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/*
* Copyright (c) 2008-2017, NVIDIA CORPORATION. All rights reserved.
*
* NVIDIA CORPORATION and its licensors retain all intellectual property
* and proprietary rights in and to this software, related documentation
* and any modifications thereto. Any use, reproduction, disclosure or
* distribution of this software and related documentation without an express
* license agreement from NVIDIA CORPORATION is strictly prohibited.
*/
#ifndef TABLE_LOOKUP_H
#define TABLE_LOOKUP_H
namespace nvidia
{
namespace apex
{
// Stored Tables
const float custom[] = { 1.00f, 1.00f, 1.00f, 1.00f, 1.00f, 1.00f, 1.00f, 1.00f, 1.00f, 1.00f, 1.00f };
const float linear[] = { 1.00f, 0.90f, 0.80f, 0.70f, 0.60f, 0.50f, 0.40f, 0.30f, 0.20f, 0.10f, 0.00f };
const float steep[] = { 1.00f, 0.99f, 0.96f, 0.91f, 0.84f, 0.75f, 0.64f, 0.51f, 0.36f, 0.19f, 0.00f };
const float scurve[] = { 1.00f, 0.99f, 0.96f, 0.91f, 0.80f, 0.50f, 0.20f, 0.09f, 0.04f, 0.01f, 0.00f };
// Table sizes must be same for all stored tables
#define NUM_ELEMENTS(X) (sizeof(X)/sizeof(*(X)))
#define TABLE_SIZE NUM_ELEMENTS(custom)
// Stored Table enums
struct TableName
{
enum Enum
{
CUSTOM = 0,
LINEAR,
STEEP,
SCURVE,
};
};
struct TableLookup
{
float xVals[TABLE_SIZE];
float yVals[TABLE_SIZE];
float x1;
float x2;
float multiplier;
#ifdef __CUDACC__
__device__ TableLookup() {}
#else
TableLookup():
x1(0),
x2(0),
multiplier(0)
{
zeroTable();
}
#endif
PX_CUDA_CALLABLE void zeroTable()
{
for (size_t i = 0; i < TABLE_SIZE; ++i)
{
xVals[i] = 0.0f;
yVals[i] = 0.0f;
}
}
PX_CUDA_CALLABLE void applyStoredTable(TableName::Enum tableName)
{
// build y values
for (size_t i = 0; i < TABLE_SIZE; ++i)
{
if (tableName == TableName::LINEAR)
{
yVals[i] = linear[i];
}
else if (tableName == TableName::STEEP)
{
yVals[i] = steep[i];
}
else if (tableName == TableName::SCURVE)
{
yVals[i] = scurve[i];
}
else if (tableName == TableName::CUSTOM)
{
yVals[i] = custom[i];
}
}
}
PX_CUDA_CALLABLE void buildTable()
{
// build x values
float interval = (x2 - x1) / (TABLE_SIZE - 1);
for (size_t i = 0; i < TABLE_SIZE; ++i)
{
xVals[i] = x1 + i * interval;
}
// apply multipler to y values
if (multiplier >= -1.0f && multiplier <= 1.0f)
{
for (size_t i = 0; i < TABLE_SIZE; ++i)
{
yVals[i] = yVals[i] * multiplier;
}
// offset = max y value in array
float max = yVals[0];
for (size_t i = 1; i < TABLE_SIZE; ++i)
{
if (yVals[i] > max)
{
max = yVals[i];
}
}
// apply offset
for (size_t i = 0; i < TABLE_SIZE; ++i)
{
yVals[i] = yVals[i] + (1.0f - max);
}
}
}
PX_CUDA_CALLABLE float lookupTableValue(float x) const
{
if (x <= xVals[0])
{
return yVals[0];
}
else if (x >= xVals[TABLE_SIZE-1])
{
return yVals[TABLE_SIZE-1];
}
else
{
// linear interpolation between x values
float interval = (xVals[TABLE_SIZE-1] - xVals[0]) / (TABLE_SIZE - 1);
uint32_t lerpPos = (uint32_t)((x - xVals[0]) / interval);
float yDiff = yVals[lerpPos+1] - yVals[lerpPos];
return yVals[lerpPos] + (x - xVals[lerpPos]) / interval * yDiff;
}
}
};
}
} // namespace nvidia::apex
#endif
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