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/*
* Project: Silx statics calculation
*
*
*
* Copyright (C) 2012-2017 European Synchrotron Radiation Facility
* Grenoble, France
*
* Principal authors: J. Kieffer (kieffer@esrf.fr)
* Last revision: 13/12/2018
*
* Permission is hereby granted, free of charge, to any person obtaining a copy
* of this software and associated documentation files (the "Software"), to deal
* in the Software without restriction, including without limitation the rights
* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
* copies of the Software, and to permit persons to whom the Software is
* furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice shall be included in
* all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
* THE SOFTWARE.
*/
/**
* \file
*
* \brief OpenCL kernels for min, max, mean and std calculation
*
* Constant to be provided at build time:
*
*/
#include "for_eclipse.h"
/* \brief read a value at given position and initialize the float8 for the reduction
*
* The float8 returned contains:
* s0: minimum value
* s1: maximum value
* s2: count number of valid pixels
* s3: count (error associated to)
* s4: sum of valid pixels
* s5: sum (error associated to)
* s6: variance*count
* s7: variance*count (error associated to)
*
*/
static inline float8 map_statistics(global float* data, int position)
{
float value = data[position];
float8 result;
if (isfinite(value))
{
result = (float8)(value, value, 1.0f, 0.0f, value, 0.0f, 0.0f, 0.0f);
// min max cnt cnt_err sum sum_err M M_err
}
else
{
result = (float8)(FLT_MAX, -FLT_MAX, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f);
}
return result;
}
/* \brief reduction function associated to the statistics.
*
* this is described in:
* https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm
*
* The float8 used here contain contains:
* s0: minimum value
* s1: maximum value
* s2: count number of valid pixels
* s3: count (error associated to)
* s4: sum of valid pixels
* s5: sum (error associated to)
* s6: M=variance*(count-1)
* s7: M=variance*(count-1) (error associated to)
*
*/
static inline float8 reduce_statistics(float8 a, float8 b)
{
float2 sum_a, sum_b, M_a, M_b, count_a, count_b;
//test on count
if (a.s2 == 0.0f)
{
return b;
}
else
{
count_a = (float2)(a.s2, a.s3);
sum_a = (float2)(a.s4, a.s5);
M_a = (float2)(a.s6, a.s7);
}
//test on count
if (b.s2 == 0.0f)
{
return a;
}
else
{
count_b = (float2)(b.s2, b.s3);
sum_b = (float2)(b.s4, b.s5);
M_b = (float2)(b.s6, b.s7);
}
// count = count_a + count_b
float2 count = compensated_sum(count_a, count_b);
// sum = sum_a + sum_b
float2 sum = compensated_sum(sum_a, sum_b);
//delta = avg_b - avg_a
//delta^2 = avg_b^2 + avg_a^2 - 2*avg_b*avg_a
//coount_a*count_b*delta^2 = count_a/count_b * sum_b^2 + count_b/count_a*sum_a^2 - 2*sum_a*sum_b
//float2 sum2_a = compensated_mul(sum_a, sum_a);
//float2 sum2_b = compensated_mul(sum_b, sum_b);
//float2 ca_over_cb = compensated_mul(count_a, compensated_inv(count_b));
//float2 cb_over_ca = compensated_mul(count_b, compensated_inv(count_a));
//float2 delta2cbca = compensated_sum(compensated_sum(
// compensated_mul(ca_over_cb, sum2_b),
// compensated_mul(cb_over_ca, sum2_a)),
// -2.0f * compensated_mul(sum_a, sum_b));
//////////////
// float2 delta = compensated_sum(
// compensated_mul(sum_b, compensated_inv(count_b)),
// -1*(compensated_mul(sum_a, compensated_inv(count_a))));
float2 delta = compensated_sum(compensated_div(sum_b, count_b),
-1*compensated_div(sum_a, count_a));
float2 delta2cbca = compensated_mul(compensated_mul(delta, delta),
compensated_mul(count_a, count_b));
float2 M2 = compensated_sum(compensated_sum(M_a, M_b),
compensated_mul(delta2cbca, compensated_inv(count)));
//M2 = M_a + M_b + delta ** 2 * count_a * count_b / (count_a + count_b)
float8 result = (float8)(min(a.s0, b.s0), max(a.s1, b.s1),
count.s0, count.s1,
sum.s0, sum.s1,
M2.s0, M2.s1);
return result;
}
/* \brief reduction function associated to the statistics without compensated arithmetics.
*
* this is described in:
* https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm
*
* The float8 used here contain contains:
* s0: minimum value
* s1: maximum value
* s2: count number of valid pixels
* s3: count (error associated to)
* s4: sum of valid pixels
* s5: sum (error associated to)
* s6: M=variance*(count-1)
* s7: M=variance*(count-1) (error associated to)
*
*/
static inline float8 reduce_statistics_simple(float8 a, float8 b)
{
float sum_a, sum_b, M_a, M_b, count_a, count_b;
//test on count
if (a.s2 == 0.0f)
{
return b;
}
else
{
count_a = a.s2;
sum_a = a.s4;
M_a = a.s6;
}
//test on count
if (b.s2 == 0.0f)
{
return a;
}
else
{
count_b = b.s2;
sum_b = b.s4;
M_b = b.s6;
}
float count = count_a + count_b;
float sum = sum_a + sum_b;
float delta = sum_a/count_a - sum_b/count_b;
float delta2cbca = count_b * count_a * delta * delta;
float M2 = M_a + M_b + delta2cbca/count;
//M2 = M_a + M_b + delta ** 2 * count_a * count_b / (count_a + count_b)
float8 result = (float8)(min(a.s0, b.s0), max(a.s1, b.s1),
count, 0.0f,
sum, 0.0f,
M2, 0.0f);
return result;
}
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