Instructions:
The project focuses on the use of FMCW radar.
It is used for data collection, point detection, tracking and other functions.
The project is broken down into several modules.
The aim of this division is to decouple as much as possible the functionalities present in each module, and make the development of new functionalities more flexible and rapid.

Some information will be presented to you in json format:
- module: the name of the module
- module_structure: the structure of the module (the files making up the module and their hierarchy)
- module_files_already_generated_doc: the documentation of the module's files if already generated
- other_modules_doc: the documentation of the other modules on which the module depends (possibly none at all)
- gen_doc_of_file: the file you will be given to generate the documentation of. If no file is given, the documentation of the entire module must be generated.
Your goal is to create a markdown documentation of the file you will be given, or the entire module if no file is given.
This documentation is intended to guide a developer who is new to the project, you can therefore add whatever you feel is relevant for this task.


Informations:
{"module": "radar_fall_detection", "module_structure": "radar_fall_detection/algo.py;radar_fall_detection/display.py;radar_fall_detection/live_fall_detection.py;radar_fall_detection\\conf/fall_conf.json;radar_fall_detection\\conf/live_fall_detection.json;radar_fall_detection\\conf/ti_conf.json;radar_fall_detection\\conf/tracking.json;", "module_files_already_generated_doc": [{"file": "radar_fall_detection\\algo.md", "doc": "# radar_fall_detection.algo Documentation\n\n## Overview\n\nThe FallDetector class within the algo.py file of the radar_fall_detection module is designed to identify fall events based on radar data processed into clusters. It receives data from the radar_tracking module which processes the radar signals to track objects in the vicinity of the FMCW radar system. This class aims to evaluate tracked clusters and determine if a fall has occurred according to predefined criteria.\n\nThe functionality of this class plays a vital role in systems that require fall detection, such as safety monitoring systems for the elderly or in industrial environments.\n\n## Dependencies\n\nThis class is dependent on the Cluster class from the radar_tracking.models.clustering.cluster module, which provides data structures representing clusters of points detected and tracked by the radar system.\n\n## Module Structure\n\nThe algo.py file contains the following class:\n\n- FallDetector: A class responsible for detecting falls based on cluster data.\n\n## Class Description\n\n### FallDetector\n\n#### Initialization Parameters:\n- minimum_height: A float specifying the minimum height of the cluster\u00e2\u20ac\u2122s center of mass to not consider it as fallen.\n- fall_duration_trigger: A float defaulting to 1, representing how long (seconds) a fall needs to be in order to be confirmed as a fall.\n\n#### Methods:\n\n- __init__(self, minimum_height: float, fall_duration_trigger: float = 1):\nInitializes the FallDetector object with its parameters.\n\n- update(self, clusters_up_to_date: List[Cluster], dt: float):\nUpdates the fall detection mechanism with the latest set of clusters and the time elapsed since the last update. This function should be called at each step of the tracking/update loop.\n\n- detect_fallen_clusters(self):\nEvaluates the clusters to detect which ones have fallen. Returns a dictionary with keys \"suspected\" and \"confirmed\", corresponding to clusters that are suspected to have fallen and those that are confirmed to have fallen, respectively.\n\n#### Attributes:\n\n- minimum_height: Holds the minimum cluster height to be considered upright.\n- fall_duration_trigger: Specifies the time threshold for confirming a fall.\n- clusters_trigger_t: A dictionary that stores clusters with their associated time since they have been detected below the minimum_height.\n\n#### Detailed Behavior:\n\n1. update method: It maintains an internal dictionary of clusters being watched for falls, referenced against the clusters_up_to_date provided. Clusters that are no longer up-to-date are removed, and the remaining ones have their internal timer updated.\n\n2. detect_fallen_clusters method: This method checks each tracked cluster\u00e2\u20ac\u2122s timer against the fall_duration_trigger to determine if the cluster has been in a fallen state long enough to confirm the fall.\n\n## Data Flow Summary\n\n1. The FallDetector is initialized with specified parameters for minimum height and duration required to trigger a fall.\n\n2. During real-time operation, the update method is called with a list of updated clusters (clusters_up_to_date) and the time elapsed since the previous call (dt), allowing the FallDetector to update its internal state.\n\n3. After updating, the detect_fallen_clusters method can be used to obtain a current analysis of which clusters are suspected or confirmed to have fallen.\n\n4. This information may then be utilized by external systems for alert generation, logging, or further analysis.\n\n## Integration with Other Modules\n\n- Interacts with radar_tracking for cluster data.\n- May utilize radar_visuals for visualizing detected falls if needed.\n\n## Example Usage\n\nTo utilize the FallDetector class, one would first instantiate it with the desired parameters:\n\n```python\nfall_detector = FallDetector(minimum_height=0.5, fall_duration_trigger=2)\n```\n\nThen, within the radar data processing loop, the update and detect_fallen_clusters methods would be called:\n\n```python\nwhile radar_active:\n    clusters = track_radar_objects()  # Assuming a function that returns a list of Cluster objects\n    fall_detector.update(clusters, dt)\n    fallen_clusters = fall_detector.detect_fallen_clusters()\n    handle_fall_detection(fallen_clusters)  # Custom function to handle the detection output\n```\n\n## Developer Notes\n\n- Ensure the FallDetector instance is consistently given up-to-date cluster information for proper function.\n- Consider the processing delay when configuring fall_duration_trigger to prevent false alarms.\n- Validate that the coordinate system of the cluster heights matches the expectations of the FallDetector. The Cluster object should be defined such that its height axis is meaningful for the height comparison.\n\n## Conclusion\n\nThe FallDetector class is a focused component for detecting falls within the context of FMCW radar data analysis. By strictly adhering to the Single Responsibility Principle, it can efficiently process cluster height information and provide pertinent fall detection notifications, making it a valuable tool in safety and monitoring applications."}, {"file": "radar_fall_detection\\display.md", "doc": "# radar_fall_detection.display Documentation\n\n## Overview\n\nThe display.py script within the radar_fall_detection module is responsible for the visual representation of detected falls checked by the FallDetector class, indicating both suspected and confirmed falls. It utilizes visualization building blocks from radar_tracking.visuals.display such as TrackingDisplay and TrackingDisplay2D, to graphically illustrate where falls might have occurred relative to the tracked clusters.\n\n## Dependencies\n\nFallDisplay and FallDisplay2D are dependent on the following external classes:\n- TrackingDisplay from radar_tracking.visuals.display\n- TrackingDisplay2D from radar_tracking.visuals.display\n\nProvided that these classes are responsible for real-time visual tracking, they render the visualization of clusters and, in this extension, highlight when a fall is detected or suspected.\n\n## Module Structure\n\nThe display.py file defines the following visualization classes:\n\n- FallDisplay: Extends TrackingDisplay to include the rendering of falls in a 3D space.\n- FallDisplay2D: Extends TrackingDisplay2D for a 2D representation.\n\n## Class Descriptions\n\n### FallDisplay\n\nThe FallDisplay is a subclass of TrackingDisplay and is used for visualizing fall events in 3D space.\n\n#### Methods:\n\n- _prepare_draw(self, clusters, noise, fallen_clusters):\nPrepares the next frame to be drawn with the latest radar data, including clusters, noise points, and fallen clusters.\n\n- show(self, clusters, noise, width_axis, depth_axis, height_axis, fallen_clusters):\nExecutes drawing onto the canvas, uses _prepare_draw to prepare the visual elements, then updates the legend and draws on the figure.\n\n#### Specifics of Visual Indicators:\n\n- Suspected falls are illustrated with an orange cuboid surrounding the cluster centroid.\n- Confirmed falls are distinguished with a red cuboid.\n\n### FallDisplay2D\n\nThis is a subclass of TrackingDisplay2D for visualizing fall events in 2D space.\n\n#### Methods:\n\n- _prepare_draw(self, clusters, fallen_clusters):\nInvokes the super()._prepare_draw method and appends data points corresponding to centroids of clusters, adding colors related to the fall status.\n\n- show(self, clusters, width_axis, depth_axis, height_axis, fallen_clusters):\nManages the rendering of points onto the 2D canvas, plotting suspected and confirmed falls with distinct colors.\n\n#### Color Coding for 2D Plot:\n\n- Suspected falls are indicated by an orange-like color [1, 0.7, 0.1].\n- Confirmed falls are indicated by a red-magenta color [1, 0.2, 0.1].\n\n## Integration with Other Modules\n\n- Inherits from TrackingDisplay and TrackingDisplay2D provided by the radar_tracking module."}, {"file": "radar_fall_detection\\live_fall_detection.md", "doc": "# radar_fall_detection.live_fall_detection Documentation\n\n## Overview\n\nThe live_fall_detection.py script is part of the radar_fall_detection module and is specifically designed for live detection of falls using mmWave radar technology. It serves as the execution point for setting up the radar sensor, processing incoming radar data in real-time, performing fall detection, and visualizing the results.\n\nThe script\u00e2\u20ac\u2122s primary function is to tie together the process from raw radar data acquisition to fall event detection visualization showing the positional and velocity information of potential falls.\n\n## Dependencies\n\nThe script leverages a combination of internal and external modules:\n- Internal dependency on algo and display modules within the radar_fall_detection package.\n- External dependencies on radar_toolbox for radar data processing utilities and radar_tracking for object tracking algorithms.\n\n## Configuration\n\nThe script requires a --conf parameter to specify the path to a JSON-formatted configuration file (live_fall_conf.json). This configuration file holds parameters necessary for initializing the radar and defining processing characteristics.\n\n## Key Components\n\n1. Radar Setup: Establishes communication with the TI IWR6843 sensor over a serial port and sends configuration settings.\n\n2. Fall Detector Initialization: Instantiates the FallDetector class with parameters from fall_detection_conf.\n\n3. Display Configuration: Creates a FallDisplay for visualizations with real-time updates.\n\n4. Run Loop: Perpetually collects and processes radar frames, tracking objects via the clustering algorithm and tracking utility, feeding the resulting clusters into the fall detection algorithm, and finally visualizing falls using the configured display classes.\n\n## Script Flow\n\n1. Parse the configuration arguments and load the specified configuration file.\n2. Obtain radar setup parameters and optionally set horizontal and vertical inclination correction angles.\n3. Initialize objects for clustering, tracking, fall detection, and visualization.\n4. Connect to the radar sensor and start the sensor data streaming.\n5. Enter a loop where radar data points are continually received and processed.\n   - Filtering for minimum velocity (if set) to discard static points.\n   - Applying spatial filters to prevent falls outside designated areas from being processed.\n   - Tracking points through clustering algorithms and time-stepping tracking utilities.\n   - Applying the fall detection mechanism by updating with the latest cluster information.\n   - Visualizing detected and current clusters along with fall events on the display.\n6. Allow for a keyboard interrupt to terminate the program safely and clean up resources.\n\n## Example Usage\n\nTo run the live fall detection script, execute the following command with the appropriate configuration file:\n\n```shell\npython live_fall_detection.py --conf=path/to/live_fall_conf.json\n```\n\n## Developer Notes\n\n- Proper configuration is critical. Ensure that the configuration files match the radar\u00e2\u20ac\u2122s setup and the environment within which the system operates.\n- Environmental noise and other factors can affect detection accuracy. This script includes filtering methods based on velocity and positional constraints; these should be tweaked to your specific use case for optimal results.\n- Ensure that the system on which the script is running has access to the necessary hardware ports for communication with the radar sensor.\n\n## Conclusion\n\nlive_fall_detection.py is an integral script within the radar_fall_detection module, showcasing the practical application of FMCW radar for fall detection systems. Its design focuses on real-time processing, which demands efficient algorithmic implementations and responsive visualization. Working in tandem with the radar processing and visualization suites (radar_toolbox and radar_visuals), it highlights the modular ecosystem\u00e2\u20ac\u2122s capacity to develop, test, and potentially deploy a robust radar-based fall detection solution."}, {"file": "radar_fall_detection\\conf\\fall_conf.md", "doc": "# Configuration for Fall Detection - radar_fall_detection.conf.fall_conf Documentation\n\n## Overview\n\nThe fall_conf.json file within the radar_fall_detection module represents the configuration for the fall detection functionality. This JSON-formatted configuration file specifies the essential parameters determining how the fall detection algorithm will infer a fall event from tracked radar data.\n\nThe configuration parameters are used to instantiate the FallDetector class and set the conditions under which a fall is detected, such as the minimum height threshold and the duration for confirming a fall.\n\n## Configuration Parameters\n\nThe fall_conf.json file contains the following parameters:\n\n- load_conf: This key specifies a path to another configuration file for loading additional settings. This inclusion enables shared configurations across different modules.\n\n- minimum_height: A float value indicating the minimum height of a cluster\u00e2\u20ac\u2122s center of mass above which it is not considered to have fallen. This value effectively acts as a threshold for the vertical position of objects that the radar tracks.\n\n- fall_duration_trigger: A float value representing the time (in seconds) a detected object must remain below the minimum_height for the fall to be confirmed. This allows for temporary dips below the threshold without triggering a false alarm."}, {"file": "radar_fall_detection\\conf\\live_fall_detection.md", "doc": "# live_fall_detection.json Configuration - radar_fall_detection.conf Documentation\n\n## Overview\n\nThe live_fall_detection.json file is part of the configuration for the radar_fall_detection module. This JSON-formatted file directs the module to specific configuration files necessary for live fall detection operations using FMCW radar data. It plays a crucial role in defining the sources of parameters that will dictate the behavior of the live fall detection script.\n\n## Configuration Content\n\nThe live_fall_detection.json file includes references to other configuration files, which are loaded to accumulate the settings required for a live fall detection scenario. Here is the breakdown of the configuration parameters:\n\n- load_conf: This key specifies a path to another configuration file for loading additional settings. This inclusion enables shared configurations across different modules.\n\n- fall_detection_conf: Indicates the path to the fall detection configuration file. This file specifies parameters such as the minimum cluster height and fall duration triggers that are critical for the fall detection algorithm."}, {"file": "radar_fall_detection\\conf\\ti_conf.md", "doc": "# ti_conf.json Configuration - radar_fall_detection.conf Documentation\n\n## Overview\n\nThe ti_conf.json file is a configuration file within the radar_fall_detection module that specifies settings for interfacing with mmWave radar hardware. This file is crucial for the correct initialization and operation of the radar sensor, particularly for the live fall detection functionality.\n\n## Configuration Parameters\n\nThe ti_conf.json file contains several key-value pairs that define the radar\u00e2\u20ac\u2122s operational parameters:\n\n- cfg_file_path: The file path to the radar configuration file, which contains specific commands for setting up the radar sensor.\n\n- model: A string identifier for the radar model, which is used to select the appropriate configuration and processing algorithms.\n\n- CLI_port: The identifier for the Command Line Interface (CLI) port through which configuration commands are sent to the radar sensor.\n\n- data_port: The identifier for the port through which radar data is received.\n\n- save_file_path: The file path where the radar data should be saved, typically in a CSV format.\n\n- capture_duration: The duration, in seconds, for which the radar data capture should run.\n\n- horizontal_inclination: The angle of horizontal inclination correction applied to the radar data, in degrees.\n\n- vertical_inclination: The angle of vertical inclination correction applied to the radar data, in degrees. If not applicable, it can be set to null.\n\n- min_point_vel: The minimum velocity threshold for point detection, which helps filter out static objects from the radar data."}], "other_modules_doc": [{"module": "radar_visuals", "doc": "# radar_visuals Module Documentation\n\n## Overview\n\nThe radar_visuals module is a dedicated visualization package within a project focused on the use of Frequency Modulated Continuous Wave (FMCW) radar. It is designed to provide tools and utilities for the rendering of radar data in various visual formats, supporting functionalities like data collection, point detection, and tracking analysis. The module aims to facilitate rapid development and integration of new visualization features through a decoupled and flexible structure.\n\n## Module Structure\n\nThe radar_visuals module currently contains the following file:\n\n- display.py: A Python script offering a suite of classes and functions for creating dynamic and static displays for radar data visualization.\n\n## Module Files Documentation\n\n### display.py\n\nThe display.py file within the radar_visuals package encompasses multiple visualization methods and is central to the module. Below is a summary of the provided documentation:\n\n#### Overview\nThe script is essential for visualizing radar data and includes capabilities for 2D, 3D, and polar plotting. It is versatile enough to accommodate both real-time and static data presentations.\n\n#### Components\nEnums such as PlayingMode, Projection, and GraphType allow the user to define the display behavior, the plot\u00e2\u20ac\u2122s projection type, and the graph style, respectively. Along with utility functions like color_from_id and plot_signal, the script provides two main classes:\n\n- UpdatableDisplay: The abstract base class for displays that can be updated, initializing the figure, axes, and other graphical elements.\n- SimpleUpdatableDisplay: A concrete implementation of UpdatableDisplay that prepares and updates displays with new data points.\n\n#### Functionalities\nThe primary functionalities include creating interactive graphs to visualize radar data, updating them according to predefined playing modes, and ensuring compatibility with different data types and visual projections.\n\n#### Data Flow\nThe typical workflow involves:\n1. Initializing a display object with the required properties.\n2. Accumulating or simulating radar data points.\n3. Updating the display as new data arrives through the show() method.\n4. Presenting the visual representation of the radar data to users based on the set playing mode.\n\n#### Developer Notes\nDevelopers are advised to match data dimensions with graph types and projections, manage update rates effectively, and assign unique colors to data sources using UUIDs for better data distinction.\n\n#### Integration\nDespite being part of the radar_visuals package, display.py operates independently from other components, specifically focusing on visualization purposes and not on signal processing.\n\n## Usage\n\nThe module can be employed to:\n\n- Visualize FMCW radar-related data points, signals, and more complex datasets.\n- Develop new visualization tools tailored to the specifics of radar data.\n- Enhance the interpretation of radar data through graphically rich plots.\n\n## Conclusion\n\nradar_visuals is a crucial module for the graphical representation and analysis of radar data within a larger FMCW radar-focused project. The modular structure accommodates efficient development, enabling users and developers to easily integrate new visualization techniques suited to their specific needs. The module\u00e2\u20ac\u2122s design streamlines the translation of complex data into understandable and insightful visual formats, supporting a wide range of functionalities from debugging to presentation."}, {"module": "radar_toolbox", "doc": "# radar_toolbox Module Documentation\n\n## Overview\n\nThe radar_toolbox module is a collection of tools designed for interfacing with Frequency Modulated Continuous Wave (FMCW) radar systems. It is utilized for various purposes such as data collection, point detection, tracking, and other radar signal processing functions. The module is structured to decouple functionalities into distinct units to promote flexibility and rapid development of new features.\n\n## Module Structure\n\nThe radar_toolbox module is composed of the following elements:\n\n- conf/\n  - Various configuration files setting parameters for radar initialization and different processing steps, such as profile_vs1642_exp.cfg which provides specific settings for radar models and data handling.\n- capture_session/\n  - This submodule contains scripts for setting up and managing radar data capture sessions, with scripts like main.py for starting, monitoring, and saving raw radar captures.\n- raw_data/\n  - A collection of code dealing with raw data handling, including adc.py for interfacing with Analog-to-Digital Converters and scripts to convert raw binary radar data into structured formats for analysis.\n- TI_data/\n  - Contains tools and scripts to communicate, configure, and process data from Texas Instruments (TI) mmWave radar boards, including sensor_com.py which outlines communication protocols and data parsing mechanisms.\n- fmcw_utils.py\n  - This utility file offers essential classes and functions for the processing and analysis of FMCW radar data, such as radar model enums and functions for loading and converting radar data.\n- utils.py\n  - General utility functions and classes that support the operation of the radar_toolbox module, with methods for loading configurations, data parsing, and more.\n\n## Module Files Documentation\n\nDevelopers new to radar_toolbox will find documentation for several key files within the module. An outline of this documentation includes:\n\n- fmcw_utils\n  - Detailed information on handling FMCW radar systems, managing configurations, processing radar signals, and transforming them into analyzable forms.\n- utils\n  - A comprehensive overview of utility functions that aid in loading configurations, preprocessing radar data, and assisting with model training and coordinate adjustments.\n- capture_session/main\n  - Instructions on setting up and conducting recording sessions with scripts that interact with the radar system to initiate and manage data capture.\n- raw_data/adc\n  - Documentation of the adc.py script which serves as an interface to ADC hardware, allowing for the capture and real-time processing of radar data streams.\n- raw_data/raw_bin_to_numpy\n  - Explains the process of converting raw binary radar data into a NumPy array and the steps involved in ensuring data integrity during conversion.\n- raw_data/record_raw_data\n  - A guide to a script focused on raw data recording, detailing the initiation of data capture and subsequent data stream management.\n- TI_data/record_ti_data\n  - Documentation for a script specific to TI radar hardware, describing real-time data capture and CSV data storage functionality.\n- TI_data/sensor_com\n  - Communication protocols and data structure parsing methods for TI mmWave radar boards are outlined for managing interactions with these devices.\n\n## Usage\n\nDevelopers can use the radar_toolbox module to:\n\n- Set up FMCW radar data capture sessions.\n- Record and process raw radar data.\n- Convert binary radar data captures into formats suitable for analysis, such as NumPy arrays.\n- Interact and configure TI mmWave radars for data acquisition.\n\n## Data Flow Summary\n\nThe data flow within the radar_toolbox module follows these general steps:\n\n1. Configuration is set up using JSON files and configuration profiles for the specific radar models.\n2. Data recording sessions are initiated, capturing the radar data and storing it in binary format.\n3. Raw binary data is processed, filtered, and organized into structured data arrays.\n4. Data is made ready for analysis, transformed into point clouds or other representations necessary for further processing and interpretation.\n\n## External Dependencies\n\nWhile the module works as an independent toolkit within the project scope, it interfaces with radar hardware and other software modules that handle advanced data analysis, visualization, and machine learning applications.\n\n## Overall Significance\n\nThe radar_toolbox module serves as the foundational toolset for FMCW radar systems, streamlining the process from initial data capture to data preparation for analytics. It emphasizes modularity, making it easier to grow and adapt the project with new functionalities."}, {"module": "radar_tracking", "doc": "# FMCW Radar Tracking Project Documentation\n\n## radar_tracking Module\n\nThe radar_tracking module sits at the heart of the FMCW radar project, providing algorithms and utilities focused on the tracking of objects detected by the radar system.\n\n### Module Structure\n\nThe module is composed of several scripts and a configuration file organized as follows:\n\n- tracking_utils.py: Utilities for configuring and initializing tracking components.\n- conf/tracking.json: A JSON-formatted configuration file specifying parameters for the tracking system.\n- inference/:\n  - example_kalman_filter.py: Perform inference using Kalman filters.\n  - example_tracking.py: An example script demonstrating the tracking process.\n- models/:\n  - kalman.py: Implementations of Kalman filter for state estimation.\n  - tracking.py: The main tracking algorithm handling the temporal correlation of clusters.\n- clustering/:\n  - cluster.py: Data structure for managing clusters of points.\n  - cluster_predictor.py: Class for predicting clustering behavior based on certain algorithms.\n  - optimizer.py: Optimization utilities for clustering performance.\n- algos/:\n  - clustering_algorithm.py: Abstract base algorithms for clustering.\n  - dbscan.py: DBSCAN clustering algorithm implementation.\n  - gaussian_mixture.py: Gaussian Mixture Model clustering implementation.\n  - meanshift.py: Mean Shift clustering algorithm implementation.\n- visuals/display.py: Visualization classes for displaying the tracking data.\n\n### Dependencies\n\nThe radar_tracking module relies on other parts of the FMCW radar project:\n\n- radar_toolbox:\nA toolkit for interacting with the radar hardware and processing raw radar data.\n\n- radar_visuals:\nProvides dynamic and static displays to visualize radar data, which can be helpful to show tracking results in an accessible form.\n\n## Development Notes\n\nDevelopers new to this project will want to familiarize themselves with the radar_tracking module by reviewing existing documentation and code comments.\n\nFor practical understanding, developers are encouraged to interact with example scripts inside the inference subdirectory. They illustrate how the various components come together to achieve object tracking.\n\n## Conclusion\n\nThe radar_tracking module integrates sophisticated algorithms and diverse data sets to track objects with high precision. It exemplifies a well-organized codebase that promotes decoupled architecture, thus expediting the development of innovative functionalities in radar-based tracking systems."}], "gen_doc_of_file": null}