/*
* Assigns the scalar value to every element of the destination raw array.
*
* Returns 0 on success, -1 on failure.
*/
NPY_NO_EXPORT int
raw_array_assign_scalar(int ndim, npy_intp *shape,
PyArray_Descr *dst_dtype, char *dst_data, npy_intp *dst_strides,
PyArray_Descr *src_dtype, char *src_data)
{
int idim;
npy_intp shape_it[NPY_MAXDIMS], dst_strides_it[NPY_MAXDIMS];
npy_intp coord[NPY_MAXDIMS];
PyArray_StridedUnaryOp *stransfer = NULL;
NpyAuxData *transferdata = NULL;
int aligned, needs_api = 0;
npy_intp src_itemsize = src_dtype->elsize;
NPY_BEGIN_THREADS_DEF;
/* Check alignment */
aligned = raw_array_is_aligned(ndim, dst_data, dst_strides,
dst_dtype->alignment);
if (!npy_is_aligned(src_data, src_dtype->alignment)) {
aligned = 0;
}
/* Use raw iteration with no heap allocation */
if (PyArray_PrepareOneRawArrayIter(
ndim, shape,
dst_data, dst_strides,
&ndim, shape_it,
&dst_data, dst_strides_it) < 0) {
return -1;
}
/* Get the function to do the casting */
if (PyArray_GetDTypeTransferFunction(aligned,
0, dst_strides_it[0],
src_dtype, dst_dtype,
0,
&stransfer, &transferdata,
&needs_api) != NPY_SUCCEED) {
return -1;
}
if (!needs_api) {
npy_intp nitems = 1, i;
for (i = 0; i < ndim; i++) {
nitems *= shape_it[i];
}
NPY_BEGIN_THREADS_THRESHOLDED(nitems);
}
NPY_RAW_ITER_START(idim, ndim, coord, shape_it) {
/* Process the innermost dimension */
stransfer(dst_data, dst_strides_it[0], src_data, 0,
shape_it[0], src_itemsize, transferdata);
} NPY_RAW_ITER_ONE_NEXT(idim, ndim, coord,
NPY_NO_EXPORT PyObject *
arr_interp(PyObject *NPY_UNUSED(self), PyObject *args, PyObject *kwdict)
{
PyObject *fp, *xp, *x;
PyObject *left = NULL, *right = NULL;
PyArrayObject *afp = NULL, *axp = NULL, *ax = NULL, *af = NULL;
npy_intp i, lenx, lenxp;
npy_double lval, rval;
const npy_double *dy, *dx, *dz;
npy_double *dres, *slopes = NULL;
static char *kwlist[] = {"x", "xp", "fp", "left", "right", NULL};
NPY_BEGIN_THREADS_DEF;
if (!PyArg_ParseTupleAndKeywords(args, kwdict, "OOO|OO", kwlist,
&x, &xp, &fp, &left, &right)) {
return NULL;
}
afp = (PyArrayObject *)PyArray_ContiguousFromAny(fp, NPY_DOUBLE, 1, 1);
if (afp == NULL) {
return NULL;
}
axp = (PyArrayObject *)PyArray_ContiguousFromAny(xp, NPY_DOUBLE, 1, 1);
if (axp == NULL) {
goto fail;
}
ax = (PyArrayObject *)PyArray_ContiguousFromAny(x, NPY_DOUBLE, 1, 0);
if (ax == NULL) {
goto fail;
}
lenxp = PyArray_SIZE(axp);
if (lenxp == 0) {
PyErr_SetString(PyExc_ValueError,
"array of sample points is empty");
goto fail;
}
if (PyArray_SIZE(afp) != lenxp) {
PyErr_SetString(PyExc_ValueError,
"fp and xp are not of the same length.");
goto fail;
}
af = (PyArrayObject *)PyArray_SimpleNew(PyArray_NDIM(ax),
PyArray_DIMS(ax), NPY_DOUBLE);
if (af == NULL) {
goto fail;
}
lenx = PyArray_SIZE(ax);
dy = (const npy_double *)PyArray_DATA(afp);
dx = (const npy_double *)PyArray_DATA(axp);
dz = (const npy_double *)PyArray_DATA(ax);
dres = (npy_double *)PyArray_DATA(af);
/* Get left and right fill values. */
if ((left == NULL) || (left == Py_None)) {
lval = dy[0];
}
else {
lval = PyFloat_AsDouble(left);
if ((lval == -1) && PyErr_Occurred()) {
goto fail;
}
}
if ((right == NULL) || (right == Py_None)) {
rval = dy[lenxp - 1];
}
else {
rval = PyFloat_AsDouble(right);
if ((rval == -1) && PyErr_Occurred()) {
goto fail;
}
}
/* binary_search_with_guess needs at least a 3 item long array */
if (lenxp == 1) {
const npy_double xp_val = dx[0];
const npy_double fp_val = dy[0];
NPY_BEGIN_THREADS_THRESHOLDED(lenx);
for (i = 0; i < lenx; ++i) {
const npy_double x_val = dz[i];
dres[i] = (x_val < xp_val) ? lval :
((x_val > xp_val) ? rval : fp_val);
}
NPY_END_THREADS;
}
else {
npy_intp j = 0;
/* only pre-calculate slopes if there are relatively few of them. */
if (lenxp <= lenx) {
slopes = PyArray_malloc((lenxp - 1) * sizeof(npy_double));
if (slopes == NULL) {
goto fail;
}
}
NPY_BEGIN_THREADS;
if (slopes != NULL) {
for (i = 0; i < lenxp - 1; ++i) {
slopes[i] = (dy[i+1] - dy[i]) / (dx[i+1] - dx[i]);
}
}
for (i = 0; i < lenx; ++i) {
const npy_double x_val = dz[i];
if (npy_isnan(x_val)) {
dres[i] = x_val;
continue;
}
j = binary_search_with_guess(x_val, dx, lenxp, j);
if (j == -1) {
dres[i] = lval;
}
else if (j == lenxp) {
dres[i] = rval;
}
else if (j == lenxp - 1) {
dres[i] = dy[j];
}
else {
const npy_double slope = (slopes != NULL) ? slopes[j] :
(dy[j+1] - dy[j]) / (dx[j+1] - dx[j]);
dres[i] = slope*(x_val - dx[j]) + dy[j];
}
}
NPY_END_THREADS;
}
PyArray_free(slopes);
Py_DECREF(afp);
Py_DECREF(axp);
Py_DECREF(ax);
return (PyObject *)af;
fail:
Py_XDECREF(afp);
Py_XDECREF(axp);
Py_XDECREF(ax);
Py_XDECREF(af);
return NULL;
}
/*
* digitize(x, bins, right=False) returns an array of integers the same length
* as x. The values i returned are such that bins[i - 1] <= x < bins[i] if
* bins is monotonically increasing, or bins[i - 1] > x >= bins[i] if bins
* is monotonically decreasing. Beyond the bounds of bins, returns either
* i = 0 or i = len(bins) as appropriate. If right == True the comparison
* is bins [i - 1] < x <= bins[i] or bins [i - 1] >= x > bins[i]
*/
NPY_NO_EXPORT PyObject *
arr_digitize(PyObject *NPY_UNUSED(self), PyObject *args, PyObject *kwds)
{
PyObject *obj_x = NULL;
PyObject *obj_bins = NULL;
PyArrayObject *arr_x = NULL;
PyArrayObject *arr_bins = NULL;
PyObject *ret = NULL;
npy_intp len_bins;
int monotonic, right = 0;
NPY_BEGIN_THREADS_DEF
static char *kwlist[] = {"x", "bins", "right", NULL};
if (!PyArg_ParseTupleAndKeywords(args, kwds, "OO|i", kwlist,
&obj_x, &obj_bins, &right)) {
goto fail;
}
/* PyArray_SearchSorted will make `x` contiguous even if we don't */
arr_x = (PyArrayObject *)PyArray_FROMANY(obj_x, NPY_DOUBLE, 0, 0,
NPY_ARRAY_CARRAY_RO);
if (arr_x == NULL) {
goto fail;
}
/* TODO: `bins` could be strided, needs change to check_array_monotonic */
arr_bins = (PyArrayObject *)PyArray_FROMANY(obj_bins, NPY_DOUBLE, 1, 1,
NPY_ARRAY_CARRAY_RO);
if (arr_bins == NULL) {
goto fail;
}
len_bins = PyArray_SIZE(arr_bins);
if (len_bins == 0) {
PyErr_SetString(PyExc_ValueError, "bins must have non-zero length");
goto fail;
}
NPY_BEGIN_THREADS_THRESHOLDED(len_bins)
monotonic = check_array_monotonic((const double *)PyArray_DATA(arr_bins),
len_bins);
NPY_END_THREADS
if (monotonic == 0) {
PyErr_SetString(PyExc_ValueError,
"bins must be monotonically increasing or decreasing");
goto fail;
}
/* PyArray_SearchSorted needs an increasing array */
if (monotonic == - 1) {
PyArrayObject *arr_tmp = NULL;
npy_intp shape = PyArray_DIM(arr_bins, 0);
npy_intp stride = -PyArray_STRIDE(arr_bins, 0);
void *data = (void *)(PyArray_BYTES(arr_bins) - stride * (shape - 1));
arr_tmp = (PyArrayObject *)PyArray_New(&PyArray_Type, 1, &shape,
NPY_DOUBLE, &stride, data, 0,
PyArray_FLAGS(arr_bins), NULL);
if (!arr_tmp) {
goto fail;
}
if (PyArray_SetBaseObject(arr_tmp, (PyObject *)arr_bins) < 0) {
Py_DECREF(arr_tmp);
goto fail;
}
arr_bins = arr_tmp;
}
ret = PyArray_SearchSorted(arr_bins, (PyObject *)arr_x,
right ? NPY_SEARCHLEFT : NPY_SEARCHRIGHT, NULL);
if (!ret) {
goto fail;
}
/* If bins is decreasing, ret has bins from end, not start */
if (monotonic == -1) {
npy_intp *ret_data =
(npy_intp *)PyArray_DATA((PyArrayObject *)ret);
npy_intp len_ret = PyArray_SIZE((PyArrayObject *)ret);
NPY_BEGIN_THREADS_THRESHOLDED(len_ret)
while (len_ret--) {
*ret_data = len_bins - *ret_data;
ret_data++;
}
NPY_END_THREADS
}
static PyObject *
PyUFunc_Accumulate(PyUFuncObject *ufunc, PyArrayObject *arr, PyArrayObject *out,
int axis, int otype)
{
PyArrayObject *op[2];
PyArray_Descr *op_dtypes[2] = {NULL, NULL};
int op_axes_arrays[2][NPY_MAXDIMS];
int *op_axes[2] = {op_axes_arrays[0], op_axes_arrays[1]};
npy_uint32 op_flags[2];
int idim, ndim, otype_final;
int needs_api, need_outer_iterator;
NpyIter *iter = NULL, *iter_inner = NULL;
/* The selected inner loop */
PyUFuncGenericFunction innerloop = NULL;
void *innerloopdata = NULL;
const char *ufunc_name = ufunc->name ? ufunc->name : "(unknown)";
/* These parameters come from extobj= or from a TLS global */
int buffersize = 0, errormask = 0;
NPY_BEGIN_THREADS_DEF;
NPY_UF_DBG_PRINT1("\nEvaluating ufunc %s.accumulate\n", ufunc_name);
#if 0
printf("Doing %s.accumulate on array with dtype : ", ufunc_name);
PyObject_Print((PyObject *)PyArray_DESCR(arr), stdout, 0);
printf("\n");
#endif
if (_get_bufsize_errmask(NULL, "accumulate", &buffersize, &errormask) < 0) {
return NULL;
}
/* Take a reference to out for later returning */
Py_XINCREF(out);
otype_final = otype;
if (get_binary_op_function(ufunc, &otype_final,
&innerloop, &innerloopdata) < 0) {
PyArray_Descr *dtype = PyArray_DescrFromType(otype);
PyErr_Format(PyExc_ValueError,
"could not find a matching type for %s.accumulate, "
"requested type has type code '%c'",
ufunc_name, dtype ? dtype->type : '-');
Py_XDECREF(dtype);
goto fail;
}
ndim = PyArray_NDIM(arr);
/*
* Set up the output data type, using the input's exact
* data type if the type number didn't change to preserve
* metadata
*/
if (PyArray_DESCR(arr)->type_num == otype_final) {
if (PyArray_ISNBO(PyArray_DESCR(arr)->byteorder)) {
op_dtypes[0] = PyArray_DESCR(arr);
Py_INCREF(op_dtypes[0]);
}
else {
op_dtypes[0] = PyArray_DescrNewByteorder(PyArray_DESCR(arr),
NPY_NATIVE);
}
}
else {
op_dtypes[0] = PyArray_DescrFromType(otype_final);
}
if (op_dtypes[0] == NULL) {
goto fail;
}
#if NPY_UF_DBG_TRACING
printf("Found %s.accumulate inner loop with dtype : ", ufunc_name);
PyObject_Print((PyObject *)op_dtypes[0], stdout, 0);
printf("\n");
#endif
/* Set up the op_axes for the outer loop */
for (idim = 0; idim < ndim; ++idim) {
op_axes_arrays[0][idim] = idim;
op_axes_arrays[1][idim] = idim;
}
/* The per-operand flags for the outer loop */
op_flags[0] = NPY_ITER_READWRITE |
NPY_ITER_NO_BROADCAST |
NPY_ITER_ALLOCATE |
NPY_ITER_NO_SUBTYPE;
op_flags[1] = NPY_ITER_READONLY;
op[0] = out;
op[1] = arr;
need_outer_iterator = (ndim > 1);
/* We can't buffer, so must do UPDATEIFCOPY */
if (!PyArray_ISALIGNED(arr) || (out && !PyArray_ISALIGNED(out)) ||
!PyArray_EquivTypes(op_dtypes[0], PyArray_DESCR(arr)) ||
(out &&
!PyArray_EquivTypes(op_dtypes[0], PyArray_DESCR(out)))) {
need_outer_iterator = 1;
}
if (need_outer_iterator) {
int ndim_iter = 0;
npy_uint32 flags = NPY_ITER_ZEROSIZE_OK|
NPY_ITER_REFS_OK;
PyArray_Descr **op_dtypes_param = NULL;
/*
* The way accumulate is set up, we can't do buffering,
* so make a copy instead when necessary.
*/
ndim_iter = ndim;
flags |= NPY_ITER_MULTI_INDEX;
/* Add some more flags */
op_flags[0] |= NPY_ITER_UPDATEIFCOPY|NPY_ITER_ALIGNED;
op_flags[1] |= NPY_ITER_COPY|NPY_ITER_ALIGNED;
op_dtypes_param = op_dtypes;
op_dtypes[1] = op_dtypes[0];
NPY_UF_DBG_PRINT("Allocating outer iterator\n");
iter = NpyIter_AdvancedNew(2, op, flags,
NPY_KEEPORDER, NPY_UNSAFE_CASTING,
op_flags,
op_dtypes_param,
ndim_iter, op_axes, NULL, 0);
if (iter == NULL) {
goto fail;
}
/* In case COPY or UPDATEIFCOPY occurred */
op[0] = NpyIter_GetOperandArray(iter)[0];
op[1] = NpyIter_GetOperandArray(iter)[1];
if (PyArray_SIZE(op[0]) == 0) {
if (out == NULL) {
out = op[0];
Py_INCREF(out);
}
goto finish;
}
if (NpyIter_RemoveAxis(iter, axis) != NPY_SUCCEED) {
goto fail;
}
if (NpyIter_RemoveMultiIndex(iter) != NPY_SUCCEED) {
goto fail;
}
}
/* Get the output */
if (out == NULL) {
if (iter) {
op[0] = out = NpyIter_GetOperandArray(iter)[0];
Py_INCREF(out);
}
else {
PyArray_Descr *dtype = op_dtypes[0];
Py_INCREF(dtype);
op[0] = out = (PyArrayObject *)PyArray_NewFromDescr(
&PyArray_Type, dtype,
ndim, PyArray_DIMS(op[1]), NULL, NULL,
0, NULL);
if (out == NULL) {
goto fail;
}
}
}
/*
* If the reduction axis has size zero, either return the reduction
* unit for UFUNC_REDUCE, or return the zero-sized output array
* for UFUNC_ACCUMULATE.
*/
if (PyArray_DIM(op[1], axis) == 0) {
goto finish;
}
else if (PyArray_SIZE(op[0]) == 0) {
goto finish;
}
if (iter && NpyIter_GetIterSize(iter) != 0) {
char *dataptr_copy[3];
npy_intp stride_copy[3];
npy_intp count_m1, stride0, stride1;
NpyIter_IterNextFunc *iternext;
char **dataptr;
int itemsize = op_dtypes[0]->elsize;
/* Get the variables needed for the loop */
iternext = NpyIter_GetIterNext(iter, NULL);
if (iternext == NULL) {
goto fail;
}
dataptr = NpyIter_GetDataPtrArray(iter);
/* Execute the loop with just the outer iterator */
count_m1 = PyArray_DIM(op[1], axis)-1;
stride0 = 0, stride1 = PyArray_STRIDE(op[1], axis);
NPY_UF_DBG_PRINT("UFunc: Reduce loop with just outer iterator\n");
stride0 = PyArray_STRIDE(op[0], axis);
stride_copy[0] = stride0;
stride_copy[1] = stride1;
stride_copy[2] = stride0;
needs_api = NpyIter_IterationNeedsAPI(iter);
NPY_BEGIN_THREADS_NDITER(iter);
do {
dataptr_copy[0] = dataptr[0];
dataptr_copy[1] = dataptr[1];
dataptr_copy[2] = dataptr[0];
/*
* Copy the first element to start the reduction.
*
* Output (dataptr[0]) and input (dataptr[1]) may point to
* the same memory, e.g. np.add.accumulate(a, out=a).
*/
if (otype == NPY_OBJECT) {
/*
* Incref before decref to avoid the possibility of the
* reference count being zero temporarily.
*/
Py_XINCREF(*(PyObject **)dataptr_copy[1]);
Py_XDECREF(*(PyObject **)dataptr_copy[0]);
*(PyObject **)dataptr_copy[0] =
*(PyObject **)dataptr_copy[1];
}
else {
memmove(dataptr_copy[0], dataptr_copy[1], itemsize);
}
if (count_m1 > 0) {
/* Turn the two items into three for the inner loop */
dataptr_copy[1] += stride1;
dataptr_copy[2] += stride0;
NPY_UF_DBG_PRINT1("iterator loop count %d\n",
(int)count_m1);
innerloop(dataptr_copy, &count_m1,
stride_copy, innerloopdata);
}
} while (iternext(iter));
NPY_END_THREADS;
}
else if (iter == NULL) {
char *dataptr_copy[3];
npy_intp stride_copy[3];
int itemsize = op_dtypes[0]->elsize;
/* Execute the loop with no iterators */
npy_intp count = PyArray_DIM(op[1], axis);
npy_intp stride0 = 0, stride1 = PyArray_STRIDE(op[1], axis);
NPY_UF_DBG_PRINT("UFunc: Reduce loop with no iterators\n");
if (PyArray_NDIM(op[0]) != PyArray_NDIM(op[1]) ||
!PyArray_CompareLists(PyArray_DIMS(op[0]),
PyArray_DIMS(op[1]),
PyArray_NDIM(op[0]))) {
PyErr_SetString(PyExc_ValueError,
"provided out is the wrong size "
"for the reduction");
goto fail;
}
stride0 = PyArray_STRIDE(op[0], axis);
stride_copy[0] = stride0;
stride_copy[1] = stride1;
stride_copy[2] = stride0;
/* Turn the two items into three for the inner loop */
dataptr_copy[0] = PyArray_BYTES(op[0]);
dataptr_copy[1] = PyArray_BYTES(op[1]);
dataptr_copy[2] = PyArray_BYTES(op[0]);
/*
* Copy the first element to start the reduction.
*
* Output (dataptr[0]) and input (dataptr[1]) may point to the
* same memory, e.g. np.add.accumulate(a, out=a).
*/
if (otype == NPY_OBJECT) {
/*
* Incref before decref to avoid the possibility of the
* reference count being zero temporarily.
*/
Py_XINCREF(*(PyObject **)dataptr_copy[1]);
Py_XDECREF(*(PyObject **)dataptr_copy[0]);
*(PyObject **)dataptr_copy[0] =
*(PyObject **)dataptr_copy[1];
}
else {
memmove(dataptr_copy[0], dataptr_copy[1], itemsize);
}
if (count > 1) {
--count;
dataptr_copy[1] += stride1;
dataptr_copy[2] += stride0;
NPY_UF_DBG_PRINT1("iterator loop count %d\n", (int)count);
needs_api = PyDataType_REFCHK(op_dtypes[0]);
if (!needs_api) {
NPY_BEGIN_THREADS_THRESHOLDED(count);
}
innerloop(dataptr_copy, &count,
stride_copy, innerloopdata);
NPY_END_THREADS;
}
}
finish:
Py_XDECREF(op_dtypes[0]);
NpyIter_Deallocate(iter);
NpyIter_Deallocate(iter_inner);
return (PyObject *)out;
fail:
Py_XDECREF(out);
Py_XDECREF(op_dtypes[0]);
NpyIter_Deallocate(iter);
NpyIter_Deallocate(iter_inner);
return NULL;
}
