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/*----------------------------- WARNING! -----------------------------
* The C functions below are NOT designed to be called externally to
* the Python function astropy/astropy/convolution/convolve.py.
* They do NOT include any of the required correct usage checking.
*
*------------------------------- NOTES ------------------------------
*
* The simplest implementation of convolution does not deal with any boundary
* treatment, and pixels within half a kernel width of the edge of the image are
* set to zero. In cases where a boundary mode is set, we pad the input array in
* the Python code. In the 1D case, this means that the input array to the C
* code has a size nx + nkx where nx is the original array size and nkx is the
* size of the kernel. If we also padded the results array, then we could use
* the exact same C code for the convolution, provided that the results array
* was 'unpadded' in the Python code after the C code.
*
* However, to avoid needlessly padding the results array, we instead adjust the
* index when accessing the results array - for example in the 1D case we shift
* the index in the results array compared to the input array by half the kernel
* size. This is done via the 'result_index' variable, and this behavior is
* triggered by the 'embed_result_within_padded_region' setting.
*
*/
#include <assert.h>
#include <math.h>
#include <stdbool.h>
#include <stdlib.h>
#include <stddef.h>
#include "convolve.h"
// Distutils on Windows automatically exports ``PyInit__convolve``,
// create dummy to prevent linker complaining about missing symbol.
#if defined(_MSC_VER)
void PyInit__convolve(void)
{
return;
}
#endif
#ifdef _OPENMP
#include <omp.h>
#endif
void convolveNd_c(DTYPE * const result,
const DTYPE * const f,
const unsigned n_dim,
const size_t * const image_shape,
const DTYPE * const g,
const size_t * const kernel_shape,
const bool nan_interpolate,
const bool embed_result_within_padded_region,
const unsigned n_threads)
{
#ifdef NDEBUG
if (!result || !f || !g || !image_shape || !kernel_shape)
return;
#else
assert(result);
assert(f);
assert(g);
assert(image_shape);
assert(kernel_shape);
#endif
if (n_dim == 1)
convolve1d_c(result, f,
image_shape[0],
g, kernel_shape[0],
nan_interpolate,
embed_result_within_padded_region,
n_threads);
else if (n_dim == 2)
convolve2d_c(result, f,
image_shape[0], image_shape[1],
g, kernel_shape[0], kernel_shape[1],
nan_interpolate,
embed_result_within_padded_region,
n_threads);
else if (n_dim == 3)
convolve3d_c(result, f,
image_shape[0], image_shape[1], image_shape[2],
g, kernel_shape[0], kernel_shape[1], kernel_shape[2],
nan_interpolate,
embed_result_within_padded_region,
n_threads);
else
assert(0); // Unimplemented: n_dim > 3
}
/*-------------------------PERFORMANCE NOTES--------------------------------
* The function wrappers below are designed to take advantage of the following:
* The preprocessor will inline convolve<N>d(), effectively
* expanding the two logical branches, replacing nan_interpolate
* for their literal equivalents. The corresponding conditionals
* within these functions will then be optimized away, this
* being the goal - removing the unnecessary conditionals from
* the loops without duplicating code.
*--------------------------------------------------------------------------
*/
void convolve1d_c(DTYPE * const result,
const DTYPE * const f, const size_t nx,
const DTYPE * const g, const size_t nkx,
const bool nan_interpolate,
const bool embed_result_within_padded_region,
const unsigned n_threads)
{
#ifdef NDEBUG
if (!result || !f || !g)
return;
#else
assert(result);
assert(f);
assert(g);
#endif
if (nan_interpolate) {
if (embed_result_within_padded_region)
convolve1d(result, f, nx, g, nkx, true, true, n_threads);
else
convolve1d(result, f, nx, g, nkx, true, false, n_threads);
} else {
if (embed_result_within_padded_region)
convolve1d(result, f, nx, g, nkx, false, true, n_threads);
else
convolve1d(result, f, nx, g, nkx, false, false, n_threads);
}
}
void convolve2d_c(DTYPE * const result,
const DTYPE * const f, const size_t nx, const size_t ny,
const DTYPE * const g, const size_t nkx, const size_t nky,
const bool nan_interpolate,
const bool embed_result_within_padded_region,
const unsigned n_threads)
{
#ifdef NDEBUG
if (!result || !f || !g)
return;
#else
assert(result);
assert(f);
assert(g);
#endif
if (nan_interpolate) {
if (embed_result_within_padded_region)
convolve2d(result, f, nx, ny, g, nkx, nky, true, true, n_threads);
else
convolve2d(result, f, nx, ny, g, nkx, nky, true, false, n_threads);
} else {
if (embed_result_within_padded_region)
convolve2d(result, f, nx, ny, g, nkx, nky, false, true, n_threads);
else
convolve2d(result, f, nx, ny, g, nkx, nky, false, false, n_threads);
}
}
void convolve3d_c(DTYPE * const result,
const DTYPE * const f, const size_t nx, const size_t ny, const size_t nz,
const DTYPE * const g, const size_t nkx, const size_t nky, const size_t nkz,
const bool nan_interpolate,
const bool embed_result_within_padded_region,
const unsigned n_threads)
{
#ifdef NDEBUG
if (!result || !f || !g)
return;
#else
assert(result);
assert(f);
assert(g);
#endif
if (nan_interpolate) {
if (embed_result_within_padded_region)
convolve3d(result, f, nx, ny, nz, g, nkx, nky, nkz, true, true, n_threads);
else
convolve3d(result, f, nx, ny, nz, g, nkx, nky, nkz, true, false, n_threads);
} else {
if (embed_result_within_padded_region)
convolve3d(result, f, nx, ny, nz, g, nkx, nky, nkz, false, true, n_threads);
else
convolve3d(result, f, nx, ny, nz, g, nkx, nky, nkz, false, false, n_threads);
}
}
// 1D
FORCE_INLINE void convolve1d(DTYPE * const result,
const DTYPE * const f, const size_t _nx,
const DTYPE * const g, const size_t _nkx,
const bool _nan_interpolate,
const bool _embed_result_within_padded_region,
const unsigned n_threads)
{
#ifdef NDEBUG
if (!result || !f || !g)
return;
#else
assert(result);
assert(f);
assert(g);
#endif
const size_t _wkx = _nkx / 2;
#ifdef NDEBUG
if (!(_nx > 2*_wkx))
return;
#else
assert(_nx > 2*_wkx);
#endif
#ifdef _OPENMP
omp_set_num_threads(n_threads); // Set number of threads to use
#pragma omp parallel
{ // Code within this block is threaded
#endif
// Copy these to thread locals to allow compiler to optimize (hoist/loads licm)
// when threaded. Without these, compile time constant conditionals may
// not be optimized away.
const size_t nx = _nx;
const size_t nkx = _nkx;
const size_t nkx_minus_1 = nkx - 1;
const bool nan_interpolate = _nan_interpolate;
const bool embed_result_within_padded_region = _embed_result_within_padded_region;
// Thread locals
const size_t wkx = _wkx;
const omp_iter_var nx_minus_wkx = nx - wkx;
size_t i_minus_wkx;
size_t result_index;
DTYPE top, bot=0., ker, val;
{omp_iter_var i;
#ifdef _OPENMP
#pragma omp for schedule(dynamic)
#endif
for (i = wkx; i < nx_minus_wkx; ++i)
{
i_minus_wkx = i - wkx;
top = 0.;
if (nan_interpolate) // compile time constant
bot = 0.;
{omp_iter_var ii;
for (ii = 0; ii < nkx; ++ii)
{
val = f[i_minus_wkx + ii];
ker = g[nkx_minus_1 - ii];
if (nan_interpolate) // compile time constant
{
if (!isnan(val))
{
top += val * ker;
bot += ker;
}
}
else
top += val * ker;
}}
if (embed_result_within_padded_region) { // compile time constant
result_index = i;
} else {
result_index = i_minus_wkx;
}
if (nan_interpolate) // compile time constant
{
if (bot == 0) // This should prob be np.isclose(kernel_sum, 0, atol=normalization_zero_tol)
result[result_index] = f[i];
else
result[result_index] = top / bot;
}
else
result[result_index] = top;
}}
#ifdef _OPENMP
}//end parallel scope
#endif
}
// 2D
FORCE_INLINE void convolve2d(DTYPE * const result,
const DTYPE * const f, const size_t _nx, const size_t _ny,
const DTYPE * const g, const size_t _nkx, const size_t _nky,
const bool _nan_interpolate,
const bool _embed_result_within_padded_region,
const unsigned n_threads)
{
#ifdef NDEBUG
if (!result || !f || !g)
return;
#else
assert(result);
assert(f);
assert(g);
#endif
const size_t _wkx = _nkx / 2;
const size_t _wky = _nky / 2;
#ifdef NDEBUG
if (!(_nx > 2*_wkx) || !(_ny > 2*_wky))
return;
#else
assert(_nx > 2*_wkx);
assert(_ny > 2*_wky);
#endif
#ifdef _OPENMP
omp_set_num_threads(n_threads); // Set number of threads to use
#pragma omp parallel
{ // Code within this block is threaded
#endif
// Copy these to thread locals to allow compiler to optimize (hoist/loads licm)
// when threaded. Without these, compile time constant conditionals may
// not be optimized away.
const size_t nx = _nx, ny = _ny;
const size_t nkx = _nkx, nky = _nky;
const size_t nkx_minus_1 = nkx - 1, nky_minus_1 = nky - 1;
const bool nan_interpolate = _nan_interpolate;
const bool embed_result_within_padded_region = _embed_result_within_padded_region;
// Thread locals
const size_t wkx = _wkx;
const size_t wky = _wky;
const omp_iter_var nx_minus_wkx = nx - wkx;
const omp_iter_var ny_minus_wky = ny - wky;
const size_t ny_minus_2wky = ny - 2 * wky;
size_t i_minus_wkx, j_minus_wky;
size_t result_cursor;
size_t f_cursor, g_cursor;
size_t result_index;
DTYPE top, bot=0., ker, val;
{omp_iter_var i;
#ifdef _OPENMP
#pragma omp for schedule(dynamic)
#endif
for (i = wkx; i < nx_minus_wkx; ++i)
{
i_minus_wkx = i - wkx;
result_cursor = i*ny;
{omp_iter_var j;
for (j = wky; j < ny_minus_wky; ++j)
{
j_minus_wky = j - wky;
top = 0.;
if (nan_interpolate) // compile time constant
bot = 0.;
{omp_iter_var ii;
for (ii = 0; ii < nkx; ++ii)
{
f_cursor = (i_minus_wkx + ii)*ny + j_minus_wky;
g_cursor = (nkx_minus_1 - ii)*nky + nky_minus_1;
{omp_iter_var jj;
for (jj = 0; jj < nky; ++jj)
{
val = f[f_cursor + jj];
ker = g[g_cursor - jj];
if (nan_interpolate) // compile time constant
{
if (!isnan(val))
{
top += val * ker;
bot += ker;
}
}
else
top += val * ker;
}}
}}
if (embed_result_within_padded_region) { // compile time constant
result_index = result_cursor + j;
} else {
result_index = i_minus_wkx * ny_minus_2wky + j_minus_wky;
}
if (nan_interpolate) // compile time constant
{
if (bot == 0) // This should prob be np.isclose(kernel_sum, 0, atol=normalization_zero_tol)
result[result_index] = f[result_cursor + j] ;
else
result[result_index] = top / bot;
}
else
result[result_index] = top;
}}
}}
#ifdef _OPENMP
}//end parallel scope
#endif
}
// 3D
FORCE_INLINE void convolve3d(DTYPE * const result,
const DTYPE * const f, const size_t _nx, const size_t _ny, const size_t _nz,
const DTYPE * const g, const size_t _nkx, const size_t _nky, const size_t _nkz,
const bool _nan_interpolate,
const bool _embed_result_within_padded_region,
const unsigned n_threads)
{
#ifdef NDEBUG
if (!result || !f || !g)
return;
#else
assert(result);
assert(f);
assert(g);
#endif
const size_t _wkx = _nkx / 2;
const size_t _wky = _nky / 2;
const size_t _wkz = _nkz / 2;
#ifdef NDEBUG
if (!(_nx > 2*_wkx) || !(_ny > 2*_wky) || !(_nz > 2*_wkz))
return;
#else
assert(_nx > 2*_wkx);
assert(_ny > 2*_wky);
assert(_nz > 2*_wkz);
#endif
#ifdef _OPENMP
omp_set_num_threads(n_threads); // Set number of threads to use
#pragma omp parallel
{ // Code within this block is threaded
#endif
// Copy these to thread locals to allow compiler to optimize (hoist/loads licm)
// when threaded. Without these, compile time constant conditionals may
// not be optimized away.
const size_t nx = _nx, ny = _ny, nz = _nz;
const size_t nkx = _nkx, nky = _nky, nkz = _nkz;
const size_t nkx_minus_1 = nkx - 1, nky_minus_1 = nky - 1, nkz_minus_1 = nkz - 1;
const bool nan_interpolate = _nan_interpolate;
const bool embed_result_within_padded_region = _embed_result_within_padded_region;
// Thread locals
const size_t wkx = _wkx;
const size_t wky = _wky;
const size_t wkz = _wkz;
const size_t nx_minus_wkx = nx - wkx;
const omp_iter_var ny_minus_wky = ny - wky;
const omp_iter_var nz_minus_wkz = nz - wkz;
const size_t ny_minus_2wky = ny - 2 * wky;
const size_t nz_minus_2wkz = nz - 2 * wkz;
size_t i_minus_wkx, j_minus_wky, k_minus_wkz;
size_t f_ii_cursor, g_ii_cursor;
size_t f_cursor, g_cursor;
size_t array_cursor, array_i_cursor;
size_t result_index;
DTYPE top, bot=0., ker, val;
{omp_iter_var i;
#ifdef _OPENMP
#pragma omp for schedule(dynamic)
#endif
for (i = wkx; i < nx_minus_wkx; ++i)
{
i_minus_wkx = i - wkx;
array_i_cursor = i*ny;
{omp_iter_var j;
for (j = wky; j < ny_minus_wky; ++j)
{
j_minus_wky = j - wky;
array_cursor = (array_i_cursor + j)*nz;
{omp_iter_var k;
for (k = wkz; k < nz_minus_wkz; ++k)
{
k_minus_wkz = k - wkz;
top = 0.;
if (nan_interpolate) // compile time constant
bot = 0.;
{omp_iter_var ii;
for (ii = 0; ii < nkx; ++ii)
{
f_ii_cursor = ((i_minus_wkx + ii)*ny + j_minus_wky)*nz + k_minus_wkz;
g_ii_cursor = ((nkx_minus_1 - ii)*nky + nky_minus_1)*nkz + nkz_minus_1;
{omp_iter_var jj;
for (jj = 0; jj < nky; ++jj)
{
f_cursor = f_ii_cursor + jj*nz;
g_cursor = g_ii_cursor - jj*nkz;
{omp_iter_var kk;
for (kk = 0; kk < nkz; ++kk)
{
val = f[f_cursor + kk];
ker = g[g_cursor - kk];
if (nan_interpolate) // compile time constant
{
if (!isnan(val))
{
top += val * ker;
bot += ker;
}
}
else
top += val * ker;
}}
}}
}}
if (embed_result_within_padded_region) { // compile time constant
result_index = array_cursor + k;
} else {
result_index = (i_minus_wkx*ny_minus_2wky + j_minus_wky)*nz_minus_2wkz + k_minus_wkz;
}
if (nan_interpolate) // compile time constant
{
if (bot == 0) // This should prob be np.isclose(kernel_sum, 0, atol=normalization_zero_tol)
result[result_index] = f[array_cursor+ k] ;
else
result[result_index] = top / bot;
}
else
result[result_index] = top;
}}
}}
}}
#ifdef _OPENMP
}//end parallel scope
#endif
}
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