +      __pyx_L9:;
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    9623   *                 if dn <= 0:
    9624 @@ -17604,7 +18227,7 @@
    9625   */
    9626        __pyx_v_Sum = (__pyx_v_Sum - (__pyx_v_pix[__pyx_v_j]));
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    9628 -    __pyx_L11_break:;
    9629 +    __pyx_L8_break:;
    9630  
    9631      /* "mtrand.pyx":4101
    9632   *                     break
    9633 @@ -17624,9 +18247,9 @@
    9634   *             i = i + d
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    9637 -      goto __pyx_L13;
    9638 +      goto __pyx_L10;
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    9640 -    __pyx_L13:;
    9641 +    __pyx_L10:;
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    9648  
    9649 -/* "mtrand.pyx":4108
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    9651 +static PyObject *__pyx_pw_6mtrand_11RandomState_99dirichlet(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
    9652 +static char __pyx_doc_6mtrand_11RandomState_98dirichlet[] = "\n        dirichlet(alpha, size=None)\n\n        Draw samples from the Dirichlet distribution.\n\n        Draw `size` samples of dimension k from a Dirichlet distribution. A\n        Dirichlet-distributed random variable can be seen as a multivariate\n        generalization of a Beta distribution. Dirichlet pdf is the conjugate\n        prior of a multinomial in Bayesian inference.\n\n        Parameters\n        ----------\n        alpha : array\n            Parameter of the distribution (k dimension for sample of\n            dimension k).\n        size : array\n            Number of samples to draw.\n\n        Returns\n        -------\n        samples : ndarray,\n            The drawn samples, of shape (alpha.ndim, size).\n\n        Notes\n        -----\n        .. math:: X \\approx \\prod_{i=1}^{k}{x^{\\alpha_i-1}_i}\n\n        Uses the following property for computation: for each dimension,\n        draw a random sample y_i from a standard gamma generator of shape\n        `alpha_i`, then\n        :math:`X = \\frac{1}{\\sum_{i=1}^k{y_i}} (y_1, \\ldots, y_n)` is\n        Dirichlet distributed.\n\n        References\n        ----------\n        .. [1] David McKay, \"Information Theory, Inference and Learning\n               Algorithms,\" chapter 23,\n               http://www.inference.phy.cam.ac.uk/mackay/\n        .. [2] Wikipedia, \"Dirichlet distribution\",\n               http://en.wikipedia.org/wiki/Dirichlet_distribution\n\n        Examples\n        --------\n        Taking an example cited in Wikipedia, this distribution can be used if\n        one wanted to cut strings (each of initial length 1.0) into K pieces\n        with different lengths, where each piece had, on average, a designated\n        average length, but allowing some variation in the relative sizes of the\n        pieces.\n\n        >>> s = np.random.dirichlet((10, 5, 3), 20).transpose()\n\n        >>> plt.barh(range(20), s[0])\n        >>> plt.barh(range(20), s[1], left=s[0], color='g')""\n        >>> plt.barh(range(20), s[2], left=s[0]+s[1], color='r')\n        >>> plt.title(\"Lengths of Strings\")\n\n        ";
    9653 +static PyObject *__pyx_pw_6mtrand_11RandomState_99dirichlet(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds) {
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    9655 +  PyObject *__pyx_v_size = 0;
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    9661 +    PyObject* values[2] = {0,0};
    9662 +
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    9664   *         return multin
    9665   *
    9666   *     def dirichlet(self, object alpha, size=None):             # <<<<<<<<<<<<<<
    9667   *         """
    9668   *         dirichlet(alpha, size=None)
    9669   */
    9670 -
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    9672 -static char __pyx_doc_6mtrand_11RandomState_49dirichlet[] = "\n        dirichlet(alpha, size=None)\n\n        Draw samples from the Dirichlet distribution.\n\n        Draw `size` samples of dimension k from a Dirichlet distribution. A\n        Dirichlet-distributed random variable can be seen as a multivariate\n        generalization of a Beta distribution. Dirichlet pdf is the conjugate\n        prior of a multinomial in Bayesian inference.\n\n        Parameters\n        ----------\n        alpha : array\n            Parameter of the distribution (k dimension for sample of\n            dimension k).\n        size : array\n            Number of samples to draw.\n\n        Returns\n        -------\n        samples : ndarray,\n            The drawn samples, of shape (alpha.ndim, size).\n\n        Notes\n        -----\n        .. math:: X \\approx \\prod_{i=1}^{k}{x^{\\alpha_i-1}_i}\n\n        Uses the following property for computation: for each dimension,\n        draw a random sample y_i from a standard gamma generator of shape\n        `alpha_i`, then\n        :math:`X = \\frac{1}{\\sum_{i=1}^k{y_i}} (y_1, \\ldots, y_n)` is\n        Dirichlet distributed.\n\n        References\n        ----------\n        .. [1] David McKay, \"Information Theory, Inference and Learning\n               Algorithms,\" chapter 23,\n               http://www.inference.phy.cam.ac.uk/mackay/\n        .. [2] Wikipedia, \"Dirichlet distribution\",\n               http://en.wikipedia.org/wiki/Dirichlet_distribution\n\n        Examples\n        --------\n        Taking an example cited in Wikipedia, this distribution can be used if\n        one wanted to cut strings (each of initial length 1.0) into K pieces\n        with different lengths, where each piece had, on average, a designated\n        average length, but allowing some variation in the relative sizes of the\n        pieces.\n\n        >>> s = np.random.dirichlet((10, 5, 3), 20).transpose()\n\n        >>> plt.barh(range(20), s[0])\n        >>> plt.barh(range(20), s[1], left=s[0], color='g')""\n        >>> plt.barh(range(20), s[2], left=s[0]+s[1], color='r')\n        >>> plt.title(\"Lengths of Strings\")\n\n        ";
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    9679 -  PyArrayObject *__pyx_v_val_arr = 0;
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    9682 -  npy_intp __pyx_v_i;
    9683 -  npy_intp __pyx_v_j;
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    9706 -      switch (PyTuple_GET_SIZE(__pyx_args)) {
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    9708 +      switch (pos_args) {
    9709          case  2: values[1] = PyTuple_GET_ITEM(__pyx_args, 1);
    9710          case  1: values[0] = PyTuple_GET_ITEM(__pyx_args, 0);
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    9714        kw_args = PyDict_Size(__pyx_kwds);
    9715 -      switch (PyTuple_GET_SIZE(__pyx_args)) {
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    9810      PyTuple_SET_ITEM(__pyx_t_4, 0, __pyx_t_2);
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    9821   *             shape = size + (k,)
    9822 @@ -17886,7 +18519,7 @@
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    9826 -  __Pyx_GOTREF(((PyObject *)__pyx_t_2));
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    9832   *                 acc             = acc + val_data[i+j]
    9833   *             invacc  = 1/acc
    9834   */
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    9838        /* "mtrand.pyx":4215
    9839   *             for j from 0 <= j < k:
    9840 @@ -18059,6 +18692,18 @@
    9841    return __pyx_r;
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    9843  
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    9845 +static PyObject *__pyx_pw_6mtrand_11RandomState_101shuffle(PyObject *__pyx_v_self, PyObject *__pyx_v_x); /*proto*/
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    9854 +}
    9855 +
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    9857   *
    9858   *     # Shuffling and permutations:
    9859 @@ -18067,9 +18712,7 @@
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    9861   */
    9862  
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    9868    npy_intp __pyx_v_j;
    9869    int __pyx_v_copy;
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    9872    const char *__pyx_filename = NULL;
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    9884 +      __pyx_t_5 = __Pyx_GetItemInt(__pyx_v_x, 0, sizeof(long), PyInt_FromLong); if (!__pyx_t_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4262; __pyx_clineno = __LINE__; goto __pyx_L3_error;}
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    9895 -    __pyx_L5_error:;
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    9937   *                 i = i - 1
    9938   */
    9939 -      __pyx_v_j = rk_interval(__pyx_v_i, ((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state);
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    9957 -        __pyx_v_j = rk_interval(__pyx_v_i, ((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state);
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    9965        }
    9966 -      goto __pyx_L18;
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    9971 @@ -18336,7 +18979,7 @@
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    9974   */
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    9992 @@ -18388,6 +19031,18 @@
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    9995  
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    10007 +
    10008  /* "mtrand.pyx":4286
    10009   *                     i = i - 1
    10010   *
    10011 @@ -18396,9 +19051,7 @@
    10012   *         permutation(x)
    10013   */
    10014  
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    10018 +static PyObject *__pyx_pf_6mtrand_11RandomState_102permutation(struct __pyx_obj_6mtrand_RandomState *__pyx_v_self, PyObject *__pyx_v_x) {
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    10406    __pyx_k_tuple_33 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_33)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1532; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10407 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_33));
    10408 +  __Pyx_GOTREF(__pyx_k_tuple_33);
    10409    __Pyx_INCREF(((PyObject *)__pyx_kp_s_31));
    10410    PyTuple_SET_ITEM(__pyx_k_tuple_33, 0, ((PyObject *)__pyx_kp_s_31));
    10411    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_31));
    10412 @@ -19325,7 +19978,7 @@
    10413   *                 raise ValueError("scale <= 0")
    10414   */
    10415    __pyx_k_tuple_35 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_35)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1612; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10416 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_35));
    10417 +  __Pyx_GOTREF(__pyx_k_tuple_35);
    10418    __Pyx_INCREF(((PyObject *)__pyx_kp_s_31));
    10419    PyTuple_SET_ITEM(__pyx_k_tuple_35, 0, ((PyObject *)__pyx_kp_s_31));
    10420    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_31));
    10421 @@ -19339,7 +19992,7 @@
    10422   *
    10423   */
    10424    __pyx_k_tuple_36 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_36)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1614; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10425 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_36));
    10426 +  __Pyx_GOTREF(__pyx_k_tuple_36);
    10427    __Pyx_INCREF(((PyObject *)__pyx_kp_s_19));
    10428    PyTuple_SET_ITEM(__pyx_k_tuple_36, 0, ((PyObject *)__pyx_kp_s_19));
    10429    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_19));
    10430 @@ -19353,7 +20006,7 @@
    10431   *             raise ValueError("scale <= 0")
    10432   */
    10433    __pyx_k_tuple_37 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_37)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1621; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10434 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_37));
    10435 +  __Pyx_GOTREF(__pyx_k_tuple_37);
    10436    __Pyx_INCREF(((PyObject *)__pyx_kp_s_31));
    10437    PyTuple_SET_ITEM(__pyx_k_tuple_37, 0, ((PyObject *)__pyx_kp_s_31));
    10438    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_31));
    10439 @@ -19367,7 +20020,7 @@
    10440   *
    10441   */
    10442    __pyx_k_tuple_38 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_38)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1623; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10443 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_38));
    10444 +  __Pyx_GOTREF(__pyx_k_tuple_38);
    10445    __Pyx_INCREF(((PyObject *)__pyx_kp_s_19));
    10446    PyTuple_SET_ITEM(__pyx_k_tuple_38, 0, ((PyObject *)__pyx_kp_s_19));
    10447    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_19));
    10448 @@ -19381,7 +20034,7 @@
    10449   *                 raise ValueError("scale <= 0")
    10450   */
    10451    __pyx_k_tuple_39 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_39)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1714; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10452 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_39));
    10453 +  __Pyx_GOTREF(__pyx_k_tuple_39);
    10454    __Pyx_INCREF(((PyObject *)__pyx_kp_s_31));
    10455    PyTuple_SET_ITEM(__pyx_k_tuple_39, 0, ((PyObject *)__pyx_kp_s_31));
    10456    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_31));
    10457 @@ -19395,7 +20048,7 @@
    10458   *
    10459   */
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    10461 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_40));
    10462 +  __Pyx_GOTREF(__pyx_k_tuple_40);
    10463    __Pyx_INCREF(((PyObject *)__pyx_kp_s_19));
    10464    PyTuple_SET_ITEM(__pyx_k_tuple_40, 0, ((PyObject *)__pyx_kp_s_19));
    10465    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_19));
    10466 @@ -19409,7 +20062,7 @@
    10467   *             raise ValueError("dfden <= 0")
    10468   */
    10469    __pyx_k_tuple_42 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_42)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1724; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10470 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_42));
    10471 +  __Pyx_GOTREF(__pyx_k_tuple_42);
    10472    __Pyx_INCREF(((PyObject *)__pyx_kp_s_41));
    10473    PyTuple_SET_ITEM(__pyx_k_tuple_42, 0, ((PyObject *)__pyx_kp_s_41));
    10474    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_41));
    10475 @@ -19423,7 +20076,7 @@
    10476   *
    10477   */
    10478    __pyx_k_tuple_44 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_44)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1726; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10479 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_44));
    10480 +  __Pyx_GOTREF(__pyx_k_tuple_44);
    10481    __Pyx_INCREF(((PyObject *)__pyx_kp_s_43));
    10482    PyTuple_SET_ITEM(__pyx_k_tuple_44, 0, ((PyObject *)__pyx_kp_s_43));
    10483    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_43));
    10484 @@ -19437,7 +20090,7 @@
    10485   *                 raise ValueError("dfden <= 0")
    10486   */
    10487    __pyx_k_tuple_46 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_46)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1801; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10488 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_46));
    10489 +  __Pyx_GOTREF(__pyx_k_tuple_46);
    10490    __Pyx_INCREF(((PyObject *)__pyx_kp_s_45));
    10491    PyTuple_SET_ITEM(__pyx_k_tuple_46, 0, ((PyObject *)__pyx_kp_s_45));
    10492    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_45));
    10493 @@ -19451,7 +20104,7 @@
    10494   *                 raise ValueError("nonc < 0")
    10495   */
    10496    __pyx_k_tuple_47 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_47)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1803; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10497 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_47));
    10498 +  __Pyx_GOTREF(__pyx_k_tuple_47);
    10499    __Pyx_INCREF(((PyObject *)__pyx_kp_s_43));
    10500    PyTuple_SET_ITEM(__pyx_k_tuple_47, 0, ((PyObject *)__pyx_kp_s_43));
    10501    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_43));
    10502 @@ -19465,7 +20118,7 @@
    10503   *                                   fdfnum, fdfden, fnonc)
    10504   */
    10505    __pyx_k_tuple_49 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_49)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1805; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10506 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_49));
    10507 +  __Pyx_GOTREF(__pyx_k_tuple_49);
    10508    __Pyx_INCREF(((PyObject *)__pyx_kp_s_48));
    10509    PyTuple_SET_ITEM(__pyx_k_tuple_49, 0, ((PyObject *)__pyx_kp_s_48));
    10510    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_48));
    10511 @@ -19479,7 +20132,7 @@
    10512   *             raise ValueError("dfden <= 0")
    10513   */
    10514    __pyx_k_tuple_50 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_50)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1816; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10515 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_50));
    10516 +  __Pyx_GOTREF(__pyx_k_tuple_50);
    10517    __Pyx_INCREF(((PyObject *)__pyx_kp_s_45));
    10518    PyTuple_SET_ITEM(__pyx_k_tuple_50, 0, ((PyObject *)__pyx_kp_s_45));
    10519    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_45));
    10520 @@ -19493,7 +20146,7 @@
    10521   *             raise ValueError("nonc < 0")
    10522   */
    10523    __pyx_k_tuple_51 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_51)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1818; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10524 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_51));
    10525 +  __Pyx_GOTREF(__pyx_k_tuple_51);
    10526    __Pyx_INCREF(((PyObject *)__pyx_kp_s_43));
    10527    PyTuple_SET_ITEM(__pyx_k_tuple_51, 0, ((PyObject *)__pyx_kp_s_43));
    10528    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_43));
    10529 @@ -19507,7 +20160,7 @@
    10530   *             odfden, ononc)
    10531   */
    10532    __pyx_k_tuple_52 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_52)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1820; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10533 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_52));
    10534 +  __Pyx_GOTREF(__pyx_k_tuple_52);
    10535    __Pyx_INCREF(((PyObject *)__pyx_kp_s_48));
    10536    PyTuple_SET_ITEM(__pyx_k_tuple_52, 0, ((PyObject *)__pyx_kp_s_48));
    10537    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_48));
    10538 @@ -19521,7 +20174,7 @@
    10539   *
    10540   */
    10541    __pyx_k_tuple_54 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_54)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1892; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10542 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_54));
    10543 +  __Pyx_GOTREF(__pyx_k_tuple_54);
    10544    __Pyx_INCREF(((PyObject *)__pyx_kp_s_53));
    10545    PyTuple_SET_ITEM(__pyx_k_tuple_54, 0, ((PyObject *)__pyx_kp_s_53));
    10546    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_53));
    10547 @@ -19535,7 +20188,7 @@
    10548   *
    10549   */
    10550    __pyx_k_tuple_55 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_55)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1899; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10551 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_55));
    10552 +  __Pyx_GOTREF(__pyx_k_tuple_55);
    10553    __Pyx_INCREF(((PyObject *)__pyx_kp_s_53));
    10554    PyTuple_SET_ITEM(__pyx_k_tuple_55, 0, ((PyObject *)__pyx_kp_s_53));
    10555    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_53));
    10556 @@ -19549,7 +20202,7 @@
    10557   *                 raise ValueError("nonc <= 0")
    10558   */
    10559    __pyx_k_tuple_56 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_56)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1977; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10560 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_56));
    10561 +  __Pyx_GOTREF(__pyx_k_tuple_56);
    10562    __Pyx_INCREF(((PyObject *)__pyx_kp_s_53));
    10563    PyTuple_SET_ITEM(__pyx_k_tuple_56, 0, ((PyObject *)__pyx_kp_s_53));
    10564    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_53));
    10565 @@ -19563,7 +20216,7 @@
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    10568    __pyx_k_tuple_58 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_58)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1979; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10569 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_58));
    10570 +  __Pyx_GOTREF(__pyx_k_tuple_58);
    10571    __Pyx_INCREF(((PyObject *)__pyx_kp_s_57));
    10572    PyTuple_SET_ITEM(__pyx_k_tuple_58, 0, ((PyObject *)__pyx_kp_s_57));
    10573    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_57));
    10574 @@ -19577,7 +20230,7 @@
    10575   *             raise ValueError("nonc < 0")
    10576   */
    10577    __pyx_k_tuple_60 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_60)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1988; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10578 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_60));
    10579 +  __Pyx_GOTREF(__pyx_k_tuple_60);
    10580    __Pyx_INCREF(((PyObject *)__pyx_kp_s_59));
    10581    PyTuple_SET_ITEM(__pyx_k_tuple_60, 0, ((PyObject *)__pyx_kp_s_59));
    10582    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_59));
    10583 @@ -19591,7 +20244,7 @@
    10584   *             odf, ononc)
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    10586    __pyx_k_tuple_61 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_61)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1990; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10587 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_61));
    10588 +  __Pyx_GOTREF(__pyx_k_tuple_61);
    10589    __Pyx_INCREF(((PyObject *)__pyx_kp_s_48));
    10590    PyTuple_SET_ITEM(__pyx_k_tuple_61, 0, ((PyObject *)__pyx_kp_s_48));
    10591    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_48));
    10592 @@ -19605,7 +20258,7 @@
    10593   *
    10594   */
    10595    __pyx_k_tuple_62 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_62)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2146; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10596 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_62));
    10597 +  __Pyx_GOTREF(__pyx_k_tuple_62);
    10598    __Pyx_INCREF(((PyObject *)__pyx_kp_s_53));
    10599    PyTuple_SET_ITEM(__pyx_k_tuple_62, 0, ((PyObject *)__pyx_kp_s_53));
    10600    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_53));
    10601 @@ -19619,7 +20272,7 @@
    10602   *
    10603   */
    10604    __pyx_k_tuple_63 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_63)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2153; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10605 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_63));
    10606 +  __Pyx_GOTREF(__pyx_k_tuple_63);
    10607    __Pyx_INCREF(((PyObject *)__pyx_kp_s_53));
    10608    PyTuple_SET_ITEM(__pyx_k_tuple_63, 0, ((PyObject *)__pyx_kp_s_53));
    10609    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_53));
    10610 @@ -19633,7 +20286,7 @@
    10611   *
    10612   */
    10613    __pyx_k_tuple_65 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_65)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2239; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10614 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_65));
    10615 +  __Pyx_GOTREF(__pyx_k_tuple_65);
    10616    __Pyx_INCREF(((PyObject *)__pyx_kp_s_64));
    10617    PyTuple_SET_ITEM(__pyx_k_tuple_65, 0, ((PyObject *)__pyx_kp_s_64));
    10618    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_64));
    10619 @@ -19647,7 +20300,7 @@
    10620   *
    10621   */
    10622    __pyx_k_tuple_66 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_66)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2247; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10623 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_66));
    10624 +  __Pyx_GOTREF(__pyx_k_tuple_66);
    10625    __Pyx_INCREF(((PyObject *)__pyx_kp_s_64));
    10626    PyTuple_SET_ITEM(__pyx_k_tuple_66, 0, ((PyObject *)__pyx_kp_s_64));
    10627    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_64));
    10628 @@ -19661,7 +20314,7 @@
    10629   *
    10630   */
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    10632 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_67));
    10633 +  __Pyx_GOTREF(__pyx_k_tuple_67);
    10634    __Pyx_INCREF(((PyObject *)__pyx_kp_s_22));
    10635    PyTuple_SET_ITEM(__pyx_k_tuple_67, 0, ((PyObject *)__pyx_kp_s_22));
    10636    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_22));
    10637 @@ -19675,7 +20328,7 @@
    10638   *
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    10641 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_68));
    10642 +  __Pyx_GOTREF(__pyx_k_tuple_68);
    10643    __Pyx_INCREF(((PyObject *)__pyx_kp_s_22));
    10644    PyTuple_SET_ITEM(__pyx_k_tuple_68, 0, ((PyObject *)__pyx_kp_s_22));
    10645    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_22));
    10646 @@ -19689,7 +20342,7 @@
    10647   *
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    10650 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_69));
    10651 +  __Pyx_GOTREF(__pyx_k_tuple_69);
    10652    __Pyx_INCREF(((PyObject *)__pyx_kp_s_22));
    10653    PyTuple_SET_ITEM(__pyx_k_tuple_69, 0, ((PyObject *)__pyx_kp_s_22));
    10654    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_22));
    10655 @@ -19703,7 +20356,7 @@
    10656   *
    10657   */
    10658    __pyx_k_tuple_70 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_70)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2443; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10659 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_70));
    10660 +  __Pyx_GOTREF(__pyx_k_tuple_70);
    10661    __Pyx_INCREF(((PyObject *)__pyx_kp_s_22));
    10662    PyTuple_SET_ITEM(__pyx_k_tuple_70, 0, ((PyObject *)__pyx_kp_s_22));
    10663    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_22));
    10664 @@ -19717,7 +20370,7 @@
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    10666   */
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    10668 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_71));
    10669 +  __Pyx_GOTREF(__pyx_k_tuple_71);
    10670    __Pyx_INCREF(((PyObject *)__pyx_kp_s_22));
    10671    PyTuple_SET_ITEM(__pyx_k_tuple_71, 0, ((PyObject *)__pyx_kp_s_22));
    10672    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_22));
    10673 @@ -19731,7 +20384,7 @@
    10674   *
    10675   */
    10676    __pyx_k_tuple_72 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_72)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2552; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10677 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_72));
    10678 +  __Pyx_GOTREF(__pyx_k_tuple_72);
    10679    __Pyx_INCREF(((PyObject *)__pyx_kp_s_22));
    10680    PyTuple_SET_ITEM(__pyx_k_tuple_72, 0, ((PyObject *)__pyx_kp_s_22));
    10681    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_22));
    10682 @@ -19745,7 +20398,7 @@
    10683   *
    10684   */
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    10686 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_75));
    10687 +  __Pyx_GOTREF(__pyx_k_tuple_75);
    10688    __Pyx_INCREF(((PyObject *)__pyx_kp_s_19));
    10689    PyTuple_SET_ITEM(__pyx_k_tuple_75, 0, ((PyObject *)__pyx_kp_s_19));
    10690    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_19));
    10691 @@ -19759,7 +20412,7 @@
    10692   *
    10693   */
    10694    __pyx_k_tuple_76 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_76)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2642; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10695 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_76));
    10696 +  __Pyx_GOTREF(__pyx_k_tuple_76);
    10697    __Pyx_INCREF(((PyObject *)__pyx_kp_s_19));
    10698    PyTuple_SET_ITEM(__pyx_k_tuple_76, 0, ((PyObject *)__pyx_kp_s_19));
    10699    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_19));
    10700 @@ -19773,7 +20426,7 @@
    10701   *
    10702   */
    10703    __pyx_k_tuple_79 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_79)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2766; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10704 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_79));
    10705 +  __Pyx_GOTREF(__pyx_k_tuple_79);
    10706    __Pyx_INCREF(((PyObject *)__pyx_kp_s_19));
    10707    PyTuple_SET_ITEM(__pyx_k_tuple_79, 0, ((PyObject *)__pyx_kp_s_19));
    10708    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_19));
    10709 @@ -19787,7 +20440,7 @@
    10710   *
    10711   */
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    10713 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_80));
    10714 +  __Pyx_GOTREF(__pyx_k_tuple_80);
    10715    __Pyx_INCREF(((PyObject *)__pyx_kp_s_19));
    10716    PyTuple_SET_ITEM(__pyx_k_tuple_80, 0, ((PyObject *)__pyx_kp_s_19));
    10717    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_19));
    10718 @@ -19801,7 +20454,7 @@
    10719   *
    10720   */
    10721    __pyx_k_tuple_83 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_83)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2854; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10722 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_83));
    10723 +  __Pyx_GOTREF(__pyx_k_tuple_83);
    10724    __Pyx_INCREF(((PyObject *)__pyx_kp_s_19));
    10725    PyTuple_SET_ITEM(__pyx_k_tuple_83, 0, ((PyObject *)__pyx_kp_s_19));
    10726    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_19));
    10727 @@ -19815,7 +20468,7 @@
    10728   *
    10729   */
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    10731 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_84));
    10732 +  __Pyx_GOTREF(__pyx_k_tuple_84);
    10733    __Pyx_INCREF(((PyObject *)__pyx_kp_s_19));
    10734    PyTuple_SET_ITEM(__pyx_k_tuple_84, 0, ((PyObject *)__pyx_kp_s_19));
    10735    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_19));
    10736 @@ -19829,7 +20482,7 @@
    10737   *
    10738   */
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    10740 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_88));
    10741 +  __Pyx_GOTREF(__pyx_k_tuple_88);
    10742    __Pyx_INCREF(((PyObject *)__pyx_kp_s_87));
    10743    PyTuple_SET_ITEM(__pyx_k_tuple_88, 0, ((PyObject *)__pyx_kp_s_87));
    10744    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_87));
    10745 @@ -19843,7 +20496,7 @@
    10746   *
    10747   */
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    10749 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_90));
    10750 +  __Pyx_GOTREF(__pyx_k_tuple_90);
    10751    __Pyx_INCREF(((PyObject *)__pyx_kp_s_89));
    10752    PyTuple_SET_ITEM(__pyx_k_tuple_90, 0, ((PyObject *)__pyx_kp_s_89));
    10753    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_89));
    10754 @@ -19857,7 +20510,7 @@
    10755   *
    10756   */
    10757    __pyx_k_tuple_92 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_92)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3057; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10758 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_92));
    10759 +  __Pyx_GOTREF(__pyx_k_tuple_92);
    10760    __Pyx_INCREF(((PyObject *)__pyx_kp_s_19));
    10761    PyTuple_SET_ITEM(__pyx_k_tuple_92, 0, ((PyObject *)__pyx_kp_s_19));
    10762    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_19));
    10763 @@ -19871,7 +20524,7 @@
    10764   *
    10765   */
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    10767 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_94));
    10768 +  __Pyx_GOTREF(__pyx_k_tuple_94);
    10769    __Pyx_INCREF(((PyObject *)__pyx_kp_s_93));
    10770    PyTuple_SET_ITEM(__pyx_k_tuple_94, 0, ((PyObject *)__pyx_kp_s_93));
    10771    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_93));
    10772 @@ -19885,7 +20538,7 @@
    10773   *                 raise ValueError("scale <= 0")
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    10775    __pyx_k_tuple_96 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_96)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3137; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10776 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_96));
    10777 +  __Pyx_GOTREF(__pyx_k_tuple_96);
    10778    __Pyx_INCREF(((PyObject *)__pyx_kp_s_95));
    10779    PyTuple_SET_ITEM(__pyx_k_tuple_96, 0, ((PyObject *)__pyx_kp_s_95));
    10780    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_95));
    10781 @@ -19899,7 +20552,7 @@
    10782   *
    10783   */
    10784    __pyx_k_tuple_97 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_97)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3139; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10785 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_97));
    10786 +  __Pyx_GOTREF(__pyx_k_tuple_97);
    10787    __Pyx_INCREF(((PyObject *)__pyx_kp_s_19));
    10788    PyTuple_SET_ITEM(__pyx_k_tuple_97, 0, ((PyObject *)__pyx_kp_s_19));
    10789    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_19));
    10790 @@ -19913,7 +20566,7 @@
    10791   *             raise ValueError("scale <= 0.0")
    10792   */
    10793    __pyx_k_tuple_99 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_99)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3146; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10794 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_99));
    10795 +  __Pyx_GOTREF(__pyx_k_tuple_99);
    10796    __Pyx_INCREF(((PyObject *)__pyx_kp_s_98));
    10797    PyTuple_SET_ITEM(__pyx_k_tuple_99, 0, ((PyObject *)__pyx_kp_s_98));
    10798    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_98));
    10799 @@ -19927,7 +20580,7 @@
    10800   *
    10801   */
    10802    __pyx_k_tuple_100 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_100)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3148; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10803 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_100));
    10804 +  __Pyx_GOTREF(__pyx_k_tuple_100);
    10805    __Pyx_INCREF(((PyObject *)__pyx_kp_s_93));
    10806    PyTuple_SET_ITEM(__pyx_k_tuple_100, 0, ((PyObject *)__pyx_kp_s_93));
    10807    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_93));
    10808 @@ -19941,7 +20594,7 @@
    10809   *                 raise ValueError("mode > right")
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    10811    __pyx_k_tuple_102 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_102)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3218; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10812 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_102));
    10813 +  __Pyx_GOTREF(__pyx_k_tuple_102);
    10814    __Pyx_INCREF(((PyObject *)__pyx_kp_s_101));
    10815    PyTuple_SET_ITEM(__pyx_k_tuple_102, 0, ((PyObject *)__pyx_kp_s_101));
    10816    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_101));
    10817 @@ -19955,7 +20608,7 @@
    10818   *                 raise ValueError("left == right")
    10819   */
    10820    __pyx_k_tuple_104 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_104)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3220; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10821 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_104));
    10822 +  __Pyx_GOTREF(__pyx_k_tuple_104);
    10823    __Pyx_INCREF(((PyObject *)__pyx_kp_s_103));
    10824    PyTuple_SET_ITEM(__pyx_k_tuple_104, 0, ((PyObject *)__pyx_kp_s_103));
    10825    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_103));
    10826 @@ -19969,7 +20622,7 @@
    10827   *                                   fmode, fright)
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    10829    __pyx_k_tuple_106 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_106)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3222; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10830 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_106));
    10831 +  __Pyx_GOTREF(__pyx_k_tuple_106);
    10832    __Pyx_INCREF(((PyObject *)__pyx_kp_s_105));
    10833    PyTuple_SET_ITEM(__pyx_k_tuple_106, 0, ((PyObject *)__pyx_kp_s_105));
    10834    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_105));
    10835 @@ -19983,7 +20636,7 @@
    10836   *             raise ValueError("mode > right")
    10837   */
    10838    __pyx_k_tuple_107 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_107)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3232; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10839 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_107));
    10840 +  __Pyx_GOTREF(__pyx_k_tuple_107);
    10841    __Pyx_INCREF(((PyObject *)__pyx_kp_s_101));
    10842    PyTuple_SET_ITEM(__pyx_k_tuple_107, 0, ((PyObject *)__pyx_kp_s_101));
    10843    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_101));
    10844 @@ -19997,7 +20650,7 @@
    10845   *             raise ValueError("left == right")
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    10847    __pyx_k_tuple_108 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_108)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3234; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10848 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_108));
    10849 +  __Pyx_GOTREF(__pyx_k_tuple_108);
    10850    __Pyx_INCREF(((PyObject *)__pyx_kp_s_103));
    10851    PyTuple_SET_ITEM(__pyx_k_tuple_108, 0, ((PyObject *)__pyx_kp_s_103));
    10852    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_103));
    10853 @@ -20011,7 +20664,7 @@
    10854   *             omode, oright)
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    10857 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_109));
    10858 +  __Pyx_GOTREF(__pyx_k_tuple_109);
    10859    __Pyx_INCREF(((PyObject *)__pyx_kp_s_105));
    10860    PyTuple_SET_ITEM(__pyx_k_tuple_109, 0, ((PyObject *)__pyx_kp_s_105));
    10861    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_105));
    10862 @@ -20025,7 +20678,7 @@
    10863   *                 raise ValueError("p < 0")
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    10866 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_111));
    10867 +  __Pyx_GOTREF(__pyx_k_tuple_111);
    10868    __Pyx_INCREF(((PyObject *)__pyx_kp_s_110));
    10869    PyTuple_SET_ITEM(__pyx_k_tuple_111, 0, ((PyObject *)__pyx_kp_s_110));
    10870    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_110));
    10871 @@ -20039,7 +20692,7 @@
    10872   *                 raise ValueError("p > 1")
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    10874    __pyx_k_tuple_113 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_113)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3332; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10875 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_113));
    10876 +  __Pyx_GOTREF(__pyx_k_tuple_113);
    10877    __Pyx_INCREF(((PyObject *)__pyx_kp_s_112));
    10878    PyTuple_SET_ITEM(__pyx_k_tuple_113, 0, ((PyObject *)__pyx_kp_s_112));
    10879    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_112));
    10880 @@ -20053,7 +20706,7 @@
    10881   *
    10882   */
    10883    __pyx_k_tuple_115 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_115)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3334; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10884 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_115));
    10885 +  __Pyx_GOTREF(__pyx_k_tuple_115);
    10886    __Pyx_INCREF(((PyObject *)__pyx_kp_s_114));
    10887    PyTuple_SET_ITEM(__pyx_k_tuple_115, 0, ((PyObject *)__pyx_kp_s_114));
    10888    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_114));
    10889 @@ -20067,7 +20720,7 @@
    10890   *             raise ValueError("p < 0")
    10891   */
    10892    __pyx_k_tuple_116 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_116)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3342; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10893 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_116));
    10894 +  __Pyx_GOTREF(__pyx_k_tuple_116);
    10895    __Pyx_INCREF(((PyObject *)__pyx_kp_s_110));
    10896    PyTuple_SET_ITEM(__pyx_k_tuple_116, 0, ((PyObject *)__pyx_kp_s_110));
    10897    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_110));
    10898 @@ -20081,7 +20734,7 @@
    10899   *             raise ValueError("p > 1")
    10900   */
    10901    __pyx_k_tuple_117 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_117)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3344; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10902 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_117));
    10903 +  __Pyx_GOTREF(__pyx_k_tuple_117);
    10904    __Pyx_INCREF(((PyObject *)__pyx_kp_s_112));
    10905    PyTuple_SET_ITEM(__pyx_k_tuple_117, 0, ((PyObject *)__pyx_kp_s_112));
    10906    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_112));
    10907 @@ -20095,7 +20748,7 @@
    10908   *
    10909   */
    10910    __pyx_k_tuple_118 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_118)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3346; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10911 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_118));
    10912 +  __Pyx_GOTREF(__pyx_k_tuple_118);
    10913    __Pyx_INCREF(((PyObject *)__pyx_kp_s_114));
    10914    PyTuple_SET_ITEM(__pyx_k_tuple_118, 0, ((PyObject *)__pyx_kp_s_114));
    10915    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_114));
    10916 @@ -20109,7 +20762,7 @@
    10917   *                 raise ValueError("p < 0")
    10918   */
    10919    __pyx_k_tuple_119 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_119)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3423; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10920 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_119));
    10921 +  __Pyx_GOTREF(__pyx_k_tuple_119);
    10922    __Pyx_INCREF(((PyObject *)__pyx_kp_s_110));
    10923    PyTuple_SET_ITEM(__pyx_k_tuple_119, 0, ((PyObject *)__pyx_kp_s_110));
    10924    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_110));
    10925 @@ -20123,7 +20776,7 @@
    10926   *                 raise ValueError("p > 1")
    10927   */
    10928    __pyx_k_tuple_120 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_120)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3425; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10929 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_120));
    10930 +  __Pyx_GOTREF(__pyx_k_tuple_120);
    10931    __Pyx_INCREF(((PyObject *)__pyx_kp_s_112));
    10932    PyTuple_SET_ITEM(__pyx_k_tuple_120, 0, ((PyObject *)__pyx_kp_s_112));
    10933    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_112));
    10934 @@ -20137,7 +20790,7 @@
    10935   *                                    size, fn, fp)
    10936   */
    10937    __pyx_k_tuple_121 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_121)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3427; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10938 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_121));
    10939 +  __Pyx_GOTREF(__pyx_k_tuple_121);
    10940    __Pyx_INCREF(((PyObject *)__pyx_kp_s_114));
    10941    PyTuple_SET_ITEM(__pyx_k_tuple_121, 0, ((PyObject *)__pyx_kp_s_114));
    10942    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_114));
    10943 @@ -20151,7 +20804,7 @@
    10944   *             raise ValueError("p < 0")
    10945   */
    10946    __pyx_k_tuple_122 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_122)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3436; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10947 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_122));
    10948 +  __Pyx_GOTREF(__pyx_k_tuple_122);
    10949    __Pyx_INCREF(((PyObject *)__pyx_kp_s_110));
    10950    PyTuple_SET_ITEM(__pyx_k_tuple_122, 0, ((PyObject *)__pyx_kp_s_110));
    10951    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_110));
    10952 @@ -20165,7 +20818,7 @@
    10953   *             raise ValueError("p > 1")
    10954   */
    10955    __pyx_k_tuple_123 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_123)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3438; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10956 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_123));
    10957 +  __Pyx_GOTREF(__pyx_k_tuple_123);
    10958    __Pyx_INCREF(((PyObject *)__pyx_kp_s_112));
    10959    PyTuple_SET_ITEM(__pyx_k_tuple_123, 0, ((PyObject *)__pyx_kp_s_112));
    10960    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_112));
    10961 @@ -20179,7 +20832,7 @@
    10962   *                             on, op)
    10963   */
    10964    __pyx_k_tuple_124 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_124)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3440; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10965 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_124));
    10966 +  __Pyx_GOTREF(__pyx_k_tuple_124);
    10967    __Pyx_INCREF(((PyObject *)__pyx_kp_s_114));
    10968    PyTuple_SET_ITEM(__pyx_k_tuple_124, 0, ((PyObject *)__pyx_kp_s_114));
    10969    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_114));
    10970 @@ -20193,7 +20846,7 @@
    10971   *                 raise ValueError("lam value too large")
    10972   */
    10973    __pyx_k_tuple_127 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_127)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3501; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10974 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_127));
    10975 +  __Pyx_GOTREF(__pyx_k_tuple_127);
    10976    __Pyx_INCREF(((PyObject *)__pyx_kp_s_126));
    10977    PyTuple_SET_ITEM(__pyx_k_tuple_127, 0, ((PyObject *)__pyx_kp_s_126));
    10978    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_126));
    10979 @@ -20207,7 +20860,7 @@
    10980   *
    10981   */
    10982    __pyx_k_tuple_129 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_129)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3503; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10983 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_129));
    10984 +  __Pyx_GOTREF(__pyx_k_tuple_129);
    10985    __Pyx_INCREF(((PyObject *)__pyx_kp_s_128));
    10986    PyTuple_SET_ITEM(__pyx_k_tuple_129, 0, ((PyObject *)__pyx_kp_s_128));
    10987    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_128));
    10988 @@ -20221,7 +20874,7 @@
    10989   *             raise ValueError("lam value too large.")
    10990   */
    10991    __pyx_k_tuple_130 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_130)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3510; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    10992 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_130));
    10993 +  __Pyx_GOTREF(__pyx_k_tuple_130);
    10994    __Pyx_INCREF(((PyObject *)__pyx_kp_s_126));
    10995    PyTuple_SET_ITEM(__pyx_k_tuple_130, 0, ((PyObject *)__pyx_kp_s_126));
    10996    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_126));
    10997 @@ -20235,7 +20888,7 @@
    10998   *
    10999   */
    11000    __pyx_k_tuple_132 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_132)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3512; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    11001 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_132));
    11002 +  __Pyx_GOTREF(__pyx_k_tuple_132);
    11003    __Pyx_INCREF(((PyObject *)__pyx_kp_s_131));
    11004    PyTuple_SET_ITEM(__pyx_k_tuple_132, 0, ((PyObject *)__pyx_kp_s_131));
    11005    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_131));
    11006 @@ -20249,7 +20902,7 @@
    11007   *
    11008   */
    11009    __pyx_k_tuple_134 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_134)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3593; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    11010 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_134));
    11011 +  __Pyx_GOTREF(__pyx_k_tuple_134);
    11012    __Pyx_INCREF(((PyObject *)__pyx_kp_s_133));
    11013    PyTuple_SET_ITEM(__pyx_k_tuple_134, 0, ((PyObject *)__pyx_kp_s_133));
    11014    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_133));
    11015 @@ -20263,7 +20916,7 @@
    11016   *
    11017   */
    11018    __pyx_k_tuple_135 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_135)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3600; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    11019 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_135));
    11020 +  __Pyx_GOTREF(__pyx_k_tuple_135);
    11021    __Pyx_INCREF(((PyObject *)__pyx_kp_s_133));
    11022    PyTuple_SET_ITEM(__pyx_k_tuple_135, 0, ((PyObject *)__pyx_kp_s_133));
    11023    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_133));
    11024 @@ -20277,7 +20930,7 @@
    11025   *                 raise ValueError("p > 1.0")
    11026   */
    11027    __pyx_k_tuple_137 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_137)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3654; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    11028 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_137));
    11029 +  __Pyx_GOTREF(__pyx_k_tuple_137);
    11030    __Pyx_INCREF(((PyObject *)__pyx_kp_s_136));
    11031    PyTuple_SET_ITEM(__pyx_k_tuple_137, 0, ((PyObject *)__pyx_kp_s_136));
    11032    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_136));
    11033 @@ -20291,7 +20944,7 @@
    11034   *
    11035   */
    11036    __pyx_k_tuple_139 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_139)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3656; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    11037 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_139));
    11038 +  __Pyx_GOTREF(__pyx_k_tuple_139);
    11039    __Pyx_INCREF(((PyObject *)__pyx_kp_s_138));
    11040    PyTuple_SET_ITEM(__pyx_k_tuple_139, 0, ((PyObject *)__pyx_kp_s_138));
    11041    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_138));
    11042 @@ -20305,7 +20958,7 @@
    11043   *             raise ValueError("p > 1.0")
    11044   */
    11045    __pyx_k_tuple_140 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_140)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3664; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    11046 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_140));
    11047 +  __Pyx_GOTREF(__pyx_k_tuple_140);
    11048    __Pyx_INCREF(((PyObject *)__pyx_kp_s_136));
    11049    PyTuple_SET_ITEM(__pyx_k_tuple_140, 0, ((PyObject *)__pyx_kp_s_136));
    11050    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_136));
    11051 @@ -20319,7 +20972,7 @@
    11052   *
    11053   */
    11054    __pyx_k_tuple_141 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_141)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3666; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    11055 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_141));
    11056 +  __Pyx_GOTREF(__pyx_k_tuple_141);
    11057    __Pyx_INCREF(((PyObject *)__pyx_kp_s_138));
    11058    PyTuple_SET_ITEM(__pyx_k_tuple_141, 0, ((PyObject *)__pyx_kp_s_138));
    11059    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_138));
    11060 @@ -20333,7 +20986,7 @@
    11061   *                 raise ValueError("nbad < 1")
    11062   */
    11063    __pyx_k_tuple_143 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_143)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3761; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    11064 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_143));
    11065 +  __Pyx_GOTREF(__pyx_k_tuple_143);
    11066    __Pyx_INCREF(((PyObject *)__pyx_kp_s_142));
    11067    PyTuple_SET_ITEM(__pyx_k_tuple_143, 0, ((PyObject *)__pyx_kp_s_142));
    11068    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_142));
    11069 @@ -20347,7 +21000,7 @@
    11070   *                 raise ValueError("nsample < 1")
    11071   */
    11072    __pyx_k_tuple_145 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_145)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3763; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    11073 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_145));
    11074 +  __Pyx_GOTREF(__pyx_k_tuple_145);
    11075    __Pyx_INCREF(((PyObject *)__pyx_kp_s_144));
    11076    PyTuple_SET_ITEM(__pyx_k_tuple_145, 0, ((PyObject *)__pyx_kp_s_144));
    11077    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_144));
    11078 @@ -20361,7 +21014,7 @@
    11079   *                 raise ValueError("ngood + nbad < nsample")
    11080   */
    11081    __pyx_k_tuple_147 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_147)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3765; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    11082 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_147));
    11083 +  __Pyx_GOTREF(__pyx_k_tuple_147);
    11084    __Pyx_INCREF(((PyObject *)__pyx_kp_s_146));
    11085    PyTuple_SET_ITEM(__pyx_k_tuple_147, 0, ((PyObject *)__pyx_kp_s_146));
    11086    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_146));
    11087 @@ -20375,7 +21028,7 @@
    11088   *                                     lngood, lnbad, lnsample)
    11089   */
    11090    __pyx_k_tuple_149 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_149)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3767; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    11091 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_149));
    11092 +  __Pyx_GOTREF(__pyx_k_tuple_149);
    11093    __Pyx_INCREF(((PyObject *)__pyx_kp_s_148));
    11094    PyTuple_SET_ITEM(__pyx_k_tuple_149, 0, ((PyObject *)__pyx_kp_s_148));
    11095    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_148));
    11096 @@ -20389,7 +21042,7 @@
    11097   *             raise ValueError("nbad < 1")
    11098   */
    11099    __pyx_k_tuple_150 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_150)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3778; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    11100 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_150));
    11101 +  __Pyx_GOTREF(__pyx_k_tuple_150);
    11102    __Pyx_INCREF(((PyObject *)__pyx_kp_s_142));
    11103    PyTuple_SET_ITEM(__pyx_k_tuple_150, 0, ((PyObject *)__pyx_kp_s_142));
    11104    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_142));
    11105 @@ -20403,7 +21056,7 @@
    11106   *             raise ValueError("nsample < 1")
    11107   */
    11108    __pyx_k_tuple_151 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_151)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3780; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    11109 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_151));
    11110 +  __Pyx_GOTREF(__pyx_k_tuple_151);
    11111    __Pyx_INCREF(((PyObject *)__pyx_kp_s_144));
    11112    PyTuple_SET_ITEM(__pyx_k_tuple_151, 0, ((PyObject *)__pyx_kp_s_144));
    11113    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_144));
    11114 @@ -20417,7 +21070,7 @@
    11115   *             raise ValueError("ngood + nbad < nsample")
    11116   */
    11117    __pyx_k_tuple_152 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_152)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3782; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    11118 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_152));
    11119 +  __Pyx_GOTREF(__pyx_k_tuple_152);
    11120    __Pyx_INCREF(((PyObject *)__pyx_kp_s_146));
    11121    PyTuple_SET_ITEM(__pyx_k_tuple_152, 0, ((PyObject *)__pyx_kp_s_146));
    11122    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_146));
    11123 @@ -20431,7 +21084,7 @@
    11124   *             ongood, onbad, onsample)
    11125   */
    11126    __pyx_k_tuple_153 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_153)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3784; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    11127 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_153));
    11128 +  __Pyx_GOTREF(__pyx_k_tuple_153);
    11129    __Pyx_INCREF(((PyObject *)__pyx_kp_s_148));
    11130    PyTuple_SET_ITEM(__pyx_k_tuple_153, 0, ((PyObject *)__pyx_kp_s_148));
    11131    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_148));
    11132 @@ -20445,7 +21098,7 @@
    11133   *                 raise ValueError("p >= 1.0")
    11134   */
    11135    __pyx_k_tuple_155 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_155)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3868; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    11136 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_155));
    11137 +  __Pyx_GOTREF(__pyx_k_tuple_155);
    11138    __Pyx_INCREF(((PyObject *)__pyx_kp_s_154));
    11139    PyTuple_SET_ITEM(__pyx_k_tuple_155, 0, ((PyObject *)__pyx_kp_s_154));
    11140    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_154));
    11141 @@ -20459,7 +21112,7 @@
    11142   *
    11143   */
    11144    __pyx_k_tuple_157 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_157)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3870; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    11145 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_157));
    11146 +  __Pyx_GOTREF(__pyx_k_tuple_157);
    11147    __Pyx_INCREF(((PyObject *)__pyx_kp_s_156));
    11148    PyTuple_SET_ITEM(__pyx_k_tuple_157, 0, ((PyObject *)__pyx_kp_s_156));
    11149    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_156));
    11150 @@ -20473,7 +21126,7 @@
    11151   *             raise ValueError("p >= 1.0")
    11152   */
    11153    __pyx_k_tuple_158 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_158)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3877; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    11154 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_158));
    11155 +  __Pyx_GOTREF(__pyx_k_tuple_158);
    11156    __Pyx_INCREF(((PyObject *)__pyx_kp_s_154));
    11157    PyTuple_SET_ITEM(__pyx_k_tuple_158, 0, ((PyObject *)__pyx_kp_s_154));
    11158    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_154));
    11159 @@ -20487,7 +21140,7 @@
    11160   *
    11161   */
    11162    __pyx_k_tuple_159 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_159)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3879; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    11163 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_159));
    11164 +  __Pyx_GOTREF(__pyx_k_tuple_159);
    11165    __Pyx_INCREF(((PyObject *)__pyx_kp_s_156));
    11166    PyTuple_SET_ITEM(__pyx_k_tuple_159, 0, ((PyObject *)__pyx_kp_s_156));
    11167    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_156));
    11168 @@ -20501,7 +21154,7 @@
    11169   *                raise ValueError("cov must be 2 dimensional and square")
    11170   */
    11171    __pyx_k_tuple_161 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_161)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3982; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    11172 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_161));
    11173 +  __Pyx_GOTREF(__pyx_k_tuple_161);
    11174    __Pyx_INCREF(((PyObject *)__pyx_kp_s_160));
    11175    PyTuple_SET_ITEM(__pyx_k_tuple_161, 0, ((PyObject *)__pyx_kp_s_160));
    11176    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_160));
    11177 @@ -20515,7 +21168,7 @@
    11178   *                raise ValueError("mean and cov must have same length")
    11179   */
    11180    __pyx_k_tuple_163 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_163)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3984; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    11181 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_163));
    11182 +  __Pyx_GOTREF(__pyx_k_tuple_163);
    11183    __Pyx_INCREF(((PyObject *)__pyx_kp_s_162));
    11184    PyTuple_SET_ITEM(__pyx_k_tuple_163, 0, ((PyObject *)__pyx_kp_s_162));
    11185    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_162));
    11186 @@ -20529,7 +21182,7 @@
    11187   *         if isinstance(shape, int):
    11188   */
    11189    __pyx_k_tuple_165 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_165)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3986; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    11190 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_165));
    11191 +  __Pyx_GOTREF(__pyx_k_tuple_165);
    11192    __Pyx_INCREF(((PyObject *)__pyx_kp_s_164));
    11193    PyTuple_SET_ITEM(__pyx_k_tuple_165, 0, ((PyObject *)__pyx_kp_s_164));
    11194    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_164));
    11195 @@ -20543,7 +21196,7 @@
    11196   *         if size is None:
    11197   */
    11198    __pyx_k_tuple_168 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_168)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 4079; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    11199 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_168));
    11200 +  __Pyx_GOTREF(__pyx_k_tuple_168);
    11201    __Pyx_INCREF(((PyObject *)__pyx_kp_s_167));
    11202    PyTuple_SET_ITEM(__pyx_k_tuple_168, 0, ((PyObject *)__pyx_kp_s_167));
    11203    __Pyx_GIVEREF(((PyObject *)__pyx_kp_s_167));
    11204 @@ -20557,13 +21210,13 @@
    11205   *     def __init__(self, seed=None):
    11206   */
    11207    __pyx_k_tuple_169 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_169)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 556; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    11208 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_169));
    11209 +  __Pyx_GOTREF(__pyx_k_tuple_169);
    11210    __Pyx_INCREF(((PyObject *)__pyx_n_s__l));
    11211    PyTuple_SET_ITEM(__pyx_k_tuple_169, 0, ((PyObject *)__pyx_n_s__l));
    11212    __Pyx_GIVEREF(((PyObject *)__pyx_n_s__l));
    11213    __Pyx_GIVEREF(((PyObject *)__pyx_k_tuple_169));
    11214    __pyx_k_tuple_170 = PyTuple_New(1); if (unlikely(!__pyx_k_tuple_170)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 556; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    11215 -  __Pyx_GOTREF(((PyObject *)__pyx_k_tuple_170));
    11216 +  __Pyx_GOTREF(__pyx_k_tuple_170);
    11217    __Pyx_INCREF(((PyObject *)__pyx_n_s__l));
    11218    PyTuple_SET_ITEM(__pyx_k_tuple_170, 0, ((PyObject *)__pyx_n_s__l));
    11219    __Pyx_GIVEREF(((PyObject *)__pyx_n_s__l));
    11220 @@ -20608,12 +21261,18 @@
    11221            Py_FatalError("failed to import 'refnanny' module");
    11222    }
    11223    #endif
    11224 -  __Pyx_RefNannySetupContext("PyMODINIT_FUNC PyInit_mtrand(void)");
    11225 +  __Pyx_RefNannySetupContext("PyMODINIT_FUNC PyInit_mtrand(void)", 0);
    11226    if ( __Pyx_check_binary_version() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    11227    __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;}
    11228    __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;}
    11229 -  #ifdef __pyx_binding_PyCFunctionType_USED
    11230 -  if (__pyx_binding_PyCFunctionType_init() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    11231 +  #ifdef __Pyx_CyFunction_USED
    11232 +  if (__Pyx_CyFunction_init() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    11233 +  #endif
    11234 +  #ifdef __Pyx_FusedFunction_USED
    11235 +  if (__pyx_FusedFunction_init() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    11236 +  #endif
    11237 +  #ifdef __Pyx_Generator_USED
    11238 +  if (__pyx_Generator_init() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    11239    #endif
    11240    /*--- Library function declarations ---*/
    11241    /*--- Threads initialization code ---*/
    11242 @@ -20624,16 +21283,15 @@
    11243    #endif
    11244    /*--- Module creation code ---*/
    11245    #if PY_MAJOR_VERSION < 3
    11246 -  __pyx_m = Py_InitModule4(__Pyx_NAMESTR("mtrand"), __pyx_methods, 0, 0, PYTHON_API_VERSION);
    11247 +  __pyx_m = Py_InitModule4(__Pyx_NAMESTR("mtrand"), __pyx_methods, 0, 0, PYTHON_API_VERSION); Py_XINCREF(__pyx_m);
    11248    #else
    11249    __pyx_m = PyModule_Create(&__pyx_moduledef);
    11250    #endif
    11251 -  if (!__pyx_m) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
    11252 -  #if PY_MAJOR_VERSION < 3
    11253 -  Py_INCREF(__pyx_m);
    11254 +  if (unlikely(!__pyx_m)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    11255 +  __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;}
    11256 +  #if CYTHON_COMPILING_IN_PYPY
    11257 +  Py_INCREF(__pyx_b);
    11258    #endif
    11259 -  __pyx_b = PyImport_AddModule(__Pyx_NAMESTR(__Pyx_BUILTIN_MODULE_NAME));
    11260 -  if (!__pyx_b) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
    11261    if (__Pyx_SetAttrString(__pyx_m, "__builtins__", __pyx_b) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;};
    11262    /*--- Initialize various global constants etc. ---*/
    11263    if (unlikely(__Pyx_InitGlobals() < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    11264 @@ -20716,7 +21374,7 @@
    11265    __Pyx_GOTREF(__pyx_t_4);
    11266    __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0;
    11267    __pyx_t_1 = PyTuple_New(1); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 556; __pyx_clineno = __LINE__; goto __pyx_L1_error;}
    11268 -  __Pyx_GOTREF(((PyObject *)__pyx_t_1));
    11269 +  __Pyx_GOTREF(__pyx_t_1);
    11270    PyTuple_SET_ITEM(__pyx_t_1, 0, __pyx_t_4);
    11271    __Pyx_GIVEREF(__pyx_t_4);
    11272    __pyx_t_4 = 0;
    11273 @@ -21667,7 +22325,6 @@
    11274  }
    11275  
    11276  /* Runtime support code */
    11277 -
    11278  #if CYTHON_REFNANNY
    11279  static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname) {
    11280      PyObject *m = NULL, *p = NULL;
    11281 @@ -21700,9 +22357,9 @@
    11282  }
    11283  
    11284  static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) {
    11285 +#if CYTHON_COMPILING_IN_CPYTHON
    11286      PyObject *tmp_type, *tmp_value, *tmp_tb;
    11287      PyThreadState *tstate = PyThreadState_GET();
    11288 -
    11289      tmp_type = tstate->curexc_type;
    11290      tmp_value = tstate->curexc_value;
    11291      tmp_tb = tstate->curexc_traceback;
    11292 @@ -21712,55 +22369,60 @@
    11293      Py_XDECREF(tmp_type);
    11294      Py_XDECREF(tmp_value);
    11295      Py_XDECREF(tmp_tb);
    11296 +#else
    11297 +    PyErr_Restore(type, value, tb);
    11298 +#endif
    11299  }
    11300 -
    11301  static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb) {
    11302 +#if CYTHON_COMPILING_IN_CPYTHON
    11303      PyThreadState *tstate = PyThreadState_GET();
    11304      *type = tstate->curexc_type;
    11305      *value = tstate->curexc_value;
    11306      *tb = tstate->curexc_traceback;
    11307 -
    11308      tstate->curexc_type = 0;
    11309      tstate->curexc_value = 0;
    11310      tstate->curexc_traceback = 0;
    11311 +#else
    11312 +    PyErr_Fetch(type, value, tb);
    11313 +#endif
    11314  }
    11315  
    11316 -
    11317  #if PY_MAJOR_VERSION < 3
    11318 -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {
    11319 -    /* cause is unused */
    11320 +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb,
    11321 +                        CYTHON_UNUSED PyObject *cause) {
    11322      Py_XINCREF(type);
    11323 -    Py_XINCREF(value);
    11324 -    Py_XINCREF(tb);
    11325 -    /* First, check the traceback argument, replacing None with NULL. */
    11326 -    if (tb == Py_None) {
    11327 -        Py_DECREF(tb);
    11328 -        tb = 0;
    11329 -    }
    11330 -    else if (tb != NULL && !PyTraceBack_Check(tb)) {
    11331 -        PyErr_SetString(PyExc_TypeError,
    11332 -            "raise: arg 3 must be a traceback or None");
    11333 -        goto raise_error;
    11334 -    }
    11335 -    /* Next, replace a missing value with None */
    11336 -    if (value == NULL) {
    11337 -        value = Py_None;
    11338 +    if (!value || value == Py_None)
    11339 +        value = NULL;
    11340 +    else
    11341          Py_INCREF(value);
    11342 +    if (!tb || tb == Py_None)
    11343 +        tb = NULL;
    11344 +    else {
    11345 +        Py_INCREF(tb);
    11346 +        if (!PyTraceBack_Check(tb)) {
    11347 +            PyErr_SetString(PyExc_TypeError,
    11348 +                "raise: arg 3 must be a traceback or None");
    11349 +            goto raise_error;
    11350 +        }
    11351      }
    11352      #if PY_VERSION_HEX < 0x02050000
    11353 -    if (!PyClass_Check(type))
    11354 +    if (PyClass_Check(type)) {
    11355      #else
    11356 -    if (!PyType_Check(type))
    11357 +    if (PyType_Check(type)) {
    11358      #endif
    11359 -    {
    11360 -        /* Raising an instance.  The value should be a dummy. */
    11361 -        if (value != Py_None) {
    11362 +#if CYTHON_COMPILING_IN_PYPY
    11363 +        if (!value) {
    11364 +            Py_INCREF(Py_None);
    11365 +            value = Py_None;
    11366 +        }
    11367 +#endif
    11368 +        PyErr_NormalizeException(&type, &value, &tb);
    11369 +    } else {
    11370 +        if (value) {
    11371              PyErr_SetString(PyExc_TypeError,
    11372                  "instance exception may not have a separate value");
    11373              goto raise_error;
    11374          }
    11375 -        /* Normalize to raise <class>, <instance> */
    11376 -        Py_DECREF(value);
    11377          value = type;
    11378          #if PY_VERSION_HEX < 0x02050000
    11379              if (PyInstance_Check(type)) {
    11380 @@ -21783,7 +22445,6 @@
    11381              }
    11382          #endif
    11383      }
    11384 -
    11385      __Pyx_ErrRestore(type, value, tb);
    11386      return;
    11387  raise_error:
    11388 @@ -21792,10 +22453,9 @@
    11389      Py_XDECREF(tb);
    11390      return;
    11391  }
    11392 -
    11393  #else /* Python 3+ */
    11394 -
    11395  static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) {
    11396 +    PyObject* owned_instance = NULL;
    11397      if (tb == Py_None) {
    11398          tb = 0;
    11399      } else if (tb && !PyTraceBack_Check(tb)) {
    11400 @@ -21805,7 +22465,6 @@
    11401      }
    11402      if (value == Py_None)
    11403          value = 0;
    11404 -
    11405      if (PyExceptionInstance_Check(type)) {
    11406          if (value) {
    11407              PyErr_SetString(PyExc_TypeError,
    11408 @@ -21814,13 +22473,36 @@
    11409          }
    11410          value = type;
    11411          type = (PyObject*) Py_TYPE(value);
    11412 -    } else if (!PyExceptionClass_Check(type)) {
    11413 +    } else if (PyExceptionClass_Check(type)) {
    11414 +        PyObject *args;
    11415 +        if (!value)
    11416 +            args = PyTuple_New(0);
    11417 +        else if (PyTuple_Check(value)) {
    11418 +            Py_INCREF(value);
    11419 +            args = value;
    11420 +        }
    11421 +        else
    11422 +            args = PyTuple_Pack(1, value);
    11423 +        if (!args)
    11424 +            goto bad;
    11425 +        owned_instance = PyEval_CallObject(type, args);
    11426 +        Py_DECREF(args);
    11427 +        if (!owned_instance)
    11428 +            goto bad;
    11429 +        value = owned_instance;
    11430 +        if (!PyExceptionInstance_Check(value)) {
    11431 +            PyErr_Format(PyExc_TypeError,
    11432 +                         "calling %R should have returned an instance of "
    11433 +                         "BaseException, not %R",
    11434 +                         type, Py_TYPE(value));
    11435 +            goto bad;
    11436 +        }
    11437 +    } else {
    11438          PyErr_SetString(PyExc_TypeError,
    11439              "raise: exception class must be a subclass of BaseException");
    11440          goto bad;
    11441      }
    11442 -
    11443 -    if (cause) {
    11444 +    if (cause && cause != Py_None) {
    11445          PyObject *fixed_cause;
    11446          if (PyExceptionClass_Check(cause)) {
    11447              fixed_cause = PyObject_CallObject(cause, NULL);
    11448 @@ -21837,14 +22519,9 @@
    11449                              "BaseException");
    11450              goto bad;
    11451          }
    11452 -        if (!value) {
    11453 -            value = PyObject_CallObject(type, NULL);
    11454 -        }
    11455          PyException_SetCause(value, fixed_cause);
    11456      }
    11457 -
    11458      PyErr_SetObject(type, value);
    11459 -
    11460      if (tb) {
    11461          PyThreadState *tstate = PyThreadState_GET();
    11462          PyObject* tmp_tb = tstate->curexc_traceback;
    11463 @@ -21854,8 +22531,8 @@
    11464              Py_XDECREF(tmp_tb);
    11465          }
    11466      }
    11467 -
    11468  bad:
    11469 +    Py_XDECREF(owned_instance);
    11470      return;
    11471  }
    11472  #endif
    11473 @@ -21869,7 +22546,7 @@
    11474          "%s() got multiple values for keyword argument '%U'", func_name, kw_name);
    11475          #else
    11476          "%s() got multiple values for keyword argument '%s'", func_name,
    11477 -        PyString_AS_STRING(kw_name));
    11478 +        PyString_AsString(kw_name));
    11479          #endif
    11480  }
    11481  
    11482 @@ -21885,55 +22562,77 @@
    11483      Py_ssize_t pos = 0;
    11484      PyObject*** name;
    11485      PyObject*** first_kw_arg = argnames + num_pos_args;
    11486 -
    11487      while (PyDict_Next(kwds, &pos, &key, &value)) {
    11488          name = first_kw_arg;
    11489          while (*name && (**name != key)) name++;
    11490          if (*name) {
    11491              values[name-argnames] = value;
    11492 -        } else {
    11493 -            #if PY_MAJOR_VERSION < 3
    11494 -            if (unlikely(!PyString_CheckExact(key)) && unlikely(!PyString_Check(key))) {
    11495 -            #else
    11496 -            if (unlikely(!PyUnicode_CheckExact(key)) && unlikely(!PyUnicode_Check(key))) {
    11497 -            #endif
    11498 -                goto invalid_keyword_type;
    11499 -            } else {
    11500 -                for (name = first_kw_arg; *name; name++) {
    11501 -                    #if PY_MAJOR_VERSION >= 3
    11502 -                    if (PyUnicode_GET_SIZE(**name) == PyUnicode_GET_SIZE(key) &&
    11503 -                        PyUnicode_Compare(**name, key) == 0) break;
    11504 -                    #else
    11505 -                    if (PyString_GET_SIZE(**name) == PyString_GET_SIZE(key) &&
    11506 -                        _PyString_Eq(**name, key)) break;
    11507 -                    #endif
    11508 -                }
    11509 -                if (*name) {
    11510 +            continue;
    11511 +        }
    11512 +        name = first_kw_arg;
    11513 +        #if PY_MAJOR_VERSION < 3
    11514 +        if (likely(PyString_CheckExact(key)) || likely(PyString_Check(key))) {
    11515 +            while (*name) {
    11516 +                if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key))
    11517 +                        && _PyString_Eq(**name, key)) {
    11518                      values[name-argnames] = value;
    11519 -                } else {
    11520 -                    /* unexpected keyword found */
    11521 -                    for (name=argnames; name != first_kw_arg; name++) {
    11522 -                        if (**name == key) goto arg_passed_twice;
    11523 -                        #if PY_MAJOR_VERSION >= 3
    11524 -                        if (PyUnicode_GET_SIZE(**name) == PyUnicode_GET_SIZE(key) &&
    11525 -                            PyUnicode_Compare(**name, key) == 0) goto arg_passed_twice;
    11526 -                        #else
    11527 -                        if (PyString_GET_SIZE(**name) == PyString_GET_SIZE(key) &&
    11528 -                            _PyString_Eq(**name, key)) goto arg_passed_twice;
    11529 -                        #endif
    11530 -                    }
    11531 -                    if (kwds2) {
    11532 -                        if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad;
    11533 -                    } else {
    11534 -                        goto invalid_keyword;
    11535 +                    break;
    11536 +                }
    11537 +                name++;
    11538 +            }
    11539 +            if (*name) continue;
    11540 +            else {
    11541 +                PyObject*** argname = argnames;
    11542 +                while (argname != first_kw_arg) {
    11543 +                    if ((**argname == key) || (
    11544 +                            (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key))
    11545 +                             && _PyString_Eq(**argname, key))) {
    11546 +                        goto arg_passed_twice;
    11547                      }
    11548 +                    argname++;
    11549                  }
    11550              }
    11551 +        } else
    11552 +        #endif
    11553 +        if (likely(PyUnicode_Check(key))) {
    11554 +            while (*name) {
    11555 +                int cmp = (**name == key) ? 0 :
    11556 +                #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
    11557 +                    (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 :
    11558 +                #endif
    11559 +                    PyUnicode_Compare(**name, key);
    11560 +                if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
    11561 +                if (cmp == 0) {
    11562 +                    values[name-argnames] = value;
    11563 +                    break;
    11564 +                }
    11565 +                name++;
    11566 +            }
    11567 +            if (*name) continue;
    11568 +            else {
    11569 +                PyObject*** argname = argnames;
    11570 +                while (argname != first_kw_arg) {
    11571 +                    int cmp = (**argname == key) ? 0 :
    11572 +                    #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3
    11573 +                        (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 :
    11574 +                    #endif
    11575 +                        PyUnicode_Compare(**argname, key);
    11576 +                    if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad;
    11577 +                    if (cmp == 0) goto arg_passed_twice;
    11578 +                    argname++;
    11579 +                }
    11580 +            }
    11581 +        } else
    11582 +            goto invalid_keyword_type;
    11583 +        if (kwds2) {
    11584 +            if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad;
    11585 +        } else {
    11586 +            goto invalid_keyword;
    11587          }
    11588      }
    11589      return 0;
    11590  arg_passed_twice:
    11591 -    __Pyx_RaiseDoubleKeywordsError(function_name, **name);
    11592 +    __Pyx_RaiseDoubleKeywordsError(function_name, key);
    11593      goto bad;
    11594  invalid_keyword_type:
    11595      PyErr_Format(PyExc_TypeError,
    11596 @@ -21961,7 +22660,6 @@
    11597  {
    11598      Py_ssize_t num_expected;
    11599      const char *more_or_less;
    11600 -
    11601      if (num_found < num_min) {
    11602          num_expected = num_min;
    11603          more_or_less = "at least";
    11604 @@ -21973,21 +22671,54 @@
    11605          more_or_less = "exactly";
    11606      }
    11607      PyErr_Format(PyExc_TypeError,
    11608 -                 "%s() takes %s %"PY_FORMAT_SIZE_T"d positional argument%s (%"PY_FORMAT_SIZE_T"d given)",
    11609 +                 "%s() takes %s %" CYTHON_FORMAT_SSIZE_T "d positional argument%s (%" CYTHON_FORMAT_SSIZE_T "d given)",
    11610                   func_name, more_or_less, num_expected,
    11611                   (num_expected == 1) ? "" : "s", num_found);
    11612  }
    11613  
    11614 +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {
    11615 +    PyErr_Format(PyExc_ValueError,
    11616 +                 "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected);
    11617 +}
    11618  
    11619  static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) {
    11620      PyErr_Format(PyExc_ValueError,
    11621 -                 "need more than %"PY_FORMAT_SIZE_T"d value%s to unpack",
    11622 +                 "need more than %" CYTHON_FORMAT_SSIZE_T "d value%s to unpack",
    11623                   index, (index == 1) ? "" : "s");
    11624  }
    11625  
    11626 -static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) {
    11627 -    PyErr_Format(PyExc_ValueError,
    11628 -                 "too many values to unpack (expected %"PY_FORMAT_SIZE_T"d)", expected);
    11629 +static CYTHON_INLINE int __Pyx_IterFinish(void) {
    11630 +#if CYTHON_COMPILING_IN_CPYTHON
    11631 +    PyThreadState *tstate = PyThreadState_GET();
    11632 +    PyObject* exc_type = tstate->curexc_type;
    11633 +    if (unlikely(exc_type)) {
    11634 +        if (likely(exc_type == PyExc_StopIteration) || PyErr_GivenExceptionMatches(exc_type, PyExc_StopIteration)) {
    11635 +            PyObject *exc_value, *exc_tb;
    11636 +            exc_value = tstate->curexc_value;
    11637 +            exc_tb = tstate->curexc_traceback;
    11638 +            tstate->curexc_type = 0;
    11639 +            tstate->curexc_value = 0;
    11640 +            tstate->curexc_traceback = 0;
    11641 +            Py_DECREF(exc_type);
    11642 +            Py_XDECREF(exc_value);
    11643 +            Py_XDECREF(exc_tb);
    11644 +            return 0;
    11645 +        } else {
    11646 +            return -1;
    11647 +        }
    11648 +    }
    11649 +    return 0;
    11650 +#else
    11651 +    if (unlikely(PyErr_Occurred())) {
    11652 +        if (likely(PyErr_ExceptionMatches(PyExc_StopIteration))) {
    11653 +            PyErr_Clear();
    11654 +            return 0;
    11655 +        } else {
    11656 +            return -1;
    11657 +        }
    11658 +    }
    11659 +    return 0;
    11660 +#endif
    11661  }
    11662  
    11663  static int __Pyx_IternextUnpackEndCheck(PyObject *retval, Py_ssize_t expected) {
    11664 @@ -21995,19 +22726,15 @@
    11665          Py_DECREF(retval);
    11666          __Pyx_RaiseTooManyValuesError(expected);
    11667          return -1;
    11668 -    } else if (PyErr_Occurred()) {
    11669 -        if (likely(PyErr_ExceptionMatches(PyExc_StopIteration))) {
    11670 -            PyErr_Clear();
    11671 -            return 0;
    11672 -        } else {
    11673 -            return -1;
    11674 -        }
    11675 +    } else {
    11676 +        return __Pyx_IterFinish();
    11677      }
    11678      return 0;
    11679  }
    11680  
    11681  static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) {
    11682      PyObject *local_type, *local_value, *local_tb;
    11683 +#if CYTHON_COMPILING_IN_CPYTHON
    11684      PyObject *tmp_type, *tmp_value, *tmp_tb;
    11685      PyThreadState *tstate = PyThreadState_GET();
    11686      local_type = tstate->curexc_type;
    11687 @@ -22016,19 +22743,27 @@
    11688      tstate->curexc_type = 0;
    11689      tstate->curexc_value = 0;
    11690      tstate->curexc_traceback = 0;
    11691 +#else
    11692 +    PyErr_Fetch(&local_type, &local_value, &local_tb);
    11693 +#endif
    11694      PyErr_NormalizeException(&local_type, &local_value, &local_tb);
    11695 +#if CYTHON_COMPILING_IN_CPYTHON
    11696      if (unlikely(tstate->curexc_type))
    11697 +#else
    11698 +    if (unlikely(PyErr_Occurred()))
    11699 +#endif
    11700          goto bad;
    11701      #if PY_MAJOR_VERSION >= 3
    11702      if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0))
    11703          goto bad;
    11704      #endif
    11705 -    *type = local_type;
    11706 -    *value = local_value;
    11707 -    *tb = local_tb;
    11708      Py_INCREF(local_type);
    11709      Py_INCREF(local_value);
    11710      Py_INCREF(local_tb);
    11711 +    *type = local_type;
    11712 +    *value = local_value;
    11713 +    *tb = local_tb;
    11714 +#if CYTHON_COMPILING_IN_CPYTHON
    11715      tmp_type = tstate->exc_type;
    11716      tmp_value = tstate->exc_value;
    11717      tmp_tb = tstate->exc_traceback;
    11718 @@ -22036,10 +22771,13 @@
    11719      tstate->exc_value = local_value;
    11720      tstate->exc_traceback = local_tb;
    11721      /* Make sure tstate is in a consistent state when we XDECREF
    11722 -       these objects (XDECREF may run arbitrary code). */
    11723 +       these objects (DECREF may run arbitrary code). */
    11724      Py_XDECREF(tmp_type);
    11725      Py_XDECREF(tmp_value);
    11726      Py_XDECREF(tmp_tb);
    11727 +#else
    11728 +    PyErr_SetExcInfo(local_type, local_value, local_tb);
    11729 +#endif
    11730      return 0;
    11731  bad:
    11732      *type = 0;
    11733 @@ -22051,7 +22789,6 @@
    11734      return -1;
    11735  }
    11736  
    11737 -
    11738  static CYTHON_INLINE int __Pyx_CheckKeywordStrings(
    11739      PyObject *kwdict,
    11740      const char* function_name,
    11741 @@ -22059,13 +22796,17 @@
    11742  {
    11743      PyObject* key = 0;
    11744      Py_ssize_t pos = 0;
    11745 +#if CPYTHON_COMPILING_IN_PYPY
    11746 +    if (!kw_allowed && PyDict_Next(kwdict, &pos, &key, 0))
    11747 +        goto invalid_keyword;
    11748 +    return 1;
    11749 +#else
    11750      while (PyDict_Next(kwdict, &pos, &key, 0)) {
    11751          #if PY_MAJOR_VERSION < 3
    11752          if (unlikely(!PyString_CheckExact(key)) && unlikely(!PyString_Check(key)))
    11753 -        #else
    11754 -        if (unlikely(!PyUnicode_CheckExact(key)) && unlikely(!PyUnicode_Check(key)))
    11755          #endif
    11756 -            goto invalid_keyword_type;
    11757 +            if (unlikely(!PyUnicode_Check(key)))
    11758 +                goto invalid_keyword_type;
    11759      }
    11760      if ((!kw_allowed) && unlikely(key))
    11761          goto invalid_keyword;
    11762 @@ -22074,6 +22815,7 @@
    11763      PyErr_Format(PyExc_TypeError,
    11764          "%s() keywords must be strings", function_name);
    11765      return 0;
    11766 +#endif
    11767  invalid_keyword:
    11768      PyErr_Format(PyExc_TypeError,
    11769      #if PY_MAJOR_VERSION < 3
    11770 @@ -22098,8 +22840,8 @@
    11771      return 0;
    11772  }
    11773  
    11774 -
    11775  static CYTHON_INLINE void __Pyx_ExceptionSave(PyObject **type, PyObject **value, PyObject **tb) {
    11776 +#if CYTHON_COMPILING_IN_CPYTHON
    11777      PyThreadState *tstate = PyThreadState_GET();
    11778      *type = tstate->exc_type;
    11779      *value = tstate->exc_value;
    11780 @@ -22107,9 +22849,12 @@
    11781      Py_XINCREF(*type);
    11782      Py_XINCREF(*value);
    11783      Py_XINCREF(*tb);
    11784 +#else
    11785 +    PyErr_GetExcInfo(type, value, tb);
    11786 +#endif
    11787  }
    11788 -
    11789  static void __Pyx_ExceptionReset(PyObject *type, PyObject *value, PyObject *tb) {
    11790 +#if CYTHON_COMPILING_IN_CPYTHON
    11791      PyObject *tmp_type, *tmp_value, *tmp_tb;
    11792      PyThreadState *tstate = PyThreadState_GET();
    11793      tmp_type = tstate->exc_type;
    11794 @@ -22121,6 +22866,9 @@
    11795      Py_XDECREF(tmp_type);
    11796      Py_XDECREF(tmp_value);
    11797      Py_XDECREF(tmp_tb);
    11798 +#else
    11799 +    PyErr_SetExcInfo(type, value, tb);
    11800 +#endif
    11801  }
    11802  
    11803  static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, long level) {
    11804 @@ -22149,12 +22897,33 @@
    11805          goto bad;
    11806      #if PY_VERSION_HEX >= 0x02050000
    11807      {
    11808 -        PyObject *py_level = PyInt_FromLong(level);
    11809 -        if (!py_level)
    11810 -            goto bad;
    11811 -        module = PyObject_CallFunctionObjArgs(py_import,
    11812 -            name, global_dict, empty_dict, list, py_level, NULL);
    11813 -        Py_DECREF(py_level);
    11814 +        #if PY_MAJOR_VERSION >= 3
    11815 +        if (level == -1) {
    11816 +            if (strchr(__Pyx_MODULE_NAME, '.')) {
    11817 +                /* try package relative import first */
    11818 +                PyObject *py_level = PyInt_FromLong(1);
    11819 +                if (!py_level)
    11820 +                    goto bad;
    11821 +                module = PyObject_CallFunctionObjArgs(py_import,
    11822 +                    name, global_dict, empty_dict, list, py_level, NULL);
    11823 +                Py_DECREF(py_level);
    11824 +                if (!module) {
    11825 +                    if (!PyErr_ExceptionMatches(PyExc_ImportError))
    11826 +                        goto bad;
    11827 +                    PyErr_Clear();
    11828 +                }
    11829 +            }
    11830 +            level = 0; /* try absolute import on failure */
    11831 +        }
    11832 +        #endif
    11833 +        if (!module) {
    11834 +            PyObject *py_level = PyInt_FromLong(level);
    11835 +            if (!py_level)
    11836 +                goto bad;
    11837 +            module = PyObject_CallFunctionObjArgs(py_import,
    11838 +                name, global_dict, empty_dict, list, py_level, NULL);
    11839 +            Py_DECREF(py_level);
    11840 +        }
    11841      }
    11842      #else
    11843      if (level>0) {
    11844 @@ -22171,66 +22940,65 @@
    11845      return module;
    11846  }
    11847  
    11848 -static CYTHON_INLINE int __Pyx_PyBytes_Equals(PyObject* s1, PyObject* s2, int equals) {
    11849 -    if (s1 == s2) {   /* as done by PyObject_RichCompareBool(); also catches the (interned) empty string */
    11850 -        return (equals == Py_EQ);
    11851 -    } else if (PyBytes_CheckExact(s1) & PyBytes_CheckExact(s2)) {
    11852 -        if (PyBytes_GET_SIZE(s1) != PyBytes_GET_SIZE(s2)) {
    11853 -            return (equals == Py_NE);
    11854 -        } else if (PyBytes_GET_SIZE(s1) == 1) {
    11855 -            if (equals == Py_EQ)
    11856 -                return (PyBytes_AS_STRING(s1)[0] == PyBytes_AS_STRING(s2)[0]);
    11857 -            else
    11858 -                return (PyBytes_AS_STRING(s1)[0] != PyBytes_AS_STRING(s2)[0]);
    11859 -        } else {
    11860 -            int result = memcmp(PyBytes_AS_STRING(s1), PyBytes_AS_STRING(s2), (size_t)PyBytes_GET_SIZE(s1));
    11861 -            return (equals == Py_EQ) ? (result == 0) : (result != 0);
    11862 +static CYTHON_INLINE npy_intp __Pyx_PyInt_from_py_npy_intp(PyObject* x) {
    11863 +    const npy_intp neg_one = (npy_intp)-1, const_zero = (npy_intp)0;
    11864 +    const int is_unsigned = const_zero < neg_one;
    11865 +    if (sizeof(npy_intp) == sizeof(char)) {
    11866 +        if (is_unsigned)
    11867 +            return (npy_intp)__Pyx_PyInt_AsUnsignedChar(x);
    11868 +        else
    11869 +            return (npy_intp)__Pyx_PyInt_AsSignedChar(x);
    11870 +    } else if (sizeof(npy_intp) == sizeof(short)) {
    11871 +        if (is_unsigned)
    11872 +            return (npy_intp)__Pyx_PyInt_AsUnsignedShort(x);
    11873 +        else
    11874 +            return (npy_intp)__Pyx_PyInt_AsSignedShort(x);
    11875 +    } else if (sizeof(npy_intp) == sizeof(int)) {
    11876 +        if (is_unsigned)
    11877 +            return (npy_intp)__Pyx_PyInt_AsUnsignedInt(x);
    11878 +        else
    11879 +            return (npy_intp)__Pyx_PyInt_AsSignedInt(x);
    11880 +    } else if (sizeof(npy_intp) == sizeof(long)) {
    11881 +        if (is_unsigned)
    11882 +            return (npy_intp)__Pyx_PyInt_AsUnsignedLong(x);
    11883 +        else
    11884 +            return (npy_intp)__Pyx_PyInt_AsSignedLong(x);
    11885 +    } else if (sizeof(npy_intp) == sizeof(PY_LONG_LONG)) {
    11886 +        if (is_unsigned)
    11887 +            return (npy_intp)__Pyx_PyInt_AsUnsignedLongLong(x);
    11888 +        else
    11889 +            return (npy_intp)__Pyx_PyInt_AsSignedLongLong(x);
    11890 +    }  else {
    11891 +        npy_intp val;
    11892 +        PyObject *v = __Pyx_PyNumber_Int(x);
    11893 +        #if PY_VERSION_HEX < 0x03000000
    11894 +        if (likely(v) && !PyLong_Check(v)) {
    11895 +            PyObject *tmp = v;
    11896 +            v = PyNumber_Long(tmp);
    11897 +            Py_DECREF(tmp);
    11898          }
    11899 -    } else if ((s1 == Py_None) & PyBytes_CheckExact(s2)) {
    11900 -        return (equals == Py_NE);
    11901 -    } else if ((s2 == Py_None) & PyBytes_CheckExact(s1)) {
    11902 -        return (equals == Py_NE);
    11903 -    } else {
    11904 -        int result;
    11905 -        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
    11906 -        if (!py_result)
    11907 -            return -1;
    11908 -        result = __Pyx_PyObject_IsTrue(py_result);
    11909 -        Py_DECREF(py_result);
    11910 -        return result;
    11911 +        #endif
    11912 +        if (likely(v)) {
    11913 +            int one = 1; int is_little = (int)*(unsigned char *)&one;
    11914 +            unsigned char *bytes = (unsigned char *)&val;
    11915 +            int ret = _PyLong_AsByteArray((PyLongObject *)v,
    11916 +                                          bytes, sizeof(val),
    11917 +                                          is_little, !is_unsigned);
    11918 +            Py_DECREF(v);
    11919 +            if (likely(!ret))
    11920 +                return val;
    11921 +        }
    11922 +        return (npy_intp)-1;
    11923      }
    11924  }
    11925  
    11926 -static CYTHON_INLINE int __Pyx_PyUnicode_Equals(PyObject* s1, PyObject* s2, int equals) {
    11927 -    if (s1 == s2) {   /* as done by PyObject_RichCompareBool(); also catches the (interned) empty string */
    11928 -        return (equals == Py_EQ);
    11929 -    } else if (PyUnicode_CheckExact(s1) & PyUnicode_CheckExact(s2)) {
    11930 -        if (PyUnicode_GET_SIZE(s1) != PyUnicode_GET_SIZE(s2)) {
    11931 -            return (equals == Py_NE);
    11932 -        } else if (PyUnicode_GET_SIZE(s1) == 1) {
    11933 -            if (equals == Py_EQ)
    11934 -                return (PyUnicode_AS_UNICODE(s1)[0] == PyUnicode_AS_UNICODE(s2)[0]);
    11935 -            else
    11936 -                return (PyUnicode_AS_UNICODE(s1)[0] != PyUnicode_AS_UNICODE(s2)[0]);
    11937 -        } else {
    11938 -            int result = PyUnicode_Compare(s1, s2);
    11939 -            if ((result == -1) && unlikely(PyErr_Occurred()))
    11940 -                return -1;
    11941 -            return (equals == Py_EQ) ? (result == 0) : (result != 0);
    11942 -        }
    11943 -    } else if ((s1 == Py_None) & PyUnicode_CheckExact(s2)) {
    11944 -        return (equals == Py_NE);
    11945 -    } else if ((s2 == Py_None) & PyUnicode_CheckExact(s1)) {
    11946 -        return (equals == Py_NE);
    11947 -    } else {
    11948 -        int result;
    11949 -        PyObject* py_result = PyObject_RichCompare(s1, s2, equals);
    11950 -        if (!py_result)
    11951 -            return -1;
    11952 -        result = __Pyx_PyObject_IsTrue(py_result);
    11953 -        Py_DECREF(py_result);
    11954 -        return result;
    11955 -    }
    11956 +static CYTHON_INLINE void __Pyx_RaiseImportError(PyObject *name) {
    11957 +#if PY_MAJOR_VERSION < 3
    11958 +    PyErr_Format(PyExc_ImportError, "cannot import name %.230s",
    11959 +                 PyString_AsString(name));
    11960 +#else
    11961 +    PyErr_Format(PyExc_ImportError, "cannot import name %S", name);
    11962 +#endif
    11963  }
    11964  
    11965  static CYTHON_INLINE PyObject *__Pyx_PyInt_to_py_npy_intp(npy_intp val) {
    11966 @@ -22658,58 +23426,6 @@
    11967      }
    11968  }
    11969  
    11970 -static CYTHON_INLINE npy_intp __Pyx_PyInt_from_py_npy_intp(PyObject* x) {
    11971 -    const npy_intp neg_one = (npy_intp)-1, const_zero = (npy_intp)0;
    11972 -    const int is_unsigned = const_zero < neg_one;
    11973 -    if (sizeof(npy_intp) == sizeof(char)) {
    11974 -        if (is_unsigned)
    11975 -            return (npy_intp)__Pyx_PyInt_AsUnsignedChar(x);
    11976 -        else
    11977 -            return (npy_intp)__Pyx_PyInt_AsSignedChar(x);
    11978 -    } else if (sizeof(npy_intp) == sizeof(short)) {
    11979 -        if (is_unsigned)
    11980 -            return (npy_intp)__Pyx_PyInt_AsUnsignedShort(x);
    11981 -        else
    11982 -            return (npy_intp)__Pyx_PyInt_AsSignedShort(x);
    11983 -    } else if (sizeof(npy_intp) == sizeof(int)) {
    11984 -        if (is_unsigned)
    11985 -            return (npy_intp)__Pyx_PyInt_AsUnsignedInt(x);
    11986 -        else
    11987 -            return (npy_intp)__Pyx_PyInt_AsSignedInt(x);
    11988 -    } else if (sizeof(npy_intp) == sizeof(long)) {
    11989 -        if (is_unsigned)
    11990 -            return (npy_intp)__Pyx_PyInt_AsUnsignedLong(x);
    11991 -        else
    11992 -            return (npy_intp)__Pyx_PyInt_AsSignedLong(x);
    11993 -    } else if (sizeof(npy_intp) == sizeof(PY_LONG_LONG)) {
    11994 -        if (is_unsigned)
    11995 -            return (npy_intp)__Pyx_PyInt_AsUnsignedLongLong(x);
    11996 -        else
    11997 -            return (npy_intp)__Pyx_PyInt_AsSignedLongLong(x);
    11998 -    }  else {
    11999 -        npy_intp val;
    12000 -        PyObject *v = __Pyx_PyNumber_Int(x);
    12001 -        #if PY_VERSION_HEX < 0x03000000
    12002 -        if (likely(v) && !PyLong_Check(v)) {
    12003 -            PyObject *tmp = v;
    12004 -            v = PyNumber_Long(tmp);
    12005 -            Py_DECREF(tmp);
    12006 -        }
    12007 -        #endif
    12008 -        if (likely(v)) {
    12009 -            int one = 1; int is_little = (int)*(unsigned char *)&one;
    12010 -            unsigned char *bytes = (unsigned char *)&val;
    12011 -            int ret = _PyLong_AsByteArray((PyLongObject *)v,
    12012 -                                          bytes, sizeof(val),
    12013 -                                          is_little, !is_unsigned);
    12014 -            Py_DECREF(v);
    12015 -            if (likely(!ret))
    12016 -                return val;
    12017 -        }
    12018 -        return (npy_intp)-1;
    12019 -    }
    12020 -}
    12021 -
    12022  static int __Pyx_check_binary_version(void) {
    12023      char ctversion[4], rtversion[4];
    12024      PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION);
    12025 @@ -22729,6 +23445,23 @@
    12026      return 0;
    12027  }
    12028  
    12029 +#ifndef __PYX_HAVE_RT_ImportModule
    12030 +#define __PYX_HAVE_RT_ImportModule
    12031 +static PyObject *__Pyx_ImportModule(const char *name) {
    12032 +    PyObject *py_name = 0;
    12033 +    PyObject *py_module = 0;
    12034 +    py_name = __Pyx_PyIdentifier_FromString(name);
    12035 +    if (!py_name)
    12036 +        goto bad;
    12037 +    py_module = PyImport_Import(py_name);
    12038 +    Py_DECREF(py_name);
    12039 +    return py_module;
    12040 +bad:
    12041 +    Py_XDECREF(py_name);
    12042 +    return 0;
    12043 +}
    12044 +#endif
    12045 +
    12046  #ifndef __PYX_HAVE_RT_ImportType
    12047  #define __PYX_HAVE_RT_ImportType
    12048  static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name,
    12049 @@ -22738,15 +23471,10 @@
    12050      PyObject *result = 0;
    12051      PyObject *py_name = 0;
    12052      char warning[200];
    12053 -
    12054      py_module = __Pyx_ImportModule(module_name);
    12055      if (!py_module)
    12056          goto bad;
    12057 -    #if PY_MAJOR_VERSION < 3
    12058 -    py_name = PyString_FromString(class_name);
    12059 -    #else
    12060 -    py_name = PyUnicode_FromString(class_name);
    12061 -    #endif
    12062 +    py_name = __Pyx_PyIdentifier_FromString(class_name);
    12063      if (!py_name)
    12064          goto bad;
    12065      result = PyObject_GetAttr(py_module, py_name);
    12066 @@ -22762,7 +23490,7 @@
    12067              module_name, class_name);
    12068          goto bad;
    12069      }
    12070 -    if (!strict && ((PyTypeObject *)result)->tp_basicsize > (Py_ssize_t)size) {
    12071 +    if (!strict && (size_t)((PyTypeObject *)result)->tp_basicsize > size) {
    12072          PyOS_snprintf(warning, sizeof(warning),
    12073              "%s.%s size changed, may indicate binary incompatibility",
    12074              module_name, class_name);
    12075 @@ -22772,7 +23500,7 @@
    12076          if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad;
    12077          #endif
    12078      }
    12079 -    else if (((PyTypeObject *)result)->tp_basicsize != (Py_ssize_t)size) {
    12080 +    else if ((size_t)((PyTypeObject *)result)->tp_basicsize != size) {
    12081          PyErr_Format(PyExc_ValueError,
    12082              "%s.%s has the wrong size, try recompiling",
    12083              module_name, class_name);
    12084 @@ -22786,51 +23514,105 @@
    12085  }
    12086  #endif
    12087  
    12088 -#ifndef __PYX_HAVE_RT_ImportModule
    12089 -#define __PYX_HAVE_RT_ImportModule
    12090 -static PyObject *__Pyx_ImportModule(const char *name) {
    12091 -    PyObject *py_name = 0;
    12092 -    PyObject *py_module = 0;
    12093 -
    12094 -    #if PY_MAJOR_VERSION < 3
    12095 -    py_name = PyString_FromString(name);
    12096 -    #else
    12097 -    py_name = PyUnicode_FromString(name);
    12098 -    #endif
    12099 -    if (!py_name)
    12100 -        goto bad;
    12101 -    py_module = PyImport_Import(py_name);
    12102 -    Py_DECREF(py_name);
    12103 -    return py_module;
    12104 -bad:
    12105 -    Py_XDECREF(py_name);
    12106 -    return 0;
    12107 +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) {
    12108 +    int start = 0, mid = 0, end = count - 1;
    12109 +    if (end >= 0 && code_line > entries[end].code_line) {
    12110 +        return count;
    12111 +    }
    12112 +    while (start < end) {
    12113 +        mid = (start + end) / 2;
    12114 +        if (code_line < entries[mid].code_line) {
    12115 +            end = mid;
    12116 +        } else if (code_line > entries[mid].code_line) {
    12117 +             start = mid + 1;
    12118 +        } else {
    12119 +            return mid;
    12120 +        }
    12121 +    }
    12122 +    if (code_line <= entries[mid].code_line) {
    12123 +        return mid;
    12124 +    } else {
    12125 +        return mid + 1;
    12126 +    }
    12127 +}
    12128 +static PyCodeObject *__pyx_find_code_object(int code_line) {
    12129 +    PyCodeObject* code_object;
    12130 +    int pos;
    12131 +    if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) {
    12132 +        return NULL;
    12133 +    }
    12134 +    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
    12135 +    if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) {
    12136 +        return NULL;
    12137 +    }
    12138 +    code_object = __pyx_code_cache.entries[pos].code_object;
    12139 +    Py_INCREF(code_object);
    12140 +    return code_object;
    12141 +}
    12142 +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) {
    12143 +    int pos, i;
    12144 +    __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries;
    12145 +    if (unlikely(!code_line)) {
    12146 +        return;
    12147 +    }
    12148 +    if (unlikely(!entries)) {
    12149 +        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry));
    12150 +        if (likely(entries)) {
    12151 +            __pyx_code_cache.entries = entries;
    12152 +            __pyx_code_cache.max_count = 64;
    12153 +            __pyx_code_cache.count = 1;
    12154 +            entries[0].code_line = code_line;
    12155 +            entries[0].code_object = code_object;
    12156 +            Py_INCREF(code_object);
    12157 +        }
    12158 +        return;
    12159 +    }
    12160 +    pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line);
    12161 +    if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) {
    12162 +        PyCodeObject* tmp = entries[pos].code_object;
    12163 +        entries[pos].code_object = code_object;
    12164 +        Py_DECREF(tmp);
    12165 +        return;
    12166 +    }
    12167 +    if (__pyx_code_cache.count == __pyx_code_cache.max_count) {
    12168 +        int new_max = __pyx_code_cache.max_count + 64;
    12169 +        entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc(
    12170 +            __pyx_code_cache.entries, new_max*sizeof(__Pyx_CodeObjectCacheEntry));
    12171 +        if (unlikely(!entries)) {
    12172 +            return;
    12173 +        }
    12174 +        __pyx_code_cache.entries = entries;
    12175 +        __pyx_code_cache.max_count = new_max;
    12176 +    }
    12177 +    for (i=__pyx_code_cache.count; i>pos; i--) {
    12178 +        entries[i] = entries[i-1];
    12179 +    }
    12180 +    entries[pos].code_line = code_line;
    12181 +    entries[pos].code_object = code_object;
    12182 +    __pyx_code_cache.count++;
    12183 +    Py_INCREF(code_object);
    12184  }
    12185 -#endif
    12186  
    12187  #include "compile.h"
    12188  #include "frameobject.h"
    12189  #include "traceback.h"
    12190 -
    12191 -static void __Pyx_AddTraceback(const char *funcname, int __pyx_clineno,
    12192 -                               int __pyx_lineno, const char *__pyx_filename) {
    12193 +static PyCodeObject* __Pyx_CreateCodeObjectForTraceback(
    12194 +            const char *funcname, int c_line,
    12195 +            int py_line, const char *filename) {
    12196 +    PyCodeObject *py_code = 0;
    12197      PyObject *py_srcfile = 0;
    12198      PyObject *py_funcname = 0;
    12199 -    PyObject *py_globals = 0;
    12200 -    PyCodeObject *py_code = 0;
    12201 -    PyFrameObject *py_frame = 0;
    12202 -
    12203      #if PY_MAJOR_VERSION < 3
    12204 -    py_srcfile = PyString_FromString(__pyx_filename);
    12205 +    py_srcfile = PyString_FromString(filename);
    12206      #else
    12207 -    py_srcfile = PyUnicode_FromString(__pyx_filename);
    12208 +    py_srcfile = PyUnicode_FromString(filename);
    12209      #endif
    12210      if (!py_srcfile) goto bad;
    12211 -    if (__pyx_clineno) {
    12212 +    if (c_line) {
    12213          #if PY_MAJOR_VERSION < 3
    12214 -        py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, __pyx_clineno);
    12215 +        py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
    12216          #else
    12217 -        py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, __pyx_clineno);
    12218 +        py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line);
    12219          #endif
    12220      }
    12221      else {
    12222 @@ -22841,28 +23623,45 @@
    12223          #endif
    12224      }
    12225      if (!py_funcname) goto bad;
    12226 -    py_globals = PyModule_GetDict(__pyx_m);
    12227 -    if (!py_globals) goto bad;
    12228 -    py_code = PyCode_New(
    12229 +    py_code = __Pyx_PyCode_New(
    12230          0,            /*int argcount,*/
    12231 -        #if PY_MAJOR_VERSION >= 3
    12232          0,            /*int kwonlyargcount,*/
    12233 -        #endif
    12234          0,            /*int nlocals,*/
    12235          0,            /*int stacksize,*/
    12236          0,            /*int flags,*/
    12237          __pyx_empty_bytes, /*PyObject *code,*/
    12238 -        __pyx_empty_tuple,  /*PyObject *consts,*/
    12239 -        __pyx_empty_tuple,  /*PyObject *names,*/
    12240 -        __pyx_empty_tuple,  /*PyObject *varnames,*/
    12241 -        __pyx_empty_tuple,  /*PyObject *freevars,*/
    12242 -        __pyx_empty_tuple,  /*PyObject *cellvars,*/
    12243 +        __pyx_empty_tuple, /*PyObject *consts,*/
    12244 +        __pyx_empty_tuple, /*PyObject *names,*/
    12245 +        __pyx_empty_tuple, /*PyObject *varnames,*/
    12246 +        __pyx_empty_tuple, /*PyObject *freevars,*/
    12247 +        __pyx_empty_tuple, /*PyObject *cellvars,*/
    12248          py_srcfile,   /*PyObject *filename,*/
    12249          py_funcname,  /*PyObject *name,*/
    12250 -        __pyx_lineno,   /*int firstlineno,*/
    12251 +        py_line,      /*int firstlineno,*/
    12252          __pyx_empty_bytes  /*PyObject *lnotab*/
    12253      );
    12254 -    if (!py_code) goto bad;
    12255 +    Py_DECREF(py_srcfile);
    12256 +    Py_DECREF(py_funcname);
    12257 +    return py_code;
    12258 +bad:
    12259 +    Py_XDECREF(py_srcfile);
    12260 +    Py_XDECREF(py_funcname);
    12261 +    return NULL;
    12262 +}
    12263 +static void __Pyx_AddTraceback(const char *funcname, int c_line,
    12264 +                               int py_line, const char *filename) {
    12265 +    PyCodeObject *py_code = 0;
    12266 +    PyObject *py_globals = 0;
    12267 +    PyFrameObject *py_frame = 0;
    12268 +    py_code = __pyx_find_code_object(c_line ? c_line : py_line);
    12269 +    if (!py_code) {
    12270 +        py_code = __Pyx_CreateCodeObjectForTraceback(
    12271 +            funcname, c_line, py_line, filename);
    12272 +        if (!py_code) goto bad;
    12273 +        __pyx_insert_code_object(c_line ? c_line : py_line, py_code);
    12274 +    }
    12275 +    py_globals = PyModule_GetDict(__pyx_m);
    12276 +    if (!py_globals) goto bad;
    12277      py_frame = PyFrame_New(
    12278          PyThreadState_GET(), /*PyThreadState *tstate,*/
    12279          py_code,             /*PyCodeObject *code,*/
    12280 @@ -22870,11 +23669,9 @@
    12281          0                    /*PyObject *locals*/
    12282      );
    12283      if (!py_frame) goto bad;
    12284 -    py_frame->f_lineno = __pyx_lineno;
    12285 +    py_frame->f_lineno = py_line;
    12286      PyTraceBack_Here(py_frame);
    12287  bad:
    12288 -    Py_XDECREF(py_srcfile);
    12289 -    Py_XDECREF(py_funcname);
    12290      Py_XDECREF(py_code);
    12291      Py_XDECREF(py_frame);
    12292  }
    12293 @@ -22909,6 +23706,7 @@
    12294      return 0;
    12295  }
    12296  
    12297 +
    12298  /* Type Conversion Functions */
    12299  
    12300  static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) {

  • deleted file dports/python/py-numpy/files/patch-python33-shape.diff 

    diff --git a/dports/python/py-numpy/files/patch-python33-shape.diff b/dports/python/py-numpy/files/patch-python33-shape.diff
deleted file mode 100644
    + -  
    1 --- numpy/core/tests/test_multiarray.py
    2 +++ numpy/core/tests/test_multiarray.py
    3 @@ -11,10 +11,19 @@
    4  
    5  from numpy.compat import asbytes, getexception, strchar
    6  
    7  from test_print import in_foreign_locale
    8  
    9 +if sys.version_info[:2] > (3, 2):
    10 +    # In Python 3.3 the representation of empty shape, strides and suboffsets
    11 +    # is an empty tuple instead of None.
    12 +    # http://docs.python.org/dev/whatsnew/3.3.html#api-changes
    13 +    EMPTY = ()
    14 +else:   
    15 +    EMPTY = None
    16 +
    17 +
    18  class TestFlags(TestCase):
    19      def setUp(self):
    20          self.a = arange(10)
    21  
    22      def test_writeable(self):
    23 @@ -2160,31 +2169,31 @@
    24              y = memoryview(x)
    25              assert_equal(y.format, 'i')
    26              assert_equal(y.shape, (5,))
    27              assert_equal(y.ndim, 1)
    28              assert_equal(y.strides, (4,))
    29 -            assert_equal(y.suboffsets, None)
    30 +            assert_equal(y.suboffsets, EMPTY)
    31              assert_equal(y.itemsize, 4)
    32  
    33          def test_export_simple_nd(self):
    34              x = np.array([[1,2],[3,4]], dtype=np.float64)
    35              y = memoryview(x)
    36              assert_equal(y.format, 'd')
    37              assert_equal(y.shape, (2, 2))
    38              assert_equal(y.ndim, 2)
    39              assert_equal(y.strides, (16, 8))
    40 -            assert_equal(y.suboffsets, None)
    41 +            assert_equal(y.suboffsets, EMPTY)
    42              assert_equal(y.itemsize, 8)
    43  
    44          def test_export_discontiguous(self):
    45              x = np.zeros((3,3,3), dtype=np.float32)[:,0,:]
    46              y = memoryview(x)
    47              assert_equal(y.format, 'f')
    48              assert_equal(y.shape, (3, 3))
    49              assert_equal(y.ndim, 2)
    50              assert_equal(y.strides, (36, 4))
    51 -            assert_equal(y.suboffsets, None)
    52 +            assert_equal(y.suboffsets, EMPTY)
    53              assert_equal(y.itemsize, 4)
    54  
    55          def test_export_record(self):
    56              dt = [('a', 'b'),
    57                    ('b', 'h'),
    58 @@ -2212,11 +2221,11 @@
    59                             asbytes('aaaa'), 'bbbb', asbytes('   '), True, 1.0)],
    60                           dtype=dt)
    61              y = memoryview(x)
    62              assert_equal(y.shape, (1,))
    63              assert_equal(y.ndim, 1)
    64 -            assert_equal(y.suboffsets, None)
    65 +            assert_equal(y.suboffsets, EMPTY)
    66  
    67              sz = sum([dtype(b).itemsize for a, b in dt])
    68              if dtype('l').itemsize == 4:
    69                  assert_equal(y.format, 'T{b:a:=h:b:i:c:l:d:^q:dx:B:e:@H:f:=I:g:L:h:^Q:hx:=f:i:d:j:^g:k:=Zf:ix:Zd:jx:^Zg:kx:4s:l:=4w:m:3x:n:?:o:@e:p:}')
    70              else:
    71 @@ -2226,14 +2235,14 @@
    72  
    73          def test_export_subarray(self):
    74              x = np.array(([[1,2],[3,4]],), dtype=[('a', ('i', (2,2)))])
    75              y = memoryview(x)
    76              assert_equal(y.format, 'T{(2,2)i:a:}')
    77 -            assert_equal(y.shape, None)
    78 +            assert_equal(y.shape, EMPTY)
    79              assert_equal(y.ndim, 0)
    80 -            assert_equal(y.strides, None)
    81 -            assert_equal(y.suboffsets, None)
    82 +            assert_equal(y.strides, EMPTY)
    83 +            assert_equal(y.suboffsets, EMPTY)
    84              assert_equal(y.itemsize, 16)
    85  
    86          def test_export_endian(self):
    87              x = np.array([1,2,3], dtype='>i')
    88              y = memoryview(x)

  • deleted file dports/python/py-numpy/files/patch-python33-unicode.diff 

    diff --git a/dports/python/py-numpy/files/patch-python33-unicode.diff b/dports/python/py-numpy/files/patch-python33-unicode.diff
deleted file mode 100644
    + -  
    1 --- numpy/core/src/multiarray/scalarapi.c
    2 +++ numpy/core/src/multiarray/scalarapi.c
    3 @@ -650,10 +650,39 @@
    4               * so round up to nearest multiple
    5               */
    6              itemsize = (((itemsize - 1) >> 2) + 1) << 2;
    7          }
    8      }
    9 +#if PY_VERSION_HEX >= 0x03030000
    10 +    if (type_num == NPY_UNICODE) {
    11 +        PyObject *u, *args;
    12 +        int byteorder;
    13 +
    14 +#if NPY_BYTE_ORDER == NPY_LITTLE_ENDIAN
    15 +        byteorder = -1;
    16 +#elif NPY_BYTE_ORDER == NPY_BIG_ENDIAN
    17 +        byteorder = +1;
    18 +#else
    19 +        #error Endianness undefined ?
    20 +#endif
    21 +        if (swap) byteorder *= -1;
    22 +
    23 +        u = PyUnicode_DecodeUTF32(data, itemsize, NULL, &byteorder);
    24 +        if (u == NULL) {
    25 +            return NULL;
    26 +        }
    27 +        args = Py_BuildValue("(O)", u);
    28 +        if (args == NULL) {
    29 +            Py_DECREF(u);
    30 +            return NULL;
    31 +        }
    32 +        obj = type->tp_new(type, args, NULL);
    33 +        Py_DECREF(u);
    34 +        Py_DECREF(args);
    35 +        return obj;
    36 +    }
    37 +#endif
    38      if (type->tp_itemsize != 0) {
    39          /* String type */
    40          obj = type->tp_alloc(type, itemsize);
    41      }
    42      else {
    43 @@ -686,10 +715,11 @@
    44              ((PyStringObject *)obj)->ob_sstate = SSTATE_NOT_INTERNED;
    45  #endif
    46              memcpy(destptr, data, itemsize);
    47              return obj;
    48          }
    49 +#if PY_VERSION_HEX < 0x03030000
    50          else if (type_num == PyArray_UNICODE) {
    51              /* tp_alloc inherited from Python PyBaseObject_Type */
    52              PyUnicodeObject *uni = (PyUnicodeObject*)obj;
    53              size_t length = itemsize >> 2;
    54              Py_UNICODE *dst;
    55 @@ -757,10 +787,11 @@
    56              uni->str[length] = 0;
    57              uni->length = length;
    58  #endif
    59              return obj;
    60          }
    61 +#endif /* PY_VERSION_HEX < 0x03030000 */
    62          else {
    63              PyVoidScalarObject *vobj = (PyVoidScalarObject *)obj;
    64              vobj->base = NULL;
    65              vobj->descr = descr;
    66              Py_INCREF(descr);
    67 --- numpy/core/src/multiarray/scalartypes.c.src
    68 +++ numpy/core/src/multiarray/scalartypes.c.src
    69 @@ -2321,11 +2321,15 @@
    70      Py_DECREF(typecode);
    71  #if @default@ == 0
    72      *((npy_@name@ *)dest) = *((npy_@name@ *)src);
    73  #elif @default@ == 1 /* unicode and strings */
    74      if (itemsize == 0) { /* unicode */
    75 +#if PY_VERSION_HEX >= 0x03030000
    76 +        itemsize = PyUnicode_GetLength(robj) * PyUnicode_KIND(robj);
    77 +#else
    78          itemsize = ((PyUnicodeObject *)robj)->length * sizeof(Py_UNICODE);
    79 +#endif
    80      }
    81      memcpy(dest, src, itemsize);
    82      /* @default@ == 2 won't get here */
    83  #endif
    84      Py_DECREF(robj);
    85 --- numpy/core/tests/test_unicode.py
    86 +++ numpy/core/tests/test_unicode.py
    87 @@ -24,14 +24,16 @@
    88      def buffer_length(arr):
    89          if isinstance(arr, ndarray):
    90              return len(arr.data)
    91          return len(buffer(arr))
    92  
    93 +# In both cases below we need to make sure that the byte swapped value (as
    94 +# UCS4) is still a valid unicode:
    95  # Value that can be represented in UCS2 interpreters
    96 -ucs2_value = u'\uFFFF'
    97 +ucs2_value = u'\u0900'
    98  # Value that cannot be represented in UCS2 interpreters (but can in UCS4)
    99 -ucs4_value = u'\U0010FFFF'
    100 +ucs4_value = u'\U00100900'
    101  
    102  
    103  ############################################################
    104  #    Creation tests
    105  ############################################################