multijitter_inline.h

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 */

namespace cugar {

// get a set of 2d stratified samples
template <typename T>
void MJSampler::sample(
    const uint32    samples_x,
    const uint32    samples_y,
    Vector<T,2>*    samples,
    Ordering        ordering)
{
    m_sample_xy.resize( samples_x * samples_y );
    for (uint32 y = 0; y < samples_y; y++)
    {
        for (uint32 x = 0; x < samples_x; x++)
        {
            const size_t index = y * samples_x + x;
            m_sample_xy[index].x = y;
            m_sample_xy[index].y = x;
        }
    }
    for (uint32 y = 0; y < samples_y; y++)
    {
        for (uint32 x = 0; x < samples_x-1; x++)
        {
            const float r = m_random.next();
            const uint32 xx = std::min( uint32(float(r) * (samples_x - x)) + x, samples_x-1u );

            const size_t index1 = y * samples_x + x;
            const size_t index2 = y * samples_x + xx;

            std::swap( m_sample_xy[index1].y, m_sample_xy[index2].y );
        }
    }
    for (uint32 x = 0; x < samples_x; x++)
    {
        for (uint32 y = 0; y < samples_y-1; y++)
        {
            const float r = m_random.next();
            const uint32 yy = std::min( uint32(float(r) * (samples_y - y)) + y, samples_y-1u );

            const size_t index1 = y * samples_x + x;
            const size_t index2 = yy * samples_x + x;

            std::swap( m_sample_xy[index1].x, m_sample_xy[index2].x );
        }
    }

    const uint32 num_samples = samples_x * samples_y;
    const T inv = T(1.0) / T(num_samples);
    for (uint32 y = 0; y < samples_y; y++)
    {
        for (uint32 x = 0; x < samples_x; x++)
        {
            const size_t index = y * samples_x + x;
            samples[index][0] = min( (T(m_sample_xy[index].x + x * samples_y) + m_random.next()) * inv, T(1) );
            samples[index][1] = min( (T(m_sample_xy[index].y + y * samples_x) + m_random.next()) * inv, T(1) );
        }
    }
    if (ordering == kRandom)
    {
        for (uint32 i = 0; i < num_samples; i++)
        {
            const float r = m_random.next();
            const uint32 j = std::min( uint32(float(r) * (num_samples - i)) + i, num_samples-1u );
            std::swap( samples[i], samples[j] );
        }
    }
}
// get a set of 3d stratified samples
// the first 2 dimensions are multi-jittered, the third one
// is selected with latin hypercube sampling wrt the first 2.
template <typename T>
void MJSampler::sample(
    const uint32    samples_x,
    const uint32    samples_y,
    Vector<T,3>*    samples)
{
    m_sample_xy.resize( samples_x * samples_y );
    for (uint32 y = 0; y < samples_y; y++)
    {
        for (uint32 x = 0; x < samples_x; x++)
        {
            const size_t index = y * samples_x + x;
            m_sample_xy[index].x = y;
            m_sample_xy[index].y = x;
        }
    }
    for (uint32 y = 0; y < samples_y; y++)
    {
        for (uint32 x = 0; x < samples_x-1; x++)
        {
            const float r = m_random.next();
            const uint32 xx = std::min( uint32(float(r) * (samples_x - x)) + x, samples_x-1u );

            const size_t index1 = y * samples_x + x;
            const size_t index2 = y * samples_x + xx;

            std::swap( m_sample_xy[index1].y, m_sample_xy[index2].y );
        }
    }
    for (uint32 x = 0; x < samples_x; x++)
    {
        for (uint32 y = 0; y < samples_y-1; y++)
        {
            const float r = m_random.next();
            const uint32 yy = std::min( uint32(float(r) * (samples_y - y)) + y, samples_y-1u );

            const size_t index1 = y * samples_x + x;
            const size_t index2 = yy * samples_x + x;

            std::swap( m_sample_xy[index1].x, m_sample_xy[index2].x );
        }
    }

    const uint32 num_samples = samples_x * samples_y;
    const T inv = T(1.0) / T(num_samples);
    for (uint32 y = 0; y < samples_y; y++)
    {
        for (uint32 x = 0; x < samples_x; x++)
        {
            const size_t index = y * samples_x + x;
            samples[index][0] = (T(m_sample_xy[index].x + x * samples_y) + m_random.next()) * inv;
            samples[index][1] = (T(m_sample_xy[index].y + y * samples_x) + m_random.next()) * inv;
            samples[index][2] = (T(index) + m_random.next()) * inv;
        }
    }
    for (uint32 i = 0; i < num_samples; i++)
    {
        const float r = m_random.next();
        const uint32 j = std::min( uint32(float(r) * (num_samples - i)) + i, num_samples-1u );
        std::swap( samples[i], samples[j] );
    }
}
// get a set of 4d stratified samples
template <typename T>
void MJSampler::sample(
    const uint32    samples_x,
    const uint32    samples_y,
    Vector<T,4>*    samples,
    Ordering        ordering)
{
    for (uint32 offset = 0; offset <= 2; offset += 2)
    {
        m_sample_xy.resize( samples_x * samples_y );
        for (uint32 y = 0; y < samples_y; y++)
        {
            for (uint32 x = 0; x < samples_x; x++)
            {
                const size_t index = y * samples_x + x;
                m_sample_xy[index].x = y;
                m_sample_xy[index].y = x;
            }
        }
        for (uint32 y = 0; y < samples_y; y++)
        {
            for (uint32 x = 0; x < samples_x-1; x++)
            {
                const float r = m_random.next();
                const uint32 xx = std::min( uint32(float(r) * (samples_x - x)) + x, samples_x-1u );

                const size_t index1 = y * samples_x + x;
                const size_t index2 = y * samples_x + xx;

                std::swap( m_sample_xy[index1].y, m_sample_xy[index2].y );
            }
        }
        for (uint32 x = 0; x < samples_x; x++)
        {
            for (uint32 y = 0; y < samples_y-1; y++)
            {
                const float r = m_random.next();
                const uint32 yy = std::min( uint32(float(r) * (samples_y - y)) + y, samples_y-1u );

                const size_t index1 = y * samples_x + x;
                const size_t index2 = yy * samples_x + x;

                std::swap( m_sample_xy[index1].x, m_sample_xy[index2].x );
            }
        }

        const uint32 num_samples = samples_x * samples_y;
        const T inv = T(1.0) / T(num_samples);
        for (uint32 y = 0; y < samples_y; y++)
        {
            for (uint32 x = 0; x < samples_x; x++)
            {
                const size_t index = y * samples_x + x;
                samples[index][offset+0] = (T(m_sample_xy[index].x + x * samples_y) + m_random.next()) * inv;
                samples[index][offset+1] = (T(m_sample_xy[index].y + y * samples_x) + m_random.next()) * inv;
            }
        }
        if (ordering == kRandom)
        {
            const uint32 num_samples = samples_x * samples_y;
            for (uint32 i = 0; i < num_samples; i++)
            {
                const float r = m_random.next();
                const uint32 j = std::min( uint32(float(r) * (num_samples - i)) + i, num_samples-1u );
                std::swap( samples[i][offset+0], samples[j][offset+0] );
                std::swap( samples[i][offset+1], samples[j][offset+1] );
            }
        }
    }
}

} // namespace cugar