DataUtils.cpp

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#include "DataUtils.hpp"
 
namespace MayaFlux::Kakshya {
 
uint64_t calculate_total_elements(const std::vector<DataDimension>& dimensions)
{
    if (dimensions.empty())
        return 0;
 
    return std::transform_reduce(dimensions.begin(), dimensions.end(),
        uint64_t(1), std::multiplies<>(),
        [](const DataDimension& dim) { return dim.size; });
}
 
uint64_t calculate_frame_size(const std::vector<DataDimension>& dimensions)
{
    if (dimensions.empty())
        return 0;
 
    return std::transform_reduce(
        dimensions.begin() + 1, dimensions.end(),
        uint64_t(1), std::multiplies<>(),
        [](const DataDimension& dim) constexpr { return dim.size; });
}
 
std::type_index get_variant_type_index(const DataVariant& data)
{
    return std::visit([](const auto& vec) -> std::type_index {
        return std::type_index(typeid(decltype(vec)));
    },
        data);
}
 
void safe_copy_data_variant(const DataVariant& input, DataVariant& output)
{
    std::visit([&](const auto& input_vec, auto& output_vec) {
        using InputType = typename std::decay_t<decltype(input_vec)>::value_type;
        using OutputType = typename std::decay_t<decltype(output_vec)>::value_type;
 
        if constexpr (ProcessableData<InputType> && ProcessableData<OutputType>) {
            std::vector<OutputType> temp_storage;
            auto input_span = extract_from_variant<OutputType>(input, temp_storage);
 
            output_vec.resize(input_span.size());
            std::copy(input_span.begin(), input_span.end(), output_vec.begin());
        } else {
            error<std::invalid_argument>(
                Journal::Component::Kakshya,
                Journal::Context::Runtime,
                std::source_location::current(),
                "Unsupported type conversion from {} to {}",
                typeid(InputType).name(),
                typeid(OutputType).name());
        }
    },
        input, output);
}
 
void set_metadata_value(std::unordered_map<std::string, std::any>& metadata, const std::string& key, std::any value)
{
    metadata[key] = std::move(value);
}
 
int find_dimension_by_role(const std::vector<DataDimension>& dimensions, DataDimension::Role role)
{
    auto it = std::ranges::find_if(dimensions,
        [role](const DataDimension& dim) { return dim.role == role; });
 
    return (it != dimensions.end()) ? static_cast<int>(std::distance(dimensions.begin(), it)) : -1;
}
 
DataModality detect_data_modality(const std::vector<DataDimension>& dimensions)
{
    if (dimensions.empty()) {
        return DataModality::UNKNOWN;
    }
 
    size_t time_dims = 0, spatial_dims = 0, channel_dims = 0, frequency_dims = 0, custom_dims = 0;
    size_t total_spatial_elements = 1;
    size_t total_channels = 0;
 
    for (const auto& dim : dimensions) {
        if (dim.grouping) {
            switch (dim.role) {
            case DataDimension::Role::POSITION:
                return DataModality::VERTEX_POSITIONS_3D;
            case DataDimension::Role::NORMAL:
                return DataModality::VERTEX_NORMALS_3D;
            case DataDimension::Role::TANGENT:
            case DataDimension::Role::BITANGENT:
                return DataModality::VERTEX_TANGENTS_3D;
            case DataDimension::Role::UV:
                return DataModality::TEXTURE_COORDS_2D;
            case DataDimension::Role::COLOR:
                if (dim.grouping->count == 3)
                    return DataModality::VERTEX_COLORS_RGB;
                if (dim.grouping->count == 4)
                    return DataModality::VERTEX_COLORS_RGBA;
                break;
            default:
                if (dim.grouping->count == 16)
                    return DataModality::TRANSFORMATION_MATRIX;
                break;
            }
        }
    }
 
    for (const auto& dim : dimensions) {
        switch (dim.role) {
        case DataDimension::Role::TIME:
            time_dims++;
            break;
        case DataDimension::Role::SPATIAL_X:
        case DataDimension::Role::SPATIAL_Y:
        case DataDimension::Role::SPATIAL_Z:
            spatial_dims++;
            total_spatial_elements *= dim.size;
            break;
        case DataDimension::Role::CHANNEL:
            channel_dims++;
            total_channels += dim.size;
            break;
        case DataDimension::Role::FREQUENCY:
            frequency_dims++;
            break;
        case DataDimension::Role::CUSTOM:
        default:
            custom_dims++;
            break;
        }
    }
 
    if (time_dims == 1 && spatial_dims == 0 && frequency_dims == 0) {
        if (channel_dims == 0) {
            return DataModality::AUDIO_1D;
        } else if (channel_dims == 1) {
            return (total_channels <= 1) ? DataModality::AUDIO_1D : DataModality::AUDIO_MULTICHANNEL;
        } else {
            return DataModality::AUDIO_MULTICHANNEL;
        }
    }
 
    if (time_dims >= 1 && frequency_dims >= 1) {
        if (spatial_dims == 0 && channel_dims <= 1) {
            return DataModality::SPECTRAL_2D;
        }
        return DataModality::TENSOR_ND;
    }
 
    if (spatial_dims >= 2 && time_dims == 0) {
        if (spatial_dims == 2) {
            if (channel_dims == 0) {
                return DataModality::IMAGE_2D;
            } else if (channel_dims == 1 && total_channels >= 3) {
                return DataModality::IMAGE_COLOR;
            } else {
                return DataModality::IMAGE_2D;
            }
        } else if (spatial_dims == 3) {
            return DataModality::VOLUMETRIC_3D;
        }
    }
 
    if (time_dims >= 1 && spatial_dims >= 2) {
        if (spatial_dims == 2) {
            if (channel_dims == 0 || (channel_dims == 1 && total_channels <= 1)) {
                return DataModality::VIDEO_GRAYSCALE;
            } else {
                return DataModality::VIDEO_COLOR;
            }
        }
        return DataModality::TENSOR_ND;
    }
 
    if (spatial_dims == 2 && time_dims == 0 && channel_dims >= 1) {
        if (total_spatial_elements >= 64 && total_channels >= 1) {
            return DataModality::TEXTURE_2D;
        }
    }
 
    return DataModality::TENSOR_ND;
}
 
DataModality detect_data_modality(
    const std::vector<DataDimension>& dimensions,
    const DataVariant& source)
{
    const DataModality base = detect_data_modality(dimensions);
 
    if (base != DataModality::AUDIO_1D && base != DataModality::TENSOR_ND) {
        return base;
    }
 
    return std::visit([&base](const auto& vec) {
        using V = typename std::decay_t<decltype(vec)>::value_type;
 
        if constexpr (ComplexData<V>) {
            return DataModality::SPECTRAL_2D;
        } else if constexpr (IntegerData<V>) {
            return DataModality::TENSOR_ND;
        } else {
            return base;
        }
    },
        source);
}
 
std::vector<DataDimension> detect_data_dimensions(const DataVariant& data)
{
    MF_WARN(Journal::Component::Kakshya, Journal::Context::Runtime,
        "{}\n{}\n{}\n{}",
        "Inferring structure from single DataVariant is not advisable as the method makes naive assumptions that can lead to massive computational errors. "
        "If the variant is part of a container, region, or segment, please use the appropriate method instead. "
        "If the variant is part of a vector, please use infer_from_data_variant_vector instead. "
        "If you are sure you want to proceed, please ignore this warning.");
 
    return std::visit([](const auto& vec) -> std::vector<DataDimension> {
        using ValueType = typename std::decay_t<decltype(vec)>::value_type;
 
        std::vector<DataDimension> dims;
 
        if constexpr (DecimalData<ValueType>) {
            dims.emplace_back(DataDimension::time(vec.size()));
 
        } else if constexpr (ComplexData<ValueType>) {
            dims.emplace_back(DataDimension::frequency(vec.size()));
 
        } else if constexpr (IntegerData<ValueType>) {
            dims.emplace_back(DataDimension::spatial(vec.size(), 'x'));
        } else if constexpr (GlmData<ValueType>) {
            constexpr size_t components = glm_component_count<ValueType>();
            DataDimension::Role role = DataDimension::Role::CUSTOM;
 
            if constexpr (GlmVec2Type<ValueType>) {
                role = DataDimension::Role::UV;
            } else if constexpr (GlmVec3Type<ValueType>) {
                role = DataDimension::Role::POSITION;
            } else if constexpr (GlmVec4Type<ValueType>) {
                role = DataDimension::Role::COLOR;
            } else if constexpr (GlmMatrixType<ValueType>) {
                role = DataDimension::Role::CUSTOM;
            }
 
            dims.push_back(DataDimension::grouped(
                "glm_structured_data",
                static_cast<uint64_t>(vec.size()),
                static_cast<uint8_t>(components),
                role));
        } else {
            dims.emplace_back(DataDimension::time(vec.size()));
        }
 
        return dims;
    },
        data);
}
 
std::vector<DataDimension> detect_data_dimensions(
    const std::vector<DataVariant>& variants)
{
    MF_WARN(Journal::Component::Kakshya, Journal::Context::Runtime,
        "{}\n{}\n{}",
        "Inferring structure from DataVariant vector is not advisable as the method makes naive assumptions that can lead to massive computational errors. "
        "If the variants are part of a container, region, or segment, please use the appropriate method instead. "
        "If you are sure you want to proceed, please ignore this warning.");
 
    if (variants.empty()) {
        std::vector<DataDimension> dims;
        dims.emplace_back("empty_variants", 0, 1, DataDimension::Role::CUSTOM);
        return dims;
    }
 
    std::vector<DataDimension> dimensions;
    size_t variant_count = variants.size();
 
    size_t first_variant_size = std::visit([](const auto& vec) -> size_t {
        return vec.size();
    },
        variants[0]);
 
    bool consistent_glm = std::ranges::all_of(variants, [](const auto& variant) {
        return std::visit([](const auto& vec) -> bool {
            using ValueType = typename std::decay_t<decltype(vec)>::value_type;
            return GlmData<ValueType>;
        },
            variant);
    });
 
    bool consistent_decimal = std::ranges::all_of(variants, [](const auto& variant) {
        return std::visit([](const auto& vec) -> bool {
            using ValueType = typename std::decay_t<decltype(vec)>::value_type;
            return MayaFlux::DecimalData<ValueType>;
        },
            variant);
    });
 
    bool consistent_complex = std::ranges::all_of(variants, [](const auto& variant) {
        return std::visit([](const auto& vec) -> bool {
            using ValueType = typename std::decay_t<decltype(vec)>::value_type;
            return MayaFlux::ComplexData<ValueType>;
        },
            variant);
    });
 
    bool consistent_integer = std::ranges::all_of(variants, [](const auto& variant) {
        return std::visit([](const auto& vec) -> bool {
            using ValueType = typename std::decay_t<decltype(vec)>::value_type;
            return MayaFlux::IntegerData<ValueType>;
        },
            variant);
    });
 
    if (consistent_glm) {
        dimensions.emplace_back(DataDimension::channel(variant_count));
 
        std::visit([&](const auto& first_vec) {
            using ValueType = typename std::decay_t<decltype(first_vec)>::value_type;
            constexpr size_t components = glm_component_count<ValueType>();
 
            DataDimension::Role role = DataDimension::Role::CUSTOM;
            if constexpr (GlmVec2Type<ValueType>) {
                role = DataDimension::Role::UV;
            } else if constexpr (GlmVec3Type<ValueType>) {
                role = DataDimension::Role::POSITION;
            } else if constexpr (GlmVec4Type<ValueType>) {
                role = DataDimension::Role::COLOR;
            }
 
            dimensions.emplace_back(DataDimension::grouped(
                "glm_elements",
                first_variant_size,
                static_cast<uint8_t>(components),
                role));
        },
            variants[0]);
 
        return dimensions;
    }
 
    if (variant_count == 1) {
        if (consistent_decimal) {
            dimensions.emplace_back(DataDimension::time(first_variant_size, "samples"));
        } else if (consistent_complex) {
            dimensions.emplace_back(DataDimension::frequency(first_variant_size, "frequency_data"));
        } else if (consistent_integer) {
            dimensions.emplace_back(DataDimension::spatial(first_variant_size, 'x', 1, "data_points"));
        } else {
            dimensions.emplace_back("unknown_data", first_variant_size, 1,
                DataDimension::Role::CUSTOM);
        }
 
    } else if (variant_count == 2 && (consistent_decimal || consistent_complex || consistent_integer)) {
        dimensions.emplace_back(DataDimension::channel(2));
        if (consistent_decimal) {
            dimensions.emplace_back(DataDimension::time(first_variant_size, "samples"));
        } else if (consistent_complex) {
            dimensions.emplace_back(DataDimension::frequency(first_variant_size, "bins"));
        } else {
            dimensions.emplace_back(DataDimension::spatial(first_variant_size, 'x', 1, "elements"));
        }
 
    } else if (variant_count <= 16 && (consistent_decimal || consistent_complex || consistent_integer)) {
        dimensions.emplace_back(DataDimension::channel(variant_count));
        if (consistent_decimal) {
            dimensions.emplace_back(DataDimension::time(first_variant_size, "samples"));
        } else if (consistent_complex) {
            dimensions.emplace_back(DataDimension::frequency(first_variant_size, "bins"));
        } else {
            dimensions.emplace_back(DataDimension::spatial(first_variant_size, 'x', 1, "pixels"));
        }
 
    } else if (consistent_decimal || consistent_complex || consistent_integer) {
        if (consistent_decimal) {
            dimensions.emplace_back(DataDimension::time(variant_count, "time_blocks"));
            dimensions.emplace_back("block_samples", first_variant_size, 1,
                DataDimension::Role::CUSTOM);
        } else if (consistent_complex) {
            dimensions.emplace_back(DataDimension::time(variant_count, "time_windows"));
            dimensions.emplace_back(DataDimension::frequency(first_variant_size, "frequency_bins"));
        } else {
            dimensions.emplace_back(DataDimension::time(variant_count, "frames"));
            dimensions.emplace_back(DataDimension::spatial(first_variant_size, 'x', 1, "frame_data"));
        }
 
    } else {
        dimensions.emplace_back("mixed_variants", variant_count, 1,
            DataDimension::Role::CUSTOM);
        dimensions.emplace_back("variant_data", first_variant_size, 1,
            DataDimension::Role::CUSTOM);
    }
 
    return dimensions;
}
 
}