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": [], "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": {"path": "radar_fall_detection\\algo.py", "content": "from typing import List\n\nfrom radar_tracking.models.clustering.cluster import Cluster\n\n\nclass FallDetector:\n    def __init__(self, minimum_height: float, fall_duration_trigger: float = 1):\n        \"\"\"\n        The aim of this class is to detect a fall on a given cluster\n        :param minimum_height: minimum height of the cluster's center of mass to not consider it as fallen.\n        :param fall_duration_trigger: how long a fall needs to be in order to trigger it.\n        \"\"\"\n        self.minimum_height = minimum_height\n        assert fall_duration_trigger > 0, \"fall_duration_trigger must be positive.\"\n        self.fall_duration_trigger = fall_duration_trigger\n        self.clusters_trigger_t = {}\n\n    def update(self, clusters_up_to_date: List[Cluster], dt: float):\n        \"\"\"\n        To call every step.\n        :param clusters_up_to_date: list of tracked clusters.\n        :param dt: time elapsed since last update.\n        \"\"\"\n        # Removing outdated clusters\n        to_remove = []\n        for cluster in self.clusters_trigger_t:\n            if cluster not in clusters_up_to_date:\n                to_remove.append(cluster)\n        for cluster_to_remove in to_remove:\n            del self.clusters_trigger_t[cluster_to_remove]\n        # Adding or Updating clusters\n        for cluster in clusters_up_to_date:\n            fall = cluster.centroid[cluster.height_axis] < self.minimum_height\n            if fall:\n                if cluster not in self.clusters_trigger_t:\n                    self.clusters_trigger_t[cluster] = dt\n                else:\n                    self.clusters_trigger_t[cluster] += dt\n            else:\n                self.clusters_trigger_t[cluster] = 0\n\n\n    def detect_fallen_clusters(self):\n        \"\"\"\n        :return: A dictionary with fallen clusters.\n\n            key: \"suspected\"\n            values: list of clusters suspected to have fallen\n\n            key: \"triggered\"\n            values: list of clusters detected as fallen.\n        \"\"\"\n        fall_dict = {\"suspected\": [], \"confirmed\": []}\n        for cluster, t in self.clusters_trigger_t.items():\n            if t > 0:\n                if t > self.fall_duration_trigger:\n                    fall_dict[\"confirmed\"].append(cluster)\n                else:\n                    fall_dict[\"suspected\"].append(cluster)\n        return fall_dict\n"}}