init commit

.gitignore

@@ -0,0 +1,221 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[codz]
+*$py.class
+build*
+# C extensions
+*.so
+*.npy
+snapshots/
+test_results*
+meta.json
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#   Usually these files are written by a python script from a template
+#   before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py.cover
+.hypothesis/
+.pytest_cache/
+cover/
+tests/artifacts/
+tests/tests/artifacts/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+# Pipfile.lock
+
+# UV
+#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+# uv.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+# poetry.lock
+# poetry.toml
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#   pdm recommends including project-wide configuration in pdm.toml, but excluding .pdm-python.
+#   https://pdm-project.org/en/latest/usage/project/#working-with-version-control
+# pdm.lock
+# pdm.toml
+.pdm-python
+.pdm-build/
+
+# pixi
+#   Similar to Pipfile.lock, it is generally recommended to include pixi.lock in version control.
+# pixi.lock
+#   Pixi creates a virtual environment in the .pixi directory, just like venv module creates one
+#   in the .venv directory. It is recommended not to include this directory in version control.
+.pixi
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# Redis
+*.rdb
+*.aof
+*.pid
+
+# RabbitMQ
+mnesia/
+rabbitmq/
+rabbitmq-data/
+
+# ActiveMQ
+activemq-data/
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.envrc
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#   JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#   be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#   and can be added to the global gitignore or merged into this file.  For a more nuclear
+#   option (not recommended) you can uncomment the following to ignore the entire idea folder.
+# .idea/
+
+# Abstra
+#   Abstra is an AI-powered process automation framework.
+#   Ignore directories containing user credentials, local state, and settings.
+#   Learn more at https://abstra.io/docs
+.abstra/
+
+# Visual Studio Code
+#   Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore 
+#   that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore
+#   and can be added to the global gitignore or merged into this file. However, if you prefer, 
+#   you could uncomment the following to ignore the entire vscode folder
+# .vscode/
+
+# Ruff stuff:
+.ruff_cache/
+
+# PyPI configuration file
+.pypirc
+
+# Marimo
+marimo/_static/
+marimo/_lsp/
+__marimo__/
+
+# Streamlit
+.streamlit/secrets.toml

Makefile

@@ -0,0 +1,83 @@
+CXX := g++
+CXXFLAGS := -std=c++20 -O2 -Wall -Wextra -Wpedantic -pthread
+LDFLAGS := -pthread -lrt
+ORCH_LDFLAGS := $(LDFLAGS) -lusb-1.0
+
+BUILD_DIR := build
+BIN_DIR := $(BUILD_DIR)/bin
+
+INCLUDES := \
+	-Idata_acq_and_processing/common_cpp/ipc/include \
+	-Idata_acq_and_processing/common_cpp/config/include \
+	-Idata_acq_and_processing/third_party \
+	-Idata_acq_and_processing/third_party/eigen \
+	-Idata_acq_and_processing/sweep_orchestrator/include \
+	-Idata_acq_and_processing/sweep_orchestrator/device_drivers/interfaces \
+	-Idata_acq_and_processing/sweep_orchestrator/device_drivers/radar \
+	-Idata_acq_and_processing/sweep_orchestrator/device_drivers/switches \
+	-Idata_acq_and_processing/preprocessing/calibration_master/include \
+	-Idata_acq_and_processing/preprocessing/reference_master/include \
+	-Idata_acq_and_processing/preprocessing/data_preprocessor/include \
+	-Idata_acq_and_processing/processing/processors/include \
+	-Idata_acq_and_processing/processing/data_processor/include
+
+COMMON_SOURCES := \
+	data_acq_and_processing/common_cpp/ipc/src/shm_ring.cpp \
+	data_acq_and_processing/common_cpp/ipc/src/shared_types.cpp \
+	data_acq_and_processing/common_cpp/config/src/run_config.cpp
+
+ORCH_SOURCES := \
+	data_acq_and_processing/sweep_orchestrator/device_drivers/radar/librevna/librevna_minimal_driver_lifecycle.cpp \
+	data_acq_and_processing/sweep_orchestrator/device_drivers/radar/librevna/librevna_minimal_driver_transport.cpp \
+	data_acq_and_processing/sweep_orchestrator/device_drivers/radar/librevna/librevna_minimal_driver_protocol.cpp \
+	data_acq_and_processing/sweep_orchestrator/device_drivers/switches/h7992_minimal_driver.cpp \
+	data_acq_and_processing/sweep_orchestrator/device_drivers/switches/hmc349a_minimal_driver.cpp \
+	data_acq_and_processing/sweep_orchestrator/src/sweep_plan.cpp \
+	data_acq_and_processing/sweep_orchestrator/src/sweep_orchestrator.cpp \
+	data_acq_and_processing/sweep_orchestrator/src/main.cpp
+
+PREPROC_SOURCES := \
+	data_acq_and_processing/preprocessing/calibration_master/src/calibration_master.cpp \
+	data_acq_and_processing/preprocessing/calibration_master/src/through_calibrator.cpp \
+	data_acq_and_processing/preprocessing/reference_master/src/reference_master.cpp \
+	data_acq_and_processing/preprocessing/data_preprocessor/src/data_preprocessor.cpp \
+	data_acq_and_processing/preprocessing/data_preprocessor/src/main.cpp
+
+PROCESSOR_SOURCES := \
+	data_acq_and_processing/processing/processors/src/bscan_processor.cpp \
+	data_acq_and_processing/processing/processors/src/passthrough_processor.cpp \
+	data_acq_and_processing/processing/data_processor/src/processing_live_config.cpp \
+	data_acq_and_processing/processing/data_processor/src/data_processor.cpp \
+	data_acq_and_processing/processing/data_processor/src/main.cpp
+
+SWEEP_ORCH_OBJS := $(addprefix $(BUILD_DIR)/,$(COMMON_SOURCES:.cpp=.o) $(ORCH_SOURCES:.cpp=.o))
+PREPROCESSOR_OBJS := $(addprefix $(BUILD_DIR)/,$(COMMON_SOURCES:.cpp=.o) $(PREPROC_SOURCES:.cpp=.o))
+DATA_PROCESSOR_OBJS := $(addprefix $(BUILD_DIR)/,$(COMMON_SOURCES:.cpp=.o) $(PROCESSOR_SOURCES:.cpp=.o))
+
+TARGETS := \
+	$(BIN_DIR)/sweep_orchestrator \
+	$(BIN_DIR)/data_preprocessor \
+	$(BIN_DIR)/data_processor
+
+.PHONY: all clean
+
+all: $(TARGETS)
+
+$(BIN_DIR)/sweep_orchestrator: $(SWEEP_ORCH_OBJS)
+	@mkdir -p $(BIN_DIR)
+	$(CXX) $(SWEEP_ORCH_OBJS) -o $@ $(ORCH_LDFLAGS)
+
+$(BIN_DIR)/data_preprocessor: $(PREPROCESSOR_OBJS)
+	@mkdir -p $(BIN_DIR)
+	$(CXX) $(PREPROCESSOR_OBJS) -o $@ $(LDFLAGS)
+
+$(BIN_DIR)/data_processor: $(DATA_PROCESSOR_OBJS)
+	@mkdir -p $(BIN_DIR)
+	$(CXX) $(DATA_PROCESSOR_OBJS) -o $@ $(LDFLAGS)
+
+$(BUILD_DIR)/%.o: %.cpp
+	@mkdir -p $(dir $@)
+	$(CXX) $(CXXFLAGS) $(INCLUDES) -c $< -o $@
+
+clean:
+	rm -rf $(BUILD_DIR)

data_acq_and_processing/common_cpp/config/include/run_config.hpp

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+#pragma once
+
+#include <cstdint>
+#include <string>
+#include <vector>
+
+#include "shared_types.hpp"
+
+namespace radar::config {
+
+enum class DriverMode {
+    Mock,
+    Native,
+};
+
+enum class SwitchDriverKind {
+    H7992,
+    HMC349A,
+};
+
+struct RadarSweepSettings {
+    // Frequency sweep bounds and acquisition settings.
+    float start_hz = 1'000'000.0F;
+    float stop_hz = 6'000'000'000.0F;
+    std::uint32_t points = 501;
+    float if_bandwidth_hz = 1'000.0F;
+    float power_dbm = -10.0F;
+};
+
+struct RadarConfig {
+    // Radar device identity and runtime mode.
+    std::string model = "librevna";
+    std::string serial{};
+    DriverMode driver_mode = DriverMode::Mock;
+    float mock_signal_hz = 5'000'000.0F;
+    RadarSweepSettings sweep{};
+};
+
+struct SwitchConfig {
+    // One RF switch attached to a specific radar port.
+    std::string name{};
+    DriverMode driver_mode = DriverMode::Mock;
+    SwitchDriverKind driver_kind = SwitchDriverKind::H7992;
+    std::uint32_t radar_port = 0;
+    std::uint32_t positions = 4;
+    std::uint32_t default_position = 0;
+    std::string gpio_chip = "/dev/gpiochip0";
+    std::int32_t pin_a = 17;
+    std::int32_t pin_b = 27;
+    bool invert_logic = false;
+};
+
+struct RingEndpointConfig {
+    // Shared-memory endpoint geometry.
+    std::string name{};
+    std::uint32_t capacity = 32;
+    std::uint32_t slot_size_bytes = 2U * 1024U * 1024U;
+};
+
+struct RingsConfig {
+    RingEndpointConfig raw{.name = "/radar_raw"};
+    RingEndpointConfig raw_tap{.name = "/radar_raw_tap"};
+    RingEndpointConfig preprocessed{.name = "/radar_preprocessed"};
+    RingEndpointConfig preprocessed_tap{.name = "/radar_preprocessed_tap"};
+    RingEndpointConfig results{.name = "/radar_results"};
+};
+
+struct RuntimeConfig {
+    // Runtime pacing and behavior flags for C++ workers.
+    std::uint32_t settling_ms = 0;
+    std::uint32_t idle_sleep_ms = 2;
+    bool continuous = true;
+    std::string processing_live_config_path = "python_app/runtime/processing_live.json";
+};
+
+struct PreprocessConfig {
+    // Names and bundle paths selected by Python GUI layer.
+    std::string calibration_set{};
+    std::string reference_set{};
+    std::string calibration_bundle_path{};
+    std::string reference_bundle_path{};
+};
+
+struct RunConfig {
+    // Single configuration object shared by all C++ processes.
+    RadarConfig radar{};
+    SwitchConfig input_switch{};
+    SwitchConfig output_switch{};
+    RingsConfig rings{};
+    RuntimeConfig runtime{};
+    PreprocessConfig preprocess{};
+    std::vector<radar::ipc::ComboKey> run_combos{};
+};
+
+[[nodiscard]] auto load_run_config(const std::string& path) -> RunConfig;
+
+}  // namespace radar::config

data_acq_and_processing/common_cpp/config/src/run_config.cpp

@@ -0,0 +1,398 @@
+#include "run_config.hpp"
+
+#include <cmath>
+#include <cstdint>
+#include <fstream>
+#include <iterator>
+#include <limits>
+#include <stdexcept>
+#include <string>
+
+#include <nlohmann/json.hpp>
+
+namespace radar::config {
+namespace {
+
+using Json = nlohmann::json;
+
+[[nodiscard]] auto required_field(const Json& object, const std::string& key) -> const Json& {
+    const auto found = object.find(key);
+    if (found == object.end()) {
+        throw std::runtime_error("Missing required config field: " + key);
+    }
+    return *found;
+}
+
+[[nodiscard]] auto optional_field(const Json& object, const std::string& key) -> const Json* {
+    const auto found = object.find(key);
+    if (found == object.end()) {
+        return nullptr;
+    }
+    return &(*found);
+}
+
+[[nodiscard]] auto as_object(const Json& value, const std::string& context) -> const Json* {
+    if (!value.is_object()) {
+        throw std::runtime_error("Expected JSON object at " + context);
+    }
+    return &value;
+}
+
+[[nodiscard]] auto as_array(const Json& value, const std::string& context) -> const Json* {
+    if (!value.is_array()) {
+        throw std::runtime_error("Expected JSON array at " + context);
+    }
+    return &value;
+}
+
+[[nodiscard]] auto as_string(const Json& value, const std::string& context) -> const std::string& {
+    if (!value.is_string()) {
+        throw std::runtime_error("Expected JSON string at " + context);
+    }
+    return value.get_ref<const std::string&>();
+}
+
+[[nodiscard]] auto as_bool(const Json& value, const std::string& context) -> bool {
+    if (!value.is_boolean()) {
+        throw std::runtime_error("Expected JSON bool at " + context);
+    }
+    return value.get<bool>();
+}
+
+[[nodiscard]] auto as_number(const Json& value, const std::string& context) -> double {
+    if (!value.is_number()) {
+        throw std::runtime_error("Expected JSON number at " + context);
+    }
+    return value.get<double>();
+}
+
+[[nodiscard]] auto number_to_u32(double value, const std::string& context) -> std::uint32_t {
+    if (value < 0.0 || value > static_cast<double>(std::numeric_limits<std::uint32_t>::max())) {
+        throw std::runtime_error("Value out of uint32 range at " + context);
+    }
+
+    const auto rounded = std::round(value);
+    if (std::fabs(value - rounded) > 0.000001) {
+        throw std::runtime_error("Expected integer value at " + context);
+    }
+    return static_cast<std::uint32_t>(rounded);
+}
+
+[[nodiscard]] auto number_to_i32(double value, const std::string& context) -> std::int32_t {
+    const auto rounded = std::round(value);
+    if (std::fabs(value - rounded) > 0.000001) {
+        throw std::runtime_error("Expected integer value at " + context);
+    }
+
+    if (rounded < static_cast<double>(std::numeric_limits<std::int32_t>::min()) ||
+        rounded > static_cast<double>(std::numeric_limits<std::int32_t>::max())) {
+        throw std::runtime_error("Value out of int32 range at " + context);
+    }
+    return static_cast<std::int32_t>(rounded);
+}
+
+[[nodiscard]] auto optional_string(
+    const Json& object,
+    const std::string& key,
+    const std::string& fallback
+) -> std::string {
+    if (const auto* value = optional_field(object, key); value != nullptr) {
+        return as_string(*value, key);
+    }
+    return fallback;
+}
+
+[[nodiscard]] auto optional_bool(const Json& object, const std::string& key, bool fallback) -> bool {
+    if (const auto* value = optional_field(object, key); value != nullptr) {
+        return as_bool(*value, key);
+    }
+    return fallback;
+}
+
+[[nodiscard]] auto optional_u32(
+    const Json& object,
+    const std::string& key,
+    std::uint32_t fallback
+) -> std::uint32_t {
+    if (const auto* value = optional_field(object, key); value != nullptr) {
+        return number_to_u32(as_number(*value, key), key);
+    }
+    return fallback;
+}
+
+[[nodiscard]] auto optional_i32(
+    const Json& object,
+    const std::string& key,
+    std::int32_t fallback
+) -> std::int32_t {
+    if (const auto* value = optional_field(object, key); value != nullptr) {
+        return number_to_i32(as_number(*value, key), key);
+    }
+    return fallback;
+}
+
+[[nodiscard]] auto optional_f32(const Json& object, const std::string& key, float fallback) -> float {
+    if (const auto* value = optional_field(object, key); value != nullptr) {
+        return static_cast<float>(as_number(*value, key));
+    }
+    return fallback;
+}
+
+[[nodiscard]] auto parse_driver_mode(const std::string& value) -> DriverMode {
+    if (value == "mock") {
+        return DriverMode::Mock;
+    }
+    if (value == "native") {
+        return DriverMode::Native;
+    }
+    throw std::runtime_error("Unsupported driver_mode value: " + value);
+}
+
+[[nodiscard]] auto parse_switch_driver_kind(const std::string& value) -> SwitchDriverKind {
+    if (value == "h7992") {
+        return SwitchDriverKind::H7992;
+    }
+    if (value == "hmc349a") {
+        return SwitchDriverKind::HMC349A;
+    }
+    throw std::runtime_error("Unsupported switch driver value: " + value);
+}
+
+void validate_radar_sweep(const RadarSweepSettings& sweep) {
+    if (sweep.points == 0U) {
+        throw std::runtime_error("radar.sweep.points must be > 0");
+    }
+    if (sweep.stop_hz < sweep.start_hz) {
+        throw std::runtime_error("radar.sweep.stop_hz must be >= start_hz");
+    }
+}
+
+void validate_switch_config(const SwitchConfig& config) {
+    if (config.radar_port != 1U && config.radar_port != 2U) {
+        throw std::runtime_error("Switch radar_port must be 1 or 2 for " + config.name);
+    }
+
+    if (config.positions == 0U) {
+        throw std::runtime_error("Switch positions must be > 0 for " + config.name);
+    }
+    if (config.default_position >= config.positions) {
+        throw std::runtime_error("Switch default_position is out of range for " + config.name);
+    }
+
+    if (config.driver_mode != DriverMode::Native) {
+        return;
+    }
+
+    if (config.gpio_chip.empty()) {
+        throw std::runtime_error("gpio_chip is required for native switch mode: " + config.name);
+    }
+
+    if (config.driver_kind == SwitchDriverKind::H7992) {
+        if (config.positions > 4U) {
+            throw std::runtime_error("Native H7992 switch driver supports positions <= 4 for " + config.name);
+        }
+        if (config.pin_a < 0 || config.pin_b < 0 || config.pin_a == config.pin_b) {
+            throw std::runtime_error("pin_a/pin_b are invalid for native H7992 mode: " + config.name);
+        }
+        return;
+    }
+
+    if (config.positions > 2U) {
+        throw std::runtime_error("Native HMC349A switch driver supports positions <= 2 for " + config.name);
+    }
+    if (config.pin_a < 0) {
+        throw std::runtime_error("pin_a (control) is required for native HMC349A mode: " + config.name);
+    }
+}
+
+void validate_combo_key(const ipc::ComboKey& key, const RunConfig& config) {
+    if (key.input_pos >= config.input_switch.positions) {
+        throw std::runtime_error("run.combos input index out of range");
+    }
+    if (key.output_pos >= config.output_switch.positions) {
+        throw std::runtime_error("run.combos output index out of range");
+    }
+}
+
+[[nodiscard]] auto parse_switch_config(
+    const Json& object,
+    const std::string& default_name,
+    std::uint32_t default_radar_port
+) -> SwitchConfig {
+    const auto* switch_obj = as_object(object, default_name);
+
+    SwitchConfig config{};
+    config.name = optional_string(*switch_obj, "name", default_name);
+    config.driver_mode = parse_driver_mode(optional_string(*switch_obj, "driver_mode", "mock"));
+    config.driver_kind = parse_switch_driver_kind(optional_string(*switch_obj, "driver", "h7992"));
+    config.radar_port = optional_u32(*switch_obj, "radar_port", default_radar_port);
+    config.positions = optional_u32(*switch_obj, "positions", 4);
+    config.default_position = optional_u32(*switch_obj, "default_position", 0);
+    config.gpio_chip = optional_string(*switch_obj, "gpio_chip", "/dev/gpiochip0");
+    config.pin_a = optional_i32(*switch_obj, "pin_a", 17);
+    config.pin_b = optional_i32(*switch_obj, "pin_b", 27);
+    config.invert_logic = optional_bool(*switch_obj, "invert_logic", false);
+
+    validate_switch_config(config);
+
+    return config;
+}
+
+[[nodiscard]] auto parse_ring_endpoint(
+    const Json& object,
+    const std::string& key,
+    const RingEndpointConfig& fallback
+) -> RingEndpointConfig {
+    RingEndpointConfig endpoint = fallback;
+    const auto* value = optional_field(object, key);
+    if (value == nullptr) {
+        return endpoint;
+    }
+
+    const auto* endpoint_obj = as_object(*value, key);
+    endpoint.name = optional_string(*endpoint_obj, "name", endpoint.name);
+    endpoint.capacity = optional_u32(*endpoint_obj, "capacity", endpoint.capacity);
+    endpoint.slot_size_bytes = optional_u32(*endpoint_obj, "slot_size_bytes", endpoint.slot_size_bytes);
+
+    if (endpoint.capacity == 0U) {
+        throw std::runtime_error("Ring capacity must be > 0 for " + key);
+    }
+    if (endpoint.slot_size_bytes == 0U) {
+        throw std::runtime_error("Ring slot_size_bytes must be > 0 for " + key);
+    }
+    if (endpoint.name.empty() || endpoint.name.front() != '/') {
+        throw std::runtime_error("Ring name must start with '/' for " + key);
+    }
+
+    return endpoint;
+}
+
+}  // namespace
+
+auto load_run_config(const std::string& path) -> RunConfig {
+    std::ifstream stream(path);
+    if (!stream.is_open()) {
+        throw std::runtime_error("Failed to open config file: " + path);
+    }
+
+    const std::string json_text((std::istreambuf_iterator<char>(stream)), std::istreambuf_iterator<char>());
+    if (json_text.empty()) {
+        throw std::runtime_error("Config file is empty: " + path);
+    }
+
+    Json root{};
+    try {
+        root = Json::parse(json_text);
+    } catch (const Json::parse_error& error) {
+        throw std::runtime_error("Failed to parse config file: " + path + ": " + std::string(error.what()));
+    }
+
+    const auto* root_obj = as_object(root, "root");
+    RunConfig config{};
+
+    {
+        const auto* radar_obj = as_object(required_field(*root_obj, "radar"), "radar");
+        config.radar.model = optional_string(*radar_obj, "model", "librevna");
+        config.radar.serial = optional_string(*radar_obj, "serial", "");
+        config.radar.driver_mode = parse_driver_mode(optional_string(*radar_obj, "driver_mode", "mock"));
+        config.radar.mock_signal_hz = optional_f32(*radar_obj, "mock_signal_hz", 5'000'000.0F);
+
+        const auto* sweep_obj = as_object(required_field(*radar_obj, "sweep"), "radar.sweep");
+        config.radar.sweep.start_hz =
+            static_cast<float>(as_number(required_field(*sweep_obj, "start_hz"), "radar.sweep.start_hz"));
+        config.radar.sweep.stop_hz =
+            static_cast<float>(as_number(required_field(*sweep_obj, "stop_hz"), "radar.sweep.stop_hz"));
+        config.radar.sweep.points =
+            number_to_u32(as_number(required_field(*sweep_obj, "points"), "radar.sweep.points"), "radar.sweep.points");
+        config.radar.sweep.if_bandwidth_hz = static_cast<float>(
+            as_number(required_field(*sweep_obj, "if_bandwidth_hz"), "radar.sweep.if_bandwidth_hz")
+        );
+        config.radar.sweep.power_dbm = optional_f32(*sweep_obj, "stimulus_power_dbm", -10.0F);
+        validate_radar_sweep(config.radar.sweep);
+    }
+
+    {
+        const auto* switches_obj = as_object(required_field(*root_obj, "switches"), "switches");
+
+        if (const auto* port1_value = optional_field(*switches_obj, "port1"); port1_value != nullptr) {
+            const auto* port1_obj = as_object(*port1_value, "switches.port1");
+            const auto* port2_obj = as_object(required_field(*switches_obj, "port2"), "switches.port2");
+            config.output_switch = parse_switch_config(*port1_obj, "port1", 1);
+            config.input_switch = parse_switch_config(*port2_obj, "port2", 2);
+        } else {
+            config.input_switch = parse_switch_config(
+                *as_object(required_field(*switches_obj, "input"), "switches.input"),
+                "input",
+                2
+            );
+            config.output_switch = parse_switch_config(
+                *as_object(required_field(*switches_obj, "output"), "switches.output"),
+                "output",
+                1
+            );
+        }
+
+        if (config.input_switch.radar_port == config.output_switch.radar_port) {
+            throw std::runtime_error("Switch radar_port mapping must be unique for two switches");
+        }
+    }
+
+    {
+        const auto* run_obj = as_object(required_field(*root_obj, "run"), "run");
+        config.runtime.settling_ms = optional_u32(*run_obj, "settling_ms", 0);
+        config.runtime.idle_sleep_ms = optional_u32(*run_obj, "idle_sleep_ms", 2);
+        config.runtime.continuous = optional_bool(*run_obj, "continuous", true);
+        config.runtime.processing_live_config_path = optional_string(
+            *run_obj,
+            "processing_live_config_path",
+            "python_app/runtime/processing_live.json"
+        );
+
+        const auto* combos = as_array(required_field(*run_obj, "combos"), "run.combos");
+        if (combos->empty()) {
+            throw std::runtime_error("run.combos must not be empty");
+        }
+
+        config.run_combos.reserve(combos->size());
+        for (const auto& combo_value : *combos) {
+            const auto* combo_obj = as_object(combo_value, "run.combos[]");
+
+            ipc::ComboKey key{};
+            key.input_pos = number_to_u32(
+                as_number(required_field(*combo_obj, "input"), "run.combos.input"),
+                "run.combos.input"
+            );
+            key.output_pos = number_to_u32(
+                as_number(required_field(*combo_obj, "output"), "run.combos.output"),
+                "run.combos.output"
+            );
+            validate_combo_key(key, config);
+            config.run_combos.push_back(key);
+        }
+    }
+
+    {
+        const auto* preprocess_obj = as_object(required_field(*root_obj, "preprocess"), "preprocess");
+        config.preprocess.calibration_set = optional_string(*preprocess_obj, "calibration_set", "");
+        config.preprocess.reference_set = optional_string(*preprocess_obj, "reference_set", "");
+        config.preprocess.calibration_bundle_path = optional_string(*preprocess_obj, "calibration_bundle_path", "");
+        config.preprocess.reference_bundle_path = optional_string(*preprocess_obj, "reference_bundle_path", "");
+    }
+
+    {
+        const auto* rings_obj = as_object(required_field(*root_obj, "rings"), "rings");
+        config.rings.raw = parse_ring_endpoint(*rings_obj, "raw", config.rings.raw);
+        config.rings.raw_tap = parse_ring_endpoint(*rings_obj, "raw_tap", config.rings.raw_tap);
+        config.rings.preprocessed = parse_ring_endpoint(*rings_obj, "preprocessed", config.rings.preprocessed);
+        config.rings.preprocessed_tap = parse_ring_endpoint(
+            *rings_obj,
+            "preprocessed_tap",
+            config.rings.preprocessed_tap
+        );
+        config.rings.results = parse_ring_endpoint(*rings_obj, "results", config.rings.results);
+    }
+
+    return config;
+}
+
+}  // namespace radar::config

data_acq_and_processing/common_cpp/ipc/include/shared_types.hpp

@@ -0,0 +1,91 @@
+#pragma once
+
+#include <cstddef>
+#include <cstdint>
+#include <span>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+namespace radar::ipc {
+
+struct Complex32 {
+    float re = 0.0F;
+    float im = 0.0F;
+};
+
+struct ComboKey {
+    // Zero-based switch positions.
+    std::uint32_t input_pos = 0;
+    std::uint32_t output_pos = 0;
+
+    [[nodiscard]] auto operator==(const ComboKey& other) const -> bool {
+        return input_pos == other.input_pos && output_pos == other.output_pos;
+    }
+};
+
+struct ComboKeyHash {
+    [[nodiscard]] auto operator()(const ComboKey& key) const noexcept -> std::size_t {
+        return (static_cast<std::size_t>(key.input_pos) << 32U) ^ static_cast<std::size_t>(key.output_pos);
+    }
+};
+
+struct SweepTraceBlock {
+    ComboKey combo{};
+    // Frequency axis in Hz. Must have the same size as `s21`.
+    std::vector<float> frequency_hz{};
+    // Complex S21 samples for matching frequency points.
+    std::vector<Complex32> s21{};
+};
+
+struct RawSweepCollection {
+    std::uint64_t collection_id = 0;
+    std::uint64_t monotonic_ns = 0;
+    std::vector<SweepTraceBlock> traces{};
+};
+
+using PreprocessedCollection = RawSweepCollection;
+
+enum class ResultKind : std::uint8_t {
+    TraceComplex = 1,
+    ScalarF32 = 2,
+};
+
+struct ResultPayload {
+    std::string processing_name{};
+    ResultKind kind = ResultKind::TraceComplex;
+    // Valid for TraceComplex payloads.
+    std::vector<float> frequency_hz{};
+    // Valid for TraceComplex payloads.
+    std::vector<Complex32> trace{};
+    // Valid for ScalarF32 payloads.
+    float scalar_value = 0.0F;
+};
+
+struct ResultBlock {
+    ComboKey combo{};
+    std::vector<ResultPayload> payloads{};
+};
+
+struct ResultCollection {
+    std::uint64_t collection_id = 0;
+    std::uint64_t monotonic_ns = 0;
+    std::vector<ResultBlock> blocks{};
+};
+
+// Wire-format serialization helpers for IPC rings.
+[[nodiscard]] auto serialize_raw_collection(const RawSweepCollection& collection) -> std::vector<std::uint8_t>;
+[[nodiscard]] auto deserialize_raw_collection(std::span<const std::uint8_t> bytes) -> RawSweepCollection;
+
+[[nodiscard]] auto serialize_preprocessed_collection(const PreprocessedCollection& collection)
+    -> std::vector<std::uint8_t>;
+[[nodiscard]] auto deserialize_preprocessed_collection(std::span<const std::uint8_t> bytes)
+    -> PreprocessedCollection;
+
+[[nodiscard]] auto serialize_result_collection(const ResultCollection& collection) -> std::vector<std::uint8_t>;
+[[nodiscard]] auto deserialize_result_collection(std::span<const std::uint8_t> bytes) -> ResultCollection;
+
+// Monotonic timestamp helper used for produced collections.
+[[nodiscard]] auto current_monotonic_ns() -> std::uint64_t;
+
+}  // namespace radar::ipc

data_acq_and_processing/common_cpp/ipc/include/shm_ring.hpp

@@ -0,0 +1,61 @@
+#pragma once
+
+#include <cstddef>
+#include <cstdint>
+#include <span>
+#include <string>
+#include <vector>
+
+namespace radar::ipc {
+
+class ShmRing {
+  public:
+    ShmRing() = default;
+    ~ShmRing();
+
+    ShmRing(const ShmRing&) = delete;
+    auto operator=(const ShmRing&) -> ShmRing& = delete;
+
+    ShmRing(ShmRing&& other) noexcept;
+    auto operator=(ShmRing&& other) noexcept -> ShmRing&;
+
+    [[nodiscard]] static auto open_or_create(
+        const std::string& name,
+        std::uint32_t capacity,
+        std::uint32_t slot_size_bytes
+    ) -> ShmRing;
+
+    // Opens ring by name and validates wire-format header fields.
+    [[nodiscard]] static auto open_existing(const std::string& name) -> ShmRing;
+    // Removes shared memory object by name (safe if it does not exist).
+    static void unlink_ring(const std::string& name);
+
+    [[nodiscard]] auto is_open() const -> bool;
+    [[nodiscard]] auto capacity() const -> std::uint32_t;
+    [[nodiscard]] auto slot_size_bytes() const -> std::uint32_t;
+    [[nodiscard]] auto dropped_count() const -> std::uint64_t;
+
+    // Pushes payload with overwrite-oldest policy on overflow.
+    [[nodiscard]] auto push(std::span<const std::uint8_t> payload) -> bool;
+    // Pops the next payload if available.
+    [[nodiscard]] auto pop(std::vector<std::uint8_t>& payload) -> bool;
+
+  private:
+    struct Header;
+    struct SlotHeader;
+
+    int fd_ = -1;
+    std::size_t mapped_size_ = 0;
+    void* mapped_ = nullptr;
+    Header* header_ = nullptr;
+
+    // Slot geometry helpers.
+    [[nodiscard]] auto slot_stride_bytes() const -> std::size_t;
+    [[nodiscard]] auto slot_header(std::uint64_t sequence) const -> SlotHeader*;
+    [[nodiscard]] auto slot_payload(SlotHeader* slot) const -> std::uint8_t*;
+
+    void close();
+    static void validate_name(const std::string& name);
+};
+
+}  // namespace radar::ipc

data_acq_and_processing/common_cpp/ipc/src/shared_types.cpp

@@ -0,0 +1,336 @@
+#include "shared_types.hpp"
+
+#include <chrono>
+#include <cstring>
+#include <limits>
+#include <stdexcept>
+#include <string>
+#include <type_traits>
+#include <utility>
+#include <vector>
+
+namespace radar::ipc {
+namespace {
+
+constexpr std::uint32_t kRawCollectionMagic = 0x31574152U;      // RAW1
+constexpr std::uint32_t kPreprocessedCollectionMagic = 0x31525050U;  // PRP1
+constexpr std::uint32_t kResultCollectionMagic = 0x314C5352U;   // RSL1
+
+template <typename T>
+concept TriviallySerializable = std::is_trivially_copyable_v<T>;
+
+[[nodiscard]] auto checked_count_to_u32(std::size_t count, const std::string& label) -> std::uint32_t {
+    if (count > std::numeric_limits<std::uint32_t>::max()) {
+        throw std::runtime_error(label + " exceeds uint32 wire-format limit");
+    }
+    return static_cast<std::uint32_t>(count);
+}
+
+void ensure_equal_sizes(std::size_t left, std::size_t right, const std::string& label) {
+    if (left != right) {
+        throw std::runtime_error(label + " has inconsistent vector sizes");
+    }
+}
+
+class BinaryWriter {
+  public:
+    template <TriviallySerializable T>
+    void write(const T& value) {
+        const auto old_size = bytes_.size();
+        bytes_.resize(old_size + sizeof(T));
+        std::memcpy(bytes_.data() + old_size, &value, sizeof(T));
+    }
+
+    void write_string(const std::string& value) {
+        if (value.size() > std::numeric_limits<std::uint16_t>::max()) {
+            throw std::runtime_error("String is too large for wire format");
+        }
+
+        write(static_cast<std::uint16_t>(value.size()));
+
+        const auto old_size = bytes_.size();
+        bytes_.resize(old_size + value.size());
+        std::memcpy(bytes_.data() + old_size, value.data(), value.size());
+    }
+
+    [[nodiscard]] auto finish() && -> std::vector<std::uint8_t> {
+        return std::move(bytes_);
+    }
+
+  private:
+    std::vector<std::uint8_t> bytes_{};
+};
+
+class BinaryReader {
+  public:
+    explicit BinaryReader(std::span<const std::uint8_t> bytes) : bytes_(bytes) {}
+
+    template <TriviallySerializable T>
+    [[nodiscard]] auto read() -> T {
+        ensure_available(sizeof(T));
+
+        T value{};
+        std::memcpy(&value, bytes_.data() + offset_, sizeof(T));
+        offset_ += sizeof(T);
+        return value;
+    }
+
+    [[nodiscard]] auto read_string() -> std::string {
+        const auto size = read<std::uint16_t>();
+        ensure_available(size);
+
+        const auto* begin = reinterpret_cast<const char*>(bytes_.data() + offset_);
+        std::string value(begin, begin + size);
+        offset_ += size;
+        return value;
+    }
+
+    [[nodiscard]] auto is_consumed() const -> bool {
+        return offset_ == bytes_.size();
+    }
+
+  private:
+    void ensure_available(std::size_t size) const {
+        if (offset_ + size > bytes_.size()) {
+            throw std::runtime_error("Unexpected end of serialized payload");
+        }
+    }
+
+    std::span<const std::uint8_t> bytes_{};
+    std::size_t offset_ = 0;
+};
+
+void write_trace_block(BinaryWriter& writer, const SweepTraceBlock& trace) {
+    ensure_equal_sizes(trace.frequency_hz.size(), trace.s21.size(), "Sweep trace");
+
+    writer.write(trace.combo.input_pos);
+    writer.write(trace.combo.output_pos);
+    writer.write(checked_count_to_u32(trace.frequency_hz.size(), "Trace point count"));
+
+    for (const auto frequency_hz : trace.frequency_hz) {
+        writer.write(frequency_hz);
+    }
+
+    for (const auto& point : trace.s21) {
+        writer.write(point.re);
+        writer.write(point.im);
+    }
+}
+
+[[nodiscard]] auto read_trace_block(BinaryReader& reader) -> SweepTraceBlock {
+    SweepTraceBlock trace{};
+    trace.combo.input_pos = reader.read<std::uint32_t>();
+    trace.combo.output_pos = reader.read<std::uint32_t>();
+
+    const auto point_count = reader.read<std::uint32_t>();
+    trace.frequency_hz.reserve(point_count);
+    trace.s21.reserve(point_count);
+
+    for (std::uint32_t index = 0; index < point_count; ++index) {
+        trace.frequency_hz.push_back(reader.read<float>());
+    }
+
+    for (std::uint32_t index = 0; index < point_count; ++index) {
+        trace.s21.push_back(Complex32{
+            .re = reader.read<float>(),
+            .im = reader.read<float>(),
+        });
+    }
+
+    return trace;
+}
+
+void write_trace_collection(BinaryWriter& writer, std::uint32_t magic, const RawSweepCollection& collection) {
+    writer.write(magic);
+    writer.write(collection.collection_id);
+    writer.write(collection.monotonic_ns);
+    writer.write(checked_count_to_u32(collection.traces.size(), "Trace count"));
+
+    for (const auto& trace : collection.traces) {
+        write_trace_block(writer, trace);
+    }
+}
+
+[[nodiscard]] auto read_trace_collection(BinaryReader& reader, std::uint32_t expected_magic) -> RawSweepCollection {
+    const auto magic = reader.read<std::uint32_t>();
+    if (magic != expected_magic) {
+        throw std::runtime_error("Unexpected trace collection magic");
+    }
+
+    RawSweepCollection collection{};
+    collection.collection_id = reader.read<std::uint64_t>();
+    collection.monotonic_ns = reader.read<std::uint64_t>();
+
+    const auto trace_count = reader.read<std::uint32_t>();
+    collection.traces.reserve(trace_count);
+    for (std::uint32_t index = 0; index < trace_count; ++index) {
+        collection.traces.push_back(read_trace_block(reader));
+    }
+
+    return collection;
+}
+
+void write_trace_result_payload(BinaryWriter& writer, const ResultPayload& payload) {
+    ensure_equal_sizes(payload.frequency_hz.size(), payload.trace.size(), "Result trace payload");
+
+    writer.write(checked_count_to_u32(payload.trace.size(), "Result trace point count"));
+    for (const auto frequency_hz : payload.frequency_hz) {
+        writer.write(frequency_hz);
+    }
+    for (const auto& sample : payload.trace) {
+        writer.write(sample.re);
+        writer.write(sample.im);
+    }
+}
+
+auto read_trace_result_payload(BinaryReader& reader, ResultPayload* payload) -> void {
+    const auto point_count = reader.read<std::uint32_t>();
+
+    payload->frequency_hz.reserve(point_count);
+    payload->trace.reserve(point_count);
+
+    for (std::uint32_t index = 0; index < point_count; ++index) {
+        payload->frequency_hz.push_back(reader.read<float>());
+    }
+    for (std::uint32_t index = 0; index < point_count; ++index) {
+        payload->trace.push_back(Complex32{
+            .re = reader.read<float>(),
+            .im = reader.read<float>(),
+        });
+    }
+}
+
+void write_result_payload(BinaryWriter& writer, const ResultPayload& payload) {
+    writer.write(static_cast<std::uint8_t>(payload.kind));
+    writer.write_string(payload.processing_name);
+
+    switch (payload.kind) {
+        case ResultKind::TraceComplex:
+            write_trace_result_payload(writer, payload);
+            return;
+        case ResultKind::ScalarF32:
+            writer.write(payload.scalar_value);
+            return;
+        default:
+            throw std::runtime_error("Unsupported result payload kind");
+    }
+}
+
+[[nodiscard]] auto read_result_payload(BinaryReader& reader) -> ResultPayload {
+    ResultPayload payload{};
+    payload.kind = static_cast<ResultKind>(reader.read<std::uint8_t>());
+    payload.processing_name = reader.read_string();
+
+    switch (payload.kind) {
+        case ResultKind::TraceComplex:
+            read_trace_result_payload(reader, &payload);
+            return payload;
+        case ResultKind::ScalarF32:
+            payload.scalar_value = reader.read<float>();
+            return payload;
+        default:
+            throw std::runtime_error("Unsupported result payload kind in stream");
+    }
+}
+
+void write_result_block(BinaryWriter& writer, const ResultBlock& block) {
+    writer.write(block.combo.input_pos);
+    writer.write(block.combo.output_pos);
+    writer.write(checked_count_to_u32(block.payloads.size(), "Result payload count"));
+
+    for (const auto& payload : block.payloads) {
+        write_result_payload(writer, payload);
+    }
+}
+
+[[nodiscard]] auto read_result_block(BinaryReader& reader) -> ResultBlock {
+    ResultBlock block{};
+    block.combo.input_pos = reader.read<std::uint32_t>();
+    block.combo.output_pos = reader.read<std::uint32_t>();
+
+    const auto payload_count = reader.read<std::uint32_t>();
+    block.payloads.reserve(payload_count);
+    for (std::uint32_t index = 0; index < payload_count; ++index) {
+        block.payloads.push_back(read_result_payload(reader));
+    }
+
+    return block;
+}
+
+void ensure_reader_consumed(const BinaryReader& reader, const std::string& payload_label) {
+    if (!reader.is_consumed()) {
+        throw std::runtime_error("Unexpected trailing bytes in " + payload_label);
+    }
+}
+
+}  // namespace
+
+auto serialize_raw_collection(const RawSweepCollection& collection) -> std::vector<std::uint8_t> {
+    BinaryWriter writer{};
+    write_trace_collection(writer, kRawCollectionMagic, collection);
+    return std::move(writer).finish();
+}
+
+auto deserialize_raw_collection(std::span<const std::uint8_t> bytes) -> RawSweepCollection {
+    BinaryReader reader(bytes);
+    auto collection = read_trace_collection(reader, kRawCollectionMagic);
+    ensure_reader_consumed(reader, "raw collection");
+    return collection;
+}
+
+auto serialize_preprocessed_collection(const PreprocessedCollection& collection) -> std::vector<std::uint8_t> {
+    BinaryWriter writer{};
+    write_trace_collection(writer, kPreprocessedCollectionMagic, collection);
+    return std::move(writer).finish();
+}
+
+auto deserialize_preprocessed_collection(std::span<const std::uint8_t> bytes) -> PreprocessedCollection {
+    BinaryReader reader(bytes);
+    auto collection = read_trace_collection(reader, kPreprocessedCollectionMagic);
+    ensure_reader_consumed(reader, "preprocessed collection");
+    return collection;
+}
+
+auto serialize_result_collection(const ResultCollection& collection) -> std::vector<std::uint8_t> {
+    BinaryWriter writer{};
+    writer.write(kResultCollectionMagic);
+    writer.write(collection.collection_id);
+    writer.write(collection.monotonic_ns);
+    writer.write(checked_count_to_u32(collection.blocks.size(), "Result block count"));
+
+    for (const auto& block : collection.blocks) {
+        write_result_block(writer, block);
+    }
+
+    return std::move(writer).finish();
+}
+
+auto deserialize_result_collection(std::span<const std::uint8_t> bytes) -> ResultCollection {
+    BinaryReader reader(bytes);
+
+    const auto magic = reader.read<std::uint32_t>();
+    if (magic != kResultCollectionMagic) {
+        throw std::runtime_error("Unexpected result collection magic");
+    }
+
+    ResultCollection collection{};
+    collection.collection_id = reader.read<std::uint64_t>();
+    collection.monotonic_ns = reader.read<std::uint64_t>();
+
+    const auto block_count = reader.read<std::uint32_t>();
+    collection.blocks.reserve(block_count);
+    for (std::uint32_t index = 0; index < block_count; ++index) {
+        collection.blocks.push_back(read_result_block(reader));
+    }
+
+    ensure_reader_consumed(reader, "result collection");
+    return collection;
+}
+
+auto current_monotonic_ns() -> std::uint64_t {
+    const auto now = std::chrono::steady_clock::now().time_since_epoch();
+    const auto now_ns = std::chrono::duration_cast<std::chrono::nanoseconds>(now).count();
+    return static_cast<std::uint64_t>(now_ns);
+}
+
+}  // namespace radar::ipc

data_acq_and_processing/common_cpp/ipc/src/shm_ring.cpp

@@ -0,0 +1,380 @@
+#include "shm_ring.hpp"
+
+#include <atomic>
+#include <chrono>
+#include <cerrno>
+#include <cstring>
+#include <fcntl.h>
+#include <new>
+#include <stdexcept>
+#include <string>
+#include <string_view>
+#include <sys/mman.h>
+#include <sys/stat.h>
+#include <thread>
+#include <unistd.h>
+#include <utility>
+
+namespace radar::ipc {
+
+struct alignas(64) ShmRing::Header {
+    char magic[8]{};
+    std::uint32_t version = 0;
+    std::uint32_t capacity = 0;
+    std::uint32_t slot_size_bytes = 0;
+    std::uint32_t reserved = 0;
+    std::atomic<std::uint64_t> write_seq{};
+    std::atomic<std::uint64_t> read_seq{};
+    std::atomic<std::uint64_t> dropped{};
+};
+
+struct alignas(16) ShmRing::SlotHeader {
+    std::uint32_t payload_size = 0;
+    std::uint32_t reserved = 0;
+    std::uint64_t sequence = 0;
+};
+
+namespace {
+
+constexpr std::string_view kRingMagic = "RDRRING2";
+constexpr std::uint32_t kRingVersion = 1;
+constexpr auto kHeaderInitWaitTimeout = std::chrono::milliseconds(1000);
+constexpr auto kHeaderInitPollInterval = std::chrono::milliseconds(2);
+
+[[nodiscard]] auto checked_u64_diff(std::uint64_t left, std::uint64_t right) -> std::uint64_t {
+    return left >= right ? left - right : 0;
+}
+
+[[nodiscard]] auto errno_message(const std::string& action, const std::string& name) -> std::runtime_error {
+    return std::runtime_error(action + " " + name + ": " + std::strerror(errno));
+}
+
+class ScopedFd {
+  public:
+    explicit ScopedFd(int fd) : fd_(fd) {}
+
+    ~ScopedFd() {
+        if (fd_ >= 0) {
+            ::close(fd_);
+        }
+    }
+
+    ScopedFd(const ScopedFd&) = delete;
+    auto operator=(const ScopedFd&) -> ScopedFd& = delete;
+
+    ScopedFd(ScopedFd&& other) noexcept : fd_(std::exchange(other.fd_, -1)) {}
+
+    auto release() -> int {
+        return std::exchange(fd_, -1);
+    }
+
+  private:
+    int fd_ = -1;
+};
+
+class ScopedMmap {
+  public:
+    ScopedMmap(void* address, std::size_t size) : address_(address), size_(size) {}
+
+    ~ScopedMmap() {
+        if (address_ != nullptr && address_ != MAP_FAILED) {
+            munmap(address_, size_);
+        }
+    }
+
+    ScopedMmap(const ScopedMmap&) = delete;
+    auto operator=(const ScopedMmap&) -> ScopedMmap& = delete;
+
+    ScopedMmap(ScopedMmap&& other) noexcept
+        : address_(std::exchange(other.address_, nullptr)),
+          size_(std::exchange(other.size_, 0U)) {}
+
+    auto release() -> std::pair<void*, std::size_t> {
+        return {std::exchange(address_, nullptr), std::exchange(size_, 0U)};
+    }
+
+  private:
+    void* address_ = nullptr;
+    std::size_t size_ = 0;
+};
+
+}  // namespace
+
+ShmRing::~ShmRing() {
+    close();
+}
+
+ShmRing::ShmRing(ShmRing&& other) noexcept {
+    *this = std::move(other);
+}
+
+auto ShmRing::operator=(ShmRing&& other) noexcept -> ShmRing& {
+    if (this != &other) {
+        close();
+
+        fd_ = std::exchange(other.fd_, -1);
+        mapped_size_ = std::exchange(other.mapped_size_, 0U);
+        mapped_ = std::exchange(other.mapped_, nullptr);
+        header_ = std::exchange(other.header_, nullptr);
+    }
+    return *this;
+}
+
+auto ShmRing::open_or_create(
+    const std::string& name,
+    std::uint32_t capacity,
+    std::uint32_t slot_size_bytes
+) -> ShmRing {
+    validate_name(name);
+    if (capacity == 0U) {
+        throw std::runtime_error("Ring capacity must be > 0");
+    }
+    if (slot_size_bytes == 0U) {
+        throw std::runtime_error("Ring slot size must be > 0");
+    }
+
+    bool created = false;
+    int fd = shm_open(name.c_str(), O_RDWR | O_CREAT | O_EXCL, 0660);
+    if (fd >= 0) {
+        created = true;
+    } else if (errno == EEXIST) {
+        fd = shm_open(name.c_str(), O_RDWR, 0660);
+    }
+
+    if (fd < 0) {
+        throw errno_message("Failed to open shared memory ring", name);
+    }
+
+    ScopedFd scoped_fd(fd);
+
+    const auto slot_stride = sizeof(SlotHeader) + static_cast<std::size_t>(slot_size_bytes);
+    const auto mapped_size = sizeof(Header) + slot_stride * capacity;
+
+    if (created) {
+        if (ftruncate(fd, static_cast<off_t>(mapped_size)) != 0) {
+            throw errno_message("Failed to resize shared memory ring", name);
+        }
+    }
+
+    void* mapped = mmap(nullptr, mapped_size, PROT_READ | PROT_WRITE, MAP_SHARED, fd, 0);
+    if (mapped == MAP_FAILED) {
+        throw errno_message("Failed to mmap shared memory ring", name);
+    }
+
+    ScopedMmap scoped_mmap(mapped, mapped_size);
+    auto* header = static_cast<Header*>(mapped);
+
+    if (created) {
+        std::memset(mapped, 0, mapped_size);
+        std::memcpy(header->magic, kRingMagic.data(), kRingMagic.size());
+        header->version = kRingVersion;
+        header->capacity = capacity;
+        header->slot_size_bytes = slot_size_bytes;
+
+        new (&header->write_seq) std::atomic<std::uint64_t>(0);
+        new (&header->read_seq) std::atomic<std::uint64_t>(0);
+        new (&header->dropped) std::atomic<std::uint64_t>(0);
+        std::atomic_thread_fence(std::memory_order_release);
+    } else {
+        const auto deadline = std::chrono::steady_clock::now() + kHeaderInitWaitTimeout;
+
+        while (true) {
+            std::atomic_thread_fence(std::memory_order_acquire);
+
+            const bool magic_ok = std::memcmp(header->magic, kRingMagic.data(), kRingMagic.size()) == 0;
+            const bool version_ok = header->version == kRingVersion;
+            const bool geometry_ok = header->capacity == capacity && header->slot_size_bytes == slot_size_bytes;
+
+            if (magic_ok && version_ok && geometry_ok) {
+                break;
+            }
+
+            if (std::chrono::steady_clock::now() >= deadline) {
+                if (!magic_ok) {
+                    throw std::runtime_error("Shared memory ring magic mismatch for " + name);
+                }
+                if (!version_ok) {
+                    throw std::runtime_error("Shared memory ring version mismatch for " + name);
+                }
+                throw std::runtime_error("Shared memory ring geometry mismatch for " + name);
+            }
+
+            std::this_thread::sleep_for(kHeaderInitPollInterval);
+        }
+    }
+
+    ShmRing ring{};
+    ring.fd_ = scoped_fd.release();
+
+    const auto [released_mapped, released_size] = scoped_mmap.release();
+    ring.mapped_ = released_mapped;
+    ring.mapped_size_ = released_size;
+    ring.header_ = static_cast<Header*>(released_mapped);
+    return ring;
+}
+
+auto ShmRing::open_existing(const std::string& name) -> ShmRing {
+    validate_name(name);
+
+    const int fd = shm_open(name.c_str(), O_RDWR, 0660);
+    if (fd < 0) {
+        throw errno_message("Failed to open shared memory ring", name);
+    }
+
+    ScopedFd scoped_fd(fd);
+
+    struct stat info {};
+    if (fstat(fd, &info) != 0) {
+        throw errno_message("Failed to stat shared memory ring", name);
+    }
+
+    if (info.st_size < static_cast<off_t>(sizeof(Header))) {
+        throw std::runtime_error("Shared memory ring size is too small for " + name);
+    }
+
+    const auto mapped_size = static_cast<std::size_t>(info.st_size);
+    void* mapped = mmap(nullptr, mapped_size, PROT_READ | PROT_WRITE, MAP_SHARED, fd, 0);
+    if (mapped == MAP_FAILED) {
+        throw errno_message("Failed to mmap shared memory ring", name);
+    }
+
+    ScopedMmap scoped_mmap(mapped, mapped_size);
+    auto* header = static_cast<Header*>(mapped);
+
+    if (std::memcmp(header->magic, kRingMagic.data(), kRingMagic.size()) != 0) {
+        throw std::runtime_error("Shared memory ring magic mismatch for " + name);
+    }
+    if (header->version != kRingVersion) {
+        throw std::runtime_error("Shared memory ring version mismatch for " + name);
+    }
+
+    ShmRing ring{};
+    ring.fd_ = scoped_fd.release();
+
+    const auto [released_mapped, released_size] = scoped_mmap.release();
+    ring.mapped_ = released_mapped;
+    ring.mapped_size_ = released_size;
+    ring.header_ = static_cast<Header*>(released_mapped);
+    return ring;
+}
+
+void ShmRing::unlink_ring(const std::string& name) {
+    validate_name(name);
+    if (shm_unlink(name.c_str()) != 0 && errno != ENOENT) {
+        throw errno_message("Failed to unlink shared memory ring", name);
+    }
+}
+
+auto ShmRing::is_open() const -> bool {
+    return header_ != nullptr;
+}
+
+auto ShmRing::capacity() const -> std::uint32_t {
+    return header_ != nullptr ? header_->capacity : 0;
+}
+
+auto ShmRing::slot_size_bytes() const -> std::uint32_t {
+    return header_ != nullptr ? header_->slot_size_bytes : 0;
+}
+
+auto ShmRing::dropped_count() const -> std::uint64_t {
+    if (header_ == nullptr) {
+        return 0;
+    }
+    return header_->dropped.load(std::memory_order_acquire);
+}
+
+auto ShmRing::push(std::span<const std::uint8_t> payload) -> bool {
+    if (header_ == nullptr) {
+        throw std::runtime_error("Ring is not open");
+    }
+    if (payload.size() > header_->slot_size_bytes) {
+        return false;
+    }
+
+    const auto write_seq = header_->write_seq.load(std::memory_order_relaxed);
+    const auto read_seq = header_->read_seq.load(std::memory_order_acquire);
+
+    if (checked_u64_diff(write_seq, read_seq) >= header_->capacity) {
+        header_->read_seq.store(read_seq + 1U, std::memory_order_release);
+        header_->dropped.fetch_add(1U, std::memory_order_relaxed);
+    }
+
+    auto* slot = slot_header(write_seq);
+    slot->payload_size = static_cast<std::uint32_t>(payload.size());
+    slot->sequence = write_seq + 1U;
+    std::memcpy(slot_payload(slot), payload.data(), payload.size());
+
+    std::atomic_thread_fence(std::memory_order_release);
+    header_->write_seq.store(write_seq + 1U, std::memory_order_release);
+    return true;
+}
+
+auto ShmRing::pop(std::vector<std::uint8_t>& payload) -> bool {
+    if (header_ == nullptr) {
+        throw std::runtime_error("Ring is not open");
+    }
+
+    const auto read_seq = header_->read_seq.load(std::memory_order_relaxed);
+    const auto write_seq = header_->write_seq.load(std::memory_order_acquire);
+    if (read_seq >= write_seq) {
+        return false;
+    }
+
+    auto* slot = slot_header(read_seq);
+    if (slot->sequence != read_seq + 1U) {
+        // Producer overwrote this slot before consumer read it. Resync to latest.
+        header_->read_seq.store(write_seq, std::memory_order_release);
+        return false;
+    }
+
+    const auto payload_size = slot->payload_size;
+    if (payload_size > header_->slot_size_bytes) {
+        header_->read_seq.store(write_seq, std::memory_order_release);
+        throw std::runtime_error("Invalid payload size in shared memory slot");
+    }
+
+    payload.resize(payload_size);
+    std::memcpy(payload.data(), slot_payload(slot), payload_size);
+
+    std::atomic_thread_fence(std::memory_order_acquire);
+    header_->read_seq.store(read_seq + 1U, std::memory_order_release);
+    return true;
+}
+
+auto ShmRing::slot_stride_bytes() const -> std::size_t {
+    return sizeof(SlotHeader) + header_->slot_size_bytes;
+}
+
+auto ShmRing::slot_header(std::uint64_t sequence) const -> SlotHeader* {
+    const auto index = sequence % header_->capacity;
+    auto* slots_begin = static_cast<std::uint8_t*>(mapped_) + sizeof(Header);
+    return reinterpret_cast<SlotHeader*>(slots_begin + index * slot_stride_bytes());
+}
+
+auto ShmRing::slot_payload(SlotHeader* slot) const -> std::uint8_t* {
+    return reinterpret_cast<std::uint8_t*>(slot) + sizeof(SlotHeader);
+}
+
+void ShmRing::close() {
+    if (mapped_ != nullptr) {
+        munmap(mapped_, mapped_size_);
+        mapped_ = nullptr;
+    }
+
+    if (fd_ >= 0) {
+        ::close(fd_);
+        fd_ = -1;
+    }
+
+    mapped_size_ = 0;
+    header_ = nullptr;
+}
+
+void ShmRing::validate_name(const std::string& name) {
+    if (name.empty() || name.front() != '/') {
+        throw std::runtime_error("POSIX shm name must start with '/'");
+    }
+}
+
+}  // namespace radar::ipc

data_acq_and_processing/preprocessing/calibration_master/include/calibration_master.hpp

@@ -0,0 +1,32 @@
+#pragma once
+
+#include <memory>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include "calibrator_interface.hpp"
+#include "shared_types.hpp"
+
+namespace radar::preprocessing {
+
+class CalibrationMaster {
+  public:
+    // `calibrator` encapsulates the actual calibration algorithm (v1: through).
+    explicit CalibrationMaster(std::unique_ptr<CalibratorInterface> calibrator);
+
+    // Loads a serialized raw sweep bundle with one calibration standard per combo.
+    void load_bundle(const std::string& path);
+    // Ensures all runtime combos are present in loaded standards.
+    void validate_combos(const std::vector<ipc::ComboKey>& combos) const;
+    // Applies calibration standard corresponding to measured trace combo.
+    [[nodiscard]] auto apply(const ipc::SweepTraceBlock& measured_trace) const -> ipc::SweepTraceBlock;
+
+  private:
+    std::unique_ptr<CalibratorInterface> calibrator_impl_;
+    std::unordered_map<ipc::ComboKey, ipc::SweepTraceBlock, ipc::ComboKeyHash> standards_by_combo_{};
+};
+
+[[nodiscard]] auto make_through_calibrator() -> std::unique_ptr<CalibratorInterface>;
+
+}  // namespace radar::preprocessing

data_acq_and_processing/preprocessing/calibration_master/include/calibrator_interface.hpp

@@ -0,0 +1,21 @@
+#pragma once
+
+#include <string>
+#include <vector>
+
+#include "shared_types.hpp"
+
+namespace radar::preprocessing {
+
+class CalibratorInterface {
+  public:
+    virtual ~CalibratorInterface() = default;
+
+    [[nodiscard]] virtual auto name() const -> std::string = 0;
+    [[nodiscard]] virtual auto apply(
+        const std::vector<ipc::Complex32>& measured,
+        const std::vector<ipc::Complex32>& calibration
+    ) const -> std::vector<ipc::Complex32> = 0;
+};
+
+}  // namespace radar::preprocessing

data_acq_and_processing/preprocessing/calibration_master/src/calibration_master.cpp

@@ -0,0 +1,101 @@
+#include "calibration_master.hpp"
+
+#include <cstdint>
+#include <fstream>
+#include <iterator>
+#include <stdexcept>
+#include <string>
+#include <vector>
+
+namespace radar::preprocessing {
+namespace {
+
+[[nodiscard]] auto combo_to_string(const ipc::ComboKey& combo) -> std::string {
+    return "input=" + std::to_string(combo.input_pos) + " output=" + std::to_string(combo.output_pos);
+}
+
+[[nodiscard]] auto read_binary_file(const std::string& path, const std::string& bundle_label)
+    -> std::vector<std::uint8_t> {
+    std::ifstream stream(path, std::ios::binary);
+    if (!stream.is_open()) {
+        throw std::runtime_error("Failed to open " + bundle_label + " bundle: " + path);
+    }
+
+    return std::vector<std::uint8_t>(std::istreambuf_iterator<char>(stream), std::istreambuf_iterator<char>());
+}
+
+void validate_trace_layout(const ipc::SweepTraceBlock& trace, const std::string& trace_label) {
+    if (trace.frequency_hz.size() != trace.s21.size()) {
+        throw std::runtime_error(
+            trace_label + " frequency/complex vector size mismatch for combo " + combo_to_string(trace.combo)
+        );
+    }
+}
+
+}  // namespace
+
+CalibrationMaster::CalibrationMaster(std::unique_ptr<CalibratorInterface> calibrator)
+    : calibrator_impl_(std::move(calibrator)) {
+    if (!calibrator_impl_) {
+        throw std::runtime_error("CalibrationMaster requires a non-null calibrator");
+    }
+}
+
+void CalibrationMaster::load_bundle(const std::string& path) {
+    if (path.empty()) {
+        throw std::runtime_error("Calibration bundle path must not be empty");
+    }
+
+    const auto bytes = read_binary_file(path, "calibration");
+    if (bytes.empty()) {
+        throw std::runtime_error("Calibration bundle is empty: " + path);
+    }
+
+    const auto collection = ipc::deserialize_raw_collection(bytes);
+    standards_by_combo_.clear();
+    standards_by_combo_.reserve(collection.traces.size());
+    for (const auto& trace : collection.traces) {
+        validate_trace_layout(trace, "Calibration standard");
+        standards_by_combo_.insert_or_assign(trace.combo, trace);
+    }
+
+    if (standards_by_combo_.empty()) {
+        throw std::runtime_error("Calibration bundle does not contain traces: " + path);
+    }
+}
+
+void CalibrationMaster::validate_combos(const std::vector<ipc::ComboKey>& combos) const {
+    for (const auto& combo : combos) {
+        if (!standards_by_combo_.contains(combo)) {
+            throw std::runtime_error("Calibration data is missing for combo " + combo_to_string(combo));
+        }
+    }
+}
+
+auto CalibrationMaster::apply(const ipc::SweepTraceBlock& measured_trace) const -> ipc::SweepTraceBlock {
+    validate_trace_layout(measured_trace, "Measured trace");
+
+    const auto found = standards_by_combo_.find(measured_trace.combo);
+    if (found == standards_by_combo_.end()) {
+        throw std::runtime_error(
+            "Calibration standard is missing for measured combo " + combo_to_string(measured_trace.combo)
+        );
+    }
+
+    const auto& standard = found->second;
+    validate_trace_layout(standard, "Calibration standard");
+    if (measured_trace.s21.size() != standard.s21.size()) {
+        throw std::runtime_error(
+            "Calibration standard point count mismatch for combo " + combo_to_string(measured_trace.combo)
+        );
+    }
+
+    ipc::SweepTraceBlock output{};
+    output.combo = measured_trace.combo;
+    output.frequency_hz = measured_trace.frequency_hz;
+    output.s21 = calibrator_impl_->apply(measured_trace.s21, standard.s21);
+
+    return output;
+}
+
+}  // namespace radar::preprocessing

data_acq_and_processing/preprocessing/calibration_master/src/through_calibrator.cpp

@@ -0,0 +1,76 @@
+#include "calibration_master.hpp"
+
+#include <cstddef>
+#include <memory>
+#include <stdexcept>
+#include <vector>
+
+#include <Eigen/Core>
+
+namespace radar::preprocessing {
+namespace {
+
+class ThroughCalibrator final : public CalibratorInterface {
+  public:
+    [[nodiscard]] auto name() const -> std::string override {
+        return "through";
+    }
+
+    [[nodiscard]] auto apply(
+        const std::vector<ipc::Complex32>& measured,
+        const std::vector<ipc::Complex32>& calibration
+    ) const -> std::vector<ipc::Complex32> override {
+        if (measured.size() != calibration.size()) {
+            throw std::runtime_error("Through calibration vector size mismatch");
+        }
+
+        static_assert(sizeof(ipc::Complex32) == sizeof(float) * 2U, "Complex32 layout must be two contiguous floats");
+
+        constexpr float epsilon = 1e-12F;
+        using InterleavedComplexView = Eigen::Matrix<float, Eigen::Dynamic, 2, Eigen::RowMajor>;
+
+        const auto point_count = static_cast<Eigen::Index>(measured.size());
+        Eigen::Map<const InterleavedComplexView> measured_view(
+            reinterpret_cast<const float*>(measured.data()),
+            point_count,
+            2
+        );
+        Eigen::Map<const InterleavedComplexView> calibration_view(
+            reinterpret_cast<const float*>(calibration.data()),
+            point_count,
+            2
+        );
+
+        const auto measured_re = measured_view.col(0).array();
+        const auto measured_im = measured_view.col(1).array();
+        const auto calibration_re = calibration_view.col(0).array();
+        const auto calibration_im = calibration_view.col(1).array();
+
+        const Eigen::ArrayXf magnitude_squared = calibration_re.square() + calibration_im.square();
+        const auto stable_division_mask = (magnitude_squared > epsilon);
+
+        const Eigen::ArrayXf corrected_re = stable_division_mask.select(
+            ((measured_re * calibration_re) + (measured_im * calibration_im)) / magnitude_squared,
+            measured_re
+        );
+        const Eigen::ArrayXf corrected_im = stable_division_mask.select(
+            ((measured_im * calibration_re) - (measured_re * calibration_im)) / magnitude_squared,
+            measured_im
+        );
+
+        std::vector<ipc::Complex32> corrected(measured.size());
+        Eigen::Map<InterleavedComplexView> corrected_view(reinterpret_cast<float*>(corrected.data()), point_count, 2);
+        corrected_view.col(0) = corrected_re.matrix();
+        corrected_view.col(1) = corrected_im.matrix();
+
+        return corrected;
+    }
+};
+
+}  // namespace
+
+auto make_through_calibrator() -> std::unique_ptr<CalibratorInterface> {
+    return std::make_unique<ThroughCalibrator>();
+}
+
+}  // namespace radar::preprocessing

data_acq_and_processing/preprocessing/data_preprocessor/include/data_preprocessor.hpp

@@ -0,0 +1,47 @@
+#pragma once
+
+#include <atomic>
+#include <cstdint>
+#include <vector>
+
+#include "calibration_master.hpp"
+#include "reference_master.hpp"
+#include "run_config.hpp"
+#include "shm_ring.hpp"
+
+namespace radar::preprocessing {
+
+class DataPreprocessor {
+  public:
+    DataPreprocessor(
+        const config::RunConfig& config,
+        CalibrationMaster& calibration_master,
+        ReferenceMaster& reference_master,
+        ipc::ShmRing& raw_ring,
+        ipc::ShmRing& preprocessed_ring,
+        ipc::ShmRing* preprocessed_tap_ring = nullptr
+    );
+
+    // Main processing loop. Runs until stop flag is set.
+    void run(const std::atomic<bool>& stop_requested);
+
+  private:
+    void validate_startup_prerequisites() const;
+    [[nodiscard]] auto try_pop_raw_collection(
+        std::vector<std::uint8_t>& serialized_raw,
+        ipc::RawSweepCollection* out_collection
+    ) -> bool;
+    void publish_preprocessed_collection(const ipc::PreprocessedCollection& collection);
+
+    [[nodiscard]] auto preprocess_collection(const ipc::RawSweepCollection& raw_collection) const
+        -> ipc::PreprocessedCollection;
+
+    const config::RunConfig& config_;
+    CalibrationMaster& calibration_master_;
+    ReferenceMaster& reference_master_;
+    ipc::ShmRing& raw_ring_;
+    ipc::ShmRing& preprocessed_ring_;
+    ipc::ShmRing* preprocessed_tap_ring_ = nullptr;
+};
+
+}  // namespace radar::preprocessing

data_acq_and_processing/preprocessing/data_preprocessor/src/data_preprocessor.cpp

@@ -0,0 +1,85 @@
+#include "data_preprocessor.hpp"
+
+#include <chrono>
+#include <stdexcept>
+#include <thread>
+#include <vector>
+
+namespace radar::preprocessing {
+
+DataPreprocessor::DataPreprocessor(
+    const config::RunConfig& config,
+    CalibrationMaster& calibration_master,
+    ReferenceMaster& reference_master,
+    ipc::ShmRing& raw_ring,
+    ipc::ShmRing& preprocessed_ring,
+    ipc::ShmRing* preprocessed_tap_ring
+)
+    : config_(config),
+      calibration_master_(calibration_master),
+      reference_master_(reference_master),
+      raw_ring_(raw_ring),
+      preprocessed_ring_(preprocessed_ring),
+      preprocessed_tap_ring_(preprocessed_tap_ring) {}
+
+void DataPreprocessor::validate_startup_prerequisites() const {
+    calibration_master_.validate_combos(config_.run_combos);
+    reference_master_.validate_combos(config_.run_combos);
+}
+
+auto DataPreprocessor::try_pop_raw_collection(
+    std::vector<std::uint8_t>& serialized_raw,
+    ipc::RawSweepCollection* out_collection
+) -> bool {
+    if (!raw_ring_.pop(serialized_raw)) {
+        std::this_thread::sleep_for(std::chrono::milliseconds(config_.runtime.idle_sleep_ms));
+        return false;
+    }
+
+    *out_collection = ipc::deserialize_raw_collection(serialized_raw);
+    return true;
+}
+
+void DataPreprocessor::publish_preprocessed_collection(const ipc::PreprocessedCollection& collection) {
+    const auto serialized_preprocessed = ipc::serialize_preprocessed_collection(collection);
+    if (!preprocessed_ring_.push(serialized_preprocessed)) {
+        throw std::runtime_error("Preprocessed ring slot is too small for serialized collection");
+    }
+    if (preprocessed_tap_ring_ != nullptr && !preprocessed_tap_ring_->push(serialized_preprocessed)) {
+        throw std::runtime_error("Preprocessed tap ring slot is too small for serialized collection");
+    }
+}
+
+void DataPreprocessor::run(const std::atomic<bool>& stop_requested) {
+    validate_startup_prerequisites();
+
+    std::vector<std::uint8_t> serialized_raw{};
+    ipc::RawSweepCollection raw_collection{};
+    while (!stop_requested.load(std::memory_order_relaxed)) {
+        if (!try_pop_raw_collection(serialized_raw, &raw_collection)) {
+            continue;
+        }
+
+        const auto preprocessed_collection = preprocess_collection(raw_collection);
+        publish_preprocessed_collection(preprocessed_collection);
+    }
+}
+
+auto DataPreprocessor::preprocess_collection(const ipc::RawSweepCollection& raw_collection) const
+    -> ipc::PreprocessedCollection {
+    ipc::PreprocessedCollection preprocessed{};
+    preprocessed.collection_id = raw_collection.collection_id;
+    preprocessed.monotonic_ns = ipc::current_monotonic_ns();
+    preprocessed.traces.reserve(raw_collection.traces.size());
+
+    for (const auto& raw_trace : raw_collection.traces) {
+        // Pipeline order is fixed: calibration first, then reference subtraction.
+        const auto calibrated = calibration_master_.apply(raw_trace);
+        const auto referenced = reference_master_.apply(calibrated);
+        preprocessed.traces.push_back(referenced);
+    }
+
+    return preprocessed;
+}
+
+}  // namespace radar::preprocessing

data_acq_and_processing/preprocessing/data_preprocessor/src/main.cpp

@@ -0,0 +1,86 @@
+#include <atomic>
+#include <csignal>
+#include <exception>
+#include <iostream>
+#include <string>
+
+#include "calibration_master.hpp"
+#include "data_preprocessor.hpp"
+#include "reference_master.hpp"
+#include "run_config.hpp"
+#include "shm_ring.hpp"
+
+namespace {
+
+constexpr const char* kDefaultConfigPath = "run_config.json";
+
+std::atomic<bool> g_stop_requested{false};
+
+void signal_handler(int /*signal*/) {
+    g_stop_requested.store(true, std::memory_order_relaxed);
+}
+
+void install_signal_handlers() {
+    std::signal(SIGINT, signal_handler);
+    std::signal(SIGTERM, signal_handler);
+}
+
+[[nodiscard]] auto read_config_path(int argc, char** argv) -> std::string {
+    std::string config_path = kDefaultConfigPath;
+    for (int index = 1; index < argc; ++index) {
+        const std::string arg = argv[index];
+        if (arg == "--config" && (index + 1) < argc) {
+            config_path = argv[++index];
+        }
+    }
+    return config_path;
+}
+
+}  // namespace
+
+int main(int argc, char** argv) {
+    install_signal_handlers();
+
+    try {
+        const auto config_path = read_config_path(argc, argv);
+        const auto config = radar::config::load_run_config(config_path);
+
+        auto raw_ring = radar::ipc::ShmRing::open_or_create(
+            config.rings.raw.name,
+            config.rings.raw.capacity,
+            config.rings.raw.slot_size_bytes
+        );
+        auto preprocessed_ring = radar::ipc::ShmRing::open_or_create(
+            config.rings.preprocessed.name,
+            config.rings.preprocessed.capacity,
+            config.rings.preprocessed.slot_size_bytes
+        );
+        auto preprocessed_tap_ring = radar::ipc::ShmRing::open_or_create(
+            config.rings.preprocessed_tap.name,
+            config.rings.preprocessed_tap.capacity,
+            config.rings.preprocessed_tap.slot_size_bytes
+        );
+
+        // Load preprocessing assets once before entering run loop.
+        radar::preprocessing::CalibrationMaster calibration_master(radar::preprocessing::make_through_calibrator());
+        calibration_master.load_bundle(config.preprocess.calibration_bundle_path);
+
+        radar::preprocessing::ReferenceMaster reference_master;
+        reference_master.load_bundle(config.preprocess.reference_bundle_path);
+        reference_master.prepare_calibrated(calibration_master);
+
+        radar::preprocessing::DataPreprocessor preprocessor(
+            config,
+            calibration_master,
+            reference_master,
+            raw_ring,
+            preprocessed_ring,
+            &preprocessed_tap_ring
+        );
+        preprocessor.run(g_stop_requested);
+        return 0;
+    } catch (const std::exception& exception) {
+        std::cerr << "data_preprocessor error: " << exception.what() << '\n';
+        return 1;
+    }
+}

data_acq_and_processing/preprocessing/reference_master/include/reference_master.hpp

@@ -0,0 +1,30 @@
+#pragma once
+
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include "shared_types.hpp"
+
+namespace radar::preprocessing {
+
+class CalibrationMaster;
+
+class ReferenceMaster {
+  public:
+    // Loads a serialized raw sweep bundle with one reference trace per combo.
+    void load_bundle(const std::string& path);
+    // Builds calibrated references in memory using currently loaded raw references.
+    // Must be called after load_bundle() and after calibration standards are loaded.
+    void prepare_calibrated(const CalibrationMaster& calibration_master);
+    // Ensures all runtime combos are present in loaded references.
+    void validate_combos(const std::vector<ipc::ComboKey>& combos) const;
+    // Subtracts per-combo reference trace from calibrated trace.
+    [[nodiscard]] auto apply(const ipc::SweepTraceBlock& calibrated_trace) const -> ipc::SweepTraceBlock;
+
+  private:
+    std::unordered_map<ipc::ComboKey, ipc::SweepTraceBlock, ipc::ComboKeyHash> raw_references_by_combo_{};
+    std::unordered_map<ipc::ComboKey, ipc::SweepTraceBlock, ipc::ComboKeyHash> calibrated_references_by_combo_{};
+};
+
+}  // namespace radar::preprocessing

data_acq_and_processing/preprocessing/reference_master/src/reference_master.cpp

@@ -0,0 +1,141 @@
+#include "reference_master.hpp"
+
+#include <cstdint>
+#include <fstream>
+#include <iterator>
+#include <stdexcept>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include <Eigen/Core>
+
+#include "calibration_master.hpp"
+
+namespace radar::preprocessing {
+namespace {
+
+[[nodiscard]] auto combo_to_string(const ipc::ComboKey& combo) -> std::string {
+    return "input=" + std::to_string(combo.input_pos) + " output=" + std::to_string(combo.output_pos);
+}
+
+[[nodiscard]] auto read_binary_file(const std::string& path, const std::string& bundle_label)
+    -> std::vector<std::uint8_t> {
+    std::ifstream stream(path, std::ios::binary);
+    if (!stream.is_open()) {
+        throw std::runtime_error("Failed to open " + bundle_label + " bundle: " + path);
+    }
+
+    return std::vector<std::uint8_t>(std::istreambuf_iterator<char>(stream), std::istreambuf_iterator<char>());
+}
+
+void validate_trace_layout(const ipc::SweepTraceBlock& trace, const std::string& trace_label) {
+    if (trace.frequency_hz.size() != trace.s21.size()) {
+        throw std::runtime_error(
+            trace_label + " frequency/complex vector size mismatch for combo " + combo_to_string(trace.combo)
+        );
+    }
+}
+
+}  // namespace
+
+void ReferenceMaster::load_bundle(const std::string& path) {
+    if (path.empty()) {
+        throw std::runtime_error("Reference bundle path must not be empty");
+    }
+
+    const auto bytes = read_binary_file(path, "reference");
+    if (bytes.empty()) {
+        throw std::runtime_error("Reference bundle is empty: " + path);
+    }
+
+    const auto collection = ipc::deserialize_raw_collection(bytes);
+    raw_references_by_combo_.clear();
+    raw_references_by_combo_.reserve(collection.traces.size());
+    calibrated_references_by_combo_.clear();
+    for (const auto& trace : collection.traces) {
+        validate_trace_layout(trace, "Reference trace");
+        raw_references_by_combo_.insert_or_assign(trace.combo, trace);
+    }
+
+    if (raw_references_by_combo_.empty()) {
+        throw std::runtime_error("Reference bundle does not contain traces: " + path);
+    }
+}
+
+void ReferenceMaster::prepare_calibrated(const CalibrationMaster& calibration_master) {
+    if (raw_references_by_combo_.empty()) {
+        throw std::runtime_error("Reference bundle must be loaded before prepare_calibrated()");
+    }
+
+    calibrated_references_by_combo_.clear();
+    calibrated_references_by_combo_.reserve(raw_references_by_combo_.size());
+    for (const auto& [combo, raw_reference] : raw_references_by_combo_) {
+        auto calibrated = calibration_master.apply(raw_reference);
+        calibrated.combo = combo;
+        calibrated_references_by_combo_.insert_or_assign(combo, std::move(calibrated));
+    }
+}
+
+void ReferenceMaster::validate_combos(const std::vector<ipc::ComboKey>& combos) const {
+    if (calibrated_references_by_combo_.empty()) {
+        throw std::runtime_error(
+            "Calibrated references are not prepared. Call ReferenceMaster::prepare_calibrated() at startup."
+        );
+    }
+
+    for (const auto& combo : combos) {
+        if (!raw_references_by_combo_.contains(combo)) {
+            throw std::runtime_error("Raw reference data is missing for combo " + combo_to_string(combo));
+        }
+        if (!calibrated_references_by_combo_.contains(combo)) {
+            throw std::runtime_error("Calibrated reference data is missing for combo " + combo_to_string(combo));
+        }
+    }
+}
+
+auto ReferenceMaster::apply(const ipc::SweepTraceBlock& calibrated_trace) const -> ipc::SweepTraceBlock {
+    validate_trace_layout(calibrated_trace, "Calibrated trace");
+
+    const auto found = calibrated_references_by_combo_.find(calibrated_trace.combo);
+    if (found == calibrated_references_by_combo_.end()) {
+        throw std::runtime_error(
+            "Calibrated reference trace is missing for combo " + combo_to_string(calibrated_trace.combo)
+        );
+    }
+
+    const auto& reference = found->second;
+    validate_trace_layout(reference, "Calibrated reference trace");
+    if (calibrated_trace.s21.size() != reference.s21.size()) {
+        throw std::runtime_error(
+            "Reference point count mismatch for combo " + combo_to_string(calibrated_trace.combo)
+        );
+    }
+
+    ipc::SweepTraceBlock output{};
+    output.combo = calibrated_trace.combo;
+    output.frequency_hz = calibrated_trace.frequency_hz;
+    output.s21.resize(calibrated_trace.s21.size());
+
+    static_assert(sizeof(ipc::Complex32) == sizeof(float) * 2U, "Complex32 layout must be two contiguous floats");
+    using InterleavedComplexView = Eigen::Matrix<float, Eigen::Dynamic, 2, Eigen::RowMajor>;
+    const auto point_count = static_cast<Eigen::Index>(calibrated_trace.s21.size());
+
+    Eigen::Map<const InterleavedComplexView> calibrated_view(
+        reinterpret_cast<const float*>(calibrated_trace.s21.data()),
+        point_count,
+        2
+    );
+    Eigen::Map<const InterleavedComplexView> reference_view(
+        reinterpret_cast<const float*>(reference.s21.data()),
+        point_count,
+        2
+    );
+    Eigen::Map<InterleavedComplexView> output_view(reinterpret_cast<float*>(output.s21.data()), point_count, 2);
+
+    output_view = calibrated_view - reference_view;
+
+    return output;
+}
+
+}  // namespace radar::preprocessing

data_acq_and_processing/processing/data_processor/include/data_processor.hpp

@@ -0,0 +1,48 @@
+#pragma once
+
+#include <atomic>
+#include <memory>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include "processor_interface.hpp"
+#include "processing_live_config.hpp"
+#include "run_config.hpp"
+#include "shm_ring.hpp"
+
+namespace radar::processing {
+
+using ProcessorRegistry = std::unordered_map<std::string, std::unique_ptr<ProcessorInterface>>;
+
+class DataProcessor {
+  public:
+    DataProcessor(
+        const config::RunConfig& config,
+        ipc::ShmRing& preprocessed_ring,
+        ipc::ShmRing& results_ring,
+        ProcessorRegistry processors
+    );
+
+    void run(const std::atomic<bool>& stop_requested);
+
+  private:
+    [[nodiscard]] auto process_collection(
+        const ipc::PreprocessedCollection& preprocessed,
+        ProcessorInterface& processor,
+        const ProcessingLiveConfig& live_config
+    ) -> ipc::ResultCollection;
+
+    [[nodiscard]] auto resolve_processor(const ProcessingLiveConfig& live_config) -> ProcessorInterface&;
+
+    const config::RunConfig& config_;
+    ipc::ShmRing& preprocessed_ring_;
+    ipc::ShmRing& results_ring_;
+    ProcessorRegistry processors_{};
+    std::string default_processor_mode_{};
+    ProcessingLiveConfigLoader live_config_loader_;
+};
+
+[[nodiscard]] auto create_default_processors() -> ProcessorRegistry;
+
+}  // namespace radar::processing

data_acq_and_processing/processing/data_processor/include/processing_live_config.hpp

@@ -0,0 +1,49 @@
+#pragma once
+
+#include <chrono>
+#include <cstdint>
+#include <filesystem>
+#include <string>
+
+namespace radar::processing {
+
+enum class HistoryCommand {
+    None,
+    RemoveLast,
+    ClearAll,
+};
+
+struct ProcessingLiveConfig {
+    std::string processor_mode = "pass_through";
+    float gain_db = 0.0F;
+    float phase_deg = 0.0F;
+    std::string bscan_axis = "abs";
+    float bscan_cut_m = 0.824F;
+    float bscan_max_depth_m = 1.0F;
+    float bscan_gain = 1.0F;
+    float bscan_start_freq_mhz = 100.0F;
+    float bscan_stop_freq_mhz = 8800.0F;
+    std::uint64_t history_command_seq = 0;
+    HistoryCommand history_command = HistoryCommand::None;
+};
+
+class ProcessingLiveConfigLoader {
+  public:
+    explicit ProcessingLiveConfigLoader(std::string path);
+
+    [[nodiscard]] auto current() const -> ProcessingLiveConfig;
+    [[nodiscard]] auto refresh_if_needed() -> ProcessingLiveConfig;
+    [[nodiscard]] auto revision() const -> std::uint64_t;
+
+  private:
+    [[nodiscard]] auto read_from_file() const -> ProcessingLiveConfig;
+
+    std::string path_{};
+    ProcessingLiveConfig current_{};
+    std::filesystem::file_time_type last_write_time_{};
+    bool has_last_write_time_ = false;
+    std::uint64_t revision_ = 0;
+    std::chrono::steady_clock::time_point next_check_at_{};
+};
+
+}  // namespace radar::processing

data_acq_and_processing/processing/data_processor/src/data_processor.cpp

@@ -0,0 +1,156 @@
+#include "data_processor.hpp"
+
+#include <algorithm>
+#include <chrono>
+#include <cstdint>
+#include <deque>
+#include <stdexcept>
+#include <thread>
+#include <utility>
+
+#include "bscan_processor.hpp"
+#include "passthrough_processor.hpp"
+
+namespace radar::processing {
+namespace {
+
+constexpr const char* kDefaultProcessorMode = "pass_through";
+constexpr std::size_t kBscanReplayWindow = 50U;
+
+[[nodiscard]] auto replay_history_limit(const config::RunConfig& config) -> std::size_t {
+    const auto preprocessed_capacity = static_cast<std::size_t>(std::max<std::uint32_t>(1U, config.rings.preprocessed.capacity));
+    const auto results_capacity = static_cast<std::size_t>(std::max<std::uint32_t>(1U, config.rings.results.capacity));
+    return std::min({preprocessed_capacity, results_capacity, kBscanReplayWindow});
+}
+
+void publish_result_collection(const ipc::ResultCollection& collection, ipc::ShmRing& results_ring) {
+    const auto out_bytes = ipc::serialize_result_collection(collection);
+    if (!results_ring.push(out_bytes)) {
+        throw std::runtime_error("Results ring slot is too small for serialized collection");
+    }
+}
+
+}  // namespace
+
+DataProcessor::DataProcessor(
+    const config::RunConfig& config,
+    ipc::ShmRing& preprocessed_ring,
+    ipc::ShmRing& results_ring,
+    ProcessorRegistry processors
+)
+    : config_(config),
+      preprocessed_ring_(preprocessed_ring),
+      results_ring_(results_ring),
+      processors_(std::move(processors)),
+      default_processor_mode_(kDefaultProcessorMode),
+      live_config_loader_(config.runtime.processing_live_config_path) {
+    if (processors_.empty()) {
+        throw std::runtime_error("DataProcessor requires at least one processor");
+    }
+    if (const auto found = processors_.find(default_processor_mode_); found == processors_.end()) {
+        default_processor_mode_ = processors_.begin()->first;
+    }
+}
+
+void DataProcessor::run(const std::atomic<bool>& stop_requested) {
+    std::vector<std::uint8_t> bytes{};
+    std::deque<ipc::PreprocessedCollection> preprocessed_history{};
+    const std::size_t history_limit = replay_history_limit(config_);
+    std::uint64_t last_replayed_revision = live_config_loader_.revision();
+    std::uint64_t last_applied_history_command_seq = 0;
+
+    while (!stop_requested.load(std::memory_order_relaxed)) {
+        const auto live_config = live_config_loader_.refresh_if_needed();
+        const auto live_revision = live_config_loader_.revision();
+        auto& processor = resolve_processor(live_config);
+
+        if (live_revision != last_replayed_revision) {
+            if (live_config.history_command_seq > last_applied_history_command_seq) {
+                if (live_config.history_command == HistoryCommand::RemoveLast) {
+                    if (!preprocessed_history.empty()) {
+                        preprocessed_history.pop_back();
+                    }
+                } else if (live_config.history_command == HistoryCommand::ClearAll) {
+                    preprocessed_history.clear();
+                }
+                last_applied_history_command_seq = live_config.history_command_seq;
+            }
+
+            if (live_config.processor_mode == "bscan") {
+                for (const auto& cached : preprocessed_history) {
+                    const auto replay_result = process_collection(cached, processor, live_config);
+                    publish_result_collection(replay_result, results_ring_);
+                }
+            } else if (!preprocessed_history.empty()) {
+                const auto replay_result = process_collection(preprocessed_history.back(), processor, live_config);
+                publish_result_collection(replay_result, results_ring_);
+            }
+            last_replayed_revision = live_revision;
+        }
+
+        if (preprocessed_ring_.pop(bytes)) {
+            auto preprocessed = ipc::deserialize_preprocessed_collection(bytes);
+            preprocessed_history.push_back(std::move(preprocessed));
+            while (preprocessed_history.size() > history_limit) {
+                preprocessed_history.pop_front();
+            }
+
+            const auto result_collection = process_collection(preprocessed_history.back(), processor, live_config);
+            publish_result_collection(result_collection, results_ring_);
+            continue;
+        }
+
+        std::this_thread::sleep_for(std::chrono::milliseconds(config_.runtime.idle_sleep_ms));
+    }
+}
+
+auto DataProcessor::process_collection(
+    const ipc::PreprocessedCollection& preprocessed,
+    ProcessorInterface& processor,
+    const ProcessingLiveConfig& live_config
+) -> ipc::ResultCollection {
+    ipc::ResultCollection results{};
+    results.collection_id = preprocessed.collection_id;
+    // Keep source monotonic timestamp stable across live-config replays.
+    results.monotonic_ns = preprocessed.monotonic_ns;
+    results.blocks.reserve(preprocessed.traces.size());
+
+    for (const auto& trace : preprocessed.traces) {
+        ipc::ResultBlock block{};
+        block.combo = trace.combo;
+        block.payloads.reserve(1U);
+        block.payloads.push_back(processor.process(trace, live_config));
+
+        results.blocks.push_back(std::move(block));
+    }
+
+    return results;
+}
+
+auto DataProcessor::resolve_processor(const ProcessingLiveConfig& live_config) -> ProcessorInterface& {
+    const std::string requested_mode =
+        live_config.processor_mode.empty() ? default_processor_mode_ : live_config.processor_mode;
+
+    if (auto found = processors_.find(requested_mode); found != processors_.end()) {
+        return *(found->second);
+    }
+    if (auto fallback = processors_.find(default_processor_mode_); fallback != processors_.end()) {
+        return *(fallback->second);
+    }
+    return *(processors_.begin()->second);
+}
+
+auto create_default_processors() -> ProcessorRegistry {
+    ProcessorRegistry processors{};
+    {
+        auto processor = std::make_unique<PassThroughProcessor>();
+        processors.emplace(processor->name(), std::move(processor));
+    }
+    {
+        auto processor = std::make_unique<BScanProcessor>();
+        processors.emplace(processor->name(), std::move(processor));
+    }
+    return processors;
+}
+
+}  // namespace radar::processing

data_acq_and_processing/processing/data_processor/src/main.cpp

@@ -0,0 +1,69 @@
+#include <atomic>
+#include <csignal>
+#include <exception>
+#include <iostream>
+#include <string>
+
+#include "data_processor.hpp"
+#include "run_config.hpp"
+#include "shm_ring.hpp"
+
+namespace {
+
+constexpr const char* kDefaultConfigPath = "run_config.json";
+
+std::atomic<bool> g_stop_requested{false};
+
+void signal_handler(int /*signal*/) {
+    g_stop_requested.store(true, std::memory_order_relaxed);
+}
+
+void install_signal_handlers() {
+    std::signal(SIGINT, signal_handler);
+    std::signal(SIGTERM, signal_handler);
+}
+
+[[nodiscard]] auto read_config_path(int argc, char** argv) -> std::string {
+    std::string config_path = kDefaultConfigPath;
+    for (int index = 1; index < argc; ++index) {
+        const std::string arg = argv[index];
+        if (arg == "--config" && (index + 1) < argc) {
+            config_path = argv[++index];
+        }
+    }
+    return config_path;
+}
+
+}  // namespace
+
+int main(int argc, char** argv) {
+    install_signal_handlers();
+
+    try {
+        const auto config_path = read_config_path(argc, argv);
+        const auto config = radar::config::load_run_config(config_path);
+
+        auto preprocessed_ring = radar::ipc::ShmRing::open_or_create(
+            config.rings.preprocessed.name,
+            config.rings.preprocessed.capacity,
+            config.rings.preprocessed.slot_size_bytes
+        );
+        auto results_ring = radar::ipc::ShmRing::open_or_create(
+            config.rings.results.name,
+            config.rings.results.capacity,
+            config.rings.results.slot_size_bytes
+        );
+
+        radar::processing::DataProcessor processor(
+            config,
+            preprocessed_ring,
+            results_ring,
+            radar::processing::create_default_processors()
+        );
+        processor.run(g_stop_requested);
+        return 0;
+    } catch (const std::exception& exception) {
+        std::cerr << "data_processor error: " << exception.what() << '\n';
+        return 1;
+    }
+}

data_acq_and_processing/processing/data_processor/src/processing_live_config.cpp

@@ -0,0 +1,189 @@
+#include "processing_live_config.hpp"
+
+#include <chrono>
+#include <cmath>
+#include <fstream>
+#include <iostream>
+#include <limits>
+#include <sstream>
+#include <stdexcept>
+#include <string>
+#include <utility>
+
+#include <nlohmann/json.hpp>
+
+namespace radar::processing {
+namespace {
+
+using Json = nlohmann::json;
+
+[[nodiscard]] auto parse_history_command(const std::string& value) -> HistoryCommand {
+    if (value == "none") {
+        return HistoryCommand::None;
+    }
+    if (value == "remove_last") {
+        return HistoryCommand::RemoveLast;
+    }
+    if (value == "clear_all") {
+        return HistoryCommand::ClearAll;
+    }
+    throw std::runtime_error("processing.history_command must be one of: none, remove_last, clear_all");
+}
+
+[[nodiscard]] auto parse_u64_number(const Json& value, const std::string& field_name) -> std::uint64_t {
+    if (!value.is_number()) {
+        throw std::runtime_error(field_name + " must be number");
+    }
+    const double numeric = value.get<double>();
+    if (numeric < 0.0 || numeric > static_cast<double>(std::numeric_limits<std::uint64_t>::max())) {
+        throw std::runtime_error(field_name + " is out of uint64 range");
+    }
+    const double rounded = std::round(numeric);
+    if (std::fabs(numeric - rounded) > 1e-6) {
+        throw std::runtime_error(field_name + " must be integer");
+    }
+    return static_cast<std::uint64_t>(rounded);
+}
+
+[[nodiscard]] auto parse_live_config(const std::string& json_text, const std::string& path) -> ProcessingLiveConfig {
+    if (json_text.empty()) {
+        throw std::runtime_error("Processing live config is empty: " + path);
+    }
+
+    Json root{};
+    try {
+        root = Json::parse(json_text);
+    } catch (const Json::parse_error& error) {
+        throw std::runtime_error("Failed to parse processing live config: " + std::string(error.what()));
+    }
+
+    if (!root.is_object()) {
+        throw std::runtime_error("Processing live config root must be JSON object");
+    }
+
+    ProcessingLiveConfig config{};
+    if (const auto found = root.find("processor_mode"); found != root.end()) {
+        if (!found->is_string()) {
+            throw std::runtime_error("processing.processor_mode must be string");
+        }
+        config.processor_mode = found->get<std::string>();
+    }
+    if (const auto found = root.find("gain_db"); found != root.end()) {
+        if (!found->is_number()) {
+            throw std::runtime_error("processing.gain_db must be number");
+        }
+        config.gain_db = static_cast<float>(found->get<double>());
+    }
+    if (const auto found = root.find("phase_deg"); found != root.end()) {
+        if (!found->is_number()) {
+            throw std::runtime_error("processing.phase_deg must be number");
+        }
+        config.phase_deg = static_cast<float>(found->get<double>());
+    }
+    if (const auto found = root.find("bscan_axis"); found != root.end()) {
+        if (!found->is_string()) {
+            throw std::runtime_error("processing.bscan_axis must be string");
+        }
+        config.bscan_axis = found->get<std::string>();
+    }
+    if (const auto found = root.find("bscan_cut_m"); found != root.end()) {
+        if (!found->is_number()) {
+            throw std::runtime_error("processing.bscan_cut_m must be number");
+        }
+        config.bscan_cut_m = static_cast<float>(found->get<double>());
+    }
+    if (const auto found = root.find("bscan_max_depth_m"); found != root.end()) {
+        if (!found->is_number()) {
+            throw std::runtime_error("processing.bscan_max_depth_m must be number");
+        }
+        config.bscan_max_depth_m = static_cast<float>(found->get<double>());
+    }
+    if (const auto found = root.find("bscan_gain"); found != root.end()) {
+        if (!found->is_number()) {
+            throw std::runtime_error("processing.bscan_gain must be number");
+        }
+        config.bscan_gain = static_cast<float>(found->get<double>());
+    }
+    if (const auto found = root.find("bscan_start_freq_mhz"); found != root.end()) {
+        if (!found->is_number()) {
+            throw std::runtime_error("processing.bscan_start_freq_mhz must be number");
+        }
+        config.bscan_start_freq_mhz = static_cast<float>(found->get<double>());
+    }
+    if (const auto found = root.find("bscan_stop_freq_mhz"); found != root.end()) {
+        if (!found->is_number()) {
+            throw std::runtime_error("processing.bscan_stop_freq_mhz must be number");
+        }
+        config.bscan_stop_freq_mhz = static_cast<float>(found->get<double>());
+    }
+    if (const auto found = root.find("history_command_seq"); found != root.end()) {
+        config.history_command_seq = parse_u64_number(*found, "processing.history_command_seq");
+    }
+    if (const auto found = root.find("history_command"); found != root.end()) {
+        if (!found->is_string()) {
+            throw std::runtime_error("processing.history_command must be string");
+        }
+        config.history_command = parse_history_command(found->get<std::string>());
+    }
+    return config;
+}
+
+}  // namespace
+
+ProcessingLiveConfigLoader::ProcessingLiveConfigLoader(std::string path)
+    : path_(std::move(path)),
+      next_check_at_(std::chrono::steady_clock::now()) {
+    if (path_.empty()) {
+        throw std::runtime_error("Processing live config path must not be empty");
+    }
+}
+
+auto ProcessingLiveConfigLoader::current() const -> ProcessingLiveConfig {
+    return current_;
+}
+
+auto ProcessingLiveConfigLoader::revision() const -> std::uint64_t {
+    return revision_;
+}
+
+auto ProcessingLiveConfigLoader::refresh_if_needed() -> ProcessingLiveConfig {
+    const auto now = std::chrono::steady_clock::now();
+    if (now < next_check_at_) {
+        return current_;
+    }
+    next_check_at_ = now + std::chrono::milliseconds(100);
+
+    std::error_code time_error{};
+    const auto file_time = std::filesystem::last_write_time(path_, time_error);
+    if (time_error) {
+        return current_;
+    }
+
+    if (has_last_write_time_ && file_time == last_write_time_) {
+        return current_;
+    }
+
+    try {
+        current_ = read_from_file();
+        last_write_time_ = file_time;
+        has_last_write_time_ = true;
+        ++revision_;
+    } catch (const std::exception& error) {
+        std::cerr << "data_processor warning: failed to refresh live processing config: " << error.what() << '\n';
+    }
+
+    return current_;
+}
+
+auto ProcessingLiveConfigLoader::read_from_file() const -> ProcessingLiveConfig {
+    std::ifstream stream(path_);
+    if (!stream.is_open()) {
+        throw std::runtime_error("Failed to open live processing config: " + path_);
+    }
+
+    std::stringstream buffer{};
+    buffer << stream.rdbuf();
+    return parse_live_config(buffer.str(), path_);
+}
+
+}  // namespace radar::processing

data_acq_and_processing/processing/processors/include/bscan_processor.hpp

@@ -0,0 +1,16 @@
+#pragma once
+
+#include "processor_interface.hpp"
+
+namespace radar::processing {
+
+class BScanProcessor final : public ProcessorInterface {
+  public:
+    [[nodiscard]] auto name() const -> std::string override;
+    [[nodiscard]] auto process(
+        const ipc::SweepTraceBlock& trace,
+        const ProcessingLiveConfig& live_config
+    ) -> ipc::ResultPayload override;
+};
+
+}  // namespace radar::processing

data_acq_and_processing/processing/processors/include/passthrough_processor.hpp

@@ -0,0 +1,16 @@
+#pragma once
+
+#include "processor_interface.hpp"
+
+namespace radar::processing {
+
+class PassThroughProcessor final : public ProcessorInterface {
+  public:
+    [[nodiscard]] auto name() const -> std::string override;
+    [[nodiscard]] auto process(
+        const ipc::SweepTraceBlock& trace,
+        const ProcessingLiveConfig& live_config
+    ) -> ipc::ResultPayload override;
+};
+
+}  // namespace radar::processing

data_acq_and_processing/processing/processors/include/processor_interface.hpp

@@ -0,0 +1,21 @@
+#pragma once
+
+#include <string>
+
+#include "processing_live_config.hpp"
+#include "shared_types.hpp"
+
+namespace radar::processing {
+
+class ProcessorInterface {
+  public:
+    virtual ~ProcessorInterface() = default;
+
+    [[nodiscard]] virtual auto name() const -> std::string = 0;
+    [[nodiscard]] virtual auto process(
+        const ipc::SweepTraceBlock& trace,
+        const ProcessingLiveConfig& live_config
+    ) -> ipc::ResultPayload = 0;
+};
+
+}  // namespace radar::processing

data_acq_and_processing/processing/processors/src/bscan_processor.cpp

@@ -0,0 +1,237 @@
+#include "bscan_processor.hpp"
+
+#include <algorithm>
+#include <cmath>
+#include <complex>
+#include <cstddef>
+#include <cstdint>
+#include <limits>
+#include <string_view>
+#include <utility>
+#include <vector>
+
+namespace radar::processing {
+namespace {
+
+constexpr double kPi = 3.14159265358979323846;
+constexpr double kSpeedOfLightMetersPerSec = 299'792'458.0;
+
+struct BScanProfile {
+    std::vector<float> depth_m{};
+    std::vector<float> response{};
+};
+
+[[nodiscard]] auto next_power_of_two(std::size_t value) -> std::size_t {
+    if (value <= 1U) {
+        return 1U;
+    }
+
+    std::size_t power = 1U;
+    while (power < value) {
+        if (power > (std::numeric_limits<std::size_t>::max() >> 1U)) {
+            return value;
+        }
+        power <<= 1U;
+    }
+    return power;
+}
+
+void fft_inplace(std::vector<std::complex<double>>& values, bool inverse) {
+    const std::size_t size = values.size();
+    if (size <= 1U) {
+        return;
+    }
+
+    for (std::size_t index = 1U, bit_reversed = 0U; index < size; ++index) {
+        std::size_t bit = size >> 1U;
+        while (bit_reversed & bit) {
+            bit_reversed ^= bit;
+            bit >>= 1U;
+        }
+        bit_reversed ^= bit;
+        if (index < bit_reversed) {
+            std::swap(values[index], values[bit_reversed]);
+        }
+    }
+
+    for (std::size_t len = 2U; len <= size; len <<= 1U) {
+        const double angle = 2.0 * kPi * (inverse ? 1.0 : -1.0) / static_cast<double>(len);
+        const std::complex<double> twiddle_step(std::cos(angle), std::sin(angle));
+        const std::size_t half_len = len >> 1U;
+
+        for (std::size_t offset = 0U; offset < size; offset += len) {
+            std::complex<double> twiddle(1.0, 0.0);
+            for (std::size_t i = 0U; i < half_len; ++i) {
+                const auto even = values[offset + i];
+                const auto odd = values[offset + i + half_len] * twiddle;
+
+                values[offset + i] = even + odd;
+                values[offset + i + half_len] = even - odd;
+                twiddle *= twiddle_step;
+            }
+        }
+    }
+
+    if (!inverse) {
+        return;
+    }
+
+    const double scale = 1.0 / static_cast<double>(size);
+    for (auto& value : values) {
+        value *= scale;
+    }
+}
+
+[[nodiscard]] auto select_axis(std::complex<double> sample, std::string_view axis) -> double {
+    if (axis == "real") {
+        return std::real(sample);
+    }
+    if (axis == "phase") {
+        return std::arg(sample);
+    }
+    return std::abs(sample);
+}
+
+[[nodiscard]] auto fallback_profile(const ipc::SweepTraceBlock& trace) -> BScanProfile {
+    const std::size_t point_count = std::min(trace.frequency_hz.size(), trace.s21.size());
+    BScanProfile fallback{};
+    fallback.depth_m.reserve(point_count);
+    fallback.response.reserve(point_count);
+
+    const float denominator = point_count > 1U ? static_cast<float>(point_count - 1U) : 1.0F;
+    for (std::size_t index = 0U; index < point_count; ++index) {
+        const auto& sample = trace.s21[index];
+        fallback.depth_m.push_back(static_cast<float>(static_cast<float>(index) / denominator));
+        fallback.response.push_back(std::sqrt(sample.re * sample.re + sample.im * sample.im));
+    }
+    return fallback;
+}
+
+[[nodiscard]] auto compute_bscan_profile(
+    const ipc::SweepTraceBlock& trace,
+    const ProcessingLiveConfig& live_config
+) -> BScanProfile {
+    const std::size_t point_count = std::min(trace.frequency_hz.size(), trace.s21.size());
+    if (point_count < 2U) {
+        return fallback_profile(trace);
+    }
+
+    const double configured_start_hz = static_cast<double>(live_config.bscan_start_freq_mhz) * 1'000'000.0;
+    const double configured_stop_hz = static_cast<double>(live_config.bscan_stop_freq_mhz) * 1'000'000.0;
+    const double start_hz = std::min(configured_start_hz, configured_stop_hz);
+    const double stop_hz = std::max(configured_start_hz, configured_stop_hz);
+
+    std::vector<double> filtered_freq_hz{};
+    std::vector<std::complex<double>> filtered_s21{};
+    filtered_freq_hz.reserve(point_count);
+    filtered_s21.reserve(point_count);
+
+    for (std::size_t index = 0U; index < point_count; ++index) {
+        const double frequency_hz = static_cast<double>(trace.frequency_hz[index]);
+        if (frequency_hz < start_hz || frequency_hz > stop_hz) {
+            continue;
+        }
+        const auto& sample = trace.s21[index];
+        filtered_freq_hz.push_back(frequency_hz);
+        filtered_s21.emplace_back(static_cast<double>(sample.re), static_cast<double>(sample.im));
+    }
+
+    if (filtered_freq_hz.size() < 2U) {
+        return fallback_profile(trace);
+    }
+
+    const std::size_t filtered_count = filtered_freq_hz.size();
+    const double df = (filtered_freq_hz.back() - filtered_freq_hz.front()) / static_cast<double>(filtered_count - 1U);
+    if (df <= 0.0) {
+        return fallback_profile(trace);
+    }
+
+    const auto start_bin = static_cast<std::int64_t>(std::llround(filtered_freq_hz.front() / df));
+    if (start_bin < 0) {
+        return fallback_profile(trace);
+    }
+
+    const auto start_index = static_cast<std::size_t>(start_bin);
+    if (start_index > (std::numeric_limits<std::size_t>::max() / 2U)) {
+        return fallback_profile(trace);
+    }
+    if (start_index > (std::numeric_limits<std::size_t>::max() - filtered_count + 1U)) {
+        return fallback_profile(trace);
+    }
+
+    const std::size_t min_fft_len = 2U * (start_index + filtered_count - 1U);
+    const std::size_t fft_len = next_power_of_two(min_fft_len);
+    if (fft_len < min_fft_len || (fft_len & (fft_len - 1U)) != 0U) {
+        return fallback_profile(trace);
+    }
+    if (start_index > fft_len || filtered_count > (fft_len - start_index)) {
+        return fallback_profile(trace);
+    }
+
+    std::vector<std::complex<double>> spectrum(fft_len, std::complex<double>(0.0, 0.0));
+    for (std::size_t index = 0U; index < filtered_count; ++index) {
+        spectrum[start_index + index] = filtered_s21[index];
+    }
+
+    fft_inplace(spectrum, true);
+
+    const double dt = 1.0 / (static_cast<double>(fft_len) * df);
+    const double cut_m = std::max(0.0, static_cast<double>(live_config.bscan_cut_m));
+    const double max_depth_m = std::max(0.0, static_cast<double>(live_config.bscan_max_depth_m));
+    const double gain = static_cast<double>(live_config.bscan_gain);
+    const double window_start = 2.0 * cut_m;
+    const double window_stop = window_start + (2.0 * max_depth_m);
+
+    BScanProfile profile{};
+    profile.depth_m.reserve(spectrum.size());
+    profile.response.reserve(spectrum.size());
+
+    const std::string_view axis = live_config.bscan_axis;
+    for (std::size_t index = 0U; index < spectrum.size(); ++index) {
+        const double depth_raw = static_cast<double>(index) * dt * kSpeedOfLightMetersPerSec;
+        if (depth_raw < window_start || depth_raw > window_stop) {
+            continue;
+        }
+
+        const double one_way_depth = (depth_raw - window_start) / 2.0;
+        double gain_shape = std::pow(one_way_depth, gain);
+        if (!std::isfinite(gain_shape)) {
+            gain_shape = 0.0;
+        }
+
+        const double axis_value = select_axis(spectrum[index], axis);
+        const double output_value = axis_value * gain_shape;
+
+        profile.depth_m.push_back(static_cast<float>(one_way_depth));
+        profile.response.push_back(static_cast<float>(output_value));
+    }
+
+    return profile;
+}
+
+}  // namespace
+
+auto BScanProcessor::name() const -> std::string {
+    return "bscan";
+}
+
+auto BScanProcessor::process(const ipc::SweepTraceBlock& trace, const ProcessingLiveConfig& live_config)
+    -> ipc::ResultPayload {
+    ipc::ResultPayload payload{};
+    payload.processing_name = name();
+    payload.kind = ipc::ResultKind::TraceComplex;
+
+    auto profile = compute_bscan_profile(trace, live_config);
+    payload.frequency_hz = std::move(profile.depth_m);
+    payload.trace.reserve(profile.response.size());
+    for (const auto value : profile.response) {
+        payload.trace.push_back(ipc::Complex32{
+            .re = value,
+            .im = 0.0F,
+        });
+    }
+
+    return payload;
+}
+
+}  // namespace radar::processing

data_acq_and_processing/processing/processors/src/passthrough_processor.cpp

@@ -0,0 +1,39 @@
+#include "passthrough_processor.hpp"
+
+#include <cmath>
+
+namespace radar::processing {
+namespace {
+
+constexpr float kPi = 3.14159265358979323846F;
+
+}  // namespace
+
+auto PassThroughProcessor::name() const -> std::string {
+    return "pass_through";
+}
+
+auto PassThroughProcessor::process(const ipc::SweepTraceBlock& trace, const ProcessingLiveConfig& live_config)
+    -> ipc::ResultPayload {
+    ipc::ResultPayload payload{};
+    payload.processing_name = name();
+    payload.kind = ipc::ResultKind::TraceComplex;
+    payload.frequency_hz = trace.frequency_hz;
+    payload.trace = trace.s21;
+
+    const float linear_gain = std::pow(10.0F, live_config.gain_db / 20.0F);
+    const float phase_rad = live_config.phase_deg * (kPi / 180.0F);
+    const float cos_phase = std::cos(phase_rad);
+    const float sin_phase = std::sin(phase_rad);
+
+    for (auto& sample : payload.trace) {
+        const float re = sample.re;
+        const float im = sample.im;
+        sample.re = linear_gain * ((re * cos_phase) - (im * sin_phase));
+        sample.im = linear_gain * ((re * sin_phase) + (im * cos_phase));
+    }
+
+    return payload;
+}
+
+}  // namespace radar::processing

data_acq_and_processing/sweep_orchestrator/device_drivers/interfaces/radar_driver.hpp

@@ -0,0 +1,37 @@
+#pragma once
+
+#include <vector>
+
+#include "shared_types.hpp"
+
+namespace radar::drivers {
+
+/**
+ * @brief One S21 sweep acquired from the radar.
+ *
+ * Both vectors must have equal size and aligned indices.
+ */
+struct SweepTrace {
+    std::vector<float> frequency_hz{};
+    std::vector<ipc::Complex32> s21{};
+};
+
+/**
+ * @brief Minimal radar interface used by the sweep orchestrator.
+ *
+ * Implementations are expected to be lightweight: configuration is handled by
+ * the Python layer, while this interface only opens/closes and acquires S21.
+ */
+class RadarDriver {
+  public:
+    virtual ~RadarDriver() = default;
+
+    /** @brief Open underlying transport and prepare acquisition. */
+    virtual void open() = 0;
+    /** @brief Release all allocated resources. */
+    virtual void close() = 0;
+    /** @brief Acquire one S21 sweep. */
+    [[nodiscard]] virtual auto acquire_s21_sweep() -> SweepTrace = 0;
+};
+
+}  // namespace radar::drivers

data_acq_and_processing/sweep_orchestrator/device_drivers/interfaces/switch_driver.hpp

@@ -0,0 +1,24 @@
+#pragma once
+
+#include <cstdint>
+
+namespace radar::drivers {
+
+/**
+ * @brief Unified RF switch interface consumed by the orchestrator.
+ */
+class SwitchDriver {
+  public:
+    virtual ~SwitchDriver() = default;
+
+    /** @brief Open underlying transport and set initial switch state. */
+    virtual void open() = 0;
+    /** @brief Release driver resources. */
+    virtual void close() = 0;
+    /** @brief Number of selectable positions exposed by this switch. */
+    [[nodiscard]] virtual auto position_count() const -> std::uint32_t = 0;
+    /** @brief Switch to a 0-based position. */
+    virtual void switch_to(std::uint32_t position) = 0;
+};
+
+}  // namespace radar::drivers

data_acq_and_processing/sweep_orchestrator/device_drivers/radar/librevna/librevna_minimal_driver_lifecycle.cpp

@@ -0,0 +1,212 @@
+#include "../librevna_minimal_driver.hpp"
+
+#include <algorithm>
+#include <array>
+#include <cmath>
+#include <exception>
+#include <string>
+#include <string_view>
+#include <stdexcept>
+#include <utility>
+#include <vector>
+
+#include "librevna_protocol_common.hpp"
+
+namespace radar::drivers {
+namespace detail = radar::drivers::librevna::detail;
+namespace {
+
+constexpr std::uint32_t kNativeAcquireMaxAttempts = 3U;
+
+[[nodiscard]] auto is_retryable_native_acquire_error(std::string_view message) -> bool {
+    constexpr std::array<std::string_view, 5> kRetryableSubstrings = {
+        "Timeout waiting for expected LibreVNA packet type",
+        "Timeout waiting for LibreVNA ACK",
+        "LibreVNA returned NACK",
+        "Failed to read LibreVNA USB bulk packet",
+        "Failed to write LibreVNA USB bulk packet",
+    };
+
+    return std::any_of(
+        kRetryableSubstrings.begin(),
+        kRetryableSubstrings.end(),
+        [message](std::string_view needle) {
+            return message.find(needle) != std::string_view::npos;
+        }
+    );
+}
+
+}  // namespace
+
+LibreVnaMinimalDriver::LibreVnaMinimalDriver(LibreVnaMinimalDriverSettings settings) : settings_(std::move(settings)) {}
+
+void LibreVnaMinimalDriver::open() {
+    if (is_open_) {
+        return;
+    }
+    if (settings_.sweep.points == 0U) {
+        throw std::runtime_error("Radar sweep points must be > 0");
+    }
+
+    if (settings_.mode == config::DriverMode::Native) {
+        open_native();
+    }
+
+    is_open_ = true;
+}
+
+void LibreVnaMinimalDriver::close() {
+    if (!is_open_) {
+        return;
+    }
+
+    if (settings_.mode == config::DriverMode::Native) {
+        close_native();
+    }
+
+    is_open_ = false;
+}
+
+auto LibreVnaMinimalDriver::acquire_s21_sweep() -> SweepTrace {
+    if (!is_open_) {
+        throw std::runtime_error("Radar driver is not open");
+    }
+
+    ++sweep_index_;
+    switch (settings_.mode) {
+        case config::DriverMode::Mock:
+            return acquire_mock();
+        case config::DriverMode::Native: {
+            std::exception_ptr last_exception{};
+            std::string last_message{};
+
+            for (std::uint32_t attempt = 1; attempt <= kNativeAcquireMaxAttempts; ++attempt) {
+                try {
+                    return acquire_native();
+                } catch (const std::exception& exception) {
+                    last_exception = std::current_exception();
+                    last_message = exception.what();
+
+                    if (!is_retryable_native_acquire_error(last_message) || attempt == kNativeAcquireMaxAttempts) {
+                        break;
+                    }
+
+                    // Recover from transient USB/protocol stalls by reconnecting the device.
+                    close_native();
+                    open_native();
+                }
+            }
+
+            if (last_exception != nullptr) {
+                std::rethrow_exception(last_exception);
+            }
+            throw std::runtime_error("Native acquisition failed without exception detail");
+        }
+        default:
+            throw std::runtime_error("Unsupported radar driver mode");
+    }
+}
+
+auto LibreVnaMinimalDriver::acquire_mock() -> SweepTrace {
+    SweepTrace trace{};
+    trace.frequency_hz.reserve(settings_.sweep.points);
+    trace.s21.reserve(settings_.sweep.points);
+
+    const auto span_hz = settings_.sweep.stop_hz - settings_.sweep.start_hz;
+    const auto denominator = settings_.sweep.points > 1U ? static_cast<float>(settings_.sweep.points - 1U) : 1.0F;
+
+    for (std::uint32_t point = 0; point < settings_.sweep.points; ++point) {
+        const auto ratio = static_cast<float>(point) / denominator;
+        const auto frequency_hz = settings_.sweep.start_hz + span_hz * ratio;
+        const auto phase = 2.0F * detail::kPi * (frequency_hz / std::max(settings_.mock_signal_hz, 1.0F)) +
+                           static_cast<float>(sweep_index_) * 0.05F;
+        const auto envelope = 0.6F + 0.4F * std::sin(0.5F * phase);
+
+        ipc::Complex32 sample{};
+        sample.re = envelope * std::cos(phase);
+        sample.im = envelope * std::sin(phase);
+
+        trace.frequency_hz.push_back(frequency_hz);
+        trace.s21.push_back(sample);
+    }
+
+    return trace;
+}
+
+auto LibreVnaMinimalDriver::acquire_native() -> SweepTrace {
+    if (usb_handle_ == nullptr) {
+        throw std::runtime_error("LibreVNA native handle is not open");
+    }
+
+    // Drop stale datapoints left in queue by previous operations.
+    while (pop_packet(detail::kPacketVnaDatapoint).has_value()) {
+    }
+
+    send_packet_no_payload(detail::kPacketInitiateSweep, true);
+
+    SweepTrace trace{};
+    trace.frequency_hz.assign(settings_.sweep.points, 0.0F);
+    trace.s21.assign(settings_.sweep.points, ipc::Complex32{});
+
+    std::vector<std::uint8_t> received(settings_.sweep.points, 0U);
+    std::uint32_t received_count = 0;
+
+    const auto ifbw_hz = std::max(settings_.sweep.if_bandwidth_hz, 1.0F);
+    const auto estimated_sweep_ms = static_cast<std::uint64_t>(
+        std::ceil((1'000.0 * static_cast<double>(settings_.sweep.points)) / static_cast<double>(ifbw_hz))
+    );
+    // Keep generous timeout margin on slower hosts.
+    const auto timeout_ms = std::max<std::uint64_t>(20'000ULL, estimated_sweep_ms * 8ULL + 1'000ULL);
+    const auto deadline = std::chrono::steady_clock::now() + std::chrono::milliseconds(timeout_ms);
+
+    while (received_count < settings_.sweep.points) {
+        NativePacket packet{};
+        try {
+            packet = wait_for_packet(detail::kPacketVnaDatapoint, deadline);
+        } catch (const std::exception& exception) {
+            throw std::runtime_error(
+                "Timeout while collecting VNADatapoints (" + std::to_string(received_count) + "/" +
+                std::to_string(settings_.sweep.points) + " points received): " + exception.what()
+            );
+        }
+
+        std::uint32_t point_number = 0;
+        float frequency_hz = 0.0F;
+        ipc::Complex32 s21{};
+
+        if (!decode_vna_datapoint_s21(packet.payload, point_number, frequency_hz, s21)) {
+            throw std::runtime_error("Failed to decode S21 from VNADatapoint packet");
+        }
+        if (point_number >= settings_.sweep.points) {
+            throw std::runtime_error("Received out-of-range VNADatapoint index");
+        }
+
+        if (received[point_number] == 0U) {
+            received[point_number] = 1U;
+            ++received_count;
+        }
+
+        trace.frequency_hz[point_number] = frequency_hz;
+        trace.s21[point_number] = s21;
+    }
+
+    return trace;
+}
+
+void LibreVnaMinimalDriver::validate_device_info_payload(std::span<const std::uint8_t> payload) const {
+    if (payload.size() != detail::kDeviceInfoPayloadSize) {
+        throw std::runtime_error("Unexpected DeviceInfo payload size");
+    }
+
+    const auto protocol = detail::read_u16_le(payload, detail::kDeviceInfoProtocolOffset);
+    if (protocol != detail::kProtocolV14) {
+        throw std::runtime_error("Unsupported LibreVNA protocol version (expected v14)");
+    }
+
+    const auto ports = payload[detail::kDeviceInfoNumPortsOffset];
+    if (ports < 2U) {
+        throw std::runtime_error("LibreVNA reported invalid number of ports");
+    }
+}
+
+}  // namespace radar::drivers

data_acq_and_processing/sweep_orchestrator/device_drivers/radar/librevna/librevna_minimal_driver_protocol.cpp

@@ -0,0 +1,130 @@
+#include "../librevna_minimal_driver.hpp"
+
+#include <array>
+#include <complex>
+#include <cstddef>
+#include <stdexcept>
+#include <vector>
+
+#include "librevna_protocol_common.hpp"
+
+namespace radar::drivers {
+namespace detail = radar::drivers::librevna::detail;
+
+auto LibreVnaMinimalDriver::encode_frame(
+    std::uint8_t packet_type,
+    std::span<const std::uint8_t> payload
+) -> std::vector<std::uint8_t> {
+    const auto length = payload.size() + detail::kFrameOverheadBytes;
+    if (length > 0xFFFFU) {
+        throw std::runtime_error("Protocol frame is too large");
+    }
+
+    std::vector<std::uint8_t> frame(length, 0);
+    frame[0] = detail::kFrameHeader;
+    detail::write_u16_le(frame, 1, static_cast<std::uint16_t>(length));
+    frame[3] = packet_type;
+    std::copy(payload.begin(), payload.end(), frame.begin() + 4);
+
+    std::uint32_t crc = 0;
+    if (packet_type != detail::kPacketVnaDatapoint) {
+        crc = crc32(std::span<const std::uint8_t>(frame.data(), frame.size() - 4U));
+    }
+    detail::write_u32_le(frame, frame.size() - 4U, crc);
+
+    return frame;
+}
+
+auto LibreVnaMinimalDriver::decode_vna_datapoint_s21(
+    std::span<const std::uint8_t> payload,
+    std::uint32_t& point_number_out,
+    float& frequency_out,
+    ipc::Complex32& s21_out
+) -> bool {
+    // VNADatapoint payload layout:
+    // [0..7]=freq_or_time, [8..9]=cdbm, [10..11]=point_number,
+    // followed by N tuples of {real:f32, imag:f32, flags:u8}.
+    if (payload.size() < 12U) {
+        return false;
+    }
+
+    const auto values_block = payload.size() - 12U;
+    if ((values_block % 9U) != 0U) {
+        return false;
+    }
+
+    const auto num_values = values_block / 9U;
+    if (num_values == 0U) {
+        return false;
+    }
+
+    point_number_out = detail::read_u16_le(payload, 10);
+    frequency_out = static_cast<float>(detail::read_u64_le(payload, 0));
+
+    const auto real_offset = 12U;
+    const auto imag_offset = real_offset + (4U * num_values);
+    const auto flags_offset = imag_offset + (4U * num_values);
+
+    std::array<std::complex<float>, 8> ref_by_stage{};
+    std::array<std::complex<float>, 8> measured_by_stage{};
+    std::array<bool, 8> has_ref{};
+    std::array<bool, 8> has_measured{};
+
+    for (std::size_t index = 0; index < num_values; ++index) {
+        const auto flags = payload[flags_offset + index];
+        const auto stage = static_cast<std::size_t>(flags >> 5U);
+        if (stage >= ref_by_stage.size()) {
+            continue;
+        }
+
+        const auto value = std::complex<float>(
+            detail::read_f32_le(payload, real_offset + (4U * index)),
+            detail::read_f32_le(payload, imag_offset + (4U * index))
+        );
+
+        const bool is_reference = (flags & detail::kReferenceFlagMask) != 0U;
+        if ((flags & detail::kPort1Mask) != 0U && is_reference) {
+            ref_by_stage[stage] = value;
+            has_ref[stage] = true;
+        }
+        if ((flags & detail::kPort2Mask) != 0U && !is_reference) {
+            measured_by_stage[stage] = value;
+            has_measured[stage] = true;
+        }
+    }
+
+    // We need one reference sample from port1 and one measured sample from port2.
+    for (std::size_t stage = 0; stage < ref_by_stage.size(); ++stage) {
+        if (!has_ref[stage] || !has_measured[stage]) {
+            continue;
+        }
+        if (std::norm(ref_by_stage[stage]) <= 0.0F) {
+            continue;
+        }
+
+        const auto ratio = measured_by_stage[stage] / ref_by_stage[stage];
+        s21_out.re = ratio.real();
+        s21_out.im = ratio.imag();
+        return true;
+    }
+
+    return false;
+}
+
+auto LibreVnaMinimalDriver::crc32(std::span<const std::uint8_t> data) -> std::uint32_t {
+    std::uint32_t crc = 0xFFFFFFFFU;
+    for (const auto byte : data) {
+        crc ^= static_cast<std::uint32_t>(byte);
+        for (int bit = 0; bit < 8; ++bit) {
+            const auto lsb = crc & 1U;
+            crc >>= 1U;
+            if (lsb != 0U) {
+                crc ^= 0xEDB88320U;
+            }
+        }
+    }
+    return ~crc;
+}
+
+}  // namespace radar::drivers
+

data_acq_and_processing/sweep_orchestrator/device_drivers/radar/librevna/librevna_minimal_driver_transport.cpp

@@ -0,0 +1,359 @@
+#include "../librevna_minimal_driver.hpp"
+
+#include <algorithm>
+#include <array>
+#include <cerrno>
+#include <cstring>
+#include <fcntl.h>
+#include <stdexcept>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "librevna_protocol_common.hpp"
+
+namespace radar::drivers {
+namespace detail = radar::drivers::librevna::detail;
+namespace {
+
+[[nodiscard]] auto libusb_error_message(const std::string& prefix, int status_code) -> std::string {
+    return prefix + ": " + libusb_error_name(status_code);
+}
+
+[[nodiscard]] auto is_supported_vid_pid(std::uint16_t vendor_id, std::uint16_t product_id) -> bool {
+    return std::any_of(
+        detail::kSupportedUsbIds.begin(),
+        detail::kSupportedUsbIds.end(),
+        [vendor_id, product_id](const auto& pair) {
+            return pair.first == vendor_id && pair.second == product_id;
+        }
+    );
+}
+
+[[nodiscard]] auto parse_serial_string(libusb_device_handle* handle, std::uint8_t serial_index) -> std::string {
+    if (handle == nullptr || serial_index == 0U) {
+        return {};
+    }
+
+    std::array<unsigned char, 256> serial_bytes{};
+    const auto length = libusb_get_string_descriptor_ascii(
+        handle,
+        serial_index,
+        serial_bytes.data(),
+        static_cast<int>(serial_bytes.size())
+    );
+    if (length <= 0) {
+        return {};
+    }
+
+    return std::string(
+        reinterpret_cast<const char*>(serial_bytes.data()),
+        reinterpret_cast<const char*>(serial_bytes.data() + length)
+    );
+}
+
+[[nodiscard]] auto find_matching_device_handle(
+    libusb_context* usb_context,
+    const std::string& expected_serial
+) -> libusb_device_handle* {
+    libusb_device** devices = nullptr;
+    const auto device_count = libusb_get_device_list(usb_context, &devices);
+    if (device_count < 0) {
+        throw std::runtime_error(
+            libusb_error_message("Failed to enumerate USB devices", static_cast<int>(device_count))
+        );
+    }
+
+    libusb_device_handle* selected_handle = nullptr;
+    for (ssize_t index = 0; index < device_count; ++index) {
+        auto* device = devices[index];
+        if (device == nullptr) {
+            continue;
+        }
+
+        libusb_device_descriptor descriptor{};
+        const auto descriptor_status = libusb_get_device_descriptor(device, &descriptor);
+        if (descriptor_status != LIBUSB_SUCCESS) {
+            continue;
+        }
+        if (!is_supported_vid_pid(descriptor.idVendor, descriptor.idProduct)) {
+            continue;
+        }
+
+        libusb_device_handle* candidate_handle = nullptr;
+        if (libusb_open(device, &candidate_handle) != LIBUSB_SUCCESS || candidate_handle == nullptr) {
+            continue;
+        }
+
+        if (!expected_serial.empty()) {
+            const auto serial = parse_serial_string(candidate_handle, descriptor.iSerialNumber);
+            if (serial != expected_serial) {
+                libusb_close(candidate_handle);
+                continue;
+            }
+        }
+
+        selected_handle = candidate_handle;
+        break;
+    }
+
+    libusb_free_device_list(devices, 1);
+    return selected_handle;
+}
+
+}  // namespace
+
+void LibreVnaMinimalDriver::open_native() {
+    if (usb_context_ != nullptr || usb_handle_ != nullptr) {
+        throw std::runtime_error("LibreVNA native state is already initialized");
+    }
+
+    const auto init_status = libusb_init(&usb_context_);
+    if (init_status != LIBUSB_SUCCESS) {
+        usb_context_ = nullptr;
+        throw std::runtime_error(libusb_error_message("Failed to initialize libusb", init_status));
+    }
+
+    try {
+        auto* selected_handle = find_matching_device_handle(usb_context_, settings_.serial);
+
+        if (selected_handle == nullptr) {
+            const auto serial_hint = settings_.serial.empty() ? std::string() : " for serial '" + settings_.serial + "'";
+            throw std::runtime_error("No compatible LibreVNA USB device found" + serial_hint);
+        }
+
+        usb_handle_ = selected_handle;
+
+        const auto auto_detach_status = libusb_set_auto_detach_kernel_driver(usb_handle_, 1);
+        if (auto_detach_status != LIBUSB_SUCCESS && auto_detach_status != LIBUSB_ERROR_NOT_SUPPORTED) {
+            throw std::runtime_error(
+                libusb_error_message("Failed to configure USB auto-detach kernel driver", auto_detach_status)
+            );
+        }
+
+        const auto claim_status = libusb_claim_interface(usb_handle_, detail::kUsbInterface);
+        if (claim_status != LIBUSB_SUCCESS) {
+            throw std::runtime_error(libusb_error_message("Failed to claim LibreVNA USB interface", claim_status));
+        }
+        interface_claimed_ = true;
+
+        rx_buffer_.clear();
+        packet_queue_.clear();
+
+        send_packet_no_payload(detail::kPacketRequestDeviceInfo, true);
+        const auto info_packet = wait_for_packet(
+            detail::kPacketDeviceInfo,
+            std::chrono::steady_clock::now() + std::chrono::seconds(detail::kDeviceInfoTimeoutSeconds)
+        );
+        validate_device_info_payload(info_packet.payload);
+        protocol_version_ = detail::read_u16_le(info_packet.payload, detail::kDeviceInfoProtocolOffset);
+        device_num_ports_ = info_packet.payload[detail::kDeviceInfoNumPortsOffset];
+    } catch (...) {
+        close_native();
+        throw;
+    }
+}
+
+void LibreVnaMinimalDriver::close_native() {
+    rx_buffer_.clear();
+    packet_queue_.clear();
+
+    if (usb_handle_ != nullptr && interface_claimed_) {
+        libusb_release_interface(usb_handle_, detail::kUsbInterface);
+        interface_claimed_ = false;
+    }
+
+    if (usb_handle_ != nullptr) {
+        libusb_close(usb_handle_);
+        usb_handle_ = nullptr;
+    }
+
+    if (usb_context_ != nullptr) {
+        libusb_exit(usb_context_);
+        usb_context_ = nullptr;
+    }
+
+    protocol_version_ = 0;
+    device_num_ports_ = 0;
+}
+
+void LibreVnaMinimalDriver::send_packet(
+    std::uint8_t packet_type,
+    std::span<const std::uint8_t> payload,
+    bool require_ack
+) {
+    if (usb_handle_ == nullptr) {
+        throw std::runtime_error("Cannot send packet: LibreVNA USB handle is not open");
+    }
+
+    auto frame = encode_frame(packet_type, payload);
+
+    int transferred = 0;
+    const auto transfer_status = libusb_bulk_transfer(
+        usb_handle_,
+        detail::kEndpointOut,
+        reinterpret_cast<unsigned char*>(frame.data()),
+        static_cast<int>(frame.size()),
+        &transferred,
+        detail::kUsbWriteTimeoutMs
+    );
+    if (transfer_status != LIBUSB_SUCCESS) {
+        throw std::runtime_error(libusb_error_message("Failed to write LibreVNA USB bulk packet", transfer_status));
+    }
+    if (transferred != static_cast<int>(frame.size())) {
+        throw std::runtime_error("Incomplete LibreVNA USB bulk write");
+    }
+
+    if (require_ack) {
+        wait_for_ack(std::chrono::steady_clock::now() + std::chrono::milliseconds(detail::kAckTimeoutMs));
+    }
+}
+
+void LibreVnaMinimalDriver::send_packet_no_payload(std::uint8_t packet_type, bool require_ack) {
+    send_packet(packet_type, std::span<const std::uint8_t>{}, require_ack);
+}
+
+void LibreVnaMinimalDriver::wait_for_ack(std::chrono::steady_clock::time_point deadline) {
+    while (std::chrono::steady_clock::now() < deadline) {
+        std::optional<NativePacket> ack_or_nack{};
+        for (auto iter = packet_queue_.begin(); iter != packet_queue_.end(); ++iter) {
+            if (iter->packet_type != detail::kPacketAck && iter->packet_type != detail::kPacketNack) {
+                continue;
+            }
+            ack_or_nack = std::move(*iter);
+            packet_queue_.erase(iter);
+            break;
+        }
+
+        if (ack_or_nack.has_value()) {
+            if (ack_or_nack->packet_type == detail::kPacketAck) {
+                return;
+            }
+            throw std::runtime_error("LibreVNA returned NACK");
+        }
+
+        pump_usb(deadline);
+    }
+
+    throw std::runtime_error("Timeout waiting for LibreVNA ACK");
+}
+
+auto LibreVnaMinimalDriver::wait_for_packet(
+    std::uint8_t expected_type,
+    std::chrono::steady_clock::time_point deadline
+) -> NativePacket {
+    while (std::chrono::steady_clock::now() < deadline) {
+        if (auto packet = pop_packet(expected_type); packet.has_value()) {
+            return *packet;
+        }
+        pump_usb(deadline);
+    }
+
+    throw std::runtime_error("Timeout waiting for expected LibreVNA packet type");
+}
+
+void LibreVnaMinimalDriver::pump_usb(std::chrono::steady_clock::time_point deadline) {
+    if (usb_handle_ == nullptr) {
+        throw std::runtime_error("Cannot read packets: LibreVNA USB handle is not open");
+    }
+
+    const auto now = std::chrono::steady_clock::now();
+    if (now >= deadline) {
+        return;
+    }
+
+    const auto remaining_ms = std::chrono::duration_cast<std::chrono::milliseconds>(deadline - now).count();
+    const auto timeout_ms = static_cast<unsigned int>(
+        std::clamp<std::int64_t>(remaining_ms, detail::kUsbReadPollMinTimeoutMs, detail::kUsbReadPollMaxTimeoutMs)
+    );
+
+    std::array<unsigned char, detail::kUsbReadChunkBytes> buffer{};
+    int transferred = 0;
+    const auto transfer_status = libusb_bulk_transfer(
+        usb_handle_,
+        detail::kEndpointIn,
+        buffer.data(),
+        static_cast<int>(buffer.size()),
+        &transferred,
+        timeout_ms
+    );
+
+    if (transfer_status == LIBUSB_ERROR_TIMEOUT) {
+        return;
+    }
+    if (transfer_status != LIBUSB_SUCCESS) {
+        throw std::runtime_error(libusb_error_message("Failed to read LibreVNA USB bulk packet", transfer_status));
+    }
+    if (transferred <= 0) {
+        return;
+    }
+
+    rx_buffer_.insert(rx_buffer_.end(), buffer.begin(), buffer.begin() + transferred);
+    decode_frames_from_buffer();
+}
+
+void LibreVnaMinimalDriver::decode_frames_from_buffer() {
+    while (true) {
+        const auto header_iter = std::find(rx_buffer_.begin(), rx_buffer_.end(), detail::kFrameHeader);
+        if (header_iter == rx_buffer_.end()) {
+            rx_buffer_.clear();
+            return;
+        }
+
+        if (header_iter != rx_buffer_.begin()) {
+            rx_buffer_.erase(rx_buffer_.begin(), header_iter);
+        }
+
+        if (rx_buffer_.size() < 4U) {
+            return;
+        }
+
+        const auto declared_length = detail::read_u16_le(rx_buffer_, 1);
+        if (declared_length < detail::kFrameOverheadBytes || declared_length > detail::kMaxFrameLengthBytes) {
+            rx_buffer_.erase(rx_buffer_.begin());
+            continue;
+        }
+
+        if (rx_buffer_.size() < declared_length) {
+            return;
+        }
+
+        const std::vector<std::uint8_t> frame(rx_buffer_.begin(), rx_buffer_.begin() + declared_length);
+        rx_buffer_.erase(rx_buffer_.begin(), rx_buffer_.begin() + declared_length);
+
+        const auto packet_type = frame[3];
+        const auto received_crc = detail::read_u32_le(frame, frame.size() - 4U);
+
+        if (packet_type == detail::kPacketVnaDatapoint) {
+            if (received_crc != 0U) {
+                throw std::runtime_error("Invalid VNADatapoint CRC (expected zero)");
+            }
+        } else {
+            const auto expected_crc = crc32(std::span<const std::uint8_t>(frame.data(), frame.size() - 4U));
+            if (received_crc != expected_crc) {
+                throw std::runtime_error("Invalid LibreVNA packet CRC");
+            }
+        }
+
+        NativePacket packet{};
+        packet.packet_type = packet_type;
+        packet.payload.assign(frame.begin() + 4, frame.end() - 4);
+        packet_queue_.push_back(std::move(packet));
+    }
+}
+
+auto LibreVnaMinimalDriver::pop_packet(std::uint8_t packet_type) -> std::optional<NativePacket> {
+    for (auto iter = packet_queue_.begin(); iter != packet_queue_.end(); ++iter) {
+        if (iter->packet_type != packet_type) {
+            continue;
+        }
+
+        auto packet = std::move(*iter);
+        packet_queue_.erase(iter);
+        return packet;
+    }
+
+    return std::nullopt;
+}
+
+}  // namespace radar::drivers

data_acq_and_processing/sweep_orchestrator/device_drivers/radar/librevna/librevna_protocol_common.hpp

@@ -0,0 +1,112 @@
+#pragma once
+
+#include <array>
+#include <cstddef>
+#include <cstdint>
+#include <cstring>
+#include <span>
+#include <stdexcept>
+#include <utility>
+
+namespace radar::drivers::librevna::detail {
+
+constexpr float kPi = 3.14159265358979323846F;
+
+// Frame format:
+// [0]=header, [1..2]=length_le, [3]=packet_type, [...payload...], [crc32_le]
+constexpr std::uint8_t kFrameHeader = 0x5A;
+constexpr std::size_t kFrameOverheadBytes = 8;
+constexpr std::size_t kMaxFrameLengthBytes = 4096;
+
+constexpr unsigned char kEndpointOut = 0x01;
+constexpr unsigned char kEndpointIn = 0x81;
+constexpr int kUsbInterface = 0;
+
+constexpr std::uint8_t kPacketDeviceInfo = 5;
+constexpr std::uint8_t kPacketAck = 7;
+constexpr std::uint8_t kPacketNack = 10;
+constexpr std::uint8_t kPacketRequestDeviceInfo = 15;
+constexpr std::uint8_t kPacketVnaDatapoint = 27;
+constexpr std::uint8_t kPacketInitiateSweep = 32;
+
+constexpr std::size_t kDeviceInfoPayloadSize = 57;
+constexpr std::size_t kDeviceInfoProtocolOffset = 0;
+constexpr std::size_t kDeviceInfoNumPortsOffset = 54;
+constexpr std::uint16_t kProtocolV14 = 14;
+
+constexpr std::uint8_t kReferenceFlagMask = 0x10;
+constexpr std::uint8_t kPort1Mask = 0x01;
+constexpr std::uint8_t kPort2Mask = 0x02;
+
+constexpr std::array<std::pair<std::uint16_t, std::uint16_t>, 3> kSupportedUsbIds = {
+    std::pair<std::uint16_t, std::uint16_t>{0x0483, 0x564E},
+    std::pair<std::uint16_t, std::uint16_t>{0x0483, 0x4121},
+    std::pair<std::uint16_t, std::uint16_t>{0x1209, 0x4121},
+};
+
+constexpr int kUsbWriteTimeoutMs = 500;
+constexpr int kAckTimeoutMs = 800;
+constexpr int kDeviceInfoTimeoutSeconds = 2;
+constexpr std::size_t kUsbReadChunkBytes = 16 * 1024;
+constexpr int kUsbReadPollMinTimeoutMs = 1;
+constexpr int kUsbReadPollMaxTimeoutMs = 50;
+
+[[nodiscard]] inline auto read_u16_le(std::span<const std::uint8_t> data, std::size_t offset) -> std::uint16_t {
+    if ((offset + sizeof(std::uint16_t)) > data.size()) {
+        throw std::runtime_error("Failed to decode uint16 from payload");
+    }
+    return static_cast<std::uint16_t>(data[offset]) |
+           (static_cast<std::uint16_t>(data[offset + 1]) << 8U);
+}
+
+[[nodiscard]] inline auto read_u32_le(std::span<const std::uint8_t> data, std::size_t offset) -> std::uint32_t {
+    if ((offset + sizeof(std::uint32_t)) > data.size()) {
+        throw std::runtime_error("Failed to decode uint32 from payload");
+    }
+    return static_cast<std::uint32_t>(data[offset]) |
+           (static_cast<std::uint32_t>(data[offset + 1]) << 8U) |
+           (static_cast<std::uint32_t>(data[offset + 2]) << 16U) |
+           (static_cast<std::uint32_t>(data[offset + 3]) << 24U);
+}
+
+[[nodiscard]] inline auto read_u64_le(std::span<const std::uint8_t> data, std::size_t offset) -> std::uint64_t {
+    if ((offset + sizeof(std::uint64_t)) > data.size()) {
+        throw std::runtime_error("Failed to decode uint64 from payload");
+    }
+
+    std::uint64_t value = 0;
+    for (std::size_t index = 0; index < sizeof(std::uint64_t); ++index) {
+        value |= static_cast<std::uint64_t>(data[offset + index]) << (index * 8U);
+    }
+    return value;
+}
+
+[[nodiscard]] inline auto read_f32_le(std::span<const std::uint8_t> data, std::size_t offset) -> float {
+    const auto raw = read_u32_le(data, offset);
+    float out = 0.0F;
+    std::memcpy(&out, &raw, sizeof(float));
+    return out;
+}
+
+inline void write_u16_le(std::span<std::uint8_t> data, std::size_t offset, std::uint16_t value) {
+    if ((offset + sizeof(std::uint16_t)) > data.size()) {
+        throw std::runtime_error("Failed to encode uint16 into frame");
+    }
+
+    data[offset] = static_cast<std::uint8_t>(value & 0xFFU);
+    data[offset + 1] = static_cast<std::uint8_t>((value >> 8U) & 0xFFU);
+}
+
+inline void write_u32_le(std::span<std::uint8_t> data, std::size_t offset, std::uint32_t value) {
+    if ((offset + sizeof(std::uint32_t)) > data.size()) {
+        throw std::runtime_error("Failed to encode uint32 into frame");
+    }
+
+    data[offset] = static_cast<std::uint8_t>(value & 0xFFU);
+    data[offset + 1] = static_cast<std::uint8_t>((value >> 8U) & 0xFFU);
+    data[offset + 2] = static_cast<std::uint8_t>((value >> 16U) & 0xFFU);
+    data[offset + 3] = static_cast<std::uint8_t>((value >> 24U) & 0xFFU);
+}
+
+}  // namespace radar::drivers::librevna::detail
+

data_acq_and_processing/sweep_orchestrator/device_drivers/radar/librevna_minimal_driver.hpp

@@ -0,0 +1,97 @@
+#pragma once
+
+#include <chrono>
+#include <cstdint>
+#include <deque>
+#include <optional>
+#include <span>
+#include <string>
+#include <vector>
+
+#if __has_include(<libusb-1.0/libusb.h>)
+#include <libusb-1.0/libusb.h>
+#else
+#include <libusb.h>
+#endif
+
+#include "radar_driver.hpp"
+#include "run_config.hpp"
+
+namespace radar::drivers {
+
+/**
+ * @brief Runtime settings for minimal LibreVNA acquisition.
+ */
+struct LibreVnaMinimalDriverSettings {
+    config::DriverMode mode = config::DriverMode::Mock;
+    std::string serial{};
+    config::RadarSweepSettings sweep{};
+    float mock_signal_hz = 5'000'000.0F;
+};
+
+/**
+ * @brief Minimal radar driver that acquires S21 sweeps from LibreVNA.
+ *
+ * This class intentionally keeps scope narrow: open/close transport and
+ * acquire one sweep. Full device configuration is expected to be done by the
+ * Python layer before this process starts.
+ */
+class LibreVnaMinimalDriver final : public RadarDriver {
+  public:
+    explicit LibreVnaMinimalDriver(LibreVnaMinimalDriverSettings settings);
+
+    void open() override;
+    void close() override;
+    [[nodiscard]] auto acquire_s21_sweep() -> SweepTrace override;
+
+  private:
+    /**
+     * @brief Raw protocol packet decoded from framed USB stream.
+     */
+    struct NativePacket {
+        std::uint8_t packet_type = 0;
+        std::vector<std::uint8_t> payload{};
+    };
+
+    [[nodiscard]] auto acquire_mock() -> SweepTrace;
+    [[nodiscard]] auto acquire_native() -> SweepTrace;
+
+    void open_native();
+    void close_native();
+
+    void send_packet(std::uint8_t packet_type, std::span<const std::uint8_t> payload, bool require_ack);
+    void send_packet_no_payload(std::uint8_t packet_type, bool require_ack);
+    void wait_for_ack(std::chrono::steady_clock::time_point deadline);
+    [[nodiscard]] auto wait_for_packet(std::uint8_t expected_type, std::chrono::steady_clock::time_point deadline)
+        -> NativePacket;
+    void pump_usb(std::chrono::steady_clock::time_point deadline);
+    void decode_frames_from_buffer();
+
+    [[nodiscard]] auto pop_packet(std::uint8_t packet_type) -> std::optional<NativePacket>;
+    [[nodiscard]] static auto encode_frame(std::uint8_t packet_type, std::span<const std::uint8_t> payload)
+        -> std::vector<std::uint8_t>;
+    [[nodiscard]] static auto decode_vna_datapoint_s21(
+        std::span<const std::uint8_t> payload,
+        std::uint32_t& point_number_out,
+        float& frequency_out,
+        ipc::Complex32& s21_out
+    ) -> bool;
+    [[nodiscard]] static auto crc32(std::span<const std::uint8_t> data) -> std::uint32_t;
+    void validate_device_info_payload(std::span<const std::uint8_t> payload) const;
+
+    LibreVnaMinimalDriverSettings settings_{};
+    bool is_open_ = false;
+    std::uint64_t sweep_index_ = 0;
+
+    libusb_context* usb_context_ = nullptr;
+    libusb_device_handle* usb_handle_ = nullptr;
+    bool interface_claimed_ = false;
+
+    std::vector<std::uint8_t> rx_buffer_{};
+    std::deque<NativePacket> packet_queue_{};
+
+    std::uint16_t protocol_version_ = 0;
+    std::uint8_t device_num_ports_ = 0;
+};
+
+}  // namespace radar::drivers

data_acq_and_processing/sweep_orchestrator/device_drivers/switches/h7992_minimal_driver.cpp

@@ -0,0 +1,160 @@
+#include "h7992_minimal_driver.hpp"
+
+#include <array>
+#include <cerrno>
+#include <cstdio>
+#include <cstring>
+#include <fcntl.h>
+#include <linux/gpio.h>
+#include <stdexcept>
+#include <sys/ioctl.h>
+#include <unistd.h>
+#include <utility>
+
+namespace radar::drivers {
+namespace {
+
+// Position mapping for H7992 control pins:
+// position -> (A, B)
+constexpr std::array<std::array<std::uint8_t, 2>, 4> kPositionToAB = {
+    std::array<std::uint8_t, 2>{0, 0},
+    std::array<std::uint8_t, 2>{0, 1},
+    std::array<std::uint8_t, 2>{1, 0},
+    std::array<std::uint8_t, 2>{1, 1},
+};
+
+void validate_open_settings(const H7992MinimalDriverSettings& settings) {
+    if (settings.positions == 0U || settings.positions > 4U) {
+        throw std::runtime_error("H7992 switch positions must be in range [1, 4] for " + settings.name);
+    }
+    if (settings.default_position >= settings.positions) {
+        throw std::runtime_error("Switch default_position out of range for " + settings.name);
+    }
+}
+
+[[nodiscard]] auto make_line_values(std::uint8_t pin_a_state, std::uint8_t pin_b_state) -> gpio_v2_line_values {
+    gpio_v2_line_values values{};
+    values.mask = (1ULL << 0U) | (1ULL << 1U);
+    values.bits =
+        (static_cast<std::uint64_t>(pin_a_state) << 0U) | (static_cast<std::uint64_t>(pin_b_state) << 1U);
+    return values;
+}
+
+}  // namespace
+
+H7992MinimalDriver::H7992MinimalDriver(H7992MinimalDriverSettings settings) : settings_(std::move(settings)) {}
+
+void H7992MinimalDriver::open() {
+    if (is_open_) {
+        return;
+    }
+    validate_open_settings(settings_);
+
+    if (settings_.mode == config::DriverMode::Native) {
+        open_native();
+    }
+
+    is_open_ = true;
+    switch_to(settings_.default_position);
+}
+
+void H7992MinimalDriver::close() {
+    if (!is_open_) {
+        return;
+    }
+
+    if (settings_.mode == config::DriverMode::Native) {
+        close_native();
+    }
+
+    is_open_ = false;
+}
+
+auto H7992MinimalDriver::position_count() const -> std::uint32_t {
+    return settings_.positions;
+}
+
+void H7992MinimalDriver::switch_to(std::uint32_t position) {
+    if (!is_open_) {
+        throw std::runtime_error("Switch driver is not open for " + settings_.name);
+    }
+    if (position >= settings_.positions) {
+        throw std::runtime_error("Switch position out of range for " + settings_.name);
+    }
+
+    switch (settings_.mode) {
+        case config::DriverMode::Mock:
+            break;
+        case config::DriverMode::Native:
+            switch_native(position);
+            break;
+        default:
+            throw std::runtime_error("Unsupported switch driver mode for " + settings_.name);
+    }
+
+    current_position_ = position;
+}
+
+void H7992MinimalDriver::open_native() {
+    if (settings_.gpio_chip.empty()) {
+        throw std::runtime_error("gpio_chip is empty for native switch " + settings_.name);
+    }
+    if (settings_.pin_a < 0 || settings_.pin_b < 0 || settings_.pin_a == settings_.pin_b) {
+        throw std::runtime_error("Invalid pin_a/pin_b for native switch " + settings_.name);
+    }
+
+    chip_fd_ = ::open(settings_.gpio_chip.c_str(), O_RDONLY | O_CLOEXEC);
+    if (chip_fd_ < 0) {
+        throw std::runtime_error(
+            "Failed to open gpio chip '" + settings_.gpio_chip + "' for " + settings_.name + ": " + std::strerror(errno)
+        );
+    }
+
+    gpio_v2_line_request request{};
+    // The first requested line maps to logical bit 0, the second to bit 1.
+    request.offsets[0] = static_cast<std::uint32_t>(settings_.pin_a);
+    request.offsets[1] = static_cast<std::uint32_t>(settings_.pin_b);
+    request.num_lines = 2;
+    request.config.flags = GPIO_V2_LINE_FLAG_OUTPUT;
+    std::snprintf(request.consumer, sizeof(request.consumer), "radar_%s", settings_.name.c_str());
+
+    if (::ioctl(chip_fd_, GPIO_V2_GET_LINE_IOCTL, &request) != 0) {
+        const auto error = std::strerror(errno);
+        ::close(chip_fd_);
+        chip_fd_ = -1;
+        throw std::runtime_error("Failed to request GPIO lines for " + settings_.name + ": " + error);
+    }
+
+    line_fd_ = request.fd;
+    if (line_fd_ < 0) {
+        ::close(chip_fd_);
+        chip_fd_ = -1;
+        throw std::runtime_error("GPIO line request returned invalid fd for " + settings_.name);
+    }
+}
+
+void H7992MinimalDriver::close_native() {
+    if (line_fd_ >= 0) {
+        ::close(line_fd_);
+        line_fd_ = -1;
+    }
+    if (chip_fd_ >= 0) {
+        ::close(chip_fd_);
+        chip_fd_ = -1;
+    }
+}
+
+void H7992MinimalDriver::switch_native(std::uint32_t position) {
+    if (line_fd_ < 0) {
+        throw std::runtime_error("Native GPIO line fd is not open for " + settings_.name);
+    }
+
+    const auto [pin_a_state, pin_b_state] = kPositionToAB[position];
+    auto values = make_line_values(pin_a_state, pin_b_state);
+
+    if (::ioctl(line_fd_, GPIO_V2_LINE_SET_VALUES_IOCTL, &values) != 0) {
+        throw std::runtime_error("Failed to switch GPIO state for " + settings_.name + ": " + std::strerror(errno));
+    }
+}
+
+}  // namespace radar::drivers

data_acq_and_processing/sweep_orchestrator/device_drivers/switches/h7992_minimal_driver.hpp

@@ -0,0 +1,51 @@
+#pragma once
+
+#include <cstdint>
+#include <string>
+
+#include "run_config.hpp"
+#include "switch_driver.hpp"
+
+namespace radar::drivers {
+
+/**
+ * @brief Runtime settings for the H7992 GPIO switch driver.
+ */
+struct H7992MinimalDriverSettings {
+    std::string name{};
+    config::DriverMode mode = config::DriverMode::Native;
+    std::uint32_t positions = 4;
+    std::uint32_t default_position = 0;
+    std::string gpio_chip = "/dev/gpiochip0";
+    std::int32_t pin_a = 17;
+    std::int32_t pin_b = 27;
+};
+
+/**
+ * @brief Minimal H7992 implementation for fast position switching.
+ *
+ * Native mode uses Linux GPIO character-device API (uAPI v2).
+ * Mock mode keeps state in memory without touching hardware.
+ */
+class H7992MinimalDriver final : public SwitchDriver {
+  public:
+    explicit H7992MinimalDriver(H7992MinimalDriverSettings settings);
+
+    void open() override;
+    void close() override;
+    [[nodiscard]] auto position_count() const -> std::uint32_t override;
+    void switch_to(std::uint32_t position) override;
+
+  private:
+    void open_native();
+    void close_native();
+    void switch_native(std::uint32_t position);
+
+    H7992MinimalDriverSettings settings_{};
+    bool is_open_ = false;
+    std::uint32_t current_position_ = 0;
+    int chip_fd_ = -1;
+    int line_fd_ = -1;
+};
+
+}  // namespace radar::drivers

data_acq_and_processing/sweep_orchestrator/device_drivers/switches/hmc349a_minimal_driver.cpp

@@ -0,0 +1,147 @@
+#include "hmc349a_minimal_driver.hpp"
+
+#include <cerrno>
+#include <cstdio>
+#include <cstring>
+#include <fcntl.h>
+#include <linux/gpio.h>
+#include <stdexcept>
+#include <sys/ioctl.h>
+#include <unistd.h>
+#include <utility>
+
+namespace radar::drivers {
+namespace {
+
+void validate_open_settings(const HMC349AMinimalDriverSettings& settings) {
+    if (settings.positions == 0U || settings.positions > 2U) {
+        throw std::runtime_error("HMC349A switch positions must be in range [1, 2] for " + settings.name);
+    }
+    if (settings.default_position >= settings.positions) {
+        throw std::runtime_error("Switch default_position out of range for " + settings.name);
+    }
+}
+
+}  // namespace
+
+HMC349AMinimalDriver::HMC349AMinimalDriver(HMC349AMinimalDriverSettings settings)
+    : settings_(std::move(settings)) {}
+
+void HMC349AMinimalDriver::open() {
+    if (is_open_) {
+        return;
+    }
+
+    validate_open_settings(settings_);
+
+    if (settings_.mode == config::DriverMode::Native) {
+        open_native();
+    }
+
+    is_open_ = true;
+    switch_to(settings_.default_position);
+}
+
+void HMC349AMinimalDriver::close() {
+    if (!is_open_) {
+        return;
+    }
+
+    if (settings_.mode == config::DriverMode::Native) {
+        close_native();
+    }
+
+    is_open_ = false;
+}
+
+auto HMC349AMinimalDriver::position_count() const -> std::uint32_t {
+    return settings_.positions;
+}
+
+void HMC349AMinimalDriver::switch_to(std::uint32_t position) {
+    if (!is_open_) {
+        throw std::runtime_error("Switch driver is not open for " + settings_.name);
+    }
+    if (position >= settings_.positions) {
+        throw std::runtime_error("Switch position out of range for " + settings_.name);
+    }
+
+    switch (settings_.mode) {
+        case config::DriverMode::Mock:
+            break;
+        case config::DriverMode::Native:
+            switch_native(position);
+            break;
+        default:
+            throw std::runtime_error("Unsupported switch driver mode for " + settings_.name);
+    }
+
+    current_position_ = position;
+}
+
+void HMC349AMinimalDriver::open_native() {
+    if (settings_.gpio_chip.empty()) {
+        throw std::runtime_error("gpio_chip is empty for native switch " + settings_.name);
+    }
+    if (settings_.pin_a < 0) {
+        throw std::runtime_error("pin_a (control) is invalid for native switch " + settings_.name);
+    }
+
+    chip_fd_ = ::open(settings_.gpio_chip.c_str(), O_RDONLY | O_CLOEXEC);
+    if (chip_fd_ < 0) {
+        throw std::runtime_error(
+            "Failed to open gpio chip '" + settings_.gpio_chip + "' for " + settings_.name + ": " + std::strerror(errno)
+        );
+    }
+
+    gpio_v2_line_request request{};
+    // Bit 0 controls pin_a.
+    request.offsets[0] = static_cast<std::uint32_t>(settings_.pin_a);
+    request.num_lines = 1;
+    request.config.flags = GPIO_V2_LINE_FLAG_OUTPUT;
+    std::snprintf(request.consumer, sizeof(request.consumer), "radar_%s", settings_.name.c_str());
+
+    if (::ioctl(chip_fd_, GPIO_V2_GET_LINE_IOCTL, &request) != 0) {
+        const auto error = std::strerror(errno);
+        ::close(chip_fd_);
+        chip_fd_ = -1;
+        throw std::runtime_error("Failed to request GPIO lines for " + settings_.name + ": " + error);
+    }
+
+    line_fd_ = request.fd;
+    if (line_fd_ < 0) {
+        ::close(chip_fd_);
+        chip_fd_ = -1;
+        throw std::runtime_error("GPIO line request returned invalid fd for " + settings_.name);
+    }
+}
+
+void HMC349AMinimalDriver::close_native() {
+    if (line_fd_ >= 0) {
+        ::close(line_fd_);
+        line_fd_ = -1;
+    }
+    if (chip_fd_ >= 0) {
+        ::close(chip_fd_);
+        chip_fd_ = -1;
+    }
+}
+
+void HMC349AMinimalDriver::switch_native(std::uint32_t position) {
+    if (line_fd_ < 0) {
+        throw std::runtime_error("Native GPIO line fd is not open for " + settings_.name);
+    }
+
+    const std::uint8_t requested_state = static_cast<std::uint8_t>(position & 0x01U);
+    const std::uint8_t control_state = settings_.invert_logic ? (requested_state ^ 0x01U) : requested_state;
+
+    gpio_v2_line_values values{};
+    values.mask = (1ULL << 0U);
+    values.bits = static_cast<std::uint64_t>(control_state);
+
+    if (::ioctl(line_fd_, GPIO_V2_LINE_SET_VALUES_IOCTL, &values) != 0) {
+        throw std::runtime_error("Failed to switch GPIO state for " + settings_.name + ": " + std::strerror(errno));
+    }
+}
+
+}  // namespace radar::drivers

data_acq_and_processing/sweep_orchestrator/device_drivers/switches/hmc349a_minimal_driver.hpp

@@ -0,0 +1,52 @@
+#pragma once
+
+#include <cstdint>
+#include <string>
+
+#include "run_config.hpp"
+#include "switch_driver.hpp"
+
+namespace radar::drivers {
+
+/**
+ * @brief Runtime settings for the HMC349A GPIO switch driver.
+ */
+struct HMC349AMinimalDriverSettings {
+    std::string name{};
+    config::DriverMode mode = config::DriverMode::Native;
+    std::uint32_t positions = 2;
+    std::uint32_t default_position = 0;
+    std::string gpio_chip = "/dev/gpiochip0";
+    std::int32_t pin_a = 17;   ///< Control pin.
+    bool invert_logic = false; ///< Invert control logic for position mapping.
+};
+
+/**
+ * @brief Minimal HMC349A implementation for deterministic GPIO control.
+ *
+ * Native mode uses Linux GPIO character-device API (uAPI v2).
+ * Mock mode keeps state in memory without touching hardware.
+ */
+class HMC349AMinimalDriver final : public SwitchDriver {
+  public:
+    explicit HMC349AMinimalDriver(HMC349AMinimalDriverSettings settings);
+
+    void open() override;
+    void close() override;
+    [[nodiscard]] auto position_count() const -> std::uint32_t override;
+    void switch_to(std::uint32_t position) override;
+
+  private:
+    void open_native();
+    void close_native();
+    void switch_native(std::uint32_t position);
+
+    HMC349AMinimalDriverSettings settings_{};
+    bool is_open_ = false;
+    std::uint32_t current_position_ = 0;
+
+    int chip_fd_ = -1;
+    int line_fd_ = -1;
+};
+
+}  // namespace radar::drivers

data_acq_and_processing/sweep_orchestrator/include/sweep_orchestrator.hpp

@@ -0,0 +1,62 @@
+#pragma once
+
+#include <atomic>
+#include <cstdint>
+
+#include "radar_driver.hpp"
+#include "run_config.hpp"
+#include "shm_ring.hpp"
+#include "sweep_plan.hpp"
+#include "switch_driver.hpp"
+
+namespace radar::acq {
+
+/**
+ * @brief Produces raw sweep collections for configured switch combinations.
+ *
+ * Lifecycle:
+ * 1. Open radar and switch drivers.
+ * 2. Iterate configured combinations, acquire one sweep per combination.
+ * 3. Publish serialized collections into the raw shared-memory ring.
+ */
+class SweepOrchestrator {
+  public:
+    /**
+     * @param config Runtime config loaded from JSON.
+     * @param radar_driver Radar device implementation.
+     * @param input_switch_driver Switch connected to radar input path.
+     * @param output_switch_driver Switch connected to radar output path.
+     * @param raw_ring Shared-memory ring for raw collections.
+     * @param raw_tap_ring Optional tap ring for GUI/debug readers.
+     */
+    SweepOrchestrator(
+        const config::RunConfig& config,
+        drivers::RadarDriver& radar_driver,
+        drivers::SwitchDriver& input_switch_driver,
+        drivers::SwitchDriver& output_switch_driver,
+        ipc::ShmRing& raw_ring,
+        ipc::ShmRing* raw_tap_ring = nullptr
+    );
+
+    /**
+     * @brief Run acquisition loop until stop is requested or single run completes.
+     */
+    void run(const std::atomic<bool>& stop_requested);
+
+  private:
+    /**
+     * @brief Acquire one collection for all combinations in `plan_`.
+     */
+    [[nodiscard]] auto acquire_one_collection(std::uint64_t collection_id, const std::atomic<bool>& stop_requested)
+        -> ipc::RawSweepCollection;
+
+    const config::RunConfig& config_;
+    drivers::RadarDriver& radar_driver_;
+    drivers::SwitchDriver& input_switch_driver_;
+    drivers::SwitchDriver& output_switch_driver_;
+    ipc::ShmRing& raw_ring_;
+    ipc::ShmRing* raw_tap_ring_ = nullptr;
+    SweepPlan plan_{};
+};
+
+}  // namespace radar::acq

data_acq_and_processing/sweep_orchestrator/include/sweep_plan.hpp

@@ -0,0 +1,22 @@
+#pragma once
+
+#include <vector>
+
+#include "run_config.hpp"
+#include "shared_types.hpp"
+
+namespace radar::acq {
+
+/**
+ * @brief Pre-validated execution order of switch combinations for one collection.
+ */
+struct SweepPlan {
+    std::vector<ipc::ComboKey> ordered_combos{};
+};
+
+/**
+ * @brief Build and validate sweep execution plan from runtime config.
+ */
+[[nodiscard]] auto build_sweep_plan(const config::RunConfig& config) -> SweepPlan;
+
+}  // namespace radar::acq

data_acq_and_processing/sweep_orchestrator/src/main.cpp

@@ -0,0 +1,135 @@
+#include <atomic>
+#include <csignal>
+#include <exception>
+#include <iostream>
+#include <memory>
+#include <stdexcept>
+#include <string>
+
+#include "h7992_minimal_driver.hpp"
+#include "hmc349a_minimal_driver.hpp"
+#include "librevna_minimal_driver.hpp"
+#include "run_config.hpp"
+#include "shm_ring.hpp"
+#include "sweep_orchestrator.hpp"
+
+namespace {
+
+constexpr const char* kDefaultConfigPath = "run_config.json";
+std::atomic<bool> g_stop_requested{false};
+
+void signal_handler(int /*signal*/) {
+    g_stop_requested.store(true, std::memory_order_relaxed);
+}
+
+void install_signal_handlers() {
+    std::signal(SIGINT, signal_handler);
+    std::signal(SIGTERM, signal_handler);
+}
+
+[[nodiscard]] auto read_config_path(int argc, char** argv) -> std::string {
+    std::string config_path = kDefaultConfigPath;
+
+    for (int index = 1; index < argc; ++index) {
+        const std::string arg = argv[index];
+        if (arg == "--config" && (index + 1) < argc) {
+            config_path = argv[++index];
+        }
+    }
+
+    return config_path;
+}
+
+[[nodiscard]] auto make_radar_driver(const radar::config::RadarConfig& config) -> radar::drivers::LibreVnaMinimalDriver {
+    return radar::drivers::LibreVnaMinimalDriver({
+        .mode = config.driver_mode,
+        .serial = config.serial,
+        .sweep = config.sweep,
+        .mock_signal_hz = config.mock_signal_hz,
+    });
+}
+
+[[nodiscard]] auto make_h7992_driver(const radar::config::SwitchConfig& config)
+    -> std::unique_ptr<radar::drivers::SwitchDriver> {
+    return std::make_unique<radar::drivers::H7992MinimalDriver>(
+        radar::drivers::H7992MinimalDriverSettings{
+            .name = config.name,
+            .mode = config.driver_mode,
+            .positions = config.positions,
+            .default_position = config.default_position,
+            .gpio_chip = config.gpio_chip,
+            .pin_a = config.pin_a,
+            .pin_b = config.pin_b,
+        }
+    );
+}
+
+[[nodiscard]] auto make_hmc349a_driver(const radar::config::SwitchConfig& config)
+    -> std::unique_ptr<radar::drivers::SwitchDriver> {
+    return std::make_unique<radar::drivers::HMC349AMinimalDriver>(
+        radar::drivers::HMC349AMinimalDriverSettings{
+            .name = config.name,
+            .mode = config.driver_mode,
+            .positions = config.positions,
+            .default_position = config.default_position,
+            .gpio_chip = config.gpio_chip,
+            .pin_a = config.pin_a,
+            .invert_logic = config.invert_logic,
+        }
+    );
+}
+
+[[nodiscard]] auto make_switch_driver(const radar::config::SwitchConfig& config)
+    -> std::unique_ptr<radar::drivers::SwitchDriver> {
+    using radar::config::SwitchDriverKind;
+
+    switch (config.driver_kind) {
+        case SwitchDriverKind::H7992:
+            return make_h7992_driver(config);
+        case SwitchDriverKind::HMC349A:
+            return make_hmc349a_driver(config);
+        default:
+            throw std::runtime_error("Unsupported switch driver kind");
+    }
+}
+
+}  // namespace
+
+int main(int argc, char** argv) {
+    install_signal_handlers();
+
+    try {
+        const auto config_path = read_config_path(argc, argv);
+        const auto config = radar::config::load_run_config(config_path);
+
+        auto raw_ring = radar::ipc::ShmRing::open_or_create(
+            config.rings.raw.name,
+            config.rings.raw.capacity,
+            config.rings.raw.slot_size_bytes
+        );
+        auto raw_tap_ring = radar::ipc::ShmRing::open_or_create(
+            config.rings.raw_tap.name,
+            config.rings.raw_tap.capacity,
+            config.rings.raw_tap.slot_size_bytes
+        );
+
+        auto radar_driver = make_radar_driver(config.radar);
+
+        auto input_switch = make_switch_driver(config.input_switch);
+        auto output_switch = make_switch_driver(config.output_switch);
+
+        radar::acq::SweepOrchestrator orchestrator(
+            config,
+            radar_driver,
+            *input_switch,
+            *output_switch,
+            raw_ring,
+            &raw_tap_ring
+        );
+        orchestrator.run(g_stop_requested);
+        return 0;
+    } catch (const std::exception& exception) {
+        std::cerr << "sweep_orchestrator error: " << exception.what() << '\n';
+        return 1;
+    }
+}

data_acq_and_processing/sweep_orchestrator/src/sweep_orchestrator.cpp

@@ -0,0 +1,178 @@
+#include "sweep_orchestrator.hpp"
+
+#include <chrono>
+#include <exception>
+#include <stdexcept>
+#include <thread>
+#include <vector>
+
+namespace radar::acq {
+namespace {
+
+[[nodiscard]] inline auto should_stop(const std::atomic<bool>& stop_requested) -> bool {
+    return stop_requested.load(std::memory_order_relaxed);
+}
+
+void sleep_if_needed_ms(std::uint32_t delay_ms) {
+    if (delay_ms == 0U) {
+        return;
+    }
+    std::this_thread::sleep_for(std::chrono::milliseconds(delay_ms));
+}
+
+void validate_sweep(const drivers::SweepTrace& sweep) {
+    if (sweep.frequency_hz.size() != sweep.s21.size()) {
+        throw std::runtime_error("Radar driver returned inconsistent sweep vectors");
+    }
+}
+
+class DriverLifecycleGuard {
+  public:
+    DriverLifecycleGuard(
+        drivers::RadarDriver& radar_driver,
+        drivers::SwitchDriver& input_switch_driver,
+        drivers::SwitchDriver& output_switch_driver
+    )
+        : radar_driver_(radar_driver),
+          input_switch_driver_(input_switch_driver),
+          output_switch_driver_(output_switch_driver) {}
+
+    ~DriverLifecycleGuard() noexcept {
+        if (!active_) {
+            return;
+        }
+
+        // Shutdown should never hide the primary error path.
+        try {
+            output_switch_driver_.close();
+        } catch (...) {
+        }
+        try {
+            input_switch_driver_.close();
+        } catch (...) {
+        }
+        try {
+            radar_driver_.close();
+        } catch (...) {
+        }
+    }
+
+    void open_all() {
+        radar_driver_.open();
+        input_switch_driver_.open();
+        output_switch_driver_.open();
+        active_ = true;
+    }
+
+    void close_all() {
+        if (!active_) {
+            return;
+        }
+        output_switch_driver_.close();
+        input_switch_driver_.close();
+        radar_driver_.close();
+        active_ = false;
+    }
+
+  private:
+    drivers::RadarDriver& radar_driver_;
+    drivers::SwitchDriver& input_switch_driver_;
+    drivers::SwitchDriver& output_switch_driver_;
+    bool active_ = false;
+};
+
+void publish_collection(ipc::ShmRing& raw_ring, ipc::ShmRing* raw_tap_ring, const ipc::RawSweepCollection& collection) {
+    const auto serialized_collection = ipc::serialize_raw_collection(collection);
+    if (!raw_ring.push(serialized_collection)) {
+        throw std::runtime_error("Raw ring slot is too small for serialized collection");
+    }
+    if (raw_tap_ring != nullptr && !raw_tap_ring->push(serialized_collection)) {
+        throw std::runtime_error("Raw tap ring slot is too small for serialized collection");
+    }
+}
+
+}  // namespace
+
+SweepOrchestrator::SweepOrchestrator(
+    const config::RunConfig& config,
+    drivers::RadarDriver& radar_driver,
+    drivers::SwitchDriver& input_switch_driver,
+    drivers::SwitchDriver& output_switch_driver,
+    ipc::ShmRing& raw_ring,
+    ipc::ShmRing* raw_tap_ring
+)
+    : config_(config),
+      radar_driver_(radar_driver),
+      input_switch_driver_(input_switch_driver),
+      output_switch_driver_(output_switch_driver),
+      raw_ring_(raw_ring),
+      raw_tap_ring_(raw_tap_ring),
+      plan_(build_sweep_plan(config)) {}
+
+void SweepOrchestrator::run(const std::atomic<bool>& stop_requested) {
+    DriverLifecycleGuard lifecycle_guard(radar_driver_, input_switch_driver_, output_switch_driver_);
+    // Open devices once and keep them active for the whole acquisition loop.
+    lifecycle_guard.open_all();
+
+    std::uint64_t collection_id = 0;
+    while (!should_stop(stop_requested)) {
+        auto raw_collection = acquire_one_collection(++collection_id, stop_requested);
+        if (raw_collection.traces.empty()) {
+            if (!config_.runtime.continuous) {
+                break;
+            }
+            sleep_if_needed_ms(config_.runtime.idle_sleep_ms);
+            continue;
+        }
+
+        publish_collection(raw_ring_, raw_tap_ring_, raw_collection);
+
+        if (!config_.runtime.continuous) {
+            break;
+        }
+    }
+
+    lifecycle_guard.close_all();
+}
+
+auto SweepOrchestrator::acquire_one_collection(
+    std::uint64_t collection_id,
+    const std::atomic<bool>& stop_requested
+) -> ipc::RawSweepCollection {
+    ipc::RawSweepCollection collection{};
+    collection.collection_id = collection_id;
+    collection.monotonic_ns = ipc::current_monotonic_ns();
+    collection.traces.reserve(plan_.ordered_combos.size());
+    bool interrupted = false;
+
+    for (const auto& combo : plan_.ordered_combos) {
+        if (should_stop(stop_requested)) {
+            interrupted = true;
+            break;
+        }
+
+        // Hardware flow for each combo:
+        // 1) switch output path, 2) switch input path, 3) optional settle.
+        output_switch_driver_.switch_to(combo.output_pos);
+        input_switch_driver_.switch_to(combo.input_pos);
+        sleep_if_needed_ms(config_.runtime.settling_ms);
+
+        auto sweep = radar_driver_.acquire_s21_sweep();
+        validate_sweep(sweep);
+
+        ipc::SweepTraceBlock trace{};
+        trace.combo = combo;
+        trace.frequency_hz = std::move(sweep.frequency_hz);
+        trace.s21 = std::move(sweep.s21);
+        collection.traces.push_back(std::move(trace));
+    }
+
+    if (interrupted) {
+        // Do not emit partial collections when stop was requested mid-cycle.
+        collection.traces.clear();
+    }
+
+    return collection;
+}
+
+}  // namespace radar::acq

data_acq_and_processing/sweep_orchestrator/src/sweep_plan.cpp

@@ -0,0 +1,19 @@
+#include "sweep_plan.hpp"
+
+#include <stdexcept>
+
+namespace radar::acq {
+
+auto build_sweep_plan(const config::RunConfig& config) -> SweepPlan {
+    // RunConfig is already validated in common_cpp/config. Keep this function
+    // focused on plan construction only.
+    if (config.run_combos.empty()) {
+        throw std::runtime_error("run.combos must not be empty");
+    }
+
+    SweepPlan plan{};
+    plan.ordered_combos = config.run_combos;
+    return plan;
+}
+
+}  // namespace radar::acq

data_acq_and_processing/third_party/eigen/COPYING.APACHE

@@ -0,0 +1,203 @@
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data_acq_and_processing/third_party/eigen/COPYING.BSD

@@ -0,0 +1,26 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
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+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
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+
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+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+*/

data_acq_and_processing/third_party/eigen/COPYING.MINPACK

@@ -0,0 +1,51 @@
+Minpack Copyright Notice (1999) University of Chicago.  All rights reserved
+
+Redistribution and use in source and binary forms, with or
+without modification, are permitted provided that the
+following conditions are met:
+
+1. Redistributions of source code must retain the above
+copyright notice, this list of conditions and the following
+disclaimer.
+
+2. Redistributions in binary form must reproduce the above
+copyright notice, this list of conditions and the following
+disclaimer in the documentation and/or other materials
+provided with the distribution.
+
+3. The end-user documentation included with the
+redistribution, if any, must include the following
+acknowledgment:
+
+   "This product includes software developed by the
+   University of Chicago, as Operator of Argonne National
+   Laboratory.
+
+Alternately, this acknowledgment may appear in the software
+itself, if and wherever such third-party acknowledgments
+normally appear.
+
+4. WARRANTY DISCLAIMER. THE SOFTWARE IS SUPPLIED "AS IS"
+WITHOUT WARRANTY OF ANY KIND. THE COPYRIGHT HOLDER, THE
+UNITED STATES, THE UNITED STATES DEPARTMENT OF ENERGY, AND
+THEIR EMPLOYEES: (1) DISCLAIM ANY WARRANTIES, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO ANY IMPLIED WARRANTIES
+OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE
+OR NON-INFRINGEMENT, (2) DO NOT ASSUME ANY LEGAL LIABILITY
+OR RESPONSIBILITY FOR THE ACCURACY, COMPLETENESS, OR
+USEFULNESS OF THE SOFTWARE, (3) DO NOT REPRESENT THAT USE OF
+THE SOFTWARE WOULD NOT INFRINGE PRIVATELY OWNED RIGHTS, (4)
+DO NOT WARRANT THAT THE SOFTWARE WILL FUNCTION
+UNINTERRUPTED, THAT IT IS ERROR-FREE OR THAT ANY ERRORS WILL
+BE CORRECTED.
+
+5. LIMITATION OF LIABILITY. IN NO EVENT WILL THE COPYRIGHT
+HOLDER, THE UNITED STATES, THE UNITED STATES DEPARTMENT OF
+ENERGY, OR THEIR EMPLOYEES: BE LIABLE FOR ANY INDIRECT,
+INCIDENTAL, CONSEQUENTIAL, SPECIAL OR PUNITIVE DAMAGES OF
+ANY KIND OR NATURE, INCLUDING BUT NOT LIMITED TO LOSS OF
+PROFITS OR LOSS OF DATA, FOR ANY REASON WHATSOEVER, WHETHER
+SUCH LIABILITY IS ASSERTED ON THE BASIS OF CONTRACT, TORT
+(INCLUDING NEGLIGENCE OR STRICT LIABILITY), OR OTHERWISE,
+EVEN IF ANY OF SAID PARTIES HAS BEEN WARNED OF THE
+POSSIBILITY OF SUCH LOSS OR DAMAGES.

data_acq_and_processing/third_party/eigen/COPYING.MPL2

@@ -0,0 +1,373 @@
+Mozilla Public License Version 2.0
+==================================
+
+1. Definitions
+--------------
+
+1.1. "Contributor"
+    means each individual or legal entity that creates, contributes to
+    the creation of, or owns Covered Software.
+
+1.2. "Contributor Version"
+    means the combination of the Contributions of others (if any) used
+    by a Contributor and that particular Contributor's Contribution.
+
+1.3. "Contribution"
+    means Covered Software of a particular Contributor.
+
+1.4. "Covered Software"
+    means Source Code Form to which the initial Contributor has attached
+    the notice in Exhibit A, the Executable Form of such Source Code
+    Form, and Modifications of such Source Code Form, in each case
+    including portions thereof.
+
+1.5. "Incompatible With Secondary Licenses"
+    means
+
+    (a) that the initial Contributor has attached the notice described
+        in Exhibit B to the Covered Software; or
+
+    (b) that the Covered Software was made available under the terms of
+        version 1.1 or earlier of the License, but not also under the
+        terms of a Secondary License.
+
+1.6. "Executable Form"
+    means any form of the work other than Source Code Form.
+
+1.7. "Larger Work"
+    means a work that combines Covered Software with other material, in 
+    a separate file or files, that is not Covered Software.
+
+1.8. "License"
+    means this document.
+
+1.9. "Licensable"
+    means having the right to grant, to the maximum extent possible,
+    whether at the time of the initial grant or subsequently, any and
+    all of the rights conveyed by this License.
+
+1.10. "Modifications"
+    means any of the following:
+
+    (a) any file in Source Code Form that results from an addition to,
+        deletion from, or modification of the contents of Covered
+        Software; or
+
+    (b) any new file in Source Code Form that contains any Covered
+        Software.
+
+1.11. "Patent Claims" of a Contributor
+    means any patent claim(s), including without limitation, method,
+    process, and apparatus claims, in any patent Licensable by such
+    Contributor that would be infringed, but for the grant of the
+    License, by the making, using, selling, offering for sale, having
+    made, import, or transfer of either its Contributions or its
+    Contributor Version.
+
+1.12. "Secondary License"
+    means either the GNU General Public License, Version 2.0, the GNU
+    Lesser General Public License, Version 2.1, the GNU Affero General
+    Public License, Version 3.0, or any later versions of those
+    licenses.
+
+1.13. "Source Code Form"
+    means the form of the work preferred for making modifications.
+
+1.14. "You" (or "Your")
+    means an individual or a legal entity exercising rights under this
+    License. For legal entities, "You" includes any entity that
+    controls, is controlled by, or is under common control with You. For
+    purposes of this definition, "control" means (a) the power, direct
+    or indirect, to cause the direction or management of such entity,
+    whether by contract or otherwise, or (b) ownership of more than
+    fifty percent (50%) of the outstanding shares or beneficial
+    ownership of such entity.
+
+2. License Grants and Conditions
+--------------------------------
+
+2.1. Grants
+
+Each Contributor hereby grants You a world-wide, royalty-free,
+non-exclusive license:
+
+(a) under intellectual property rights (other than patent or trademark)
+    Licensable by such Contributor to use, reproduce, make available,
+    modify, display, perform, distribute, and otherwise exploit its
+    Contributions, either on an unmodified basis, with Modifications, or
+    as part of a Larger Work; and
+
+(b) under Patent Claims of such Contributor to make, use, sell, offer
+    for sale, have made, import, and otherwise transfer either its
+    Contributions or its Contributor Version.
+
+2.2. Effective Date
+
+The licenses granted in Section 2.1 with respect to any Contribution
+become effective for each Contribution on the date the Contributor first
+distributes such Contribution.
+
+2.3. Limitations on Grant Scope
+
+The licenses granted in this Section 2 are the only rights granted under
+this License. No additional rights or licenses will be implied from the
+distribution or licensing of Covered Software under this License.
+Notwithstanding Section 2.1(b) above, no patent license is granted by a
+Contributor:
+
+(a) for any code that a Contributor has removed from Covered Software;
+    or
+
+(b) for infringements caused by: (i) Your and any other third party's
+    modifications of Covered Software, or (ii) the combination of its
+    Contributions with other software (except as part of its Contributor
+    Version); or
+
+(c) under Patent Claims infringed by Covered Software in the absence of
+    its Contributions.
+
+This License does not grant any rights in the trademarks, service marks,
+or logos of any Contributor (except as may be necessary to comply with
+the notice requirements in Section 3.4).
+
+2.4. Subsequent Licenses
+
+No Contributor makes additional grants as a result of Your choice to
+distribute the Covered Software under a subsequent version of this
+License (see Section 10.2) or under the terms of a Secondary License (if
+permitted under the terms of Section 3.3).
+
+2.5. Representation
+
+Each Contributor represents that the Contributor believes its
+Contributions are its original creation(s) or it has sufficient rights
+to grant the rights to its Contributions conveyed by this License.
+
+2.6. Fair Use
+
+This License is not intended to limit any rights You have under
+applicable copyright doctrines of fair use, fair dealing, or other
+equivalents.
+
+2.7. Conditions
+
+Sections 3.1, 3.2, 3.3, and 3.4 are conditions of the licenses granted
+in Section 2.1.
+
+3. Responsibilities
+-------------------
+
+3.1. Distribution of Source Form
+
+All distribution of Covered Software in Source Code Form, including any
+Modifications that You create or to which You contribute, must be under
+the terms of this License. You must inform recipients that the Source
+Code Form of the Covered Software is governed by the terms of this
+License, and how they can obtain a copy of this License. You may not
+attempt to alter or restrict the recipients' rights in the Source Code
+Form.
+
+3.2. Distribution of Executable Form
+
+If You distribute Covered Software in Executable Form then:
+
+(a) such Covered Software must also be made available in Source Code
+    Form, as described in Section 3.1, and You must inform recipients of
+    the Executable Form how they can obtain a copy of such Source Code
+    Form by reasonable means in a timely manner, at a charge no more
+    than the cost of distribution to the recipient; and
+
+(b) You may distribute such Executable Form under the terms of this
+    License, or sublicense it under different terms, provided that the
+    license for the Executable Form does not attempt to limit or alter
+    the recipients' rights in the Source Code Form under this License.
+
+3.3. Distribution of a Larger Work
+
+You may create and distribute a Larger Work under terms of Your choice,
+provided that You also comply with the requirements of this License for
+the Covered Software. If the Larger Work is a combination of Covered
+Software with a work governed by one or more Secondary Licenses, and the
+Covered Software is not Incompatible With Secondary Licenses, this
+License permits You to additionally distribute such Covered Software
+under the terms of such Secondary License(s), so that the recipient of
+the Larger Work may, at their option, further distribute the Covered
+Software under the terms of either this License or such Secondary
+License(s).
+
+3.4. Notices
+
+You may not remove or alter the substance of any license notices
+(including copyright notices, patent notices, disclaimers of warranty,
+or limitations of liability) contained within the Source Code Form of
+the Covered Software, except that You may alter any license notices to
+the extent required to remedy known factual inaccuracies.
+
+3.5. Application of Additional Terms
+
+You may choose to offer, and to charge a fee for, warranty, support,
+indemnity or liability obligations to one or more recipients of Covered
+Software. However, You may do so only on Your own behalf, and not on
+behalf of any Contributor. You must make it absolutely clear that any
+such warranty, support, indemnity, or liability obligation is offered by
+You alone, and You hereby agree to indemnify every Contributor for any
+liability incurred by such Contributor as a result of warranty, support,
+indemnity or liability terms You offer. You may include additional
+disclaimers of warranty and limitations of liability specific to any
+jurisdiction.
+
+4. Inability to Comply Due to Statute or Regulation
+---------------------------------------------------
+
+If it is impossible for You to comply with any of the terms of this
+License with respect to some or all of the Covered Software due to
+statute, judicial order, or regulation then You must: (a) comply with
+the terms of this License to the maximum extent possible; and (b)
+describe the limitations and the code they affect. Such description must
+be placed in a text file included with all distributions of the Covered
+Software under this License. Except to the extent prohibited by statute
+or regulation, such description must be sufficiently detailed for a
+recipient of ordinary skill to be able to understand it.
+
+5. Termination
+--------------
+
+5.1. The rights granted under this License will terminate automatically
+if You fail to comply with any of its terms. However, if You become
+compliant, then the rights granted under this License from a particular
+Contributor are reinstated (a) provisionally, unless and until such
+Contributor explicitly and finally terminates Your grants, and (b) on an
+ongoing basis, if such Contributor fails to notify You of the
+non-compliance by some reasonable means prior to 60 days after You have
+come back into compliance. Moreover, Your grants from a particular
+Contributor are reinstated on an ongoing basis if such Contributor
+notifies You of the non-compliance by some reasonable means, this is the
+first time You have received notice of non-compliance with this License
+from such Contributor, and You become compliant prior to 30 days after
+Your receipt of the notice.
+
+5.2. If You initiate litigation against any entity by asserting a patent
+infringement claim (excluding declaratory judgment actions,
+counter-claims, and cross-claims) alleging that a Contributor Version
+directly or indirectly infringes any patent, then the rights granted to
+You by any and all Contributors for the Covered Software under Section
+2.1 of this License shall terminate.
+
+5.3. In the event of termination under Sections 5.1 or 5.2 above, all
+end user license agreements (excluding distributors and resellers) which
+have been validly granted by You or Your distributors under this License
+prior to termination shall survive termination.
+
+************************************************************************
+*                                                                      *
+*  6. Disclaimer of Warranty                                           *
+*  -------------------------                                           *
+*                                                                      *
+*  Covered Software is provided under this License on an "as is"       *
+*  basis, without warranty of any kind, either expressed, implied, or  *
+*  statutory, including, without limitation, warranties that the       *
+*  Covered Software is free of defects, merchantable, fit for a        *
+*  particular purpose or non-infringing. The entire risk as to the     *
+*  quality and performance of the Covered Software is with You.        *
+*  Should any Covered Software prove defective in any respect, You     *
+*  (not any Contributor) assume the cost of any necessary servicing,   *
+*  repair, or correction. This disclaimer of warranty constitutes an   *
+*  essential part of this License. No use of any Covered Software is   *
+*  authorized under this License except under this disclaimer.         *
+*                                                                      *
+************************************************************************
+
+************************************************************************
+*                                                                      *
+*  7. Limitation of Liability                                          *
+*  --------------------------                                          *
+*                                                                      *
+*  Under no circumstances and under no legal theory, whether tort      *
+*  (including negligence), contract, or otherwise, shall any           *
+*  Contributor, or anyone who distributes Covered Software as          *
+*  permitted above, be liable to You for any direct, indirect,         *
+*  special, incidental, or consequential damages of any character      *
+*  including, without limitation, damages for lost profits, loss of    *
+*  goodwill, work stoppage, computer failure or malfunction, or any    *
+*  and all other commercial damages or losses, even if such party      *
+*  shall have been informed of the possibility of such damages. This   *
+*  limitation of liability shall not apply to liability for death or   *
+*  personal injury resulting from such party's negligence to the       *
+*  extent applicable law prohibits such limitation. Some               *
+*  jurisdictions do not allow the exclusion or limitation of           *
+*  incidental or consequential damages, so this exclusion and          *
+*  limitation may not apply to You.                                    *
+*                                                                      *
+************************************************************************
+
+8. Litigation
+-------------
+
+Any litigation relating to this License may be brought only in the
+courts of a jurisdiction where the defendant maintains its principal
+place of business and such litigation shall be governed by laws of that
+jurisdiction, without reference to its conflict-of-law provisions.
+Nothing in this Section shall prevent a party's ability to bring
+cross-claims or counter-claims.
+
+9. Miscellaneous
+----------------
+
+This License represents the complete agreement concerning the subject
+matter hereof. If any provision of this License is held to be
+unenforceable, such provision shall be reformed only to the extent
+necessary to make it enforceable. Any law or regulation which provides
+that the language of a contract shall be construed against the drafter
+shall not be used to construe this License against a Contributor.
+
+10. Versions of the License
+---------------------------
+
+10.1. New Versions
+
+Mozilla Foundation is the license steward. Except as provided in Section
+10.3, no one other than the license steward has the right to modify or
+publish new versions of this License. Each version will be given a
+distinguishing version number.
+
+10.2. Effect of New Versions
+
+You may distribute the Covered Software under the terms of the version
+of the License under which You originally received the Covered Software,
+or under the terms of any subsequent version published by the license
+steward.
+
+10.3. Modified Versions
+
+If you create software not governed by this License, and you want to
+create a new license for such software, you may create and use a
+modified version of this License if you rename the license and remove
+any references to the name of the license steward (except to note that
+such modified license differs from this License).
+
+10.4. Distributing Source Code Form that is Incompatible With Secondary
+Licenses
+
+If You choose to distribute Source Code Form that is Incompatible With
+Secondary Licenses under the terms of this version of the License, the
+notice described in Exhibit B of this License must be attached.
+
+Exhibit A - Source Code Form License Notice
+-------------------------------------------
+
+  This Source Code Form is subject to the terms of the Mozilla Public
+  License, v. 2.0. If a copy of the MPL was not distributed with this
+  file, You can obtain one at https://mozilla.org/MPL/2.0/.
+
+If it is not possible or desirable to put the notice in a particular
+file, then You may include the notice in a location (such as a LICENSE
+file in a relevant directory) where a recipient would be likely to look
+for such a notice.
+
+You may add additional accurate notices of copyright ownership.
+
+Exhibit B - "Incompatible With Secondary Licenses" Notice
+---------------------------------------------------------
+
+  This Source Code Form is "Incompatible With Secondary Licenses", as
+  defined by the Mozilla Public License, v. 2.0.

data_acq_and_processing/third_party/eigen/COPYING.README

@@ -0,0 +1,11 @@
+Eigen is primarily MPL2 licensed. See COPYING.MPL2 and these links:
+  http://www.mozilla.org/MPL/2.0/
+  http://www.mozilla.org/MPL/2.0/FAQ.html
+
+Some files contain third-party code under BSD, LGPL, Apache, or other
+MPL2-compatible licenses, hence the other COPYING.* files here.
+
+Note that some optional external dependencies (e.g. FFTW, MPFR C++)
+and some bundled benchmark code (bench/btl/) are distributed under
+different licenses, including the GPL. Refer to the individual source
+files and their respective COPYING files for details.

data_acq_and_processing/third_party/eigen/Eigen/AccelerateSupport

@@ -0,0 +1,52 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ACCELERATESUPPORT_MODULE_H
+#define EIGEN_ACCELERATESUPPORT_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** \ingroup Support_modules
+ * \defgroup AccelerateSupport_Module AccelerateSupport module
+ *
+ * This module provides an interface to the Apple Accelerate library.
+ * It provides the seven following main factorization classes:
+ * - class AccelerateLLT: a Cholesky (LL^T) factorization.
+ * - class AccelerateLDLT: the default LDL^T factorization.
+ * - class AccelerateLDLTUnpivoted: a Cholesky-like LDL^T factorization with only 1x1 pivots and no pivoting
+ * - class AccelerateLDLTSBK: an LDL^T factorization with Supernode Bunch-Kaufman and static pivoting
+ * - class AccelerateLDLTTPP: an LDL^T factorization with full threshold partial pivoting
+ * - class AccelerateQR: a QR factorization
+ * - class AccelerateCholeskyAtA: a QR factorization without storing Q (equivalent to A^TA = R^T R)
+ *
+ * \code
+ * #include <Eigen/AccelerateSupport>
+ * \endcode
+ *
+ * In order to use this module, the Accelerate headers must be accessible from
+ * the include paths, and your binary must be linked to the Accelerate framework.
+ * The Accelerate library is only available on Apple hardware.
+ *
+ * Note that many of the algorithms can be influenced by the UpLo template
+ * argument. All matrices are assumed to be symmetric. For example, the following
+ * creates an LDLT factorization where your matrix is symmetric (implicit) and
+ * uses the lower triangle:
+ *
+ * \code
+ * AccelerateLDLT<SparseMatrix<float>, Lower> ldlt;
+ * \endcode
+ */
+
+// IWYU pragma: begin_exports
+#include "src/AccelerateSupport/AccelerateSupport.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_ACCELERATESUPPORT_MODULE_H

data_acq_and_processing/third_party/eigen/Eigen/Cholesky

@@ -0,0 +1,43 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CHOLESKY_MODULE_H
+#define EIGEN_CHOLESKY_MODULE_H
+
+#include "Core"
+#include "Jacobi"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** \defgroup Cholesky_Module Cholesky module
+ *
+ *
+ *
+ * This module provides two variants of the Cholesky decomposition for selfadjoint (hermitian) matrices.
+ * Those decompositions are also accessible via the following methods:
+ *  - MatrixBase::llt()
+ *  - MatrixBase::ldlt()
+ *  - SelfAdjointView::llt()
+ *  - SelfAdjointView::ldlt()
+ *
+ * \code
+ * #include <Eigen/Cholesky>
+ * \endcode
+ */
+
+// IWYU pragma: begin_exports
+#include "src/Cholesky/LLT.h"
+#include "src/Cholesky/LDLT.h"
+#ifdef EIGEN_USE_LAPACKE
+#include "src/misc/lapacke_helpers.h"
+#include "src/Cholesky/LLT_LAPACKE.h"
+#endif
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_CHOLESKY_MODULE_H

data_acq_and_processing/third_party/eigen/Eigen/CholmodSupport

@@ -0,0 +1,48 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CHOLMODSUPPORT_MODULE_H
+#define EIGEN_CHOLMODSUPPORT_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+#include <cholmod.h>
+
+/** \ingroup Support_modules
+ * \defgroup CholmodSupport_Module CholmodSupport module
+ *
+ * This module provides an interface to the Cholmod library which is part of the <a
+ * href="http://www.suitesparse.com">suitesparse</a> package. It provides the two following main factorization classes:
+ * - class CholmodSupernodalLLT: a supernodal LLT Cholesky factorization.
+ * - class CholmodDecomposition: a general L(D)LT Cholesky factorization with automatic or explicit runtime selection of
+ * the underlying factorization method (supernodal or simplicial).
+ *
+ * For the sake of completeness, this module also propose the two following classes:
+ * - class CholmodSimplicialLLT
+ * - class CholmodSimplicialLDLT
+ * Note that these classes does not bring any particular advantage compared to the built-in
+ * SimplicialLLT and SimplicialLDLT factorization classes.
+ *
+ * \code
+ * #include <Eigen/CholmodSupport>
+ * \endcode
+ *
+ * In order to use this module, the cholmod headers must be accessible from the include paths, and your binary must be
+ * linked to the cholmod library and its dependencies. The dependencies depend on how cholmod has been compiled. For a
+ * cmake based project, you can use our FindCholmod.cmake module to help you in this task.
+ *
+ */
+
+// IWYU pragma: begin_exports
+#include "src/CholmodSupport/CholmodSupport.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_CHOLMODSUPPORT_MODULE_H

data_acq_and_processing/third_party/eigen/Eigen/Core

@@ -0,0 +1,482 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2007-2011 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CORE_MODULE_H
+#define EIGEN_CORE_MODULE_H
+
+// Eigen version information.
+#include "Version"
+
+// first thing Eigen does: stop the compiler from reporting useless warnings.
+#include "src/Core/util/DisableStupidWarnings.h"
+
+// then include this file where all our macros are defined. It's really important to do it first because
+// it's where we do all the compiler/OS/arch detections and define most defaults.
+#include "src/Core/util/Macros.h"
+
+// This detects SSE/AVX/NEON/etc. and configure alignment settings
+#include "src/Core/util/ConfigureVectorization.h"
+
+// We need cuda_runtime.h/hip_runtime.h to ensure that
+// the EIGEN_USING_STD macro works properly on the device side
+#if defined(EIGEN_CUDACC)
+#include <cuda_runtime.h>
+#elif defined(EIGEN_HIPCC)
+#include <hip/hip_runtime.h>
+#endif
+
+#ifdef EIGEN_EXCEPTIONS
+#include <new>
+#endif
+
+// Disable the ipa-cp-clone optimization flag with MinGW 6.x or older (enabled by default with -O3)
+// See http://eigen.tuxfamily.org/bz/show_bug.cgi?id=556 for details.
+#if EIGEN_COMP_MINGW && EIGEN_GNUC_STRICT_LESS_THAN(6, 0, 0)
+#pragma GCC optimize("-fno-ipa-cp-clone")
+#endif
+
+// Prevent ICC from specializing std::complex operators that silently fail
+// on device. This allows us to use our own device-compatible specializations
+// instead.
+#if EIGEN_COMP_ICC && defined(EIGEN_GPU_COMPILE_PHASE) && !defined(_OVERRIDE_COMPLEX_SPECIALIZATION_)
+#define _OVERRIDE_COMPLEX_SPECIALIZATION_ 1
+#endif
+#include <complex>
+
+// this include file manages BLAS and MKL related macros
+// and inclusion of their respective header files
+#include "src/Core/util/MKL_support.h"
+#include "src/Core/util/AOCL_Support.h"    // ← ADD THIS
+
+
+#if defined(EIGEN_HAS_CUDA_FP16) || defined(EIGEN_HAS_HIP_FP16)
+#define EIGEN_HAS_GPU_FP16
+#endif
+
+#if defined(EIGEN_HAS_CUDA_BF16) || defined(EIGEN_HAS_HIP_BF16)
+#define EIGEN_HAS_GPU_BF16
+#endif
+
+#if (defined _OPENMP) && (!defined EIGEN_DONT_PARALLELIZE)
+#define EIGEN_HAS_OPENMP
+#endif
+
+#ifdef EIGEN_HAS_OPENMP
+#include <atomic>
+#include <omp.h>
+#endif
+
+// MSVC for windows mobile does not have the errno.h file
+#if !(EIGEN_COMP_MSVC && EIGEN_OS_WINCE) && !EIGEN_COMP_ARM
+#define EIGEN_HAS_ERRNO
+#endif
+
+#ifdef EIGEN_HAS_ERRNO
+#include <cerrno>
+#endif
+#include <cstddef>
+#include <cstdlib>
+#include <cmath>
+#include <functional>
+#ifndef EIGEN_NO_IO
+#include <sstream>
+#include <iosfwd>
+#endif
+#include <cstring>
+#include <string>
+#include <limits>
+#include <climits>  // for CHAR_BIT
+// for min/max:
+#include <algorithm>
+
+#include <array>
+#include <memory>
+#include <vector>
+
+// for std::is_nothrow_move_assignable
+#include <type_traits>
+
+// for std::this_thread::yield().
+#if !defined(EIGEN_USE_BLAS) && (defined(EIGEN_HAS_OPENMP) || defined(EIGEN_GEMM_THREADPOOL))
+#include <thread>
+#endif
+
+// for __cpp_lib feature test macros
+#if defined(__has_include) && __has_include(<version>)
+#include <version>
+#endif
+
+// for std::bit_cast()
+#if defined(__cpp_lib_bit_cast) && __cpp_lib_bit_cast >= 201806L
+#include <bit>
+#endif
+
+// for outputting debug info
+#ifdef EIGEN_DEBUG_ASSIGN
+#include <iostream>
+#endif
+
+// required for __cpuid, needs to be included after cmath
+// also required for _BitScanReverse on Windows on ARM
+#if EIGEN_COMP_MSVC && (EIGEN_ARCH_i386_OR_x86_64 || EIGEN_ARCH_ARM64) && !EIGEN_OS_WINCE
+#include <intrin.h>
+#endif
+
+#if defined(EIGEN_USE_SYCL)
+#undef min
+#undef max
+#undef isnan
+#undef isinf
+#undef isfinite
+#include <CL/sycl.hpp>
+#include <map>
+#include <thread>
+#include <utility>
+#ifndef EIGEN_SYCL_LOCAL_THREAD_DIM0
+#define EIGEN_SYCL_LOCAL_THREAD_DIM0 16
+#endif
+#ifndef EIGEN_SYCL_LOCAL_THREAD_DIM1
+#define EIGEN_SYCL_LOCAL_THREAD_DIM1 16
+#endif
+#endif
+
+#if defined EIGEN2_SUPPORT_STAGE40_FULL_EIGEN3_STRICTNESS || defined EIGEN2_SUPPORT_STAGE30_FULL_EIGEN3_API || \
+    defined EIGEN2_SUPPORT_STAGE20_RESOLVE_API_CONFLICTS || defined EIGEN2_SUPPORT_STAGE10_FULL_EIGEN2_API ||  \
+    defined EIGEN2_SUPPORT
+// This will generate an error message:
+#error Eigen2-support is only available up to version 3.2. Please go to "http://eigen.tuxfamily.org/index.php?title=Eigen2" for further information
+#endif
+
+namespace Eigen {
+
+// we use size_t frequently and we'll never remember to prepend it with std:: every time just to
+// ensure QNX/QCC support
+using std::size_t;
+// gcc 4.6.0 wants std:: for ptrdiff_t
+using std::ptrdiff_t;
+
+}  // namespace Eigen
+
+/** \defgroup Core_Module Core module
+ * This is the main module of Eigen providing dense matrix and vector support
+ * (both fixed and dynamic size) with all the features corresponding to a BLAS library
+ * and much more...
+ *
+ * \code
+ * #include <Eigen/Core>
+ * \endcode
+ */
+
+#ifdef EIGEN_USE_LAPACKE
+#ifdef EIGEN_USE_MKL
+#include "mkl_lapacke.h"
+#else
+#include "src/misc/lapacke.h"
+#endif
+#endif
+
+// IWYU pragma: begin_exports
+#include "src/Core/util/Constants.h"
+#include "src/Core/util/Meta.h"
+#include "src/Core/util/Assert.h"
+#include "src/Core/util/ForwardDeclarations.h"
+#include "src/Core/util/StaticAssert.h"
+#include "src/Core/util/XprHelper.h"
+#include "src/Core/util/Memory.h"
+#include "src/Core/util/IntegralConstant.h"
+#include "src/Core/util/Serializer.h"
+#include "src/Core/util/SymbolicIndex.h"
+#include "src/Core/util/EmulateArray.h"
+#include "src/Core/util/MoreMeta.h"
+
+#include "src/Core/NumTraits.h"
+#include "src/Core/MathFunctions.h"
+#include "src/Core/RandomImpl.h"
+#include "src/Core/GenericPacketMath.h"
+#include "src/Core/MathFunctionsImpl.h"
+#include "src/Core/arch/Default/ConjHelper.h"
+// Generic half float support
+#include "src/Core/arch/Default/Half.h"
+#include "src/Core/arch/Default/BFloat16.h"
+#include "src/Core/arch/Default/GenericPacketMathFunctionsFwd.h"
+
+#if defined(EIGEN_VECTORIZE_GENERIC) && !defined(EIGEN_DONT_VECTORIZE)
+#include "src/Core/arch/clang/PacketMath.h"
+#include "src/Core/arch/clang/TypeCasting.h"
+#include "src/Core/arch/clang/Complex.h"
+#include "src/Core/arch/clang/Reductions.h"
+#include "src/Core/arch/clang/MathFunctions.h"
+#else
+#if defined EIGEN_VECTORIZE_AVX512
+#include "src/Core/arch/SSE/PacketMath.h"
+#include "src/Core/arch/SSE/Reductions.h"
+#include "src/Core/arch/AVX/PacketMath.h"
+#include "src/Core/arch/AVX/Reductions.h"
+#include "src/Core/arch/AVX512/PacketMath.h"
+#include "src/Core/arch/AVX512/Reductions.h"
+#if defined EIGEN_VECTORIZE_AVX512FP16
+#include "src/Core/arch/AVX512/PacketMathFP16.h"
+#endif
+#include "src/Core/arch/SSE/TypeCasting.h"
+#include "src/Core/arch/AVX/TypeCasting.h"
+#include "src/Core/arch/AVX512/TypeCasting.h"
+#if defined EIGEN_VECTORIZE_AVX512FP16
+#include "src/Core/arch/AVX512/TypeCastingFP16.h"
+#endif
+#include "src/Core/arch/SSE/Complex.h"
+#include "src/Core/arch/AVX/Complex.h"
+#include "src/Core/arch/AVX512/Complex.h"
+#include "src/Core/arch/SSE/MathFunctions.h"
+#include "src/Core/arch/AVX/MathFunctions.h"
+#include "src/Core/arch/AVX512/MathFunctions.h"
+#if defined EIGEN_VECTORIZE_AVX512FP16
+#include "src/Core/arch/AVX512/MathFunctionsFP16.h"
+#endif
+#include "src/Core/arch/AVX512/TrsmKernel.h"
+#elif defined EIGEN_VECTORIZE_AVX
+// Use AVX for floats and doubles, SSE for integers
+#include "src/Core/arch/SSE/PacketMath.h"
+#include "src/Core/arch/SSE/Reductions.h"
+#include "src/Core/arch/SSE/TypeCasting.h"
+#include "src/Core/arch/SSE/Complex.h"
+#include "src/Core/arch/AVX/PacketMath.h"
+#include "src/Core/arch/AVX/Reductions.h"
+#include "src/Core/arch/AVX/TypeCasting.h"
+#include "src/Core/arch/AVX/Complex.h"
+#include "src/Core/arch/SSE/MathFunctions.h"
+#include "src/Core/arch/AVX/MathFunctions.h"
+#elif defined EIGEN_VECTORIZE_SSE
+#include "src/Core/arch/SSE/PacketMath.h"
+#include "src/Core/arch/SSE/Reductions.h"
+#include "src/Core/arch/SSE/TypeCasting.h"
+#include "src/Core/arch/SSE/MathFunctions.h"
+#include "src/Core/arch/SSE/Complex.h"
+#endif
+
+#if defined(EIGEN_VECTORIZE_ALTIVEC) || defined(EIGEN_VECTORIZE_VSX)
+#include "src/Core/arch/AltiVec/PacketMath.h"
+#include "src/Core/arch/AltiVec/TypeCasting.h"
+#include "src/Core/arch/AltiVec/MathFunctions.h"
+#include "src/Core/arch/AltiVec/Complex.h"
+#elif defined EIGEN_VECTORIZE_NEON
+#include "src/Core/arch/NEON/PacketMath.h"
+#include "src/Core/arch/NEON/TypeCasting.h"
+#include "src/Core/arch/NEON/MathFunctions.h"
+#include "src/Core/arch/NEON/Complex.h"
+#elif defined EIGEN_VECTORIZE_LSX
+#include "src/Core/arch/LSX/PacketMath.h"
+#include "src/Core/arch/LSX/TypeCasting.h"
+#include "src/Core/arch/LSX/MathFunctions.h"
+#include "src/Core/arch/LSX/Complex.h"
+#elif defined EIGEN_VECTORIZE_SVE
+#include "src/Core/arch/SVE/PacketMath.h"
+#include "src/Core/arch/SVE/TypeCasting.h"
+#include "src/Core/arch/SVE/MathFunctions.h"
+#elif defined EIGEN_VECTORIZE_RVV10
+#include "src/Core/arch/RVV10/PacketMath.h"
+#include "src/Core/arch/RVV10/PacketMath4.h"
+#include "src/Core/arch/RVV10/PacketMath2.h"
+#include "src/Core/arch/RVV10/TypeCasting.h"
+#include "src/Core/arch/RVV10/MathFunctions.h"
+#if defined EIGEN_VECTORIZE_RVV10FP16
+#include "src/Core/arch/RVV10/PacketMathFP16.h"
+#endif
+#if defined EIGEN_VECTORIZE_RVV10BF16
+#include "src/Core/arch/RVV10/PacketMathBF16.h"
+#endif
+#elif defined EIGEN_VECTORIZE_ZVECTOR
+#include "src/Core/arch/ZVector/PacketMath.h"
+#include "src/Core/arch/ZVector/MathFunctions.h"
+#include "src/Core/arch/ZVector/Complex.h"
+#elif defined EIGEN_VECTORIZE_MSA
+#include "src/Core/arch/MSA/PacketMath.h"
+#include "src/Core/arch/MSA/MathFunctions.h"
+#include "src/Core/arch/MSA/Complex.h"
+#elif defined EIGEN_VECTORIZE_HVX
+#include "src/Core/arch/HVX/PacketMath.h"
+#endif
+
+#if defined EIGEN_VECTORIZE_GPU
+#include "src/Core/arch/GPU/PacketMath.h"
+#include "src/Core/arch/GPU/MathFunctions.h"
+#include "src/Core/arch/GPU/TypeCasting.h"
+#endif
+
+#if defined(EIGEN_USE_SYCL)
+#include "src/Core/arch/SYCL/InteropHeaders.h"
+#if !defined(EIGEN_DONT_VECTORIZE_SYCL)
+#include "src/Core/arch/SYCL/PacketMath.h"
+#include "src/Core/arch/SYCL/MathFunctions.h"
+#include "src/Core/arch/SYCL/TypeCasting.h"
+#endif
+#endif
+
+#endif  // #ifndef EIGEN_VECTORIZE_GENERIC
+
+#include "src/Core/arch/Default/Settings.h"
+// This file provides generic implementations valid for scalar as well
+#include "src/Core/arch/Default/GenericPacketMathFunctions.h"
+
+#include "src/Core/functors/TernaryFunctors.h"
+#include "src/Core/functors/BinaryFunctors.h"
+#include "src/Core/functors/UnaryFunctors.h"
+#include "src/Core/functors/NullaryFunctors.h"
+#include "src/Core/functors/StlFunctors.h"
+#include "src/Core/functors/AssignmentFunctors.h"
+
+// Specialized functors for GPU.
+#ifdef EIGEN_GPUCC
+#include "src/Core/arch/GPU/Complex.h"
+#endif
+
+// Specializations of vectorized activation functions for NEON.
+#ifdef EIGEN_VECTORIZE_NEON
+#include "src/Core/arch/NEON/UnaryFunctors.h"
+#endif
+
+#include "src/Core/util/IndexedViewHelper.h"
+#include "src/Core/util/ReshapedHelper.h"
+#include "src/Core/ArithmeticSequence.h"
+#ifndef EIGEN_NO_IO
+#include "src/Core/IO.h"
+#endif
+#include "src/Core/DenseCoeffsBase.h"
+#include "src/Core/DenseBase.h"
+#include "src/Core/MatrixBase.h"
+#include "src/Core/EigenBase.h"
+
+#include "src/Core/Product.h"
+#include "src/Core/CoreEvaluators.h"
+#include "src/Core/AssignEvaluator.h"
+#include "src/Core/RealView.h"
+#include "src/Core/Assign.h"
+
+#include "src/Core/ArrayBase.h"
+#include "src/Core/util/BlasUtil.h"
+#include "src/Core/DenseStorage.h"
+#include "src/Core/NestByValue.h"
+
+// #include "src/Core/ForceAlignedAccess.h"
+
+#include "src/Core/ReturnByValue.h"
+#include "src/Core/NoAlias.h"
+#include "src/Core/PlainObjectBase.h"
+#include "src/Core/Matrix.h"
+#include "src/Core/Array.h"
+#include "src/Core/Fill.h"
+#include "src/Core/CwiseTernaryOp.h"
+#include "src/Core/CwiseBinaryOp.h"
+#include "src/Core/CwiseUnaryOp.h"
+#include "src/Core/CwiseNullaryOp.h"
+#include "src/Core/CwiseUnaryView.h"
+#include "src/Core/SelfCwiseBinaryOp.h"
+#include "src/Core/InnerProduct.h"
+#include "src/Core/Dot.h"
+#include "src/Core/StableNorm.h"
+#include "src/Core/Stride.h"
+#include "src/Core/MapBase.h"
+#include "src/Core/Map.h"
+#include "src/Core/Ref.h"
+#include "src/Core/Block.h"
+#include "src/Core/VectorBlock.h"
+#include "src/Core/IndexedView.h"
+#include "src/Core/Reshaped.h"
+#include "src/Core/Transpose.h"
+#include "src/Core/DiagonalMatrix.h"
+#include "src/Core/Diagonal.h"
+#include "src/Core/DiagonalProduct.h"
+#include "src/Core/SkewSymmetricMatrix3.h"
+#include "src/Core/Redux.h"
+#include "src/Core/Visitor.h"
+#include "src/Core/FindCoeff.h"
+#include "src/Core/Fuzzy.h"
+#include "src/Core/Swap.h"
+#include "src/Core/CommaInitializer.h"
+#include "src/Core/GeneralProduct.h"
+#include "src/Core/Solve.h"
+#include "src/Core/Inverse.h"
+#include "src/Core/SolverBase.h"
+#include "src/Core/PermutationMatrix.h"
+#include "src/Core/Transpositions.h"
+#include "src/Core/TriangularMatrix.h"
+#include "src/Core/SelfAdjointView.h"
+#include "src/Core/products/GeneralBlockPanelKernel.h"
+#include "src/Core/DeviceWrapper.h"
+#ifdef EIGEN_GEMM_THREADPOOL
+#include "ThreadPool"
+#endif
+#include "src/Core/products/Parallelizer.h"
+#include "src/Core/ProductEvaluators.h"
+#include "src/Core/products/GeneralMatrixVector.h"
+#include "src/Core/products/GeneralMatrixMatrix.h"
+#include "src/Core/SolveTriangular.h"
+#include "src/Core/products/GeneralMatrixMatrixTriangular.h"
+#include "src/Core/products/SelfadjointMatrixVector.h"
+#include "src/Core/products/SelfadjointMatrixMatrix.h"
+#include "src/Core/products/SelfadjointProduct.h"
+#include "src/Core/products/SelfadjointRank2Update.h"
+#include "src/Core/products/TriangularMatrixVector.h"
+#include "src/Core/products/TriangularMatrixMatrix.h"
+#include "src/Core/products/TriangularSolverMatrix.h"
+#include "src/Core/products/TriangularSolverVector.h"
+#include "src/Core/BandMatrix.h"
+#include "src/Core/CoreIterators.h"
+#include "src/Core/ConditionEstimator.h"
+
+#if !defined(EIGEN_VECTORIZE_GENERIC)
+#if defined(EIGEN_VECTORIZE_VSX)
+#include "src/Core/arch/AltiVec/MatrixProduct.h"
+#elif defined EIGEN_VECTORIZE_NEON
+#include "src/Core/arch/NEON/GeneralBlockPanelKernel.h"
+#elif defined EIGEN_VECTORIZE_LSX
+#include "src/Core/arch/LSX/GeneralBlockPanelKernel.h"
+#elif defined EIGEN_VECTORIZE_RVV10
+#include "src/Core/arch/RVV10/GeneralBlockPanelKernel.h"
+#endif
+
+#if defined(EIGEN_VECTORIZE_AVX512)
+#include "src/Core/arch/AVX512/GemmKernel.h"
+#endif
+#endif
+
+#include "src/Core/Select.h"
+#include "src/Core/VectorwiseOp.h"
+#include "src/Core/PartialReduxEvaluator.h"
+#include "src/Core/Random.h"
+#include "src/Core/Replicate.h"
+#include "src/Core/Reverse.h"
+#include "src/Core/ArrayWrapper.h"
+#include "src/Core/StlIterators.h"
+
+#ifdef EIGEN_USE_BLAS
+#include "src/Core/products/GeneralMatrixMatrix_BLAS.h"
+#include "src/Core/products/GeneralMatrixVector_BLAS.h"
+#include "src/Core/products/GeneralMatrixMatrixTriangular_BLAS.h"
+#include "src/Core/products/SelfadjointMatrixMatrix_BLAS.h"
+#include "src/Core/products/SelfadjointMatrixVector_BLAS.h"
+#include "src/Core/products/TriangularMatrixMatrix_BLAS.h"
+#include "src/Core/products/TriangularMatrixVector_BLAS.h"
+#include "src/Core/products/TriangularSolverMatrix_BLAS.h"
+#endif  // EIGEN_USE_BLAS
+
+#ifdef EIGEN_USE_MKL_VML
+#include "src/Core/Assign_MKL.h"
+#endif
+
+#ifdef EIGEN_USE_AOCL_VML
+#include "src/Core/Assign_AOCL.h"
+#endif
+
+#include "src/Core/GlobalFunctions.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_CORE_MODULE_H

data_acq_and_processing/third_party/eigen/Eigen/Dense

@@ -0,0 +1,7 @@
+#include "Core"
+#include "LU"
+#include "Cholesky"
+#include "QR"
+#include "SVD"
+#include "Geometry"
+#include "Eigenvalues"

data_acq_and_processing/third_party/eigen/Eigen/Eigen

@@ -0,0 +1,2 @@
+#include "Dense"
+#include "Sparse"

data_acq_and_processing/third_party/eigen/Eigen/Eigenvalues

@@ -0,0 +1,63 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_EIGENVALUES_MODULE_H
+#define EIGEN_EIGENVALUES_MODULE_H
+
+#include "Core"
+
+#include "Cholesky"
+#include "Jacobi"
+#include "Householder"
+#include "LU"
+#include "Geometry"
+#include "Sparse"  // Needed by ComplexQZ.
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** \defgroup Eigenvalues_Module Eigenvalues module
+ *
+ *
+ *
+ * This module mainly provides various eigenvalue solvers.
+ * This module also provides some MatrixBase methods, including:
+ *  - MatrixBase::eigenvalues(),
+ *  - MatrixBase::operatorNorm()
+ *
+ * \code
+ * #include <Eigen/Eigenvalues>
+ * \endcode
+ */
+
+// IWYU pragma: begin_exports
+#include "src/Eigenvalues/Tridiagonalization.h"
+#include "src/Eigenvalues/RealSchur.h"
+#include "src/Eigenvalues/EigenSolver.h"
+#include "src/Eigenvalues/SelfAdjointEigenSolver.h"
+#include "src/Eigenvalues/GeneralizedSelfAdjointEigenSolver.h"
+#include "src/Eigenvalues/HessenbergDecomposition.h"
+#include "src/Eigenvalues/ComplexSchur.h"
+#include "src/Eigenvalues/ComplexEigenSolver.h"
+#include "src/Eigenvalues/RealQZ.h"
+#include "src/Eigenvalues/ComplexQZ.h"
+#include "src/Eigenvalues/GeneralizedEigenSolver.h"
+#include "src/Eigenvalues/MatrixBaseEigenvalues.h"
+#ifdef EIGEN_USE_LAPACKE
+#ifdef EIGEN_USE_MKL
+#include "mkl_lapacke.h"
+#else
+#include "src/misc/lapacke.h"
+#endif
+#include "src/Eigenvalues/RealSchur_LAPACKE.h"
+#include "src/Eigenvalues/ComplexSchur_LAPACKE.h"
+#include "src/Eigenvalues/SelfAdjointEigenSolver_LAPACKE.h"
+#endif
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_EIGENVALUES_MODULE_H

data_acq_and_processing/third_party/eigen/Eigen/Geometry

@@ -0,0 +1,62 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_GEOMETRY_MODULE_H
+#define EIGEN_GEOMETRY_MODULE_H
+
+#include "Core"
+
+#include "SVD"
+#include "LU"
+#include <limits>
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** \defgroup Geometry_Module Geometry module
+ *
+ * This module provides support for:
+ *  - fixed-size homogeneous transformations
+ *  - translation, scaling, 2D and 3D rotations
+ *  - \link Quaternion quaternions \endlink
+ *  - cross products (\ref MatrixBase::cross(), \ref MatrixBase::cross3())
+ *  - orthogonal vector generation (MatrixBase::unitOrthogonal)
+ *  - some linear components: \link ParametrizedLine parametrized-lines \endlink and \link Hyperplane hyperplanes \endlink
+ *  - \link AlignedBox axis aligned bounding boxes \endlink
+ *  - \link umeyama() least-square transformation fitting \endlink
+ * \code
+ * #include <Eigen/Geometry>
+ * \endcode
+ */
+
+// IWYU pragma: begin_exports
+#include "src/Geometry/OrthoMethods.h"
+#include "src/Geometry/EulerAngles.h"
+#include "src/Geometry/Homogeneous.h"
+#include "src/Geometry/RotationBase.h"
+#include "src/Geometry/Rotation2D.h"
+#include "src/Geometry/Quaternion.h"
+#include "src/Geometry/AngleAxis.h"
+#include "src/Geometry/Transform.h"
+#include "src/Geometry/Translation.h"
+#include "src/Geometry/Scaling.h"
+#include "src/Geometry/Hyperplane.h"
+#include "src/Geometry/ParametrizedLine.h"
+#include "src/Geometry/AlignedBox.h"
+#include "src/Geometry/Umeyama.h"
+
+#ifndef EIGEN_VECTORIZE_GENERIC
+// TODO(rmlarsen): Make these work with generic vectorization if possible.
+// Use the SSE optimized version whenever possible.
+#if (defined EIGEN_VECTORIZE_SSE) || (defined EIGEN_VECTORIZE_NEON)
+#include "src/Geometry/arch/Geometry_SIMD.h"
+#endif
+#endif
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_GEOMETRY_MODULE_H

data_acq_and_processing/third_party/eigen/Eigen/Householder

@@ -0,0 +1,31 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_HOUSEHOLDER_MODULE_H
+#define EIGEN_HOUSEHOLDER_MODULE_H
+
+#include "Core"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** \defgroup Householder_Module Householder module
+ * This module provides Householder transformations.
+ *
+ * \code
+ * #include <Eigen/Householder>
+ * \endcode
+ */
+
+// IWYU pragma: begin_exports
+#include "src/Householder/Householder.h"
+#include "src/Householder/HouseholderSequence.h"
+#include "src/Householder/BlockHouseholder.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_HOUSEHOLDER_MODULE_H

data_acq_and_processing/third_party/eigen/Eigen/IterativeLinearSolvers

@@ -0,0 +1,52 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ITERATIVELINEARSOLVERS_MODULE_H
+#define EIGEN_ITERATIVELINEARSOLVERS_MODULE_H
+
+#include "SparseCore"
+#include "OrderingMethods"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/**
+  * \defgroup IterativeLinearSolvers_Module IterativeLinearSolvers module
+  *
+  * This module currently provides iterative methods to solve problems of the form \c A \c x = \c b, where \c A is a
+  squared matrix, usually very large and sparse.
+  * Those solvers are accessible via the following classes:
+  *  - ConjugateGradient for selfadjoint (hermitian) matrices,
+  *  - LeastSquaresConjugateGradient for rectangular least-square problems,
+  *  - BiCGSTAB for general square matrices.
+  *
+  * These iterative solvers are associated with some preconditioners:
+  *  - IdentityPreconditioner - not really useful
+  *  - DiagonalPreconditioner - also called Jacobi preconditioner, work very well on diagonal dominant matrices.
+  *  - IncompleteLUT - incomplete LU factorization with dual thresholding
+  *
+  * Such problems can also be solved using the direct sparse decomposition modules: SparseCholesky, CholmodSupport,
+  UmfPackSupport, SuperLUSupport, AccelerateSupport.
+  *
+    \code
+    #include <Eigen/IterativeLinearSolvers>
+    \endcode
+  */
+
+// IWYU pragma: begin_exports
+#include "src/IterativeLinearSolvers/SolveWithGuess.h"
+#include "src/IterativeLinearSolvers/IterativeSolverBase.h"
+#include "src/IterativeLinearSolvers/BasicPreconditioners.h"
+#include "src/IterativeLinearSolvers/ConjugateGradient.h"
+#include "src/IterativeLinearSolvers/LeastSquareConjugateGradient.h"
+#include "src/IterativeLinearSolvers/BiCGSTAB.h"
+#include "src/IterativeLinearSolvers/IncompleteLUT.h"
+#include "src/IterativeLinearSolvers/IncompleteCholesky.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_ITERATIVELINEARSOLVERS_MODULE_H

data_acq_and_processing/third_party/eigen/Eigen/Jacobi

@@ -0,0 +1,33 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_JACOBI_MODULE_H
+#define EIGEN_JACOBI_MODULE_H
+
+#include "Core"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** \defgroup Jacobi_Module Jacobi module
+ * This module provides Jacobi and Givens rotations.
+ *
+ * \code
+ * #include <Eigen/Jacobi>
+ * \endcode
+ *
+ * In addition to listed classes, it defines the two following MatrixBase methods to apply a Jacobi or Givens rotation:
+ *  - MatrixBase::applyOnTheLeft()
+ *  - MatrixBase::applyOnTheRight().
+ */
+
+// IWYU pragma: begin_exports
+#include "src/Jacobi/Jacobi.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_JACOBI_MODULE_H

data_acq_and_processing/third_party/eigen/Eigen/KLUSupport

@@ -0,0 +1,43 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_KLUSUPPORT_MODULE_H
+#define EIGEN_KLUSUPPORT_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+extern "C" {
+#include <btf.h>
+#include <klu.h>
+}
+
+/** \ingroup Support_modules
+ * \defgroup KLUSupport_Module KLUSupport module
+ *
+ * This module provides an interface to the KLU library which is part of the <a
+ * href="http://www.suitesparse.com">suitesparse</a> package. It provides the following factorization class:
+ * - class KLU: a sparse LU factorization, well-suited for circuit simulation.
+ *
+ * \code
+ * #include <Eigen/KLUSupport>
+ * \endcode
+ *
+ * In order to use this module, the klu and btf headers must be accessible from the include paths, and your binary must
+ * be linked to the klu library and its dependencies. The dependencies depend on how umfpack has been compiled. For a
+ * cmake based project, you can use our FindKLU.cmake module to help you in this task.
+ *
+ */
+
+// IWYU pragma: begin_exports
+#include "src/KLUSupport/KLUSupport.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_KLUSUPPORT_MODULE_H

data_acq_and_processing/third_party/eigen/Eigen/LU

@@ -0,0 +1,49 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_LU_MODULE_H
+#define EIGEN_LU_MODULE_H
+
+#include "Core"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** \defgroup LU_Module LU module
+ * This module includes %LU decomposition and related notions such as matrix inversion and determinant.
+ * This module defines the following MatrixBase methods:
+ *  - MatrixBase::inverse()
+ *  - MatrixBase::determinant()
+ *
+ * \code
+ * #include <Eigen/LU>
+ * \endcode
+ */
+
+#include "src/misc/Kernel.h"
+#include "src/misc/Image.h"
+
+// IWYU pragma: begin_exports
+#include "src/LU/FullPivLU.h"
+#include "src/LU/PartialPivLU.h"
+#ifdef EIGEN_USE_LAPACKE
+#include "src/misc/lapacke_helpers.h"
+#include "src/LU/PartialPivLU_LAPACKE.h"
+#endif
+#include "src/LU/Determinant.h"
+#include "src/LU/InverseImpl.h"
+
+#ifndef EIGEN_VECTORIZE_GENERIC
+// TODO(rmlarsen): Make these work with generic vectorization if possible.
+#if defined EIGEN_VECTORIZE_SSE || defined EIGEN_VECTORIZE_NEON
+#include "src/LU/arch/InverseSize4.h"
+#endif
+#endif
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_LU_MODULE_H

data_acq_and_processing/third_party/eigen/Eigen/MetisSupport

@@ -0,0 +1,35 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_METISSUPPORT_MODULE_H
+#define EIGEN_METISSUPPORT_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+extern "C" {
+#include <metis.h>
+}
+
+/** \ingroup Support_modules
+ * \defgroup MetisSupport_Module MetisSupport module
+ *
+ * \code
+ * #include <Eigen/MetisSupport>
+ * \endcode
+ * This module defines an interface to the METIS reordering package (http://glaros.dtc.umn.edu/gkhome/views/metis).
+ * It can be used just as any other built-in method as explained in \link OrderingMethods_Module here. \endlink
+ */
+
+// IWYU pragma: begin_exports
+#include "src/MetisSupport/MetisSupport.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_METISSUPPORT_MODULE_H

data_acq_and_processing/third_party/eigen/Eigen/OrderingMethods

@@ -0,0 +1,73 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ORDERINGMETHODS_MODULE_H
+#define EIGEN_ORDERINGMETHODS_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/**
+ * \defgroup OrderingMethods_Module OrderingMethods module
+ *
+ * This module is currently for internal use only
+ *
+ * It defines various built-in and external ordering methods for sparse matrices.
+ * They are typically used to reduce the number of elements during
+ * the sparse matrix decomposition (LLT, LU, QR).
+ * Precisely, in a preprocessing step, a permutation matrix P is computed using
+ * those ordering methods and applied to the columns of the matrix.
+ * Using for instance the sparse Cholesky decomposition, it is expected that
+ * the nonzeros elements in LLT(A*P) will be much smaller than that in LLT(A).
+ *
+ *
+ * Usage :
+ * \code
+ * #include <Eigen/OrderingMethods>
+ * \endcode
+ *
+ * A simple usage is as a template parameter in the sparse decomposition classes :
+ *
+ * \code
+ * SparseLU<MatrixType, COLAMDOrdering<int> > solver;
+ * \endcode
+ *
+ * \code
+ * SparseQR<MatrixType, COLAMDOrdering<int> > solver;
+ * \endcode
+ *
+ * It is possible as well to call directly a particular ordering method for your own purpose,
+ * \code
+ * AMDOrdering<int> ordering;
+ * PermutationMatrix<Dynamic, Dynamic, int> perm;
+ * SparseMatrix<double> A;
+ * //Fill the matrix ...
+ *
+ * ordering(A, perm); // Call AMD
+ * \endcode
+ *
+ * \note Some of these methods (like AMD or METIS), need the sparsity pattern
+ * of the input matrix to be symmetric. When the matrix is structurally unsymmetric,
+ * Eigen computes internally the pattern of \f$A^T*A\f$ before calling the method.
+ * If your matrix is already symmetric (at least in structure), you can avoid that
+ * by calling the method with a SelfAdjointView type.
+ *
+ * \code
+ *  // Call the ordering on the pattern of the lower triangular matrix A
+ * ordering(A.selfadjointView<Lower>(), perm);
+ * \endcode
+ */
+
+// IWYU pragma: begin_exports
+#include "src/OrderingMethods/Amd.h"
+#include "src/OrderingMethods/Ordering.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_ORDERINGMETHODS_MODULE_H

data_acq_and_processing/third_party/eigen/Eigen/PaStiXSupport

@@ -0,0 +1,51 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PASTIXSUPPORT_MODULE_H
+#define EIGEN_PASTIXSUPPORT_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+extern "C" {
+#include <pastix_nompi.h>
+#include <pastix.h>
+}
+
+#ifdef complex
+#undef complex
+#endif
+
+/** \ingroup Support_modules
+ * \defgroup PaStiXSupport_Module PaStiXSupport module
+ *
+ * This module provides an interface to the <a href="http://pastix.gforge.inria.fr/">PaSTiX</a> library.
+ * PaSTiX is a general \b supernodal, \b parallel and \b opensource sparse solver.
+ * It provides the two following main factorization classes:
+ * - class PastixLLT : a supernodal, parallel LLt Cholesky factorization.
+ * - class PastixLDLT: a supernodal, parallel LDLt Cholesky factorization.
+ * - class PastixLU : a supernodal, parallel LU factorization (optimized for a symmetric pattern).
+ *
+ * \code
+ * #include <Eigen/PaStiXSupport>
+ * \endcode
+ *
+ * In order to use this module, the PaSTiX headers must be accessible from the include paths, and your binary must be
+ * linked to the PaSTiX library and its dependencies. This wrapper resuires PaStiX version 5.x compiled without MPI
+ * support. The dependencies depend on how PaSTiX has been compiled. For a cmake based project, you can use our
+ * FindPaSTiX.cmake module to help you in this task.
+ *
+ */
+
+// IWYU pragma: begin_exports
+#include "src/PaStiXSupport/PaStiXSupport.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_PASTIXSUPPORT_MODULE_H

data_acq_and_processing/third_party/eigen/Eigen/PardisoSupport

@@ -0,0 +1,38 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_PARDISOSUPPORT_MODULE_H
+#define EIGEN_PARDISOSUPPORT_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+#include <mkl_pardiso.h>
+
+/** \ingroup Support_modules
+ * \defgroup PardisoSupport_Module PardisoSupport module
+ *
+ * This module brings support for the Intel(R) MKL PARDISO direct sparse solvers.
+ *
+ * \code
+ * #include <Eigen/PardisoSupport>
+ * \endcode
+ *
+ * In order to use this module, the MKL headers must be accessible from the include paths, and your binary must be
+ * linked to the MKL library and its dependencies. See this \ref TopicUsingIntelMKL "page" for more information on
+ * MKL-Eigen integration.
+ *
+ */
+
+// IWYU pragma: begin_exports
+#include "src/PardisoSupport/PardisoSupport.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_PARDISOSUPPORT_MODULE_H

data_acq_and_processing/third_party/eigen/Eigen/QR

@@ -0,0 +1,48 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_QR_MODULE_H
+#define EIGEN_QR_MODULE_H
+
+#include "Core"
+
+#include "Cholesky"
+#include "Jacobi"
+#include "Householder"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** \defgroup QR_Module QR module
+ *
+ *
+ *
+ * This module provides various QR decompositions
+ * This module also provides some MatrixBase methods, including:
+ *  - MatrixBase::householderQr()
+ *  - MatrixBase::colPivHouseholderQr()
+ *  - MatrixBase::fullPivHouseholderQr()
+ *
+ * \code
+ * #include <Eigen/QR>
+ * \endcode
+ */
+
+// IWYU pragma: begin_exports
+#include "src/QR/HouseholderQR.h"
+#include "src/QR/FullPivHouseholderQR.h"
+#include "src/QR/ColPivHouseholderQR.h"
+#include "src/QR/CompleteOrthogonalDecomposition.h"
+#ifdef EIGEN_USE_LAPACKE
+#include "src/misc/lapacke_helpers.h"
+#include "src/QR/HouseholderQR_LAPACKE.h"
+#include "src/QR/ColPivHouseholderQR_LAPACKE.h"
+#endif
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_QR_MODULE_H

data_acq_and_processing/third_party/eigen/Eigen/QtAlignedMalloc

@@ -0,0 +1,32 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_QTMALLOC_MODULE_H
+#define EIGEN_QTMALLOC_MODULE_H
+
+#include "Core"
+
+#if (!EIGEN_MALLOC_ALREADY_ALIGNED)
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+void *qMalloc(std::size_t size) { return Eigen::internal::aligned_malloc(size); }
+
+void qFree(void *ptr) { Eigen::internal::aligned_free(ptr); }
+
+void *qRealloc(void *ptr, std::size_t size) {
+  void *newPtr = Eigen::internal::aligned_malloc(size);
+  std::memcpy(newPtr, ptr, size);
+  Eigen::internal::aligned_free(ptr);
+  return newPtr;
+}
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif
+
+#endif  // EIGEN_QTMALLOC_MODULE_H

data_acq_and_processing/third_party/eigen/Eigen/SPQRSupport

@@ -0,0 +1,41 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPQRSUPPORT_MODULE_H
+#define EIGEN_SPQRSUPPORT_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+#include "SuiteSparseQR.hpp"
+
+/** \ingroup Support_modules
+ * \defgroup SPQRSupport_Module SuiteSparseQR module
+ *
+ * This module provides an interface to the SPQR library, which is part of the <a
+ * href="http://www.suitesparse.com">suitesparse</a> package.
+ *
+ * \code
+ * #include <Eigen/SPQRSupport>
+ * \endcode
+ *
+ * In order to use this module, the SPQR headers must be accessible from the include paths, and your binary must be
+ * linked to the SPQR library and its dependencies (Cholmod, AMD, COLAMD,...). For a cmake based project, you can use
+ * our FindSPQR.cmake and FindCholmod.Cmake modules
+ *
+ */
+
+#include "CholmodSupport"
+
+// IWYU pragma: begin_exports
+#include "src/SPQRSupport/SuiteSparseQRSupport.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif

data_acq_and_processing/third_party/eigen/Eigen/SVD

@@ -0,0 +1,55 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SVD_MODULE_H
+#define EIGEN_SVD_MODULE_H
+
+#include "QR"
+#include "Householder"
+#include "Jacobi"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** \defgroup SVD_Module SVD module
+ *
+ *
+ *
+ * This module provides SVD decomposition for matrices (both real and complex).
+ * Two decomposition algorithms are provided:
+ *  - JacobiSVD implementing two-sided Jacobi iterations is numerically very accurate, fast for small matrices, but very
+ * slow for larger ones.
+ *  - BDCSVD implementing a recursive divide & conquer strategy on top of an upper-bidiagonalization which remains fast
+ * for large problems. These decompositions are accessible via the respective classes and following MatrixBase methods:
+ *  - MatrixBase::jacobiSvd()
+ *  - MatrixBase::bdcSvd()
+ *
+ * \code
+ * #include <Eigen/SVD>
+ * \endcode
+ */
+
+// IWYU pragma: begin_exports
+#include "src/SVD/UpperBidiagonalization.h"
+#include "src/SVD/SVDBase.h"
+#include "src/SVD/JacobiSVD.h"
+#include "src/SVD/BDCSVD.h"
+#ifdef EIGEN_USE_LAPACKE
+#ifdef EIGEN_USE_MKL
+#include "mkl_lapacke.h"
+#else
+#include "src/misc/lapacke.h"
+#endif
+#ifndef EIGEN_USE_LAPACKE_STRICT
+#include "src/SVD/JacobiSVD_LAPACKE.h"
+#endif
+#include "src/SVD/BDCSVD_LAPACKE.h"
+#endif
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_SVD_MODULE_H

data_acq_and_processing/third_party/eigen/Eigen/Sparse

@@ -0,0 +1,33 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSE_MODULE_H
+#define EIGEN_SPARSE_MODULE_H
+
+/** \defgroup Sparse_Module Sparse meta-module
+  *
+  * Meta-module including all related modules:
+  * - \ref SparseCore_Module
+  * - \ref OrderingMethods_Module
+  * - \ref SparseCholesky_Module
+  * - \ref SparseLU_Module
+  * - \ref SparseQR_Module
+  * - \ref IterativeLinearSolvers_Module
+  *
+    \code
+    #include <Eigen/Sparse>
+    \endcode
+  */
+
+#include "SparseCore"
+#include "OrderingMethods"
+#include "SparseCholesky"
+#include "SparseLU"
+#include "SparseQR"
+#include "IterativeLinearSolvers"
+
+#endif  // EIGEN_SPARSE_MODULE_H

data_acq_and_processing/third_party/eigen/Eigen/SparseCholesky

@@ -0,0 +1,40 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2013 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSECHOLESKY_MODULE_H
+#define EIGEN_SPARSECHOLESKY_MODULE_H
+
+#include "SparseCore"
+#include "OrderingMethods"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/**
+ * \defgroup SparseCholesky_Module SparseCholesky module
+ *
+ * This module currently provides two variants of the direct sparse Cholesky decomposition for selfadjoint (hermitian)
+ * matrices. Those decompositions are accessible via the following classes:
+ *  - SimplicialLLt,
+ *  - SimplicialLDLt
+ *
+ * Such problems can also be solved using the ConjugateGradient solver from the IterativeLinearSolvers module.
+ *
+ * \code
+ * #include <Eigen/SparseCholesky>
+ * \endcode
+ */
+
+// IWYU pragma: begin_exports
+#include "src/SparseCholesky/SimplicialCholesky.h"
+#include "src/SparseCholesky/SimplicialCholesky_impl.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_SPARSECHOLESKY_MODULE_H

data_acq_and_processing/third_party/eigen/Eigen/SparseCore

@@ -0,0 +1,70 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSECORE_MODULE_H
+#define EIGEN_SPARSECORE_MODULE_H
+
+#include "Core"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+#include <vector>
+#include <map>
+#include <cstdlib>
+#include <cstring>
+#include <algorithm>
+#include <numeric>
+
+/**
+ * \defgroup SparseCore_Module SparseCore module
+ *
+ * This module provides a sparse matrix representation, and basic associated matrix manipulations
+ * and operations.
+ *
+ * See the \ref TutorialSparse "Sparse tutorial"
+ *
+ * \code
+ * #include <Eigen/SparseCore>
+ * \endcode
+ *
+ * This module depends on: Core.
+ */
+
+// IWYU pragma: begin_exports
+#include "src/SparseCore/SparseUtil.h"
+#include "src/SparseCore/SparseMatrixBase.h"
+#include "src/SparseCore/SparseAssign.h"
+#include "src/SparseCore/CompressedStorage.h"
+#include "src/SparseCore/AmbiVector.h"
+#include "src/SparseCore/SparseCompressedBase.h"
+#include "src/SparseCore/SparseMatrix.h"
+#include "src/SparseCore/SparseMap.h"
+#include "src/SparseCore/SparseVector.h"
+#include "src/SparseCore/SparseRef.h"
+#include "src/SparseCore/SparseCwiseUnaryOp.h"
+#include "src/SparseCore/SparseCwiseBinaryOp.h"
+#include "src/SparseCore/SparseTranspose.h"
+#include "src/SparseCore/SparseBlock.h"
+#include "src/SparseCore/SparseDot.h"
+#include "src/SparseCore/SparseRedux.h"
+#include "src/SparseCore/SparseView.h"
+#include "src/SparseCore/SparseDiagonalProduct.h"
+#include "src/SparseCore/ConservativeSparseSparseProduct.h"
+#include "src/SparseCore/SparseSparseProductWithPruning.h"
+#include "src/SparseCore/SparseProduct.h"
+#include "src/SparseCore/SparseDenseProduct.h"
+#include "src/SparseCore/SparseSelfAdjointView.h"
+#include "src/SparseCore/SparseTriangularView.h"
+#include "src/SparseCore/TriangularSolver.h"
+#include "src/SparseCore/SparsePermutation.h"
+#include "src/SparseCore/SparseFuzzy.h"
+#include "src/SparseCore/SparseSolverBase.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_SPARSECORE_MODULE_H

data_acq_and_processing/third_party/eigen/Eigen/SparseLU

@@ -0,0 +1,50 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2012 Désiré Nuentsa-Wakam <desire.nuentsa_wakam@inria.fr>
+// Copyright (C) 2012 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSELU_MODULE_H
+#define EIGEN_SPARSELU_MODULE_H
+
+#include "SparseCore"
+
+/**
+ * \defgroup SparseLU_Module SparseLU module
+ * This module defines a supernodal factorization of general sparse matrices.
+ * The code is fully optimized for supernode-panel updates with specialized kernels.
+ * Please, see the documentation of the SparseLU class for more details.
+ */
+
+// Ordering interface
+#include "OrderingMethods"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+// IWYU pragma: begin_exports
+#include "src/SparseLU/SparseLU_Structs.h"
+#include "src/SparseLU/SparseLU_SupernodalMatrix.h"
+#include "src/SparseLU/SparseLUImpl.h"
+#include "src/SparseCore/SparseColEtree.h"
+#include "src/SparseLU/SparseLU_Memory.h"
+#include "src/SparseLU/SparseLU_heap_relax_snode.h"
+#include "src/SparseLU/SparseLU_relax_snode.h"
+#include "src/SparseLU/SparseLU_pivotL.h"
+#include "src/SparseLU/SparseLU_panel_dfs.h"
+#include "src/SparseLU/SparseLU_kernel_bmod.h"
+#include "src/SparseLU/SparseLU_panel_bmod.h"
+#include "src/SparseLU/SparseLU_column_dfs.h"
+#include "src/SparseLU/SparseLU_column_bmod.h"
+#include "src/SparseLU/SparseLU_copy_to_ucol.h"
+#include "src/SparseLU/SparseLU_pruneL.h"
+#include "src/SparseLU/SparseLU_Utils.h"
+#include "src/SparseLU/SparseLU.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_SPARSELU_MODULE_H

data_acq_and_processing/third_party/eigen/Eigen/SparseQR

@@ -0,0 +1,38 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SPARSEQR_MODULE_H
+#define EIGEN_SPARSEQR_MODULE_H
+
+#include "SparseCore"
+#include "OrderingMethods"
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** \defgroup SparseQR_Module SparseQR module
+ * \brief Provides QR decomposition for sparse matrices
+ *
+ * This module provides a simplicial version of the left-looking Sparse QR decomposition.
+ * The columns of the input matrix should be reordered to limit the fill-in during the
+ * decomposition. Built-in methods (COLAMD, AMD) or external  methods (METIS) can be used to this end.
+ * See the \link OrderingMethods_Module OrderingMethods\endlink module for the list
+ * of built-in and external ordering methods.
+ *
+ * \code
+ * #include <Eigen/SparseQR>
+ * \endcode
+ *
+ *
+ */
+
+// IWYU pragma: begin_exports
+#include "src/SparseCore/SparseColEtree.h"
+#include "src/SparseQR/SparseQR.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif

data_acq_and_processing/third_party/eigen/Eigen/StdDeque

@@ -0,0 +1,30 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_STDDEQUE_MODULE_H
+#define EIGEN_STDDEQUE_MODULE_H
+
+#include "Core"
+#include <deque>
+
+#if EIGEN_COMP_MSVC && EIGEN_OS_WIN64 && \
+    (EIGEN_MAX_STATIC_ALIGN_BYTES <= 16) /* MSVC auto aligns up to 16 bytes in 64 bit builds */
+
+#define EIGEN_DEFINE_STL_DEQUE_SPECIALIZATION(...)
+
+#else
+
+// IWYU pragma: begin_exports
+#include "src/StlSupport/StdDeque.h"
+// IWYU pragma: end_exports
+
+#endif
+
+#endif  // EIGEN_STDDEQUE_MODULE_H

data_acq_and_processing/third_party/eigen/Eigen/StdList

@@ -0,0 +1,29 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_STDLIST_MODULE_H
+#define EIGEN_STDLIST_MODULE_H
+
+#include "Core"
+#include <list>
+
+#if EIGEN_COMP_MSVC && EIGEN_OS_WIN64 && \
+    (EIGEN_MAX_STATIC_ALIGN_BYTES <= 16) /* MSVC auto aligns up to 16 bytes in 64 bit builds */
+
+#define EIGEN_DEFINE_STL_LIST_SPECIALIZATION(...)
+
+#else
+
+// IWYU pragma: begin_exports
+#include "src/StlSupport/StdList.h"
+// IWYU pragma: end_exports
+
+#endif
+
+#endif  // EIGEN_STDLIST_MODULE_H

data_acq_and_processing/third_party/eigen/Eigen/StdVector

@@ -0,0 +1,30 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2009 Hauke Heibel <hauke.heibel@googlemail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_STDVECTOR_MODULE_H
+#define EIGEN_STDVECTOR_MODULE_H
+
+#include "Core"
+#include <vector>
+
+#if EIGEN_COMP_MSVC && EIGEN_OS_WIN64 && \
+    (EIGEN_MAX_STATIC_ALIGN_BYTES <= 16) /* MSVC auto aligns up to 16 bytes in 64 bit builds */
+
+#define EIGEN_DEFINE_STL_VECTOR_SPECIALIZATION(...)
+
+#else
+
+// IWYU pragma: begin_exports
+#include "src/StlSupport/StdVector.h"
+// IWYU pragma: end_exports
+
+#endif
+
+#endif  // EIGEN_STDVECTOR_MODULE_H

data_acq_and_processing/third_party/eigen/Eigen/SuperLUSupport

@@ -0,0 +1,70 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_SUPERLUSUPPORT_MODULE_H
+#define EIGEN_SUPERLUSUPPORT_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+#ifdef EMPTY
+#define EIGEN_EMPTY_WAS_ALREADY_DEFINED
+#endif
+
+typedef int int_t;
+#include <slu_Cnames.h>
+#include <supermatrix.h>
+#include <slu_util.h>
+
+// slu_util.h defines a preprocessor token named EMPTY which is really polluting,
+// so we remove it in favor of a SUPERLU_EMPTY token.
+// If EMPTY was already defined then we don't undef it.
+
+#if defined(EIGEN_EMPTY_WAS_ALREADY_DEFINED)
+#undef EIGEN_EMPTY_WAS_ALREADY_DEFINED
+#elif defined(EMPTY)
+#undef EMPTY
+#endif
+
+#define SUPERLU_EMPTY (-1)
+
+namespace Eigen {
+struct SluMatrix;
+}
+
+/** \ingroup Support_modules
+ * \defgroup SuperLUSupport_Module SuperLUSupport module
+ *
+ * This module provides an interface to the <a href="http://crd-legacy.lbl.gov/~xiaoye/SuperLU/">SuperLU</a> library.
+ * It provides the following factorization class:
+ * - class SuperLU: a supernodal sequential LU factorization.
+ * - class SuperILU: a supernodal sequential incomplete LU factorization (to be used as a preconditioner for iterative
+ * methods).
+ *
+ * \warning This wrapper requires at least versions 4.0 of SuperLU. The 3.x versions are not supported.
+ *
+ * \warning When including this module, you have to use SUPERLU_EMPTY instead of EMPTY which is no longer defined
+ * because it is too polluting.
+ *
+ * \code
+ * #include <Eigen/SuperLUSupport>
+ * \endcode
+ *
+ * In order to use this module, the superlu headers must be accessible from the include paths, and your binary must be
+ * linked to the superlu library and its dependencies. The dependencies depend on how superlu has been compiled. For a
+ * cmake based project, you can use our FindSuperLU.cmake module to help you in this task.
+ *
+ */
+
+// IWYU pragma: begin_exports
+#include "src/SuperLUSupport/SuperLUSupport.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_SUPERLUSUPPORT_MODULE_H

data_acq_and_processing/third_party/eigen/Eigen/ThreadPool

@@ -0,0 +1,80 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2016 Benoit Steiner <benoit.steiner.goog@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_THREADPOOL_MODULE_H
+#define EIGEN_THREADPOOL_MODULE_H
+
+#include "Core"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+/** \defgroup ThreadPool_Module ThreadPool Module
+ *
+ * This module provides 2 threadpool implementations
+ *  - a simple reference implementation
+ *  - a faster non blocking implementation
+ *
+ * \code
+ * #include <Eigen/ThreadPool>
+ * \endcode
+ */
+
+#include <cstddef>
+#include <cstring>
+#include <time.h>
+
+#include <vector>
+#include <atomic>
+#include <condition_variable>
+#include <deque>
+#include <mutex>
+#include <thread>
+#include <functional>
+#include <memory>
+#include <utility>
+
+// There are non-parenthesized calls to "max" in the  <unordered_map> header,
+// which trigger a check in test/main.h causing compilation to fail.
+// We work around the check here by removing the check for max in
+// the case where we have to emulate thread_local.
+#ifdef max
+#undef max
+#endif
+#include <unordered_map>
+
+#include "src/Core/util/Meta.h"
+#include "src/Core/util/MaxSizeVector.h"
+
+#ifndef EIGEN_MUTEX
+#define EIGEN_MUTEX std::mutex
+#endif
+#ifndef EIGEN_MUTEX_LOCK
+#define EIGEN_MUTEX_LOCK std::unique_lock<std::mutex>
+#endif
+#ifndef EIGEN_CONDVAR
+#define EIGEN_CONDVAR std::condition_variable
+#endif
+
+// IWYU pragma: begin_exports
+#include "src/ThreadPool/ThreadLocal.h"
+#include "src/ThreadPool/ThreadYield.h"
+#include "src/ThreadPool/ThreadCancel.h"
+#include "src/ThreadPool/EventCount.h"
+#include "src/ThreadPool/RunQueue.h"
+#include "src/ThreadPool/ThreadPoolInterface.h"
+#include "src/ThreadPool/ThreadEnvironment.h"
+#include "src/ThreadPool/Barrier.h"
+#include "src/ThreadPool/NonBlockingThreadPool.h"
+#include "src/ThreadPool/CoreThreadPoolDevice.h"
+#include "src/ThreadPool/ForkJoin.h"
+// IWYU pragma: end_exports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_CXX11_THREADPOOL_MODULE_H

data_acq_and_processing/third_party/eigen/Eigen/UmfPackSupport

@@ -0,0 +1,42 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_UMFPACKSUPPORT_MODULE_H
+#define EIGEN_UMFPACKSUPPORT_MODULE_H
+
+#include "SparseCore"
+
+#include "src/Core/util/DisableStupidWarnings.h"
+
+extern "C" {
+#include <umfpack.h>
+}
+
+/** \ingroup Support_modules
+ * \defgroup UmfPackSupport_Module UmfPackSupport module
+ *
+ * This module provides an interface to the UmfPack library which is part of the <a
+ * href="http://www.suitesparse.com">suitesparse</a> package. It provides the following factorization class:
+ * - class UmfPackLU: a multifrontal sequential LU factorization.
+ *
+ * \code
+ * #include <Eigen/UmfPackSupport>
+ * \endcode
+ *
+ * In order to use this module, the umfpack headers must be accessible from the include paths, and your binary must be
+ * linked to the umfpack library and its dependencies. The dependencies depend on how umfpack has been compiled. For a
+ * cmake based project, you can use our FindUmfPack.cmake module to help you in this task.
+ *
+ */
+
+// IWYU pragma: begin_exports
+#include "src/UmfPackSupport/UmfPackSupport.h"
+// IWYU pragma: endexports
+
+#include "src/Core/util/ReenableStupidWarnings.h"
+
+#endif  // EIGEN_UMFPACKSUPPORT_MODULE_H

data_acq_and_processing/third_party/eigen/Eigen/Version

@@ -0,0 +1,14 @@
+#ifndef EIGEN_VERSION_H
+#define EIGEN_VERSION_H
+
+// The "WORLD" version will forever remain "3" for the "Eigen3" library.
+#define EIGEN_WORLD_VERSION 3
+// As of Eigen3 5.0.0, we have moved to Semantic Versioning (semver.org).
+#define EIGEN_MAJOR_VERSION 5
+#define EIGEN_MINOR_VERSION 0
+#define EIGEN_PATCH_VERSION 1
+#define EIGEN_PRERELEASE_VERSION "dev"
+#define EIGEN_BUILD_VERSION "master"
+#define EIGEN_VERSION_STRING "5.0.1-dev+master"
+
+#endif  // EIGEN_VERSION_H

data_acq_and_processing/third_party/eigen/Eigen/src/AccelerateSupport/AccelerateSupport.h

@@ -0,0 +1,423 @@
+#ifndef EIGEN_ACCELERATESUPPORT_H
+#define EIGEN_ACCELERATESUPPORT_H
+
+#include <Accelerate/Accelerate.h>
+
+#include <Eigen/Sparse>
+
+namespace Eigen {
+
+template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
+class AccelerateImpl;
+
+/** \ingroup AccelerateSupport_Module
+ * \typedef AccelerateLLT
+ * \brief A direct Cholesky (LLT) factorization and solver based on Accelerate
+ *
+ * \warning Only single and double precision real scalar types are supported by Accelerate
+ *
+ * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+ * \tparam UpLo_ additional information about the matrix structure. Default is Lower.
+ *
+ * \sa \ref TutorialSparseSolverConcept, class AccelerateLLT
+ */
+template <typename MatrixType, int UpLo = Lower>
+using AccelerateLLT = AccelerateImpl<MatrixType, UpLo | Symmetric, SparseFactorizationCholesky, true>;
+
+/** \ingroup AccelerateSupport_Module
+ * \typedef AccelerateLDLT
+ * \brief The default Cholesky (LDLT) factorization and solver based on Accelerate
+ *
+ * \warning Only single and double precision real scalar types are supported by Accelerate
+ *
+ * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+ * \tparam UpLo_ additional information about the matrix structure. Default is Lower.
+ *
+ * \sa \ref TutorialSparseSolverConcept, class AccelerateLDLT
+ */
+template <typename MatrixType, int UpLo = Lower>
+using AccelerateLDLT = AccelerateImpl<MatrixType, UpLo | Symmetric, SparseFactorizationLDLT, true>;
+
+/** \ingroup AccelerateSupport_Module
+ * \typedef AccelerateLDLTUnpivoted
+ * \brief A direct Cholesky-like LDL^T factorization and solver based on Accelerate with only 1x1 pivots and no pivoting
+ *
+ * \warning Only single and double precision real scalar types are supported by Accelerate
+ *
+ * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+ * \tparam UpLo_ additional information about the matrix structure. Default is Lower.
+ *
+ * \sa \ref TutorialSparseSolverConcept, class AccelerateLDLTUnpivoted
+ */
+template <typename MatrixType, int UpLo = Lower>
+using AccelerateLDLTUnpivoted = AccelerateImpl<MatrixType, UpLo | Symmetric, SparseFactorizationLDLTUnpivoted, true>;
+
+/** \ingroup AccelerateSupport_Module
+ * \typedef AccelerateLDLTSBK
+ * \brief A direct Cholesky (LDLT) factorization and solver based on Accelerate with Supernode Bunch-Kaufman and static
+ * pivoting
+ *
+ * \warning Only single and double precision real scalar types are supported by Accelerate
+ *
+ * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+ * \tparam UpLo_ additional information about the matrix structure. Default is Lower.
+ *
+ * \sa \ref TutorialSparseSolverConcept, class AccelerateLDLTSBK
+ */
+template <typename MatrixType, int UpLo = Lower>
+using AccelerateLDLTSBK = AccelerateImpl<MatrixType, UpLo | Symmetric, SparseFactorizationLDLTSBK, true>;
+
+/** \ingroup AccelerateSupport_Module
+ * \typedef AccelerateLDLTTPP
+ * \brief A direct Cholesky (LDLT) factorization and solver based on Accelerate with full threshold partial pivoting
+ *
+ * \warning Only single and double precision real scalar types are supported by Accelerate
+ *
+ * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+ * \tparam UpLo_ additional information about the matrix structure. Default is Lower.
+ *
+ * \sa \ref TutorialSparseSolverConcept, class AccelerateLDLTTPP
+ */
+template <typename MatrixType, int UpLo = Lower>
+using AccelerateLDLTTPP = AccelerateImpl<MatrixType, UpLo | Symmetric, SparseFactorizationLDLTTPP, true>;
+
+/** \ingroup AccelerateSupport_Module
+ * \typedef AccelerateQR
+ * \brief A QR factorization and solver based on Accelerate
+ *
+ * \warning Only single and double precision real scalar types are supported by Accelerate
+ *
+ * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+ *
+ * \sa \ref TutorialSparseSolverConcept, class AccelerateQR
+ */
+template <typename MatrixType>
+using AccelerateQR = AccelerateImpl<MatrixType, 0, SparseFactorizationQR, false>;
+
+/** \ingroup AccelerateSupport_Module
+ * \typedef AccelerateCholeskyAtA
+ * \brief A QR factorization and solver based on Accelerate without storing Q (equivalent to A^TA = R^T R)
+ *
+ * \warning Only single and double precision real scalar types are supported by Accelerate
+ *
+ * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+ *
+ * \sa \ref TutorialSparseSolverConcept, class AccelerateCholeskyAtA
+ */
+template <typename MatrixType>
+using AccelerateCholeskyAtA = AccelerateImpl<MatrixType, 0, SparseFactorizationCholeskyAtA, false>;
+
+namespace internal {
+template <typename T>
+struct AccelFactorizationDeleter {
+  void operator()(T* sym) {
+    if (sym) {
+      SparseCleanup(*sym);
+      delete sym;
+      sym = nullptr;
+    }
+  }
+};
+
+template <typename DenseVecT, typename DenseMatT, typename SparseMatT, typename NumFactT>
+struct SparseTypesTraitBase {
+  typedef DenseVecT AccelDenseVector;
+  typedef DenseMatT AccelDenseMatrix;
+  typedef SparseMatT AccelSparseMatrix;
+
+  typedef SparseOpaqueSymbolicFactorization SymbolicFactorization;
+  typedef NumFactT NumericFactorization;
+
+  typedef AccelFactorizationDeleter<SymbolicFactorization> SymbolicFactorizationDeleter;
+  typedef AccelFactorizationDeleter<NumericFactorization> NumericFactorizationDeleter;
+};
+
+template <typename Scalar>
+struct SparseTypesTrait {};
+
+template <>
+struct SparseTypesTrait<double> : SparseTypesTraitBase<DenseVector_Double, DenseMatrix_Double, SparseMatrix_Double,
+                                                       SparseOpaqueFactorization_Double> {};
+
+template <>
+struct SparseTypesTrait<float>
+    : SparseTypesTraitBase<DenseVector_Float, DenseMatrix_Float, SparseMatrix_Float, SparseOpaqueFactorization_Float> {
+};
+
+}  // end namespace internal
+
+template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
+class AccelerateImpl : public SparseSolverBase<AccelerateImpl<MatrixType_, UpLo_, Solver_, EnforceSquare_> > {
+ protected:
+  using Base = SparseSolverBase<AccelerateImpl>;
+  using Base::derived;
+  using Base::m_isInitialized;
+
+ public:
+  using Base::_solve_impl;
+
+  typedef MatrixType_ MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  enum { ColsAtCompileTime = Dynamic, MaxColsAtCompileTime = Dynamic };
+  enum { UpLo = UpLo_ };
+
+  using AccelDenseVector = typename internal::SparseTypesTrait<Scalar>::AccelDenseVector;
+  using AccelDenseMatrix = typename internal::SparseTypesTrait<Scalar>::AccelDenseMatrix;
+  using AccelSparseMatrix = typename internal::SparseTypesTrait<Scalar>::AccelSparseMatrix;
+  using SymbolicFactorization = typename internal::SparseTypesTrait<Scalar>::SymbolicFactorization;
+  using NumericFactorization = typename internal::SparseTypesTrait<Scalar>::NumericFactorization;
+  using SymbolicFactorizationDeleter = typename internal::SparseTypesTrait<Scalar>::SymbolicFactorizationDeleter;
+  using NumericFactorizationDeleter = typename internal::SparseTypesTrait<Scalar>::NumericFactorizationDeleter;
+
+  AccelerateImpl() {
+    m_isInitialized = false;
+
+    auto check_flag_set = [](int value, int flag) { return ((value & flag) == flag); };
+
+    if (check_flag_set(UpLo_, Symmetric)) {
+      m_sparseKind = SparseSymmetric;
+      m_triType = (UpLo_ & Lower) ? SparseLowerTriangle : SparseUpperTriangle;
+    } else if (check_flag_set(UpLo_, UnitLower)) {
+      m_sparseKind = SparseUnitTriangular;
+      m_triType = SparseLowerTriangle;
+    } else if (check_flag_set(UpLo_, UnitUpper)) {
+      m_sparseKind = SparseUnitTriangular;
+      m_triType = SparseUpperTriangle;
+    } else if (check_flag_set(UpLo_, StrictlyLower)) {
+      m_sparseKind = SparseTriangular;
+      m_triType = SparseLowerTriangle;
+    } else if (check_flag_set(UpLo_, StrictlyUpper)) {
+      m_sparseKind = SparseTriangular;
+      m_triType = SparseUpperTriangle;
+    } else if (check_flag_set(UpLo_, Lower)) {
+      m_sparseKind = SparseTriangular;
+      m_triType = SparseLowerTriangle;
+    } else if (check_flag_set(UpLo_, Upper)) {
+      m_sparseKind = SparseTriangular;
+      m_triType = SparseUpperTriangle;
+    } else {
+      m_sparseKind = SparseOrdinary;
+      m_triType = (UpLo_ & Lower) ? SparseLowerTriangle : SparseUpperTriangle;
+    }
+
+    m_order = SparseOrderDefault;
+  }
+
+  explicit AccelerateImpl(const MatrixType& matrix) : AccelerateImpl() { compute(matrix); }
+
+  ~AccelerateImpl() {}
+
+  inline Index cols() const { return m_nCols; }
+  inline Index rows() const { return m_nRows; }
+
+  ComputationInfo info() const {
+    eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+    return m_info;
+  }
+
+  void analyzePattern(const MatrixType& matrix);
+
+  void factorize(const MatrixType& matrix);
+
+  void compute(const MatrixType& matrix);
+
+  template <typename Rhs, typename Dest>
+  void _solve_impl(const MatrixBase<Rhs>& b, MatrixBase<Dest>& dest) const;
+
+  /** Sets the ordering algorithm to use. */
+  void setOrder(SparseOrder_t order) { m_order = order; }
+
+ private:
+  template <typename T>
+  void buildAccelSparseMatrix(const SparseMatrix<T>& a, AccelSparseMatrix& A, std::vector<long>& columnStarts) {
+    const Index nColumnsStarts = a.cols() + 1;
+
+    columnStarts.resize(nColumnsStarts);
+
+    for (Index i = 0; i < nColumnsStarts; i++) columnStarts[i] = a.outerIndexPtr()[i];
+
+    SparseAttributes_t attributes{};
+    attributes.transpose = false;
+    attributes.triangle = m_triType;
+    attributes.kind = m_sparseKind;
+
+    SparseMatrixStructure structure{};
+    structure.attributes = attributes;
+    structure.rowCount = static_cast<int>(a.rows());
+    structure.columnCount = static_cast<int>(a.cols());
+    structure.blockSize = 1;
+    structure.columnStarts = columnStarts.data();
+    structure.rowIndices = const_cast<int*>(a.innerIndexPtr());
+
+    A.structure = structure;
+    A.data = const_cast<T*>(a.valuePtr());
+  }
+
+  void doAnalysis(AccelSparseMatrix& A) {
+    m_numericFactorization.reset(nullptr);
+
+    SparseSymbolicFactorOptions opts{};
+    opts.control = SparseDefaultControl;
+    opts.orderMethod = m_order;
+    opts.order = nullptr;
+    opts.ignoreRowsAndColumns = nullptr;
+    opts.malloc = malloc;
+    opts.free = free;
+    opts.reportError = nullptr;
+
+    m_symbolicFactorization.reset(new SymbolicFactorization(SparseFactor(Solver_, A.structure, opts)));
+
+    SparseStatus_t status = m_symbolicFactorization->status;
+
+    updateInfoStatus(status);
+
+    if (status != SparseStatusOK) m_symbolicFactorization.reset(nullptr);
+  }
+
+  void doFactorization(AccelSparseMatrix& A) {
+    SparseStatus_t status = SparseStatusReleased;
+
+    if (m_symbolicFactorization) {
+      m_numericFactorization.reset(new NumericFactorization(SparseFactor(*m_symbolicFactorization, A)));
+
+      status = m_numericFactorization->status;
+
+      if (status != SparseStatusOK) m_numericFactorization.reset(nullptr);
+    }
+
+    updateInfoStatus(status);
+  }
+
+ protected:
+  void updateInfoStatus(SparseStatus_t status) const {
+    switch (status) {
+      case SparseStatusOK:
+        m_info = Success;
+        break;
+      case SparseFactorizationFailed:
+      case SparseMatrixIsSingular:
+        m_info = NumericalIssue;
+        break;
+      case SparseInternalError:
+      case SparseParameterError:
+      case SparseStatusReleased:
+      default:
+        m_info = InvalidInput;
+        break;
+    }
+  }
+
+  mutable ComputationInfo m_info;
+  Index m_nRows, m_nCols;
+  std::unique_ptr<SymbolicFactorization, SymbolicFactorizationDeleter> m_symbolicFactorization;
+  std::unique_ptr<NumericFactorization, NumericFactorizationDeleter> m_numericFactorization;
+  SparseKind_t m_sparseKind;
+  SparseTriangle_t m_triType;
+  SparseOrder_t m_order;
+};
+
+/** Computes the symbolic and numeric decomposition of matrix \a a */
+template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
+void AccelerateImpl<MatrixType_, UpLo_, Solver_, EnforceSquare_>::compute(const MatrixType& a) {
+  if (EnforceSquare_) eigen_assert(a.rows() == a.cols());
+
+  m_nRows = a.rows();
+  m_nCols = a.cols();
+
+  AccelSparseMatrix A{};
+  std::vector<long> columnStarts;
+
+  buildAccelSparseMatrix(a, A, columnStarts);
+
+  doAnalysis(A);
+
+  if (m_symbolicFactorization) doFactorization(A);
+
+  m_isInitialized = true;
+}
+
+/** Performs a symbolic decomposition on the sparsity pattern of matrix \a a.
+ *
+ * This function is particularly useful when solving for several problems having the same structure.
+ *
+ * \sa factorize()
+ */
+template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
+void AccelerateImpl<MatrixType_, UpLo_, Solver_, EnforceSquare_>::analyzePattern(const MatrixType& a) {
+  if (EnforceSquare_) eigen_assert(a.rows() == a.cols());
+
+  m_nRows = a.rows();
+  m_nCols = a.cols();
+
+  AccelSparseMatrix A{};
+  std::vector<long> columnStarts;
+
+  buildAccelSparseMatrix(a, A, columnStarts);
+
+  doAnalysis(A);
+
+  m_isInitialized = true;
+}
+
+/** Performs a numeric decomposition of matrix \a a.
+ *
+ * The given matrix must have the same sparsity pattern as the matrix on which the symbolic decomposition has been
+ * performed.
+ *
+ * \sa analyzePattern()
+ */
+template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
+void AccelerateImpl<MatrixType_, UpLo_, Solver_, EnforceSquare_>::factorize(const MatrixType& a) {
+  eigen_assert(m_symbolicFactorization && "You must first call analyzePattern()");
+  eigen_assert(m_nRows == a.rows() && m_nCols == a.cols());
+
+  if (EnforceSquare_) eigen_assert(a.rows() == a.cols());
+
+  AccelSparseMatrix A{};
+  std::vector<long> columnStarts;
+
+  buildAccelSparseMatrix(a, A, columnStarts);
+
+  doFactorization(A);
+}
+
+template <typename MatrixType_, int UpLo_, SparseFactorization_t Solver_, bool EnforceSquare_>
+template <typename Rhs, typename Dest>
+void AccelerateImpl<MatrixType_, UpLo_, Solver_, EnforceSquare_>::_solve_impl(const MatrixBase<Rhs>& b,
+                                                                              MatrixBase<Dest>& x) const {
+  if (!m_numericFactorization) {
+    m_info = InvalidInput;
+    return;
+  }
+
+  eigen_assert(m_nRows == b.rows());
+  eigen_assert(((b.cols() == 1) || b.outerStride() == b.rows()));
+
+  SparseStatus_t status = SparseStatusOK;
+
+  Scalar* b_ptr = const_cast<Scalar*>(b.derived().data());
+  Scalar* x_ptr = const_cast<Scalar*>(x.derived().data());
+
+  AccelDenseMatrix xmat{};
+  xmat.attributes = SparseAttributes_t();
+  xmat.columnCount = static_cast<int>(x.cols());
+  xmat.rowCount = static_cast<int>(x.rows());
+  xmat.columnStride = xmat.rowCount;
+  xmat.data = x_ptr;
+
+  AccelDenseMatrix bmat{};
+  bmat.attributes = SparseAttributes_t();
+  bmat.columnCount = static_cast<int>(b.cols());
+  bmat.rowCount = static_cast<int>(b.rows());
+  bmat.columnStride = bmat.rowCount;
+  bmat.data = b_ptr;
+
+  SparseSolve(*m_numericFactorization, bmat, xmat);
+
+  updateInfoStatus(status);
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_ACCELERATESUPPORT_H

data_acq_and_processing/third_party/eigen/Eigen/src/AccelerateSupport/InternalHeaderCheck.h

@@ -0,0 +1,3 @@
+#ifndef EIGEN_ACCELERATESUPPORT_MODULE_H
+#error "Please include Eigen/AccelerateSupport instead of including headers inside the src directory directly."
+#endif

data_acq_and_processing/third_party/eigen/Eigen/src/Cholesky/InternalHeaderCheck.h

@@ -0,0 +1,3 @@
+#ifndef EIGEN_CHOLESKY_MODULE_H
+#error "Please include Eigen/Cholesky instead of including headers inside the src directory directly."
+#endif

data_acq_and_processing/third_party/eigen/Eigen/src/Cholesky/LDLT.h

@@ -0,0 +1,661 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2011 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2009 Keir Mierle <mierle@gmail.com>
+// Copyright (C) 2009 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2011 Timothy E. Holy <tim.holy@gmail.com >
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_LDLT_H
+#define EIGEN_LDLT_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename MatrixType_, int UpLo_>
+struct traits<LDLT<MatrixType_, UpLo_> > : traits<MatrixType_> {
+  typedef MatrixXpr XprKind;
+  typedef SolverStorage StorageKind;
+  typedef int StorageIndex;
+  enum { Flags = 0 };
+};
+
+template <typename MatrixType, int UpLo>
+struct LDLT_Traits;
+
+// PositiveSemiDef means positive semi-definite and non-zero; same for NegativeSemiDef
+enum SignMatrix { PositiveSemiDef, NegativeSemiDef, ZeroSign, Indefinite };
+}  // namespace internal
+
+/** \ingroup Cholesky_Module
+ *
+ * \class LDLT
+ *
+ * \brief Robust Cholesky decomposition of a matrix with pivoting
+ *
+ * \tparam MatrixType_ the type of the matrix of which to compute the LDL^T Cholesky decomposition
+ * \tparam UpLo_ the triangular part that will be used for the decomposition: Lower (default) or Upper.
+ *             The other triangular part won't be read.
+ *
+ * Perform a robust Cholesky decomposition of a positive semidefinite or negative semidefinite
+ * matrix \f$ A \f$ such that \f$ A =  P^TLDL^*P \f$, where P is a permutation matrix, L
+ * is lower triangular with a unit diagonal and D is a diagonal matrix.
+ *
+ * The decomposition uses pivoting to ensure stability, so that D will have
+ * zeros in the bottom right rank(A) - n submatrix. Avoiding the square root
+ * on D also stabilizes the computation.
+ *
+ * Remember that Cholesky decompositions are not rank-revealing. Also, do not use a Cholesky
+ * decomposition to determine whether a system of equations has a solution.
+ *
+ * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
+ *
+ * \sa MatrixBase::ldlt(), SelfAdjointView::ldlt(), class LLT
+ */
+template <typename MatrixType_, int UpLo_>
+class LDLT : public SolverBase<LDLT<MatrixType_, UpLo_> > {
+ public:
+  typedef MatrixType_ MatrixType;
+  typedef SolverBase<LDLT> Base;
+  friend class SolverBase<LDLT>;
+
+  EIGEN_GENERIC_PUBLIC_INTERFACE(LDLT)
+  enum {
+    MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime,
+    UpLo = UpLo_
+  };
+  typedef Matrix<Scalar, RowsAtCompileTime, 1, 0, MaxRowsAtCompileTime, 1> TmpMatrixType;
+
+  typedef Transpositions<RowsAtCompileTime, MaxRowsAtCompileTime> TranspositionType;
+  typedef PermutationMatrix<RowsAtCompileTime, MaxRowsAtCompileTime> PermutationType;
+
+  typedef internal::LDLT_Traits<MatrixType, UpLo> Traits;
+
+  /** \brief Default Constructor.
+   *
+   * The default constructor is useful in cases in which the user intends to
+   * perform decompositions via LDLT::compute(const MatrixType&).
+   */
+  LDLT()
+      : m_matrix(),
+        m_l1_norm(0),
+        m_transpositions(),
+        m_sign(internal::ZeroSign),
+        m_isInitialized(false),
+        m_info(InvalidInput) {}
+
+  /** \brief Default Constructor with memory preallocation
+   *
+   * Like the default constructor but with preallocation of the internal data
+   * according to the specified problem \a size.
+   * \sa LDLT()
+   */
+  explicit LDLT(Index size)
+      : m_matrix(size, size),
+        m_l1_norm(0),
+        m_transpositions(size),
+        m_temporary(size),
+        m_sign(internal::ZeroSign),
+        m_isInitialized(false),
+        m_info(InvalidInput) {}
+
+  /** \brief Constructor with decomposition
+   *
+   * This calculates the decomposition for the input \a matrix.
+   *
+   * \sa LDLT(Index size)
+   */
+  template <typename InputType>
+  explicit LDLT(const EigenBase<InputType>& matrix)
+      : m_matrix(matrix.rows(), matrix.cols()),
+        m_l1_norm(0),
+        m_transpositions(matrix.rows()),
+        m_temporary(matrix.rows()),
+        m_sign(internal::ZeroSign),
+        m_isInitialized(false),
+        m_info(InvalidInput) {
+    compute(matrix.derived());
+  }
+
+  /** \brief Constructs a LDLT factorization from a given matrix
+   *
+   * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when \c
+   * MatrixType is a Eigen::Ref.
+   *
+   * \sa LDLT(const EigenBase&)
+   */
+  template <typename InputType>
+  explicit LDLT(EigenBase<InputType>& matrix)
+      : m_matrix(matrix.derived()),
+        m_l1_norm(0),
+        m_transpositions(matrix.rows()),
+        m_temporary(matrix.rows()),
+        m_sign(internal::ZeroSign),
+        m_isInitialized(false),
+        m_info(InvalidInput) {
+    compute(matrix.derived());
+  }
+
+  /** Clear any existing decomposition
+   * \sa rankUpdate(w,sigma)
+   */
+  void setZero() { m_isInitialized = false; }
+
+  /** \returns a view of the upper triangular matrix U */
+  inline typename Traits::MatrixU matrixU() const {
+    eigen_assert(m_isInitialized && "LDLT is not initialized.");
+    return Traits::getU(m_matrix);
+  }
+
+  /** \returns a view of the lower triangular matrix L */
+  inline typename Traits::MatrixL matrixL() const {
+    eigen_assert(m_isInitialized && "LDLT is not initialized.");
+    return Traits::getL(m_matrix);
+  }
+
+  /** \returns the permutation matrix P as a transposition sequence.
+   */
+  inline const TranspositionType& transpositionsP() const {
+    eigen_assert(m_isInitialized && "LDLT is not initialized.");
+    return m_transpositions;
+  }
+
+  /** \returns the coefficients of the diagonal matrix D */
+  inline Diagonal<const MatrixType> vectorD() const {
+    eigen_assert(m_isInitialized && "LDLT is not initialized.");
+    return m_matrix.diagonal();
+  }
+
+  /** \returns true if the matrix is positive (semidefinite) */
+  inline bool isPositive() const {
+    eigen_assert(m_isInitialized && "LDLT is not initialized.");
+    return m_sign == internal::PositiveSemiDef || m_sign == internal::ZeroSign;
+  }
+
+  /** \returns true if the matrix is negative (semidefinite) */
+  inline bool isNegative(void) const {
+    eigen_assert(m_isInitialized && "LDLT is not initialized.");
+    return m_sign == internal::NegativeSemiDef || m_sign == internal::ZeroSign;
+  }
+
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+  /** \returns a solution x of \f$ A x = b \f$ using the current decomposition of A.
+   *
+   * This function also supports in-place solves using the syntax <tt>x = decompositionObject.solve(x)</tt> .
+   *
+   * \note_about_checking_solutions
+   *
+   * More precisely, this method solves \f$ A x = b \f$ using the decomposition \f$ A = P^T L D L^* P \f$
+   * by solving the systems \f$ P^T y_1 = b \f$, \f$ L y_2 = y_1 \f$, \f$ D y_3 = y_2 \f$,
+   * \f$ L^* y_4 = y_3 \f$ and \f$ P x = y_4 \f$ in succession. If the matrix \f$ A \f$ is singular, then
+   * \f$ D \f$ will also be singular (all the other matrices are invertible). In that case, the
+   * least-square solution of \f$ D y_3 = y_2 \f$ is computed. This does not mean that this function
+   * computes the least-square solution of \f$ A x = b \f$ if \f$ A \f$ is singular.
+   *
+   * \sa MatrixBase::ldlt(), SelfAdjointView::ldlt()
+   */
+  template <typename Rhs>
+  inline Solve<LDLT, Rhs> solve(const MatrixBase<Rhs>& b) const;
+#endif
+
+  template <typename Derived>
+  bool solveInPlace(MatrixBase<Derived>& bAndX) const;
+
+  template <typename InputType>
+  LDLT& compute(const EigenBase<InputType>& matrix);
+
+  /** \returns an estimate of the reciprocal condition number of the matrix of
+   *  which \c *this is the LDLT decomposition.
+   */
+  RealScalar rcond() const {
+    eigen_assert(m_isInitialized && "LDLT is not initialized.");
+    return internal::rcond_estimate_helper(m_l1_norm, *this);
+  }
+
+  template <typename Derived>
+  LDLT& rankUpdate(const MatrixBase<Derived>& w, const RealScalar& alpha = 1);
+
+  /** \returns the internal LDLT decomposition matrix
+   *
+   * TODO: document the storage layout.
+   */
+  inline const MatrixType& matrixLDLT() const {
+    eigen_assert(m_isInitialized && "LDLT is not initialized.");
+    return m_matrix;
+  }
+
+  MatrixType reconstructedMatrix() const;
+
+  /** \returns the adjoint of \c *this, that is, a const reference to the decomposition itself as the underlying matrix
+   * is self-adjoint.
+   *
+   * This method is provided for compatibility with other matrix decompositions, thus enabling generic code such as:
+   * \code x = decomposition.adjoint().solve(b) \endcode
+   */
+  const LDLT& adjoint() const { return *this; }
+
+  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return m_matrix.rows(); }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return m_matrix.cols(); }
+
+  /** \brief Reports whether previous computation was successful.
+   *
+   * \returns \c Success if computation was successful,
+   *          \c NumericalIssue if the factorization failed because of a zero pivot.
+   */
+  ComputationInfo info() const {
+    eigen_assert(m_isInitialized && "LDLT is not initialized.");
+    return m_info;
+  }
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  template <typename RhsType, typename DstType>
+  void _solve_impl(const RhsType& rhs, DstType& dst) const;
+
+  template <bool Conjugate, typename RhsType, typename DstType>
+  void _solve_impl_transposed(const RhsType& rhs, DstType& dst) const;
+#endif
+
+ protected:
+  EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
+
+  /** \internal
+   * Used to compute and store the Cholesky decomposition A = L D L^* = U^* D U.
+   * The strict upper part is used during the decomposition, the strict lower
+   * part correspond to the coefficients of L (its diagonal is equal to 1 and
+   * is not stored), and the diagonal entries correspond to D.
+   */
+  MatrixType m_matrix;
+  RealScalar m_l1_norm;
+  TranspositionType m_transpositions;
+  TmpMatrixType m_temporary;
+  internal::SignMatrix m_sign;
+  bool m_isInitialized;
+  ComputationInfo m_info;
+};
+
+namespace internal {
+
+template <int UpLo>
+struct ldlt_inplace;
+
+template <>
+struct ldlt_inplace<Lower> {
+  template <typename MatrixType, typename TranspositionType, typename Workspace>
+  static bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp, SignMatrix& sign) {
+    using std::abs;
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::RealScalar RealScalar;
+    typedef typename TranspositionType::StorageIndex IndexType;
+    eigen_assert(mat.rows() == mat.cols());
+    const Index size = mat.rows();
+    bool found_zero_pivot = false;
+    bool ret = true;
+
+    if (size <= 1) {
+      transpositions.setIdentity();
+      if (size == 0)
+        sign = ZeroSign;
+      else if (numext::real(mat.coeff(0, 0)) > static_cast<RealScalar>(0))
+        sign = PositiveSemiDef;
+      else if (numext::real(mat.coeff(0, 0)) < static_cast<RealScalar>(0))
+        sign = NegativeSemiDef;
+      else
+        sign = ZeroSign;
+      return true;
+    }
+
+    for (Index k = 0; k < size; ++k) {
+      // Find largest diagonal element
+      Index index_of_biggest_in_corner;
+      mat.diagonal().tail(size - k).cwiseAbs().maxCoeff(&index_of_biggest_in_corner);
+      index_of_biggest_in_corner += k;
+
+      transpositions.coeffRef(k) = IndexType(index_of_biggest_in_corner);
+      if (k != index_of_biggest_in_corner) {
+        // apply the transposition while taking care to consider only
+        // the lower triangular part
+        Index s = size - index_of_biggest_in_corner - 1;  // trailing size after the biggest element
+        mat.row(k).head(k).swap(mat.row(index_of_biggest_in_corner).head(k));
+        mat.col(k).tail(s).swap(mat.col(index_of_biggest_in_corner).tail(s));
+        std::swap(mat.coeffRef(k, k), mat.coeffRef(index_of_biggest_in_corner, index_of_biggest_in_corner));
+        for (Index i = k + 1; i < index_of_biggest_in_corner; ++i) {
+          Scalar tmp = mat.coeffRef(i, k);
+          mat.coeffRef(i, k) = numext::conj(mat.coeffRef(index_of_biggest_in_corner, i));
+          mat.coeffRef(index_of_biggest_in_corner, i) = numext::conj(tmp);
+        }
+        if (NumTraits<Scalar>::IsComplex)
+          mat.coeffRef(index_of_biggest_in_corner, k) = numext::conj(mat.coeff(index_of_biggest_in_corner, k));
+      }
+
+      // partition the matrix:
+      //       A00 |  -  |  -
+      // lu  = A10 | A11 |  -
+      //       A20 | A21 | A22
+      Index rs = size - k - 1;
+      Block<MatrixType, Dynamic, 1> A21(mat, k + 1, k, rs, 1);
+      Block<MatrixType, 1, Dynamic> A10(mat, k, 0, 1, k);
+      Block<MatrixType, Dynamic, Dynamic> A20(mat, k + 1, 0, rs, k);
+
+      if (k > 0) {
+        temp.head(k) = mat.diagonal().real().head(k).asDiagonal() * A10.adjoint();
+        mat.coeffRef(k, k) -= (A10 * temp.head(k)).value();
+        if (rs > 0) A21.noalias() -= A20 * temp.head(k);
+      }
+
+      // In some previous versions of Eigen (e.g., 3.2.1), the scaling was omitted if the pivot
+      // was smaller than the cutoff value. However, since LDLT is not rank-revealing
+      // we should only make sure that we do not introduce INF or NaN values.
+      // Remark that LAPACK also uses 0 as the cutoff value.
+      RealScalar realAkk = numext::real(mat.coeffRef(k, k));
+      bool pivot_is_valid = (abs(realAkk) > RealScalar(0));
+
+      if (k == 0 && !pivot_is_valid) {
+        // The entire diagonal is zero, there is nothing more to do
+        // except filling the transpositions, and checking whether the matrix is zero.
+        sign = ZeroSign;
+        for (Index j = 0; j < size; ++j) {
+          transpositions.coeffRef(j) = IndexType(j);
+          ret = ret && (mat.col(j).tail(size - j - 1).array() == Scalar(0)).all();
+        }
+        return ret;
+      }
+
+      if ((rs > 0) && pivot_is_valid)
+        A21 /= realAkk;
+      else if (rs > 0)
+        ret = ret && (A21.array() == Scalar(0)).all();
+
+      if (found_zero_pivot && pivot_is_valid)
+        ret = false;  // factorization failed
+      else if (!pivot_is_valid)
+        found_zero_pivot = true;
+
+      if (sign == PositiveSemiDef) {
+        if (realAkk < static_cast<RealScalar>(0)) sign = Indefinite;
+      } else if (sign == NegativeSemiDef) {
+        if (realAkk > static_cast<RealScalar>(0)) sign = Indefinite;
+      } else if (sign == ZeroSign) {
+        if (realAkk > static_cast<RealScalar>(0))
+          sign = PositiveSemiDef;
+        else if (realAkk < static_cast<RealScalar>(0))
+          sign = NegativeSemiDef;
+      }
+    }
+
+    return ret;
+  }
+
+  // Reference for the algorithm: Davis and Hager, "Multiple Rank
+  // Modifications of a Sparse Cholesky Factorization" (Algorithm 1)
+  // Trivial rearrangements of their computations (Timothy E. Holy)
+  // allow their algorithm to work for rank-1 updates even if the
+  // original matrix is not of full rank.
+  // Here only rank-1 updates are implemented, to reduce the
+  // requirement for intermediate storage and improve accuracy
+  template <typename MatrixType, typename WDerived>
+  static bool updateInPlace(MatrixType& mat, MatrixBase<WDerived>& w,
+                            const typename MatrixType::RealScalar& sigma = 1) {
+    using numext::isfinite;
+    typedef typename MatrixType::Scalar Scalar;
+    typedef typename MatrixType::RealScalar RealScalar;
+
+    const Index size = mat.rows();
+    eigen_assert(mat.cols() == size && w.size() == size);
+
+    RealScalar alpha = 1;
+
+    // Apply the update
+    for (Index j = 0; j < size; j++) {
+      // Check for termination due to an original decomposition of low-rank
+      if (!(isfinite)(alpha)) break;
+
+      // Update the diagonal terms
+      RealScalar dj = numext::real(mat.coeff(j, j));
+      Scalar wj = w.coeff(j);
+      RealScalar swj2 = sigma * numext::abs2(wj);
+      RealScalar gamma = dj * alpha + swj2;
+
+      mat.coeffRef(j, j) += swj2 / alpha;
+      alpha += swj2 / dj;
+
+      // Update the terms of L
+      Index rs = size - j - 1;
+      w.tail(rs) -= wj * mat.col(j).tail(rs);
+      if (!numext::is_exactly_zero(gamma)) mat.col(j).tail(rs) += (sigma * numext::conj(wj) / gamma) * w.tail(rs);
+    }
+    return true;
+  }
+
+  template <typename MatrixType, typename TranspositionType, typename Workspace, typename WType>
+  static bool update(MatrixType& mat, const TranspositionType& transpositions, Workspace& tmp, const WType& w,
+                     const typename MatrixType::RealScalar& sigma = 1) {
+    // Apply the permutation to the input w
+    tmp = transpositions * w;
+
+    return ldlt_inplace<Lower>::updateInPlace(mat, tmp, sigma);
+  }
+};
+
+template <>
+struct ldlt_inplace<Upper> {
+  template <typename MatrixType, typename TranspositionType, typename Workspace>
+  static EIGEN_STRONG_INLINE bool unblocked(MatrixType& mat, TranspositionType& transpositions, Workspace& temp,
+                                            SignMatrix& sign) {
+    Transpose<MatrixType> matt(mat);
+    return ldlt_inplace<Lower>::unblocked(matt, transpositions, temp, sign);
+  }
+
+  template <typename MatrixType, typename TranspositionType, typename Workspace, typename WType>
+  static EIGEN_STRONG_INLINE bool update(MatrixType& mat, TranspositionType& transpositions, Workspace& tmp, WType& w,
+                                         const typename MatrixType::RealScalar& sigma = 1) {
+    Transpose<MatrixType> matt(mat);
+    return ldlt_inplace<Lower>::update(matt, transpositions, tmp, w.conjugate(), sigma);
+  }
+};
+
+template <typename MatrixType>
+struct LDLT_Traits<MatrixType, Lower> {
+  typedef const TriangularView<const MatrixType, UnitLower> MatrixL;
+  typedef const TriangularView<const typename MatrixType::AdjointReturnType, UnitUpper> MatrixU;
+  static inline MatrixL getL(const MatrixType& m) { return MatrixL(m); }
+  static inline MatrixU getU(const MatrixType& m) { return MatrixU(m.adjoint()); }
+};
+
+template <typename MatrixType>
+struct LDLT_Traits<MatrixType, Upper> {
+  typedef const TriangularView<const typename MatrixType::AdjointReturnType, UnitLower> MatrixL;
+  typedef const TriangularView<const MatrixType, UnitUpper> MatrixU;
+  static inline MatrixL getL(const MatrixType& m) { return MatrixL(m.adjoint()); }
+  static inline MatrixU getU(const MatrixType& m) { return MatrixU(m); }
+};
+
+}  // end namespace internal
+
+/** Compute / recompute the LDLT decomposition A = L D L^* = U^* D U of \a matrix
+ */
+template <typename MatrixType, int UpLo_>
+template <typename InputType>
+LDLT<MatrixType, UpLo_>& LDLT<MatrixType, UpLo_>::compute(const EigenBase<InputType>& a) {
+  eigen_assert(a.rows() == a.cols());
+  const Index size = a.rows();
+
+  m_matrix = a.derived();
+
+  // Compute matrix L1 norm = max abs column sum.
+  m_l1_norm = RealScalar(0);
+  // TODO: move this code to SelfAdjointView
+  for (Index col = 0; col < size; ++col) {
+    RealScalar abs_col_sum;
+    if (UpLo_ == Lower)
+      abs_col_sum =
+          m_matrix.col(col).tail(size - col).template lpNorm<1>() + m_matrix.row(col).head(col).template lpNorm<1>();
+    else
+      abs_col_sum =
+          m_matrix.col(col).head(col).template lpNorm<1>() + m_matrix.row(col).tail(size - col).template lpNorm<1>();
+    if (abs_col_sum > m_l1_norm) m_l1_norm = abs_col_sum;
+  }
+
+  m_transpositions.resize(size);
+  m_isInitialized = false;
+  m_temporary.resize(size);
+  m_sign = internal::ZeroSign;
+
+  m_info = internal::ldlt_inplace<UpLo>::unblocked(m_matrix, m_transpositions, m_temporary, m_sign) ? Success
+                                                                                                    : NumericalIssue;
+
+  m_isInitialized = true;
+  return *this;
+}
+
+/** Update the LDLT decomposition:  given A = L D L^T, efficiently compute the decomposition of A + sigma w w^T.
+ * \param w a vector to be incorporated into the decomposition.
+ * \param sigma a scalar, +1 for updates and -1 for "downdates," which correspond to removing previously-added column
+ * vectors. Optional; default value is +1. \sa setZero()
+ */
+template <typename MatrixType, int UpLo_>
+template <typename Derived>
+LDLT<MatrixType, UpLo_>& LDLT<MatrixType, UpLo_>::rankUpdate(
+    const MatrixBase<Derived>& w, const typename LDLT<MatrixType, UpLo_>::RealScalar& sigma) {
+  typedef typename TranspositionType::StorageIndex IndexType;
+  const Index size = w.rows();
+  if (m_isInitialized) {
+    eigen_assert(m_matrix.rows() == size);
+  } else {
+    m_matrix.resize(size, size);
+    m_matrix.setZero();
+    m_transpositions.resize(size);
+    for (Index i = 0; i < size; i++) m_transpositions.coeffRef(i) = IndexType(i);
+    m_temporary.resize(size);
+    m_sign = sigma >= 0 ? internal::PositiveSemiDef : internal::NegativeSemiDef;
+    m_isInitialized = true;
+  }
+
+  internal::ldlt_inplace<UpLo>::update(m_matrix, m_transpositions, m_temporary, w, sigma);
+
+  return *this;
+}
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template <typename MatrixType_, int UpLo_>
+template <typename RhsType, typename DstType>
+void LDLT<MatrixType_, UpLo_>::_solve_impl(const RhsType& rhs, DstType& dst) const {
+  _solve_impl_transposed<true>(rhs, dst);
+}
+
+template <typename MatrixType_, int UpLo_>
+template <bool Conjugate, typename RhsType, typename DstType>
+void LDLT<MatrixType_, UpLo_>::_solve_impl_transposed(const RhsType& rhs, DstType& dst) const {
+  // dst = P b
+  dst = m_transpositions * rhs;
+
+  // dst = L^-1 (P b)
+  // dst = L^-*T (P b)
+  matrixL().template conjugateIf<!Conjugate>().solveInPlace(dst);
+
+  // dst = D^-* (L^-1 P b)
+  // dst = D^-1 (L^-*T P b)
+  // more precisely, use pseudo-inverse of D (see bug 241)
+  using std::abs;
+  const typename Diagonal<const MatrixType>::RealReturnType vecD(vectorD());
+  // In some previous versions, tolerance was set to the max of 1/highest (or rather numeric_limits::min())
+  // and the maximal diagonal entry * epsilon as motivated by LAPACK's xGELSS:
+  // RealScalar tolerance = numext::maxi(vecD.array().abs().maxCoeff() * NumTraits<RealScalar>::epsilon(),RealScalar(1)
+  // / NumTraits<RealScalar>::highest()); However, LDLT is not rank revealing, and so adjusting the tolerance wrt to the
+  // highest diagonal element is not well justified and leads to numerical issues in some cases. Moreover, Lapack's
+  // xSYTRS routines use 0 for the tolerance. Using numeric_limits::min() gives us more robustness to denormals.
+  RealScalar tolerance = (std::numeric_limits<RealScalar>::min)();
+  for (Index i = 0; i < vecD.size(); ++i) {
+    if (abs(vecD(i)) > tolerance)
+      dst.row(i) /= vecD(i);
+    else
+      dst.row(i).setZero();
+  }
+
+  // dst = L^-* (D^-* L^-1 P b)
+  // dst = L^-T (D^-1 L^-*T P b)
+  matrixL().transpose().template conjugateIf<Conjugate>().solveInPlace(dst);
+
+  // dst = P^T (L^-* D^-* L^-1 P b) = A^-1 b
+  // dst = P^-T (L^-T D^-1 L^-*T P b) = A^-1 b
+  dst = m_transpositions.transpose() * dst;
+}
+#endif
+
+/** \internal use x = ldlt_object.solve(x);
+ *
+ * This is the \em in-place version of solve().
+ *
+ * \param bAndX represents both the right-hand side matrix b and result x.
+ *
+ * \returns true always! If you need to check for existence of solutions, use another decomposition like LU, QR, or SVD.
+ *
+ * This version avoids a copy when the right hand side matrix b is not
+ * needed anymore.
+ *
+ * \sa LDLT::solve(), MatrixBase::ldlt()
+ */
+template <typename MatrixType, int UpLo_>
+template <typename Derived>
+bool LDLT<MatrixType, UpLo_>::solveInPlace(MatrixBase<Derived>& bAndX) const {
+  eigen_assert(m_isInitialized && "LDLT is not initialized.");
+  eigen_assert(m_matrix.rows() == bAndX.rows());
+
+  bAndX = this->solve(bAndX);
+
+  return true;
+}
+
+/** \returns the matrix represented by the decomposition,
+ * i.e., it returns the product: P^T L D L^* P.
+ * This function is provided for debug purpose. */
+template <typename MatrixType, int UpLo_>
+MatrixType LDLT<MatrixType, UpLo_>::reconstructedMatrix() const {
+  eigen_assert(m_isInitialized && "LDLT is not initialized.");
+  const Index size = m_matrix.rows();
+  MatrixType res(size, size);
+
+  // P
+  res.setIdentity();
+  res = transpositionsP() * res;
+  // L^* P
+  res = matrixU() * res;
+  // D(L^*P)
+  res = vectorD().real().asDiagonal() * res;
+  // L(DL^*P)
+  res = matrixL() * res;
+  // P^T (LDL^*P)
+  res = transpositionsP().transpose() * res;
+
+  return res;
+}
+
+/** \cholesky_module
+ * \returns the Cholesky decomposition with full pivoting without square root of \c *this
+ * \sa MatrixBase::ldlt()
+ */
+template <typename MatrixType, unsigned int UpLo>
+inline LDLT<typename SelfAdjointView<MatrixType, UpLo>::PlainObject, UpLo> SelfAdjointView<MatrixType, UpLo>::ldlt()
+    const {
+  return LDLT<PlainObject, UpLo>(m_matrix);
+}
+
+/** \cholesky_module
+ * \returns the Cholesky decomposition with full pivoting without square root of \c *this
+ * \sa SelfAdjointView::ldlt()
+ */
+template <typename Derived>
+inline LDLT<typename MatrixBase<Derived>::PlainObject> MatrixBase<Derived>::ldlt() const {
+  return LDLT<PlainObject>(derived());
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_LDLT_H

data_acq_and_processing/third_party/eigen/Eigen/src/Cholesky/LLT.h

@@ -0,0 +1,516 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_LLT_H
+#define EIGEN_LLT_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename MatrixType_, int UpLo_>
+struct traits<LLT<MatrixType_, UpLo_> > : traits<MatrixType_> {
+  typedef MatrixXpr XprKind;
+  typedef SolverStorage StorageKind;
+  typedef int StorageIndex;
+  enum { Flags = 0 };
+};
+
+template <typename MatrixType, int UpLo>
+struct LLT_Traits;
+}  // namespace internal
+
+/** \ingroup Cholesky_Module
+ *
+ * \class LLT
+ *
+ * \brief Standard Cholesky decomposition (LL^T) of a matrix and associated features
+ *
+ * \tparam MatrixType_ the type of the matrix of which we are computing the LL^T Cholesky decomposition
+ * \tparam UpLo_ the triangular part that will be used for the decomposition: Lower (default) or Upper.
+ *               The other triangular part won't be read.
+ *
+ * This class performs a LL^T Cholesky decomposition of a symmetric, positive definite
+ * matrix A such that A = LL^* = U^*U, where L is lower triangular.
+ *
+ * While the Cholesky decomposition is particularly useful to solve selfadjoint problems like  D^*D x = b,
+ * for that purpose, we recommend the Cholesky decomposition without square root which is more stable
+ * and even faster. Nevertheless, this standard Cholesky decomposition remains useful in many other
+ * situations like generalised eigen problems with hermitian matrices.
+ *
+ * Remember that Cholesky decompositions are not rank-revealing. This LLT decomposition is only stable on positive
+ * definite matrices, use LDLT instead for the semidefinite case. Also, do not use a Cholesky decomposition to determine
+ * whether a system of equations has a solution.
+ *
+ * Example: \include LLT_example.cpp
+ * Output: \verbinclude LLT_example.out
+ *
+ * \b Performance: for best performance, it is recommended to use a column-major storage format
+ * with the Lower triangular part (the default), or, equivalently, a row-major storage format
+ * with the Upper triangular part. Otherwise, you might get a 20% slowdown for the full factorization
+ * step, and rank-updates can be up to 3 times slower.
+ *
+ * This class supports the \link InplaceDecomposition inplace decomposition \endlink mechanism.
+ *
+ * Note that during the decomposition, only the lower (or upper, as defined by UpLo_) triangular part of A is
+ * considered. Therefore, the strict lower part does not have to store correct values.
+ *
+ * \sa MatrixBase::llt(), SelfAdjointView::llt(), class LDLT
+ */
+template <typename MatrixType_, int UpLo_>
+class LLT : public SolverBase<LLT<MatrixType_, UpLo_> > {
+ public:
+  typedef MatrixType_ MatrixType;
+  typedef SolverBase<LLT> Base;
+  friend class SolverBase<LLT>;
+
+  EIGEN_GENERIC_PUBLIC_INTERFACE(LLT)
+  enum { MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime };
+
+  enum { PacketSize = internal::packet_traits<Scalar>::size, AlignmentMask = int(PacketSize) - 1, UpLo = UpLo_ };
+
+  typedef internal::LLT_Traits<MatrixType, UpLo> Traits;
+
+  /**
+   * \brief Default Constructor.
+   *
+   * The default constructor is useful in cases in which the user intends to
+   * perform decompositions via LLT::compute(const MatrixType&).
+   */
+  LLT() : m_matrix(), m_l1_norm(0), m_isInitialized(false), m_info(InvalidInput) {}
+
+  /** \brief Default Constructor with memory preallocation
+   *
+   * Like the default constructor but with preallocation of the internal data
+   * according to the specified problem \a size.
+   * \sa LLT()
+   */
+  explicit LLT(Index size) : m_matrix(size, size), m_l1_norm(0), m_isInitialized(false), m_info(InvalidInput) {}
+
+  template <typename InputType>
+  explicit LLT(const EigenBase<InputType>& matrix)
+      : m_matrix(matrix.rows(), matrix.cols()), m_l1_norm(0), m_isInitialized(false), m_info(InvalidInput) {
+    compute(matrix.derived());
+  }
+
+  /** \brief Constructs a LLT factorization from a given matrix
+   *
+   * This overloaded constructor is provided for \link InplaceDecomposition inplace decomposition \endlink when
+   * \c MatrixType is a Eigen::Ref.
+   *
+   * \sa LLT(const EigenBase&)
+   */
+  template <typename InputType>
+  explicit LLT(EigenBase<InputType>& matrix)
+      : m_matrix(matrix.derived()), m_l1_norm(0), m_isInitialized(false), m_info(InvalidInput) {
+    compute(matrix.derived());
+  }
+
+  /** \returns a view of the upper triangular matrix U */
+  inline typename Traits::MatrixU matrixU() const {
+    eigen_assert(m_isInitialized && "LLT is not initialized.");
+    return Traits::getU(m_matrix);
+  }
+
+  /** \returns a view of the lower triangular matrix L */
+  inline typename Traits::MatrixL matrixL() const {
+    eigen_assert(m_isInitialized && "LLT is not initialized.");
+    return Traits::getL(m_matrix);
+  }
+
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+  /** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
+   *
+   * Since this LLT class assumes anyway that the matrix A is invertible, the solution
+   * theoretically exists and is unique regardless of b.
+   *
+   * Example: \include LLT_solve.cpp
+   * Output: \verbinclude LLT_solve.out
+   *
+   * \sa solveInPlace(), MatrixBase::llt(), SelfAdjointView::llt()
+   */
+  template <typename Rhs>
+  inline Solve<LLT, Rhs> solve(const MatrixBase<Rhs>& b) const;
+#endif
+
+  template <typename Derived>
+  void solveInPlace(const MatrixBase<Derived>& bAndX) const;
+
+  template <typename InputType>
+  LLT& compute(const EigenBase<InputType>& matrix);
+
+  /** \returns an estimate of the reciprocal condition number of the matrix of
+   *  which \c *this is the Cholesky decomposition.
+   */
+  RealScalar rcond() const {
+    eigen_assert(m_isInitialized && "LLT is not initialized.");
+    eigen_assert(m_info == Success && "LLT failed because matrix appears to be negative");
+    return internal::rcond_estimate_helper(m_l1_norm, *this);
+  }
+
+  /** \returns the LLT decomposition matrix
+   *
+   * TODO: document the storage layout
+   */
+  inline const MatrixType& matrixLLT() const {
+    eigen_assert(m_isInitialized && "LLT is not initialized.");
+    return m_matrix;
+  }
+
+  MatrixType reconstructedMatrix() const;
+
+  /** \brief Reports whether previous computation was successful.
+   *
+   * \returns \c Success if computation was successful,
+   *          \c NumericalIssue if the matrix.appears not to be positive definite.
+   */
+  ComputationInfo info() const {
+    eigen_assert(m_isInitialized && "LLT is not initialized.");
+    return m_info;
+  }
+
+  /** \returns the adjoint of \c *this, that is, a const reference to the decomposition itself as the underlying matrix
+   * is self-adjoint.
+   *
+   * This method is provided for compatibility with other matrix decompositions, thus enabling generic code such as:
+   * \code x = decomposition.adjoint().solve(b) \endcode
+   */
+  const LLT& adjoint() const noexcept { return *this; }
+
+  constexpr Index rows() const noexcept { return m_matrix.rows(); }
+  constexpr Index cols() const noexcept { return m_matrix.cols(); }
+
+  template <typename VectorType>
+  LLT& rankUpdate(const VectorType& vec, const RealScalar& sigma = 1);
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  template <typename RhsType, typename DstType>
+  void _solve_impl(const RhsType& rhs, DstType& dst) const;
+
+  template <bool Conjugate, typename RhsType, typename DstType>
+  void _solve_impl_transposed(const RhsType& rhs, DstType& dst) const;
+#endif
+
+ protected:
+  EIGEN_STATIC_ASSERT_NON_INTEGER(Scalar)
+
+  /** \internal
+   * Used to compute and store L
+   * The strict upper part is not used and even not initialized.
+   */
+  MatrixType m_matrix;
+  RealScalar m_l1_norm;
+  bool m_isInitialized;
+  ComputationInfo m_info;
+};
+
+namespace internal {
+
+template <typename Scalar, int UpLo>
+struct llt_inplace;
+
+template <typename MatrixType, typename VectorType>
+static Index llt_rank_update_lower(MatrixType& mat, const VectorType& vec,
+                                   const typename MatrixType::RealScalar& sigma) {
+  using std::sqrt;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef typename MatrixType::ColXpr ColXpr;
+  typedef internal::remove_all_t<ColXpr> ColXprCleaned;
+  typedef typename ColXprCleaned::SegmentReturnType ColXprSegment;
+  typedef Matrix<Scalar, Dynamic, 1> TempVectorType;
+  typedef typename TempVectorType::SegmentReturnType TempVecSegment;
+
+  Index n = mat.cols();
+  eigen_assert(mat.rows() == n && vec.size() == n);
+
+  TempVectorType temp;
+
+  if (sigma > 0) {
+    // This version is based on Givens rotations.
+    // It is faster than the other one below, but only works for updates,
+    // i.e., for sigma > 0
+    temp = sqrt(sigma) * vec;
+
+    for (Index i = 0; i < n; ++i) {
+      JacobiRotation<Scalar> g;
+      g.makeGivens(mat(i, i), -temp(i), &mat(i, i));
+
+      Index rs = n - i - 1;
+      if (rs > 0) {
+        ColXprSegment x(mat.col(i).tail(rs));
+        TempVecSegment y(temp.tail(rs));
+        apply_rotation_in_the_plane(x, y, g);
+      }
+    }
+  } else {
+    temp = vec;
+    RealScalar beta = 1;
+    for (Index j = 0; j < n; ++j) {
+      RealScalar Ljj = numext::real(mat.coeff(j, j));
+      RealScalar dj = numext::abs2(Ljj);
+      Scalar wj = temp.coeff(j);
+      RealScalar swj2 = sigma * numext::abs2(wj);
+      RealScalar gamma = dj * beta + swj2;
+
+      RealScalar x = dj + swj2 / beta;
+      if (x <= RealScalar(0)) return j;
+      RealScalar nLjj = sqrt(x);
+      mat.coeffRef(j, j) = nLjj;
+      beta += swj2 / dj;
+
+      // Update the terms of L
+      Index rs = n - j - 1;
+      if (rs) {
+        temp.tail(rs) -= (wj / Ljj) * mat.col(j).tail(rs);
+        if (!numext::is_exactly_zero(gamma))
+          mat.col(j).tail(rs) =
+              (nLjj / Ljj) * mat.col(j).tail(rs) + (nLjj * sigma * numext::conj(wj) / gamma) * temp.tail(rs);
+      }
+    }
+  }
+  return -1;
+}
+
+template <typename Scalar>
+struct llt_inplace<Scalar, Lower> {
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+  template <typename MatrixType>
+  static Index unblocked(MatrixType& mat) {
+    using std::sqrt;
+
+    eigen_assert(mat.rows() == mat.cols());
+    const Index size = mat.rows();
+    for (Index k = 0; k < size; ++k) {
+      Index rs = size - k - 1;  // remaining size
+
+      Block<MatrixType, Dynamic, 1> A21(mat, k + 1, k, rs, 1);
+      Block<MatrixType, 1, Dynamic> A10(mat, k, 0, 1, k);
+      Block<MatrixType, Dynamic, Dynamic> A20(mat, k + 1, 0, rs, k);
+
+      RealScalar x = numext::real(mat.coeff(k, k));
+      if (k > 0) x -= A10.squaredNorm();
+      if (x <= RealScalar(0)) return k;
+      mat.coeffRef(k, k) = x = sqrt(x);
+      if (k > 0 && rs > 0) A21.noalias() -= A20 * A10.adjoint();
+      if (rs > 0) A21 /= x;
+    }
+    return -1;
+  }
+
+  template <typename MatrixType>
+  static Index blocked(MatrixType& m) {
+    eigen_assert(m.rows() == m.cols());
+    Index size = m.rows();
+    if (size < 32) return unblocked(m);
+
+    Index blockSize = size / 8;
+    blockSize = (blockSize / 16) * 16;
+    blockSize = (std::min)((std::max)(blockSize, Index(8)), Index(128));
+
+    for (Index k = 0; k < size; k += blockSize) {
+      // partition the matrix:
+      //       A00 |  -  |  -
+      // lu  = A10 | A11 |  -
+      //       A20 | A21 | A22
+      Index bs = (std::min)(blockSize, size - k);
+      Index rs = size - k - bs;
+      Block<MatrixType, Dynamic, Dynamic> A11(m, k, k, bs, bs);
+      Block<MatrixType, Dynamic, Dynamic> A21(m, k + bs, k, rs, bs);
+      Block<MatrixType, Dynamic, Dynamic> A22(m, k + bs, k + bs, rs, rs);
+
+      Index ret;
+      if ((ret = unblocked(A11)) >= 0) return k + ret;
+      if (rs > 0) A11.adjoint().template triangularView<Upper>().template solveInPlace<OnTheRight>(A21);
+      if (rs > 0)
+        A22.template selfadjointView<Lower>().rankUpdate(A21,
+                                                         typename NumTraits<RealScalar>::Literal(-1));  // bottleneck
+    }
+    return -1;
+  }
+
+  template <typename MatrixType, typename VectorType>
+  static Index rankUpdate(MatrixType& mat, const VectorType& vec, const RealScalar& sigma) {
+    return Eigen::internal::llt_rank_update_lower(mat, vec, sigma);
+  }
+};
+
+template <typename Scalar>
+struct llt_inplace<Scalar, Upper> {
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+
+  template <typename MatrixType>
+  static EIGEN_STRONG_INLINE Index unblocked(MatrixType& mat) {
+    Transpose<MatrixType> matt(mat);
+    return llt_inplace<Scalar, Lower>::unblocked(matt);
+  }
+  template <typename MatrixType>
+  static EIGEN_STRONG_INLINE Index blocked(MatrixType& mat) {
+    Transpose<MatrixType> matt(mat);
+    return llt_inplace<Scalar, Lower>::blocked(matt);
+  }
+  template <typename MatrixType, typename VectorType>
+  static Index rankUpdate(MatrixType& mat, const VectorType& vec, const RealScalar& sigma) {
+    Transpose<MatrixType> matt(mat);
+    return llt_inplace<Scalar, Lower>::rankUpdate(matt, vec.conjugate(), sigma);
+  }
+};
+
+template <typename MatrixType>
+struct LLT_Traits<MatrixType, Lower> {
+  typedef const TriangularView<const MatrixType, Lower> MatrixL;
+  typedef const TriangularView<const typename MatrixType::AdjointReturnType, Upper> MatrixU;
+  static inline MatrixL getL(const MatrixType& m) { return MatrixL(m); }
+  static inline MatrixU getU(const MatrixType& m) { return MatrixU(m.adjoint()); }
+  static bool inplace_decomposition(MatrixType& m) {
+    return llt_inplace<typename MatrixType::Scalar, Lower>::blocked(m) == -1;
+  }
+};
+
+template <typename MatrixType>
+struct LLT_Traits<MatrixType, Upper> {
+  typedef const TriangularView<const typename MatrixType::AdjointReturnType, Lower> MatrixL;
+  typedef const TriangularView<const MatrixType, Upper> MatrixU;
+  static inline MatrixL getL(const MatrixType& m) { return MatrixL(m.adjoint()); }
+  static inline MatrixU getU(const MatrixType& m) { return MatrixU(m); }
+  static bool inplace_decomposition(MatrixType& m) {
+    return llt_inplace<typename MatrixType::Scalar, Upper>::blocked(m) == -1;
+  }
+};
+
+}  // end namespace internal
+
+/** Computes / recomputes the Cholesky decomposition A = LL^* = U^*U of \a matrix
+ *
+ * \returns a reference to *this
+ *
+ * Example: \include TutorialLinAlgComputeTwice.cpp
+ * Output: \verbinclude TutorialLinAlgComputeTwice.out
+ */
+template <typename MatrixType, int UpLo_>
+template <typename InputType>
+LLT<MatrixType, UpLo_>& LLT<MatrixType, UpLo_>::compute(const EigenBase<InputType>& a) {
+  eigen_assert(a.rows() == a.cols());
+  const Index size = a.rows();
+  m_matrix.resize(size, size);
+  if (!internal::is_same_dense(m_matrix, a.derived())) m_matrix = a.derived();
+
+  // Compute matrix L1 norm = max abs column sum.
+  m_l1_norm = RealScalar(0);
+  // TODO: move this code to SelfAdjointView
+  for (Index col = 0; col < size; ++col) {
+    RealScalar abs_col_sum;
+    if (UpLo_ == Lower)
+      abs_col_sum =
+          m_matrix.col(col).tail(size - col).template lpNorm<1>() + m_matrix.row(col).head(col).template lpNorm<1>();
+    else
+      abs_col_sum =
+          m_matrix.col(col).head(col).template lpNorm<1>() + m_matrix.row(col).tail(size - col).template lpNorm<1>();
+    if (abs_col_sum > m_l1_norm) m_l1_norm = abs_col_sum;
+  }
+
+  m_isInitialized = true;
+  bool ok = Traits::inplace_decomposition(m_matrix);
+  m_info = ok ? Success : NumericalIssue;
+
+  return *this;
+}
+
+/** Performs a rank one update (or dowdate) of the current decomposition.
+ * If A = LL^* before the rank one update,
+ * then after it we have LL^* = A + sigma * v v^* where \a v must be a vector
+ * of same dimension.
+ */
+template <typename MatrixType_, int UpLo_>
+template <typename VectorType>
+LLT<MatrixType_, UpLo_>& LLT<MatrixType_, UpLo_>::rankUpdate(const VectorType& v, const RealScalar& sigma) {
+  EIGEN_STATIC_ASSERT_VECTOR_ONLY(VectorType);
+  eigen_assert(v.size() == m_matrix.cols());
+  eigen_assert(m_isInitialized);
+  if (internal::llt_inplace<typename MatrixType::Scalar, UpLo>::rankUpdate(m_matrix, v, sigma) >= 0)
+    m_info = NumericalIssue;
+  else
+    m_info = Success;
+
+  return *this;
+}
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+template <typename MatrixType_, int UpLo_>
+template <typename RhsType, typename DstType>
+void LLT<MatrixType_, UpLo_>::_solve_impl(const RhsType& rhs, DstType& dst) const {
+  _solve_impl_transposed<true>(rhs, dst);
+}
+
+template <typename MatrixType_, int UpLo_>
+template <bool Conjugate, typename RhsType, typename DstType>
+void LLT<MatrixType_, UpLo_>::_solve_impl_transposed(const RhsType& rhs, DstType& dst) const {
+  dst = rhs;
+
+  matrixL().template conjugateIf<!Conjugate>().solveInPlace(dst);
+  matrixU().template conjugateIf<!Conjugate>().solveInPlace(dst);
+}
+#endif
+
+/** \internal use x = llt_object.solve(x);
+ *
+ * This is the \em in-place version of solve().
+ *
+ * \param bAndX represents both the right-hand side matrix b and result x.
+ *
+ * This version avoids a copy when the right hand side matrix b is not needed anymore.
+ *
+ * \warning The parameter is only marked 'const' to make the C++ compiler accept a temporary expression here.
+ * This function will const_cast it, so constness isn't honored here.
+ *
+ * \sa LLT::solve(), MatrixBase::llt()
+ */
+template <typename MatrixType, int UpLo_>
+template <typename Derived>
+void LLT<MatrixType, UpLo_>::solveInPlace(const MatrixBase<Derived>& bAndX) const {
+  eigen_assert(m_isInitialized && "LLT is not initialized.");
+  eigen_assert(m_matrix.rows() == bAndX.rows());
+  matrixL().solveInPlace(bAndX);
+  matrixU().solveInPlace(bAndX);
+}
+
+/** \returns the matrix represented by the decomposition,
+ * i.e., it returns the product: L L^*.
+ * This function is provided for debug purpose. */
+template <typename MatrixType, int UpLo_>
+MatrixType LLT<MatrixType, UpLo_>::reconstructedMatrix() const {
+  eigen_assert(m_isInitialized && "LLT is not initialized.");
+  return matrixL() * matrixL().adjoint().toDenseMatrix();
+}
+
+/** \cholesky_module
+ * \returns the LLT decomposition of \c *this
+ * \sa SelfAdjointView::llt()
+ */
+template <typename Derived>
+inline LLT<typename MatrixBase<Derived>::PlainObject> MatrixBase<Derived>::llt() const {
+  return LLT<PlainObject>(derived());
+}
+
+/** \cholesky_module
+ * \returns the LLT decomposition of \c *this
+ * \sa SelfAdjointView::llt()
+ */
+template <typename MatrixType, unsigned int UpLo>
+inline LLT<typename SelfAdjointView<MatrixType, UpLo>::PlainObject, UpLo> SelfAdjointView<MatrixType, UpLo>::llt()
+    const {
+  return LLT<PlainObject, UpLo>(m_matrix);
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_LLT_H

data_acq_and_processing/third_party/eigen/Eigen/src/Cholesky/LLT_LAPACKE.h

@@ -0,0 +1,124 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to LAPACKe
+ *     LLt decomposition based on LAPACKE_?potrf function.
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_LLT_LAPACKE_H
+#define EIGEN_LLT_LAPACKE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+namespace lapacke_helpers {
+// -------------------------------------------------------------------------------------------------------------------
+//        Dispatch for rank update handling upper and lower parts
+// -------------------------------------------------------------------------------------------------------------------
+
+template <UpLoType Mode>
+struct rank_update {};
+
+template <>
+struct rank_update<Lower> {
+  template <typename MatrixType, typename VectorType>
+  static Index run(MatrixType &mat, const VectorType &vec, const typename MatrixType::RealScalar &sigma) {
+    return Eigen::internal::llt_rank_update_lower(mat, vec, sigma);
+  }
+};
+
+template <>
+struct rank_update<Upper> {
+  template <typename MatrixType, typename VectorType>
+  static Index run(MatrixType &mat, const VectorType &vec, const typename MatrixType::RealScalar &sigma) {
+    Transpose<MatrixType> matt(mat);
+    return Eigen::internal::llt_rank_update_lower(matt, vec.conjugate(), sigma);
+  }
+};
+
+// -------------------------------------------------------------------------------------------------------------------
+//        Generic lapacke llt implementation that hands of to the dispatches
+// -------------------------------------------------------------------------------------------------------------------
+
+template <typename Scalar, UpLoType Mode>
+struct lapacke_llt {
+  EIGEN_STATIC_ASSERT(((Mode == Lower) || (Mode == Upper)), MODE_MUST_BE_UPPER_OR_LOWER)
+  template <typename MatrixType>
+  static Index blocked(MatrixType &m) {
+    eigen_assert(m.rows() == m.cols());
+    if (m.rows() == 0) {
+      return -1;
+    }
+    /* Set up parameters for ?potrf */
+    lapack_int size = to_lapack(m.rows());
+    lapack_int matrix_order = lapack_storage_of(m);
+    constexpr char uplo = Mode == Upper ? 'U' : 'L';
+    Scalar *a = &(m.coeffRef(0, 0));
+    lapack_int lda = to_lapack(m.outerStride());
+
+    lapack_int info = potrf(matrix_order, uplo, size, to_lapack(a), lda);
+    info = (info == 0) ? -1 : info > 0 ? info - 1 : size;
+    return info;
+  }
+
+  template <typename MatrixType, typename VectorType>
+  static Index rankUpdate(MatrixType &mat, const VectorType &vec, const typename MatrixType::RealScalar &sigma) {
+    return rank_update<Mode>::run(mat, vec, sigma);
+  }
+};
+}  // namespace lapacke_helpers
+// end namespace lapacke_helpers
+
+/*
+ * Here, we just put the generic implementation from lapacke_llt into a full specialization of the llt_inplace
+ * type. By being a full specialization, the versions defined here thus get precedence over the generic implementation
+ * in LLT.h for double, float and complex double, complex float types.
+ */
+
+#define EIGEN_LAPACKE_LLT(EIGTYPE)                                                             \
+  template <>                                                                                  \
+  struct llt_inplace<EIGTYPE, Lower> : public lapacke_helpers::lapacke_llt<EIGTYPE, Lower> {}; \
+  template <>                                                                                  \
+  struct llt_inplace<EIGTYPE, Upper> : public lapacke_helpers::lapacke_llt<EIGTYPE, Upper> {};
+
+EIGEN_LAPACKE_LLT(double)
+EIGEN_LAPACKE_LLT(float)
+EIGEN_LAPACKE_LLT(std::complex<double>)
+EIGEN_LAPACKE_LLT(std::complex<float>)
+
+#undef EIGEN_LAPACKE_LLT
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_LLT_LAPACKE_H

data_acq_and_processing/third_party/eigen/Eigen/src/CholmodSupport/CholmodSupport.h

@@ -0,0 +1,738 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_CHOLMODSUPPORT_H
+#define EIGEN_CHOLMODSUPPORT_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Scalar>
+struct cholmod_configure_matrix;
+
+template <>
+struct cholmod_configure_matrix<double> {
+  template <typename CholmodType>
+  static void run(CholmodType& mat) {
+    mat.xtype = CHOLMOD_REAL;
+    mat.dtype = CHOLMOD_DOUBLE;
+  }
+};
+
+template <>
+struct cholmod_configure_matrix<std::complex<double> > {
+  template <typename CholmodType>
+  static void run(CholmodType& mat) {
+    mat.xtype = CHOLMOD_COMPLEX;
+    mat.dtype = CHOLMOD_DOUBLE;
+  }
+};
+
+// Other scalar types are not yet supported by Cholmod
+// template<> struct cholmod_configure_matrix<float> {
+//   template<typename CholmodType>
+//   static void run(CholmodType& mat) {
+//     mat.xtype = CHOLMOD_REAL;
+//     mat.dtype = CHOLMOD_SINGLE;
+//   }
+// };
+//
+// template<> struct cholmod_configure_matrix<std::complex<float> > {
+//   template<typename CholmodType>
+//   static void run(CholmodType& mat) {
+//     mat.xtype = CHOLMOD_COMPLEX;
+//     mat.dtype = CHOLMOD_SINGLE;
+//   }
+// };
+
+}  // namespace internal
+
+/** Wraps the Eigen sparse matrix \a mat into a Cholmod sparse matrix object.
+ * Note that the data are shared.
+ */
+template <typename Scalar_, int Options_, typename StorageIndex_>
+cholmod_sparse viewAsCholmod(Ref<SparseMatrix<Scalar_, Options_, StorageIndex_> > mat) {
+  cholmod_sparse res;
+  res.nzmax = mat.nonZeros();
+  res.nrow = mat.rows();
+  res.ncol = mat.cols();
+  res.p = mat.outerIndexPtr();
+  res.i = mat.innerIndexPtr();
+  res.x = mat.valuePtr();
+  res.z = 0;
+  res.sorted = 1;
+  if (mat.isCompressed()) {
+    res.packed = 1;
+    res.nz = 0;
+  } else {
+    res.packed = 0;
+    res.nz = mat.innerNonZeroPtr();
+  }
+
+  res.dtype = 0;
+  res.stype = -1;
+
+  if (internal::is_same<StorageIndex_, int>::value) {
+    res.itype = CHOLMOD_INT;
+  } else if (internal::is_same<StorageIndex_, SuiteSparse_long>::value) {
+    res.itype = CHOLMOD_LONG;
+  } else {
+    eigen_assert(false && "Index type not supported yet");
+  }
+
+  // setup res.xtype
+  internal::cholmod_configure_matrix<Scalar_>::run(res);
+
+  res.stype = 0;
+
+  return res;
+}
+
+template <typename Scalar_, int Options_, typename Index_>
+const cholmod_sparse viewAsCholmod(const SparseMatrix<Scalar_, Options_, Index_>& mat) {
+  cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<Scalar_, Options_, Index_> >(mat.const_cast_derived()));
+  return res;
+}
+
+template <typename Scalar_, int Options_, typename Index_>
+const cholmod_sparse viewAsCholmod(const SparseVector<Scalar_, Options_, Index_>& mat) {
+  cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<Scalar_, Options_, Index_> >(mat.const_cast_derived()));
+  return res;
+}
+
+/** Returns a view of the Eigen sparse matrix \a mat as Cholmod sparse matrix.
+ * The data are not copied but shared. */
+template <typename Scalar_, int Options_, typename Index_, unsigned int UpLo>
+cholmod_sparse viewAsCholmod(const SparseSelfAdjointView<const SparseMatrix<Scalar_, Options_, Index_>, UpLo>& mat) {
+  cholmod_sparse res = viewAsCholmod(Ref<SparseMatrix<Scalar_, Options_, Index_> >(mat.matrix().const_cast_derived()));
+
+  if (UpLo == Upper) res.stype = 1;
+  if (UpLo == Lower) res.stype = -1;
+  // swap stype for rowmajor matrices (only works for real matrices)
+  EIGEN_STATIC_ASSERT((Options_ & RowMajorBit) == 0 || NumTraits<Scalar_>::IsComplex == 0,
+                      THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
+  if (Options_ & RowMajorBit) res.stype *= -1;
+
+  return res;
+}
+
+/** Returns a view of the Eigen \b dense matrix \a mat as Cholmod dense matrix.
+ * The data are not copied but shared. */
+template <typename Derived>
+cholmod_dense viewAsCholmod(MatrixBase<Derived>& mat) {
+  EIGEN_STATIC_ASSERT((internal::traits<Derived>::Flags & RowMajorBit) == 0,
+                      THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
+  typedef typename Derived::Scalar Scalar;
+
+  cholmod_dense res;
+  res.nrow = mat.rows();
+  res.ncol = mat.cols();
+  res.nzmax = res.nrow * res.ncol;
+  res.d = Derived::IsVectorAtCompileTime ? mat.derived().size() : mat.derived().outerStride();
+  res.x = (void*)(mat.derived().data());
+  res.z = 0;
+
+  internal::cholmod_configure_matrix<Scalar>::run(res);
+
+  return res;
+}
+
+/** Returns a view of the Cholmod sparse matrix \a cm as an Eigen sparse matrix.
+ * The data are not copied but shared. */
+template <typename Scalar, typename StorageIndex>
+Map<const SparseMatrix<Scalar, ColMajor, StorageIndex> > viewAsEigen(cholmod_sparse& cm) {
+  return Map<const SparseMatrix<Scalar, ColMajor, StorageIndex> >(
+      cm.nrow, cm.ncol, static_cast<StorageIndex*>(cm.p)[cm.ncol], static_cast<StorageIndex*>(cm.p),
+      static_cast<StorageIndex*>(cm.i), static_cast<Scalar*>(cm.x));
+}
+
+/** Returns a view of the Cholmod sparse matrix factor \a cm as an Eigen sparse matrix.
+ * The data are not copied but shared. */
+template <typename Scalar, typename StorageIndex>
+Map<const SparseMatrix<Scalar, ColMajor, StorageIndex> > viewAsEigen(cholmod_factor& cm) {
+  return Map<const SparseMatrix<Scalar, ColMajor, StorageIndex> >(
+      cm.n, cm.n, static_cast<StorageIndex*>(cm.p)[cm.n], static_cast<StorageIndex*>(cm.p),
+      static_cast<StorageIndex*>(cm.i), static_cast<Scalar*>(cm.x));
+}
+
+namespace internal {
+
+// template specializations for int and long that call the correct cholmod method
+
+#define EIGEN_CHOLMOD_SPECIALIZE0(ret, name)                        \
+  template <typename StorageIndex_>                                 \
+  inline ret cm_##name(cholmod_common& Common) {                    \
+    return cholmod_##name(&Common);                                 \
+  }                                                                 \
+  template <>                                                       \
+  inline ret cm_##name<SuiteSparse_long>(cholmod_common & Common) { \
+    return cholmod_l_##name(&Common);                               \
+  }
+
+#define EIGEN_CHOLMOD_SPECIALIZE1(ret, name, t1, a1)                         \
+  template <typename StorageIndex_>                                          \
+  inline ret cm_##name(t1& a1, cholmod_common& Common) {                     \
+    return cholmod_##name(&a1, &Common);                                     \
+  }                                                                          \
+  template <>                                                                \
+  inline ret cm_##name<SuiteSparse_long>(t1 & a1, cholmod_common & Common) { \
+    return cholmod_l_##name(&a1, &Common);                                   \
+  }
+
+EIGEN_CHOLMOD_SPECIALIZE0(int, start)
+EIGEN_CHOLMOD_SPECIALIZE0(int, finish)
+
+EIGEN_CHOLMOD_SPECIALIZE1(int, free_factor, cholmod_factor*, L)
+EIGEN_CHOLMOD_SPECIALIZE1(int, free_dense, cholmod_dense*, X)
+EIGEN_CHOLMOD_SPECIALIZE1(int, free_sparse, cholmod_sparse*, A)
+
+EIGEN_CHOLMOD_SPECIALIZE1(cholmod_factor*, analyze, cholmod_sparse, A)
+EIGEN_CHOLMOD_SPECIALIZE1(cholmod_sparse*, factor_to_sparse, cholmod_factor, L)
+
+template <typename StorageIndex_>
+inline cholmod_dense* cm_solve(int sys, cholmod_factor& L, cholmod_dense& B, cholmod_common& Common) {
+  return cholmod_solve(sys, &L, &B, &Common);
+}
+template <>
+inline cholmod_dense* cm_solve<SuiteSparse_long>(int sys, cholmod_factor& L, cholmod_dense& B, cholmod_common& Common) {
+  return cholmod_l_solve(sys, &L, &B, &Common);
+}
+
+template <typename StorageIndex_>
+inline cholmod_sparse* cm_spsolve(int sys, cholmod_factor& L, cholmod_sparse& B, cholmod_common& Common) {
+  return cholmod_spsolve(sys, &L, &B, &Common);
+}
+template <>
+inline cholmod_sparse* cm_spsolve<SuiteSparse_long>(int sys, cholmod_factor& L, cholmod_sparse& B,
+                                                    cholmod_common& Common) {
+  return cholmod_l_spsolve(sys, &L, &B, &Common);
+}
+
+template <typename StorageIndex_>
+inline int cm_factorize_p(cholmod_sparse* A, double beta[2], StorageIndex_* fset, std::size_t fsize, cholmod_factor* L,
+                          cholmod_common& Common) {
+  return cholmod_factorize_p(A, beta, fset, fsize, L, &Common);
+}
+template <>
+inline int cm_factorize_p<SuiteSparse_long>(cholmod_sparse* A, double beta[2], SuiteSparse_long* fset,
+                                            std::size_t fsize, cholmod_factor* L, cholmod_common& Common) {
+  return cholmod_l_factorize_p(A, beta, fset, fsize, L, &Common);
+}
+
+#undef EIGEN_CHOLMOD_SPECIALIZE0
+#undef EIGEN_CHOLMOD_SPECIALIZE1
+
+}  // namespace internal
+
+enum CholmodMode { CholmodAuto, CholmodSimplicialLLt, CholmodSupernodalLLt, CholmodLDLt };
+
+/** \ingroup CholmodSupport_Module
+ * \class CholmodBase
+ * \brief The base class for the direct Cholesky factorization of Cholmod
+ * \sa class CholmodSupernodalLLT, class CholmodSimplicialLDLT, class CholmodSimplicialLLT
+ */
+template <typename MatrixType_, int UpLo_, typename Derived>
+class CholmodBase : public SparseSolverBase<Derived> {
+ protected:
+  typedef SparseSolverBase<Derived> Base;
+  using Base::derived;
+  using Base::m_isInitialized;
+
+ public:
+  typedef MatrixType_ MatrixType;
+  enum { UpLo = UpLo_ };
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef MatrixType CholMatrixType;
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  enum { ColsAtCompileTime = MatrixType::ColsAtCompileTime, MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime };
+
+ public:
+  CholmodBase() : m_cholmodFactor(0), m_info(Success), m_factorizationIsOk(false), m_analysisIsOk(false) {
+    EIGEN_STATIC_ASSERT((internal::is_same<double, RealScalar>::value), CHOLMOD_SUPPORTS_DOUBLE_PRECISION_ONLY);
+    m_shiftOffset[0] = m_shiftOffset[1] = 0.0;
+    internal::cm_start<StorageIndex>(m_cholmod);
+  }
+
+  explicit CholmodBase(const MatrixType& matrix)
+      : m_cholmodFactor(0), m_info(Success), m_factorizationIsOk(false), m_analysisIsOk(false) {
+    EIGEN_STATIC_ASSERT((internal::is_same<double, RealScalar>::value), CHOLMOD_SUPPORTS_DOUBLE_PRECISION_ONLY);
+    m_shiftOffset[0] = m_shiftOffset[1] = 0.0;
+    internal::cm_start<StorageIndex>(m_cholmod);
+    compute(matrix);
+  }
+
+  ~CholmodBase() {
+    if (m_cholmodFactor) internal::cm_free_factor<StorageIndex>(m_cholmodFactor, m_cholmod);
+    internal::cm_finish<StorageIndex>(m_cholmod);
+  }
+
+  inline StorageIndex cols() const { return internal::convert_index<StorageIndex, Index>(m_cholmodFactor->n); }
+  inline StorageIndex rows() const { return internal::convert_index<StorageIndex, Index>(m_cholmodFactor->n); }
+
+  /** \brief Reports whether previous computation was successful.
+   *
+   * \returns \c Success if computation was successful,
+   *          \c NumericalIssue if the matrix.appears to be negative.
+   */
+  ComputationInfo info() const {
+    eigen_assert(m_isInitialized && "Decomposition is not initialized.");
+    return m_info;
+  }
+
+  /** Computes the sparse Cholesky decomposition of \a matrix */
+  Derived& compute(const MatrixType& matrix) {
+    analyzePattern(matrix);
+    factorize(matrix);
+    return derived();
+  }
+
+  /** Performs a symbolic decomposition on the sparsity pattern of \a matrix.
+   *
+   * This function is particularly useful when solving for several problems having the same structure.
+   *
+   * \sa factorize()
+   */
+  void analyzePattern(const MatrixType& matrix) {
+    if (m_cholmodFactor) {
+      internal::cm_free_factor<StorageIndex>(m_cholmodFactor, m_cholmod);
+      m_cholmodFactor = 0;
+    }
+    cholmod_sparse A = viewAsCholmod(matrix.template selfadjointView<UpLo>());
+    m_cholmodFactor = internal::cm_analyze<StorageIndex>(A, m_cholmod);
+
+    this->m_isInitialized = true;
+    this->m_info = Success;
+    m_analysisIsOk = true;
+    m_factorizationIsOk = false;
+  }
+
+  /** Performs a numeric decomposition of \a matrix
+   *
+   * The given matrix must have the same sparsity pattern as the matrix on which the symbolic decomposition has been
+   * performed.
+   *
+   * \sa analyzePattern()
+   */
+  void factorize(const MatrixType& matrix) {
+    eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
+    cholmod_sparse A = viewAsCholmod(matrix.template selfadjointView<UpLo>());
+    internal::cm_factorize_p<StorageIndex>(&A, m_shiftOffset, 0, 0, m_cholmodFactor, m_cholmod);
+
+    // If the factorization failed, either the input matrix was zero (so m_cholmodFactor == nullptr), or minor is the
+    // column at which it failed. On success minor == n.
+    this->m_info =
+        (m_cholmodFactor != nullptr && m_cholmodFactor->minor == m_cholmodFactor->n ? Success : NumericalIssue);
+    m_factorizationIsOk = true;
+  }
+
+  /** Returns a reference to the Cholmod's configuration structure to get a full control over the performed operations.
+   *  See the Cholmod user guide for details. */
+  cholmod_common& cholmod() { return m_cholmod; }
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  /** \internal */
+  template <typename Rhs, typename Dest>
+  void _solve_impl(const MatrixBase<Rhs>& b, MatrixBase<Dest>& dest) const {
+    eigen_assert(m_factorizationIsOk &&
+                 "The decomposition is not in a valid state for solving, you must first call either compute() or "
+                 "symbolic()/numeric()");
+    const Index size = m_cholmodFactor->n;
+    EIGEN_UNUSED_VARIABLE(size);
+    eigen_assert(size == b.rows());
+
+    // Cholmod needs column-major storage without inner-stride, which corresponds to the default behavior of Ref.
+    Ref<const Matrix<typename Rhs::Scalar, Dynamic, Dynamic, ColMajor> > b_ref(b.derived());
+
+    cholmod_dense b_cd = viewAsCholmod(b_ref);
+    cholmod_dense* x_cd = internal::cm_solve<StorageIndex>(CHOLMOD_A, *m_cholmodFactor, b_cd, m_cholmod);
+    if (!x_cd) {
+      this->m_info = NumericalIssue;
+      return;
+    }
+    // TODO: optimize this copy by swapping when possible (be careful with alignment, etc.)
+    // NOTE Actually, the copy can be avoided by calling cholmod_solve2 instead of cholmod_solve
+    dest = Matrix<Scalar, Dest::RowsAtCompileTime, Dest::ColsAtCompileTime>::Map(reinterpret_cast<Scalar*>(x_cd->x),
+                                                                                 b.rows(), b.cols());
+    internal::cm_free_dense<StorageIndex>(x_cd, m_cholmod);
+  }
+
+  /** \internal */
+  template <typename RhsDerived, typename DestDerived>
+  void _solve_impl(const SparseMatrixBase<RhsDerived>& b, SparseMatrixBase<DestDerived>& dest) const {
+    eigen_assert(m_factorizationIsOk &&
+                 "The decomposition is not in a valid state for solving, you must first call either compute() or "
+                 "symbolic()/numeric()");
+    const Index size = m_cholmodFactor->n;
+    EIGEN_UNUSED_VARIABLE(size);
+    eigen_assert(size == b.rows());
+
+    // note: cs stands for Cholmod Sparse
+    Ref<SparseMatrix<typename RhsDerived::Scalar, ColMajor, typename RhsDerived::StorageIndex> > b_ref(
+        b.const_cast_derived());
+    cholmod_sparse b_cs = viewAsCholmod(b_ref);
+    cholmod_sparse* x_cs = internal::cm_spsolve<StorageIndex>(CHOLMOD_A, *m_cholmodFactor, b_cs, m_cholmod);
+    if (!x_cs) {
+      this->m_info = NumericalIssue;
+      return;
+    }
+    // TODO: optimize this copy by swapping when possible (be careful with alignment, etc.)
+    // NOTE cholmod_spsolve in fact just calls the dense solver for blocks of 4 columns at a time (similar to Eigen's
+    // sparse solver)
+    dest.derived() = viewAsEigen<typename DestDerived::Scalar, typename DestDerived::StorageIndex>(*x_cs);
+    internal::cm_free_sparse<StorageIndex>(x_cs, m_cholmod);
+  }
+#endif  // EIGEN_PARSED_BY_DOXYGEN
+
+  /** Sets the shift parameter that will be used to adjust the diagonal coefficients during the numerical factorization.
+   *
+   * During the numerical factorization, an offset term is added to the diagonal coefficients:\n
+   * \c d_ii = \a offset + \c d_ii
+   *
+   * The default is \a offset=0.
+   *
+   * \returns a reference to \c *this.
+   */
+  Derived& setShift(const RealScalar& offset) {
+    m_shiftOffset[0] = double(offset);
+    return derived();
+  }
+
+  /** \returns the determinant of the underlying matrix from the current factorization */
+  Scalar determinant() const {
+    using std::exp;
+    return exp(logDeterminant());
+  }
+
+  /** \returns the log determinant of the underlying matrix from the current factorization */
+  Scalar logDeterminant() const {
+    using numext::real;
+    using std::log;
+    eigen_assert(m_factorizationIsOk &&
+                 "The decomposition is not in a valid state for solving, you must first call either compute() or "
+                 "symbolic()/numeric()");
+
+    RealScalar logDet = 0;
+    Scalar* x = static_cast<Scalar*>(m_cholmodFactor->x);
+    if (m_cholmodFactor->is_super) {
+      // Supernodal factorization stored as a packed list of dense column-major blocks,
+      // as described by the following structure:
+
+      // super[k] == index of the first column of the j-th super node
+      StorageIndex* super = static_cast<StorageIndex*>(m_cholmodFactor->super);
+      // pi[k] == offset to the description of row indices
+      StorageIndex* pi = static_cast<StorageIndex*>(m_cholmodFactor->pi);
+      // px[k] == offset to the respective dense block
+      StorageIndex* px = static_cast<StorageIndex*>(m_cholmodFactor->px);
+
+      Index nb_super_nodes = m_cholmodFactor->nsuper;
+      for (Index k = 0; k < nb_super_nodes; ++k) {
+        StorageIndex ncols = super[k + 1] - super[k];
+        StorageIndex nrows = pi[k + 1] - pi[k];
+
+        Map<const Array<Scalar, 1, Dynamic>, 0, InnerStride<> > sk(x + px[k], ncols, InnerStride<>(nrows + 1));
+        logDet += sk.real().log().sum();
+      }
+    } else {
+      // Simplicial factorization stored as standard CSC matrix.
+      StorageIndex* p = static_cast<StorageIndex*>(m_cholmodFactor->p);
+      Index size = m_cholmodFactor->n;
+      for (Index k = 0; k < size; ++k) logDet += log(real(x[p[k]]));
+    }
+    if (m_cholmodFactor->is_ll) logDet *= 2.0;
+    return logDet;
+  }
+
+  template <typename Stream>
+  void dumpMemory(Stream& /*s*/) {}
+
+ protected:
+  mutable cholmod_common m_cholmod;
+  cholmod_factor* m_cholmodFactor;
+  double m_shiftOffset[2];
+  mutable ComputationInfo m_info;
+  int m_factorizationIsOk;
+  int m_analysisIsOk;
+};
+
+/** \ingroup CholmodSupport_Module
+ * \class CholmodSimplicialLLT
+ * \brief A simplicial direct Cholesky (LLT) factorization and solver based on Cholmod
+ *
+ * This class allows to solve for A.X = B sparse linear problems via a simplicial LL^T Cholesky factorization
+ * using the Cholmod library.
+ * This simplicial variant is equivalent to Eigen's built-in SimplicialLLT class. Therefore, it has little practical
+ * interest. The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices X and B can be
+ * either dense or sparse.
+ *
+ * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+ * \tparam UpLo_ the triangular part that will be used for the computations. It can be Lower
+ *               or Upper. Default is Lower.
+ *
+ * \implsparsesolverconcept
+ *
+ * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non
+ * compressed.
+ *
+ * \warning Only double precision real and complex scalar types are supported by Cholmod.
+ *
+ * \sa \ref TutorialSparseSolverConcept, class CholmodSupernodalLLT, class SimplicialLLT
+ */
+template <typename MatrixType_, int UpLo_ = Lower>
+class CholmodSimplicialLLT : public CholmodBase<MatrixType_, UpLo_, CholmodSimplicialLLT<MatrixType_, UpLo_> > {
+  typedef CholmodBase<MatrixType_, UpLo_, CholmodSimplicialLLT> Base;
+  using Base::m_cholmod;
+
+ public:
+  typedef MatrixType_ MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  typedef TriangularView<const MatrixType, Eigen::Lower> MatrixL;
+  typedef TriangularView<const typename MatrixType::AdjointReturnType, Eigen::Upper> MatrixU;
+
+  CholmodSimplicialLLT() : Base() { init(); }
+
+  CholmodSimplicialLLT(const MatrixType& matrix) : Base() {
+    init();
+    this->compute(matrix);
+  }
+
+  ~CholmodSimplicialLLT() {}
+
+  /** \returns an expression of the factor L */
+  inline MatrixL matrixL() const { return viewAsEigen<Scalar, StorageIndex>(*Base::m_cholmodFactor); }
+
+  /** \returns an expression of the factor U (= L^*) */
+  inline MatrixU matrixU() const { return matrixL().adjoint(); }
+
+ protected:
+  void init() {
+    m_cholmod.final_asis = 0;
+    m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
+    m_cholmod.final_ll = 1;
+  }
+};
+
+/** \ingroup CholmodSupport_Module
+ * \class CholmodSimplicialLDLT
+ * \brief A simplicial direct Cholesky (LDLT) factorization and solver based on Cholmod
+ *
+ * This class allows to solve for A.X = B sparse linear problems via a simplicial LDL^T Cholesky factorization
+ * using the Cholmod library.
+ * This simplicial variant is equivalent to Eigen's built-in SimplicialLDLT class. Therefore, it has little practical
+ * interest. The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices X and B can be
+ * either dense or sparse.
+ *
+ * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+ * \tparam UpLo_ the triangular part that will be used for the computations. It can be Lower
+ *               or Upper. Default is Lower.
+ *
+ * \implsparsesolverconcept
+ *
+ * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non
+ * compressed.
+ *
+ * \warning Only double precision real and complex scalar types are supported by Cholmod.
+ *
+ * \sa \ref TutorialSparseSolverConcept, class CholmodSupernodalLLT, class SimplicialLDLT
+ */
+template <typename MatrixType_, int UpLo_ = Lower>
+class CholmodSimplicialLDLT : public CholmodBase<MatrixType_, UpLo_, CholmodSimplicialLDLT<MatrixType_, UpLo_> > {
+  typedef CholmodBase<MatrixType_, UpLo_, CholmodSimplicialLDLT> Base;
+  using Base::m_cholmod;
+
+ public:
+  typedef MatrixType_ MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef typename MatrixType::StorageIndex StorageIndex;
+  typedef Matrix<Scalar, Dynamic, 1> VectorType;
+  typedef TriangularView<const MatrixType, Eigen::UnitLower> MatrixL;
+  typedef TriangularView<const typename MatrixType::AdjointReturnType, Eigen::UnitUpper> MatrixU;
+
+  CholmodSimplicialLDLT() : Base() { init(); }
+
+  CholmodSimplicialLDLT(const MatrixType& matrix) : Base() {
+    init();
+    this->compute(matrix);
+  }
+
+  ~CholmodSimplicialLDLT() {}
+
+  /** \returns a vector expression of the diagonal D */
+  inline VectorType vectorD() const {
+    auto cholmodL = viewAsEigen<Scalar, StorageIndex>(*Base::m_cholmodFactor);
+
+    VectorType D{cholmodL.rows()};
+
+    for (Index k = 0; k < cholmodL.outerSize(); ++k) {
+      typename decltype(cholmodL)::InnerIterator it{cholmodL, k};
+      D(k) = it.value();
+    }
+
+    return D;
+  }
+
+  /** \returns an expression of the factor L */
+  inline MatrixL matrixL() const { return viewAsEigen<Scalar, StorageIndex>(*Base::m_cholmodFactor); }
+
+  /** \returns an expression of the factor U (= L^*) */
+  inline MatrixU matrixU() const { return matrixL().adjoint(); }
+
+ protected:
+  void init() {
+    m_cholmod.final_asis = 1;
+    m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
+  }
+};
+
+/** \ingroup CholmodSupport_Module
+ * \class CholmodSupernodalLLT
+ * \brief A supernodal Cholesky (LLT) factorization and solver based on Cholmod
+ *
+ * This class allows to solve for A.X = B sparse linear problems via a supernodal LL^T Cholesky factorization
+ * using the Cholmod library.
+ * This supernodal variant performs best on dense enough problems, e.g., 3D FEM, or very high order 2D FEM.
+ * The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
+ * X and B can be either dense or sparse.
+ *
+ * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+ * \tparam UpLo_ the triangular part that will be used for the computations. It can be Lower
+ *               or Upper. Default is Lower.
+ *
+ * \implsparsesolverconcept
+ *
+ * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non
+ * compressed.
+ *
+ * \warning Only double precision real and complex scalar types are supported by Cholmod.
+ *
+ * \sa \ref TutorialSparseSolverConcept
+ */
+template <typename MatrixType_, int UpLo_ = Lower>
+class CholmodSupernodalLLT : public CholmodBase<MatrixType_, UpLo_, CholmodSupernodalLLT<MatrixType_, UpLo_> > {
+  typedef CholmodBase<MatrixType_, UpLo_, CholmodSupernodalLLT> Base;
+  using Base::m_cholmod;
+
+ public:
+  typedef MatrixType_ MatrixType;
+  typedef typename MatrixType::Scalar Scalar;
+  typedef typename MatrixType::RealScalar RealScalar;
+  typedef typename MatrixType::StorageIndex StorageIndex;
+
+  CholmodSupernodalLLT() : Base() { init(); }
+
+  CholmodSupernodalLLT(const MatrixType& matrix) : Base() {
+    init();
+    this->compute(matrix);
+  }
+
+  ~CholmodSupernodalLLT() {}
+
+  /** \returns an expression of the factor L */
+  inline MatrixType matrixL() const {
+    // Convert Cholmod factor's supernodal storage format to Eigen's CSC storage format
+    cholmod_sparse* cholmodL = internal::cm_factor_to_sparse(*Base::m_cholmodFactor, m_cholmod);
+    MatrixType L = viewAsEigen<Scalar, StorageIndex>(*cholmodL);
+    internal::cm_free_sparse<StorageIndex>(cholmodL, m_cholmod);
+
+    return L;
+  }
+
+  /** \returns an expression of the factor U (= L^*) */
+  inline MatrixType matrixU() const { return matrixL().adjoint(); }
+
+ protected:
+  void init() {
+    m_cholmod.final_asis = 1;
+    m_cholmod.supernodal = CHOLMOD_SUPERNODAL;
+  }
+};
+
+/** \ingroup CholmodSupport_Module
+ * \class CholmodDecomposition
+ * \brief A general Cholesky factorization and solver based on Cholmod
+ *
+ * This class allows to solve for A.X = B sparse linear problems via a LL^T or LDL^T Cholesky factorization
+ * using the Cholmod library. The sparse matrix A must be selfadjoint and positive definite. The vectors or matrices
+ * X and B can be either dense or sparse.
+ *
+ * This variant permits to change the underlying Cholesky method at runtime.
+ * On the other hand, it does not provide access to the result of the factorization.
+ * The default is to let Cholmod automatically choose between a simplicial and supernodal factorization.
+ *
+ * \tparam MatrixType_ the type of the sparse matrix A, it must be a SparseMatrix<>
+ * \tparam UpLo_ the triangular part that will be used for the computations. It can be Lower
+ *               or Upper. Default is Lower.
+ *
+ * \implsparsesolverconcept
+ *
+ * This class supports all kind of SparseMatrix<>: row or column major; upper, lower, or both; compressed or non
+ * compressed.
+ *
+ * \warning Only double precision real and complex scalar types are supported by Cholmod.
+ *
+ * \sa \ref TutorialSparseSolverConcept
+ */
+template <typename MatrixType_, int UpLo_ = Lower>
+class CholmodDecomposition : public CholmodBase<MatrixType_, UpLo_, CholmodDecomposition<MatrixType_, UpLo_> > {
+  typedef CholmodBase<MatrixType_, UpLo_, CholmodDecomposition> Base;
+  using Base::m_cholmod;
+
+ public:
+  typedef MatrixType_ MatrixType;
+
+  CholmodDecomposition() : Base() { init(); }
+
+  CholmodDecomposition(const MatrixType& matrix) : Base() {
+    init();
+    this->compute(matrix);
+  }
+
+  ~CholmodDecomposition() {}
+
+  void setMode(CholmodMode mode) {
+    switch (mode) {
+      case CholmodAuto:
+        m_cholmod.final_asis = 1;
+        m_cholmod.supernodal = CHOLMOD_AUTO;
+        break;
+      case CholmodSimplicialLLt:
+        m_cholmod.final_asis = 0;
+        m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
+        m_cholmod.final_ll = 1;
+        break;
+      case CholmodSupernodalLLt:
+        m_cholmod.final_asis = 1;
+        m_cholmod.supernodal = CHOLMOD_SUPERNODAL;
+        break;
+      case CholmodLDLt:
+        m_cholmod.final_asis = 1;
+        m_cholmod.supernodal = CHOLMOD_SIMPLICIAL;
+        break;
+      default:
+        break;
+    }
+  }
+
+ protected:
+  void init() {
+    m_cholmod.final_asis = 1;
+    m_cholmod.supernodal = CHOLMOD_AUTO;
+  }
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_CHOLMODSUPPORT_H

data_acq_and_processing/third_party/eigen/Eigen/src/CholmodSupport/InternalHeaderCheck.h

@@ -0,0 +1,3 @@
+#ifndef EIGEN_CHOLMODSUPPORT_MODULE_H
+#error "Please include Eigen/CholmodSupport instead of including headers inside the src directory directly."
+#endif

data_acq_and_processing/third_party/eigen/Eigen/src/Core/ArithmeticSequence.h

@@ -0,0 +1,239 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2017 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ARITHMETIC_SEQUENCE_H
+#define EIGEN_ARITHMETIC_SEQUENCE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+// Helper to cleanup the type of the increment:
+template <typename T>
+struct cleanup_seq_incr {
+  typedef typename cleanup_index_type<T, DynamicIndex>::type type;
+};
+
+}  // namespace internal
+
+//--------------------------------------------------------------------------------
+// seq(first,last,incr) and seqN(first,size,incr)
+//--------------------------------------------------------------------------------
+
+template <typename FirstType = Index, typename SizeType = Index, typename IncrType = internal::FixedInt<1> >
+class ArithmeticSequence;
+
+template <typename FirstType, typename SizeType, typename IncrType>
+ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,
+                   typename internal::cleanup_index_type<SizeType>::type,
+                   typename internal::cleanup_seq_incr<IncrType>::type>
+seqN(FirstType first, SizeType size, IncrType incr);
+
+/** \class ArithmeticSequence
+ * \ingroup Core_Module
+ *
+ * This class represents an arithmetic progression \f$ a_0, a_1, a_2, ..., a_{n-1}\f$ defined by
+ * its \em first value \f$ a_0 \f$, its \em size (aka length) \em n, and the \em increment (aka stride)
+ * that is equal to \f$ a_{i+1}-a_{i}\f$ for any \em i.
+ *
+ * It is internally used as the return type of the Eigen::seq and Eigen::seqN functions, and as the input arguments
+ * of DenseBase::operator()(const RowIndices&, const ColIndices&), and most of the time this is the
+ * only way it is used.
+ *
+ * \tparam FirstType type of the first element, usually an Index,
+ *                   but internally it can be a symbolic expression
+ * \tparam SizeType type representing the size of the sequence, usually an Index
+ *                  or a compile time integral constant. Internally, it can also be a symbolic expression
+ * \tparam IncrType type of the increment, can be a runtime Index, or a compile time integral constant (default is
+ * compile-time 1)
+ *
+ * \sa Eigen::seq, Eigen::seqN, DenseBase::operator()(const RowIndices&, const ColIndices&), class IndexedView
+ */
+template <typename FirstType, typename SizeType, typename IncrType>
+class ArithmeticSequence {
+ public:
+  constexpr ArithmeticSequence() = default;
+  constexpr ArithmeticSequence(FirstType first, SizeType size) : m_first(first), m_size(size) {}
+  constexpr ArithmeticSequence(FirstType first, SizeType size, IncrType incr)
+      : m_first(first), m_size(size), m_incr(incr) {}
+
+  enum {
+    // SizeAtCompileTime = internal::get_fixed_value<SizeType>::value,
+    IncrAtCompileTime = internal::get_fixed_value<IncrType, DynamicIndex>::value
+  };
+
+  /** \returns the size, i.e., number of elements, of the sequence */
+  constexpr Index size() const { return m_size; }
+
+  /** \returns the first element \f$ a_0 \f$ in the sequence */
+  constexpr Index first() const { return m_first; }
+
+  /** \returns the value \f$ a_i \f$ at index \a i in the sequence. */
+  constexpr Index operator[](Index i) const { return m_first + i * m_incr; }
+
+  constexpr const FirstType& firstObject() const { return m_first; }
+  constexpr const SizeType& sizeObject() const { return m_size; }
+  constexpr const IncrType& incrObject() const { return m_incr; }
+
+ protected:
+  FirstType m_first;
+  SizeType m_size;
+  IncrType m_incr;
+
+ public:
+  constexpr auto reverse() const -> decltype(Eigen::seqN(m_first + (m_size + fix<-1>()) * m_incr, m_size, -m_incr)) {
+    return seqN(m_first + (m_size + fix<-1>()) * m_incr, m_size, -m_incr);
+  }
+};
+
+/** \returns an ArithmeticSequence starting at \a first, of length \a size, and increment \a incr
+ *
+ * \sa seqN(FirstType,SizeType), seq(FirstType,LastType,IncrType) */
+template <typename FirstType, typename SizeType, typename IncrType>
+ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,
+                   typename internal::cleanup_index_type<SizeType>::type,
+                   typename internal::cleanup_seq_incr<IncrType>::type>
+seqN(FirstType first, SizeType size, IncrType incr) {
+  return ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,
+                            typename internal::cleanup_index_type<SizeType>::type,
+                            typename internal::cleanup_seq_incr<IncrType>::type>(first, size, incr);
+}
+
+/** \returns an ArithmeticSequence starting at \a first, of length \a size, and unit increment
+ *
+ * \sa seqN(FirstType,SizeType,IncrType), seq(FirstType,LastType) */
+template <typename FirstType, typename SizeType>
+ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,
+                   typename internal::cleanup_index_type<SizeType>::type>
+seqN(FirstType first, SizeType size) {
+  return ArithmeticSequence<typename internal::cleanup_index_type<FirstType>::type,
+                            typename internal::cleanup_index_type<SizeType>::type>(first, size);
+}
+
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+
+/** \returns an ArithmeticSequence starting at \a f, up (or down) to \a l, and with positive (or negative) increment \a
+ * incr
+ *
+ * It is essentially an alias to:
+ * \code
+ * seqN(f, (l-f+incr)/incr, incr);
+ * \endcode
+ *
+ * \sa seqN(FirstType,SizeType,IncrType), seq(FirstType,LastType)
+ */
+template <typename FirstType, typename LastType, typename IncrType>
+auto seq(FirstType f, LastType l, IncrType incr);
+
+/** \returns an ArithmeticSequence starting at \a f, up (or down) to \a l, and unit increment
+ *
+ * It is essentially an alias to:
+ * \code
+ * seqN(f,l-f+1);
+ * \endcode
+ *
+ * \sa seqN(FirstType,SizeType), seq(FirstType,LastType,IncrType)
+ */
+template <typename FirstType, typename LastType>
+auto seq(FirstType f, LastType l);
+
+#else  // EIGEN_PARSED_BY_DOXYGEN
+
+template <typename FirstType, typename LastType>
+auto seq(FirstType f, LastType l)
+    -> decltype(seqN(typename internal::cleanup_index_type<FirstType>::type(f),
+                     (typename internal::cleanup_index_type<LastType>::type(l) -
+                      typename internal::cleanup_index_type<FirstType>::type(f) + fix<1>()))) {
+  return seqN(typename internal::cleanup_index_type<FirstType>::type(f),
+              (typename internal::cleanup_index_type<LastType>::type(l) -
+               typename internal::cleanup_index_type<FirstType>::type(f) + fix<1>()));
+}
+
+template <typename FirstType, typename LastType, typename IncrType>
+auto seq(FirstType f, LastType l, IncrType incr)
+    -> decltype(seqN(typename internal::cleanup_index_type<FirstType>::type(f),
+                     (typename internal::cleanup_index_type<LastType>::type(l) -
+                      typename internal::cleanup_index_type<FirstType>::type(f) +
+                      typename internal::cleanup_seq_incr<IncrType>::type(incr)) /
+                         typename internal::cleanup_seq_incr<IncrType>::type(incr),
+                     typename internal::cleanup_seq_incr<IncrType>::type(incr))) {
+  typedef typename internal::cleanup_seq_incr<IncrType>::type CleanedIncrType;
+  return seqN(typename internal::cleanup_index_type<FirstType>::type(f),
+              (typename internal::cleanup_index_type<LastType>::type(l) -
+               typename internal::cleanup_index_type<FirstType>::type(f) + CleanedIncrType(incr)) /
+                  CleanedIncrType(incr),
+              CleanedIncrType(incr));
+}
+
+#endif  // EIGEN_PARSED_BY_DOXYGEN
+
+namespace placeholders {
+
+/** \cpp11
+ * \returns a symbolic ArithmeticSequence representing the last \a size elements with increment \a incr.
+ *
+ * It is a shortcut for: \code seqN(last-(size-fix<1>)*incr, size, incr) \endcode
+ * \anchor Eigen_placeholders_lastN
+ * \sa lastN(SizeType), seqN(FirstType,SizeType), seq(FirstType,LastType,IncrType) */
+template <typename SizeType, typename IncrType>
+auto lastN(SizeType size, IncrType incr)
+    -> decltype(seqN(Eigen::placeholders::last - (size - fix<1>()) * incr, size, incr)) {
+  return seqN(Eigen::placeholders::last - (size - fix<1>()) * incr, size, incr);
+}
+
+/** \cpp11
+ * \returns a symbolic ArithmeticSequence representing the last \a size elements with a unit increment.
+ *
+ *  It is a shortcut for: \code seq(last+fix<1>-size, last) \endcode
+ *
+ * \sa lastN(SizeType,IncrType, seqN(FirstType,SizeType), seq(FirstType,LastType) */
+template <typename SizeType>
+auto lastN(SizeType size) -> decltype(seqN(Eigen::placeholders::last + fix<1>() - size, size)) {
+  return seqN(Eigen::placeholders::last + fix<1>() - size, size);
+}
+
+}  // namespace placeholders
+
+/** \namespace Eigen::indexing
+  * \ingroup Core_Module
+  *
+  * The sole purpose of this namespace is to be able to import all functions
+  * and symbols that are expected to be used within operator() for indexing
+  * and slicing. If you already imported the whole Eigen namespace:
+  * \code using namespace Eigen; \endcode
+  * then you are already all set. Otherwise, if you don't want/cannot import
+  * the whole Eigen namespace, the following line:
+  * \code using namespace Eigen::indexing; \endcode
+  * is equivalent to:
+  * \code
+  using Eigen::fix;
+  using Eigen::seq;
+  using Eigen::seqN;
+  using Eigen::placeholders::all;
+  using Eigen::placeholders::last;
+  using Eigen::placeholders::lastN;  // c++11 only
+  using Eigen::placeholders::lastp1;
+  \endcode
+  */
+namespace indexing {
+using Eigen::fix;
+using Eigen::seq;
+using Eigen::seqN;
+using Eigen::placeholders::all;
+using Eigen::placeholders::last;
+using Eigen::placeholders::lastN;
+using Eigen::placeholders::lastp1;
+}  // namespace indexing
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_ARITHMETIC_SEQUENCE_H

data_acq_and_processing/third_party/eigen/Eigen/src/Core/Array.h

@@ -0,0 +1,374 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ARRAY_H
+#define EIGEN_ARRAY_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename Scalar_, int Rows_, int Cols_, int Options_, int MaxRows_, int MaxCols_>
+struct traits<Array<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_>>
+    : traits<Matrix<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_>> {
+  typedef ArrayXpr XprKind;
+  typedef ArrayBase<Array<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_>> XprBase;
+};
+}  // namespace internal
+
+/** \class Array
+ * \ingroup Core_Module
+ *
+ * \brief General-purpose arrays with easy API for coefficient-wise operations
+ *
+ * The %Array class is very similar to the Matrix class. It provides
+ * general-purpose one- and two-dimensional arrays. The difference between the
+ * %Array and the %Matrix class is primarily in the API: the API for the
+ * %Array class provides easy access to coefficient-wise operations, while the
+ * API for the %Matrix class provides easy access to linear-algebra
+ * operations.
+ *
+ * See documentation of class Matrix for detailed information on the template parameters
+ * storage layout.
+ *
+ * This class can be extended with the help of the plugin mechanism described on the page
+ * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_ARRAY_PLUGIN.
+ *
+ * \sa \blank \ref TutorialArrayClass, \ref TopicClassHierarchy
+ */
+template <typename Scalar_, int Rows_, int Cols_, int Options_, int MaxRows_, int MaxCols_>
+class Array : public PlainObjectBase<Array<Scalar_, Rows_, Cols_, Options_, MaxRows_, MaxCols_>> {
+ public:
+  typedef PlainObjectBase<Array> Base;
+  EIGEN_DENSE_PUBLIC_INTERFACE(Array)
+
+  enum { Options = Options_ };
+  typedef typename Base::PlainObject PlainObject;
+
+ protected:
+  template <typename Derived, typename OtherDerived, bool IsVector>
+  friend struct internal::conservative_resize_like_impl;
+
+  using Base::m_storage;
+
+ public:
+  using Base::base;
+  using Base::coeff;
+  using Base::coeffRef;
+
+  /**
+   * The usage of
+   *   using Base::operator=;
+   * fails on MSVC. Since the code below is working with GCC and MSVC, we skipped
+   * the usage of 'using'. This should be done only for operator=.
+   */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Array& operator=(const EigenBase<OtherDerived>& other) {
+    return Base::operator=(other);
+  }
+
+  /** Set all the entries to \a value.
+   * \sa DenseBase::setConstant(), DenseBase::fill()
+   */
+  /* This overload is needed because the usage of
+   *   using Base::operator=;
+   * fails on MSVC. Since the code below is working with GCC and MSVC, we skipped
+   * the usage of 'using'. This should be done only for operator=.
+   */
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Array& operator=(const Scalar& value) {
+    Base::setConstant(value);
+    return *this;
+  }
+
+  /** Copies the value of the expression \a other into \c *this with automatic resizing.
+   *
+   * *this might be resized to match the dimensions of \a other. If *this was a null matrix (not already initialized),
+   * it will be initialized.
+   *
+   * Note that copying a row-vector into a vector (and conversely) is allowed.
+   * The resizing, if any, is then done in the appropriate way so that row-vectors
+   * remain row-vectors and vectors remain vectors.
+   */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Array& operator=(const DenseBase<OtherDerived>& other) {
+    return Base::_set(other);
+  }
+
+  /**
+   * \brief Assigns arrays to each other.
+   *
+   * \note This is a special case of the templated operator=. Its purpose is
+   * to prevent a default operator= from hiding the templated operator=.
+   *
+   * \callgraph
+   */
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Array& operator=(const Array& other) { return Base::_set(other); }
+
+  /** Default constructor.
+   *
+   * For fixed-size matrices, does nothing.
+   *
+   * For dynamic-size matrices, creates an empty matrix of size 0. Does not allocate any array. Such a matrix
+   * is called a null matrix. This constructor is the unique way to create null matrices: resizing
+   * a matrix to 0 is not supported.
+   *
+   * \sa resize(Index,Index)
+   */
+#ifdef EIGEN_INITIALIZE_COEFFS
+  EIGEN_DEVICE_FUNC constexpr Array() : Base() { EIGEN_INITIALIZE_COEFFS_IF_THAT_OPTION_IS_ENABLED }
+#else
+  EIGEN_DEVICE_FUNC constexpr Array() = default;
+#endif
+  /** \brief Move constructor */
+  EIGEN_DEVICE_FUNC constexpr Array(Array&&) = default;
+  EIGEN_DEVICE_FUNC Array& operator=(Array&& other) noexcept(std::is_nothrow_move_assignable<Scalar>::value) {
+    Base::operator=(std::move(other));
+    return *this;
+  }
+
+  /** \brief Construct a row of column vector with fixed size from an arbitrary number of coefficients.
+   *
+   * \only_for_vectors
+   *
+   * This constructor is for 1D array or vectors with more than 4 coefficients.
+   *
+   * \warning To construct a column (resp. row) vector of fixed length, the number of values passed to this
+   * constructor must match the fixed number of rows (resp. columns) of \c *this.
+   *
+   *
+   * Example: \include Array_variadic_ctor_cxx11.cpp
+   * Output: \verbinclude Array_variadic_ctor_cxx11.out
+   *
+   * \sa Array(const std::initializer_list<std::initializer_list<Scalar>>&)
+   * \sa Array(const Scalar&), Array(const Scalar&,const Scalar&)
+   */
+  template <typename... ArgTypes>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3,
+                                              const ArgTypes&... args)
+      : Base(a0, a1, a2, a3, args...) {}
+
+  /** \brief Constructs an array and initializes it from the coefficients given as initializer-lists grouped by row.
+   * \cpp11
+   *
+   * In the general case, the constructor takes a list of rows, each row being represented as a list of coefficients:
+   *
+   * Example: \include Array_initializer_list_23_cxx11.cpp
+   * Output: \verbinclude Array_initializer_list_23_cxx11.out
+   *
+   * Each of the inner initializer lists must contain the exact same number of elements, otherwise an assertion is
+   * triggered.
+   *
+   * In the case of a compile-time column 1D array, implicit transposition from a single row is allowed.
+   * Therefore <code> Array<int,Dynamic,1>{{1,2,3,4,5}}</code> is legal and the more verbose syntax
+   * <code>Array<int,Dynamic,1>{{1},{2},{3},{4},{5}}</code> can be avoided:
+   *
+   * Example: \include Array_initializer_list_vector_cxx11.cpp
+   * Output: \verbinclude Array_initializer_list_vector_cxx11.out
+   *
+   * In the case of fixed-sized arrays, the initializer list sizes must exactly match the array sizes,
+   * and implicit transposition is allowed for compile-time 1D arrays only.
+   *
+   * \sa  Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args)
+   */
+  EIGEN_DEVICE_FUNC constexpr Array(const std::initializer_list<std::initializer_list<Scalar>>& list) : Base(list) {}
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  template <typename T>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit Array(const T& x) {
+    Base::template _init1<T>(x);
+  }
+
+  template <typename T0, typename T1>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Array(const T0& val0, const T1& val1) {
+    this->template _init2<T0, T1>(val0, val1);
+  }
+
+#else
+  /** \brief Constructs a fixed-sized array initialized with coefficients starting at \a data */
+  EIGEN_DEVICE_FUNC explicit Array(const Scalar* data);
+  /** Constructs a vector or row-vector with given dimension. \only_for_vectors
+   *
+   * Note that this is only useful for dynamic-size vectors. For fixed-size vectors,
+   * it is redundant to pass the dimension here, so it makes more sense to use the default
+   * constructor Array() instead.
+   */
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE explicit Array(Index dim);
+  /** constructs an initialized 1x1 Array with the given coefficient
+   * \sa const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args */
+  Array(const Scalar& value);
+  /** constructs an uninitialized array with \a rows rows and \a cols columns.
+   *
+   * This is useful for dynamic-size arrays. For fixed-size arrays,
+   * it is redundant to pass these parameters, so one should use the default constructor
+   * Array() instead. */
+  Array(Index rows, Index cols);
+  /** constructs an initialized 2D vector with given coefficients
+   * \sa Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args) */
+  Array(const Scalar& val0, const Scalar& val1);
+#endif  // end EIGEN_PARSED_BY_DOXYGEN
+
+  /** constructs an initialized 3D vector with given coefficients
+   * \sa Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args)
+   */
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Array(const Scalar& val0, const Scalar& val1, const Scalar& val2) {
+    EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Array, 3)
+    m_storage.data()[0] = val0;
+    m_storage.data()[1] = val1;
+    m_storage.data()[2] = val2;
+  }
+  /** constructs an initialized 4D vector with given coefficients
+   * \sa Array(const Scalar& a0, const Scalar& a1, const Scalar& a2, const Scalar& a3, const ArgTypes&... args)
+   */
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Array(const Scalar& val0, const Scalar& val1, const Scalar& val2,
+                                              const Scalar& val3) {
+    EIGEN_STATIC_ASSERT_VECTOR_SPECIFIC_SIZE(Array, 4)
+    m_storage.data()[0] = val0;
+    m_storage.data()[1] = val1;
+    m_storage.data()[2] = val2;
+    m_storage.data()[3] = val3;
+  }
+
+  /** Copy constructor */
+  EIGEN_DEVICE_FUNC constexpr Array(const Array&) = default;
+
+ private:
+  struct PrivateType {};
+
+ public:
+  /** \sa MatrixBase::operator=(const EigenBase<OtherDerived>&) */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Array(
+      const EigenBase<OtherDerived>& other,
+      std::enable_if_t<internal::is_convertible<typename OtherDerived::Scalar, Scalar>::value, PrivateType> =
+          PrivateType())
+      : Base(other.derived()) {}
+
+  EIGEN_DEVICE_FUNC constexpr Index innerStride() const noexcept { return 1; }
+  EIGEN_DEVICE_FUNC constexpr Index outerStride() const noexcept { return this->innerSize(); }
+
+#ifdef EIGEN_ARRAY_PLUGIN
+#include EIGEN_ARRAY_PLUGIN
+#endif
+
+ private:
+  template <typename MatrixType, typename OtherDerived, bool SwapPointers>
+  friend struct internal::matrix_swap_impl;
+};
+
+/** \defgroup arraytypedefs Global array typedefs
+ * \ingroup Core_Module
+ *
+ * %Eigen defines several typedef shortcuts for most common 1D and 2D array types.
+ *
+ * The general patterns are the following:
+ *
+ * \c ArrayRowsColsType where \c Rows and \c Cols can be \c 2,\c 3,\c 4 for fixed size square matrices or \c X for
+ * dynamic size, and where \c Type can be \c i for integer, \c f for float, \c d for double, \c cf for complex float, \c
+ * cd for complex double.
+ *
+ * For example, \c Array33d is a fixed-size 3x3 array type of doubles, and \c ArrayXXf is a dynamic-size matrix of
+ * floats.
+ *
+ * There are also \c ArraySizeType which are self-explanatory. For example, \c Array4cf is
+ * a fixed-size 1D array of 4 complex floats.
+ *
+ * With \cpp11, template alias are also defined for common sizes.
+ * They follow the same pattern as above except that the scalar type suffix is replaced by a
+ * template parameter, i.e.:
+ *   - `ArrayRowsCols<Type>` where `Rows` and `Cols` can be \c 2,\c 3,\c 4, or \c X for fixed or dynamic size.
+ *   - `ArraySize<Type>` where `Size` can be \c 2,\c 3,\c 4 or \c X for fixed or dynamic size 1D arrays.
+ *
+ * \sa class Array
+ */
+
+#define EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, Size, SizeSuffix)        \
+  /** \ingroup arraytypedefs */                                              \
+  typedef Array<Type, Size, Size> Array##SizeSuffix##SizeSuffix##TypeSuffix; \
+  /** \ingroup arraytypedefs */                                              \
+  typedef Array<Type, Size, 1> Array##SizeSuffix##TypeSuffix;
+
+#define EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, Size)  \
+  /** \ingroup arraytypedefs */                                  \
+  typedef Array<Type, Size, Dynamic> Array##Size##X##TypeSuffix; \
+  /** \ingroup arraytypedefs */                                  \
+  typedef Array<Type, Dynamic, Size> Array##X##Size##TypeSuffix;
+
+#define EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(Type, TypeSuffix) \
+  EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, 2, 2)           \
+  EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, 3, 3)           \
+  EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, 4, 4)           \
+  EIGEN_MAKE_ARRAY_TYPEDEFS(Type, TypeSuffix, Dynamic, X)     \
+  EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, 2)        \
+  EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, 3)        \
+  EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Type, TypeSuffix, 4)
+
+EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(int, i)
+EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(float, f)
+EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(double, d)
+EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(std::complex<float>, cf)
+EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES(std::complex<double>, cd)
+
+#undef EIGEN_MAKE_ARRAY_TYPEDEFS_ALL_SIZES
+#undef EIGEN_MAKE_ARRAY_TYPEDEFS
+#undef EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS
+
+#define EIGEN_MAKE_ARRAY_TYPEDEFS(Size, SizeSuffix)              \
+  /** \ingroup arraytypedefs */                                  \
+  /** \brief \cpp11 */                                           \
+  template <typename Type>                                       \
+  using Array##SizeSuffix##SizeSuffix = Array<Type, Size, Size>; \
+  /** \ingroup arraytypedefs */                                  \
+  /** \brief \cpp11 */                                           \
+  template <typename Type>                                       \
+  using Array##SizeSuffix = Array<Type, Size, 1>;
+
+#define EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(Size)        \
+  /** \ingroup arraytypedefs */                      \
+  /** \brief \cpp11 */                               \
+  template <typename Type>                           \
+  using Array##Size##X = Array<Type, Size, Dynamic>; \
+  /** \ingroup arraytypedefs */                      \
+  /** \brief \cpp11 */                               \
+  template <typename Type>                           \
+  using Array##X##Size = Array<Type, Dynamic, Size>;
+
+EIGEN_MAKE_ARRAY_TYPEDEFS(2, 2)
+EIGEN_MAKE_ARRAY_TYPEDEFS(3, 3)
+EIGEN_MAKE_ARRAY_TYPEDEFS(4, 4)
+EIGEN_MAKE_ARRAY_TYPEDEFS(Dynamic, X)
+EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(2)
+EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(3)
+EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS(4)
+
+#undef EIGEN_MAKE_ARRAY_TYPEDEFS
+#undef EIGEN_MAKE_ARRAY_FIXED_TYPEDEFS
+
+#define EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, SizeSuffix) \
+  using Eigen::Matrix##SizeSuffix##TypeSuffix;                               \
+  using Eigen::Vector##SizeSuffix##TypeSuffix;                               \
+  using Eigen::RowVector##SizeSuffix##TypeSuffix;
+
+#define EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(TypeSuffix)       \
+  EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 2) \
+  EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 3) \
+  EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, 4) \
+  EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE_AND_SIZE(TypeSuffix, X)
+
+#define EIGEN_USING_ARRAY_TYPEDEFS        \
+  EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(i)  \
+  EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(f)  \
+  EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(d)  \
+  EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(cf) \
+  EIGEN_USING_ARRAY_TYPEDEFS_FOR_TYPE(cd)
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_ARRAY_H

data_acq_and_processing/third_party/eigen/Eigen/src/Core/ArrayBase.h

@@ -0,0 +1,210 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ARRAYBASE_H
+#define EIGEN_ARRAYBASE_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template <typename ExpressionType>
+class MatrixWrapper;
+
+/** \class ArrayBase
+ * \ingroup Core_Module
+ *
+ * \brief Base class for all 1D and 2D array, and related expressions
+ *
+ * An array is similar to a dense vector or matrix. While matrices are mathematical
+ * objects with well defined linear algebra operators, an array is just a collection
+ * of scalar values arranged in a one or two dimensional fashion. As the main consequence,
+ * all operations applied to an array are performed coefficient wise. Furthermore,
+ * arrays support scalar math functions of the c++ standard library (e.g., std::sin(x)), and convenient
+ * constructors allowing to easily write generic code working for both scalar values
+ * and arrays.
+ *
+ * This class is the base that is inherited by all array expression types.
+ *
+ * \tparam Derived is the derived type, e.g., an array or an expression type.
+ *
+ * This class can be extended with the help of the plugin mechanism described on the page
+ * \ref TopicCustomizing_Plugins by defining the preprocessor symbol \c EIGEN_ARRAYBASE_PLUGIN.
+ *
+ * \sa class MatrixBase, \ref TopicClassHierarchy
+ */
+template <typename Derived>
+class ArrayBase : public DenseBase<Derived> {
+ public:
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  /** The base class for a given storage type. */
+  typedef ArrayBase StorageBaseType;
+
+  typedef ArrayBase Eigen_BaseClassForSpecializationOfGlobalMathFuncImpl;
+
+  typedef typename internal::traits<Derived>::StorageKind StorageKind;
+  typedef typename internal::traits<Derived>::Scalar Scalar;
+  typedef typename internal::packet_traits<Scalar>::type PacketScalar;
+  typedef typename NumTraits<Scalar>::Real RealScalar;
+
+  typedef DenseBase<Derived> Base;
+  using Base::ColsAtCompileTime;
+  using Base::Flags;
+  using Base::IsVectorAtCompileTime;
+  using Base::MaxColsAtCompileTime;
+  using Base::MaxRowsAtCompileTime;
+  using Base::MaxSizeAtCompileTime;
+  using Base::RowsAtCompileTime;
+  using Base::SizeAtCompileTime;
+
+  using Base::coeff;
+  using Base::coeffRef;
+  using Base::cols;
+  using Base::const_cast_derived;
+  using Base::derived;
+  using Base::lazyAssign;
+  using Base::rows;
+  using Base::size;
+  using Base::operator-;
+  using Base::operator=;
+  using Base::operator+=;
+  using Base::operator-=;
+  using Base::operator*=;
+  using Base::operator/=;
+
+  typedef typename Base::CoeffReturnType CoeffReturnType;
+
+  typedef typename Base::PlainObject PlainObject;
+
+  /** \internal Represents a matrix with all coefficients equal to one another*/
+  typedef CwiseNullaryOp<internal::scalar_constant_op<Scalar>, PlainObject> ConstantReturnType;
+#endif  // not EIGEN_PARSED_BY_DOXYGEN
+
+#define EIGEN_CURRENT_STORAGE_BASE_CLASS Eigen::ArrayBase
+#define EIGEN_DOC_UNARY_ADDONS(X, Y)
+#include "../plugins/MatrixCwiseUnaryOps.inc"
+#include "../plugins/ArrayCwiseUnaryOps.inc"
+#include "../plugins/CommonCwiseBinaryOps.inc"
+#include "../plugins/MatrixCwiseBinaryOps.inc"
+#include "../plugins/ArrayCwiseBinaryOps.inc"
+#ifdef EIGEN_ARRAYBASE_PLUGIN
+#include EIGEN_ARRAYBASE_PLUGIN
+#endif
+#undef EIGEN_CURRENT_STORAGE_BASE_CLASS
+#undef EIGEN_DOC_UNARY_ADDONS
+
+  /** Special case of the template operator=, in order to prevent the compiler
+   * from generating a default operator= (issue hit with g++ 4.1)
+   */
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const ArrayBase& other) {
+    internal::call_assignment(derived(), other.derived());
+    return derived();
+  }
+
+  /** Set all the entries to \a value.
+   * \sa DenseBase::setConstant(), DenseBase::fill() */
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator=(const Scalar& value) {
+    Base::setConstant(value);
+    return derived();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator+=(const Scalar& other) {
+    internal::call_assignment(this->derived(), PlainObject::Constant(rows(), cols(), other),
+                              internal::add_assign_op<Scalar, Scalar>());
+    return derived();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator-=(const Scalar& other) {
+    internal::call_assignment(this->derived(), PlainObject::Constant(rows(), cols(), other),
+                              internal::sub_assign_op<Scalar, Scalar>());
+    return derived();
+  }
+
+  /** replaces \c *this by \c *this + \a other.
+   *
+   * \returns a reference to \c *this
+   */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator+=(const ArrayBase<OtherDerived>& other) {
+    call_assignment(derived(), other.derived(), internal::add_assign_op<Scalar, typename OtherDerived::Scalar>());
+    return derived();
+  }
+
+  /** replaces \c *this by \c *this - \a other.
+   *
+   * \returns a reference to \c *this
+   */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator-=(const ArrayBase<OtherDerived>& other) {
+    call_assignment(derived(), other.derived(), internal::sub_assign_op<Scalar, typename OtherDerived::Scalar>());
+    return derived();
+  }
+
+  /** replaces \c *this by \c *this * \a other coefficient wise.
+   *
+   * \returns a reference to \c *this
+   */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator*=(const ArrayBase<OtherDerived>& other) {
+    call_assignment(derived(), other.derived(), internal::mul_assign_op<Scalar, typename OtherDerived::Scalar>());
+    return derived();
+  }
+
+  /** replaces \c *this by \c *this / \a other coefficient wise.
+   *
+   * \returns a reference to \c *this
+   */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Derived& operator/=(const ArrayBase<OtherDerived>& other) {
+    call_assignment(derived(), other.derived(), internal::div_assign_op<Scalar, typename OtherDerived::Scalar>());
+    return derived();
+  }
+
+ public:
+  EIGEN_DEVICE_FUNC constexpr ArrayBase<Derived>& array() { return *this; }
+  EIGEN_DEVICE_FUNC constexpr const ArrayBase<Derived>& array() const { return *this; }
+
+  /** \returns an \link Eigen::MatrixBase Matrix \endlink expression of this array
+   * \sa MatrixBase::array() */
+  EIGEN_DEVICE_FUNC constexpr MatrixWrapper<Derived> matrix() { return MatrixWrapper<Derived>(derived()); }
+  EIGEN_DEVICE_FUNC constexpr const MatrixWrapper<const Derived> matrix() const {
+    return MatrixWrapper<const Derived>(derived());
+  }
+
+ protected:
+  EIGEN_DEFAULT_COPY_CONSTRUCTOR(ArrayBase)
+  EIGEN_DEFAULT_EMPTY_CONSTRUCTOR_AND_DESTRUCTOR(ArrayBase)
+
+ private:
+  explicit ArrayBase(Index);
+  ArrayBase(Index, Index);
+  template <typename OtherDerived>
+  explicit ArrayBase(const ArrayBase<OtherDerived>&);
+
+ protected:
+  // mixing arrays and matrices is not legal
+  template <typename OtherDerived>
+  Derived& operator+=(const MatrixBase<OtherDerived>&) {
+    EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar)) == -1,
+                        YOU_CANNOT_MIX_ARRAYS_AND_MATRICES);
+    return *this;
+  }
+  // mixing arrays and matrices is not legal
+  template <typename OtherDerived>
+  Derived& operator-=(const MatrixBase<OtherDerived>&) {
+    EIGEN_STATIC_ASSERT(std::ptrdiff_t(sizeof(typename OtherDerived::Scalar)) == -1,
+                        YOU_CANNOT_MIX_ARRAYS_AND_MATRICES);
+    return *this;
+  }
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_ARRAYBASE_H

data_acq_and_processing/third_party/eigen/Eigen/src/Core/ArrayWrapper.h

@@ -0,0 +1,166 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009-2010 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ARRAYWRAPPER_H
+#define EIGEN_ARRAYWRAPPER_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \class ArrayWrapper
+ * \ingroup Core_Module
+ *
+ * \brief Expression of a mathematical vector or matrix as an array object
+ *
+ * This class is the return type of MatrixBase::array(), and most of the time
+ * this is the only way it is used.
+ *
+ * \sa MatrixBase::array(), class MatrixWrapper
+ */
+
+namespace internal {
+template <typename ExpressionType>
+struct traits<ArrayWrapper<ExpressionType> > : public traits<remove_all_t<typename ExpressionType::Nested> > {
+  typedef ArrayXpr XprKind;
+  // Let's remove NestByRefBit
+  enum {
+    Flags0 = traits<remove_all_t<typename ExpressionType::Nested> >::Flags,
+    LvalueBitFlag = is_lvalue<ExpressionType>::value ? LvalueBit : 0,
+    Flags = (Flags0 & ~(NestByRefBit | LvalueBit)) | LvalueBitFlag
+  };
+};
+}  // namespace internal
+
+template <typename ExpressionType>
+class ArrayWrapper : public ArrayBase<ArrayWrapper<ExpressionType> > {
+ public:
+  typedef ArrayBase<ArrayWrapper> Base;
+  EIGEN_DENSE_PUBLIC_INTERFACE(ArrayWrapper)
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(ArrayWrapper)
+  typedef internal::remove_all_t<ExpressionType> NestedExpression;
+
+  typedef std::conditional_t<internal::is_lvalue<ExpressionType>::value, Scalar, const Scalar>
+      ScalarWithConstIfNotLvalue;
+
+  typedef typename internal::ref_selector<ExpressionType>::non_const_type NestedExpressionType;
+
+  using Base::coeffRef;
+
+  EIGEN_DEVICE_FUNC constexpr explicit EIGEN_STRONG_INLINE ArrayWrapper(ExpressionType& matrix)
+      : m_expression(matrix) {}
+
+  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return m_expression.rows(); }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return m_expression.cols(); }
+  EIGEN_DEVICE_FUNC constexpr Index outerStride() const noexcept { return m_expression.outerStride(); }
+  EIGEN_DEVICE_FUNC constexpr Index innerStride() const noexcept { return m_expression.innerStride(); }
+
+  EIGEN_DEVICE_FUNC constexpr ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
+  EIGEN_DEVICE_FUNC constexpr const Scalar* data() const { return m_expression.data(); }
+
+  EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index rowId, Index colId) const {
+    return m_expression.coeffRef(rowId, colId);
+  }
+
+  EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index index) const { return m_expression.coeffRef(index); }
+
+  template <typename Dest>
+  EIGEN_DEVICE_FUNC inline void evalTo(Dest& dst) const {
+    dst = m_expression;
+  }
+
+  EIGEN_DEVICE_FUNC constexpr const internal::remove_all_t<NestedExpressionType>& nestedExpression() const {
+    return m_expression;
+  }
+
+  /** Forwards the resizing request to the nested expression
+   * \sa DenseBase::resize(Index)  */
+  EIGEN_DEVICE_FUNC void resize(Index newSize) { m_expression.resize(newSize); }
+  /** Forwards the resizing request to the nested expression
+   * \sa DenseBase::resize(Index,Index)*/
+  EIGEN_DEVICE_FUNC void resize(Index rows, Index cols) { m_expression.resize(rows, cols); }
+
+ protected:
+  NestedExpressionType m_expression;
+};
+
+/** \class MatrixWrapper
+ * \ingroup Core_Module
+ *
+ * \brief Expression of an array as a mathematical vector or matrix
+ *
+ * This class is the return type of ArrayBase::matrix(), and most of the time
+ * this is the only way it is used.
+ *
+ * \sa MatrixBase::matrix(), class ArrayWrapper
+ */
+
+namespace internal {
+template <typename ExpressionType>
+struct traits<MatrixWrapper<ExpressionType> > : public traits<remove_all_t<typename ExpressionType::Nested> > {
+  typedef MatrixXpr XprKind;
+  // Let's remove NestByRefBit
+  enum {
+    Flags0 = traits<remove_all_t<typename ExpressionType::Nested> >::Flags,
+    LvalueBitFlag = is_lvalue<ExpressionType>::value ? LvalueBit : 0,
+    Flags = (Flags0 & ~(NestByRefBit | LvalueBit)) | LvalueBitFlag
+  };
+};
+}  // namespace internal
+
+template <typename ExpressionType>
+class MatrixWrapper : public MatrixBase<MatrixWrapper<ExpressionType> > {
+ public:
+  typedef MatrixBase<MatrixWrapper<ExpressionType> > Base;
+  EIGEN_DENSE_PUBLIC_INTERFACE(MatrixWrapper)
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(MatrixWrapper)
+  typedef internal::remove_all_t<ExpressionType> NestedExpression;
+
+  typedef std::conditional_t<internal::is_lvalue<ExpressionType>::value, Scalar, const Scalar>
+      ScalarWithConstIfNotLvalue;
+
+  typedef typename internal::ref_selector<ExpressionType>::non_const_type NestedExpressionType;
+
+  using Base::coeffRef;
+
+  EIGEN_DEVICE_FUNC constexpr explicit inline MatrixWrapper(ExpressionType& matrix) : m_expression(matrix) {}
+
+  EIGEN_DEVICE_FUNC constexpr Index rows() const noexcept { return m_expression.rows(); }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const noexcept { return m_expression.cols(); }
+  EIGEN_DEVICE_FUNC constexpr Index outerStride() const noexcept { return m_expression.outerStride(); }
+  EIGEN_DEVICE_FUNC constexpr Index innerStride() const noexcept { return m_expression.innerStride(); }
+
+  EIGEN_DEVICE_FUNC constexpr ScalarWithConstIfNotLvalue* data() { return m_expression.data(); }
+  EIGEN_DEVICE_FUNC constexpr const Scalar* data() const { return m_expression.data(); }
+
+  EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index rowId, Index colId) const {
+    return m_expression.derived().coeffRef(rowId, colId);
+  }
+
+  EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index index) const { return m_expression.coeffRef(index); }
+
+  EIGEN_DEVICE_FUNC constexpr const internal::remove_all_t<NestedExpressionType>& nestedExpression() const {
+    return m_expression;
+  }
+
+  /** Forwards the resizing request to the nested expression
+   * \sa DenseBase::resize(Index)  */
+  EIGEN_DEVICE_FUNC void resize(Index newSize) { m_expression.resize(newSize); }
+  /** Forwards the resizing request to the nested expression
+   * \sa DenseBase::resize(Index,Index)*/
+  EIGEN_DEVICE_FUNC void resize(Index rows, Index cols) { m_expression.resize(rows, cols); }
+
+ protected:
+  NestedExpressionType m_expression;
+};
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_ARRAYWRAPPER_H

data_acq_and_processing/third_party/eigen/Eigen/src/Core/Assign.h

@@ -0,0 +1,84 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2007 Michael Olbrich <michael.olbrich@gmx.net>
+// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_ASSIGN_H
+#define EIGEN_ASSIGN_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+template <typename Derived>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::lazyAssign(
+    const DenseBase<OtherDerived>& other) {
+  enum { SameType = internal::is_same<typename Derived::Scalar, typename OtherDerived::Scalar>::value };
+
+  EIGEN_STATIC_ASSERT_LVALUE(Derived)
+  EIGEN_STATIC_ASSERT_SAME_MATRIX_SIZE(Derived, OtherDerived)
+  EIGEN_STATIC_ASSERT(
+      SameType,
+      YOU_MIXED_DIFFERENT_NUMERIC_TYPES__YOU_NEED_TO_USE_THE_CAST_METHOD_OF_MATRIXBASE_TO_CAST_NUMERIC_TYPES_EXPLICITLY)
+
+  eigen_assert(rows() == other.rows() && cols() == other.cols());
+  internal::call_assignment_no_alias(derived(), other.derived());
+
+  return derived();
+}
+
+template <typename Derived>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator=(
+    const DenseBase<OtherDerived>& other) {
+  internal::call_assignment(derived(), other.derived());
+  return derived();
+}
+
+template <typename Derived>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& DenseBase<Derived>::operator=(const DenseBase& other) {
+  internal::call_assignment(derived(), other.derived());
+  return derived();
+}
+
+template <typename Derived>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(const MatrixBase& other) {
+  internal::call_assignment(derived(), other.derived());
+  return derived();
+}
+
+template <typename Derived>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(
+    const DenseBase<OtherDerived>& other) {
+  internal::call_assignment(derived(), other.derived());
+  return derived();
+}
+
+template <typename Derived>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(
+    const EigenBase<OtherDerived>& other) {
+  internal::call_assignment(derived(), other.derived());
+  return derived();
+}
+
+template <typename Derived>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Derived& MatrixBase<Derived>::operator=(
+    const ReturnByValue<OtherDerived>& other) {
+  other.derived().evalTo(derived());
+  return derived();
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_ASSIGN_H

data_acq_and_processing/third_party/eigen/Eigen/src/Core/Assign_AOCL.h

@@ -0,0 +1,301 @@
+/*
+ * This Source Code Form is subject to the terms of the Mozilla Public
+ * License, v. 2.0. If a copy of the MPL was not distributed with this
+ * file, You can obtain one at https://mozilla.org/MPL/2.0/.
+ *
+ * Assign_AOCL.h - AOCL Vectorized Math Dispatch Layer for Eigen
+ *
+ * Copyright (c) 2025, Advanced Micro Devices, Inc. All rights reserved.
+ *
+ * Description:
+ * ------------
+ * This file implements a high-performance dispatch layer that automatically
+ * routes Eigen's element-wise mathematical operations to AMD Optimizing CPU
+ * Libraries (AOCL) Vector Math Library (VML) functions when beneficial for
+ * performance.
+ *
+ * The dispatch system uses C++ template specialization to intercept Eigen's
+ * assignment operations and redirect them to AOCL's VRDA functions, which
+ * provide optimized implementations for AMD Zen architectures.
+ *
+ * Key Features:
+ * -------------
+ * 1. Automatic Dispatch: Seamlessly routes supported operations to AOCL without
+ *    requiring code changes in user applications
+ *
+ * 2. Performance Optimization: Uses AOCL VRDA functions optimized for Zen
+ * family processors with automatic SIMD instruction selection (AVX2, AVX-512)
+ *
+ * 3. Threshold-Based Activation: Only activates for vectors larger than
+ *    EIGEN_AOCL_VML_THRESHOLD (default: 128 elements) to avoid overhead on
+ * small vectors
+ *
+ * 4. Precision-Specific Handling:
+ *    - Double precision: AOCL VRDA vectorized functions
+ *    - Single precision: Scalar fallback (preserves correctness)
+ *
+ * 5. Memory Layout Compatibility: Ensures direct memory access and compatible
+ *    storage orders between source and destination for optimal performance
+ *
+ * Supported Operations:
+ * ---------------------
+ * UNARY OPERATIONS (vector → vector):
+ * - Transcendental: exp(), sin(), cos(), sqrt(), log(), log10(), log2()
+ *
+ * BINARY OPERATIONS (vector op vector → vector):
+ * - Arithmetic: +, *, pow()
+ *
+ * Template Specialization Mechanism:
+ * -----------------------------------
+ * The system works by specializing Eigen's Assignment template for:
+ * 1. CwiseUnaryOp with scalar_*_op functors (unary operations)
+ * 2. CwiseBinaryOp with scalar_*_op functors (binary operations)
+ * 3. Dense2Dense assignment context with AOCL-compatible traits
+ *
+ * Dispatch conditions (all must be true):
+ * - Source and destination have DirectAccessBit (contiguous memory)
+ * - Compatible storage orders (both row-major or both column-major)
+ * - Vector size ≥ EIGEN_AOCL_VML_THRESHOLD or Dynamic size
+ * - Supported data type (currently double precision for VRDA)
+ *
+ * Integration Example:
+ * --------------------
+ * // Standard Eigen code - no changes required
+ * VectorXd x = VectorXd::Random(10000);
+ * VectorXd y = VectorXd::Random(10000);
+ * VectorXd result;
+ *
+ * // These operations are automatically dispatched to AOCL:
+ * result = x.array().exp();              // → amd_vrda_exp()
+ * result = x.array().sin();              // → amd_vrda_sin()
+ * result = x.array() + y.array();        // → amd_vrda_add()
+ * result = x.array().pow(y.array());     // → amd_vrda_pow()
+ *
+ * Configuration:
+ * --------------
+ * Required preprocessor definitions:
+ * - EIGEN_USE_AOCL_ALL or EIGEN_USE_AOCL_MT: Enable AOCL integration
+ * - EIGEN_USE_AOCL_VML: Enable Vector Math Library dispatch
+ *
+ * Compilation Requirements:
+ * -------------------------
+ * Include paths:
+ * - AOCL headers: -I${AOCL_ROOT}/include
+ * - Eigen headers: -I/path/to/eigen
+ *
+ * Link libraries:
+ * - AOCL MathLib: -lamdlibm
+ * - Standard math: -lm
+ *
+ * Compiler flags:
+ * - Optimization: -O3 (required for inlining)
+ * - Architecture: -march=znver5 or -march=native
+ * - Vectorization: -mfma -mavx512f (if supported)
+ *
+ * Platform Support:
+ * ------------------
+ * - Primary: Linux x86_64 with AMD Zen family processors
+ * - Compilers: GCC 8+, Clang 10+, AOCC (recommended)
+ * - AOCL Version: 4.0+ (with VRDA support)
+ *
+ * Error Handling:
+ * ---------------
+ * - Graceful fallback to scalar operations for unsupported configurations
+ * - Compile-time detection of AOCL availability
+ * - Runtime size and alignment validation with eigen_assert()
+ *
+ * Developer:
+ * ----------
+ * Name: Sharad Saurabh Bhaskar
+ * Email: shbhaska@amd.com
+ * Organization: Advanced Micro Devices, Inc.
+ */
+
+
+#ifndef EIGEN_ASSIGN_AOCL_H
+#define EIGEN_ASSIGN_AOCL_H
+
+namespace Eigen {
+namespace internal {
+
+// Traits for unary operations.
+template <typename Dst, typename Src> class aocl_assign_traits {
+private:
+  enum {
+    DstHasDirectAccess = !!(Dst::Flags & DirectAccessBit),
+    SrcHasDirectAccess = !!(Src::Flags & DirectAccessBit),
+    StorageOrdersAgree = (int(Dst::IsRowMajor) == int(Src::IsRowMajor)),
+    InnerSize = Dst::IsVectorAtCompileTime   ? int(Dst::SizeAtCompileTime)
+                : (Dst::Flags & RowMajorBit) ? int(Dst::ColsAtCompileTime)
+                                             : int(Dst::RowsAtCompileTime),
+    LargeEnough =
+        (InnerSize == Dynamic) || (InnerSize >= EIGEN_AOCL_VML_THRESHOLD)
+  };
+
+public:
+  enum {
+    EnableAoclVML = DstHasDirectAccess && SrcHasDirectAccess &&
+                    StorageOrdersAgree && LargeEnough,
+    Traversal = LinearTraversal
+  };
+};
+
+// Traits for binary operations (e.g., add, pow).
+template <typename Dst, typename Lhs, typename Rhs>
+class aocl_assign_binary_traits {
+private:
+  enum {
+    DstHasDirectAccess = !!(Dst::Flags & DirectAccessBit),
+    LhsHasDirectAccess = !!(Lhs::Flags & DirectAccessBit),
+    RhsHasDirectAccess = !!(Rhs::Flags & DirectAccessBit),
+    StorageOrdersAgree = (int(Dst::IsRowMajor) == int(Lhs::IsRowMajor)) &&
+                         (int(Dst::IsRowMajor) == int(Rhs::IsRowMajor)),
+    InnerSize = Dst::IsVectorAtCompileTime   ? int(Dst::SizeAtCompileTime)
+                : (Dst::Flags & RowMajorBit) ? int(Dst::ColsAtCompileTime)
+                                             : int(Dst::RowsAtCompileTime),
+    LargeEnough =
+        (InnerSize == Dynamic) || (InnerSize >= EIGEN_AOCL_VML_THRESHOLD)
+  };
+
+public:
+  enum {
+    EnableAoclVML = DstHasDirectAccess && LhsHasDirectAccess &&
+                    RhsHasDirectAccess && StorageOrdersAgree && LargeEnough
+  };
+};
+
+// Unary operation dispatch for float (scalar fallback).
+#define EIGEN_AOCL_VML_UNARY_CALL_FLOAT(EIGENOP)                               \
+  template <typename DstXprType, typename SrcXprNested>                        \
+  struct Assignment<                                                           \
+      DstXprType, CwiseUnaryOp<scalar_##EIGENOP##_op<float>, SrcXprNested>,    \
+      assign_op<float, float>, Dense2Dense,                                    \
+      std::enable_if_t<                                                        \
+          aocl_assign_traits<DstXprType, SrcXprNested>::EnableAoclVML>> {      \
+    typedef CwiseUnaryOp<scalar_##EIGENOP##_op<float>, SrcXprNested>           \
+        SrcXprType;                                                            \
+    static void run(DstXprType &dst, const SrcXprType &src,                    \
+                    const assign_op<float, float> &) {                         \
+      eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());      \
+      Eigen::Index n = dst.size();                                             \
+      if (n <= 0)                                                              \
+        return;                                                                \
+      const float *input =                                                     \
+          reinterpret_cast<const float *>(src.nestedExpression().data());      \
+      float *output = reinterpret_cast<float *>(dst.data());                   \
+      for (Eigen::Index i = 0; i < n; ++i) {                                   \
+        output[i] = std::EIGENOP(input[i]);                                    \
+      }                                                                        \
+    }                                                                          \
+  };
+
+// Unary operation dispatch for double (AOCL vectorized).
+#define EIGEN_AOCL_VML_UNARY_CALL_DOUBLE(EIGENOP, AOCLOP)                      \
+  template <typename DstXprType, typename SrcXprNested>                        \
+  struct Assignment<                                                           \
+      DstXprType, CwiseUnaryOp<scalar_##EIGENOP##_op<double>, SrcXprNested>,   \
+      assign_op<double, double>, Dense2Dense,                                  \
+      std::enable_if_t<                                                        \
+          aocl_assign_traits<DstXprType, SrcXprNested>::EnableAoclVML>> {      \
+    typedef CwiseUnaryOp<scalar_##EIGENOP##_op<double>, SrcXprNested>          \
+        SrcXprType;                                                            \
+    static void run(DstXprType &dst, const SrcXprType &src,                    \
+                    const assign_op<double, double> &) {                       \
+      eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());      \
+      Eigen::Index n = dst.size();                                             \
+      eigen_assert(n <= INT_MAX && "AOCL does not support arrays larger than INT_MAX"); \
+      if (n <= 0)                                                              \
+        return;                                                                \
+      const double *input =                                                    \
+          reinterpret_cast<const double *>(src.nestedExpression().data());     \
+      double *output = reinterpret_cast<double *>(dst.data());                 \
+      int aocl_n = internal::convert_index<int>(n);                            \
+      AOCLOP(aocl_n, const_cast<double *>(input), output);                     \
+    }                                                                          \
+  };
+
+// Instantiate unary calls for float (scalar).
+// EIGEN_AOCL_VML_UNARY_CALL_FLOAT(exp)
+
+// Instantiate unary calls for double (AOCL vectorized).
+EIGEN_AOCL_VML_UNARY_CALL_DOUBLE(exp2, amd_vrda_exp2)
+EIGEN_AOCL_VML_UNARY_CALL_DOUBLE(exp, amd_vrda_exp)
+EIGEN_AOCL_VML_UNARY_CALL_DOUBLE(sin, amd_vrda_sin)
+EIGEN_AOCL_VML_UNARY_CALL_DOUBLE(cos, amd_vrda_cos)
+EIGEN_AOCL_VML_UNARY_CALL_DOUBLE(sqrt, amd_vrda_sqrt)
+EIGEN_AOCL_VML_UNARY_CALL_DOUBLE(cbrt, amd_vrda_cbrt)
+EIGEN_AOCL_VML_UNARY_CALL_DOUBLE(abs, amd_vrda_fabs)
+EIGEN_AOCL_VML_UNARY_CALL_DOUBLE(log, amd_vrda_log)
+EIGEN_AOCL_VML_UNARY_CALL_DOUBLE(log10, amd_vrda_log10)
+EIGEN_AOCL_VML_UNARY_CALL_DOUBLE(log2, amd_vrda_log2)
+
+// Binary operation dispatch for float (scalar fallback).
+#define EIGEN_AOCL_VML_BINARY_CALL_FLOAT(EIGENOP, STDFUNC)                     \
+  template <typename DstXprType, typename LhsXprNested, typename RhsXprNested> \
+  struct Assignment<                                                           \
+      DstXprType,                                                              \
+      CwiseBinaryOp<scalar_##EIGENOP##_op<float, float>, LhsXprNested,         \
+                    RhsXprNested>,                                             \
+      assign_op<float, float>, Dense2Dense,                                    \
+      std::enable_if_t<aocl_assign_binary_traits<                              \
+          DstXprType, LhsXprNested, RhsXprNested>::EnableAoclVML>> {           \
+    typedef CwiseBinaryOp<scalar_##EIGENOP##_op<float, float>, LhsXprNested,   \
+                          RhsXprNested>                                        \
+        SrcXprType;                                                            \
+    static void run(DstXprType &dst, const SrcXprType &src,                    \
+                    const assign_op<float, float> &) {                         \
+      eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());      \
+      Eigen::Index n = dst.size();                                             \
+      if (n <= 0)                                                              \
+        return;                                                                \
+      const float *lhs = reinterpret_cast<const float *>(src.lhs().data());    \
+      const float *rhs = reinterpret_cast<const float *>(src.rhs().data());    \
+      float *output = reinterpret_cast<float *>(dst.data());                   \
+      for (Eigen::Index i = 0; i < n; ++i) {                                   \
+        output[i] = STDFUNC(lhs[i], rhs[i]);                                   \
+      }                                                                        \
+    }                                                                          \
+  };
+
+// Binary operation dispatch for double (AOCL vectorized).
+#define EIGEN_AOCL_VML_BINARY_CALL_DOUBLE(EIGENOP, AOCLOP)                     \
+  template <typename DstXprType, typename LhsXprNested, typename RhsXprNested> \
+  struct Assignment<                                                           \
+      DstXprType,                                                              \
+      CwiseBinaryOp<scalar_##EIGENOP##_op<double, double>, LhsXprNested,       \
+                    RhsXprNested>,                                             \
+      assign_op<double, double>, Dense2Dense,                                  \
+      std::enable_if_t<aocl_assign_binary_traits<                              \
+          DstXprType, LhsXprNested, RhsXprNested>::EnableAoclVML>> {           \
+    typedef CwiseBinaryOp<scalar_##EIGENOP##_op<double, double>, LhsXprNested, \
+                          RhsXprNested>                                        \
+        SrcXprType;                                                            \
+    static void run(DstXprType &dst, const SrcXprType &src,                    \
+                    const assign_op<double, double> &) {                       \
+      eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());      \
+      Eigen::Index n = dst.size();                                             \
+      eigen_assert(n <= INT_MAX && "AOCL does not support arrays larger than INT_MAX"); \
+      if (n <= 0)                                                              \
+        return;                                                                \
+      const double *lhs = reinterpret_cast<const double *>(src.lhs().data());  \
+      const double *rhs = reinterpret_cast<const double *>(src.rhs().data());  \
+      double *output = reinterpret_cast<double *>(dst.data());                 \
+      int aocl_n = internal::convert_index<int>(n);                            \
+      AOCLOP(aocl_n, const_cast<double *>(lhs), const_cast<double *>(rhs), output); \
+    }                                                                          \
+  };
+
+// Instantiate binary calls for float (scalar).
+// EIGEN_AOCL_VML_BINARY_CALL_FLOAT(sum, std::plus<float>)  // Using
+// scalar_sum_op for addition EIGEN_AOCL_VML_BINARY_CALL_FLOAT(pow, std::pow)
+
+// Instantiate binary calls for double (AOCL vectorized).
+EIGEN_AOCL_VML_BINARY_CALL_DOUBLE(sum, amd_vrda_add) // Using scalar_sum_op for addition
+EIGEN_AOCL_VML_BINARY_CALL_DOUBLE(pow, amd_vrda_pow)
+EIGEN_AOCL_VML_BINARY_CALL_DOUBLE(max, amd_vrda_fmax)
+EIGEN_AOCL_VML_BINARY_CALL_DOUBLE(min, amd_vrda_fmin)
+
+} // namespace internal
+} // namespace Eigen
+
+#endif // EIGEN_ASSIGN_AOCL_H

data_acq_and_processing/third_party/eigen/Eigen/src/Core/Assign_MKL.h

@@ -0,0 +1,183 @@
+/*
+ Copyright (c) 2011, Intel Corporation. All rights reserved.
+ Copyright (C) 2015 Gael Guennebaud <gael.guennebaud@inria.fr>
+
+ Redistribution and use in source and binary forms, with or without modification,
+ are permitted provided that the following conditions are met:
+
+ * Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+ * Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+ * Neither the name of Intel Corporation nor the names of its contributors may
+   be used to endorse or promote products derived from this software without
+   specific prior written permission.
+
+ THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
+ ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
+ WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
+ ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
+ (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
+ LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
+ ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
+ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+ ********************************************************************************
+ *   Content : Eigen bindings to Intel(R) MKL
+ *   MKL VML support for coefficient-wise unary Eigen expressions like a=b.sin()
+ ********************************************************************************
+*/
+
+#ifndef EIGEN_ASSIGN_VML_H
+#define EIGEN_ASSIGN_VML_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Dst, typename Src>
+class vml_assign_traits {
+ private:
+  enum {
+    DstHasDirectAccess = Dst::Flags & DirectAccessBit,
+    SrcHasDirectAccess = Src::Flags & DirectAccessBit,
+    StorageOrdersAgree = (int(Dst::IsRowMajor) == int(Src::IsRowMajor)),
+    InnerSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::SizeAtCompileTime)
+                : int(Dst::Flags) & RowMajorBit ? int(Dst::ColsAtCompileTime)
+                                                : int(Dst::RowsAtCompileTime),
+    InnerMaxSize = int(Dst::IsVectorAtCompileTime) ? int(Dst::MaxSizeAtCompileTime)
+                   : int(Dst::Flags) & RowMajorBit ? int(Dst::MaxColsAtCompileTime)
+                                                   : int(Dst::MaxRowsAtCompileTime),
+    MaxSizeAtCompileTime = Dst::SizeAtCompileTime,
+
+    MightEnableVml = bool(StorageOrdersAgree) && bool(DstHasDirectAccess) && bool(SrcHasDirectAccess) &&
+                     Src::InnerStrideAtCompileTime == 1 && Dst::InnerStrideAtCompileTime == 1,
+    MightLinearize = bool(MightEnableVml) && (int(Dst::Flags) & int(Src::Flags) & LinearAccessBit),
+    VmlSize = bool(MightLinearize) ? MaxSizeAtCompileTime : InnerMaxSize,
+    LargeEnough = (VmlSize == Dynamic) || VmlSize >= EIGEN_MKL_VML_THRESHOLD
+  };
+
+ public:
+  enum { EnableVml = MightEnableVml && LargeEnough, Traversal = MightLinearize ? LinearTraversal : DefaultTraversal };
+};
+
+#define EIGEN_PP_EXPAND(ARG) ARG
+#if !defined(EIGEN_FAST_MATH) || (EIGEN_FAST_MATH != 1)
+#define EIGEN_VMLMODE_EXPAND_xLA , VML_HA
+#else
+#define EIGEN_VMLMODE_EXPAND_xLA , VML_LA
+#endif
+
+#define EIGEN_VMLMODE_EXPAND_x_
+
+#define EIGEN_VMLMODE_PREFIX_xLA vm
+#define EIGEN_VMLMODE_PREFIX_x_ v
+#define EIGEN_VMLMODE_PREFIX(VMLMODE) EIGEN_CAT(EIGEN_VMLMODE_PREFIX_x, VMLMODE)
+
+#define EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE, VMLMODE)                      \
+  template <typename DstXprType, typename SrcXprNested>                                                    \
+  struct Assignment<DstXprType, CwiseUnaryOp<scalar_##EIGENOP##_op<EIGENTYPE>, SrcXprNested>,              \
+                    assign_op<EIGENTYPE, EIGENTYPE>, Dense2Dense,                                          \
+                    std::enable_if_t<vml_assign_traits<DstXprType, SrcXprNested>::EnableVml>> {            \
+    typedef CwiseUnaryOp<scalar_##EIGENOP##_op<EIGENTYPE>, SrcXprNested> SrcXprType;                       \
+    static void run(DstXprType &dst, const SrcXprType &src, const assign_op<EIGENTYPE, EIGENTYPE> &func) { \
+      resize_if_allowed(dst, src, func);                                                                   \
+      eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());                                  \
+      if (vml_assign_traits<DstXprType, SrcXprNested>::Traversal == (int)LinearTraversal) {                \
+        VMLOP(dst.size(), (const VMLTYPE *)src.nestedExpression().data(),                                  \
+              (VMLTYPE *)dst.data() EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_x##VMLMODE));                     \
+      } else {                                                                                             \
+        const Index outerSize = dst.outerSize();                                                           \
+        for (Index outer = 0; outer < outerSize; ++outer) {                                                \
+          const EIGENTYPE *src_ptr = src.IsRowMajor ? &(src.nestedExpression().coeffRef(outer, 0))         \
+                                                    : &(src.nestedExpression().coeffRef(0, outer));        \
+          EIGENTYPE *dst_ptr = dst.IsRowMajor ? &(dst.coeffRef(outer, 0)) : &(dst.coeffRef(0, outer));     \
+          VMLOP(dst.innerSize(), (const VMLTYPE *)src_ptr,                                                 \
+                (VMLTYPE *)dst_ptr EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_x##VMLMODE));                      \
+        }                                                                                                  \
+      }                                                                                                    \
+    }                                                                                                      \
+  };
+
+#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(EIGENOP, VMLOP, VMLMODE)                                                \
+  EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE), s##VMLOP), float, float, VMLMODE) \
+  EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE), d##VMLOP), double, double, VMLMODE)
+
+#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS_CPLX(EIGENOP, VMLOP, VMLMODE)                                   \
+  EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE), c##VMLOP), scomplex, \
+                                   MKL_Complex8, VMLMODE)                                                 \
+  EIGEN_MKL_VML_DECLARE_UNARY_CALL(EIGENOP, EIGEN_CAT(EIGEN_VMLMODE_PREFIX(VMLMODE), z##VMLOP), dcomplex, \
+                                   MKL_Complex16, VMLMODE)
+
+#define EIGEN_MKL_VML_DECLARE_UNARY_CALLS(EIGENOP, VMLOP, VMLMODE) \
+  EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(EIGENOP, VMLOP, VMLMODE)  \
+  EIGEN_MKL_VML_DECLARE_UNARY_CALLS_CPLX(EIGENOP, VMLOP, VMLMODE)
+
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(sin, Sin, LA)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(asin, Asin, LA)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(sinh, Sinh, LA)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(cos, Cos, LA)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(acos, Acos, LA)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(cosh, Cosh, LA)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(tan, Tan, LA)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(atan, Atan, LA)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(tanh, Tanh, LA)
+// EIGEN_MKL_VML_DECLARE_UNARY_CALLS(abs,   Abs,    _)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(exp, Exp, LA)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(log, Ln, LA)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(log10, Log10, LA)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS(sqrt, Sqrt, _)
+
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(square, Sqr, _)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS_CPLX(arg, Arg, _)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(round, Round, _)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(floor, Floor, _)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(ceil, Ceil, _)
+EIGEN_MKL_VML_DECLARE_UNARY_CALLS_REAL(cbrt, Cbrt, _)
+
+#define EIGEN_MKL_VML_DECLARE_POW_CALL(EIGENOP, VMLOP, EIGENTYPE, VMLTYPE, VMLMODE)                        \
+  template <typename DstXprType, typename SrcXprNested, typename Plain>                                    \
+  struct Assignment<DstXprType,                                                                            \
+                    CwiseBinaryOp<scalar_##EIGENOP##_op<EIGENTYPE, EIGENTYPE>, SrcXprNested,               \
+                                  const CwiseNullaryOp<internal::scalar_constant_op<EIGENTYPE>, Plain>>,   \
+                    assign_op<EIGENTYPE, EIGENTYPE>, Dense2Dense,                                          \
+                    std::enable_if_t<vml_assign_traits<DstXprType, SrcXprNested>::EnableVml>> {            \
+    typedef CwiseBinaryOp<scalar_##EIGENOP##_op<EIGENTYPE, EIGENTYPE>, SrcXprNested,                       \
+                          const CwiseNullaryOp<internal::scalar_constant_op<EIGENTYPE>, Plain>>            \
+        SrcXprType;                                                                                        \
+    static void run(DstXprType &dst, const SrcXprType &src, const assign_op<EIGENTYPE, EIGENTYPE> &func) { \
+      resize_if_allowed(dst, src, func);                                                                   \
+      eigen_assert(dst.rows() == src.rows() && dst.cols() == src.cols());                                  \
+      VMLTYPE exponent = reinterpret_cast<const VMLTYPE &>(src.rhs().functor().m_other);                   \
+      if (vml_assign_traits<DstXprType, SrcXprNested>::Traversal == LinearTraversal) {                     \
+        VMLOP(dst.size(), (const VMLTYPE *)src.lhs().data(), exponent,                                     \
+              (VMLTYPE *)dst.data() EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_x##VMLMODE));                     \
+      } else {                                                                                             \
+        const Index outerSize = dst.outerSize();                                                           \
+        for (Index outer = 0; outer < outerSize; ++outer) {                                                \
+          const EIGENTYPE *src_ptr =                                                                       \
+              src.IsRowMajor ? &(src.lhs().coeffRef(outer, 0)) : &(src.lhs().coeffRef(0, outer));          \
+          EIGENTYPE *dst_ptr = dst.IsRowMajor ? &(dst.coeffRef(outer, 0)) : &(dst.coeffRef(0, outer));     \
+          VMLOP(dst.innerSize(), (const VMLTYPE *)src_ptr, exponent,                                       \
+                (VMLTYPE *)dst_ptr EIGEN_PP_EXPAND(EIGEN_VMLMODE_EXPAND_x##VMLMODE));                      \
+        }                                                                                                  \
+      }                                                                                                    \
+    }                                                                                                      \
+  };
+
+EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmsPowx, float, float, LA)
+EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmdPowx, double, double, LA)
+EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmcPowx, scomplex, MKL_Complex8, LA)
+EIGEN_MKL_VML_DECLARE_POW_CALL(pow, vmzPowx, dcomplex, MKL_Complex16, LA)
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_ASSIGN_VML_H

data_acq_and_processing/third_party/eigen/Eigen/src/Core/BandMatrix.h

@@ -0,0 +1,338 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2009 Gael Guennebaud <gael.guennebaud@inria.fr>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BANDMATRIX_H
+#define EIGEN_BANDMATRIX_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+
+template <typename Derived>
+class BandMatrixBase : public EigenBase<Derived> {
+ public:
+  enum {
+    Flags = internal::traits<Derived>::Flags,
+    CoeffReadCost = internal::traits<Derived>::CoeffReadCost,
+    RowsAtCompileTime = internal::traits<Derived>::RowsAtCompileTime,
+    ColsAtCompileTime = internal::traits<Derived>::ColsAtCompileTime,
+    MaxRowsAtCompileTime = internal::traits<Derived>::MaxRowsAtCompileTime,
+    MaxColsAtCompileTime = internal::traits<Derived>::MaxColsAtCompileTime,
+    Supers = internal::traits<Derived>::Supers,
+    Subs = internal::traits<Derived>::Subs,
+    Options = internal::traits<Derived>::Options
+  };
+  typedef typename internal::traits<Derived>::Scalar Scalar;
+  typedef Matrix<Scalar, RowsAtCompileTime, ColsAtCompileTime> DenseMatrixType;
+  typedef typename DenseMatrixType::StorageIndex StorageIndex;
+  typedef typename internal::traits<Derived>::CoefficientsType CoefficientsType;
+  typedef EigenBase<Derived> Base;
+
+ protected:
+  enum {
+    DataRowsAtCompileTime = ((Supers != Dynamic) && (Subs != Dynamic)) ? 1 + Supers + Subs : Dynamic,
+    SizeAtCompileTime = min_size_prefer_dynamic(RowsAtCompileTime, ColsAtCompileTime)
+  };
+
+ public:
+  using Base::cols;
+  using Base::derived;
+  using Base::rows;
+
+  /** \returns the number of super diagonals */
+  inline Index supers() const { return derived().supers(); }
+
+  /** \returns the number of sub diagonals */
+  inline Index subs() const { return derived().subs(); }
+
+  /** \returns an expression of the underlying coefficient matrix */
+  inline const CoefficientsType& coeffs() const { return derived().coeffs(); }
+
+  /** \returns an expression of the underlying coefficient matrix */
+  inline CoefficientsType& coeffs() { return derived().coeffs(); }
+
+  /** \returns a vector expression of the \a i -th column,
+   * only the meaningful part is returned.
+   * \warning the internal storage must be column major. */
+  inline Block<CoefficientsType, Dynamic, 1> col(Index i) {
+    EIGEN_STATIC_ASSERT((int(Options) & int(RowMajor)) == 0, THIS_METHOD_IS_ONLY_FOR_COLUMN_MAJOR_MATRICES);
+    Index start = 0;
+    Index len = coeffs().rows();
+    if (i <= supers()) {
+      start = supers() - i;
+      len = (std::min)(rows(), std::max<Index>(0, coeffs().rows() - (supers() - i)));
+    } else if (i >= rows() - subs())
+      len = std::max<Index>(0, coeffs().rows() - (i + 1 - rows() + subs()));
+    return Block<CoefficientsType, Dynamic, 1>(coeffs(), start, i, len, 1);
+  }
+
+  /** \returns a vector expression of the main diagonal */
+  inline Block<CoefficientsType, 1, SizeAtCompileTime> diagonal() {
+    return Block<CoefficientsType, 1, SizeAtCompileTime>(coeffs(), supers(), 0, 1, (std::min)(rows(), cols()));
+  }
+
+  /** \returns a vector expression of the main diagonal (const version) */
+  inline const Block<const CoefficientsType, 1, SizeAtCompileTime> diagonal() const {
+    return Block<const CoefficientsType, 1, SizeAtCompileTime>(coeffs(), supers(), 0, 1, (std::min)(rows(), cols()));
+  }
+
+  template <int Index>
+  struct DiagonalIntReturnType {
+    enum {
+      ReturnOpposite =
+          (int(Options) & int(SelfAdjoint)) && (((Index) > 0 && Supers == 0) || ((Index) < 0 && Subs == 0)),
+      Conjugate = ReturnOpposite && NumTraits<Scalar>::IsComplex,
+      ActualIndex = ReturnOpposite ? -Index : Index,
+      DiagonalSize =
+          (RowsAtCompileTime == Dynamic || ColsAtCompileTime == Dynamic)
+              ? Dynamic
+              : (ActualIndex < 0 ? min_size_prefer_dynamic(ColsAtCompileTime, RowsAtCompileTime + ActualIndex)
+                                 : min_size_prefer_dynamic(RowsAtCompileTime, ColsAtCompileTime - ActualIndex))
+    };
+    typedef Block<CoefficientsType, 1, DiagonalSize> BuildType;
+    typedef std::conditional_t<Conjugate, CwiseUnaryOp<internal::scalar_conjugate_op<Scalar>, BuildType>, BuildType>
+        Type;
+  };
+
+  /** \returns a vector expression of the \a N -th sub or super diagonal */
+  template <int N>
+  inline typename DiagonalIntReturnType<N>::Type diagonal() {
+    return typename DiagonalIntReturnType<N>::BuildType(coeffs(), supers() - N, (std::max)(0, N), 1, diagonalLength(N));
+  }
+
+  /** \returns a vector expression of the \a N -th sub or super diagonal */
+  template <int N>
+  inline const typename DiagonalIntReturnType<N>::Type diagonal() const {
+    return typename DiagonalIntReturnType<N>::BuildType(coeffs(), supers() - N, (std::max)(0, N), 1, diagonalLength(N));
+  }
+
+  /** \returns a vector expression of the \a i -th sub or super diagonal */
+  inline Block<CoefficientsType, 1, Dynamic> diagonal(Index i) {
+    eigen_assert((i < 0 && -i <= subs()) || (i >= 0 && i <= supers()));
+    return Block<CoefficientsType, 1, Dynamic>(coeffs(), supers() - i, std::max<Index>(0, i), 1, diagonalLength(i));
+  }
+
+  /** \returns a vector expression of the \a i -th sub or super diagonal */
+  inline const Block<const CoefficientsType, 1, Dynamic> diagonal(Index i) const {
+    eigen_assert((i < 0 && -i <= subs()) || (i >= 0 && i <= supers()));
+    return Block<const CoefficientsType, 1, Dynamic>(coeffs(), supers() - i, std::max<Index>(0, i), 1,
+                                                     diagonalLength(i));
+  }
+
+  template <typename Dest>
+  inline void evalTo(Dest& dst) const {
+    dst.resize(rows(), cols());
+    dst.setZero();
+    dst.diagonal() = diagonal();
+    for (Index i = 1; i <= supers(); ++i) dst.diagonal(i) = diagonal(i);
+    for (Index i = 1; i <= subs(); ++i) dst.diagonal(-i) = diagonal(-i);
+  }
+
+  DenseMatrixType toDenseMatrix() const {
+    DenseMatrixType res(rows(), cols());
+    evalTo(res);
+    return res;
+  }
+
+ protected:
+  inline Index diagonalLength(Index i) const {
+    return i < 0 ? (std::min)(cols(), rows() + i) : (std::min)(rows(), cols() - i);
+  }
+};
+
+/**
+ * \class BandMatrix
+ * \ingroup Core_Module
+ *
+ * \brief Represents a rectangular matrix with a banded storage
+ *
+ * \tparam Scalar_ Numeric type, i.e. float, double, int
+ * \tparam Rows_ Number of rows, or \b Dynamic
+ * \tparam Cols_ Number of columns, or \b Dynamic
+ * \tparam Supers_ Number of super diagonal
+ * \tparam Subs_ Number of sub diagonal
+ * \tparam Options_ A combination of either \b #RowMajor or \b #ColMajor, and of \b #SelfAdjoint
+ *                  The former controls \ref TopicStorageOrders "storage order", and defaults to
+ *                  column-major. The latter controls whether the matrix represents a selfadjoint
+ *                  matrix in which case either Supers of Subs have to be null.
+ *
+ * \sa class TridiagonalMatrix
+ */
+
+template <typename Scalar_, int Rows_, int Cols_, int Supers_, int Subs_, int Options_>
+struct traits<BandMatrix<Scalar_, Rows_, Cols_, Supers_, Subs_, Options_> > {
+  typedef Scalar_ Scalar;
+  typedef Dense StorageKind;
+  typedef Eigen::Index StorageIndex;
+  enum {
+    CoeffReadCost = NumTraits<Scalar>::ReadCost,
+    RowsAtCompileTime = Rows_,
+    ColsAtCompileTime = Cols_,
+    MaxRowsAtCompileTime = Rows_,
+    MaxColsAtCompileTime = Cols_,
+    Flags = LvalueBit,
+    Supers = Supers_,
+    Subs = Subs_,
+    Options = Options_,
+    DataRowsAtCompileTime = ((Supers != Dynamic) && (Subs != Dynamic)) ? 1 + Supers + Subs : Dynamic
+  };
+  typedef Matrix<Scalar, DataRowsAtCompileTime, ColsAtCompileTime, int(Options) & int(RowMajor) ? RowMajor : ColMajor>
+      CoefficientsType;
+};
+
+template <typename Scalar_, int Rows, int Cols, int Supers, int Subs, int Options>
+class BandMatrix : public BandMatrixBase<BandMatrix<Scalar_, Rows, Cols, Supers, Subs, Options> > {
+ public:
+  typedef typename internal::traits<BandMatrix>::Scalar Scalar;
+  typedef typename internal::traits<BandMatrix>::StorageIndex StorageIndex;
+  typedef typename internal::traits<BandMatrix>::CoefficientsType CoefficientsType;
+
+  explicit inline BandMatrix(Index rows = Rows, Index cols = Cols, Index supers = Supers, Index subs = Subs)
+      : m_coeffs(1 + supers + subs, cols), m_rows(rows), m_supers(supers), m_subs(subs) {}
+
+  /** \returns the number of columns */
+  constexpr Index rows() const { return m_rows.value(); }
+
+  /** \returns the number of rows */
+  constexpr Index cols() const { return m_coeffs.cols(); }
+
+  /** \returns the number of super diagonals */
+  constexpr Index supers() const { return m_supers.value(); }
+
+  /** \returns the number of sub diagonals */
+  constexpr Index subs() const { return m_subs.value(); }
+
+  inline const CoefficientsType& coeffs() const { return m_coeffs; }
+  inline CoefficientsType& coeffs() { return m_coeffs; }
+
+ protected:
+  CoefficientsType m_coeffs;
+  internal::variable_if_dynamic<Index, Rows> m_rows;
+  internal::variable_if_dynamic<Index, Supers> m_supers;
+  internal::variable_if_dynamic<Index, Subs> m_subs;
+};
+
+template <typename CoefficientsType_, int Rows_, int Cols_, int Supers_, int Subs_, int Options_>
+class BandMatrixWrapper;
+
+template <typename CoefficientsType_, int Rows_, int Cols_, int Supers_, int Subs_, int Options_>
+struct traits<BandMatrixWrapper<CoefficientsType_, Rows_, Cols_, Supers_, Subs_, Options_> > {
+  typedef typename CoefficientsType_::Scalar Scalar;
+  typedef typename CoefficientsType_::StorageKind StorageKind;
+  typedef typename CoefficientsType_::StorageIndex StorageIndex;
+  enum {
+    CoeffReadCost = internal::traits<CoefficientsType_>::CoeffReadCost,
+    RowsAtCompileTime = Rows_,
+    ColsAtCompileTime = Cols_,
+    MaxRowsAtCompileTime = Rows_,
+    MaxColsAtCompileTime = Cols_,
+    Flags = LvalueBit,
+    Supers = Supers_,
+    Subs = Subs_,
+    Options = Options_,
+    DataRowsAtCompileTime = ((Supers != Dynamic) && (Subs != Dynamic)) ? 1 + Supers + Subs : Dynamic
+  };
+  typedef CoefficientsType_ CoefficientsType;
+};
+
+template <typename CoefficientsType_, int Rows_, int Cols_, int Supers_, int Subs_, int Options_>
+class BandMatrixWrapper
+    : public BandMatrixBase<BandMatrixWrapper<CoefficientsType_, Rows_, Cols_, Supers_, Subs_, Options_> > {
+ public:
+  typedef typename internal::traits<BandMatrixWrapper>::Scalar Scalar;
+  typedef typename internal::traits<BandMatrixWrapper>::CoefficientsType CoefficientsType;
+  typedef typename internal::traits<BandMatrixWrapper>::StorageIndex StorageIndex;
+
+  explicit inline BandMatrixWrapper(const CoefficientsType& coeffs, Index rows = Rows_, Index cols = Cols_,
+                                    Index supers = Supers_, Index subs = Subs_)
+      : m_coeffs(coeffs), m_rows(rows), m_supers(supers), m_subs(subs) {
+    EIGEN_UNUSED_VARIABLE(cols);
+    // eigen_assert(coeffs.cols()==cols() && (supers()+subs()+1)==coeffs.rows());
+  }
+
+  /** \returns the number of columns */
+  constexpr Index rows() const { return m_rows.value(); }
+
+  /** \returns the number of rows */
+  constexpr Index cols() const { return m_coeffs.cols(); }
+
+  /** \returns the number of super diagonals */
+  constexpr Index supers() const { return m_supers.value(); }
+
+  /** \returns the number of sub diagonals */
+  constexpr Index subs() const { return m_subs.value(); }
+
+  inline const CoefficientsType& coeffs() const { return m_coeffs; }
+
+ protected:
+  const CoefficientsType& m_coeffs;
+  internal::variable_if_dynamic<Index, Rows_> m_rows;
+  internal::variable_if_dynamic<Index, Supers_> m_supers;
+  internal::variable_if_dynamic<Index, Subs_> m_subs;
+};
+
+/**
+ * \class TridiagonalMatrix
+ * \ingroup Core_Module
+ *
+ * \brief Represents a tridiagonal matrix with a compact banded storage
+ *
+ * \tparam Scalar Numeric type, i.e. float, double, int
+ * \tparam Size Number of rows and cols, or \b Dynamic
+ * \tparam Options Can be 0 or \b SelfAdjoint
+ *
+ * \sa class BandMatrix
+ */
+template <typename Scalar, int Size, int Options>
+class TridiagonalMatrix : public BandMatrix<Scalar, Size, Size, Options & SelfAdjoint ? 0 : 1, 1, Options | RowMajor> {
+  typedef BandMatrix<Scalar, Size, Size, Options & SelfAdjoint ? 0 : 1, 1, Options | RowMajor> Base;
+  typedef typename Base::StorageIndex StorageIndex;
+
+ public:
+  explicit TridiagonalMatrix(Index size = Size) : Base(size, size, Options & SelfAdjoint ? 0 : 1, 1) {}
+
+  inline typename Base::template DiagonalIntReturnType<1>::Type super() { return Base::template diagonal<1>(); }
+  inline const typename Base::template DiagonalIntReturnType<1>::Type super() const {
+    return Base::template diagonal<1>();
+  }
+  inline typename Base::template DiagonalIntReturnType<-1>::Type sub() { return Base::template diagonal<-1>(); }
+  inline const typename Base::template DiagonalIntReturnType<-1>::Type sub() const {
+    return Base::template diagonal<-1>();
+  }
+
+ protected:
+};
+
+struct BandShape {};
+
+template <typename Scalar_, int Rows_, int Cols_, int Supers_, int Subs_, int Options_>
+struct evaluator_traits<BandMatrix<Scalar_, Rows_, Cols_, Supers_, Subs_, Options_> >
+    : public evaluator_traits_base<BandMatrix<Scalar_, Rows_, Cols_, Supers_, Subs_, Options_> > {
+  typedef BandShape Shape;
+};
+
+template <typename CoefficientsType_, int Rows_, int Cols_, int Supers_, int Subs_, int Options_>
+struct evaluator_traits<BandMatrixWrapper<CoefficientsType_, Rows_, Cols_, Supers_, Subs_, Options_> >
+    : public evaluator_traits_base<BandMatrixWrapper<CoefficientsType_, Rows_, Cols_, Supers_, Subs_, Options_> > {
+  typedef BandShape Shape;
+};
+
+template <>
+struct AssignmentKind<DenseShape, BandShape> {
+  typedef EigenBase2EigenBase Kind;
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_BANDMATRIX_H

data_acq_and_processing/third_party/eigen/Eigen/src/Core/Block.h

@@ -0,0 +1,427 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2010 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_BLOCK_H
+#define EIGEN_BLOCK_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+namespace internal {
+template <typename XprType_, int BlockRows, int BlockCols, bool InnerPanel_>
+struct traits<Block<XprType_, BlockRows, BlockCols, InnerPanel_>> : traits<XprType_> {
+  typedef typename traits<XprType_>::Scalar Scalar;
+  typedef typename traits<XprType_>::StorageKind StorageKind;
+  typedef typename traits<XprType_>::XprKind XprKind;
+  typedef typename ref_selector<XprType_>::type XprTypeNested;
+  typedef std::remove_reference_t<XprTypeNested> XprTypeNested_;
+  enum {
+    MatrixRows = traits<XprType_>::RowsAtCompileTime,
+    MatrixCols = traits<XprType_>::ColsAtCompileTime,
+    RowsAtCompileTime = MatrixRows == 0 ? 0 : BlockRows,
+    ColsAtCompileTime = MatrixCols == 0 ? 0 : BlockCols,
+    MaxRowsAtCompileTime = BlockRows == 0                 ? 0
+                           : RowsAtCompileTime != Dynamic ? int(RowsAtCompileTime)
+                                                          : int(traits<XprType_>::MaxRowsAtCompileTime),
+    MaxColsAtCompileTime = BlockCols == 0                 ? 0
+                           : ColsAtCompileTime != Dynamic ? int(ColsAtCompileTime)
+                                                          : int(traits<XprType_>::MaxColsAtCompileTime),
+
+    XprTypeIsRowMajor = (int(traits<XprType_>::Flags) & RowMajorBit) != 0,
+    IsRowMajor = (MaxRowsAtCompileTime == 1 && MaxColsAtCompileTime != 1)   ? 1
+                 : (MaxColsAtCompileTime == 1 && MaxRowsAtCompileTime != 1) ? 0
+                                                                            : XprTypeIsRowMajor,
+    HasSameStorageOrderAsXprType = (IsRowMajor == XprTypeIsRowMajor),
+    InnerSize = IsRowMajor ? int(ColsAtCompileTime) : int(RowsAtCompileTime),
+    InnerStrideAtCompileTime = HasSameStorageOrderAsXprType ? int(inner_stride_at_compile_time<XprType_>::ret)
+                                                            : int(outer_stride_at_compile_time<XprType_>::ret),
+    OuterStrideAtCompileTime = HasSameStorageOrderAsXprType ? int(outer_stride_at_compile_time<XprType_>::ret)
+                                                            : int(inner_stride_at_compile_time<XprType_>::ret),
+
+    // FIXME, this traits is rather specialized for dense object and it needs to be cleaned further
+    FlagsLvalueBit = is_lvalue<XprType_>::value ? LvalueBit : 0,
+    FlagsRowMajorBit = IsRowMajor ? RowMajorBit : 0,
+    Flags = (traits<XprType_>::Flags & (DirectAccessBit | (InnerPanel_ ? CompressedAccessBit : 0))) | FlagsLvalueBit |
+            FlagsRowMajorBit,
+    // FIXME DirectAccessBit should not be handled by expressions
+    //
+    // Alignment is needed by MapBase's assertions
+    // We can sefely set it to false here. Internal alignment errors will be detected by an eigen_internal_assert in the
+    // respective evaluator
+    Alignment = 0,
+    InnerPanel = InnerPanel_ ? 1 : 0
+  };
+};
+
+template <typename XprType, int BlockRows = Dynamic, int BlockCols = Dynamic, bool InnerPanel = false,
+          bool HasDirectAccess = internal::has_direct_access<XprType>::ret>
+class BlockImpl_dense;
+
+}  // end namespace internal
+
+template <typename XprType, int BlockRows, int BlockCols, bool InnerPanel, typename StorageKind>
+class BlockImpl;
+
+/** \class Block
+ * \ingroup Core_Module
+ *
+ * \brief Expression of a fixed-size or dynamic-size block
+ *
+ * \tparam XprType the type of the expression in which we are taking a block
+ * \tparam BlockRows the number of rows of the block we are taking at compile time (optional)
+ * \tparam BlockCols the number of columns of the block we are taking at compile time (optional)
+ * \tparam InnerPanel is true, if the block maps to a set of rows of a row major matrix or
+ *         to set of columns of a column major matrix (optional). The parameter allows to determine
+ *         at compile time whether aligned access is possible on the block expression.
+ *
+ * This class represents an expression of either a fixed-size or dynamic-size block. It is the return
+ * type of DenseBase::block(Index,Index,Index,Index) and DenseBase::block<int,int>(Index,Index) and
+ * most of the time this is the only way it is used.
+ *
+ * However, if you want to directly manipulate block expressions,
+ * for instance if you want to write a function returning such an expression, you
+ * will need to use this class.
+ *
+ * Here is an example illustrating the dynamic case:
+ * \include class_Block.cpp
+ * Output: \verbinclude class_Block.out
+ *
+ * \note Even though this expression has dynamic size, in the case where \a XprType
+ * has fixed size, this expression inherits a fixed maximal size which means that evaluating
+ * it does not cause a dynamic memory allocation.
+ *
+ * Here is an example illustrating the fixed-size case:
+ * \include class_FixedBlock.cpp
+ * Output: \verbinclude class_FixedBlock.out
+ *
+ * \sa DenseBase::block(Index,Index,Index,Index), DenseBase::block(Index,Index), class VectorBlock
+ */
+template <typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
+class Block
+    : public BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, typename internal::traits<XprType>::StorageKind> {
+  typedef BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, typename internal::traits<XprType>::StorageKind> Impl;
+  using BlockHelper = internal::block_xpr_helper<Block>;
+
+ public:
+  // typedef typename Impl::Base Base;
+  typedef Impl Base;
+  EIGEN_GENERIC_PUBLIC_INTERFACE(Block)
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(Block)
+
+  typedef internal::remove_all_t<XprType> NestedExpression;
+
+  /** Column or Row constructor
+   */
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Block(XprType& xpr, Index i) : Impl(xpr, i) {
+    eigen_assert((i >= 0) && (((BlockRows == 1) && (BlockCols == XprType::ColsAtCompileTime) && i < xpr.rows()) ||
+                              ((BlockRows == XprType::RowsAtCompileTime) && (BlockCols == 1) && i < xpr.cols())));
+  }
+
+  /** Fixed-size constructor
+   */
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Block(XprType& xpr, Index startRow, Index startCol)
+      : Impl(xpr, startRow, startCol) {
+    EIGEN_STATIC_ASSERT(RowsAtCompileTime != Dynamic && ColsAtCompileTime != Dynamic,
+                        THIS_METHOD_IS_ONLY_FOR_FIXED_SIZE)
+    eigen_assert(startRow >= 0 && BlockRows >= 0 && startRow + BlockRows <= xpr.rows() && startCol >= 0 &&
+                 BlockCols >= 0 && startCol + BlockCols <= xpr.cols());
+  }
+
+  /** Dynamic-size constructor
+   */
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE Block(XprType& xpr, Index startRow, Index startCol, Index blockRows,
+                                                        Index blockCols)
+      : Impl(xpr, startRow, startCol, blockRows, blockCols) {
+    eigen_assert((RowsAtCompileTime == Dynamic || RowsAtCompileTime == blockRows) &&
+                 (ColsAtCompileTime == Dynamic || ColsAtCompileTime == blockCols));
+    eigen_assert(startRow >= 0 && blockRows >= 0 && startRow <= xpr.rows() - blockRows && startCol >= 0 &&
+                 blockCols >= 0 && startCol <= xpr.cols() - blockCols);
+  }
+
+  // convert nested blocks (e.g. Block<Block<MatrixType>>) to a simple block expression (Block<MatrixType>)
+
+  using ConstUnwindReturnType = Block<const typename BlockHelper::BaseType, BlockRows, BlockCols, InnerPanel>;
+  using UnwindReturnType = Block<typename BlockHelper::BaseType, BlockRows, BlockCols, InnerPanel>;
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE ConstUnwindReturnType unwind() const {
+    return ConstUnwindReturnType(BlockHelper::base(*this), BlockHelper::row(*this, 0), BlockHelper::col(*this, 0),
+                                 this->rows(), this->cols());
+  }
+
+  template <typename T = Block, typename EnableIf = std::enable_if_t<!std::is_const<T>::value>>
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE UnwindReturnType unwind() {
+    return UnwindReturnType(BlockHelper::base(*this), BlockHelper::row(*this, 0), BlockHelper::col(*this, 0),
+                            this->rows(), this->cols());
+  }
+};
+
+// The generic default implementation for dense block simply forward to the internal::BlockImpl_dense
+// that must be specialized for direct and non-direct access...
+template <typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
+class BlockImpl<XprType, BlockRows, BlockCols, InnerPanel, Dense>
+    : public internal::BlockImpl_dense<XprType, BlockRows, BlockCols, InnerPanel> {
+  typedef internal::BlockImpl_dense<XprType, BlockRows, BlockCols, InnerPanel> Impl;
+  typedef typename XprType::StorageIndex StorageIndex;
+
+ public:
+  typedef Impl Base;
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl)
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE BlockImpl(XprType& xpr, Index i) : Impl(xpr, i) {}
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE BlockImpl(XprType& xpr, Index startRow, Index startCol)
+      : Impl(xpr, startRow, startCol) {}
+  EIGEN_DEVICE_FUNC constexpr EIGEN_STRONG_INLINE BlockImpl(XprType& xpr, Index startRow, Index startCol,
+                                                            Index blockRows, Index blockCols)
+      : Impl(xpr, startRow, startCol, blockRows, blockCols) {}
+};
+
+namespace internal {
+
+/** \internal Internal implementation of dense Blocks in the general case. */
+template <typename XprType, int BlockRows, int BlockCols, bool InnerPanel, bool HasDirectAccess>
+class BlockImpl_dense : public internal::dense_xpr_base<Block<XprType, BlockRows, BlockCols, InnerPanel>>::type {
+  typedef Block<XprType, BlockRows, BlockCols, InnerPanel> BlockType;
+  typedef typename internal::ref_selector<XprType>::non_const_type XprTypeNested;
+
+ public:
+  typedef typename internal::dense_xpr_base<BlockType>::type Base;
+  EIGEN_DENSE_PUBLIC_INTERFACE(BlockType)
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl_dense)
+
+  /** Column or Row constructor
+   */
+  EIGEN_DEVICE_FUNC constexpr BlockImpl_dense(XprType& xpr, Index i)
+      : m_xpr(xpr),
+        // It is a row if and only if BlockRows==1 and BlockCols==XprType::ColsAtCompileTime,
+        // and it is a column if and only if BlockRows==XprType::RowsAtCompileTime and BlockCols==1,
+        // all other cases are invalid.
+        // The case a 1x1 matrix seems ambiguous, but the result is the same anyway.
+        m_startRow((BlockRows == 1) && (BlockCols == XprType::ColsAtCompileTime) ? i : 0),
+        m_startCol((BlockRows == XprType::RowsAtCompileTime) && (BlockCols == 1) ? i : 0),
+        m_blockRows(BlockRows == 1 ? 1 : xpr.rows()),
+        m_blockCols(BlockCols == 1 ? 1 : xpr.cols()) {}
+
+  /** Fixed-size constructor
+   */
+  EIGEN_DEVICE_FUNC constexpr BlockImpl_dense(XprType& xpr, Index startRow, Index startCol)
+      : m_xpr(xpr), m_startRow(startRow), m_startCol(startCol), m_blockRows(BlockRows), m_blockCols(BlockCols) {}
+
+  /** Dynamic-size constructor
+   */
+  EIGEN_DEVICE_FUNC constexpr BlockImpl_dense(XprType& xpr, Index startRow, Index startCol, Index blockRows,
+                                              Index blockCols)
+      : m_xpr(xpr), m_startRow(startRow), m_startCol(startCol), m_blockRows(blockRows), m_blockCols(blockCols) {}
+
+  EIGEN_DEVICE_FUNC constexpr Index rows() const { return m_blockRows.value(); }
+  EIGEN_DEVICE_FUNC constexpr Index cols() const { return m_blockCols.value(); }
+
+  EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index rowId, Index colId) {
+    EIGEN_STATIC_ASSERT_LVALUE(XprType)
+    return m_xpr.coeffRef(rowId + m_startRow.value(), colId + m_startCol.value());
+  }
+
+  EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index rowId, Index colId) const {
+    return m_xpr.derived().coeffRef(rowId + m_startRow.value(), colId + m_startCol.value());
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const CoeffReturnType coeff(Index rowId, Index colId) const {
+    return m_xpr.coeff(rowId + m_startRow.value(), colId + m_startCol.value());
+  }
+
+  EIGEN_DEVICE_FUNC inline Scalar& coeffRef(Index index) {
+    EIGEN_STATIC_ASSERT_LVALUE(XprType)
+    return m_xpr.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
+                          m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
+  }
+
+  EIGEN_DEVICE_FUNC inline const Scalar& coeffRef(Index index) const {
+    return m_xpr.coeffRef(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
+                          m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
+  }
+
+  EIGEN_DEVICE_FUNC inline const CoeffReturnType coeff(Index index) const {
+    return m_xpr.coeff(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
+                       m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
+  }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC inline PacketScalar packet(Index rowId, Index colId) const {
+    return m_xpr.template packet<Unaligned>(rowId + m_startRow.value(), colId + m_startCol.value());
+  }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC inline void writePacket(Index rowId, Index colId, const PacketScalar& val) {
+    m_xpr.template writePacket<Unaligned>(rowId + m_startRow.value(), colId + m_startCol.value(), val);
+  }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC inline PacketScalar packet(Index index) const {
+    return m_xpr.template packet<Unaligned>(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
+                                            m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0));
+  }
+
+  template <int LoadMode>
+  EIGEN_DEVICE_FUNC inline void writePacket(Index index, const PacketScalar& val) {
+    m_xpr.template writePacket<Unaligned>(m_startRow.value() + (RowsAtCompileTime == 1 ? 0 : index),
+                                          m_startCol.value() + (RowsAtCompileTime == 1 ? index : 0), val);
+  }
+
+#ifdef EIGEN_PARSED_BY_DOXYGEN
+  /** \sa MapBase::data() */
+  EIGEN_DEVICE_FUNC constexpr const Scalar* data() const;
+  EIGEN_DEVICE_FUNC inline Index innerStride() const;
+  EIGEN_DEVICE_FUNC inline Index outerStride() const;
+#endif
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const internal::remove_all_t<XprTypeNested>& nestedExpression() const {
+    return m_xpr;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE XprType& nestedExpression() { return m_xpr; }
+
+  EIGEN_DEVICE_FUNC constexpr StorageIndex startRow() const noexcept { return m_startRow.value(); }
+
+  EIGEN_DEVICE_FUNC constexpr StorageIndex startCol() const noexcept { return m_startCol.value(); }
+
+ protected:
+  XprTypeNested m_xpr;
+  const internal::variable_if_dynamic<StorageIndex, (XprType::RowsAtCompileTime == 1 && BlockRows == 1) ? 0 : Dynamic>
+      m_startRow;
+  const internal::variable_if_dynamic<StorageIndex, (XprType::ColsAtCompileTime == 1 && BlockCols == 1) ? 0 : Dynamic>
+      m_startCol;
+  const internal::variable_if_dynamic<StorageIndex, RowsAtCompileTime> m_blockRows;
+  const internal::variable_if_dynamic<StorageIndex, ColsAtCompileTime> m_blockCols;
+};
+
+/** \internal Internal implementation of dense Blocks in the direct access case.*/
+template <typename XprType, int BlockRows, int BlockCols, bool InnerPanel>
+class BlockImpl_dense<XprType, BlockRows, BlockCols, InnerPanel, true>
+    : public MapBase<Block<XprType, BlockRows, BlockCols, InnerPanel>> {
+  typedef Block<XprType, BlockRows, BlockCols, InnerPanel> BlockType;
+  typedef typename internal::ref_selector<XprType>::non_const_type XprTypeNested;
+  enum { XprTypeIsRowMajor = (int(traits<XprType>::Flags) & RowMajorBit) != 0 };
+
+  /** \internal Returns base+offset (unless base is null, in which case returns null).
+   * Adding an offset to nullptr is undefined behavior, so we must avoid it.
+   */
+  template <typename Scalar>
+  EIGEN_DEVICE_FUNC constexpr EIGEN_ALWAYS_INLINE static Scalar* add_to_nullable_pointer(Scalar* base, Index offset) {
+    return base != nullptr ? base + offset : nullptr;
+  }
+
+ public:
+  typedef MapBase<BlockType> Base;
+  EIGEN_DENSE_PUBLIC_INTERFACE(BlockType)
+  EIGEN_INHERIT_ASSIGNMENT_OPERATORS(BlockImpl_dense)
+
+  /** Column or Row constructor
+   */
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE BlockImpl_dense(XprType& xpr, Index i)
+      : Base((BlockRows == 0 || BlockCols == 0)
+                 ? nullptr
+                 : add_to_nullable_pointer(
+                       xpr.data(),
+                       i * (((BlockRows == 1) && (BlockCols == XprType::ColsAtCompileTime) && (!XprTypeIsRowMajor)) ||
+                                    ((BlockRows == XprType::RowsAtCompileTime) && (BlockCols == 1) &&
+                                     (XprTypeIsRowMajor))
+                                ? xpr.innerStride()
+                                : xpr.outerStride())),
+             BlockRows == 1 ? 1 : xpr.rows(), BlockCols == 1 ? 1 : xpr.cols()),
+        m_xpr(xpr),
+        m_startRow((BlockRows == 1) && (BlockCols == XprType::ColsAtCompileTime) ? i : 0),
+        m_startCol((BlockRows == XprType::RowsAtCompileTime) && (BlockCols == 1) ? i : 0) {
+    init();
+  }
+
+  /** Fixed-size constructor
+   */
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE BlockImpl_dense(XprType& xpr, Index startRow, Index startCol)
+      : Base((BlockRows == 0 || BlockCols == 0)
+                 ? nullptr
+                 : add_to_nullable_pointer(xpr.data(),
+                                           xpr.innerStride() * (XprTypeIsRowMajor ? startCol : startRow) +
+                                               xpr.outerStride() * (XprTypeIsRowMajor ? startRow : startCol))),
+        m_xpr(xpr),
+        m_startRow(startRow),
+        m_startCol(startCol) {
+    init();
+  }
+
+  /** Dynamic-size constructor
+   */
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE BlockImpl_dense(XprType& xpr, Index startRow, Index startCol, Index blockRows,
+                                                        Index blockCols)
+      : Base((blockRows == 0 || blockCols == 0)
+                 ? nullptr
+                 : add_to_nullable_pointer(xpr.data(),
+                                           xpr.innerStride() * (XprTypeIsRowMajor ? startCol : startRow) +
+                                               xpr.outerStride() * (XprTypeIsRowMajor ? startRow : startCol)),
+             blockRows, blockCols),
+        m_xpr(xpr),
+        m_startRow(startRow),
+        m_startCol(startCol) {
+    init();
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const internal::remove_all_t<XprTypeNested>& nestedExpression() const noexcept {
+    return m_xpr;
+  }
+
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE XprType& nestedExpression() { return m_xpr; }
+
+  /** \sa MapBase::innerStride() */
+  EIGEN_DEVICE_FUNC constexpr Index innerStride() const noexcept {
+    return internal::traits<BlockType>::HasSameStorageOrderAsXprType ? m_xpr.innerStride() : m_xpr.outerStride();
+  }
+
+  /** \sa MapBase::outerStride() */
+  EIGEN_DEVICE_FUNC constexpr Index outerStride() const noexcept {
+    return internal::traits<BlockType>::HasSameStorageOrderAsXprType ? m_xpr.outerStride() : m_xpr.innerStride();
+  }
+
+  EIGEN_DEVICE_FUNC constexpr StorageIndex startRow() const noexcept { return m_startRow.value(); }
+
+  EIGEN_DEVICE_FUNC constexpr StorageIndex startCol() const noexcept { return m_startCol.value(); }
+
+#ifndef __SUNPRO_CC
+  // FIXME sunstudio is not friendly with the above friend...
+  // META-FIXME there is no 'friend' keyword around here. Is this obsolete?
+ protected:
+#endif
+
+#ifndef EIGEN_PARSED_BY_DOXYGEN
+  /** \internal used by allowAligned() */
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE BlockImpl_dense(XprType& xpr, const Scalar* data, Index blockRows,
+                                                        Index blockCols)
+      : Base(data, blockRows, blockCols), m_xpr(xpr) {
+    init();
+  }
+#endif
+
+ protected:
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void init() {
+    m_outerStride =
+        internal::traits<BlockType>::HasSameStorageOrderAsXprType ? m_xpr.outerStride() : m_xpr.innerStride();
+  }
+
+  XprTypeNested m_xpr;
+  const internal::variable_if_dynamic<StorageIndex, (XprType::RowsAtCompileTime == 1 && BlockRows == 1) ? 0 : Dynamic>
+      m_startRow;
+  const internal::variable_if_dynamic<StorageIndex, (XprType::ColsAtCompileTime == 1 && BlockCols == 1) ? 0 : Dynamic>
+      m_startCol;
+  Index m_outerStride;
+};
+
+}  // end namespace internal
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_BLOCK_H

data_acq_and_processing/third_party/eigen/Eigen/src/Core/CommaInitializer.h

@@ -0,0 +1,148 @@
+// This file is part of Eigen, a lightweight C++ template library
+// for linear algebra.
+//
+// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
+// Copyright (C) 2006-2008 Benoit Jacob <jacob.benoit.1@gmail.com>
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#ifndef EIGEN_COMMAINITIALIZER_H
+#define EIGEN_COMMAINITIALIZER_H
+
+// IWYU pragma: private
+#include "./InternalHeaderCheck.h"
+
+namespace Eigen {
+
+/** \class CommaInitializer
+ * \ingroup Core_Module
+ *
+ * \brief Helper class used by the comma initializer operator
+ *
+ * This class is internally used to implement the comma initializer feature. It is
+ * the return type of MatrixBase::operator<<, and most of the time this is the only
+ * way it is used.
+ *
+ * \sa \blank \ref MatrixBaseCommaInitRef "MatrixBase::operator<<", CommaInitializer::finished()
+ */
+template <typename XprType>
+struct CommaInitializer {
+  typedef typename XprType::Scalar Scalar;
+
+  EIGEN_DEVICE_FUNC constexpr CommaInitializer(XprType& xpr, const Scalar& s)
+      : m_xpr(xpr), m_row(0), m_col(1), m_currentBlockRows(1) {
+    eigen_assert(m_xpr.rows() > 0 && m_xpr.cols() > 0 && "Cannot comma-initialize a 0x0 matrix (operator<<)");
+    m_xpr.coeffRef(0, 0) = s;
+  }
+
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC inline CommaInitializer(XprType& xpr, const DenseBase<OtherDerived>& other)
+      : m_xpr(xpr), m_row(0), m_col(other.cols()), m_currentBlockRows(other.rows()) {
+    eigen_assert(m_xpr.rows() >= other.rows() && m_xpr.cols() >= other.cols() &&
+                 "Cannot comma-initialize a 0x0 matrix (operator<<)");
+    m_xpr.template block<OtherDerived::RowsAtCompileTime, OtherDerived::ColsAtCompileTime>(0, 0, other.rows(),
+                                                                                           other.cols()) = other;
+  }
+
+  /* Copy/Move constructor which transfers ownership. This is crucial in
+   * absence of return value optimization to avoid assertions during destruction. */
+  EIGEN_DEVICE_FUNC inline CommaInitializer(const CommaInitializer& o)
+      : m_xpr(o.m_xpr), m_row(o.m_row), m_col(o.m_col), m_currentBlockRows(o.m_currentBlockRows) {
+    // Mark original object as finished. In absence of R-value references we need to const_cast:
+    const_cast<CommaInitializer&>(o).m_row = m_xpr.rows();
+    const_cast<CommaInitializer&>(o).m_col = m_xpr.cols();
+    const_cast<CommaInitializer&>(o).m_currentBlockRows = 0;
+  }
+
+  /* inserts a scalar value in the target matrix */
+  EIGEN_DEVICE_FUNC CommaInitializer &operator,(const Scalar& s) {
+    if (m_col == m_xpr.cols()) {
+      m_row += m_currentBlockRows;
+      m_col = 0;
+      m_currentBlockRows = 1;
+      eigen_assert(m_row < m_xpr.rows() && "Too many rows passed to comma initializer (operator<<)");
+    }
+    eigen_assert(m_col < m_xpr.cols() && "Too many coefficients passed to comma initializer (operator<<)");
+    eigen_assert(m_currentBlockRows == 1);
+    m_xpr.coeffRef(m_row, m_col++) = s;
+    return *this;
+  }
+
+  /* inserts a matrix expression in the target matrix */
+  template <typename OtherDerived>
+  EIGEN_DEVICE_FUNC CommaInitializer &operator,(const DenseBase<OtherDerived>& other) {
+    if (m_col == m_xpr.cols() && (other.cols() != 0 || other.rows() != m_currentBlockRows)) {
+      m_row += m_currentBlockRows;
+      m_col = 0;
+      m_currentBlockRows = other.rows();
+      eigen_assert(m_row + m_currentBlockRows <= m_xpr.rows() &&
+                   "Too many rows passed to comma initializer (operator<<)");
+    }
+    eigen_assert((m_col + other.cols() <= m_xpr.cols()) &&
+                 "Too many coefficients passed to comma initializer (operator<<)");
+    eigen_assert(m_currentBlockRows == other.rows());
+    m_xpr.template block<OtherDerived::RowsAtCompileTime, OtherDerived::ColsAtCompileTime>(m_row, m_col, other.rows(),
+                                                                                           other.cols()) = other;
+    m_col += other.cols();
+    return *this;
+  }
+
+  EIGEN_DEVICE_FUNC inline ~CommaInitializer()
+#if defined VERIFY_RAISES_ASSERT && (!defined EIGEN_NO_ASSERTION_CHECKING) && defined EIGEN_EXCEPTIONS
+      noexcept(false)  // Eigen::eigen_assert_exception
+#endif
+  {
+    finished();
+  }
+
+  /** \returns the built matrix once all its coefficients have been set.
+   * Calling finished is 100% optional. Its purpose is to write expressions
+   * like this:
+   * \code
+   * quaternion.fromRotationMatrix((Matrix3f() << axis0, axis1, axis2).finished());
+   * \endcode
+   */
+  EIGEN_DEVICE_FUNC inline XprType& finished() {
+    eigen_assert(((m_row + m_currentBlockRows) == m_xpr.rows() || m_xpr.cols() == 0) && m_col == m_xpr.cols() &&
+                 "Too few coefficients passed to comma initializer (operator<<)");
+    return m_xpr;
+  }
+
+  XprType& m_xpr;            // target expression
+  Index m_row;               // current row id
+  Index m_col;               // current col id
+  Index m_currentBlockRows;  // current block height
+};
+
+/** \anchor MatrixBaseCommaInitRef
+ * Convenient operator to set the coefficients of a matrix.
+ *
+ * The coefficients must be provided in a row major order and exactly match
+ * the size of the matrix. Otherwise an assertion is raised.
+ *
+ * Example: \include MatrixBase_set.cpp
+ * Output: \verbinclude MatrixBase_set.out
+ *
+ * \note According the c++ standard, the argument expressions of this comma initializer are evaluated in arbitrary
+ * order.
+ *
+ * \sa CommaInitializer::finished(), class CommaInitializer
+ */
+template <typename Derived>
+EIGEN_DEVICE_FUNC inline CommaInitializer<Derived> DenseBase<Derived>::operator<<(const Scalar& s) {
+  return CommaInitializer<Derived>(*static_cast<Derived*>(this), s);
+}
+
+/** \sa operator<<(const Scalar&) */
+template <typename Derived>
+template <typename OtherDerived>
+EIGEN_DEVICE_FUNC inline CommaInitializer<Derived> DenseBase<Derived>::operator<<(
+    const DenseBase<OtherDerived>& other) {
+  return CommaInitializer<Derived>(*static_cast<Derived*>(this), other);
+}
+
+}  // end namespace Eigen
+
+#endif  // EIGEN_COMMAINITIALIZER_H