inline static void import_numpy() {
static bool init = false;
if (!init) {
_import_array();
// std::cerr << "importing array"<<std::endl;
init = true;
}
}
PyObject* ICoinPackedMatrix::np_getIndices(){
npy_intp dims = this->getNumElements();
_import_array();
PyObject *Arr = PyArray_SimpleNewFromData( 1, &dims, PyArray_INT32, this->IgetIndices() );
return Arr;
}
static PyObject *
initaubio (void)
{
PyObject *m = NULL;
int err;
// fvec is defined in __init__.py
if ( (PyType_Ready (&Py_cvecType) < 0)
|| (PyType_Ready (&Py_filterType) < 0)
|| (PyType_Ready (&Py_filterbankType) < 0)
|| (PyType_Ready (&Py_fftType) < 0)
|| (PyType_Ready (&Py_pvocType) < 0)
|| (PyType_Ready (&Py_sourceType) < 0)
|| (PyType_Ready (&Py_sinkType) < 0)
// generated objects
|| (generated_types_ready() < 0 )
) {
return m;
}
#if PY_MAJOR_VERSION >= 3
m = PyModule_Create(&moduledef);
#else
m = Py_InitModule3 ("_aubio", aubio_methods, aubio_module_doc);
#endif
if (m == NULL) {
return m;
}
err = _import_array ();
if (err != 0) {
fprintf (stderr,
"Unable to import Numpy array from aubio module (error %d)\n", err);
}
Py_INCREF (&Py_cvecType);
PyModule_AddObject (m, "cvec", (PyObject *) & Py_cvecType);
Py_INCREF (&Py_filterType);
PyModule_AddObject (m, "digital_filter", (PyObject *) & Py_filterType);
Py_INCREF (&Py_filterbankType);
PyModule_AddObject (m, "filterbank", (PyObject *) & Py_filterbankType);
Py_INCREF (&Py_fftType);
PyModule_AddObject (m, "fft", (PyObject *) & Py_fftType);
Py_INCREF (&Py_pvocType);
PyModule_AddObject (m, "pvoc", (PyObject *) & Py_pvocType);
Py_INCREF (&Py_sourceType);
PyModule_AddObject (m, "source", (PyObject *) & Py_sourceType);
Py_INCREF (&Py_sinkType);
PyModule_AddObject (m, "sink", (PyObject *) & Py_sinkType);
// add generated objects
add_generated_objects(m);
// add ufunc
add_ufuncs(m);
return m;
}
void initializeNumpy() {
// Use _import_array() because import_array() is a macro that contains a
// return statement.
if (_import_array() != 0) {
throw std::runtime_error(
"initializeNumpy: numpy.core.multiarray failed to import.");
}
}
PYBIND11_MODULE(superutils, m) {
_import_array();
m.doc() = "fast utils";
vaex::init_hash_primitives(m);
vaex::init_hash_string(m);
vaex::init_hash_object(m);
}
void Invocation::numpy_init()
{
// this macro is defined be NumPy and must be included
if (_import_array() < 0)
{
std::ostringstream oss;
oss << "unable to initialize NumPy with error '" << getPythonTraceback() << "'";
throw python_error(oss.str());
}
}
int initialize_numpy(void)
{
// lock here so only one thread can initialize NumPy
boost::mutex::scoped_lock lock(numpy_initialize_mtx);
if (!numpy_initialized) {
_import_array(); // import NumPy
numpy_initialized = true; // numpy successfully initialized
}
return GADGET_OK;
}
PyMODINIT_FUNC
init_aubio (void)
{
PyObject *m;
int err;
// fvec is defined in __init__.py
if ( (PyType_Ready (&Py_cvecType) < 0)
|| (PyType_Ready (&Py_filterType) < 0)
|| (PyType_Ready (&Py_filterbankType) < 0)
|| (PyType_Ready (&Py_fftType) < 0)
|| (PyType_Ready (&Py_pvocType) < 0)
// generated objects
|| (generated_types_ready() < 0 )
) {
return;
}
m = Py_InitModule3 ("_aubio", aubio_methods, aubio_module_doc);
if (m == NULL) {
return;
}
err = _import_array ();
if (err != 0) {
fprintf (stderr,
"Unable to import Numpy array from aubio module (error %d)\n", err);
}
Py_INCREF (&Py_cvecType);
PyModule_AddObject (m, "cvec", (PyObject *) & Py_cvecType);
Py_INCREF (&Py_filterType);
PyModule_AddObject (m, "digital_filter", (PyObject *) & Py_filterType);
Py_INCREF (&Py_filterbankType);
PyModule_AddObject (m, "filterbank", (PyObject *) & Py_filterbankType);
Py_INCREF (&Py_fftType);
PyModule_AddObject (m, "fft", (PyObject *) & Py_fftType);
Py_INCREF (&Py_pvocType);
PyModule_AddObject (m, "pvoc", (PyObject *) & Py_pvocType);
// add generated objects
add_generated_objects(m);
// add ufunc
add_ufuncs(m);
}
/**
* Try and initialize numpy module, setting the useNumPy flag appropriately
* Note that this can only be called once and must be called from the init section of the Python module
*/
void MantidVecHelper::initializeDependencies()
{
if( MantidVecHelper::g_isInitialized ) return;
// Import numpy and check it succeeded
int result = _import_array();
if( result < 0 )
{
PyErr_Clear();
MantidVecHelper::g_useNumPy = false;
}
else
{
MantidVecHelper::g_useNumPy = true;
boost::python::numeric::array::set_module_and_type("numpy", "ndarray");
}
MantidVecHelper::g_isInitialized = true;
}
PyMODINIT_FUNC
init_chebyfit(void)
#endif
{
PyObject *module;
char *doc = (char *)PyMem_Malloc(sizeof(module_doc) + sizeof(_VERSION_));
PyOS_snprintf(doc, sizeof(module_doc) + sizeof(_VERSION_),
module_doc, _VERSION_);
#if PY_MAJOR_VERSION >= 3
moduledef.m_doc = doc;
module = PyModule_Create(&moduledef);
#else
module = Py_InitModule3("_chebyfit", module_methods, doc);
#endif
PyMem_Free(doc);
if (module == NULL)
INITERROR;
if (_import_array() < 0) {
Py_DECREF(module);
INITERROR;
}
{
#if PY_MAJOR_VERSION < 3
PyObject *s = PyString_FromString(_VERSION_);
#else
PyObject *s = PyUnicode_FromString(_VERSION_);
#endif
PyObject *dict = PyModule_GetDict(module);
PyDict_SetItemString(dict, "__version__", s);
Py_DECREF(s);
}
#if PY_MAJOR_VERSION >= 3
return module;
#endif
}
Kernel::DblMatrix MantidVecHelper::getMatrixFromArray(PyObject *p)
{
_import_array();
if(PyArray_Check(p)==1)
{
boost::python::numeric::array a=boost::python::extract<boost::python::numeric::array>(p);
a=(boost::python::numeric::array) a.astype('d');//force the array to be of double type (in case it was int)
boost::python::tuple shape( a.attr("shape") );
if( boost::python::len( shape ) != 2 ) throw std::invalid_argument( "numeric::array must have 2 dimensions" );
size_t nx,ny,i,j;
nx=(size_t)(boost::python::extract< unsigned >( shape[0] ));
ny=(size_t)(boost::python::extract< unsigned >( shape[1] ));
Kernel::Matrix<double> m(nx,ny);
for( i = 0; i < nx; i++ )
{
for( j = 0; j < ny; j++ )
{
m[i][j] = boost::python::extract< double >( a[ boost::python::make_tuple( i, j ) ] );
}
}
return m;
}
else throw std::invalid_argument("Not a numpy array");
}
void _formatinput_init(void) { _import_array(); }
numpy_extractor ( PyObject * X, bool allow_copy) {
if (X==NULL) TRIQS_RUNTIME_ERROR<<"numpy interface : the python object is NULL !";
if (_import_array()!=0) TRIQS_RUNTIME_ERROR <<"Internal Error in importing numpy";
// make sure IndexMap is cuboid ?s
static const char Order = IndexMapType::index_order_type::C_or_F;
static_assert( ((Order=='F')||(Order=='C')), "Ordering must be C or Fortran");
if ((!PyArray_Check(X)) && (Order=='D'))
TRIQS_RUNTIME_ERROR<<"numpy interface : the python object is not a numpy and you ask me to deduce the ordering in memory !";
const int elementsType (numpy_to_C_type<typename boost::remove_const<ValueType>::type>::arraytype);
int rank = IndexMapType::rank;
static const char * error_msg = " A deep copy of the object would be necessary while views are supposed to guarantee to present a *view* of the python data.\n";
if (!allow_copy) {
// in case of a view, we decide ourselves if we can do it...
// a previous uses PyArray_FromAny, but behaviour changes between different version of numpy, so it is not portable...
if (!PyArray_Check(X))
throw copy_exception () << error_msg<<" Indeed the object was not even an array !\n";
if ( elementsType != PyArray_TYPE((PyArrayObject*)X))
throw copy_exception () << error_msg<<" The deep copy is caused by a type mismatch of the elements. \n";
PyArrayObject *arr = (PyArrayObject *)X;
if ( arr->nd != rank)
throw copy_exception () << error_msg<<" Rank mismatch . numpy array is of rank "<< arr->nd << "while you ask for rank "<< rank<<". \n";
if ((Order == 'C') && (PyArray_ISFORTRAN(arr)))
throw copy_exception () << error_msg<<" The numpy is in Fortran order while it is expected in C order. \n";
if ((Order == 'F') && (!PyArray_ISFORTRAN(arr)))
throw copy_exception () << error_msg<<" The numpy is not in Fortran order as it is expected. \n";
numpy_obj = X; Py_INCREF(X);
}
else {
// From X, we ask the numpy library to make a numpy, and of the correct type.
// This handles automatically the cases where :
// - we have list, or list of list/tuple
// - the numpy type is not the one we want.
// - adjust the dimension if needed
// If X is an array :
// - if Order is same, don't change it
// - else impose it (may provoque a copy).
// if X is not array :
// - Order = FortranOrder or SameOrder - > Fortran order otherwise C
bool ForceCast = false;// Unless FORCECAST is present in flags, this call will generate an error if the data type cannot be safely obtained from the object.
int flags = (ForceCast ? NPY_FORCECAST : 0) ;// do NOT force a copy | (make_copy ? NPY_ENSURECOPY : 0);
if (!(PyArray_Check(X) && (Order=='D'))) flags |= (Order =='F' ? NPY_F_CONTIGUOUS : NPY_C_CONTIGUOUS); //impose mem order
//if (!(PyArray_Check(X) && (Order=='D'))) flags |= (Order =='F' ? NPY_FARRAY : NPY_CARRAY); //impose mem order
numpy_obj= PyArray_FromAny(X,PyArray_DescrFromType(elementsType), rank,rank, flags , NULL );
// do several checks
if (!numpy_obj) {// The convertion of X to a numpy has failed !
if (PyErr_Occurred()) {PyErr_Print();PyErr_Clear();}
TRIQS_RUNTIME_ERROR<<"numpy interface : the python object is not convertible to a numpy. ";
}
assert (PyArray_Check(numpy_obj)); assert((numpy_obj->ob_refcnt==1) || ((numpy_obj ==X)));
arr_obj = (PyArrayObject *)numpy_obj;
try {
if (arr_obj->nd!=rank) TRIQS_RUNTIME_ERROR<<"numpy interface : internal error : dimensions do not match";
if (arr_obj->descr->type_num != elementsType)
TRIQS_RUNTIME_ERROR<<"numpy interface : internal error : incorrect type of element :" <<arr_obj->descr->type_num <<" vs "<<elementsType;
if (Order == 'F') { if (!PyArray_ISFORTRAN(numpy_obj)) TRIQS_RUNTIME_ERROR<<"numpy interface : internal error : should be Fortran array";}
else {if (!PyArray_ISCONTIGUOUS(numpy_obj)) TRIQS_RUNTIME_ERROR<<"numpy interface : internal error : should be contiguous";}
}
catch(...) { Py_DECREF(numpy_obj); throw;} // make sure that in case of problem, the reference counting of python is still ok...
}
arr_obj = (PyArrayObject *)numpy_obj;
}
PyMODINIT_FUNC
initflow_mod(void)
{
_import_array();
Py_InitModule3("flow_mod", flow_mod_methods, flow_mod_doc);
}
PyMODINIT_FUNC init5numpy4core5umath(void)
#endif
{
PyObject *m, *d, *s, *s2, *c_api;
int UFUNC_FLOATING_POINT_SUPPORT = 1;
#ifdef NO_UFUNC_FLOATING_POINT_SUPPORT
UFUNC_FLOATING_POINT_SUPPORT = 0;
#endif
/* Create the module and add the functions */
#if defined(NPY_PY3K)
m = PyModule_Create(&moduledef);
#else
m = Py_InitModule("numpy.core.umath", methods);
#endif
if (!m) {
return RETVAL;
}
/* Import the array */
if (_import_array() < 0) {
if (!PyErr_Occurred()) {
PyErr_SetString(PyExc_ImportError,
"umath failed: Could not import array core.");
}
return RETVAL;
}
/* Initialize the types */
if (PyType_Ready(&PyUFunc_Type) < 0)
return RETVAL;
/* Add some symbolic constants to the module */
d = PyModule_GetDict(m);
c_api = NpyCapsule_FromVoidPtr((void *)PyUFunc_API, NULL);
if (PyErr_Occurred()) {
goto err;
}
PyDict_SetItemString(d, "_UFUNC_API", c_api);
Py_DECREF(c_api);
if (PyErr_Occurred()) {
goto err;
}
s = PyString_FromString("0.4.0");
PyDict_SetItemString(d, "__version__", s);
Py_DECREF(s);
/* Load the ufunc operators into the array module's namespace */
InitOperators(d);
PyDict_SetItemString(d, "pi", s = PyFloat_FromDouble(NPY_PI));
Py_DECREF(s);
PyDict_SetItemString(d, "e", s = PyFloat_FromDouble(NPY_E));
Py_DECREF(s);
PyDict_SetItemString(d, "euler_gamma", s = PyFloat_FromDouble(NPY_EULER));
Py_DECREF(s);
#define ADDCONST(str) PyModule_AddIntConstant(m, #str, UFUNC_##str)
#define ADDSCONST(str) PyModule_AddStringConstant(m, "UFUNC_" #str, UFUNC_##str)
ADDCONST(ERR_IGNORE);
ADDCONST(ERR_WARN);
ADDCONST(ERR_CALL);
ADDCONST(ERR_RAISE);
ADDCONST(ERR_PRINT);
ADDCONST(ERR_LOG);
ADDCONST(ERR_DEFAULT);
ADDCONST(SHIFT_DIVIDEBYZERO);
ADDCONST(SHIFT_OVERFLOW);
ADDCONST(SHIFT_UNDERFLOW);
ADDCONST(SHIFT_INVALID);
ADDCONST(FPE_DIVIDEBYZERO);
ADDCONST(FPE_OVERFLOW);
ADDCONST(FPE_UNDERFLOW);
ADDCONST(FPE_INVALID);
ADDCONST(FLOATING_POINT_SUPPORT);
ADDSCONST(PYVALS_NAME);
#undef ADDCONST
#undef ADDSCONST
PyModule_AddIntConstant(m, "UFUNC_BUFSIZE_DEFAULT", (long)NPY_BUFSIZE);
PyModule_AddObject(m, "PINF", PyFloat_FromDouble(NPY_INFINITY));
PyModule_AddObject(m, "NINF", PyFloat_FromDouble(-NPY_INFINITY));
PyModule_AddObject(m, "PZERO", PyFloat_FromDouble(NPY_PZERO));
PyModule_AddObject(m, "NZERO", PyFloat_FromDouble(NPY_NZERO));
PyModule_AddObject(m, "NAN", PyFloat_FromDouble(NPY_NAN));
#if defined(NPY_PY3K)
s = PyDict_GetItemString(d, "true_divide");
PyDict_SetItemString(d, "divide", s);
#endif
s = PyDict_GetItemString(d, "conjugate");
s2 = PyDict_GetItemString(d, "remainder");
/* Setup the array object's numerical structures with appropriate
ufuncs in d*/
PyArray_SetNumericOps(d);
PyDict_SetItemString(d, "conj", s);
PyDict_SetItemString(d, "mod", s2);
initscalarmath(m);
if (!intern_strings()) {
goto err;
}
return RETVAL;
err:
/* Check for errors */
if (!PyErr_Occurred()) {
PyErr_SetString(PyExc_RuntimeError,
"cannot load umath module.");
}
return RETVAL;
}
static PyObject *
initaubio (void)
{
PyObject *m = NULL;
int err;
// fvec is defined in __init__.py
if ( (PyType_Ready (&Py_cvecType) < 0)
|| (PyType_Ready (&Py_filterType) < 0)
|| (PyType_Ready (&Py_filterbankType) < 0)
|| (PyType_Ready (&Py_fftType) < 0)
|| (PyType_Ready (&Py_pvocType) < 0)
|| (PyType_Ready (&Py_sourceType) < 0)
|| (PyType_Ready (&Py_sinkType) < 0)
// generated objects
|| (generated_types_ready() < 0 )
) {
return m;
}
#if PY_MAJOR_VERSION >= 3
m = PyModule_Create(&moduledef);
#else
m = Py_InitModule3 ("_aubio", aubio_methods, aubio_module_doc);
#endif
if (m == NULL) {
return m;
}
err = _import_array ();
if (err != 0) {
fprintf (stderr,
"Unable to import Numpy array from aubio module (error %d)\n", err);
}
Py_INCREF (&Py_cvecType);
PyModule_AddObject (m, "cvec", (PyObject *) & Py_cvecType);
Py_INCREF (&Py_filterType);
PyModule_AddObject (m, "digital_filter", (PyObject *) & Py_filterType);
Py_INCREF (&Py_filterbankType);
PyModule_AddObject (m, "filterbank", (PyObject *) & Py_filterbankType);
Py_INCREF (&Py_fftType);
PyModule_AddObject (m, "fft", (PyObject *) & Py_fftType);
Py_INCREF (&Py_pvocType);
PyModule_AddObject (m, "pvoc", (PyObject *) & Py_pvocType);
Py_INCREF (&Py_sourceType);
PyModule_AddObject (m, "source", (PyObject *) & Py_sourceType);
Py_INCREF (&Py_sinkType);
PyModule_AddObject (m, "sink", (PyObject *) & Py_sinkType);
PyModule_AddStringConstant(m, "float_type", AUBIO_NPY_SMPL_STR);
PyModule_AddStringConstant(m, "__version__", DEFINEDSTRING(AUBIO_VERSION));
// add generated objects
add_generated_objects(m);
// add ufunc
add_ufuncs(m);
aubio_log_set_level_function(AUBIO_LOG_ERR, aubio_log_function, NULL);
aubio_log_set_level_function(AUBIO_LOG_WRN, aubio_log_function, NULL);
return m;
}
PyObject* ICoinPackedMatrix::np_getVectorStarts(){
npy_intp dims = this->getMajorDim() + 1;
_import_array();
PyObject *Arr = PyArray_SimpleNewFromData( 1, &dims, PyArray_INT32, this->IgetVectorStarts() );
return Arr;
}
// return a NEW (owned) reference
//
inline PyObject * numpy_extractor_impl ( PyObject * X, bool allow_copy, std::string type_name,
int elementsType, int rank, size_t * lengths, std::ptrdiff_t * strides, size_t size_of_ValueType) {
PyObject * numpy_obj;
if (X==NULL) TRIQS_RUNTIME_ERROR<<"numpy interface : the python object is NULL !";
if (_import_array()!=0) TRIQS_RUNTIME_ERROR <<"Internal Error in importing numpy";
static const char * error_msg = " A deep copy of the object would be necessary while views are supposed to guarantee to present a *view* of the python data.\n";
if (!allow_copy) {
if (!PyArray_Check(X)) throw copy_exception () << error_msg<<" Indeed the object was not even an array !\n";
if ( elementsType != PyArray_TYPE((PyArrayObject*)X))
throw copy_exception () << error_msg<<" The deep copy is caused by a type mismatch of the elements. Expected "<< type_name<< " and found XXX \n";
PyArrayObject *arr = (PyArrayObject *)X;
#ifdef TRIQS_NUMPY_VERSION_LT_17
if ( arr->nd != rank) throw copy_exception () << error_msg<<" Rank mismatch . numpy array is of rank "<< arr->nd << "while you ask for rank "<< rank<<". \n";
#else
if ( PyArray_NDIM(arr) != rank) throw copy_exception () << error_msg<<" Rank mismatch . numpy array is of rank "<< PyArray_NDIM(arr) << "while you ask for rank "<< rank<<". \n";
#endif
numpy_obj = X; Py_INCREF(X);
}
else {
// From X, we ask the numpy library to make a numpy, and of the correct type.
// This handles automatically the cases where :
// - we have list, or list of list/tuple
// - the numpy type is not the one we want.
// - adjust the dimension if needed
// If X is an array :
// - if Order is same, don't change it
// - else impose it (may provoque a copy).
// if X is not array :
// - Order = FortranOrder or SameOrder - > Fortran order otherwise C
//bool ForceCast = false;// Unless FORCECAST is present in flags, this call will generate an error if the data type cannot be safely obtained from the object.
int flags = 0; //(ForceCast ? NPY_FORCECAST : 0) ;// do NOT force a copy | (make_copy ? NPY_ENSURECOPY : 0);
if (!(PyArray_Check(X) ))
//flags |= ( IndexMapType::traversal_order == indexmaps::mem_layout::c_order(rank) ? NPY_C_CONTIGUOUS : NPY_F_CONTIGUOUS); //impose mem order
#ifdef TRIQS_NUMPY_VERSION_LT_17
flags |= (NPY_C_CONTIGUOUS); //impose mem order
#else
flags |= (NPY_ARRAY_C_CONTIGUOUS); //impose mem order
#endif
numpy_obj= PyArray_FromAny(X,PyArray_DescrFromType(elementsType), rank,rank, flags , NULL );
// do several checks
if (!numpy_obj) {// The convertion of X to a numpy has failed !
if (PyErr_Occurred()) {PyErr_Print();PyErr_Clear();}
TRIQS_RUNTIME_ERROR<<"numpy interface : the python object is not convertible to a numpy. ";
}
assert (PyArray_Check(numpy_obj)); assert((numpy_obj->ob_refcnt==1) || ((numpy_obj ==X)));
PyArrayObject *arr_obj;
arr_obj = (PyArrayObject *)numpy_obj;
try {
#ifdef TRIQS_NUMPY_VERSION_LT_17
if (arr_obj->nd!=rank) TRIQS_RUNTIME_ERROR<<"numpy interface : internal error : dimensions do not match";
if (arr_obj->descr->type_num != elementsType)
TRIQS_RUNTIME_ERROR<<"numpy interface : internal error : incorrect type of element :" <<arr_obj->descr->type_num <<" vs "<<elementsType;
#else
if ( PyArray_NDIM(arr_obj) !=rank) TRIQS_RUNTIME_ERROR<<"numpy interface : internal error : dimensions do not match";
if ( PyArray_DESCR(arr_obj)->type_num != elementsType)
TRIQS_RUNTIME_ERROR<<"numpy interface : internal error : incorrect type of element :" <<PyArray_DESCR(arr_obj)->type_num <<" vs "<<elementsType;
#endif
}
catch(...) { Py_DECREF(numpy_obj); throw;} // make sure that in case of problem, the reference counting of python is still ok...
}
// extract strides and lengths
PyArrayObject *arr_obj;
arr_obj = (PyArrayObject *)numpy_obj;
#ifdef TRIQS_NUMPY_VERSION_LT_17
const size_t dim =arr_obj->nd; // we know that dim == rank
for (size_t i=0; i< dim ; ++i) {
lengths[i] = size_t(arr_obj-> dimensions[i]);
strides[i] = std::ptrdiff_t(arr_obj-> strides[i])/ size_of_ValueType;
}
#else
const size_t dim = PyArray_NDIM(arr_obj); // we know that dim == rank
for (size_t i=0; i< dim ; ++i) {
lengths[i] = size_t( PyArray_DIMS(arr_obj)[i]);
strides[i] = std::ptrdiff_t( PyArray_STRIDES(arr_obj)[i])/ size_of_ValueType;
}
#endif
return numpy_obj;
}