[Numpy-svn] r2892 - in branches/ver1.0/numpy: core/include/numpy doc/pyrex linalg random/mtrand
numpy-svn at scipy.org
numpy-svn at scipy.org
Tue Jul 25 18:06:16 EDT 2006
Author: oliphant
Date: 2006-07-25 17:06:02 -0500 (Tue, 25 Jul 2006)
New Revision: 2892
Modified:
branches/ver1.0/numpy/core/include/numpy/arrayobject.h
branches/ver1.0/numpy/doc/pyrex/c_numpy.pxd
branches/ver1.0/numpy/linalg/linalg.py
branches/ver1.0/numpy/random/mtrand/generate_mtrand_c.py
branches/ver1.0/numpy/random/mtrand/mtrand.c
branches/ver1.0/numpy/random/mtrand/mtrand.pyx
branches/ver1.0/numpy/random/mtrand/numpy.pxi
Log:
Add broadcasting behavior to random-number generators. Fix cholesky to keep matrix return.
Modified: branches/ver1.0/numpy/core/include/numpy/arrayobject.h
===================================================================
--- branches/ver1.0/numpy/core/include/numpy/arrayobject.h 2006-07-25 17:17:08 UTC (rev 2891)
+++ branches/ver1.0/numpy/core/include/numpy/arrayobject.h 2006-07-25 22:06:02 UTC (rev 2892)
@@ -1032,90 +1032,90 @@
#define PyArrayIter_Check(op) PyObject_TypeCheck(op, &PyArrayIter_Type)
#define PyArray_ITER_RESET(it) { \
- it->index = 0; \
- it->dataptr = it->ao->data; \
- memset(it->coordinates, 0, (it->nd_m1+1)*sizeof(npy_intp)); \
+ (it)->index = 0; \
+ (it)->dataptr = (it)->ao->data; \
+ memset((it)->coordinates, 0, ((it)->nd_m1+1)*sizeof(npy_intp)); \
}
#define _PyArray_ITER_NEXT1(it) { \
- it->dataptr += it->strides[0]; \
- it->coordinates[0]++; \
+ (it)->dataptr += (it)->strides[0]; \
+ (it)->coordinates[0]++; \
}
#define _PyArray_ITER_NEXT2(it) { \
- if (it->coordinates[1] < it->dims_m1[1]) { \
- it->coordinates[1]++; \
- it->dataptr += it->strides[1]; \
+ if ((it)->coordinates[1] < (it)->dims_m1[1]) { \
+ (it)->coordinates[1]++; \
+ (it)->dataptr += (it)->strides[1]; \
} \
else { \
- it->coordinates[1] = 0; \
- it->coordinates[0]++; \
- it->dataptr += it->strides[0] - \
- it->backstrides[1]; \
+ (it)->coordinates[1] = 0; \
+ (it)->coordinates[0]++; \
+ (it)->dataptr += (it)->strides[0] - \
+ (it)->backstrides[1]; \
} \
}
#define PyArray_ITER_NEXT(it) { \
- it->index++; \
- if (it->nd_m1 == 0) { \
+ (it)->index++; \
+ if ((it)->nd_m1 == 0) { \
_PyArray_ITER_NEXT1(it); \
} \
- else if (it->contiguous) it->dataptr += it->ao->descr->elsize; \
- else if (it->nd_m1 == 1) { \
+ else if ((it)->contiguous) (it)->dataptr += (it)->ao->descr->elsize; \
+ else if ((it)->nd_m1 == 1) { \
_PyArray_ITER_NEXT2(it); \
} \
else { \
- int _i_; \
- for (_i_ = it->nd_m1; _i_ >= 0; _i_--) { \
- if (it->coordinates[_i_] < \
- it->dims_m1[_i_]) { \
- it->coordinates[_i_]++; \
- it->dataptr += it->strides[_i_]; \
+ int __npy_i; \
+ for (__npy_i = (it)->nd_m1; __npy_i >= 0; __npy_i--) { \
+ if ((it)->coordinates[__npy_i] < \
+ (it)->dims_m1[__npy_i]) { \
+ (it)->coordinates[__npy_i]++; \
+ (it)->dataptr += (it)->strides[__npy_i]; \
break; \
} \
else { \
- it->coordinates[_i_] = 0; \
- it->dataptr -= it->backstrides[_i_]; \
+ (it)->coordinates[__npy_i] = 0; \
+ (it)->dataptr -= (it)->backstrides[__npy_i]; \
} \
} \
} \
}
#define PyArray_ITER_GOTO(it, destination) { \
- int _i_; \
- it->index = 0; \
- it->dataptr = it->ao->data; \
- for (_i_ = it->nd_m1; _i_>=0; _i_--) { \
- if (destination[_i_] < 0) { \
- destination[_i_] += it->dims_m1[_i_]+1; \
+ int __npy_i; \
+ (it)->index = 0; \
+ (it)->dataptr = (it)->ao->data; \
+ for (__npy_i = (it)->nd_m1; __npy_i>=0; __npy_i--) { \
+ if (destination[__npy_i] < 0) { \
+ destination[__npy_i] += (it)->dims_m1[__npy_i]+1; \
} \
- it->dataptr += destination[_i_] * \
- it->strides[_i_]; \
- it->coordinates[_i_] = destination[_i_]; \
- it->index += destination[_i_] * \
- ( _i_==it->nd_m1 ? 1 : \
- it->dims_m1[_i_+1]+1) ; \
+ (it)->dataptr += destination[__npy_i] * \
+ (it)->strides[__npy_i]; \
+ (it)->coordinates[__npy_i] = destination[__npy_i]; \
+ (it)->index += destination[__npy_i] * \
+ ( __npy_i==(it)->nd_m1 ? 1 : \
+ (it)->dims_m1[__npy_i+1]+1) ; \
} \
}
#define PyArray_ITER_GOTO1D(it, ind) { \
- int _i_; \
- npy_intp _lind_ = (npy_intp) (ind); \
- if (_lind_ < 0) _lind_ += it->size; \
- it->index = _lind_; \
- if (it->nd_m1 == 0) { \
- it->dataptr = it->ao->data + _lind_ * \
- it->strides[0]; \
+ int __npy_i; \
+ npy_intp __npy_ind = (npy_intp) (ind); \
+ if (__npy_ind < 0) __npy_ind += (it)->size; \
+ (it)->index = __npy_ind; \
+ if ((it)->nd_m1 == 0) { \
+ (it)->dataptr = (it)->ao->data + __npy_ind * \
+ (it)->strides[0]; \
} \
- else if (it->contiguous) \
- it->dataptr = it->ao->data + _lind_ * \
- it->ao->descr->elsize; \
+ else if ((it)->contiguous) \
+ (it)->dataptr = (it)->ao->data + __npy_ind * \
+ (it)->ao->descr->elsize; \
else { \
- it->dataptr = it->ao->data; \
- for (_i_ = 0; _i_<=it->nd_m1; _i_++) { \
- it->dataptr += (_lind_ / it->factors[_i_]) \
- * it->strides[_i_]; \
- _lind_ %= it->factors[_i_]; \
+ (it)->dataptr = (it)->ao->data; \
+ for (__npy_i = 0; __npy_i<=(it)->nd_m1; __npy_i++) { \
+ (it)->dataptr += (__npy_ind / (it)->factors[__npy_i]) \
+ * (it)->strides[__npy_i]; \
+ __npy_ind %= (it)->factors[__npy_i]; \
} \
} \
}
@@ -1139,47 +1139,49 @@
} PyArrayMultiIterObject;
#define PyArray_MultiIter_RESET(multi) { \
- int _mi_; \
- PyArrayMultiIterObject *_mul_ = (multi); \
- _mul_->index = 0; \
- for (_mi_ = 0; _mi_ < _mul_->numiter; _mi_++) { \
- PyArray_ITER_RESET(_mul_->iters[_mi_]); \
+ int __npy_mi; \
+ PyArrayMultiIterObject *__npy_mul = (multi); \
+ __npy_mul->index = 0; \
+ for (__npy_mi = 0; __npy_mi < __npy_mul->numiter; __npy_mi++) { \
+ PyArray_ITER_RESET(__npy_mul->iters[__npy_mi]); \
} \
}
#define PyArray_MultiIter_NEXT(multi) { \
- int _mi_; \
- PyArrayMultiIterObject *_mul_ = (multi); \
- _mul_->index += 1; \
- for (_mi_=0; _mi_<_mul_->numiter; _mi_++) { \
- PyArray_ITER_NEXT(_mul_->iters[_mi_]); \
+ int __npy_mi; \
+ PyArrayMultiIterObject *__npy_mul = (multi); \
+ __npy_mul->index += 1; \
+ for (__npy_mi=0; __npy_mi<__npy_mul->numiter; __npy_mi++) { \
+ PyArray_ITER_NEXT(__npy_mul->iters[__npy_mi]); \
} \
}
#define PyArray_MultiIter_GOTO(multi, dest) { \
- int _mi_; \
- PyArrayMultiIterObject *_mul_ = (multi); \
- for (_mi_=0; _mi_<_mul_->numiter; _mi_++) { \
- PyArray_ITER_GOTO(_mul_->iters[_mi_], dest); \
+ int __npy_mi; \
+ PyArrayMultiIterObject *__npy_mul = (multi); \
+ for (__npy_mi=0; __npy_mi<__npy_mul->numiter; __npy_mi++) { \
+ PyArray_ITER_GOTO(__npy_mul->iters[__npy_mi], dest); \
} \
- _mul_->index = _mul_->iters[0]->index; \
+ __npy_mul->index = __npy_mul->iters[0]->index; \
}
#define PyArray_MultiIter_GOTO1D(multi, ind) { \
- int _mi_; \
- PyArrayMultiIterObject *_mul_ = (multi); \
- for (_mi_=0; _mi_<_mul_->numiter; _mi_++) { \
- PyArray_ITER_GOTO1D(_mul_->iters[_mi_], ind); \
+ int __npy_mi; \
+ PyArrayMultiIterObject *__npy_mul = (multi); \
+ for (__npy_mi=0; __npy_mi<__npy_mul->numiter; __npy_mi++) { \
+ PyArray_ITER_GOTO1D(__npy_mul->iters[__npy_mi], ind); \
} \
- _mul_->index = _mul_->iters[0]->index; \
+ __npy_mul->index = __npy_mul->iters[0]->index; \
}
-#define PyArray_MultiIter_DATA(multi, i) \
- ((PyArrayMultiIterObject *)multi)->iters[i]->dataptr
+#define PyArray_MultiIter_DATA(multi, i) \
+ (((PyArrayMultiIterObject *)multi)->iters[i]->dataptr)
-#define PyArray_MultiIter_SIZE(multi) \
- ((PyArrayMultiIterObject *)multi)->size;
+#define PyArray_MultiIter_SIZE(multi) \
+ ((PyArrayMultiIterObject *)multi)->size
+#define PyArray_MultiIter_NEXTi(multi, i) \
+ PyArray_ITER_NEXT(((PyArrayMultiIterObject *)multi)->iters[i])
/* Store the information needed for fancy-indexing over an array */
Modified: branches/ver1.0/numpy/doc/pyrex/c_numpy.pxd
===================================================================
--- branches/ver1.0/numpy/doc/pyrex/c_numpy.pxd 2006-07-25 17:17:08 UTC (rev 2891)
+++ branches/ver1.0/numpy/doc/pyrex/c_numpy.pxd 2006-07-25 22:06:02 UTC (rev 2892)
@@ -1,6 +1,4 @@
-# :Author: Robert Kern
-# :Copyright: 2004, Enthought, Inc.
-# :License: BSD Style
+# :Author: Travis Oliphant
cdef extern from "numpy/arrayobject.h":
@@ -60,6 +58,9 @@
NPY_UPDATE_ALL
+ cdef enum defines:
+ NPY_MAXDIMS
+
ctypedef struct npy_cdouble:
double real
double imag
@@ -84,6 +85,19 @@
cdef dtype descr
cdef int flags
+ ctypedef extern class numpy.flatiter [object PyArrayIterObject]:
+ cdef int nd_m1
+ cdef npy_intp index, size
+ cdef ndarray ao
+ cdef char *dataptr
+
+ ctypedef extern class numpy.broadcast [object PyArrayMultiIterObject]:
+ cdef int numiter
+ cdef npy_intp size, index
+ cdef int nd
+ cdef npy_intp dimensions[NPY_MAXDIMS]
+ cdef flatiter iters[NPY_MAXDIMS]
+
object PyArray_ZEROS(int ndims, npy_intp* dims, NPY_TYPES type_num, int fortran)
object PyArray_EMPTY(int ndims, npy_intp* dims, NPY_TYPES type_num, int fortran)
dtype PyArray_DescrFromTypeNum(NPY_TYPES type_num)
@@ -102,4 +116,6 @@
npy_intp* dims, npy_intp* strides, void* data,
int flags, object parent)
+ void PyArray_ITER_NEXT(flatiter it)
+
void import_array()
Modified: branches/ver1.0/numpy/linalg/linalg.py
===================================================================
--- branches/ver1.0/numpy/linalg/linalg.py 2006-07-25 17:17:08 UTC (rev 2891)
+++ branches/ver1.0/numpy/linalg/linalg.py 2006-07-25 22:06:02 UTC (rev 2892)
@@ -165,7 +165,8 @@
if results['info'] > 0:
raise LinAlgError, 'Matrix is not positive definite - Cholesky decomposition cannot be computed'
s = triu(a, k=0).transpose()
- return array(s, dtype=result_t, copy=True)
+ if (s.dtype != result_t):
+ return s.astype(result_t)
# Eigenvalues
def eigvals(a):
Modified: branches/ver1.0/numpy/random/mtrand/generate_mtrand_c.py
===================================================================
--- branches/ver1.0/numpy/random/mtrand/generate_mtrand_c.py 2006-07-25 17:17:08 UTC (rev 2891)
+++ branches/ver1.0/numpy/random/mtrand/generate_mtrand_c.py 2006-07-25 22:06:02 UTC (rev 2892)
@@ -8,7 +8,6 @@
'__Pyx_ReRaise',
'__Pyx_GetExcValue',
'__Pyx_ArgTypeTest',
- '__Pyx_TypeTest',
'__Pyx_SetVtable',
'__Pyx_GetVtable',
'__Pyx_CreateClass']
Modified: branches/ver1.0/numpy/random/mtrand/mtrand.c
===================================================================
--- branches/ver1.0/numpy/random/mtrand/mtrand.c 2006-07-25 17:17:08 UTC (rev 2891)
+++ branches/ver1.0/numpy/random/mtrand/mtrand.c 2006-07-25 22:06:02 UTC (rev 2892)
@@ -1,10 +1,16 @@
-/* Generated by Pyrex 0.9.3.1cpp on Thu Jul 20 17:10:39 2006 */
+/* Generated by Pyrex 0.9.4.1 on Tue Jul 25 16:03:16 2006 */
#include "Python.h"
#include "structmember.h"
#ifndef PY_LONG_LONG
#define PY_LONG_LONG LONG_LONG
#endif
+#ifdef __cplusplus
+#define __PYX_EXTERN_C extern "C"
+#else
+#define __PYX_EXTERN_C extern
+#endif
+__PYX_EXTERN_C double pow(double, double);
#include "string.h"
#include "math.h"
#include "numpy/arrayobject.h"
@@ -19,13 +25,13 @@
static int __Pyx_EndUnpack(PyObject *, int); /*proto*/
static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb); /*proto*/
static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list); /*proto*/
+static int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); /*proto*/
static int __Pyx_GetStarArgs(PyObject **args, PyObject **kwds, char *kwd_list[], int nargs, PyObject **args2, PyObject **kwds2); /*proto*/
static void __Pyx_WriteUnraisable(char *name); /*proto*/
static void __Pyx_AddTraceback(char *funcname); /*proto*/
static PyTypeObject *__Pyx_ImportType(char *module_name, char *class_name, long size); /*proto*/
static int __Pyx_InternStrings(__Pyx_InternTabEntry *t); /*proto*/
static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); /*proto*/
-static char **__Pyx_FileNames(void); /*proto*/
static PyObject *__Pyx_GetName(PyObject *dict, PyObject *name); /*proto*/
static PyObject *__pyx_m;
@@ -34,5651 +40,8364 @@
static char *__pyx_filename;
static char **__pyx_f;
-#ifdef __cplusplus
-extern "C" {
- #endif
+/* Declarations from mtrand */
- /* Declarations from mtrand */
+struct __pyx_obj_6mtrand_RandomState {
+ PyObject_HEAD
+ rk_state (*internal_state);
+};
- struct __pyx_obj_6mtrand_RandomState {
- PyObject_HEAD
- rk_state (*internal_state);
- };
+static PyTypeObject *__pyx_ptype_6mtrand_dtype = 0;
+static PyTypeObject *__pyx_ptype_6mtrand_ndarray = 0;
+static PyTypeObject *__pyx_ptype_6mtrand_flatiter = 0;
+static PyTypeObject *__pyx_ptype_6mtrand_broadcast = 0;
+static PyTypeObject *__pyx_ptype_6mtrand_RandomState = 0;
+static PyObject *__pyx_k2;
+static PyObject *__pyx_k3;
+static PyObject *__pyx_k4;
+static PyObject *__pyx_k5;
+static PyObject *__pyx_k6;
+static PyObject *__pyx_k7;
+static PyObject *__pyx_k8;
+static PyObject *__pyx_k9;
+static PyObject *__pyx_k10;
+static PyObject *__pyx_k11;
+static PyObject *__pyx_k12;
+static PyObject *__pyx_k13;
+static PyObject *__pyx_k14;
+static PyObject *__pyx_k15;
+static PyObject *__pyx_k16;
+static PyObject *__pyx_k17;
+static PyObject *__pyx_k18;
+static PyObject *__pyx_k19;
+static PyObject *__pyx_k20;
+static PyObject *__pyx_k21;
+static PyObject *__pyx_k22;
+static PyObject *__pyx_k23;
+static PyObject *__pyx_k24;
+static PyObject *__pyx_k25;
+static PyObject *__pyx_k26;
+static PyObject *__pyx_k27;
+static PyObject *__pyx_k28;
+static PyObject *__pyx_k29;
+static PyObject *__pyx_k30;
+static PyObject *__pyx_k31;
+static PyObject *__pyx_k32;
+static PyObject *__pyx_k33;
+static PyObject *__pyx_k34;
+static PyObject *__pyx_k35;
+static PyObject *__pyx_k36;
+static PyObject *__pyx_k37;
+static PyObject *__pyx_k38;
+static PyObject *__pyx_k39;
+static PyObject *__pyx_k40;
+static PyObject *__pyx_k41;
+static PyObject *__pyx_k42;
+static PyObject *__pyx_k43;
+static PyObject *__pyx_k44;
+static PyObject *__pyx_k45;
+static PyObject *__pyx_k46;
+static PyObject *__pyx_k47;
+static PyObject *__pyx_k48;
+static PyObject *__pyx_k49;
+static PyObject *__pyx_k50;
+static PyObject *__pyx_k51;
+static PyObject *__pyx_k52;
+static PyObject *__pyx_k53;
+static PyObject *__pyx_k54;
+static PyObject *__pyx_k55;
+static PyObject *__pyx_k56;
+static PyObject *__pyx_k57;
+static PyObject *__pyx_k58;
+static PyObject *__pyx_k59;
+static PyObject *(__pyx_f_6mtrand_cont0_array(rk_state (*),double ((*)(rk_state (*))),PyObject *)); /*proto*/
+static PyObject *(__pyx_f_6mtrand_cont1_array(rk_state (*),double ((*)(rk_state (*),double )),PyObject *,PyArrayObject *)); /*proto*/
+static PyObject *(__pyx_f_6mtrand_cont2_array(rk_state (*),double ((*)(rk_state (*),double ,double )),PyObject *,PyArrayObject *,PyArrayObject *)); /*proto*/
+static PyObject *(__pyx_f_6mtrand_cont3_array(rk_state (*),double ((*)(rk_state (*),double ,double ,double )),PyObject *,PyArrayObject *,PyArrayObject *,PyArrayObject *)); /*proto*/
+static PyObject *(__pyx_f_6mtrand_disc0_array(rk_state (*),long ((*)(rk_state (*))),PyObject *)); /*proto*/
+static PyObject *(__pyx_f_6mtrand_discnp_array(rk_state (*),long ((*)(rk_state (*),long ,double )),PyObject *,PyArrayObject *,PyArrayObject *)); /*proto*/
+static PyObject *(__pyx_f_6mtrand_discnmN_array(rk_state (*),long ((*)(rk_state (*),long ,long ,long )),PyObject *,PyArrayObject *,PyArrayObject *,PyArrayObject *)); /*proto*/
+static PyObject *(__pyx_f_6mtrand_discd_array(rk_state (*),long ((*)(rk_state (*),double )),PyObject *,PyArrayObject *)); /*proto*/
+static double (__pyx_f_6mtrand_kahan_sum(double (*),long )); /*proto*/
- static PyTypeObject *__pyx_ptype_6mtrand_dtype = 0;
- static PyTypeObject *__pyx_ptype_6mtrand_ndarray = 0;
- static PyTypeObject *__pyx_ptype_6mtrand_RandomState = 0;
- static PyObject *__pyx_k2;
- static PyObject *__pyx_k3;
- static PyObject *__pyx_k4;
- static PyObject *__pyx_k5;
- static PyObject *__pyx_k6;
- static PyObject *__pyx_k7;
- static double __pyx_k8;
- static double __pyx_k9;
- static PyObject *__pyx_k10;
- static PyObject *__pyx_k11;
- static PyObject *__pyx_k12;
- static PyObject *__pyx_k13;
- static double __pyx_k14;
- static double __pyx_k15;
- static PyObject *__pyx_k16;
- static PyObject *__pyx_k17;
- static double __pyx_k18;
- static PyObject *__pyx_k19;
- static PyObject *__pyx_k20;
- static PyObject *__pyx_k21;
- static double __pyx_k22;
- static PyObject *__pyx_k23;
- static PyObject *__pyx_k24;
- static PyObject *__pyx_k25;
- static PyObject *__pyx_k26;
- static PyObject *__pyx_k27;
- static PyObject *__pyx_k28;
- static PyObject *__pyx_k29;
- static PyObject *__pyx_k30;
- static PyObject *__pyx_k31;
- static PyObject *__pyx_k32;
- static PyObject *__pyx_k33;
- static double __pyx_k34;
- static double __pyx_k35;
- static PyObject *__pyx_k36;
- static double __pyx_k37;
- static double __pyx_k38;
- static PyObject *__pyx_k39;
- static double __pyx_k40;
- static double __pyx_k41;
- static PyObject *__pyx_k42;
- static double __pyx_k43;
- static double __pyx_k44;
- static PyObject *__pyx_k45;
- static double __pyx_k46;
- static PyObject *__pyx_k47;
- static PyObject *__pyx_k48;
- static PyObject *__pyx_k49;
- static PyObject *__pyx_k50;
- static PyObject *__pyx_k51;
- static double __pyx_k52;
- static PyObject *__pyx_k53;
- static PyObject *__pyx_k54;
- static PyObject *__pyx_k55;
- static PyObject *__pyx_k56;
- static PyObject *__pyx_k57;
- static PyObject *__pyx_k58;
- static PyObject *__pyx_k59;
- static PyObject *(__pyx_f_6mtrand_cont0_array(rk_state (*),double ((*)(rk_state (*))),PyObject *)); /*proto*/
- static PyObject *(__pyx_f_6mtrand_cont1_array(rk_state (*),double ((*)(rk_state (*),double )),PyObject *,double )); /*proto*/
- static PyObject *(__pyx_f_6mtrand_cont2_array(rk_state (*),double ((*)(rk_state (*),double ,double )),PyObject *,double ,double )); /*proto*/
- static PyObject *(__pyx_f_6mtrand_cont3_array(rk_state (*),double ((*)(rk_state (*),double ,double ,double )),PyObject *,double ,double ,double )); /*proto*/
- static PyObject *(__pyx_f_6mtrand_disc0_array(rk_state (*),long ((*)(rk_state (*))),PyObject *)); /*proto*/
- static PyObject *(__pyx_f_6mtrand_discnp_array(rk_state (*),long ((*)(rk_state (*),long ,double )),PyObject *,long ,double )); /*proto*/
- static PyObject *(__pyx_f_6mtrand_discnmN_array(rk_state (*),long ((*)(rk_state (*),long ,long ,long )),PyObject *,long ,long ,long )); /*proto*/
- static PyObject *(__pyx_f_6mtrand_discd_array(rk_state (*),long ((*)(rk_state (*),double )),PyObject *,double )); /*proto*/
- static double (__pyx_f_6mtrand_kahan_sum(double (*),long )); /*proto*/
+/* Implementation of mtrand */
- /* Implementation of mtrand */
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+static PyObject *__pyx_n__rand;
+static PyObject *__pyx_n_seed;
+static PyObject *__pyx_n_get_state;
+static PyObject *__pyx_n_set_state;
+static PyObject *__pyx_n_random_sample;
+static PyObject *__pyx_n_randint;
+static PyObject *__pyx_n_bytes;
+static PyObject *__pyx_n_uniform;
+static PyObject *__pyx_n_rand;
+static PyObject *__pyx_n_randn;
+static PyObject *__pyx_n_random_integers;
+static PyObject *__pyx_n_standard_normal;
+static PyObject *__pyx_n_normal;
+static PyObject *__pyx_n_beta;
+static PyObject *__pyx_n_exponential;
+static PyObject *__pyx_n_standard_exponential;
+static PyObject *__pyx_n_standard_gamma;
+static PyObject *__pyx_n_gamma;
+static PyObject *__pyx_n_f;
+static PyObject *__pyx_n_noncentral_f;
+static PyObject *__pyx_n_chisquare;
+static PyObject *__pyx_n_noncentral_chisquare;
+static PyObject *__pyx_n_standard_cauchy;
+static PyObject *__pyx_n_standard_t;
+static PyObject *__pyx_n_vonmises;
+static PyObject *__pyx_n_pareto;
+static PyObject *__pyx_n_weibull;
+static PyObject *__pyx_n_power;
+static PyObject *__pyx_n_laplace;
+static PyObject *__pyx_n_gumbel;
+static PyObject *__pyx_n_logistic;
+static PyObject *__pyx_n_lognormal;
+static PyObject *__pyx_n_rayleigh;
+static PyObject *__pyx_n_wald;
+static PyObject *__pyx_n_triangular;
+static PyObject *__pyx_n_binomial;
+static PyObject *__pyx_n_negative_binomial;
+static PyObject *__pyx_n_poisson;
+static PyObject *__pyx_n_zipf;
+static PyObject *__pyx_n_geometric;
+static PyObject *__pyx_n_hypergeometric;
+static PyObject *__pyx_n_logseries;
+static PyObject *__pyx_n_multivariate_normal;
+static PyObject *__pyx_n_multinomial;
+static PyObject *__pyx_n_shuffle;
+static PyObject *__pyx_n_permutation;
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- static PyObject *__pyx_n__rand;
- static PyObject *__pyx_n_seed;
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- static PyObject *__pyx_n_randint;
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- static PyObject *__pyx_n_rand;
- static PyObject *__pyx_n_randn;
- static PyObject *__pyx_n_random_integers;
- static PyObject *__pyx_n_standard_normal;
- static PyObject *__pyx_n_normal;
- static PyObject *__pyx_n_beta;
- static PyObject *__pyx_n_exponential;
- static PyObject *__pyx_n_standard_exponential;
- static PyObject *__pyx_n_standard_gamma;
- static PyObject *__pyx_n_gamma;
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- static PyObject *__pyx_n_vonmises;
- static PyObject *__pyx_n_pareto;
- static PyObject *__pyx_n_weibull;
- static PyObject *__pyx_n_power;
- static PyObject *__pyx_n_laplace;
- static PyObject *__pyx_n_gumbel;
- static PyObject *__pyx_n_logistic;
- static PyObject *__pyx_n_lognormal;
- static PyObject *__pyx_n_rayleigh;
- static PyObject *__pyx_n_wald;
- static PyObject *__pyx_n_triangular;
- static PyObject *__pyx_n_binomial;
- static PyObject *__pyx_n_negative_binomial;
- static PyObject *__pyx_n_poisson;
- static PyObject *__pyx_n_zipf;
- static PyObject *__pyx_n_geometric;
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- static PyObject *__pyx_n_shuffle;
- static PyObject *__pyx_n_permutation;
- static PyObject *__pyx_n_numpy;
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+static PyObject *__pyx_n_float64;
- static PyObject *__pyx_n_empty;
- static PyObject *__pyx_n_float64;
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+ double (*__pyx_v_array_data);
+ PyArrayObject *arrayObject;
+ long __pyx_v_length;
+ long __pyx_v_i;
+ PyObject *__pyx_r;
+ int __pyx_1;
+ PyObject *__pyx_2 = 0;
+ PyObject *__pyx_3 = 0;
+ PyObject *__pyx_4 = 0;
+ Py_INCREF(__pyx_v_size);
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- static PyObject *__pyx_f_6mtrand_cont0_array(rk_state (*__pyx_v_state),double ((*__pyx_v_func)(rk_state (*))),PyObject *__pyx_v_size) {
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- int __pyx_1;
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+ /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":133 */
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+static PyObject *__pyx_n_ValueError;
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+static PyObject *__pyx_k60p;
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+static PyObject *__pyx_f_6mtrand_cont1_array(rk_state (*__pyx_v_state),double ((*__pyx_v_func)(rk_state (*),double )),PyObject *__pyx_v_size,PyArrayObject *__pyx_v_oa) {
+ double (*__pyx_v_array_data);
+ double (*__pyx_v_oa_data);
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+ npy_intp __pyx_v_i;
+ PyArrayIterObject *__pyx_v_itera;
+ PyArrayMultiIterObject *__pyx_v_multi;
+ int __pyx_v_scalar;
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__pyx_2 = 0;
goto __pyx_L0;
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+ {&__pyx_k123p, __pyx_k123, sizeof(__pyx_k123)},
+ {0, 0, 0}
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- {&__pyx_n_zeros, "zeros"},
- {&__pyx_n_zipf, "zipf"},
- {0, 0}
- };
+static PyObject *__pyx_tp_new_6mtrand_RandomState(PyTypeObject *t, PyObject *a, PyObject *k) {
+ PyObject *o = (*t->tp_alloc)(t, 0);
+ struct __pyx_obj_6mtrand_RandomState *p = (struct __pyx_obj_6mtrand_RandomState *)o;
+ return o;
+}
- static __Pyx_StringTabEntry __pyx_string_tab[] = {
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- static PyObject *__pyx_tp_new_6mtrand_RandomState(PyTypeObject *t, PyObject *a, PyObject *k) {
- PyObject *o = (*t->tp_alloc)(t, 0);
- struct __pyx_obj_6mtrand_RandomState *p = (struct __pyx_obj_6mtrand_RandomState *)o;
- return o;
+static void __pyx_tp_dealloc_6mtrand_RandomState(PyObject *o) {
+ struct __pyx_obj_6mtrand_RandomState *p = (struct __pyx_obj_6mtrand_RandomState *)o;
+ {
+ PyObject *etype, *eval, *etb;
+ PyErr_Fetch(&etype, &eval, &etb);
+ ++o->ob_refcnt;
+ __pyx_f_6mtrand_11RandomState___dealloc__(o);
+ if (PyErr_Occurred()) PyErr_WriteUnraisable(o);
+ --o->ob_refcnt;
+ PyErr_Restore(etype, eval, etb);
}
+ (*o->ob_type->tp_free)(o);
+}
- static void __pyx_tp_dealloc_6mtrand_RandomState(PyObject *o) {
- struct __pyx_obj_6mtrand_RandomState *p = (struct __pyx_obj_6mtrand_RandomState *)o;
- {
- PyObject *etype, *eval, *etb;
- PyErr_Fetch(&etype, &eval, &etb);
- ++o->ob_refcnt;
- __pyx_f_6mtrand_11RandomState___dealloc__(o);
- if (PyErr_Occurred()) PyErr_WriteUnraisable(o);
- --o->ob_refcnt;
- PyErr_Restore(etype, eval, etb);
- }
- (*o->ob_type->tp_free)(o);
- }
+static int __pyx_tp_traverse_6mtrand_RandomState(PyObject *o, visitproc v, void *a) {
+ int e;
+ struct __pyx_obj_6mtrand_RandomState *p = (struct __pyx_obj_6mtrand_RandomState *)o;
+ return 0;
+}
- static int __pyx_tp_traverse_6mtrand_RandomState(PyObject *o, visitproc v, void *a) {
- int e;
- struct __pyx_obj_6mtrand_RandomState *p = (struct __pyx_obj_6mtrand_RandomState *)o;
- return 0;
- }
+static int __pyx_tp_clear_6mtrand_RandomState(PyObject *o) {
+ struct __pyx_obj_6mtrand_RandomState *p = (struct __pyx_obj_6mtrand_RandomState *)o;
+ return 0;
+}
- static int __pyx_tp_clear_6mtrand_RandomState(PyObject *o) {
- struct __pyx_obj_6mtrand_RandomState *p = (struct __pyx_obj_6mtrand_RandomState *)o;
- return 0;
- }
+static struct PyMethodDef __pyx_methods_6mtrand_RandomState[] = {
+ {"seed", (PyCFunction)__pyx_f_6mtrand_11RandomState_seed, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_seed},
+ {"get_state", (PyCFunction)__pyx_f_6mtrand_11RandomState_get_state, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_get_state},
+ {"set_state", (PyCFunction)__pyx_f_6mtrand_11RandomState_set_state, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_set_state},
+ {"__getstate__", (PyCFunction)__pyx_f_6mtrand_11RandomState___getstate__, METH_VARARGS|METH_KEYWORDS, 0},
+ {"__setstate__", (PyCFunction)__pyx_f_6mtrand_11RandomState___setstate__, METH_VARARGS|METH_KEYWORDS, 0},
+ {"__reduce__", (PyCFunction)__pyx_f_6mtrand_11RandomState___reduce__, METH_VARARGS|METH_KEYWORDS, 0},
+ {"random_sample", (PyCFunction)__pyx_f_6mtrand_11RandomState_random_sample, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_random_sample},
+ {"tomaxint", (PyCFunction)__pyx_f_6mtrand_11RandomState_tomaxint, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_tomaxint},
+ {"randint", (PyCFunction)__pyx_f_6mtrand_11RandomState_randint, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_randint},
+ {"bytes", (PyCFunction)__pyx_f_6mtrand_11RandomState_bytes, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_bytes},
+ {"uniform", (PyCFunction)__pyx_f_6mtrand_11RandomState_uniform, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_uniform},
+ {"rand", (PyCFunction)__pyx_f_6mtrand_11RandomState_rand, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_rand},
+ {"randn", (PyCFunction)__pyx_f_6mtrand_11RandomState_randn, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_randn},
+ {"random_integers", (PyCFunction)__pyx_f_6mtrand_11RandomState_random_integers, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_random_integers},
+ {"standard_normal", (PyCFunction)__pyx_f_6mtrand_11RandomState_standard_normal, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_standard_normal},
+ {"normal", (PyCFunction)__pyx_f_6mtrand_11RandomState_normal, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_normal},
+ {"beta", (PyCFunction)__pyx_f_6mtrand_11RandomState_beta, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_beta},
+ {"exponential", (PyCFunction)__pyx_f_6mtrand_11RandomState_exponential, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_exponential},
+ {"standard_exponential", (PyCFunction)__pyx_f_6mtrand_11RandomState_standard_exponential, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_standard_exponential},
+ {"standard_gamma", (PyCFunction)__pyx_f_6mtrand_11RandomState_standard_gamma, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_standard_gamma},
+ {"gamma", (PyCFunction)__pyx_f_6mtrand_11RandomState_gamma, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_gamma},
+ {"f", (PyCFunction)__pyx_f_6mtrand_11RandomState_f, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_f},
+ {"noncentral_f", (PyCFunction)__pyx_f_6mtrand_11RandomState_noncentral_f, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_noncentral_f},
+ {"chisquare", (PyCFunction)__pyx_f_6mtrand_11RandomState_chisquare, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_chisquare},
+ {"noncentral_chisquare", (PyCFunction)__pyx_f_6mtrand_11RandomState_noncentral_chisquare, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_noncentral_chisquare},
+ {"standard_cauchy", (PyCFunction)__pyx_f_6mtrand_11RandomState_standard_cauchy, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_standard_cauchy},
+ {"standard_t", (PyCFunction)__pyx_f_6mtrand_11RandomState_standard_t, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_standard_t},
+ {"vonmises", (PyCFunction)__pyx_f_6mtrand_11RandomState_vonmises, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_vonmises},
+ {"pareto", (PyCFunction)__pyx_f_6mtrand_11RandomState_pareto, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_pareto},
+ {"weibull", (PyCFunction)__pyx_f_6mtrand_11RandomState_weibull, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_weibull},
+ {"power", (PyCFunction)__pyx_f_6mtrand_11RandomState_power, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_power},
+ {"laplace", (PyCFunction)__pyx_f_6mtrand_11RandomState_laplace, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_laplace},
+ {"gumbel", (PyCFunction)__pyx_f_6mtrand_11RandomState_gumbel, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_gumbel},
+ {"logistic", (PyCFunction)__pyx_f_6mtrand_11RandomState_logistic, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_logistic},
+ {"lognormal", (PyCFunction)__pyx_f_6mtrand_11RandomState_lognormal, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_lognormal},
+ {"rayleigh", (PyCFunction)__pyx_f_6mtrand_11RandomState_rayleigh, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_rayleigh},
+ {"wald", (PyCFunction)__pyx_f_6mtrand_11RandomState_wald, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_wald},
+ {"triangular", (PyCFunction)__pyx_f_6mtrand_11RandomState_triangular, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_triangular},
+ {"binomial", (PyCFunction)__pyx_f_6mtrand_11RandomState_binomial, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_binomial},
+ {"negative_binomial", (PyCFunction)__pyx_f_6mtrand_11RandomState_negative_binomial, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_negative_binomial},
+ {"poisson", (PyCFunction)__pyx_f_6mtrand_11RandomState_poisson, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_poisson},
+ {"zipf", (PyCFunction)__pyx_f_6mtrand_11RandomState_zipf, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_zipf},
+ {"geometric", (PyCFunction)__pyx_f_6mtrand_11RandomState_geometric, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_geometric},
+ {"hypergeometric", (PyCFunction)__pyx_f_6mtrand_11RandomState_hypergeometric, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_hypergeometric},
+ {"logseries", (PyCFunction)__pyx_f_6mtrand_11RandomState_logseries, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_logseries},
+ {"multivariate_normal", (PyCFunction)__pyx_f_6mtrand_11RandomState_multivariate_normal, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_multivariate_normal},
+ {"multinomial", (PyCFunction)__pyx_f_6mtrand_11RandomState_multinomial, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_multinomial},
+ {"shuffle", (PyCFunction)__pyx_f_6mtrand_11RandomState_shuffle, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_shuffle},
+ {"permutation", (PyCFunction)__pyx_f_6mtrand_11RandomState_permutation, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_permutation},
+ {0, 0, 0, 0}
+};
- static struct PyMethodDef __pyx_methods_6mtrand_RandomState[] = {
- {"seed", (PyCFunction)__pyx_f_6mtrand_11RandomState_seed, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_seed},
- {"get_state", (PyCFunction)__pyx_f_6mtrand_11RandomState_get_state, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_get_state},
- {"set_state", (PyCFunction)__pyx_f_6mtrand_11RandomState_set_state, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_set_state},
- {"__getstate__", (PyCFunction)__pyx_f_6mtrand_11RandomState___getstate__, METH_VARARGS|METH_KEYWORDS, 0},
- {"__setstate__", (PyCFunction)__pyx_f_6mtrand_11RandomState___setstate__, METH_VARARGS|METH_KEYWORDS, 0},
- {"__reduce__", (PyCFunction)__pyx_f_6mtrand_11RandomState___reduce__, METH_VARARGS|METH_KEYWORDS, 0},
- {"random_sample", (PyCFunction)__pyx_f_6mtrand_11RandomState_random_sample, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_random_sample},
- {"tomaxint", (PyCFunction)__pyx_f_6mtrand_11RandomState_tomaxint, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_tomaxint},
- {"randint", (PyCFunction)__pyx_f_6mtrand_11RandomState_randint, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_randint},
- {"bytes", (PyCFunction)__pyx_f_6mtrand_11RandomState_bytes, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_bytes},
- {"uniform", (PyCFunction)__pyx_f_6mtrand_11RandomState_uniform, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_uniform},
- {"rand", (PyCFunction)__pyx_f_6mtrand_11RandomState_rand, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_rand},
- {"randn", (PyCFunction)__pyx_f_6mtrand_11RandomState_randn, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_randn},
- {"random_integers", (PyCFunction)__pyx_f_6mtrand_11RandomState_random_integers, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_random_integers},
- {"standard_normal", (PyCFunction)__pyx_f_6mtrand_11RandomState_standard_normal, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_standard_normal},
- {"normal", (PyCFunction)__pyx_f_6mtrand_11RandomState_normal, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_normal},
- {"beta", (PyCFunction)__pyx_f_6mtrand_11RandomState_beta, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_beta},
- {"exponential", (PyCFunction)__pyx_f_6mtrand_11RandomState_exponential, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_exponential},
- {"standard_exponential", (PyCFunction)__pyx_f_6mtrand_11RandomState_standard_exponential, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_standard_exponential},
- {"standard_gamma", (PyCFunction)__pyx_f_6mtrand_11RandomState_standard_gamma, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_standard_gamma},
- {"gamma", (PyCFunction)__pyx_f_6mtrand_11RandomState_gamma, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_gamma},
- {"f", (PyCFunction)__pyx_f_6mtrand_11RandomState_f, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_f},
- {"noncentral_f", (PyCFunction)__pyx_f_6mtrand_11RandomState_noncentral_f, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_noncentral_f},
- {"chisquare", (PyCFunction)__pyx_f_6mtrand_11RandomState_chisquare, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_chisquare},
- {"noncentral_chisquare", (PyCFunction)__pyx_f_6mtrand_11RandomState_noncentral_chisquare, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_noncentral_chisquare},
- {"standard_cauchy", (PyCFunction)__pyx_f_6mtrand_11RandomState_standard_cauchy, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_standard_cauchy},
- {"standard_t", (PyCFunction)__pyx_f_6mtrand_11RandomState_standard_t, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_standard_t},
- {"vonmises", (PyCFunction)__pyx_f_6mtrand_11RandomState_vonmises, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_vonmises},
- {"pareto", (PyCFunction)__pyx_f_6mtrand_11RandomState_pareto, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_pareto},
- {"weibull", (PyCFunction)__pyx_f_6mtrand_11RandomState_weibull, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_weibull},
- {"power", (PyCFunction)__pyx_f_6mtrand_11RandomState_power, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_power},
- {"laplace", (PyCFunction)__pyx_f_6mtrand_11RandomState_laplace, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_laplace},
- {"gumbel", (PyCFunction)__pyx_f_6mtrand_11RandomState_gumbel, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_gumbel},
- {"logistic", (PyCFunction)__pyx_f_6mtrand_11RandomState_logistic, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_logistic},
- {"lognormal", (PyCFunction)__pyx_f_6mtrand_11RandomState_lognormal, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_lognormal},
- {"rayleigh", (PyCFunction)__pyx_f_6mtrand_11RandomState_rayleigh, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_rayleigh},
- {"wald", (PyCFunction)__pyx_f_6mtrand_11RandomState_wald, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_wald},
- {"triangular", (PyCFunction)__pyx_f_6mtrand_11RandomState_triangular, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_triangular},
- {"binomial", (PyCFunction)__pyx_f_6mtrand_11RandomState_binomial, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_binomial},
- {"negative_binomial", (PyCFunction)__pyx_f_6mtrand_11RandomState_negative_binomial, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_negative_binomial},
- {"poisson", (PyCFunction)__pyx_f_6mtrand_11RandomState_poisson, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_poisson},
- {"zipf", (PyCFunction)__pyx_f_6mtrand_11RandomState_zipf, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_zipf},
- {"geometric", (PyCFunction)__pyx_f_6mtrand_11RandomState_geometric, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_geometric},
- {"hypergeometric", (PyCFunction)__pyx_f_6mtrand_11RandomState_hypergeometric, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_hypergeometric},
- {"logseries", (PyCFunction)__pyx_f_6mtrand_11RandomState_logseries, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_logseries},
- {"multivariate_normal", (PyCFunction)__pyx_f_6mtrand_11RandomState_multivariate_normal, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_multivariate_normal},
- {"multinomial", (PyCFunction)__pyx_f_6mtrand_11RandomState_multinomial, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_multinomial},
- {"shuffle", (PyCFunction)__pyx_f_6mtrand_11RandomState_shuffle, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_shuffle},
- {"permutation", (PyCFunction)__pyx_f_6mtrand_11RandomState_permutation, METH_VARARGS|METH_KEYWORDS, __pyx_doc_6mtrand_11RandomState_permutation},
- {0, 0, 0, 0}
- };
+static PyNumberMethods __pyx_tp_as_number_RandomState = {
+ 0, /*nb_add*/
+ 0, /*nb_subtract*/
+ 0, /*nb_multiply*/
+ 0, /*nb_divide*/
+ 0, /*nb_remainder*/
+ 0, /*nb_divmod*/
+ 0, /*nb_power*/
+ 0, /*nb_negative*/
+ 0, /*nb_positive*/
+ 0, /*nb_absolute*/
+ 0, /*nb_nonzero*/
+ 0, /*nb_invert*/
+ 0, /*nb_lshift*/
+ 0, /*nb_rshift*/
+ 0, /*nb_and*/
+ 0, /*nb_xor*/
+ 0, /*nb_or*/
+ 0, /*nb_coerce*/
+ 0, /*nb_int*/
+ 0, /*nb_long*/
+ 0, /*nb_float*/
+ 0, /*nb_oct*/
+ 0, /*nb_hex*/
+ 0, /*nb_inplace_add*/
+ 0, /*nb_inplace_subtract*/
+ 0, /*nb_inplace_multiply*/
+ 0, /*nb_inplace_divide*/
+ 0, /*nb_inplace_remainder*/
+ 0, /*nb_inplace_power*/
+ 0, /*nb_inplace_lshift*/
+ 0, /*nb_inplace_rshift*/
+ 0, /*nb_inplace_and*/
+ 0, /*nb_inplace_xor*/
+ 0, /*nb_inplace_or*/
+ 0, /*nb_floor_divide*/
+ 0, /*nb_true_divide*/
+ 0, /*nb_inplace_floor_divide*/
+ 0, /*nb_inplace_true_divide*/
+};
- static PyNumberMethods __pyx_tp_as_number_RandomState = {
- 0, /*nb_add*/
- 0, /*nb_subtract*/
- 0, /*nb_multiply*/
- 0, /*nb_divide*/
- 0, /*nb_remainder*/
- 0, /*nb_divmod*/
- 0, /*nb_power*/
- 0, /*nb_negative*/
- 0, /*nb_positive*/
- 0, /*nb_absolute*/
- 0, /*nb_nonzero*/
- 0, /*nb_invert*/
- 0, /*nb_lshift*/
- 0, /*nb_rshift*/
- 0, /*nb_and*/
- 0, /*nb_xor*/
- 0, /*nb_or*/
- 0, /*nb_coerce*/
- 0, /*nb_int*/
- 0, /*nb_long*/
- 0, /*nb_float*/
- 0, /*nb_oct*/
- 0, /*nb_hex*/
- 0, /*nb_inplace_add*/
- 0, /*nb_inplace_subtract*/
- 0, /*nb_inplace_multiply*/
- 0, /*nb_inplace_divide*/
- 0, /*nb_inplace_remainder*/
- 0, /*nb_inplace_power*/
- 0, /*nb_inplace_lshift*/
- 0, /*nb_inplace_rshift*/
- 0, /*nb_inplace_and*/
- 0, /*nb_inplace_xor*/
- 0, /*nb_inplace_or*/
- 0, /*nb_floor_divide*/
- 0, /*nb_true_divide*/
- 0, /*nb_inplace_floor_divide*/
- 0, /*nb_inplace_true_divide*/
- };
+static PySequenceMethods __pyx_tp_as_sequence_RandomState = {
+ 0, /*sq_length*/
+ 0, /*sq_concat*/
+ 0, /*sq_repeat*/
+ 0, /*sq_item*/
+ 0, /*sq_slice*/
+ 0, /*sq_ass_item*/
+ 0, /*sq_ass_slice*/
+ 0, /*sq_contains*/
+ 0, /*sq_inplace_concat*/
+ 0, /*sq_inplace_repeat*/
+};
- static PySequenceMethods __pyx_tp_as_sequence_RandomState = {
- 0, /*sq_length*/
- 0, /*sq_concat*/
- 0, /*sq_repeat*/
- 0, /*sq_item*/
- 0, /*sq_slice*/
- 0, /*sq_ass_item*/
- 0, /*sq_ass_slice*/
- 0, /*sq_contains*/
- 0, /*sq_inplace_concat*/
- 0, /*sq_inplace_repeat*/
- };
+static PyMappingMethods __pyx_tp_as_mapping_RandomState = {
+ 0, /*mp_length*/
+ 0, /*mp_subscript*/
+ 0, /*mp_ass_subscript*/
+};
- static PyMappingMethods __pyx_tp_as_mapping_RandomState = {
- 0, /*mp_length*/
- 0, /*mp_subscript*/
- 0, /*mp_ass_subscript*/
- };
+static PyBufferProcs __pyx_tp_as_buffer_RandomState = {
+ 0, /*bf_getreadbuffer*/
+ 0, /*bf_getwritebuffer*/
+ 0, /*bf_getsegcount*/
+ 0, /*bf_getcharbuffer*/
+};
- static PyBufferProcs __pyx_tp_as_buffer_RandomState = {
- 0, /*bf_getreadbuffer*/
- 0, /*bf_getwritebuffer*/
- 0, /*bf_getsegcount*/
- 0, /*bf_getcharbuffer*/
- };
+PyTypeObject __pyx_type_6mtrand_RandomState = {
+ PyObject_HEAD_INIT(0)
+ 0, /*ob_size*/
+ "mtrand.RandomState", /*tp_name*/
+ sizeof(struct __pyx_obj_6mtrand_RandomState), /*tp_basicsize*/
+ 0, /*tp_itemsize*/
+ __pyx_tp_dealloc_6mtrand_RandomState, /*tp_dealloc*/
+ 0, /*tp_print*/
+ 0, /*tp_getattr*/
+ 0, /*tp_setattr*/
+ 0, /*tp_compare*/
+ 0, /*tp_repr*/
+ &__pyx_tp_as_number_RandomState, /*tp_as_number*/
+ &__pyx_tp_as_sequence_RandomState, /*tp_as_sequence*/
+ &__pyx_tp_as_mapping_RandomState, /*tp_as_mapping*/
+ 0, /*tp_hash*/
+ 0, /*tp_call*/
+ 0, /*tp_str*/
+ 0, /*tp_getattro*/
+ 0, /*tp_setattro*/
+ &__pyx_tp_as_buffer_RandomState, /*tp_as_buffer*/
+ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_BASETYPE|Py_TPFLAGS_HAVE_GC, /*tp_flags*/
+ "Container for the Mersenne Twister PRNG.\n\n Constructor\n -----------\n RandomState(seed=None): initializes the PRNG with the given seed. See the\n seed() method for details.\n\n Distribution Methods\n -----------------\n RandomState exposes a number of methods for generating random numbers drawn\n from a variety of probability distributions. In addition to the\n distribution-specific arguments, each method takes a keyword argument\n size=None. If size is None, then a single value is generated and returned.\n If size is an integer, then a 1-D numpy array filled with generated values\n is returned. If size is a tuple, then a numpy array with that shape is\n filled and returned.\n ", /*tp_doc*/
+ __pyx_tp_traverse_6mtrand_RandomState, /*tp_traverse*/
+ __pyx_tp_clear_6mtrand_RandomState, /*tp_clear*/
+ 0, /*tp_richcompare*/
+ 0, /*tp_weaklistoffset*/
+ 0, /*tp_iter*/
+ 0, /*tp_iternext*/
+ __pyx_methods_6mtrand_RandomState, /*tp_methods*/
+ 0, /*tp_members*/
+ 0, /*tp_getset*/
+ 0, /*tp_base*/
+ 0, /*tp_dict*/
+ 0, /*tp_descr_get*/
+ 0, /*tp_descr_set*/
+ 0, /*tp_dictoffset*/
+ __pyx_f_6mtrand_11RandomState___init__, /*tp_init*/
+ 0, /*tp_alloc*/
+ __pyx_tp_new_6mtrand_RandomState, /*tp_new*/
+ 0, /*tp_free*/
+ 0, /*tp_is_gc*/
+ 0, /*tp_bases*/
+ 0, /*tp_mro*/
+ 0, /*tp_cache*/
+ 0, /*tp_subclasses*/
+ 0, /*tp_weaklist*/
+};
- static PyTypeObject __pyx_type_6mtrand_RandomState = {
- PyObject_HEAD_INIT(0)
- 0, /*ob_size*/
- "mtrand.RandomState", /*tp_name*/
- sizeof(struct __pyx_obj_6mtrand_RandomState), /*tp_basicsize*/
- 0, /*tp_itemsize*/
- __pyx_tp_dealloc_6mtrand_RandomState, /*tp_dealloc*/
- 0, /*tp_print*/
- 0, /*tp_getattr*/
- 0, /*tp_setattr*/
- 0, /*tp_compare*/
- 0, /*tp_repr*/
- &__pyx_tp_as_number_RandomState, /*tp_as_number*/
- &__pyx_tp_as_sequence_RandomState, /*tp_as_sequence*/
- &__pyx_tp_as_mapping_RandomState, /*tp_as_mapping*/
- 0, /*tp_hash*/
- 0, /*tp_call*/
- 0, /*tp_str*/
- 0, /*tp_getattro*/
- 0, /*tp_setattro*/
- &__pyx_tp_as_buffer_RandomState, /*tp_as_buffer*/
- Py_TPFLAGS_DEFAULT|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_BASETYPE, /*tp_flags*/
- "Container for the Mersenne Twister PRNG.\n\n Constructor\n -----------\n RandomState(seed=None): initializes the PRNG with the given seed. See the\n seed() method for details.\n\n Distribution Methods\n -----------------\n RandomState exposes a number of methods for generating random numbers drawn\n from a variety of probability distributions. In addition to the\n distribution-specific arguments, each method takes a keyword argument\n size=None. If size is None, then a single value is generated and returned.\n If size is an integer, then a 1-D numpy array filled with generated values\n is returned. If size is a tuple, then a numpy array with that shape is\n filled and returned.\n ", /*tp_doc*/
- __pyx_tp_traverse_6mtrand_RandomState, /*tp_traverse*/
- __pyx_tp_clear_6mtrand_RandomState, /*tp_clear*/
- 0, /*tp_richcompare*/
- 0, /*tp_weaklistoffset*/
- 0, /*tp_iter*/
- 0, /*tp_iternext*/
- __pyx_methods_6mtrand_RandomState, /*tp_methods*/
- 0, /*tp_members*/
- 0, /*tp_getset*/
- 0, /*tp_base*/
- 0, /*tp_dict*/
- 0, /*tp_descr_get*/
- 0, /*tp_descr_set*/
- 0, /*tp_dictoffset*/
- __pyx_f_6mtrand_11RandomState___init__, /*tp_init*/
- 0, /*tp_alloc*/
- __pyx_tp_new_6mtrand_RandomState, /*tp_new*/
- 0, /*tp_free*/
- 0, /*tp_is_gc*/
- 0, /*tp_bases*/
- 0, /*tp_mro*/
- 0, /*tp_cache*/
- 0, /*tp_subclasses*/
- 0, /*tp_weaklist*/
- };
+static struct PyMethodDef __pyx_methods[] = {
+ {0, 0, 0, 0}
+};
- static struct PyMethodDef __pyx_methods[] = {
- {0, 0, 0, 0}
- };
+static void __pyx_init_filenames(void); /*proto*/
- DL_EXPORT(void) initmtrand(void); /*proto*/
- DL_EXPORT(void) initmtrand(void) {
- PyObject *__pyx_1 = 0;
- PyObject *__pyx_2 = 0;
- __pyx_f = __Pyx_FileNames();
- __pyx_m = Py_InitModule4("mtrand", __pyx_methods, 0, 0, PYTHON_API_VERSION);
- if (!__pyx_m) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 24; goto __pyx_L1;};
- __pyx_b = PyImport_AddModule("__builtin__");
- if (!__pyx_b) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 24; goto __pyx_L1;};
- if (PyObject_SetAttrString(__pyx_m, "__builtins__", __pyx_b) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 24; goto __pyx_L1;};
- if (__Pyx_InternStrings(__pyx_intern_tab) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 24; goto __pyx_L1;};
- if (__Pyx_InitStrings(__pyx_string_tab) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 24; goto __pyx_L1;};
- __pyx_ptype_6mtrand_dtype = __Pyx_ImportType("numpy", "dtype", sizeof(PyArray_Descr)); if (!__pyx_ptype_6mtrand_dtype) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 32; goto __pyx_L1;}
- __pyx_ptype_6mtrand_ndarray = __Pyx_ImportType("numpy", "ndarray", sizeof(PyArrayObject)); if (!__pyx_ptype_6mtrand_ndarray) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 36; goto __pyx_L1;}
- if (PyType_Ready(&__pyx_type_6mtrand_RandomState) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 266; goto __pyx_L1;}
- if (PyObject_SetAttrString(__pyx_m, "RandomState", (PyObject *)&__pyx_type_6mtrand_RandomState) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 266; goto __pyx_L1;}
- __pyx_ptype_6mtrand_RandomState = &__pyx_type_6mtrand_RandomState;
+PyMODINIT_FUNC initmtrand(void); /*proto*/
+PyMODINIT_FUNC initmtrand(void) {
+ PyObject *__pyx_1 = 0;
+ PyObject *__pyx_2 = 0;
+ PyObject *__pyx_3 = 0;
+ PyObject *__pyx_4 = 0;
+ PyObject *__pyx_5 = 0;
+ PyObject *__pyx_6 = 0;
+ PyObject *__pyx_7 = 0;
+ PyObject *__pyx_8 = 0;
+ PyObject *__pyx_9 = 0;
+ PyObject *__pyx_10 = 0;
+ PyObject *__pyx_11 = 0;
+ PyObject *__pyx_12 = 0;
+ PyObject *__pyx_13 = 0;
+ PyObject *__pyx_14 = 0;
+ PyObject *__pyx_15 = 0;
+ PyObject *__pyx_16 = 0;
+ PyObject *__pyx_17 = 0;
+ PyObject *__pyx_18 = 0;
+ __pyx_init_filenames();
+ __pyx_m = Py_InitModule4("mtrand", __pyx_methods, 0, 0, PYTHON_API_VERSION);
+ if (!__pyx_m) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 24; goto __pyx_L1;};
+ __pyx_b = PyImport_AddModule("__builtin__");
+ if (!__pyx_b) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 24; goto __pyx_L1;};
+ if (PyObject_SetAttrString(__pyx_m, "__builtins__", __pyx_b) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 24; goto __pyx_L1;};
+ if (__Pyx_InternStrings(__pyx_intern_tab) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 24; goto __pyx_L1;};
+ if (__Pyx_InitStrings(__pyx_string_tab) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 24; goto __pyx_L1;};
+ __pyx_ptype_6mtrand_dtype = __Pyx_ImportType("numpy", "dtype", sizeof(PyArray_Descr)); if (!__pyx_ptype_6mtrand_dtype) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 74; goto __pyx_L1;}
+ __pyx_ptype_6mtrand_ndarray = __Pyx_ImportType("numpy", "ndarray", sizeof(PyArrayObject)); if (!__pyx_ptype_6mtrand_ndarray) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 79; goto __pyx_L1;}
+ __pyx_ptype_6mtrand_flatiter = __Pyx_ImportType("numpy", "flatiter", sizeof(PyArrayIterObject)); if (!__pyx_ptype_6mtrand_flatiter) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 88; goto __pyx_L1;}
+ __pyx_ptype_6mtrand_broadcast = __Pyx_ImportType("numpy", "broadcast", sizeof(PyArrayMultiIterObject)); if (!__pyx_ptype_6mtrand_broadcast) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 94; goto __pyx_L1;}
+ if (PyType_Ready(&__pyx_type_6mtrand_RandomState) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 454; goto __pyx_L1;}
+ if (PyObject_SetAttrString(__pyx_m, "RandomState", (PyObject *)&__pyx_type_6mtrand_RandomState) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 454; goto __pyx_L1;}
+ __pyx_ptype_6mtrand_RandomState = &__pyx_type_6mtrand_RandomState;
- /* "/Users/kern/svn/numpy/numpy/random/mtrand/mtrand.pyx":118 */
- import_array();
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+ import_array();
- /* "/Users/kern/svn/numpy/numpy/random/mtrand/mtrand.pyx":120 */
- __pyx_1 = __Pyx_Import(__pyx_n_numpy, 0); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 120; goto __pyx_L1;}
- if (PyObject_SetAttr(__pyx_m, __pyx_n__sp, __pyx_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 120; goto __pyx_L1;}
- Py_DECREF(__pyx_1); __pyx_1 = 0;
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+ if (PyObject_SetAttr(__pyx_m, __pyx_n__sp, __pyx_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 120; goto __pyx_L1;}
+ Py_DECREF(__pyx_1); __pyx_1 = 0;
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- Py_INCREF(Py_None);
- __pyx_k2 = Py_None;
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+ Py_INCREF(Py_None);
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- Py_INCREF(Py_None);
- __pyx_k3 = Py_None;
+ /* "/home/oliphant/numpy/numpy/random/mtrand/mtrand.pyx":484 */
+ Py_INCREF(Py_None);
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- /* "/Users/kern/svn/numpy/numpy/random/mtrand/mtrand.pyx":357 */
- Py_INCREF(Py_None);
- __pyx_k4 = Py_None;
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+ Py_INCREF(Py_None);
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- Py_INCREF(Py_None);
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+ Py_XDECREF(__pyx_1);
+ Py_XDECREF(__pyx_2);
+ Py_XDECREF(__pyx_3);
+ Py_XDECREF(__pyx_4);
+ Py_XDECREF(__pyx_5);
+ Py_XDECREF(__pyx_6);
+ Py_XDECREF(__pyx_7);
+ Py_XDECREF(__pyx_8);
+ Py_XDECREF(__pyx_9);
+ Py_XDECREF(__pyx_10);
+ Py_XDECREF(__pyx_11);
+ Py_XDECREF(__pyx_12);
+ Py_XDECREF(__pyx_13);
+ Py_XDECREF(__pyx_14);
+ Py_XDECREF(__pyx_15);
+ Py_XDECREF(__pyx_16);
+ Py_XDECREF(__pyx_17);
+ Py_XDECREF(__pyx_18);
+ __Pyx_AddTraceback("mtrand");
+}
- #ifdef __cplusplus
-} // extern "C"
-#endif
+static char *__pyx_filenames[] = {
+ "mtrand.pyx",
+ "numpy.pxi",
+};
/* Runtime support code */
-static char **__Pyx_FileNames(void) {
- static char *filenames[] = {
- "mtrand.pyx",
- "numpy.pxi",
- };
- return filenames;
+static void __pyx_init_filenames(void) {
+ __pyx_f = __pyx_filenames;
}
static int __Pyx_GetStarArgs(
@@ -5793,17 +8512,6 @@
return result;
}
-static void __Pyx_WriteUnraisable(char *name) {
- PyObject *old_exc, *old_val, *old_tb;
- PyObject *ctx;
- PyErr_Fetch(&old_exc, &old_val, &old_tb);
- ctx = PyString_FromString(name);
- PyErr_Restore(old_exc, old_val, old_tb);
- if (!ctx)
- ctx = Py_None;
- PyErr_WriteUnraisable(ctx);
-}
-
static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb) {
Py_XINCREF(type);
Py_XINCREF(value);
@@ -5866,6 +8574,17 @@
return;
}
+static void __Pyx_WriteUnraisable(char *name) {
+ PyObject *old_exc, *old_val, *old_tb;
+ PyObject *ctx;
+ PyErr_Fetch(&old_exc, &old_val, &old_tb);
+ ctx = PyString_FromString(name);
+ PyErr_Restore(old_exc, old_val, old_tb);
+ if (!ctx)
+ ctx = Py_None;
+ PyErr_WriteUnraisable(ctx);
+}
+
static void __Pyx_UnpackError(void) {
PyErr_SetString(PyExc_ValueError, "unpack sequence of wrong size");
}
@@ -5890,6 +8609,18 @@
return 0;
}
+static int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) {
+ if (!type) {
+ PyErr_Format(PyExc_SystemError, "Missing type object");
+ return 0;
+ }
+ if (obj == Py_None || PyObject_TypeCheck(obj, type))
+ return 1;
+ PyErr_Format(PyExc_TypeError, "Cannot convert %s to %s",
+ obj->ob_type->tp_name, type->tp_name);
+ return 0;
+}
+
static int __Pyx_InternStrings(__Pyx_InternTabEntry *t) {
while (t->p) {
*t->p = PyString_InternFromString(t->s);
Modified: branches/ver1.0/numpy/random/mtrand/mtrand.pyx
===================================================================
--- branches/ver1.0/numpy/random/mtrand/mtrand.pyx 2006-07-25 17:17:08 UTC (rev 2891)
+++ branches/ver1.0/numpy/random/mtrand/mtrand.pyx 2006-07-25 22:06:02 UTC (rev 2892)
@@ -135,56 +135,149 @@
array_data[i] = func(state)
return array
-cdef object cont1_array(rk_state *state, rk_cont1 func, object size, double a):
+cdef object cont1_array(rk_state *state, rk_cont1 func, object size, ndarray oa):
cdef double *array_data
+ cdef double *oa_data
cdef ndarray array "arrayObject"
- cdef long length
- cdef long i
+ cdef npy_intp length
+ cdef npy_intp i
+ cdef flatiter itera
+ cdef broadcast multi
+ cdef int scalar
+ scalar = 0
+ if oa.nd == 0:
+ oa_data = <double *>oa.data
+ scalar = 1
+
if size is None:
- return func(state, a)
+ if scalar:
+ return func(state, oa_data[0])
+ else:
+ array = <ndarray>PyArray_SimpleNew(oa.nd, oa.dimensions, NPY_DOUBLE)
+ length = PyArray_SIZE(array)
+ array_data = <double *>array.data
+ itera = <flatiter>PyArray_IterNew(<object>oa)
+ for i from 0 <= i < length:
+ array_data[i] = func(state, (<double *>(itera.dataptr))[0])
+ PyArray_ITER_NEXT(itera)
else:
array = <ndarray>_sp.empty(size, _sp.float64)
- length = PyArray_SIZE(array)
array_data = <double *>array.data
- for i from 0 <= i < length:
- array_data[i] = func(state, a)
- return array
+ if scalar:
+ length = PyArray_SIZE(array)
+ for i from 0 <= i < length:
+ array_data[i] = func(state, oa_data[0])
+ else:
+ multi = <broadcast>PyArray_MultiIterNew(2, <void *>array, <void *>oa)
+ if (multi.size != PyArray_SIZE(array)):
+ raise ValueError("size is not compatible with inputs")
+ for i from 0 <= i < multi.size:
+ oa_data = <double *>PyArray_MultiIter_DATA(multi, 1)
+ array_data[i] = func(state, oa_data[0])
+ PyArray_MultiIter_NEXTi(multi, 1)
+ return array
-cdef object cont2_array(rk_state *state, rk_cont2 func, object size, double a,
- double b):
+cdef object cont2_array(rk_state *state, rk_cont2 func, object size, ndarray oa,
+ ndarray ob):
cdef double *array_data
+ cdef double *oa_data
+ cdef double *ob_data
cdef ndarray array "arrayObject"
- cdef long length
- cdef long i
+ cdef npy_intp length
+ cdef npy_intp i
+ cdef broadcast multi
+ cdef int scalar
+ scalar = 0
+ if oa.nd == 0 and ob.nd == 0:
+ oa_data = <double *>oa.data
+ ob_data = <double *>ob.data
+ scalar = 1
+
if size is None:
- return func(state, a, b)
+ if scalar:
+ return func(state, oa_data[0], ob_data[0])
+ else:
+ multi = <broadcast> PyArray_MultiIterNew(2, <void *>oa, <void *>ob)
+ array = <ndarray> PyArray_SimpleNew(multi.nd, multi.dimensions, NPY_DOUBLE)
+ array_data = <double *>array.data
+ for i from 0 <= i < multi.size:
+ oa_data = <double *>PyArray_MultiIter_DATA(multi, 0)
+ ob_data = <double *>PyArray_MultiIter_DATA(multi, 1)
+ array_data[i] = func(state, oa_data[0], ob_data[0])
+ PyArray_MultiIter_NEXT(multi)
else:
array = <ndarray>_sp.empty(size, _sp.float64)
- length = PyArray_SIZE(array)
array_data = <double *>array.data
- for i from 0 <= i < length:
- array_data[i] = func(state, a, b)
- return array
+ if scalar:
+ length = PyArray_SIZE(array)
+ for i from 0 <= i < length:
+ array_data[i] = func(state, oa_data[0], ob_data[0])
+ else:
+ multi = <broadcast>PyArray_MultiIterNew(3, <void*>array, <void *>oa, <void *>ob)
+ if (multi.size != PyArray_SIZE(array)):
+ raise ValueError("size is not compatible with inputs")
+ for i from 0 <= i < multi.size:
+ oa_data = <double *>PyArray_MultiIter_DATA(multi, 1)
+ ob_data = <double *>PyArray_MultiIter_DATA(multi, 2)
+ array_data[i] = func(state, oa_data[0], ob_data[0])
+ PyArray_MultiIter_NEXTi(multi, 1)
+ PyArray_MultiIter_NEXTi(multi, 2)
+ return array
-cdef object cont3_array(rk_state *state, rk_cont3 func, object size, double a,
- double b, double c):
+cdef object cont3_array(rk_state *state, rk_cont3 func, object size, ndarray oa,
+ ndarray ob, ndarray oc):
cdef double *array_data
+ cdef double *oa_data
+ cdef double *ob_data
+ cdef double *oc_data
cdef ndarray array "arrayObject"
- cdef long length
- cdef long i
-
+ cdef npy_intp length
+ cdef npy_intp i
+ cdef broadcast multi
+ cdef int scalar
+
+ scalar = 0
+ if (oa.nd ==0 and ob.nd==0 and oc.nd == 0):
+ oa_data = <double *>oa.data
+ ob_data = <double *>ob.data
+ oc_data = <double *>oc.data
+ scalar = 1
+
if size is None:
- return func(state, a, b, c)
+ if scalar:
+ return func(state, oa_data[0], ob_data[0], oc_data[0])
+ else:
+ multi = <broadcast> PyArray_MultiIterNew(3, <void *>oa, <void *>ob, <void *>oc)
+ array = <ndarray> PyArray_SimpleNew(multi.nd, multi.dimensions, NPY_DOUBLE)
+ array_data = <double *>array.data
+ for i from 0 <= i < multi.size:
+ oa_data = <double *>PyArray_MultiIter_DATA(multi, 0)
+ ob_data = <double *>PyArray_MultiIter_DATA(multi, 1)
+ oc_data = <double *>PyArray_MultiIter_DATA(multi, 2)
+ array_data[i] = func(state, oa_data[0], ob_data[0], oc_data[0])
+ PyArray_MultiIter_NEXT(multi)
else:
array = <ndarray>_sp.empty(size, _sp.float64)
- length = PyArray_SIZE(array)
- array_data = <double *>array.data
- for i from 0 <= i < length:
- array_data[i] = func(state, a, b, c)
- return array
+ array_data = <double *>array.data
+ if scalar:
+ length = PyArray_SIZE(array)
+ for i from 0 <= i < length:
+ array_data[i] = func(state, oa_data[0], ob_data[0], oc_data[0])
+ else:
+ multi = <broadcast>PyArray_MultiIterNew(4, <void*>array, <void *>oa,
+ <void *>ob, <void *>oc)
+ if (multi.size != PyArray_SIZE(array)):
+ raise ValueError("size is not compatible with inputs")
+ for i from 0 <= i < multi.size:
+ oa_data = <double *>PyArray_MultiIter_DATA(multi, 1)
+ ob_data = <double *>PyArray_MultiIter_DATA(multi, 2)
+ oc_data = <double *>PyArray_MultiIter_DATA(multi, 3)
+ array_data[i] = func(state, oa_data[0], ob_data[0], oc_data[0])
+ PyArray_MultiIter_NEXT(multi)
+ return array
cdef object disc0_array(rk_state *state, rk_disc0 func, object size):
cdef long *array_data
@@ -202,54 +295,149 @@
array_data[i] = func(state)
return array
-cdef object discnp_array(rk_state *state, rk_discnp func, object size, long n, double p):
+cdef object discnp_array(rk_state *state, rk_discnp func, object size, ndarray on, ndarray op):
cdef long *array_data
cdef ndarray array "arrayObject"
- cdef long length
- cdef long i
+ cdef npy_intp length
+ cdef npy_intp i
+ cdef double *op_data
+ cdef long *on_data
+ cdef int scalar
+ cdef broadcast multi
+ scalar = 0
+ if (on.nd == 0 and op.nd == 0):
+ on_data = <long *>on.data
+ op_data = <double *>op.data
+ scalar = 1
+
if size is None:
- return func(state, n, p)
+ if (scalar):
+ return func(state, on_data[0], op_data[0])
+ else:
+ multi = <broadcast> PyArray_MultiIterNew(2, <void *>on, <void *>op)
+ array = <ndarray> PyArray_SimpleNew(multi.nd, multi.dimensions, NPY_LONG)
+ array_data = <long *>array.data
+ for i from 0 <= i < multi.size:
+ on_data = <long *>PyArray_MultiIter_DATA(multi, 0)
+ op_data = <double *>PyArray_MultiIter_DATA(multi, 1)
+ array_data[i] = func(state, on_data[0], op_data[0])
+ PyArray_MultiIter_NEXT(multi)
else:
- array = <ndarray>_sp.empty(size, int)
- length = PyArray_SIZE(array)
- array_data = <long *>array.data
- for i from 0 <= i < length:
- array_data[i] = func(state, n, p)
- return array
+ array = <ndarray>_sp.empty(size, int)
+ if (scalar):
+ length = PyArray_SIZE(array)
+ array_data = <long *>array.data
+ for i from 0 <= i < length:
+ array_data[i] = func(state, on_data[0], op_data[0])
+ else:
+ multi = <broadcast>PyArray_MultiIterNew(3, <void*>array, <void *>on, <void *>op)
+ if (multi.size != PyArray_SIZE(array)):
+ raise ValueError("size is not compatible with inputs")
+ for i from 0 <= i < multi.size:
+ on_data = <long *>PyArray_MultiIter_DATA(multi, 1)
+ op_data = <double *>PyArray_MultiIter_DATA(multi, 2)
+ array_data[i] = func(state, on_data[0], op_data[0])
+ PyArray_MultiIter_NEXTi(multi, 1)
+ PyArray_MultiIter_NEXTi(multi, 2)
+
+ return array
cdef object discnmN_array(rk_state *state, rk_discnmN func, object size,
- long n, long m, long N):
+ ndarray on, ndarray om, ndarray oN):
cdef long *array_data
+ cdef long *on_data
+ cdef long *om_data
+ cdef long *oN_data
cdef ndarray array "arrayObject"
- cdef long length
- cdef long i
+ cdef npy_intp length
+ cdef npy_intp i
+ cdef broadcast multi
+ cdef int scalar
+ scalar = 0
+ if (on.nd == 0 and om.nd == 0 and oN.nd==0):
+ scalar = 1
+ on_data = <long *>on.data
+ om_data = <long *>om.data
+ oN_data = <long *>oN.data
+
if size is None:
- return func(state, n, m, N)
+ if (scalar):
+ return func(state, on_data[0], om_data[0], oN_data[0])
+ else:
+ multi = <broadcast> PyArray_MultiIterNew(3, <void *>on, <void *>om, <void *>oN)
+ array = <ndarray> PyArray_SimpleNew(multi.nd, multi.dimensions, NPY_LONG)
+ array_data = <long *>array.data
+ for i from 0 <= i < multi.size:
+ on_data = <long *>PyArray_MultiIter_DATA(multi, 0)
+ om_data = <long *>PyArray_MultiIter_DATA(multi, 1)
+ oN_data = <long *>PyArray_MultiIter_DATA(multi, 2)
+ array_data[i] = func(state, on_data[0], om_data[0], oN_data[0])
+ PyArray_MultiIter_NEXT(multi)
else:
array = <ndarray>_sp.empty(size, int)
- length = PyArray_SIZE(array)
array_data = <long *>array.data
- for i from 0 <= i < length:
- array_data[i] = func(state, n, m, N)
- return array
+ if (scalar):
+ length = PyArray_SIZE(array)
+ for i from 0 <= i < length:
+ array_data[i] = func(state, on_data[0], om_data[0], oN_data[0])
+ else:
+ multi = <broadcast>PyArray_MultiIterNew(4, <void*>array, <void *>on, <void *>om,
+ <void *>oN)
+ if (multi.size != PyArray_SIZE(array)):
+ raise ValueError("size is not compatible with inputs")
+ for i from 0 <= i < multi.size:
+ on_data = <long *>PyArray_MultiIter_DATA(multi, 1)
+ om_data = <long *>PyArray_MultiIter_DATA(multi, 2)
+ oN_data = <long *>PyArray_MultiIter_DATA(multi, 3)
+ array_data[i] = func(state, on_data[0], om_data[0], oN_data[0])
+ PyArray_MultiIter_NEXT(multi)
-cdef object discd_array(rk_state *state, rk_discd func, object size, double a):
+ return array
+
+cdef object discd_array(rk_state *state, rk_discd func, object size, ndarray oa):
cdef long *array_data
+ cdef double *oa_data
cdef ndarray array "arrayObject"
- cdef long length
- cdef long i
+ cdef npy_intp length
+ cdef npy_intp i
+ cdef broadcast multi
+ cdef flatiter itera
+ cdef int scalar
+ scalar = 0
+ if (oa.nd == 0):
+ oa_data = <double *>oa.data
+ scalar =1
+
if size is None:
- return func(state, a)
+ if (scalar):
+ return func(state, oa_data[0])
+ else:
+ array = <ndarray>PyArray_SimpleNew(oa.nd, oa.dimensions, NPY_LONG)
+ length = PyArray_SIZE(array)
+ array_data = <long *>array.data
+ itera = <flatiter>PyArray_IterNew(<object>oa)
+ for i from 0 <= i < length:
+ array_data[i] = func(state, (<double *>(itera.dataptr))[0])
+ PyArray_ITER_NEXT(itera)
else:
array = <ndarray>_sp.empty(size, int)
- length = PyArray_SIZE(array)
array_data = <long *>array.data
- for i from 0 <= i < length:
- array_data[i] = func(state, a)
- return array
+ if (scalar):
+ length = PyArray_SIZE(array)
+ for i from 0 <= i < length:
+ array_data[i] = func(state, oa_data[0])
+ else:
+ multi = <broadcast>PyArray_MultiIterNew(2, <void *>array, <void *>oa)
+ if (multi.size != PyArray_SIZE(array)):
+ raise ValueError("size is not compatible with inputs")
+ for i from 0 <= i < multi.size:
+ oa_data = <double *>PyArray_MultiIter_DATA(multi, 1)
+ array_data[i] = func(state, oa_data[0])
+ PyArray_MultiIter_NEXTi(multi, 1)
+ return array
cdef double kahan_sum(double *darr, long n):
cdef double c, y, t, sum
@@ -310,7 +498,7 @@
elif type(seed) is int:
rk_seed(seed, self.internal_state)
else:
- obj = <ndarray>PyArray_ContiguousFromObject(seed, PyArray_LONG, 1, 1)
+ obj = <ndarray>PyArray_ContiguousFromObject(seed, NPY_LONG, 1, 1)
init_by_array(self.internal_state, <unsigned long *>(obj.data),
obj.dimensions[0])
@@ -337,7 +525,7 @@
if algorithm_name != 'MT19937':
raise ValueError("algorithm must be 'MT19937'")
key, pos = state[1:]
- obj = <ndarray>PyArray_ContiguousFromObject(key, PyArray_LONG, 1, 1)
+ obj = <ndarray>PyArray_ContiguousFromObject(key, NPY_LONG, 1, 1)
if obj.dimensions[0] != 624:
raise ValueError("state must be 624 longs")
memcpy(self.internal_state.key, <void*>(obj.data), 624*sizeof(long))
@@ -414,13 +602,18 @@
PyMem_Free(bytes)
return bytestring
- def uniform(self, double low=0.0, double high=1.0, size=None):
+ def uniform(self, low=0.0, high=1.0, size=None):
"""Uniform distribution over [low, high).
uniform(low=0.0, high=1.0, size=None) -> random values
"""
- return cont2_array(self.internal_state, rk_uniform, size, low,
- high-low)
+ cdef ndarray olow
+ cdef ndarray ohigh
+ cdef ndarray odiff
+ olow = <ndarray>PyArray_FROM_OTF(low, NPY_DOUBLE, NPY_ALIGNED)
+ ohigh = <ndarray>PyArray_FROM_OTF(high, NPY_DOUBLE, NPY_ALIGNED)
+ odiff = <ndarray>_sp.subtract(ohigh,olow)
+ return cont2_array(self.internal_state, rk_uniform, size, olow, odiff)
def rand(self, *args):
"""Return an array of the given dimensions which is initialized to
@@ -473,34 +666,45 @@
"""
return cont0_array(self.internal_state, rk_gauss, size)
- def normal(self, double loc=0.0, double scale=1.0, size=None):
+ def normal(self, loc=0.0, scale=1.0, size=None):
"""Normal distribution (mean=loc, stdev=scale).
normal(loc=0.0, scale=1.0, size=None) -> random values
"""
- if scale <= 0:
+ cdef ndarray oloc
+ cdef ndarray oscale
+ oloc = <ndarray>PyArray_FROM_OTF(loc, NPY_DOUBLE, NPY_ALIGNED)
+ oscale = <ndarray>PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED)
+ if _sp.any(_sp.less_equal(oscale, 0)):
raise ValueError("scale <= 0")
- return cont2_array(self.internal_state, rk_normal, size, loc, scale)
+ return cont2_array(self.internal_state, rk_normal, size, oloc, oscale)
- def beta(self, double a, double b, size=None):
+ def beta(self, a, b, size=None):
"""Beta distribution over [0, 1].
beta(a, b, size=None) -> random values
"""
- if a <= 0:
+ cdef ndarray oa
+ cdef ndarray ob
+ oa = <ndarray>PyArray_FROM_OTF(a, NPY_DOUBLE, NPY_ALIGNED)
+ ob = <ndarray>PyArray_FROM_OTF(b, NPY_DOUBLE, NPY_ALIGNED)
+
+ if _sp.any(_sp.less_equal(oa, 0)):
raise ValueError("a <= 0")
- elif b <= 0:
+ if _sp.any(_sp.less_equal(ob, 0)):
raise ValueError("b <= 0")
- return cont2_array(self.internal_state, rk_beta, size, a, b)
+ return cont2_array(self.internal_state, rk_beta, size, oa, ob)
- def exponential(self, double scale=1.0, size=None):
+ def exponential(self, scale=1.0, size=None):
"""Exponential distribution.
exponential(scale=1.0, size=None) -> random values
"""
- if scale <= 0:
+ cdef ndarray oscale
+ oscale = <ndarray> PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED)
+ if _sp.any(_sp.less_equal(oscale, 0.0)):
raise ValueError("scale <= 0")
- return cont1_array(self.internal_state, rk_exponential, size, scale)
+ return cont1_array(self.internal_state, rk_exponential, size, oscale)
def standard_exponential(self, size=None):
"""Standard exponential distribution (scale=1).
@@ -509,71 +713,94 @@
"""
return cont0_array(self.internal_state, rk_standard_exponential, size)
- def standard_gamma(self, double shape, size=None):
+ def standard_gamma(self, shape, size=None):
"""Standard Gamma distribution.
standard_gamma(shape, size=None) -> random values
"""
- if shape <= 0:
+ cdef ndarray oshape
+ oshape = <ndarray> PyArray_FROM_OTF(shape, NPY_DOUBLE, NPY_ALIGNED)
+ if _sp.any(_sp.less_equal(oshape, 0.0)):
raise ValueError("shape <= 0")
- return cont1_array(self.internal_state, rk_standard_gamma, size, shape)
+ return cont1_array(self.internal_state, rk_standard_gamma, size, oshape)
- def gamma(self, double shape, double scale=1.0, size=None):
+ def gamma(self, shape, scale=1.0, size=None):
"""Gamma distribution.
gamma(shape, scale=1.0, size=None) -> random values
"""
- if shape <= 0:
+ cdef ndarray oshape
+ cdef ndarray oscale
+ oshape = <ndarray>PyArray_FROM_OTF(shape, NPY_DOUBLE, NPY_ALIGNED)
+ oscale = <ndarray>PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED)
+ if _sp.any(_sp.less_equal(oshape, 0.0)):
raise ValueError("shape <= 0")
- elif scale <= 0:
+ if _sp.any(_sp.less_equal(oscale, 0.0)):
raise ValueError("scale <= 0")
- return cont2_array(self.internal_state, rk_gamma, size, shape, scale)
+ return cont2_array(self.internal_state, rk_gamma, size, oshape, oscale)
- def f(self, double dfnum, double dfden, size=None):
+ def f(self, dfnum, dfden, size=None):
"""F distribution.
f(dfnum, dfden, size=None) -> random values
"""
- if dfnum <= 0:
- raise ValueError("dfnum <= 0")
- elif dfden <= 0:
+ cdef ndarray odfnum
+ cdef ndarray odfden
+ odfnum = <ndarray>PyArray_FROM_OTF(dfnum, NPY_DOUBLE, NPY_ALIGNED)
+ odfden = <ndarray>PyArray_FROM_OTF(dfden, NPY_DOUBLE, NPY_ALIGNED)
+ if _sp.any(_sp.less_equal(odfnum, 0.0)):
+ raise ValueError("dfnum <= 0")
+ if _sp.any(_sp.less_equal(odfden, 0.0)):
raise ValueError("dfden <= 0")
- return cont2_array(self.internal_state, rk_f, size, dfnum, dfden)
+ return cont2_array(self.internal_state, rk_f, size, odfnum, odfden)
- def noncentral_f(self, double dfnum, double dfden, double nonc, size=None):
+ def noncentral_f(self, dfnum, dfden, nonc, size=None):
"""Noncentral F distribution.
noncentral_f(dfnum, dfden, nonc, size=None) -> random values
"""
- if dfnum <= 1:
- raise ValueError("dfnum <= 1")
- elif dfden <= 0:
+ cdef ndarray odfnum
+ cdef ndarray odfden
+ cdef ndarray ononc
+ odfnum = <ndarray>PyArray_FROM_OTF(dfnum, NPY_DOUBLE, NPY_ALIGNED)
+ odfden = <ndarray>PyArray_FROM_OTF(dfden, NPY_DOUBLE, NPY_ALIGNED)
+ ononc = <ndarray>PyArray_FROM_OTF(nonc, NPY_DOUBLE, NPY_ALIGNED)
+
+ if _sp.any(_sp.less_equal(odfnum, 1.0)):
+ raise ValueError("dfnum <= 1")
+ if _sp.any(_sp.less_equal(odfden, 0.0)):
raise ValueError("dfden <= 0")
- elif nonc < 0:
+ if _sp.any(_sp.less(ononc, 0.0)):
raise ValueError("nonc < 0")
- return cont3_array(self.internal_state, rk_noncentral_f, size, dfnum,
- dfden, nonc)
+ return cont3_array(self.internal_state, rk_noncentral_f, size, odfnum,
+ odfden, ononc)
- def chisquare(self, double df, size=None):
+ def chisquare(self, df, size=None):
"""Chi^2 distribution.
chisquare(df, size=None) -> random values
"""
- if df <= 0:
+ cdef ndarray odf
+ odf = <ndarray>PyArray_FROM_OTF(df, NPY_DOUBLE, NPY_ALIGNED)
+ if _sp.any(_sp.less_equal(odf, 0.0)):
raise ValueError("df <= 0")
- return cont1_array(self.internal_state, rk_chisquare, size, df)
+ return cont1_array(self.internal_state, rk_chisquare, size, odf)
- def noncentral_chisquare(self, double df, double nonc, size=None):
+ def noncentral_chisquare(self, df, nonc, size=None):
"""Noncentral Chi^2 distribution.
noncentral_chisquare(df, nonc, size=None) -> random values
"""
- if df <= 1:
+ cdef ndarray odf
+ cdef ndarray ononc
+ odf = <ndarray>PyArray_FROM_OTF(df, NPY_DOUBLE, NPY_ALIGNED)
+ ononc = <ndarray>PyArray_FROM_OTF(nonc, NPY_DOUBLE, NPY_ALIGNED)
+ if _sp.any(_sp.less_equal(odf, 0.0)):
raise ValueError("df <= 1")
- elif nonc < 0:
+ if _sp.any(_sp.less_equal(ononc, 0.0)):
raise ValueError("nonc < 0")
return cont2_array(self.internal_state, rk_noncentral_chisquare, size,
- df, nonc)
+ odf, ononc)
def standard_cauchy(self, size=None):
"""Standard Cauchy with mode=0.
@@ -582,80 +809,104 @@
"""
return cont0_array(self.internal_state, rk_standard_cauchy, size)
- def standard_t(self, double df, size=None):
+ def standard_t(self, df, size=None):
"""Standard Student's t distribution with df degrees of freedom.
standard_t(df, size=None)
"""
- if df <= 0:
+ cdef ndarray odf
+ odf = <ndarray> PyArray_FROM_OTF(df, NPY_DOUBLE, NPY_ALIGNED)
+ if _sp.any(_sp.less_equal(odf, 0.0)):
raise ValueError("df <= 0")
- return cont1_array(self.internal_state, rk_standard_t, size, df)
+ return cont1_array(self.internal_state, rk_standard_t, size, odf)
- def vonmises(self, double mu, double kappa, size=None):
+ def vonmises(self, mu, kappa, size=None):
"""von Mises circular distribution with mode mu and dispersion parameter
kappa on [-pi, pi].
vonmises(mu, kappa, size=None)
"""
- if kappa < 0:
+ cdef ndarray omu
+ cdef ndarray okappa
+ omu = <ndarray> PyArray_FROM_OTF(mu, NPY_DOUBLE, NPY_ALIGNED)
+ okappa = <ndarray> PyArray_FROM_OTF(kappa, NPY_DOUBLE, NPY_ALIGNED)
+ if _sp.any(_sp.less_equal(okappa, 0.0)):
raise ValueError("kappa < 0")
- return cont2_array(self.internal_state, rk_vonmises, size, mu, kappa)
+ return cont2_array(self.internal_state, rk_vonmises, size, omu, okappa)
- def pareto(self, double a, size=None):
+ def pareto(self, a, size=None):
"""Pareto distribution.
pareto(a, size=None)
"""
- if a <= 0:
+ cdef ndarray oa
+ oa = <ndarray>PyArray_FROM_OTF(a, NPY_DOUBLE, NPY_ALIGNED)
+ if _sp.any(_sp.less_equal(oa, 0.0)):
raise ValueError("a <= 0")
- return cont1_array(self.internal_state, rk_pareto, size, a)
+ return cont1_array(self.internal_state, rk_pareto, size, oa)
def weibull(self, double a, size=None):
"""Weibull distribution.
weibull(a, size=None)
"""
- if a <= 0:
+ cdef ndarray oa
+ oa = <ndarray>PyArray_FROM_OTF(a, NPY_DOUBLE, NPY_ALIGNED)
+ if _sp.any(_sp.less_equal(oa, 0.0)):
raise ValueError("a <= 0")
- return cont1_array(self.internal_state, rk_weibull, size, a)
+ return cont1_array(self.internal_state, rk_weibull, size, oa)
def power(self, double a, size=None):
"""Power distribution.
power(a, size=None)
"""
- if a <= 0:
+ cdef ndarray oa
+ oa = <ndarray>PyArray_FROM_OTF(a, NPY_DOUBLE, NPY_ALIGNED)
+ if _sp.any(_sp.less_equal(oa, 0.0)):
raise ValueError("a <= 0")
- return cont1_array(self.internal_state, rk_power, size, a)
+ return cont1_array(self.internal_state, rk_power, size, oa)
- def laplace(self, double loc=0.0, double scale=1.0, size=None):
+ def laplace(self, loc=0.0, scale=1.0, size=None):
"""Laplace distribution.
laplace(loc=0.0, scale=1.0, size=None)
"""
- if scale <= 0.0:
- raise ValueError("scale <= 0.0")
- return cont2_array(self.internal_state, rk_laplace, size, loc, scale)
+ cdef ndarray oloc
+ cdef ndarray oscale
+ oloc = PyArray_FROM_OTF(loc, NPY_DOUBLE, NPY_ALIGNED)
+ oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED)
+ if _sp.any(_sp.less_equal(oscale, 0.0)):
+ raise ValueError("scale <= 0")
+ return cont2_array(self.internal_state, rk_laplace, size, oloc, oscale)
- def gumbel(self, double loc=0.0, double scale=1.0, size=None):
+ def gumbel(self, loc=0.0, scale=1.0, size=None):
"""Gumbel distribution.
gumbel(loc=0.0, scale=1.0, size=None)
"""
- if scale <= 0.0:
- raise ValueError("scale <= 0.0")
- return cont2_array(self.internal_state, rk_gumbel, size, loc, scale)
+ cdef ndarray oloc
+ cdef ndarray oscale
+ oloc = PyArray_FROM_OTF(loc, NPY_DOUBLE, NPY_ALIGNED)
+ oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED)
+ if _sp.any(_sp.less_equal(oscale, 0.0)):
+ raise ValueError("scale <= 0")
+ return cont2_array(self.internal_state, rk_gumbel, size, oloc, oscale)
- def logistic(self, double loc=0.0, double scale=1.0, size=None):
+ def logistic(self, loc=0.0, scale=1.0, size=None):
"""Logistic distribution.
logistic(loc=0.0, scale=1.0, size=None)
"""
- if scale <= 0.0:
- raise ValueError("scale <= 0.0")
- return cont2_array(self.internal_state, rk_logistic, size, loc, scale)
+ cdef ndarray oloc
+ cdef ndarray oscale
+ oloc = PyArray_FROM_OTF(loc, NPY_DOUBLE, NPY_ALIGNED)
+ oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED)
+ if _sp.any(_sp.less_equal(oscale, 0.0)):
+ raise ValueError("scale <= 0")
+ return cont2_array(self.internal_state, rk_logistic, size, oloc, oscale)
- def lognormal(self, double mean=0.0, double sigma=1.0, size=None):
+ def lognormal(self, mean=0.0, sigma=1.0, size=None):
"""Log-normal distribution.
Note that the mean parameter is not the mean of this distribution, but
@@ -665,104 +916,135 @@
lognormal(mean=0.0, sigma=1.0, size=None)
"""
- if sigma <= 0.0:
+ cdef ndarray omean
+ cdef ndarray osigma
+ omean = PyArray_FROM_OTF(mean, NPY_DOUBLE, NPY_ALIGNED)
+ osigma = PyArray_FROM_OTF(sigma, NPY_DOUBLE, NPY_ALIGNED)
+ if _sp.any(_sp.less_equal(osigma, 0.0)):
raise ValueError("sigma <= 0.0")
return cont2_array(self.internal_state, rk_lognormal, size, mean, sigma)
- def rayleigh(self, double scale=1.0, size=None):
+ def rayleigh(self, scale=1.0, size=None):
"""Rayleigh distribution.
rayleigh(scale=1.0, size=None)
"""
- if scale <= 0.0:
+ cdef ndarray oscale
+ oscale = <ndarray>PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED)
+ if _sp.any(_sp.less_equal(oscale, 0.0)):
raise ValueError("scale <= 0.0")
- return cont1_array(self.internal_state, rk_rayleigh, size, scale)
-
- def wald(self, double mean, double scale, size=None):
+ return cont1_array(self.internal_state, rk_rayleigh, size, oscale)
+
+ def wald(self, mean, scale, size=None):
"""Wald (inverse Gaussian) distribution.
wald(mean, scale, size=None)
"""
- if mean <= 0.0:
+ cdef ndarray omean
+ cdef ndarray oscale
+ omean = PyArray_FROM_OTF(mean, NPY_DOUBLE, NPY_ALIGNED)
+ oscale = PyArray_FROM_OTF(scale, NPY_DOUBLE, NPY_ALIGNED)
+ if _sp.any(_sp.less_equal(omean,0.0)):
raise ValueError("mean <= 0.0")
- elif scale <= 0.0:
+ elif _sp.any(_sp.less_equal(oscale,0.0)):
raise ValueError("scale <= 0.0")
- return cont2_array(self.internal_state, rk_wald, size, mean, scale)
+ return cont2_array(self.internal_state, rk_wald, size, omean, oscale)
- def triangular(self, double left, double mode, double right, size=None):
+ def triangular(self, left, mode, right, size=None):
"""Triangular distribution starting at left, peaking at mode, and
ending at right (left <= mode <= right).
triangular(left, mode, right, size=None)
"""
- if left > mode:
+ cdef ndarray oleft
+ cdef ndarray omode
+ cdef ndarray oright
+ oleft = <ndarray>PyArray_FROM_OTF(left, NPY_DOUBLE, NPY_ALIGNED)
+ omode = <ndarray>PyArray_FROM_OTF(mode, NPY_DOUBLE, NPY_ALIGNED)
+ oright = <ndarray>PyArray_FROM_OTF(right, NPY_DOUBLE, NPY_ALIGNED)
+
+ if _sp.any(_sp.greater(oleft, omode)):
raise ValueError("left > mode")
- elif mode > right:
+ if _sp.any(_sp.greater(omode, oright)):
raise ValueError("mode > right")
- elif left == right:
+ if _sp.any(_sp.equal(oleft, oright)):
raise ValueError("left == right")
- return cont3_array(self.internal_state, rk_triangular, size, left,
- mode, right)
+ return cont3_array(self.internal_state, rk_triangular, size, oleft,
+ omode, oright)
# Complicated, discrete distributions:
- def binomial(self, long n, double p, size=None):
+ def binomial(self, n, p, size=None):
"""Binomial distribution of n trials and p probability of success.
binomial(n, p, size=None) -> random values
"""
- if n <= 0:
+ cdef ndarray on
+ cdef ndarray op
+ on = <ndarray>PyArray_FROM_OTF(n, NPY_LONG, NPY_ALIGNED)
+ op = <ndarray>PyArray_FROM_OTF(p, NPY_DOUBLE, NPY_ALIGNED)
+ if _sp.any(_sp.less_equal(n, 0)):
raise ValueError("n <= 0")
- elif p < 0:
+ if _sp.any(_sp.less(p, 0)):
raise ValueError("p < 0")
- elif p > 1:
+ if _sp.any(_sp.greater(p, 1)):
raise ValueError("p > 1")
- return discnp_array(self.internal_state, rk_binomial, size, n, p)
+ return discnp_array(self.internal_state, rk_binomial, size, on, op)
- def negative_binomial(self, long n, double p, size=None):
+ def negative_binomial(self, n, p, size=None):
"""Negative Binomial distribution.
negative_binomial(n, p, size=None) -> random values
"""
- if n <= 0:
+ cdef ndarray on
+ cdef ndarray op
+ on = <ndarray>PyArray_FROM_OTF(n, NPY_LONG, NPY_ALIGNED)
+ op = <ndarray>PyArray_FROM_OTF(p, NPY_DOUBLE, NPY_ALIGNED)
+ if _sp.any(_sp.less_equal(n, 0)):
raise ValueError("n <= 0")
- elif p < 0:
+ if _sp.any(_sp.less(p, 0)):
raise ValueError("p < 0")
- elif p > 1:
+ if _sp.any(_sp.greater(p, 1)):
raise ValueError("p > 1")
- return discnp_array(self.internal_state, rk_negative_binomial, size, n,
- p)
+ return discnp_array(self.internal_state, rk_negative_binomial, size,
+ on, op)
- def poisson(self, double lam=1.0, size=None):
+ def poisson(self, lam=1.0, size=None):
"""Poisson distribution.
poisson(lam=1.0, size=None) -> random values
"""
- if lam <= 0:
+ cdef ndarray olam
+ olam = <ndarray>PyArray_FROM_OTF(lam, NPY_DOUBLE, NPY_ALIGNED)
+ if _sp.any(_sp.less_equal(olam, 0)):
raise ValueError("lam <= 0")
- return discd_array(self.internal_state, rk_poisson, size, lam)
+ return discd_array(self.internal_state, rk_poisson, size, olam)
- def zipf(self, double a, size=None):
+ def zipf(self, a, size=None):
"""Zipf distribution.
zipf(a, size=None)
"""
- if a <= 1.0:
+ cdef ndarray oa
+ oa = <ndarray>PyArray_FROM_OTF(a, NPY_DOUBLE, NPY_ALIGNED)
+ if _sp.any(_sp.less_equal(oa, 1.0)):
raise ValueError("a <= 1.0")
- return discd_array(self.internal_state, rk_zipf, size, a)
+ return discd_array(self.internal_state, rk_zipf, size, oa)
- def geometric(self, double p, size=None):
+ def geometric(self, p, size=None):
"""Geometric distribution with p being the probability of "success" on
an individual trial.
geometric(p, size=None)
"""
- if p < 0.0:
+ cdef ndarray op
+ op = <ndarray>PyArray_FROM_OTF(p, NPY_DOUBLE, NPY_ALIGNED)
+ if _sp.any(_sp.less(op, 0.0)):
raise ValueError("p < 0.0")
- elif p > 1.0:
+ if _sp.any(_sp.greater(op, 1.0)):
raise ValueError("p > 1.0")
- return discd_array(self.internal_state, rk_geometric, size, p)
+ return discd_array(self.internal_state, rk_geometric, size, op)
- def hypergeometric(self, long ngood, long nbad, long nsample, size=None):
+ def hypergeometric(self, ngood, nbad, nsample, size=None):
"""Hypergeometric distribution.
Consider an urn with ngood "good" balls and nbad "bad" balls. If one
@@ -772,27 +1054,36 @@
hypergeometric(ngood, nbad, nsample, size=None)
"""
- if ngood < 1:
+ cdef ndarray ongood
+ cdef ndarray onbad
+ cdef ndarray onsample
+
+ ongood = <ndarray>PyArray_FROM_OTF(ngood, NPY_LONG, NPY_ALIGNED)
+ onbad = <ndarray>PyArray_FROM_OTF(nbad, NPY_LONG, NPY_ALIGNED)
+ onsample = <ndarray>PyArray_FROM_OTF(nsample, NPY_LONG, NPY_ALIGNED)
+ if _sp.any(_sp.less(ongood, 1)):
raise ValueError("ngood < 1")
- elif nbad < 1:
+ if _sp.any(_sp.less(onbad, 1)):
raise ValueError("nbad < 1")
- elif ngood + nbad < nsample:
+ if _sp.any(_sp.less(_sp.add(ongood, onbad),onsample)):
raise ValueError("ngood + nbad < nsample")
- elif nsample < 1:
+ if _sp.any(_sp.less(onsample, 1)):
raise ValueError("nsample < 1")
return discnmN_array(self.internal_state, rk_hypergeometric, size,
- ngood, nbad, nsample)
+ ongood, onbad, onsample)
- def logseries(self, double p, size=None):
+ def logseries(self, p, size=None):
"""Logarithmic series distribution.
logseries(p, size=None)
"""
- if p < 0:
- raise ValueError("p < 0")
- elif p > 1:
- raise ValueError("p > 1")
- return discd_array(self.internal_state, rk_logseries, size, p)
+ cdef ndarray op
+ op = <ndarray>PyArray_FROM_OTF(p, NPY_DOUBLE, NPY_ALIGNED)
+ if _sp.any(_sp.less(op, 0.0)):
+ raise ValueError("p < 0.0")
+ if _sp.any(_sp.greater(op, 1.0)):
+ raise ValueError("p > 1.0")
+ return discd_array(self.internal_state, rk_logseries, size, op)
# Multivariate distributions:
def multivariate_normal(self, mean, cov, size=None):
@@ -870,7 +1161,7 @@
cdef double Sum
d = len(pvals)
- parr = <ndarray>PyArray_ContiguousFromObject(pvals, PyArray_DOUBLE, 1, 1)
+ parr = <ndarray>PyArray_ContiguousFromObject(pvals, NPY_DOUBLE, 1, 1)
pix = <double*>parr.data
if kahan_sum(pix, d-1) > 1.0:
Modified: branches/ver1.0/numpy/random/mtrand/numpy.pxi
===================================================================
--- branches/ver1.0/numpy/random/mtrand/numpy.pxi 2006-07-25 17:17:08 UTC (rev 2891)
+++ branches/ver1.0/numpy/random/mtrand/numpy.pxi 2006-07-25 22:06:02 UTC (rev 2892)
@@ -1,52 +1,130 @@
-# :Author: Robert Kern
-# :Copyright: 2004, Enthought, Inc.
-# :License: BSD Style
+# :Author: Travis Oliphant
-
cdef extern from "numpy/arrayobject.h":
- ctypedef enum PyArray_TYPES:
- PyArray_BOOL
- PyArray_BYTE
- PyArray_UBYTE
- PyArray_SHORT
- PyArray_USHORT
- PyArray_INT
- PyArray_UINT
- PyArray_LONG
- PyArray_ULONG
- PyArray_FLOAT
- PyArray_DOUBLE
- PyArray_LONGDOUBLE
- PyArray_CFLOAT
- PyArray_CDOUBLE
- PyArray_CLONGDOUBLE
- PyArray_OBJECT
- PyArray_STRING
- PyArray_UNICODE
- PyArray_VOID
- PyArray_NTYPES
- PyArray_NOTYPE
- ctypedef int intp
+ cdef enum NPY_TYPES:
+ NPY_BOOL
+ NPY_BYTE
+ NPY_UBYTE
+ NPY_SHORT
+ NPY_USHORT
+ NPY_INT
+ NPY_UINT
+ NPY_LONG
+ NPY_ULONG
+ NPY_LONGLONG
+ NPY_ULONGLONG
+ NPY_FLOAT
+ NPY_DOUBLE
+ NPY_LONGDOUBLE
+ NPY_CFLOAT
+ NPY_CDOUBLE
+ NPY_CLONGDOUBLE
+ NPY_OBJECT
+ NPY_STRING
+ NPY_UNICODE
+ NPY_VOID
+ NPY_NTYPES
+ NPY_NOTYPE
+ cdef enum requirements:
+ NPY_CONTIGUOUS
+ NPY_FORTRAN
+ NPY_OWNDATA
+ NPY_FORCECAST
+ NPY_ENSURECOPY
+ NPY_ENSUREARRAY
+ NPY_ELEMENTSTRIDES
+ NPY_ALIGNED
+ NPY_NOTSWAPPED
+ NPY_WRITEABLE
+ NPY_UPDATEIFCOPY
+ NPY_ARR_HAS_DESCR
+
+ NPY_BEHAVED
+ NPY_BEHAVED_NS
+ NPY_CARRAY
+ NPY_CARRAY_RO
+ NPY_FARRAY
+ NPY_FARRAY_RO
+ NPY_DEFAULT
+
+ NPY_IN_ARRAY
+ NPY_OUT_ARRAY
+ NPY_INOUT_ARRAY
+ NPY_IN_FARRAY
+ NPY_OUT_FARRAY
+ NPY_INOUT_FARRAY
+
+ NPY_UPDATE_ALL
+
+ cdef enum defines:
+ NPY_MAXDIMS
+
+ ctypedef struct npy_cdouble:
+ double real
+ double imag
+
+ ctypedef struct npy_cfloat:
+ double real
+ double imag
+
+ ctypedef int npy_intp
+
ctypedef extern class numpy.dtype [object PyArray_Descr]:
- cdef int type_num, elsize
- cdef char type
+ cdef int type_num, elsize, alignment
+ cdef char type, kind, byteorder, hasobject
+ cdef object fields, typeobj
ctypedef extern class numpy.ndarray [object PyArrayObject]:
cdef char *data
cdef int nd
- cdef intp *dimensions
- cdef intp *strides
+ cdef npy_intp *dimensions
+ cdef npy_intp *strides
cdef object base
cdef dtype descr
cdef int flags
- ndarray PyArray_SimpleNew(int ndims, intp* dims, int item_type)
+ ctypedef extern class numpy.flatiter [object PyArrayIterObject]:
+ cdef int nd_m1
+ cdef npy_intp index, size
+ cdef ndarray ao
+ cdef char *dataptr
+
+ ctypedef extern class numpy.broadcast [object PyArrayMultiIterObject]:
+ cdef int numiter
+ cdef npy_intp size, index
+ cdef int nd
+ cdef npy_intp dimensions[NPY_MAXDIMS]
+ cdef void **iters
+
+ object PyArray_ZEROS(int ndims, npy_intp* dims, NPY_TYPES type_num, int fortran)
+ object PyArray_EMPTY(int ndims, npy_intp* dims, NPY_TYPES type_num, int fortran)
+ dtype PyArray_DescrFromTypeNum(NPY_TYPES type_num)
+ object PyArray_SimpleNew(int ndims, npy_intp* dims, NPY_TYPES type_num)
int PyArray_Check(object obj)
- ndarray PyArray_ContiguousFromObject(object obj, PyArray_TYPES type,
+ object PyArray_ContiguousFromAny(object obj, NPY_TYPES type,
int mindim, int maxdim)
- intp PyArray_SIZE(ndarray arr)
+ npy_intp PyArray_SIZE(ndarray arr)
+ npy_intp PyArray_NBYTES(ndarray arr)
void *PyArray_DATA(ndarray arr)
+ object PyArray_FromAny(object obj, dtype newtype, int mindim, int maxdim,
+ int requirements, object context)
+ object PyArray_FROMANY(object obj, NPY_TYPES type_num, int min,
+ int max, int requirements)
+ object PyArray_NewFromDescr(object subtype, dtype newtype, int nd,
+ npy_intp* dims, npy_intp* strides, void* data,
+ int flags, object parent)
+ object PyArray_FROM_OTF(object obj, NPY_TYPES type, int flags)
+
+ object PyArray_MultiIterNew(int n, ...)
+
+ char *PyArray_MultiIter_DATA(broadcast multi, int i)
+ void PyArray_MultiIter_NEXTi(broadcast multi, int i)
+ void PyArray_MultiIter_NEXT(broadcast multi)
+
+ object PyArray_IterNew(object arr)
+ void PyArray_ITER_NEXT(flatiter it)
+
void import_array()
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