[Numpy-svn] r5964 - trunk/numpy/random/mtrand
numpy-svn at scipy.org
numpy-svn at scipy.org
Mon Oct 27 20:24:37 EDT 2008
Author: ptvirtan
Date: 2008-10-27 19:24:27 -0500 (Mon, 27 Oct 2008)
New Revision: 5964
Modified:
trunk/numpy/random/mtrand/mtrand.c
Log:
Regenerate mtrand.c due to docstring changes
Modified: trunk/numpy/random/mtrand/mtrand.c
===================================================================
--- trunk/numpy/random/mtrand/mtrand.c 2008-10-28 00:13:44 UTC (rev 5963)
+++ trunk/numpy/random/mtrand/mtrand.c 2008-10-28 00:24:27 UTC (rev 5964)
@@ -1,4 +1,4 @@
-/* Generated by Pyrex 0.9.6.4 on Sun Aug 24 16:14:30 2008 */
+/* 0.9.7 on Tue Oct 28 02:15:49 2008 */
#define PY_SSIZE_T_CLEAN
#include "Python.h"
@@ -247,7 +247,7 @@
Py_INCREF(__pyx_v_size);
arrayObject = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":131 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":131 */
__pyx_1 = __pyx_v_size == Py_None;
if (__pyx_1) {
__pyx_2 = PyFloat_FromDouble(__pyx_v_func(__pyx_v_state)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 132; goto __pyx_L1;}
@@ -258,7 +258,7 @@
}
/*else*/ {
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":134 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":134 */
__pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n_np); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 134; goto __pyx_L1;}
__pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_n_empty); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 134; goto __pyx_L1;}
Py_DECREF(__pyx_2); __pyx_2 = 0;
@@ -278,18 +278,18 @@
arrayObject = ((PyArrayObject *)__pyx_4);
Py_DECREF(__pyx_4); __pyx_4 = 0;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":135 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":135 */
__pyx_v_length = PyArray_SIZE(arrayObject);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":136 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":136 */
__pyx_v_array_data = ((double *)arrayObject->data);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":137 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":137 */
for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; ++__pyx_v_i) {
(__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state);
}
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":139 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":139 */
Py_INCREF(((PyObject *)arrayObject));
__pyx_r = ((PyObject *)arrayObject);
goto __pyx_L0;
@@ -323,7 +323,7 @@
Py_INCREF(__pyx_v_size);
arrayObject = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":148 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":148 */
__pyx_1 = __pyx_v_size == Py_None;
if (__pyx_1) {
__pyx_2 = PyFloat_FromDouble(__pyx_v_func(__pyx_v_state,__pyx_v_a)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 149; goto __pyx_L1;}
@@ -334,7 +334,7 @@
}
/*else*/ {
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":151 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":151 */
__pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n_np); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 151; goto __pyx_L1;}
__pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_n_empty); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 151; goto __pyx_L1;}
Py_DECREF(__pyx_2); __pyx_2 = 0;
@@ -354,18 +354,18 @@
arrayObject = ((PyArrayObject *)__pyx_4);
Py_DECREF(__pyx_4); __pyx_4 = 0;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":152 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":152 */
__pyx_v_length = PyArray_SIZE(arrayObject);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":153 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":153 */
__pyx_v_array_data = ((double *)arrayObject->data);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":154 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":154 */
for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; ++__pyx_v_i) {
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}
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":156 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":156 */
Py_INCREF(((PyObject *)arrayObject));
__pyx_r = ((PyObject *)arrayObject);
goto __pyx_L0;
@@ -386,8 +386,6 @@
return __pyx_r;
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static PyObject *__pyx_k61p;
static char __pyx_k61[] = "size is not compatible with inputs";
@@ -412,44 +410,44 @@
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__pyx_v_multi = ((PyArrayMultiIterObject *)Py_None); Py_INCREF(Py_None);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":167 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":167 */
__pyx_1 = __pyx_v_size == Py_None;
if (__pyx_1) {
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":168 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":168 */
__pyx_2 = PyArray_SimpleNew(__pyx_v_oa->nd,__pyx_v_oa->dimensions,NPY_DOUBLE); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 168; goto __pyx_L1;}
Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_2)));
Py_DECREF(((PyObject *)arrayObject));
arrayObject = ((PyArrayObject *)__pyx_2);
Py_DECREF(__pyx_2); __pyx_2 = 0;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":169 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":169 */
__pyx_v_length = PyArray_SIZE(arrayObject);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":170 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":170 */
__pyx_v_array_data = ((double *)arrayObject->data);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":171 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":171 */
__pyx_2 = PyArray_IterNew(((PyObject *)__pyx_v_oa)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 171; goto __pyx_L1;}
Py_INCREF(((PyObject *)((PyArrayIterObject *)__pyx_2)));
Py_DECREF(((PyObject *)__pyx_v_itera));
__pyx_v_itera = ((PyArrayIterObject *)__pyx_2);
Py_DECREF(__pyx_2); __pyx_2 = 0;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":172 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":172 */
for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; ++__pyx_v_i) {
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":173 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":173 */
(__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,(((double *)__pyx_v_itera->dataptr)[0]));
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":174 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":174 */
PyArray_ITER_NEXT(__pyx_v_itera);
}
goto __pyx_L2;
}
/*else*/ {
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":176 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":176 */
__pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n_np); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 176; goto __pyx_L1;}
__pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_n_empty); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 176; goto __pyx_L1;}
Py_DECREF(__pyx_2); __pyx_2 = 0;
@@ -469,50 +467,48 @@
arrayObject = ((PyArrayObject *)__pyx_4);
Py_DECREF(__pyx_4); __pyx_4 = 0;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":177 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":177 */
__pyx_v_array_data = ((double *)arrayObject->data);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":178 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":178 */
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Py_INCREF(((PyObject *)((PyArrayMultiIterObject *)__pyx_3)));
Py_DECREF(((PyObject *)__pyx_v_multi));
__pyx_v_multi = ((PyArrayMultiIterObject *)__pyx_3);
Py_DECREF(__pyx_3); __pyx_3 = 0;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":180 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":180 */
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- __pyx_2 = __Pyx_GetName(__pyx_b, __pyx_n_ValueError); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 181; goto __pyx_L1;}
- __pyx_4 = PyTuple_New(1); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 181; goto __pyx_L1;}
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Py_INCREF(__pyx_k61p);
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- __pyx_3 = PyObject_CallObject(__pyx_2, __pyx_4); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 181; goto __pyx_L1;}
+ PyTuple_SET_ITEM(__pyx_2, 0, __pyx_k61p);
+ __pyx_4 = PyObject_CallObject(PyExc_ValueError, __pyx_2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 181; goto __pyx_L1;}
Py_DECREF(__pyx_2); __pyx_2 = 0;
+ __Pyx_Raise(__pyx_4, 0, 0);
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{__pyx_filename = __pyx_f[0]; __pyx_lineno = 181; goto __pyx_L1;}
goto __pyx_L5;
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__pyx_L5:;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":182 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":182 */
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- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":183 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":183 */
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- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":184 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":184 */
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- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":185 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":185 */
PyArray_MultiIter_NEXTi(__pyx_v_multi,1);
}
}
__pyx_L2:;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":186 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":186 */
Py_INCREF(((PyObject *)arrayObject));
__pyx_r = ((PyObject *)arrayObject);
goto __pyx_L0;
@@ -547,7 +543,7 @@
Py_INCREF(__pyx_v_size);
arrayObject = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":195 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":195 */
__pyx_1 = __pyx_v_size == Py_None;
if (__pyx_1) {
__pyx_2 = PyFloat_FromDouble(__pyx_v_func(__pyx_v_state,__pyx_v_a,__pyx_v_b)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 196; goto __pyx_L1;}
@@ -558,7 +554,7 @@
}
/*else*/ {
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":198 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":198 */
__pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n_np); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 198; goto __pyx_L1;}
__pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_n_empty); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 198; goto __pyx_L1;}
Py_DECREF(__pyx_2); __pyx_2 = 0;
@@ -578,18 +574,18 @@
arrayObject = ((PyArrayObject *)__pyx_4);
Py_DECREF(__pyx_4); __pyx_4 = 0;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":199 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":199 */
__pyx_v_length = PyArray_SIZE(arrayObject);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":200 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":200 */
__pyx_v_array_data = ((double *)arrayObject->data);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":201 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":201 */
for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; ++__pyx_v_i) {
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}
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":203 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":203 */
Py_INCREF(((PyObject *)arrayObject));
__pyx_r = ((PyObject *)arrayObject);
goto __pyx_L0;
@@ -633,48 +629,48 @@
arrayObject = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
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- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":216 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":216 */
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if (__pyx_1) {
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":217 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":217 */
__pyx_2 = PyArray_MultiIterNew(2,((void *)__pyx_v_oa),((void *)__pyx_v_ob)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 217; goto __pyx_L1;}
Py_INCREF(((PyObject *)((PyArrayMultiIterObject *)__pyx_2)));
Py_DECREF(((PyObject *)__pyx_v_multi));
__pyx_v_multi = ((PyArrayMultiIterObject *)__pyx_2);
Py_DECREF(__pyx_2); __pyx_2 = 0;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":218 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":218 */
__pyx_2 = PyArray_SimpleNew(__pyx_v_multi->nd,__pyx_v_multi->dimensions,NPY_DOUBLE); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 218; goto __pyx_L1;}
Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_2)));
Py_DECREF(((PyObject *)arrayObject));
arrayObject = ((PyArrayObject *)__pyx_2);
Py_DECREF(__pyx_2); __pyx_2 = 0;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":219 */
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__pyx_v_array_data = ((double *)arrayObject->data);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":220 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":220 */
__pyx_3 = __pyx_v_multi->size;
for (__pyx_v_i = 0; __pyx_v_i < __pyx_3; ++__pyx_v_i) {
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":221 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":221 */
__pyx_v_oa_data = ((double *)PyArray_MultiIter_DATA(__pyx_v_multi,0));
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":222 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":222 */
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- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":223 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":223 */
(__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,(__pyx_v_oa_data[0]),(__pyx_v_ob_data[0]));
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":224 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":224 */
PyArray_MultiIter_NEXT(__pyx_v_multi);
}
goto __pyx_L2;
}
/*else*/ {
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":226 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":226 */
__pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n_np); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 226; goto __pyx_L1;}
__pyx_4 = PyObject_GetAttr(__pyx_2, __pyx_n_empty); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 226; goto __pyx_L1;}
Py_DECREF(__pyx_2); __pyx_2 = 0;
@@ -694,56 +690,54 @@
arrayObject = ((PyArrayObject *)__pyx_5);
Py_DECREF(__pyx_5); __pyx_5 = 0;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":227 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":227 */
__pyx_v_array_data = ((double *)arrayObject->data);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":228 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":228 */
__pyx_4 = PyArray_MultiIterNew(3,((void *)arrayObject),((void *)__pyx_v_oa),((void *)__pyx_v_ob)); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 228; goto __pyx_L1;}
Py_INCREF(((PyObject *)((PyArrayMultiIterObject *)__pyx_4)));
Py_DECREF(((PyObject *)__pyx_v_multi));
__pyx_v_multi = ((PyArrayMultiIterObject *)__pyx_4);
Py_DECREF(__pyx_4); __pyx_4 = 0;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":229 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":229 */
__pyx_1 = (__pyx_v_multi->size != PyArray_SIZE(arrayObject));
if (__pyx_1) {
- __pyx_2 = __Pyx_GetName(__pyx_b, __pyx_n_ValueError); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 230; goto __pyx_L1;}
- __pyx_5 = PyTuple_New(1); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 230; goto __pyx_L1;}
+ __pyx_2 = PyTuple_New(1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 230; goto __pyx_L1;}
Py_INCREF(__pyx_k62p);
- PyTuple_SET_ITEM(__pyx_5, 0, __pyx_k62p);
- __pyx_4 = PyObject_CallObject(__pyx_2, __pyx_5); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 230; goto __pyx_L1;}
+ PyTuple_SET_ITEM(__pyx_2, 0, __pyx_k62p);
+ __pyx_5 = PyObject_CallObject(PyExc_ValueError, __pyx_2); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 230; goto __pyx_L1;}
Py_DECREF(__pyx_2); __pyx_2 = 0;
+ __Pyx_Raise(__pyx_5, 0, 0);
Py_DECREF(__pyx_5); __pyx_5 = 0;
- __Pyx_Raise(__pyx_4, 0, 0);
- Py_DECREF(__pyx_4); __pyx_4 = 0;
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 230; goto __pyx_L1;}
goto __pyx_L5;
}
__pyx_L5:;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":231 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":231 */
__pyx_3 = __pyx_v_multi->size;
for (__pyx_v_i = 0; __pyx_v_i < __pyx_3; ++__pyx_v_i) {
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":232 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":232 */
__pyx_v_oa_data = ((double *)PyArray_MultiIter_DATA(__pyx_v_multi,1));
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":233 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":233 */
__pyx_v_ob_data = ((double *)PyArray_MultiIter_DATA(__pyx_v_multi,2));
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":234 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":234 */
(__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,(__pyx_v_oa_data[0]),(__pyx_v_ob_data[0]));
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":235 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":235 */
PyArray_MultiIter_NEXTi(__pyx_v_multi,1);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":236 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":236 */
PyArray_MultiIter_NEXTi(__pyx_v_multi,2);
}
}
__pyx_L2:;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":237 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":237 */
Py_INCREF(((PyObject *)arrayObject));
__pyx_r = ((PyObject *)arrayObject);
goto __pyx_L0;
@@ -778,7 +772,7 @@
Py_INCREF(__pyx_v_size);
arrayObject = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":247 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":247 */
__pyx_1 = __pyx_v_size == Py_None;
if (__pyx_1) {
__pyx_2 = PyFloat_FromDouble(__pyx_v_func(__pyx_v_state,__pyx_v_a,__pyx_v_b,__pyx_v_c)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 248; goto __pyx_L1;}
@@ -789,7 +783,7 @@
}
/*else*/ {
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":250 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":250 */
__pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n_np); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 250; goto __pyx_L1;}
__pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_n_empty); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 250; goto __pyx_L1;}
Py_DECREF(__pyx_2); __pyx_2 = 0;
@@ -809,18 +803,18 @@
arrayObject = ((PyArrayObject *)__pyx_4);
Py_DECREF(__pyx_4); __pyx_4 = 0;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":251 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":251 */
__pyx_v_length = PyArray_SIZE(arrayObject);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":252 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":252 */
__pyx_v_array_data = ((double *)arrayObject->data);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":253 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":253 */
for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; ++__pyx_v_i) {
(__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,__pyx_v_a,__pyx_v_b,__pyx_v_c);
}
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":255 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":255 */
Py_INCREF(((PyObject *)arrayObject));
__pyx_r = ((PyObject *)arrayObject);
goto __pyx_L0;
@@ -866,51 +860,51 @@
arrayObject = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
__pyx_v_multi = ((PyArrayMultiIterObject *)Py_None); Py_INCREF(Py_None);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":269 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":269 */
__pyx_1 = __pyx_v_size == Py_None;
if (__pyx_1) {
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":270 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":270 */
__pyx_2 = PyArray_MultiIterNew(3,((void *)__pyx_v_oa),((void *)__pyx_v_ob),((void *)__pyx_v_oc)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 270; goto __pyx_L1;}
Py_INCREF(((PyObject *)((PyArrayMultiIterObject *)__pyx_2)));
Py_DECREF(((PyObject *)__pyx_v_multi));
__pyx_v_multi = ((PyArrayMultiIterObject *)__pyx_2);
Py_DECREF(__pyx_2); __pyx_2 = 0;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":271 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":271 */
__pyx_2 = PyArray_SimpleNew(__pyx_v_multi->nd,__pyx_v_multi->dimensions,NPY_DOUBLE); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 271; goto __pyx_L1;}
Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_2)));
Py_DECREF(((PyObject *)arrayObject));
arrayObject = ((PyArrayObject *)__pyx_2);
Py_DECREF(__pyx_2); __pyx_2 = 0;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":272 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":272 */
__pyx_v_array_data = ((double *)arrayObject->data);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":273 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":273 */
__pyx_3 = __pyx_v_multi->size;
for (__pyx_v_i = 0; __pyx_v_i < __pyx_3; ++__pyx_v_i) {
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":274 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":274 */
__pyx_v_oa_data = ((double *)PyArray_MultiIter_DATA(__pyx_v_multi,0));
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":275 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":275 */
__pyx_v_ob_data = ((double *)PyArray_MultiIter_DATA(__pyx_v_multi,1));
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":276 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":276 */
__pyx_v_oc_data = ((double *)PyArray_MultiIter_DATA(__pyx_v_multi,2));
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":277 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":277 */
(__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,(__pyx_v_oa_data[0]),(__pyx_v_ob_data[0]),(__pyx_v_oc_data[0]));
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":278 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":278 */
PyArray_MultiIter_NEXT(__pyx_v_multi);
}
goto __pyx_L2;
}
/*else*/ {
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":280 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":280 */
__pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n_np); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 280; goto __pyx_L1;}
__pyx_4 = PyObject_GetAttr(__pyx_2, __pyx_n_empty); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 280; goto __pyx_L1;}
Py_DECREF(__pyx_2); __pyx_2 = 0;
@@ -930,56 +924,54 @@
arrayObject = ((PyArrayObject *)__pyx_5);
Py_DECREF(__pyx_5); __pyx_5 = 0;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":281 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":281 */
__pyx_v_array_data = ((double *)arrayObject->data);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":282 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":282 */
__pyx_4 = PyArray_MultiIterNew(4,((void *)arrayObject),((void *)__pyx_v_oa),((void *)__pyx_v_ob),((void *)__pyx_v_oc)); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 282; goto __pyx_L1;}
Py_INCREF(((PyObject *)((PyArrayMultiIterObject *)__pyx_4)));
Py_DECREF(((PyObject *)__pyx_v_multi));
__pyx_v_multi = ((PyArrayMultiIterObject *)__pyx_4);
Py_DECREF(__pyx_4); __pyx_4 = 0;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":284 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":284 */
__pyx_1 = (__pyx_v_multi->size != PyArray_SIZE(arrayObject));
if (__pyx_1) {
- __pyx_2 = __Pyx_GetName(__pyx_b, __pyx_n_ValueError); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 285; goto __pyx_L1;}
- __pyx_5 = PyTuple_New(1); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 285; goto __pyx_L1;}
+ __pyx_2 = PyTuple_New(1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 285; goto __pyx_L1;}
Py_INCREF(__pyx_k63p);
- PyTuple_SET_ITEM(__pyx_5, 0, __pyx_k63p);
- __pyx_4 = PyObject_CallObject(__pyx_2, __pyx_5); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 285; goto __pyx_L1;}
+ PyTuple_SET_ITEM(__pyx_2, 0, __pyx_k63p);
+ __pyx_5 = PyObject_CallObject(PyExc_ValueError, __pyx_2); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 285; goto __pyx_L1;}
Py_DECREF(__pyx_2); __pyx_2 = 0;
+ __Pyx_Raise(__pyx_5, 0, 0);
Py_DECREF(__pyx_5); __pyx_5 = 0;
- __Pyx_Raise(__pyx_4, 0, 0);
- Py_DECREF(__pyx_4); __pyx_4 = 0;
{__pyx_filename = __pyx_f[0]; __pyx_lineno = 285; goto __pyx_L1;}
goto __pyx_L5;
}
__pyx_L5:;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":286 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":286 */
__pyx_3 = __pyx_v_multi->size;
for (__pyx_v_i = 0; __pyx_v_i < __pyx_3; ++__pyx_v_i) {
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":287 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":287 */
__pyx_v_oa_data = ((double *)PyArray_MultiIter_DATA(__pyx_v_multi,1));
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":288 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":288 */
__pyx_v_ob_data = ((double *)PyArray_MultiIter_DATA(__pyx_v_multi,2));
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":289 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":289 */
__pyx_v_oc_data = ((double *)PyArray_MultiIter_DATA(__pyx_v_multi,3));
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":290 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":290 */
(__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,(__pyx_v_oa_data[0]),(__pyx_v_ob_data[0]),(__pyx_v_oc_data[0]));
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":291 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":291 */
PyArray_MultiIter_NEXT(__pyx_v_multi);
}
}
__pyx_L2:;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":292 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":292 */
Py_INCREF(((PyObject *)arrayObject));
__pyx_r = ((PyObject *)arrayObject);
goto __pyx_L0;
@@ -1002,8 +994,6 @@
return __pyx_r;
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-static PyObject *__pyx_n_int;
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static PyObject *__pyx_f_6mtrand_disc0_array(rk_state *__pyx_v_state,__pyx_t_6mtrand_rk_disc0 __pyx_v_func,PyObject *__pyx_v_size) {
long *__pyx_v_array_data;
PyArrayObject *arrayObject;
@@ -1017,7 +1007,7 @@
Py_INCREF(__pyx_v_size);
arrayObject = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":300 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":300 */
__pyx_1 = __pyx_v_size == Py_None;
if (__pyx_1) {
__pyx_2 = PyInt_FromLong(__pyx_v_func(__pyx_v_state)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 301; goto __pyx_L1;}
@@ -1028,36 +1018,35 @@
}
/*else*/ {
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":303 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":303 */
__pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n_np); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 303; goto __pyx_L1;}
__pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_n_empty); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 303; goto __pyx_L1;}
Py_DECREF(__pyx_2); __pyx_2 = 0;
- __pyx_2 = __Pyx_GetName(__pyx_b, __pyx_n_int); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 303; goto __pyx_L1;}
- __pyx_4 = PyTuple_New(2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 303; goto __pyx_L1;}
+ __pyx_2 = PyTuple_New(2); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 303; goto __pyx_L1;}
Py_INCREF(__pyx_v_size);
- PyTuple_SET_ITEM(__pyx_4, 0, __pyx_v_size);
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- __pyx_2 = 0;
- __pyx_2 = PyObject_CallObject(__pyx_3, __pyx_4); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 303; goto __pyx_L1;}
+ PyTuple_SET_ITEM(__pyx_2, 0, __pyx_v_size);
+ Py_INCREF(((PyObject *)(&PyInt_Type)));
+ PyTuple_SET_ITEM(__pyx_2, 1, ((PyObject *)(&PyInt_Type)));
+ __pyx_4 = PyObject_CallObject(__pyx_3, __pyx_2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 303; goto __pyx_L1;}
Py_DECREF(__pyx_3); __pyx_3 = 0;
- Py_DECREF(__pyx_4); __pyx_4 = 0;
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- Py_DECREF(((PyObject *)arrayObject));
- arrayObject = ((PyArrayObject *)__pyx_2);
Py_DECREF(__pyx_2); __pyx_2 = 0;
+ Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_4)));
+ Py_DECREF(((PyObject *)arrayObject));
+ arrayObject = ((PyArrayObject *)__pyx_4);
+ Py_DECREF(__pyx_4); __pyx_4 = 0;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":304 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":304 */
__pyx_v_length = PyArray_SIZE(arrayObject);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":305 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":305 */
__pyx_v_array_data = ((long *)arrayObject->data);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":306 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":306 */
for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; ++__pyx_v_i) {
(__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state);
}
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":308 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":308 */
Py_INCREF(((PyObject *)arrayObject));
__pyx_r = ((PyObject *)arrayObject);
goto __pyx_L0;
@@ -1091,7 +1080,7 @@
Py_INCREF(__pyx_v_size);
arrayObject = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":316 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":316 */
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@@ -1329,36 +1314,35 @@
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+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":455 */
__pyx_v_om_data = ((long *)PyArray_MultiIter_DATA(__pyx_v_multi,2));
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":456 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":456 */
__pyx_v_oN_data = ((long *)PyArray_MultiIter_DATA(__pyx_v_multi,3));
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":457 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":457 */
(__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,(__pyx_v_on_data[0]),(__pyx_v_om_data[0]),(__pyx_v_oN_data[0]));
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":458 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":458 */
PyArray_MultiIter_NEXT(__pyx_v_multi);
}
}
__pyx_L2:;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":460 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":460 */
Py_INCREF(((PyObject *)arrayObject));
__pyx_r = ((PyObject *)arrayObject);
goto __pyx_L0;
@@ -1778,7 +1755,7 @@
Py_INCREF(__pyx_v_size);
arrayObject = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":468 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":468 */
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if (__pyx_1) {
__pyx_2 = PyInt_FromLong(__pyx_v_func(__pyx_v_state,__pyx_v_a)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 469; goto __pyx_L1;}
@@ -1789,36 +1766,35 @@
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/*else*/ {
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":471 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":471 */
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__pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_n_empty); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 471; goto __pyx_L1;}
Py_DECREF(__pyx_2); __pyx_2 = 0;
- __pyx_2 = __Pyx_GetName(__pyx_b, __pyx_n_int); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 471; goto __pyx_L1;}
- __pyx_4 = PyTuple_New(2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 471; goto __pyx_L1;}
+ __pyx_2 = PyTuple_New(2); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 471; goto __pyx_L1;}
Py_INCREF(__pyx_v_size);
- PyTuple_SET_ITEM(__pyx_4, 0, __pyx_v_size);
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- __pyx_2 = 0;
- __pyx_2 = PyObject_CallObject(__pyx_3, __pyx_4); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 471; goto __pyx_L1;}
+ PyTuple_SET_ITEM(__pyx_2, 0, __pyx_v_size);
+ Py_INCREF(((PyObject *)(&PyInt_Type)));
+ PyTuple_SET_ITEM(__pyx_2, 1, ((PyObject *)(&PyInt_Type)));
+ __pyx_4 = PyObject_CallObject(__pyx_3, __pyx_2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 471; goto __pyx_L1;}
Py_DECREF(__pyx_3); __pyx_3 = 0;
- Py_DECREF(__pyx_4); __pyx_4 = 0;
- Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_2)));
- Py_DECREF(((PyObject *)arrayObject));
- arrayObject = ((PyArrayObject *)__pyx_2);
Py_DECREF(__pyx_2); __pyx_2 = 0;
+ Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_4)));
+ Py_DECREF(((PyObject *)arrayObject));
+ arrayObject = ((PyArrayObject *)__pyx_4);
+ Py_DECREF(__pyx_4); __pyx_4 = 0;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":472 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":472 */
__pyx_v_length = PyArray_SIZE(arrayObject);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":473 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":473 */
__pyx_v_array_data = ((long *)arrayObject->data);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":474 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":474 */
for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; ++__pyx_v_i) {
(__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,__pyx_v_a);
}
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":476 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":476 */
Py_INCREF(((PyObject *)arrayObject));
__pyx_r = ((PyObject *)arrayObject);
goto __pyx_L0;
@@ -1863,105 +1839,102 @@
__pyx_v_multi = ((PyArrayMultiIterObject *)Py_None); Py_INCREF(Py_None);
__pyx_v_itera = ((PyArrayIterObject *)Py_None); Py_INCREF(Py_None);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":487 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":487 */
__pyx_1 = __pyx_v_size == Py_None;
if (__pyx_1) {
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":488 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":488 */
__pyx_2 = PyArray_SimpleNew(__pyx_v_oa->nd,__pyx_v_oa->dimensions,NPY_LONG); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 488; goto __pyx_L1;}
Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_2)));
Py_DECREF(((PyObject *)arrayObject));
arrayObject = ((PyArrayObject *)__pyx_2);
Py_DECREF(__pyx_2); __pyx_2 = 0;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":489 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":489 */
__pyx_v_length = PyArray_SIZE(arrayObject);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":490 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":490 */
__pyx_v_array_data = ((long *)arrayObject->data);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":491 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":491 */
__pyx_2 = PyArray_IterNew(((PyObject *)__pyx_v_oa)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 491; goto __pyx_L1;}
Py_INCREF(((PyObject *)((PyArrayIterObject *)__pyx_2)));
Py_DECREF(((PyObject *)__pyx_v_itera));
__pyx_v_itera = ((PyArrayIterObject *)__pyx_2);
Py_DECREF(__pyx_2); __pyx_2 = 0;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":492 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":492 */
for (__pyx_v_i = 0; __pyx_v_i < __pyx_v_length; ++__pyx_v_i) {
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":493 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":493 */
(__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,(((double *)__pyx_v_itera->dataptr)[0]));
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":494 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":494 */
PyArray_ITER_NEXT(__pyx_v_itera);
}
goto __pyx_L2;
}
/*else*/ {
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":496 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":496 */
__pyx_2 = __Pyx_GetName(__pyx_m, __pyx_n_np); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 496; goto __pyx_L1;}
__pyx_3 = PyObject_GetAttr(__pyx_2, __pyx_n_empty); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 496; goto __pyx_L1;}
Py_DECREF(__pyx_2); __pyx_2 = 0;
- __pyx_2 = __Pyx_GetName(__pyx_b, __pyx_n_int); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 496; goto __pyx_L1;}
- __pyx_4 = PyTuple_New(2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 496; goto __pyx_L1;}
+ __pyx_2 = PyTuple_New(2); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 496; goto __pyx_L1;}
Py_INCREF(__pyx_v_size);
- PyTuple_SET_ITEM(__pyx_4, 0, __pyx_v_size);
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- __pyx_2 = 0;
- __pyx_2 = PyObject_CallObject(__pyx_3, __pyx_4); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 496; goto __pyx_L1;}
+ PyTuple_SET_ITEM(__pyx_2, 0, __pyx_v_size);
+ Py_INCREF(((PyObject *)(&PyInt_Type)));
+ PyTuple_SET_ITEM(__pyx_2, 1, ((PyObject *)(&PyInt_Type)));
+ __pyx_4 = PyObject_CallObject(__pyx_3, __pyx_2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 496; goto __pyx_L1;}
Py_DECREF(__pyx_3); __pyx_3 = 0;
- Py_DECREF(__pyx_4); __pyx_4 = 0;
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- Py_DECREF(((PyObject *)arrayObject));
- arrayObject = ((PyArrayObject *)__pyx_2);
Py_DECREF(__pyx_2); __pyx_2 = 0;
+ Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_4)));
+ Py_DECREF(((PyObject *)arrayObject));
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+ Py_DECREF(__pyx_4); __pyx_4 = 0;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":497 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":497 */
__pyx_v_array_data = ((long *)arrayObject->data);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":498 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":498 */
__pyx_3 = PyArray_MultiIterNew(2,((void *)arrayObject),((void *)__pyx_v_oa)); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 498; goto __pyx_L1;}
Py_INCREF(((PyObject *)((PyArrayMultiIterObject *)__pyx_3)));
Py_DECREF(((PyObject *)__pyx_v_multi));
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Py_DECREF(__pyx_3); __pyx_3 = 0;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":499 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":499 */
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- __pyx_4 = __Pyx_GetName(__pyx_b, __pyx_n_ValueError); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 500; goto __pyx_L1;}
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Py_INCREF(__pyx_k67p);
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- __pyx_3 = PyObject_CallObject(__pyx_4, __pyx_2); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 500; goto __pyx_L1;}
- Py_DECREF(__pyx_4); __pyx_4 = 0;
+ __pyx_4 = PyObject_CallObject(PyExc_ValueError, __pyx_2); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 500; goto __pyx_L1;}
Py_DECREF(__pyx_2); __pyx_2 = 0;
- __Pyx_Raise(__pyx_3, 0, 0);
- Py_DECREF(__pyx_3); __pyx_3 = 0;
+ __Pyx_Raise(__pyx_4, 0, 0);
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{__pyx_filename = __pyx_f[0]; __pyx_lineno = 500; goto __pyx_L1;}
goto __pyx_L5;
}
__pyx_L5:;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":501 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":501 */
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for (__pyx_v_i = 0; __pyx_v_i < __pyx_5; ++__pyx_v_i) {
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":502 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":502 */
__pyx_v_oa_data = ((double *)PyArray_MultiIter_DATA(__pyx_v_multi,1));
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":503 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":503 */
(__pyx_v_array_data[__pyx_v_i]) = __pyx_v_func(__pyx_v_state,(__pyx_v_oa_data[0]));
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":504 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":504 */
PyArray_MultiIter_NEXTi(__pyx_v_multi,1);
}
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__pyx_L2:;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":505 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":505 */
Py_INCREF(((PyObject *)arrayObject));
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goto __pyx_L0;
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long __pyx_v_i;
double __pyx_r;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":510 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":510 */
__pyx_v_sum = (__pyx_v_darr[0]);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":511 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":511 */
__pyx_v_c = 0.0;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":512 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":512 */
for (__pyx_v_i = 1; __pyx_v_i < __pyx_v_n; ++__pyx_v_i) {
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+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":513 */
__pyx_v_y = ((__pyx_v_darr[__pyx_v_i]) - __pyx_v_c);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":514 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":514 */
__pyx_v_t = (__pyx_v_sum + __pyx_v_y);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":515 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":515 */
__pyx_v_c = ((__pyx_v_t - __pyx_v_sum) - __pyx_v_y);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":516 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":516 */
__pyx_v_sum = __pyx_v_t;
}
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":517 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":517 */
__pyx_r = __pyx_v_sum;
goto __pyx_L0;
@@ -2035,10 +2008,10 @@
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Py_INCREF(__pyx_v_seed);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":547 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":547 */
((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state = ((rk_state *)PyMem_Malloc((sizeof(rk_state))));
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":549 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":549 */
__pyx_1 = PyObject_GetAttr(__pyx_v_self, __pyx_n_seed); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 549; goto __pyx_L1;}
__pyx_2 = PyTuple_New(1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 549; goto __pyx_L1;}
Py_INCREF(__pyx_v_seed);
@@ -2069,10 +2042,10 @@
__pyx_1 = (((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state != NULL);
if (__pyx_1) {
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":553 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":553 */
PyMem_Free(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":554 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":554 */
((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state = NULL;
goto __pyx_L2;
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@@ -2081,7 +2054,6 @@
Py_DECREF(__pyx_v_self);
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static PyObject *__pyx_n_integer;
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@@ -2095,8 +2067,7 @@
int __pyx_1;
PyObject *__pyx_2 = 0;
PyObject *__pyx_3 = 0;
- PyObject *__pyx_4 = 0;
- unsigned long __pyx_5;
+ unsigned long __pyx_4;
static char *__pyx_argnames[] = {"seed",0};
__pyx_v_seed = __pyx_k3;
if (!PyArg_ParseTupleAndKeywords(__pyx_args, __pyx_kwds, "|O", __pyx_argnames, &__pyx_v_seed)) return 0;
@@ -2105,62 +2076,56 @@
arrayObject_obj = ((PyArrayObject *)Py_None); Py_INCREF(Py_None);
__pyx_v_iseed = Py_None; Py_INCREF(Py_None);
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":570 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":570 */
__pyx_1 = __pyx_v_seed == Py_None;
if (__pyx_1) {
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goto __pyx_L2;
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- __pyx_2 = __Pyx_GetName(__pyx_b, __pyx_n_type); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 572; goto __pyx_L1;}
- __pyx_3 = PyTuple_New(1); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 572; goto __pyx_L1;}
+ __pyx_2 = PyTuple_New(1); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 572; goto __pyx_L1;}
Py_INCREF(__pyx_v_seed);
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Py_XDECREF(__pyx_6);
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Py_DECREF(((PyObject *)arrayObject));
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}
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":717 */
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Py_DECREF(__pyx_v_bytestring);
__pyx_v_bytestring = __pyx_1;
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+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":743 */
__pyx_v_bytes = PyString_AS_STRING(__pyx_v_bytestring);
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rk_fill(__pyx_v_bytes,__pyx_v_length,((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state);
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+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":745 */
Py_INCREF(__pyx_v_bytestring);
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__pyx_v_flow = PyFloat_AsDouble(__pyx_v_low);
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__pyx_v_fhigh = PyFloat_AsDouble(__pyx_v_high);
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+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":824 */
__pyx_1 = (!PyErr_Occurred());
if (__pyx_1) {
__pyx_2 = __pyx_f_6mtrand_cont2_array_sc(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state,rk_uniform,__pyx_v_size,__pyx_v_flow,(__pyx_v_fhigh - __pyx_v_flow)); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 825; goto __pyx_L1;}
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PyErr_Clear();
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":827 */
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Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_2)));
Py_DECREF(((PyObject *)__pyx_v_olow));
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- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":828 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":828 */
__pyx_2 = PyArray_FROM_OTF(__pyx_v_high,NPY_DOUBLE,NPY_ALIGNED); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 828; goto __pyx_L1;}
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Py_DECREF(((PyObject *)__pyx_v_ohigh));
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Py_INCREF(__pyx_v_temp);
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Py_INCREF(((PyObject *)((PyArrayObject *)__pyx_3)));
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- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":1007 */
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PyErr_Clear();
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static char __pyx_k91[] = "dfden <= 0";
static PyObject *__pyx_f_6mtrand_11RandomState_f(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_f[] = "\n f(dfnum, dfden, size=None)\n\n F distribution.\n\n ";
+static char __pyx_doc_6mtrand_11RandomState_f[] = "\n f(dfnum, dfden, size=None)\n\n Draw samples from a F distribution.\n\n Samples are drawn from an F distribution with specified parameters,\n `dfnum` (degrees of freedom in numerator) and `dfden` (degrees of freedom\n in denominator), where both parameters should be greater than zero.\n\n The random variate of the F distribution (also known as the\n Fisher distribution) is a continuous probability distribution\n that arises in ANOVA tests, and is the ratio of two chi-square\n variates.\n\n Parameters\n ----------\n dfnum : float\n Degrees of freedom in numerator. Should be greater than zero.\n dfden : float\n Degrees of freedom in denominator. Should be greater than zero.\n size : {tuple, int}, optional\n Output shape. If the given shape is, e.g., ``(m, n, k)``,\n then ``m * n * k`` samples are drawn. By default only one sample\n is returned.\n\n Returns\n -------\n samples : {ndarray, scalar}\n Samples from the Fisher distribution.\n\n See Also\n --------\n scipy.stats.distributions.f : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n\n The F statistic is used to compare in-group variances to between-group\n variances. Calculating the distribution depends on the sampling, and\n so it is a function of the respective degrees of freedom in the\n problem. The variable `dfnum` is the number of samples minus one, the\n between-groups degrees of freedom, while `dfden` is the within-groups\n degrees of freedom, the sum of the number of samples in each group\n minus the number of groups.\n\n References\n ----------\n .. [1] Glantz, Stanton A. \"Primer of Biostatistics.\", McGraw-Hill,\n Fifth Edition, 2002.\n .. [2] Wikipedia, \"F-distribution\",\n http://en.wikipedia.org/wiki/F-distribution\n\n Examples\n --------\n An example from Glantz[1], pp 47-40.\n Two groups, children of diabetics (25 people) and children from people\n without diabetes (25 controls). Fasting blood glucose was measured,\n case group had a mean value of 86.1, controls had a mean value of\n 82.2. Standard deviations were 2.09 and 2.49 respectively. Are these\n data consistent with the null hypothesis that the parents diabetic\n status does not affect their children\'s blood glucose levels?\n Calculating the F statistic from the data gives a value of 36.01.\n\n Draw samples from the distribution:\n\n >>> dfnum = 1. # between group degrees of freedom\n >>> dfden = 48. # within groups degrees of freedom\n >>> s = np.random.f(dfnum, dfden, 1000)\n\n The lower bound for the top 1% of the samples is :\n\n >>> sort(s)[-10]\n 7.61988120985\n\n So there is about a 1% chance that the F statistic will exceed 7.62,\n the measured value is 36, so the null hypothesis is rejected at the 1%\n level.\n\n ";
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- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":1197 */
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__pyx_v_fdfnum = PyFloat_AsDouble(__pyx_v_dfnum);
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+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":1341 */
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@@ -4124,105 +4042,101 @@
}
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+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":1720 */
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+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":1888 */
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@@ -5736,7 +5602,7 @@
static char __pyx_k117[] = "scale <= 0";
static PyObject *__pyx_f_6mtrand_11RandomState_gumbel(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_gumbel[] = "\n gumbel(loc=0.0, scale=1.0, size=None)\n\n Gumbel distribution.\n\n Draw samples from a Gumbel distribution with specified location (or mean)\n and scale (or standard deviation).\n\n The Gumbel (or Smallest Extreme Value (SEV) or the Smallest Extreme Value\n Type I) distribution is one of a class of Generalized Extreme Value (GEV)\n distributions used in modeling extreme value problems. The Gumbel is a\n special case of the Extreme Value Type I distribution for maximums from\n distributions with \"exponential-like\" tails, it may be derived by\n considering a Gaussian process of measurements, and generating the pdf for\n the maximum values from that set of measurements (see examples).\n\n Parameters\n ----------\n loc : float\n The location of the mode of the distribution.\n scale : float\n The scale parameter of the distribution.\n size : tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n See Also\n --------\n scipy.stats.gumbel : probability density function,\n distribution or cumulative density function, etc.\n weibull, scipy.stats.genextreme\n\n Notes\n -----\n The probability density for the Gumbel distribution is\n\n .. math:: p(x) = \\frac{e^{-(x - \\mu)/ \\beta}}{\\beta} e^{ -e^{-(x - \\mu)/\n \\beta}},\n\n where :math:`\\mu` is the mode, a location parameter, and :math:`\\beta`\n is the scale parameter.\n\n The Gumbel (named for German mathematician Emil Julius Gumbel) was used\n very early in the hydrology literature, for modeling the occurrence of\n flood events. It is also used for modeling maximum wind speed and rainfall\n rates. It is a \"fat-tailed\" distribution - the probability of an event in\n the tail of the distribution is larger than if one used a Gaussian, hence\n the surprisingly frequent occurrence of 100-year floods. Floods were\n initially modeled as a Gaussian process, which underestimated the frequency\n of extreme events.\n\n It is one of a class of extreme value distributions, the Generalized\n Extreme Value (GEV) distributions, which also includes the Weibull and\n Frechet.\n\n The function has a mean of :math:`\\mu + 0.57721\\beta` and a variance of\n :math:`\\frac{\\pi^2}{6}\\beta^2`.\n\n References\n ----------\n .. [1] Gumbel, E.J. (1958). Statistics of Extremes. Columbia University\n Press.\n .. [2] Reiss, R.-D. and Thomas M. (2001), Statistical Analysis of Extreme\n Values, from Insurance, Finance, Hydrology and Other Fields,\n Birkhauser Verlag, Basel: Boston : Berlin.\n .. [3] Wikipedia, \"Gumbel distribution\",\n http://en.wikipedia.org/wiki/Gumbel_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> mu, beta = 0, 0.1 # location and scale\n >>> s = np.random.gumbel(mu, beta, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)*\n ... np.exp( -np.exp( -(bins - mu) /beta) ),\n ... linewidth=2, color=\'r\')\n >>> plt.show()\n\n Show how an extreme value distribution can arise from a Gaussian process\n and compare to a Gaussian:\n\n >>> means = []\n >>> maxima = []\n >>> for i in range(0,1000) :\n ... a = np.random.normal(mu, beta, 1000)\n ... means.append(a.mean())\n ... maxima.append(a.max())\n >>> count, bins, ignored = plt.hist(maxima, 30, normed=True)\n >>> beta = np.std(maxima)*np.pi/np.sqrt(6)\n >>> mu = np.mean(maxima) - 0.57721*beta\n >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)*\n ... np.exp( -np.exp( -(bins - mu) /beta) ),\n ... linewidth=2, color=\'r\')\n >>> plt.plot(bins, 1/(beta * np.sqrt(2 * np.pi)) *\n ... np.exp( - (bins - mu)**2 / (2 * beta**2) ),\n ... linewidth=2, color=\'g\')\n >>> plt.show()\n\n ";
+static char __pyx_doc_6mtrand_11RandomState_gumbel[] = "\n gumbel(loc=0.0, scale=1.0, size=None)\n\n Gumbel distribution.\n\n Draw samples from a Gumbel distribution with specified location (or mean)\n and scale (or standard deviation).\n\n The Gumbel (or Smallest Extreme Value (SEV) or the Smallest Extreme Value\n Type I) distribution is one of a class of Generalized Extreme Value (GEV)\n distributions used in modeling extreme value problems. The Gumbel is a\n special case of the Extreme Value Type I distribution for maximums from\n distributions with \"exponential-like\" tails, it may be derived by\n considering a Gaussian process of measurements, and generating the pdf for\n the maximum values from that set of measurements (see examples).\n\n Parameters\n ----------\n loc : float\n The location of the mode of the distribution.\n scale : float\n The scale parameter of the distribution.\n size : tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n See Also\n --------\n scipy.stats.gumbel : probability density function,\n distribution or cumulative density function, etc.\n weibull, scipy.stats.genextreme\n\n Notes\n -----\n The probability density for the Gumbel distribution is\n\n .. math:: p(x) = \\frac{e^{-(x - \\mu)/ \\beta}}{\\beta} e^{ -e^{-(x - \\mu)/\n \\beta}},\n\n where :math:`\\mu` is the mode, a location parameter, and :math:`\\beta`\n is the scale parameter.\n\n The Gumbel (named for German mathematician Emil Julius Gumbel) was used\n very early in the hydrology literature, for modeling the occurrence of\n flood events. It is also used for modeling maximum wind speed and rainfall\n rates. It is a \"fat-tailed\" distribution - the probability of an event in\n the tail of the distribution is larger than if one used a Gaussian, hence\n the surprisingly frequent occurrence of 100-year floods. Floods were\n initially modeled as a Gaussian process, which underestimated the frequency\n of extreme events.\n\n It is one of a class of extreme value distributions, the Generalized\n Extreme Value (GEV) distributions, which also includes the Weibull and\n Frechet.\n\n The function has a mean of :math:`\\mu + 0.57721\\beta` and a variance of\n :math:`\\frac{\\pi^2}{6}\\beta^2`.\n\n References\n ----------\n .. [1] Gumbel, E.J. (1958). Statistics of Extremes. Columbia University\n Press.\n .. [2] Reiss, R.-D. and Thomas M. (2001), Statistical Analysis of Extreme\n Values, from Insurance, Finance, Hydrology and Other Fields,\n Birkhauser Verlag, Basel: Boston : Berlin.\n .. [3] Wikipedia, \"Gumbel distribution\",\n http://en.wikipedia.org/wiki/Gumbel_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> mu, beta = 0, 0.1 # location and scale\n >>> s = np.random.gumbel(mu, beta, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, 30, normed=True)\n >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n ... * np.exp( -np.exp( -(bins - mu) /beta) ),\n ... linewidth=2, color=\'r\')\n >>> plt.show()\n\n Show how an extreme value distribution can arise from a Gaussian process\n and compare to a Gaussian:\n\n >>> means = []\n >>> maxima = []\n >>> for i in range(0,1000) :\n ... a = np.random.normal(mu, beta, 1000)\n ... means.append(a.mean())\n ... maxima.append(a.max())\n >>> count, bins, ignored = plt.hist(maxima, 30, normed=True)\n >>> beta = np.std(maxima)*np.pi/np.sqrt(6)\n >>> mu = np.mean(maxima) - 0.57721*beta\n >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)\n ... * np.exp(-np.exp(-(bins - mu)/beta)),\n ... linewidth=2, color=\'r\')\n >>> plt.plot(bins, 1/(beta * np.sqrt(2 * np.pi))\n ... * np.exp(-(bins - mu)**2 / (2 * beta**2)),\n ... linewidth=2, color=\'g\')\n >>> plt.show()\n\n ";
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static PyObject *__pyx_f_6mtrand_11RandomState_logistic(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_logistic[] = "\n logistic(loc=0.0, scale=1.0, size=None)\n\n Logistic distribution.\n\n ";
+static char __pyx_doc_6mtrand_11RandomState_logistic[] = "\n logistic(loc=0.0, scale=1.0, size=None)\n\n Draw samples from a Logistic distribution.\n\n Samples are drawn from a Logistic distribution with specified\n parameters, loc (location or mean, also median), and scale (>0).\n\n Parameters\n ----------\n loc : float\n\n scale : float > 0.\n\n size : {tuple, int}\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : {ndarray, scalar}\n where the values are all integers in [0, n].\n\n See Also\n --------\n scipy.stats.distributions.logistic : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Logistic distribution is\n\n .. math:: P(x) = P(x) = \\frac{e^{-(x-\\mu)/s}}{s(1+e^{-(x-\\mu)/s})^2},\n\n where :math:`\\mu` = location and :math:`s` = scale.\n\n The Logistic distribution is used in Extreme Value problems where it\n can act as a mixture of Gumbel distributions, in Epidemiology, and by\n the World Chess Federation (FIDE) where it is used in the Elo ranking\n system, assuming the performance of each player is a logistically\n distributed random variable.\n\n References\n ----------\n .. [1] Reiss, R.-D. and Thomas M. (2001), Statistical Analysis of Extreme\n Values, from Insurance, Finance, Hydrology and Other Fields,\n Birkhauser Verlag, Basel, pp 132-133.\n .. [2] Weisstein, Eric W. \"Logistic Distribution.\" From\n MathWorld--A Wolfram Web Resource.\n http://mathworld.wolfram.com/LogisticDistribution.html\n .. [3] Wikipedia, \"Logistic-distribution\",\n http://en.wikipedia.org/wiki/Logistic-distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> loc, scale = 10, 1\n >>> s = np.random.logistic(loc, scale, 10000)\n >>> count, bins, ignored = plt.hist(s, bins=50)\n\n # plot against distribution\n\n >>> def logist(x, loc, scale):\n ... return exp((loc-x)/scale)/(scale*(1+exp((loc-x)/scale))**2)\n >>> plt.plot(bins, logist(bins, loc, scale)*count.max()/\\\n ... logist(bins, loc, scale).max())\n >>> plt.show()\n\n ";
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@@ -6038,7 +5896,7 @@
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-static char __pyx_doc_6mtrand_11RandomState_lognormal[] = "\n lognormal(mean=0.0, sigma=1.0, size=None)\n\n Log-normal distribution.\n\n Draw samples from a log-normal distribution with specified mean, standard\n deviation, and shape. Note that the mean and standard deviation are not the\n values for the distribution itself, but of the underlying normal\n distribution it is derived from.\n\n\n Parameters\n ----------\n mean : float\n Mean value of the underlying normal distribution\n sigma : float, >0.\n Standard deviation of the underlying normal distribution\n size : tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n See Also\n --------\n scipy.stats.lognorm : probability density function, distribution,\n cumulative density function, etc.\n\n Notes\n -----\n A variable `x` has a log-normal distribution if `log(x)` is normally\n distributed.\n\n The probability density function for the log-normal distribution is\n\n .. math:: p(x) = \\frac{1}{\\sigma x \\sqrt{2\\pi}}\n e^{(-\\frac{(ln(x)-\\mu)^2}{2\\sigma^2})}\n\n where :math:`\\mu` is the mean and :math:`\\sigma` is the standard deviation\n of the normally distributed logarithm of the variable.\n\n A log normal distribution results if a random variable is the *product* of\n a large number of independent, identically-distributed variables in the\n same way that a normal distribution results if the variable is the *sum*\n of a large number of independent, identically-distributed variables\n (see the last example). It is one of the so-called \"fat-tailed\"\n distributions.\n\n The log-normal distribution is commonly used to model the lifespan of units\n with fatigue-stress failure modes. Since this includes\n most mechanical systems, the lognormal distribution has widespread\n application.\n\n It is also commonly used to model oil field sizes, species abundance, and\n latent periods of infectious diseases.\n\n References\n ----------\n .. [1] Eckhard Limpert, Werner A. Stahel, and Markus Abbt, \"Log-normal\n Distributions across the Sciences: Keys and Clues\", May 2001\n Vol. 51 No. 5 BioScience\n http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf\n .. [2] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme\n Values, Birkhauser Verlag, Basel, pp 31-32.\n .. [3] Wikipedia, \"Lognormal distribution\",\n http://en.wikipedia.org/wiki/Lognormal_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> mu, sigma = 3., 1. # mean and standard deviation\n >>> s = np.random.lognormal(mu, sigma, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, 100, normed=True, align=\'center\')\n\n >>> x = np.linspace(min(bins), max(bins), 10000)\n >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n ... / (x * sigma * np.sqrt(2 * np.pi)))\n\n >>> plt.plot(x, pdf, linewidth=2, color=\'r\')\n >>> plt.axis(\'tight\')\n >>> plt.show()\n\n Demonstrate that taking the products of random samples from a uniform\n distribution can be fit well by a log-normal pdf.\n\n >>> # Generate a thousand samples: each is the product of 100 random\n >>> # values, drawn from a normal distribution.\n >>> b = []\n >>> for i in range(1000):\n ... a = 10. + np.random.random(100)\n ... b.append(np.product(a))\n\n >>> b = np.array(b) / np.min(b) # scale values to be positive\n\n >>> count, bins, ignored = plt.hist(b, 100, normed=True, align=\'center\')\n\n >>> sigma = np.std(np.log(b))\n >>> mu = np.mean(np.log(b))\n\n >>> x = np.linspace(min(bins), max(bins), 10000)\n >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n ... / (x * sigma * np.sqrt(2 * np.pi)))\n\n >>> plt.plot(x, pdf, color=\'r\', linewidth=2)\n >>> plt.show()\n\n ";
+static char __pyx_doc_6mtrand_11RandomState_lognormal[] = "\n lognormal(mean=0.0, sigma=1.0, size=None)\n\n Return samples drawn from a log-normal distribution.\n\n Draw samples from a log-normal distribution with specified mean, standard\n deviation, and shape. Note that the mean and standard deviation are not the\n values for the distribution itself, but of the underlying normal\n distribution it is derived from.\n\n\n Parameters\n ----------\n mean : float\n Mean value of the underlying normal distribution\n sigma : float, >0.\n Standard deviation of the underlying normal distribution\n size : tuple of ints\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n See Also\n --------\n scipy.stats.lognorm : probability density function, distribution,\n cumulative density function, etc.\n\n Notes\n -----\n A variable `x` has a log-normal distribution if `log(x)` is normally\n distributed.\n\n The probability density function for the log-normal distribution is\n\n .. math:: p(x) = \\frac{1}{\\sigma x \\sqrt{2\\pi}}\n e^{(-\\frac{(ln(x)-\\mu)^2}{2\\sigma^2})}\n\n where :math:`\\mu` is the mean and :math:`\\sigma` is the standard deviation\n of the normally distributed logarithm of the variable.\n\n A log-normal distribution results if a random variable is the *product* of\n a large number of independent, identically-distributed variables in the\n same way that a normal distribution results if the variable is the *sum*\n of a large number of independent, identically-distributed variables\n (see the last example). It is one of the so-called \"fat-tailed\"\n distributions.\n\n The log-normal distribution is commonly used to model the lifespan of units\n with fatigue-stress failure modes. Since this includes\n most mechanical systems, the log-normal distribution has widespread\n application.\n\n It is also commonly used to model oil field sizes, species abundance, and\n latent periods of infectious diseases.\n\n References\n ----------\n .. [1] Eckhard Limpert, Werner A. Stahel, and Markus Abbt, \"Log-normal\n Distributions across the Sciences: Keys and Clues\", May 2001\n Vol. 51 No. 5 BioScience\n http://stat.ethz.ch/~stahel/lognormal/bioscience.pdf\n .. [2] Reiss, R.D., Thomas, M.(2001), Statistical Analysis of Extreme\n Values, Birkhauser Verlag, Basel, pp 31-32.\n .. [3] Wikipedia, \"Lognormal distribution\",\n http://en.wikipedia.org/wiki/Lognormal_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> mu, sigma = 3., 1. # mean and standard deviation\n >>> s = np.random.lognormal(mu, sigma, 1000)\n\n Display the histogram of the samples, along with\n the probability density function:\n\n >>> import matplotlib.pyplot as plt\n >>> count, bins, ignored = plt.hist(s, 100, normed=True, align=\'center\')\n\n >>> x = np.linspace(min(bins), max(bins), 10000)\n >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n ... / (x * sigma * np.sqrt(2 * np.pi)))\n\n >>> plt.plot(x, pdf, linewidth=2, color=\'r\')\n >>> plt.axis(\'tight\')\n >>> plt.show()\n\n Demonstrate that taking the products of random samples from a uniform\n distribution can be fit well by a log-normal probability density function.\n\n >>> # Generate a thousand samples: each is the product of 100 random\n >>> # values, drawn from a normal distribution.\n >>> b = []\n >>> for i in range(1000):\n ... a = 10. + np.random.random(100)\n ... b.append(np.product(a))\n\n >>> b = np.array(b) / np.min(b) # scale values to be positive\n\n >>> count, bins, ignored = plt.hist(b, 100, normed=True, align=\'center\')\n\n >>> sigma = np.std(np.log(b))\n >>> mu = np.mean(np.log(b))\n\n >>> x = np.linspace(min(bins), max(bins), 10000)\n >>> pdf = (np.exp(-(np.log(x) - mu)**2 / (2 * sigma**2))\n ... / (x * sigma * np.sqrt(2 * np.pi)))\n\n >>> plt.plot(x, pdf, color=\'r\', linewidth=2)\n >>> plt.show()\n\n ";
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- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":2017 */
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+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":2223 */
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+static char __pyx_doc_6mtrand_11RandomState_binomial[] = "\n binomial(n, p, size=None)\n\n Draw samples from a binomial distribution.\n\n Samples are drawn from a Binomial distribution with specified\n parameters, n trials and p probability of success where\n n an integer > 0 and p is in the interval [0,1]. (n may be\n input as a float, but it is truncated to an integer in use)\n\n Parameters\n ----------\n n : float (but truncated to an integer)\n parameter, > 0.\n p : float\n parameter, >= 0 and <=1.\n size : {tuple, int}\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : {ndarray, scalar}\n where the values are all integers in [0, n].\n\n See Also\n --------\n scipy.stats.distributions.binom : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Binomial distribution is\n\n .. math:: P(N) = \\binom{n}{N}p^N(1-p)^{n-N},\n\n where :math:`n` is the number of trials, :math:`p` is the probability\n of success, and :math:`N` is the number of successes.\n\n When estimating the standard error of a proportion in a population by\n using a random sample, the normal distribution works well unless the\n product p*n <=5, where p = population proportion estimate, and n =\n number of samples, in which case the binomial distribution is used\n instead. For example, a sample of 15 people shows 4 who are left\n handed, and 11 who are right handed. Then p = 4/15 = 27%. 0.27*15 = 4,\n so the binomial distribution should be used in this case.\n\n References\n ----------\n .. [1] Dalgaard, Peter, \"Introductory Statistics with R\",\n Springer-Verlag, 2002.\n .. [2] Glantz, Stanton A. \"Primer of Biostatistics.\", McGraw-Hill,\n Fifth Edition, 2002.\n .. [3] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n and Quigley, 1972.\n .. [4] Weisstein, Eric W. \"Binomial Distribution.\" From MathWorld--A\n Wolfram Web Resource.\n http://mathworld.wolfram.com/BinomialDistribution.html\n .. [5] Wikipedia, \"Binomial-distribution\",\n http://en.wikipedia.org/wiki/Binomial_distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> n, p = 10, .5 # number of trials, probability of each trial\n >>> s = np.random.binomial(n, p, 1000)\n # result of flipping a coin 10 times, tested 1000 times.\n\n A real world example. A company drills 9 wild-cat oil exploration\n wells, each with an estimated probability of success of 0.1. All nine\n wells fail. What is the probability of that happening?\n\n Let\'s do 20,000 trials of the model, and count the number that\n generate zero positive results.\n\n >>> sum(np.random.binomial(9,0.1,20000)==0)/20000.\n answer = 0.38885, or 38%.\n\n ";
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static PyObject *__pyx_f_6mtrand_11RandomState_hypergeometric(PyObject *__pyx_v_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/
-static char __pyx_doc_6mtrand_11RandomState_hypergeometric[] = "\n hypergeometric(ngood, nbad, nsample, size=None)\n\n Hypergeometric distribution.\n\n Consider an urn with ngood \"good\" balls and nbad \"bad\" balls. If one\n were to draw nsample balls from the urn without replacement, then\n the hypergeometric distribution describes the distribution of \"good\"\n balls in the sample.\n\n ";
+static char __pyx_doc_6mtrand_11RandomState_hypergeometric[] = "\n hypergeometric(ngood, nbad, nsample, size=None)\n\n Draw samples from a Hypergeometric distribution.\n\n Samples are drawn from a Hypergeometric distribution with specified\n parameters, ngood (ways to make a good selection), nbad (ways to make\n a bad selection), and nsample = number of items sampled, which is less\n than or equal to the sum ngood + nbad.\n\n Parameters\n ----------\n ngood : float (but truncated to an integer)\n parameter, > 0.\n nbad : float\n parameter, >= 0.\n nsample : float\n parameter, > 0 and <= ngood+nbad\n size : {tuple, int}\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : {ndarray, scalar}\n where the values are all integers in [0, n].\n\n See Also\n --------\n scipy.stats.distributions.hypergeom : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Hypergeometric distribution is\n\n .. math:: P(x) = \\frac{\\binom{m}{n}\\binom{N-m}{n-x}}{\\binom{N}{n}},\n\n where :math:`0 \\le x \\le m` and :math:`n+m-N \\le x \\le n`\n\n for P(x) the probability of x successes, n = ngood, m = nbad, and\n N = number of samples.\n\n Consider an urn with black and white marbles in it, ngood of them\n black and nbad are white. If you draw nsample balls without\n replacement, then the Hypergeometric distribution describes the\n distribution of black balls in the drawn sample.\n\n Note that this distribution is very similar to the Binomial\n distribution, except that in this case, samples are drawn without\n replacement, whereas in the Binomial case samples are drawn with\n replacement (or the sample space is infinite). As the sample space\n becomes large, this distribution approaches the Binomial.\n\n References\n ----------\n .. [1] Lentner, Marvin, \"Elementary Applied Statistics\", Bogden\n and Quigley, 1972.\n .. [2] Weisstein, Eric W. \"Hypergeometric Distribution.\" From\n MathWorld--A Wolfram Web Resource.\n http://mathworld.wolfram.com/HypergeometricDistribution.html\n .. [3] Wikipedia, \"Hypergeometric-distribution\",\n http://en.wikipedia.org/wiki/Hypergeometric-distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> ngood, nbad, nsamp = 100, 2, 10\n # number of good, number of bad, and number of samples\n >>> s = np.random.hypergeometric(ngood, nbad, nsamp, 1000)\n >>> hist(s)\n # note that it is very unlikely to grab both bad items\n\n Suppose you have an urn with 15 white and 15 black marbles.\n If you pull 15 marbles at random, how likely is it that\n 12 or more of them are one color?\n\n >>> s = np.random.hypergeometric(15, 15, 15, 100000)\n >>> sum(s>=12)/100000. + sum(s<=3)/100000.\n # answer = 0.003 ... pretty unlikely!\n\n ";
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+static char __pyx_doc_6mtrand_11RandomState_logseries[] = "\n logseries(p, size=None)\n\n Draw samples from a Logarithmic Series distribution.\n\n Samples are drawn from a Log Series distribution with specified\n parameter, p (probability, 0 < p < 1).\n\n Parameters\n ----------\n loc : float\n\n scale : float > 0.\n\n size : {tuple, int}\n Output shape. If the given shape is, e.g., ``(m, n, k)``, then\n ``m * n * k`` samples are drawn.\n\n Returns\n -------\n samples : {ndarray, scalar}\n where the values are all integers in [0, n].\n\n See Also\n --------\n scipy.stats.distributions.logser : probability density function,\n distribution or cumulative density function, etc.\n\n Notes\n -----\n The probability density for the Log Series distribution is\n\n .. math:: P(k) = \\frac{-p^k}{k \\ln(1-p)},\n\n where p = probability.\n\n The Log Series distribution is frequently used to represent species\n richness and occurrence, first proposed by Fisher, Corbet, and\n Williams in 1943 [2]. It may also be used to model the numbers of\n occupants seen in cars [3].\n\n References\n ----------\n .. [1] Buzas, Martin A.; Culver, Stephen J., Understanding regional\n species diversity through the log series distribution of\n occurrences: BIODIVERSITY RESEARCH Diversity & Distributions,\n Volume 5, Number 5, September 1999 , pp. 187-195(9).\n .. [2] Fisher, R.A,, A.S. Corbet, and C.B. Williams. 1943. The\n relation between the number of species and the number of\n individuals in a random sample of an animal population.\n Journal of Animal Ecology, 12:42-58.\n .. [3] D. J. Hand, F. Daly, D. Lunn, E. Ostrowski, A Handbook of Small\n Data Sets, CRC Press, 1994.\n .. [4] Wikipedia, \"Logarithmic-distribution\",\n http://en.wikipedia.org/wiki/Logarithmic-distribution\n\n Examples\n --------\n Draw samples from the distribution:\n\n >>> a = .6\n >>> s = np.random.logseries(a, 10000)\n >>> count, bins, ignored = plt.hist(s)\n\n # plot against distribution\n\n >>> def logseries(k, p):\n ... return -p**k/(k*log(1-p))\n >>> plt.plot(bins, logseries(bins, a)*count.max()/\\\n logseries(bins, a).max(),\'r\')\n >>> plt.show()\n\n ";
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+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":3074 */
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+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":3075 */
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if (!__pyx_3) break;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":2662 */
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__pyx_v_Sum = 1.0;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":2663 */
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__pyx_v_dn = __pyx_v_n;
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+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":3078 */
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- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":2665 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":3079 */
(__pyx_v_mnix[(__pyx_v_i + __pyx_v_j)]) = rk_binomial(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state,__pyx_v_dn,((__pyx_v_pix[__pyx_v_j]) / __pyx_v_Sum));
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+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":3080 */
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+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":3081 */
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goto __pyx_L7;
@@ -8939,12 +8672,12 @@
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}
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while (1) {
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if (!__pyx_3) break;
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for (__pyx_v_j = 0; __pyx_v_j < __pyx_v_k; ++__pyx_v_j) {
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+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":3175 */
(__pyx_v_val_data[(__pyx_v_i + __pyx_v_j)]) = rk_standard_gamma(((struct __pyx_obj_6mtrand_RandomState *)__pyx_v_self)->internal_state,(__pyx_v_alpha_data[__pyx_v_j]));
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for (__pyx_v_j = 0; __pyx_v_j < __pyx_v_k; ++__pyx_v_j) {
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@@ -9184,28 +8913,25 @@
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Py_DECREF(__pyx_2); __pyx_2 = 0;
- Py_DECREF(__pyx_1); __pyx_1 = 0;
+ Py_DECREF(__pyx_4); __pyx_4 = 0;
Py_DECREF(__pyx_v_arr);
- __pyx_v_arr = __pyx_3;
- __pyx_3 = 0;
+ __pyx_v_arr = __pyx_1;
+ __pyx_1 = 0;
}
__pyx_L2:;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":2838 */
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- __pyx_1 = PyTuple_New(1); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2838; goto __pyx_L1;}
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":3252 */
+ __pyx_2 = PyObject_GetAttr(__pyx_v_self, __pyx_n_shuffle); if (!__pyx_2) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3252; goto __pyx_L1;}
+ __pyx_4 = PyTuple_New(1); if (!__pyx_4) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3252; goto __pyx_L1;}
Py_INCREF(__pyx_v_arr);
- PyTuple_SET_ITEM(__pyx_1, 0, __pyx_v_arr);
- __pyx_3 = PyObject_CallObject(__pyx_2, __pyx_1); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2838; goto __pyx_L1;}
+ PyTuple_SET_ITEM(__pyx_4, 0, __pyx_v_arr);
+ __pyx_1 = PyObject_CallObject(__pyx_2, __pyx_4); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3252; goto __pyx_L1;}
Py_DECREF(__pyx_2); __pyx_2 = 0;
+ Py_DECREF(__pyx_4); __pyx_4 = 0;
Py_DECREF(__pyx_1); __pyx_1 = 0;
- Py_DECREF(__pyx_3); __pyx_3 = 0;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":2839 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":3253 */
Py_INCREF(__pyx_v_arr);
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goto __pyx_L0;
@@ -9425,7 +9124,7 @@
__pyx_L1:;
Py_XDECREF(__pyx_1);
Py_XDECREF(__pyx_2);
- Py_XDECREF(__pyx_3);
+ Py_XDECREF(__pyx_4);
__Pyx_AddTraceback("mtrand.RandomState.permutation");
__pyx_r = 0;
__pyx_L0:;
@@ -9437,8 +9136,6 @@
static __Pyx_InternTabEntry __pyx_intern_tab[] = {
{&__pyx_n_MT19937, "MT19937"},
- {&__pyx_n_TypeError, "TypeError"},
- {&__pyx_n_ValueError, "ValueError"},
{&__pyx_n___RandomState_ctor, "__RandomState_ctor"},
{&__pyx_n__rand, "_rand"},
{&__pyx_n_add, "add"},
@@ -9466,12 +9163,10 @@
{&__pyx_n_greater_equal, "greater_equal"},
{&__pyx_n_gumbel, "gumbel"},
{&__pyx_n_hypergeometric, "hypergeometric"},
- {&__pyx_n_int, "int"},
{&__pyx_n_integer, "integer"},
{&__pyx_n_laplace, "laplace"},
{&__pyx_n_less, "less"},
{&__pyx_n_less_equal, "less_equal"},
- {&__pyx_n_list, "list"},
{&__pyx_n_logistic, "logistic"},
{&__pyx_n_lognormal, "lognormal"},
{&__pyx_n_logseries, "logseries"},
@@ -9510,8 +9205,6 @@
{&__pyx_n_subtract, "subtract"},
{&__pyx_n_svd, "svd"},
{&__pyx_n_triangular, "triangular"},
- {&__pyx_n_tuple, "tuple"},
- {&__pyx_n_type, "type"},
{&__pyx_n_uint, "uint"},
{&__pyx_n_uint32, "uint32"},
{&__pyx_n_uniform, "uniform"},
@@ -9653,14 +9346,6 @@
(*o->ob_type->tp_free)(o);
}
-static int __pyx_tp_traverse_6mtrand_RandomState(PyObject *o, visitproc v, void *a) {
- return 0;
-}
-
-static int __pyx_tp_clear_6mtrand_RandomState(PyObject *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},
@@ -9806,10 +9491,10 @@
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*/
- "\n RandomState(seed=None)\n\n Container for the Mersenne Twister PRNG.\n\n `RandomState` exposes a number of methods for generating random numbers\n drawn from a variety of probability distributions. In addition to the\n distribution-specific arguments, each method takes a keyword argument\n `size` that defaults to ``None``. If `size` is ``None``, then a single\n value is generated and returned. If `size` is an integer, then a 1-D\n numpy array filled with generated values is returned. If size is a tuple,\n then a numpy array with that shape is filled and returned.\n\n Parameters\n ----------\n seed : {None, int, array-like}\n Random seed initializing the PRNG.\n Can be an integer, an array (or other sequence) of integers of\n any length, or ``None``.\n If `seed` is ``None``, then `RandomState` will try to read data from\n ``/dev/urandom`` (or the Windows analogue) if available or seed from\n the clock otherwise.\n\n ", /*tp_doc*/
- __pyx_tp_traverse_6mtrand_RandomState, /*tp_traverse*/
- __pyx_tp_clear_6mtrand_RandomState, /*tp_clear*/
+ Py_TPFLAGS_DEFAULT|Py_TPFLAGS_CHECKTYPES|Py_TPFLAGS_BASETYPE, /*tp_flags*/
+ "\n RandomState(seed=None)\n\n Container for the Mersenne Twister PRNG.\n\n `RandomState` exposes a number of methods for generating random numbers\n drawn from a variety of probability distributions. In addition to the\n distribution-specific arguments, each method takes a keyword argument\n `size` that defaults to ``None``. If `size` is ``None``, then a single\n value is generated and returned. If `size` is an integer, then a 1-D\n numpy array filled with generated values is returned. If size is a tuple,\n then a numpy array with that shape is filled and returned.\n\n Parameters\n ----------\n seed : array_like, int, optional\n Random seed initializing the PRNG.\n Can be an integer, an array (or other sequence) of integers of\n any length, or ``None``.\n If `seed` is ``None``, then `RandomState` will try to read data from\n ``/dev/urandom`` (or the Windows analogue) if available or seed from\n the clock otherwise.\n\n ", /*tp_doc*/
+ 0, /*tp_traverse*/
+ 0, /*tp_clear*/
0, /*tp_richcompare*/
0, /*tp_weaklistoffset*/
0, /*tp_iter*/
@@ -9877,37 +9562,37 @@
if (PyObject_SetAttrString(__pyx_m, "RandomState", (PyObject *)&__pyx_type_6mtrand_RandomState) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 519; goto __pyx_L1;}
__pyx_ptype_6mtrand_RandomState = &__pyx_type_6mtrand_RandomState;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":121 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":121 */
import_array();
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":123 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":123 */
__pyx_1 = __Pyx_Import(__pyx_n_numpy, 0); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 123; goto __pyx_L1;}
if (PyObject_SetAttr(__pyx_m, __pyx_n_np, __pyx_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 123; goto __pyx_L1;}
Py_DECREF(__pyx_1); __pyx_1 = 0;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":546 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":546 */
Py_INCREF(Py_None);
__pyx_k2 = Py_None;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":556 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":556 */
Py_INCREF(Py_None);
__pyx_k3 = Py_None;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":646 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":646 */
Py_INCREF(Py_None);
__pyx_k4 = Py_None;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":655 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":655 */
Py_INCREF(Py_None);
__pyx_k5 = Py_None;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":683 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":683 */
Py_INCREF(Py_None);
__pyx_k6 = Py_None;
Py_INCREF(Py_None);
__pyx_k7 = Py_None;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":747 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":747 */
__pyx_1 = PyFloat_FromDouble(0.0); if (!__pyx_1) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 747; goto __pyx_L1;}
__pyx_k8 = __pyx_1;
__pyx_1 = 0;
@@ -9917,17 +9602,17 @@
Py_INCREF(Py_None);
__pyx_k10 = Py_None;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":895 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":895 */
Py_INCREF(Py_None);
__pyx_k11 = Py_None;
Py_INCREF(Py_None);
__pyx_k12 = Py_None;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":910 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":910 */
Py_INCREF(Py_None);
__pyx_k13 = Py_None;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":919 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":919 */
__pyx_3 = PyFloat_FromDouble(0.0); if (!__pyx_3) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 919; goto __pyx_L1;}
__pyx_k14 = __pyx_3;
__pyx_3 = 0;
@@ -9937,495 +9622,495 @@
Py_INCREF(Py_None);
__pyx_k16 = Py_None;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":1019 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":1019 */
Py_INCREF(Py_None);
__pyx_k17 = Py_None;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":1078 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":1078 */
__pyx_5 = PyFloat_FromDouble(1.0); if (!__pyx_5) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1078; goto __pyx_L1;}
__pyx_k18 = __pyx_5;
__pyx_5 = 0;
Py_INCREF(Py_None);
__pyx_k19 = Py_None;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":1128 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":1128 */
Py_INCREF(Py_None);
__pyx_k20 = Py_None;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":1137 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":1137 */
Py_INCREF(Py_None);
__pyx_k21 = Py_None;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":1159 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":1159 */
__pyx_6 = PyFloat_FromDouble(1.0); if (!__pyx_6) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1159; goto __pyx_L1;}
__pyx_k22 = __pyx_6;
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Py_INCREF(Py_None);
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- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":1187 */
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Py_INCREF(Py_None);
__pyx_k24 = Py_None;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":1216 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":1353 */
Py_INCREF(Py_None);
__pyx_k25 = Py_None;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":1254 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":1391 */
Py_INCREF(Py_None);
__pyx_k26 = Py_None;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":1334 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":1471 */
Py_INCREF(Py_None);
__pyx_k27 = Py_None;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":1376 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":1513 */
Py_INCREF(Py_None);
__pyx_k28 = Py_None;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":1385 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":1522 */
Py_INCREF(Py_None);
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- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":1408 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":1545 */
Py_INCREF(Py_None);
__pyx_k30 = Py_None;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":1503 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":1640 */
Py_INCREF(Py_None);
__pyx_k31 = Py_None;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":1592 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":1729 */
Py_INCREF(Py_None);
__pyx_k32 = Py_None;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":1691 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":1828 */
Py_INCREF(Py_None);
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+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":1851 */
+ __pyx_7 = PyFloat_FromDouble(0.0); if (!__pyx_7) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1851; goto __pyx_L1;}
__pyx_k34 = __pyx_7;
__pyx_7 = 0;
- __pyx_8 = PyFloat_FromDouble(1.0); if (!__pyx_8) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1714; goto __pyx_L1;}
+ __pyx_8 = PyFloat_FromDouble(1.0); if (!__pyx_8) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1851; goto __pyx_L1;}
__pyx_k35 = __pyx_8;
__pyx_8 = 0;
Py_INCREF(Py_None);
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- __pyx_9 = PyFloat_FromDouble(0.0); if (!__pyx_9) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1753; goto __pyx_L1;}
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":1890 */
+ __pyx_9 = PyFloat_FromDouble(0.0); if (!__pyx_9) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1890; goto __pyx_L1;}
__pyx_k37 = __pyx_9;
__pyx_9 = 0;
- __pyx_10 = PyFloat_FromDouble(1.0); if (!__pyx_10) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1753; goto __pyx_L1;}
+ __pyx_10 = PyFloat_FromDouble(1.0); if (!__pyx_10) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1890; goto __pyx_L1;}
__pyx_k38 = __pyx_10;
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Py_INCREF(Py_None);
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+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":2014 */
+ __pyx_11 = PyFloat_FromDouble(0.0); if (!__pyx_11) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2014; goto __pyx_L1;}
__pyx_k40 = __pyx_11;
__pyx_11 = 0;
- __pyx_12 = PyFloat_FromDouble(1.0); if (!__pyx_12) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1877; goto __pyx_L1;}
+ __pyx_12 = PyFloat_FromDouble(1.0); if (!__pyx_12) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2014; goto __pyx_L1;}
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Py_INCREF(Py_None);
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+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":2102 */
+ __pyx_13 = PyFloat_FromDouble(0.0); if (!__pyx_13) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2102; goto __pyx_L1;}
__pyx_k43 = __pyx_13;
__pyx_13 = 0;
- __pyx_14 = PyFloat_FromDouble(1.0); if (!__pyx_14) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1901; goto __pyx_L1;}
+ __pyx_14 = PyFloat_FromDouble(1.0); if (!__pyx_14) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2102; goto __pyx_L1;}
__pyx_k44 = __pyx_14;
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Py_INCREF(Py_None);
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+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":2233 */
+ __pyx_15 = PyFloat_FromDouble(1.0); if (!__pyx_15) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 2233; goto __pyx_L1;}
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Py_INCREF(Py_None);
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Py_INCREF(Py_None);
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Py_INCREF(Py_None);
__pyx_k49 = Py_None;
- /* "/Users/rkern/svn/numpy/numpy/random/mtrand/mtrand.pyx":2125 */
+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":2326 */
Py_INCREF(Py_None);
__pyx_k50 = Py_None;
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+ /* "/home/pauli/koodi/proj/numpy/one-shot/numpy/random/mtrand/mtrand.pyx":2434 */
Py_INCREF(Py_None);
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Py_DECREF(__pyx_17); __pyx_17 = 0;
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Py_DECREF(__pyx_18); __pyx_18 = 0;
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+ if (PyObject_SetAttr(__pyx_m, __pyx_n_shuffle, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3303; goto __pyx_L1;}
Py_DECREF(__pyx_18); __pyx_18 = 0;
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Py_DECREF(__pyx_17); __pyx_17 = 0;
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+ if (PyObject_SetAttr(__pyx_m, __pyx_n_permutation, __pyx_18) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 3304; goto __pyx_L1;}
Py_DECREF(__pyx_18); __pyx_18 = 0;
return;
__pyx_L1:;
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