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/*
* SPDX-FileCopyrightText: All Contributors to the PyTango project
*
* SPDX-License-Identifier: LGPL-3.0-or-later
*/
// This header file is just some template functions moved apart from
// attribute.cpp, and should only be included there.
#pragma once
#include "tango_numpy.h"
#define fast_python_to_tango_buffer_fallback__() \
fast_python_to_tango_buffer_sequence<tangoScalarTypeConst>( \
py_val, pdim_x, pdim_y, fname, isImage, res_dim_x, res_dim_y)
template <long tangoScalarTypeConst>
inline typename TANGO_const2type(tangoScalarTypeConst) * fast_python_to_tango_buffer_numpy(PyObject *py_val,
long *pdim_x,
long *pdim_y,
const std::string &fname,
bool isImage,
long &res_dim_x,
long &res_dim_y)
{
typedef typename TANGO_const2type(tangoScalarTypeConst) TangoScalarType;
static const int typenum = TANGO_const2numpy(tangoScalarTypeConst);
if(!PyArray_Check(py_val))
{
return fast_python_to_tango_buffer_fallback__();
}
int nd = PyArray_NDIM(to_PyArrayObject(py_val));
npy_intp *dims = PyArray_DIMS(to_PyArrayObject(py_val));
long len = 0;
// Is the array exactly what we need? I mean: The type we need and with
// continuous aligned memory?
const bool exact_array = (PyArray_CHKFLAGS(to_PyArrayObject(py_val), NPY_ARRAY_C_CONTIGUOUS | NPY_ARRAY_ALIGNED) &&
(PyArray_TYPE(to_PyArrayObject(py_val)) == typenum));
if(isImage)
{
// If dimensions are manually specified (nd must be 1), I don't
// know how to handle from numpy
if(nd == 1)
{
return fast_python_to_tango_buffer_fallback__();
}
// Check: This is an image!
if(nd != 2)
{
Tango::Except::throw_exception("PyDs_WrongNumpyArrayDimensions",
"Expecting a 2 dimensional numpy array (IMAGE attribute).",
fname + "()");
}
// If dimensions are manually limited and it's an image, I just
// know that if the limit is the real size, then I can safely
// ignore it, else let the default path take care...
bool dims_ok = true;
if(pdim_x)
{
dims_ok = dims_ok && (*pdim_x == dims[1]);
}
if(pdim_y)
{
dims_ok = dims_ok && (*pdim_y == dims[0]);
}
if(!dims_ok)
{
return fast_python_to_tango_buffer_fallback__();
}
len = static_cast<long>(dims[0] * dims[1]);
res_dim_x = static_cast<long>(dims[1]);
res_dim_y = static_cast<long>(dims[0]);
}
else
{
// Check: This is an spectrum!
if(nd != 1)
{
Tango::Except::throw_exception("PyDs_WrongNumpyArrayDimensions",
"Expecting a 1 dimensional numpy array (SPECTRUM attribute).",
fname + "()");
}
// If x dimension is limited then I only know how to behave
// if the array is exact
if(pdim_x)
{
// if x_dim is wrong, instead of throwing an exception I will let
// fast_python_to_tgbuffer_sequence throw it:
const bool dims_ok = (*pdim_x <= dims[0]);
if(!exact_array || !dims_ok)
{
return fast_python_to_tango_buffer_fallback__();
}
len = *pdim_x;
}
else
{
len = static_cast<long>(dims[0]);
}
res_dim_x = len;
res_dim_y = 0;
}
TangoScalarType *tg_data = new TangoScalarType[len];
void *vd_data = tg_data;
if(exact_array)
{
// The array is exactly what we need, so a plain memcpy is
// enough!
/// @todo If it is read only we need the copy, but if the
/// attribute is read/write, Tango will do the copy himself on
/// the calll to set_value(...), so there's no need for us
/// to make an extra one...
memcpy(vd_data, PyArray_DATA(to_PyArrayObject(py_val)), len * sizeof(TangoScalarType));
}
else
{
// We use numpy to create a copy of the array into the continuous
// memory location that we specify.
PyObject *py_cont;
py_cont = PyArray_SimpleNewFromData(nd, dims, typenum, vd_data);
if(!py_cont)
{
fast_python_to_tango_buffer_deleter__<tangoScalarTypeConst>(tg_data, len);
bopy::throw_error_already_set();
}
if(PyArray_CopyInto((PyArrayObject *) py_cont, (PyArrayObject *) py_val) < 0)
{
Py_DECREF(py_cont);
fast_python_to_tango_buffer_deleter__<tangoScalarTypeConst>(tg_data, len);
bopy::throw_error_already_set();
}
Py_DECREF(py_cont);
}
return tg_data;
}
template <>
inline TANGO_const2type(Tango::DEV_STRING) *
fast_python_to_tango_buffer_numpy<Tango::DEV_STRING>(PyObject *py_val,
long *pdim_x,
long *pdim_y,
const std::string &fname,
bool isImage,
long &res_dim_x,
long &res_dim_y)
{
static const long tangoScalarTypeConst = Tango::DEV_STRING;
return fast_python_to_tango_buffer_fallback__();
}
template <>
inline TANGO_const2type(Tango::DEV_ENCODED) *
fast_python_to_tango_buffer_numpy<Tango::DEV_ENCODED>(PyObject *py_val,
long *pdim_x,
long *pdim_y,
const std::string &fname,
bool isImage,
long &res_dim_x,
long &res_dim_y)
{
static const long tangoScalarTypeConst = Tango::DEV_ENCODED;
return fast_python_to_tango_buffer_fallback__();
}
#define fast_python_to_corba_buffer_fallback__() \
fast_python_to_corba_buffer_sequence<tangoArrayTypeConst>(py_val, pdim_x, fname, res_dim_x)
template <long tangoArrayTypeConst>
inline typename TANGO_const2scalartype(tangoArrayTypeConst) *
fast_python_to_corba_buffer_numpy(PyObject *py_val, long *pdim_x, const std::string &fname, long &res_dim_x)
{
typedef typename TANGO_const2type(tangoArrayTypeConst) TangoArrayType;
typedef typename TANGO_const2scalartype(tangoArrayTypeConst) TangoScalarType;
static const int typenum = TANGO_const2scalarnumpy(tangoArrayTypeConst);
if(!PyArray_Check(py_val))
{
return fast_python_to_corba_buffer_fallback__();
}
int nd = PyArray_NDIM(to_PyArrayObject(py_val));
npy_intp *dims = PyArray_DIMS(to_PyArrayObject(py_val));
long len = 0;
// Is the array exactly what we need? I mean: The type we need and with
// continuous aligned memory?
const bool exact_array = (PyArray_CHKFLAGS(to_PyArrayObject(py_val), NPY_ARRAY_C_CONTIGUOUS | NPY_ARRAY_ALIGNED) &&
(PyArray_TYPE(to_PyArrayObject(py_val)) == typenum));
// Check: This is an spectrum!
if(nd != 1)
{
Tango::Except::throw_exception("PyDs_WrongNumpyArrayDimensions",
"Expecting a 1 dimensional numpy array (SPECTRUM attribute).",
fname + "()");
}
// If x dimension is limited then I only know how to behave
// if the array is exact
if(pdim_x)
{
// if x_dim is wrong, instead of throwing an exception I will let
// fast_python_to_tgbuffer_sequence throw it:
const bool dims_ok = (*pdim_x <= dims[0]);
if(!exact_array || !dims_ok)
{
return fast_python_to_corba_buffer_fallback__();
}
len = *pdim_x;
}
else
{
len = static_cast<long>(dims[0]);
}
res_dim_x = len;
TangoScalarType *tg_data = TangoArrayType::allocbuf(len);
void *vd_data = tg_data;
if(exact_array)
{
// The array is exactly what we need, so a plain memcpy is
// enough!
/// @todo If it is read only we need the copy, but if the
/// attribute is read/write, Tango will do the copy himself on
/// the calll to set_value(...), so there's no need for us
/// to make an extra one...
memcpy(vd_data, PyArray_DATA(to_PyArrayObject(py_val)), len * sizeof(TangoScalarType));
}
else
{
// We use numpy to create a copy of the array into the continuous
// memory location that we specify.
PyObject *py_cont;
py_cont = PyArray_SimpleNewFromData(nd, dims, typenum, vd_data);
if(!py_cont)
{
TangoArrayType::freebuf(tg_data);
bopy::throw_error_already_set();
}
if(PyArray_CopyInto((PyArrayObject *) py_cont, (PyArrayObject *) py_val) < 0)
{
Py_DECREF(py_cont);
TangoArrayType::freebuf(tg_data);
bopy::throw_error_already_set();
}
Py_DECREF(py_cont);
}
return tg_data;
}
template <>
inline TANGO_const2scalartype(Tango::DEVVAR_STRINGARRAY) *
fast_python_to_corba_buffer_numpy<Tango::DEVVAR_STRINGARRAY>(PyObject *py_val,
long *pdim_x,
const std::string &fname,
long &res_dim_x)
{
static const long tangoArrayTypeConst = Tango::DEVVAR_STRINGARRAY;
return fast_python_to_corba_buffer_fallback__();
}
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