/*NUMPY_API
* Update Several Flags at once.
*/
NPY_NO_EXPORT void
PyArray_UpdateFlags(PyArrayObject *ret, int flagmask)
{
/* Always update both, as its not trivial to guess one from the other */
if (flagmask & (NPY_ARRAY_F_CONTIGUOUS | NPY_ARRAY_C_CONTIGUOUS)) {
_UpdateContiguousFlags(ret);
}
if (flagmask & NPY_ARRAY_ALIGNED) {
if (_IsAligned(ret)) {
PyArray_ENABLEFLAGS(ret, NPY_ARRAY_ALIGNED);
}
else {
PyArray_CLEARFLAGS(ret, NPY_ARRAY_ALIGNED);
}
}
/*
* This is not checked by default WRITEABLE is not
* part of UPDATE_ALL
*/
if (flagmask & NPY_ARRAY_WRITEABLE) {
if (_IsWriteable(ret)) {
PyArray_ENABLEFLAGS(ret, NPY_ARRAY_WRITEABLE);
}
else {
PyArray_CLEARFLAGS(ret, NPY_ARRAY_WRITEABLE);
}
}
return;
}
/*NUMPY_API
* Update Several Flags at once.
*/
NPY_NO_EXPORT void
PyArray_UpdateFlags(PyArrayObject *ret, int flagmask)
{
if (flagmask & NPY_ARRAY_F_CONTIGUOUS) {
if (_IsFortranContiguous(ret)) {
PyArray_ENABLEFLAGS(ret, NPY_ARRAY_F_CONTIGUOUS);
if (PyArray_NDIM(ret) > 1) {
PyArray_CLEARFLAGS(ret, NPY_ARRAY_C_CONTIGUOUS);
}
}
else {
PyArray_CLEARFLAGS(ret, NPY_ARRAY_F_CONTIGUOUS);
}
}
if (flagmask & NPY_ARRAY_C_CONTIGUOUS) {
if (_IsContiguous(ret)) {
PyArray_ENABLEFLAGS(ret, NPY_ARRAY_C_CONTIGUOUS);
if (PyArray_NDIM(ret) > 1) {
PyArray_CLEARFLAGS(ret, NPY_ARRAY_F_CONTIGUOUS);
}
}
else {
PyArray_CLEARFLAGS(ret, NPY_ARRAY_C_CONTIGUOUS);
}
}
if (flagmask & NPY_ARRAY_ALIGNED) {
if (_IsAligned(ret)) {
PyArray_ENABLEFLAGS(ret, NPY_ARRAY_ALIGNED);
}
else {
PyArray_CLEARFLAGS(ret, NPY_ARRAY_ALIGNED);
}
}
/*
* This is not checked by default WRITEABLE is not
* part of UPDATE_ALL
*/
if (flagmask & NPY_ARRAY_WRITEABLE) {
if (_IsWriteable(ret)) {
PyArray_ENABLEFLAGS(ret, NPY_ARRAY_WRITEABLE);
}
else {
PyArray_CLEARFLAGS(ret, NPY_ARRAY_WRITEABLE);
}
}
return;
}
NRT_adapt_ndarray_to_python(arystruct_t* arystruct, int ndim,
int writeable, PyArray_Descr *descr)
{
PyArrayObject *array;
MemInfoObject *miobj = NULL;
PyObject *args;
npy_intp *shape, *strides;
int flags = 0;
if (!PyArray_DescrCheck(descr)) {
PyErr_Format(PyExc_TypeError,
"expected dtype object, got '%.200s'",
Py_TYPE(descr)->tp_name);
return NULL;
}
if (arystruct->parent) {
PyObject *obj = try_to_return_parent(arystruct, ndim, descr);
if (obj) {
/* Release NRT reference to the numpy array */
NRT_MemInfo_release(arystruct->meminfo);
return obj;
}
}
if (arystruct->meminfo) {
/* wrap into MemInfoObject */
miobj = PyObject_New(MemInfoObject, &MemInfoType);
args = PyTuple_New(1);
/* SETITEM steals reference */
PyTuple_SET_ITEM(args, 0, PyLong_FromVoidPtr(arystruct->meminfo));
/* Note: MemInfo_init() does not incref. This function steals the
* NRT reference.
*/
if (MemInfo_init(miobj, args, NULL)) {
return NULL;
}
Py_DECREF(args);
}
shape = arystruct->shape_and_strides;
strides = shape + ndim;
Py_INCREF((PyObject *) descr);
array = (PyArrayObject *) PyArray_NewFromDescr(&PyArray_Type, descr, ndim,
shape, strides, arystruct->data,
flags, (PyObject *) miobj);
if (array == NULL)
return NULL;
/* Set writable */
#if NPY_API_VERSION >= 0x00000007
if (writeable) {
PyArray_ENABLEFLAGS(array, NPY_ARRAY_WRITEABLE);
}
else {
PyArray_CLEARFLAGS(array, NPY_ARRAY_WRITEABLE);
}
#else
if (writeable) {
array->flags |= NPY_WRITEABLE;
}
else {
array->flags &= ~NPY_WRITEABLE;
}
#endif
if (miobj) {
/* Set the MemInfoObject as the base object */
#if NPY_API_VERSION >= 0x00000007
if (-1 == PyArray_SetBaseObject(array,
(PyObject *) miobj))
{
Py_DECREF(array);
Py_DECREF(miobj);
return NULL;
}
#else
PyArray_BASE(array) = (PyObject *) miobj;
#endif
}
return (PyObject *) array;
}
/*
* Check whether the given array is stored contiguously
* in memory. And update the passed in ap flags appropriately.
*
* The traditional rule is that for an array to be flagged as C contiguous,
* the following must hold:
*
* strides[-1] == itemsize
* strides[i] == shape[i+1] * strides[i + 1]
*
* And for an array to be flagged as F contiguous, the obvious reversal:
*
* strides[0] == itemsize
* strides[i] == shape[i - 1] * strides[i - 1]
*
* According to these rules, a 0- or 1-dimensional array is either both
* C- and F-contiguous, or neither; and an array with 2+ dimensions
* can be C- or F- contiguous, or neither, but not both. Though there
* there are exceptions for arrays with zero or one item, in the first
* case the check is relaxed up to and including the first dimension
* with shape[i] == 0. In the second case `strides == itemsize` will
* can be true for all dimensions and both flags are set.
*
* When NPY_RELAXED_STRIDES_CHECKING is set, we use a more accurate
* definition of C- and F-contiguity, in which all 0-sized arrays are
* contiguous (regardless of dimensionality), and if shape[i] == 1
* then we ignore strides[i] (since it has no affect on memory layout).
* With these new rules, it is possible for e.g. a 10x1 array to be both
* C- and F-contiguous -- but, they break downstream code which assumes
* that for contiguous arrays strides[-1] (resp. strides[0]) always
* contains the itemsize.
*/
static void
_UpdateContiguousFlags(PyArrayObject *ap)
{
npy_intp sd;
npy_intp dim;
int i;
npy_bool is_c_contig = 1;
sd = PyArray_ITEMSIZE(ap);
for (i = PyArray_NDIM(ap) - 1; i >= 0; --i) {
dim = PyArray_DIMS(ap)[i];
#if NPY_RELAXED_STRIDES_CHECKING
/* contiguous by definition */
if (dim == 0) {
PyArray_ENABLEFLAGS(ap, NPY_ARRAY_C_CONTIGUOUS);
PyArray_ENABLEFLAGS(ap, NPY_ARRAY_F_CONTIGUOUS);
return;
}
if (dim != 1) {
if (PyArray_STRIDES(ap)[i] != sd) {
is_c_contig = 0;
}
sd *= dim;
}
#else /* not NPY_RELAXED_STRIDES_CHECKING */
if (PyArray_STRIDES(ap)[i] != sd) {
is_c_contig = 0;
break;
}
/* contiguous, if it got this far */
if (dim == 0) {
break;
}
sd *= dim;
#endif /* not NPY_RELAXED_STRIDES_CHECKING */
}
if (is_c_contig) {
PyArray_ENABLEFLAGS(ap, NPY_ARRAY_C_CONTIGUOUS);
}
else {
PyArray_CLEARFLAGS(ap, NPY_ARRAY_C_CONTIGUOUS);
}
/* check if fortran contiguous */
sd = PyArray_ITEMSIZE(ap);
for (i = 0; i < PyArray_NDIM(ap); ++i) {
dim = PyArray_DIMS(ap)[i];
#if NPY_RELAXED_STRIDES_CHECKING
if (dim != 1) {
if (PyArray_STRIDES(ap)[i] != sd) {
PyArray_CLEARFLAGS(ap, NPY_ARRAY_F_CONTIGUOUS);
return;
}
sd *= dim;
}
#else /* not NPY_RELAXED_STRIDES_CHECKING */
if (PyArray_STRIDES(ap)[i] != sd) {
PyArray_CLEARFLAGS(ap, NPY_ARRAY_F_CONTIGUOUS);
return;
}
if (dim == 0) {
break;
}
sd *= dim;
#endif /* not NPY_RELAXED_STRIDES_CHECKING */
}
PyArray_ENABLEFLAGS(ap, NPY_ARRAY_F_CONTIGUOUS);
return;
}
static PyObject * enz_vnmpocnp(PyObject *self, PyObject *args,
PyObject *keywds)
{
static char *kwlist[] = { "r", "f", "n", "m", "L_max",
"bessel_name", "ncpus", "verb", NULL };
long syscpus = sysconf(_SC_NPROCESSORS_ONLN);
PyObject *r_o, *f_o, *n_o, *m_o;
PyObject *r = NULL;
PyObject *f = NULL;
PyObject *n = NULL;
PyObject *m = NULL;
const char *error = NULL;
npy_intp r_n, f_n, beta_n;
PyObject *vnm = NULL;
npy_intp vnm_dims[3];
double *datar = NULL;
complex double *dataf = NULL;
int *datan = NULL;
int *datam = NULL;
complex double *datap = NULL;
int kL_max = 35;
int kncpus = -1;
int kverb = 0;
char *kbessel_name = "jn";
int ret;
if (syscpus <= 0)
syscpus = 1;
if (!PyArg_ParseTupleAndKeywords(args, keywds, "O!O!O!O!|isii",
kwlist,
&PyArray_Type, &r_o,
&PyArray_Type, &f_o,
&PyArray_Type, &n_o,
&PyArray_Type, &m_o,
&kL_max, &kbessel_name, &kncpus, &kverb)) {
PyErr_SetString(PyExc_SyntaxError, "failed to parse args");
return NULL;
}
r = PyArray_FROM_OTF(r_o, NPY_FLOAT64, NPY_ARRAY_IN_ARRAY);
f = PyArray_FROM_OTF(f_o, NPY_COMPLEX128, NPY_ARRAY_IN_ARRAY);
n = PyArray_FROM_OTF(n_o, NPY_INT64, NPY_ARRAY_IN_ARRAY);
m = PyArray_FROM_OTF(m_o, NPY_INT64, NPY_ARRAY_IN_ARRAY);
if (!r) {
PyErr_SetString(PyExc_ValueError, "cannot convert r to PyArray");
return NULL;
}
if (!f) {
PyErr_SetString(PyExc_ValueError, "cannot convert f to PyArray");
return NULL;
}
if (!n) {
PyErr_SetString(PyExc_ValueError, "cannot convert n to PyArray");
return NULL;
}
if (!m) {
PyErr_SetString(PyExc_ValueError, "cannot convert m to PyArray");
return NULL;
}
if (!r || not_vect(r) || PyArray_TYPE((PyArrayObject*)r) != NPY_FLOAT64) {
error = "r is not a vector of doubles";
goto exit_decrement;
}
r_n = PyArray_DIM((PyArrayObject*)r, 0);
if (!f || not_vect(f) ||
PyArray_TYPE((PyArrayObject*)f) != NPY_COMPLEX128) {
error = "f is not a vector of complex numbers";
goto exit_decrement;
}
f_n = PyArray_DIM((PyArrayObject*)f, 0);
if (!n || not_vect(n) || PyArray_TYPE((PyArrayObject*)n) != NPY_INT64) {
error = "n is not a vector of integers";
goto exit_decrement;
}
if (!m || not_vect(m) || PyArray_TYPE((PyArrayObject*)m) != NPY_INT64) {
error = "m is not a vector of integers";
goto exit_decrement;
}
if (PyArray_DIM((PyArrayObject*)n, 0) !=
PyArray_DIM((PyArrayObject*)m, 0)) {
error = "n and m must have the same length";
goto exit_decrement;
}
beta_n = PyArray_DIM((PyArrayObject*)n, 0);
vnm_dims[0] = r_n;
vnm_dims[1] = f_n;
vnm_dims[2] = beta_n;
vnm = PyArray_New(&PyArray_Type, 3, vnm_dims, NPY_COMPLEX128, NULL, NULL,
0, NPY_ARRAY_F_CONTIGUOUS | NPY_ARRAY_ALIGNED, NULL);
if (!vnm) {
error = "cannot create vnm";
goto exit_decrement;
}
PyArray_CLEARFLAGS((PyArrayObject*)vnm, NPY_ARRAY_C_CONTIGUOUS);
assert(PyArray_Size(vnm) == (r_n * f_n * beta_n));
assert(PyArray_NBYTES(vnm) == (r_n * f_n * beta_n * sizeof(double complex)));
datap = PyArray_DATA((PyArrayObject*)vnm);
if (kncpus < 0)
kncpus = beta_n < syscpus ? beta_n : syscpus;
else
kncpus = kncpus > syscpus ? syscpus : kncpus;
if ((r_n * f_n * beta_n) != 0) {
datar = PyArray_DATA((PyArrayObject*)r);
dataf = PyArray_DATA((PyArrayObject*)f);
datan = PyArray_DATA((PyArrayObject*)n);
datam = PyArray_DATA((PyArrayObject*)m);
Py_BEGIN_ALLOW_THREADS
ret = vnmpocnp(r_n, datar,
f_n, dataf,
beta_n,
datan, datam,
kL_max, kbessel_name,
kncpus,
kverb, datap, &error);
Py_END_ALLOW_THREADS
if (ret) {
Py_XDECREF(vnm);
goto exit_decrement;
}
}