Analysis Manager

Run the script: /home/thh3/dev/DebugPlayer/analysis_manager/main_analysis_manager.py

Most missing packages should be installed on the fly. If you encounter any issues with missing packages, install them manually and notify me if needed.

Running a Trip from the Menu

Steps:

1. On the Menu:

   • Press File -> Open Trips Folder: Select the folder where all your trips are located.
   • Press File -> Select Trip: Choose a trip from the list of available trips.

2. To open the playback window, press the RUN button.

Loading a Trip with Command-Line Arguments

1. Go to launch.json and ensure the following configuration is set:

   "args": ["--trip", "trip_path"]

2. Alternatively, you can call it directly from the terminal:

   python analysis_manager/main_analysis_manager.py --trip "/home/thamam/data/trips/2024-09-19T13_14_48/"

personal-folder setting

1. This project supports a prsonal folder so you can configure the vscode according to your preference. For, example, you can config launch.json according to your local file structure, where trip are saved, or define list of extension that will be included when this project is in use.
2. personal-folder and its content are already in the project .gitignore file.

Loading the prject

To properly process the usage of personal-folder, please make sure that the project Workspace is Opened Correctly: Go to File > Open Workspace and select your DebugPlayer.code-workspace

DebugPlayer - UNDER CONSTRUCTIONS

Preparations

Install the necessary Python packages:

pip install pyside6 matplotlib pyqtgraph SciPy NumPy spatialmath-python polars pandas 

Optional Installs

pip install "dask[complete]" json5

Install libxcb Dependencies

1. Update your system:

   sudo apt update

2. Install the required packages:

   pip install setuptools
   sudo apt-get install libxcb-randr0-dev libxcb-xtest0-dev libxcb-xinerama0-dev libxcb-shape0-dev libxcb-xkb-dev
   sudo apt install libxcb-xinerama0 libxcb-cursor0 libxcb1 libxcb-util1 libxkbcommon-x11-0

Overview of File Structure

• main.py: Entry point for launching the application.
• playback_viewer.py: Sets up the main window, integrates different modules, and manages interactions.
• data_handler.py: Manages data loading, storage, and access.
• playback_controls.py: Handles playback control logic, including play/pause, speed adjustment, and time navigation.
• plot_manager.py: Responsible for managing all plots and updating them based on the data.
• signal_manager.py: Manages the signal selection UI and updates the temporal plot based on user selections.

Utils

How to Add New Data - Step-by-Step Guide

1. Data Preparation: Ensure your data is in a suitable format (e.g., CSV or JSON).
2. Loading the Data: Modify the DataHandler to load and structure your data.
3. Visualizing Data: Update the PlotManager to plot your specific data.
4. Interactive Features: Verify and customize tooltips, crosshairs, and other features to align with your data structure.
5. Testing and Refining: Test the integration and refine the UI/UX based on your needs.

Adding a new Plugin

1. Step 1: Define the API Interface. The first step will be to create a new class that adheres to the UserPluginInterface. This class will read the data file, parse the data, and provide functionality for visualizing the data over time.

2. Step 2: Create the NewPlugin Plugin Class

import pandas as pd
from api_interfaces import UserPluginInterface

class NewPlugin(UserPluginInterface):
    def __init__(self, file_path):
        self.file_path = file_path
        self.data = None  # Placeholder for the loaded CSV data
        self.current_paths = None  # Current paths at the selected timestamp

    def load_data(self):
        """Load the CSV data."""
        try:
            self.data = pd.read_csv(self.file_path)
            # Ensure the data is sorted by timestamp
            self.data = self.data.sort_values(by='timestamp')
            print(f"Loaded data from {self.file_path}")
        except Exception as e:
            print(f"Failed to load CSV file: {e}")

    def set_display(self, plot_widget):
        """Display the paths on the plot widget."""
        if self.current_paths is not None and not self.current_paths.empty:
            # Extract the x and y coordinates
            x_values = self.current_paths['x']
            y_values = self.current_paths['y']
            
            # Clear the plot and replot the current paths
            plot_widget.clear()
            plot_widget.plot(x_values, y_values, pen=None, symbol='o', symbolSize=5, symbolBrush='b')
        else:
            print("No paths available for the current timestamp.")

Explanation:

load_data: Loads the CSV file and stores the data in a Pandas DataFrame.

sync_data_with_timestamp: Filters the data for the given timestamp and extracts the corresponding paths.

display: Displays the paths on the provided plot widget.

3. Step 3: Register the Plugin Once we have the NewPlugin class, we need to register it in the main application.

we do so by instantiating it in main() in the main_path_regression.py.

First, add it in the include, e.g. from plugins.newplugin import NewPlugin

Then, add the two liines by writing two lines under the comment "# REGISTER PLUGINS": newplugin_plugin = NewPlugin() regression_app.load_plugins([newplugin_plugin])

Some tips and guidelines regarding data format:

# Check if the DataFrame index is already in Unix timestamp format
if self.path_obj.timestamps.dtype != 'int64':
    self.timestamps = self.path_obj.get_timestamps().astype('int64') // 10**6 
else:
    self.timestamps = self.path_obj.timestamps

Explanation:

1. Check Data Type:

   • The code first checks if the timestamps attribute of self.path_obj is not of type int64.
   • This is done using the condition self.path_obj.timestamps.dtype != 'int64'.

2. Convert to Unix Timestamp:

   • If the condition is true (i.e., the timestamps are not in int64 format), it converts the timestamps to Unix timestamp format.
   • This is achieved by calling self.path_obj.get_timestamps().astype('int64') // 10**6.
   • The astype('int64') method changes the data type to int64, and the division by 10**9 converts the timestamps to seconds.

3. Assign Timestamps:

   • If the timestamps are already in int64 format, it directly assigns self.path_obj.timestamps to self.timestamps.

This code ensures that the timestamps are always in Unix timestamp format (in seconds) before proceeding with further operations.

Top-level diagram

graph TD;
  MainPathRegression --> PluginRegistry
  MainPathRegression --> PathViewPlugin
  MainPathRegression --> TimestampSlider
  PluginRegistry --> sync_all_plugins
  PluginRegistry --> display_all_plugins
  PathViewPlugin --> load_data
  PathViewPlugin --> sync_data_with_timestamp
  PathViewPlugin --> display

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