commit be34e325d6ffbe443c233646d374314a83fa53b8 Author: turtlebasket Date: Thu Oct 3 20:21:55 2024 +0000 deploy: 54c7e5fbc735b32edac8fdddb0f2908912c17bb0 diff --git a/.nojekyll b/.nojekyll new file mode 100644 index 0000000..e69de29 diff --git a/404.html b/404.html new file mode 100644 index 0000000..196cc6a --- /dev/null +++ b/404.html @@ -0,0 +1,21 @@ + + + + + + 404 | micromissiles-unity + + + + + + + + + + +
+ + + + \ No newline at end of file diff --git a/Development_Guide.html b/Development_Guide.html new file mode 100644 index 0000000..1c6a4d9 --- /dev/null +++ b/Development_Guide.html @@ -0,0 +1,24 @@ + + + + + + Development Guide | micromissiles-unity + + + + + + + + + + + + + +
micromissiles-unity

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Development Guide ​

This guide will help you set up and run the project in development mode. You'll learn how to install Unity Hub, open the project, and navigate the main scene.

Table of Contents ​

Prerequisites ​

  • A computer with internet access.
  • Administrative privileges to install software.
  • Git installed on your system (optional, for cloning the repository).

Installation Steps ​

1. Install Unity Hub ​

Unity Hub is a desktop application that manages your Unity projects and installations. It simplifies the process of installing different Unity versions and launching projects.

Steps to Install Unity Hub:

  1. Visit the Unity Download Page.
  2. Click on "Download Unity Hub".
  3. Run the downloaded installer and follow the on-screen instructions to complete the installation.

2. Clone the Project Repository ​

Obtain the project source code by cloning the repository from GitHub.

bash
git clone https://github.com/PisterLab/micromissiles-unity.git

Alternatively, you can download the repository as a ZIP file and extract it to a preferred location.

3. Launch the Project via Unity Hub ​

Unity Hub

  1. Open Unity Hub.

  2. Add the Project to Unity Hub:

    • Navigate to the "Projects" tab.
    • Click on the "ADD" button.
    • Browse to the folder where you cloned or extracted the project.
    • Select the folder containing the Assets folder and click on "Select Folder".

  3. Install the Required Unity Version:

    • Unity Hub will detect if the project requires a Unity version that is not currently installed.
    • A notification or warning icon may appear next to the project name.
    • Click on the notification and select "Install Unity [version]".
    • Unity Hub will download and install the required Unity version automatically.

  4. Open the Project:

    • Once the required Unity version is installed, click on the project name in Unity Hub to open it.

4. Open the Main Scene ​

After the project opens in Unity:

  1. In the Project window (usually located at the bottom), navigate to:

    Assets/Scenes/

  2. Find the main scene file, usually named MainScene.unity.

  3. Double-click on MainScene.unity to open it.

  4. The scene will load in the Scene view. You can now run the simulation by clicking the Play button at the top of the Unity Editor.

Additional Resources ​

Released under the BSD-3-Clause License.

Copyright © 2024-present, The Regents of the University of California (Regents). All Rights Reserved.

+ + + + \ No newline at end of file diff --git a/Keybinds_and_Controls.html b/Keybinds_and_Controls.html new file mode 100644 index 0000000..358bca7 --- /dev/null +++ b/Keybinds_and_Controls.html @@ -0,0 +1,24 @@ + + + + + + Keybinds and Controls | micromissiles-unity + + + + + + + + + + + + + +
micromissiles-unity

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Keybinds and Controls ​

This guide will help you navigate and interact with the environment using your mouse and keyboard. Below are the keybindings organized into easy-to-read tables, along with detailed descriptions to enhance your experience.

Table of Contents ​

Mouse Controls ​

Use your mouse to control the camera's orientation and zoom level.

ActionKeybindDescription
Orbit CameraLeft Mouse Button (Hold)Rotate the camera around a focal point by holding the left mouse button and moving the mouse.
Rotate CameraRight Mouse Button (Hold)Adjust the camera's rotation by holding the right mouse button and moving the mouse.
Zoom In/OutMouse Scroll WheelScroll the wheel up to zoom in and scroll down to zoom out.

Keyboard Controls ​

Camera Movement ​

Navigate the camera through the simulation using the following keys:

ActionKeysDescription
Move ForwardW or Up ArrowMoves the camera forward in the simulation space.
Move LeftA or Left ArrowMoves the camera to the left side.
Move BackwardS or Down ArrowMoves the camera backward.
Move RightD or Right ArrowMoves the camera to the right side.
Move UpwardQRaises the camera upward.
Move DownwardELowers the camera downward.

Camera Speed ​

Adjust the speed of the camera movement to suit your preference:

ActionKeybindDescription
Increase SpeedLeft Shift (Hold)Hold down the Left Shift key to move the camera faster.
Normal SpeedRelease Left ShiftReleases the Left Shift key to return to normal movement speed.

Simulation Controls ​

Manage the simulation state with these keybinds:

ActionKeybindDescription
Pause/ResumeSpacebarToggles between pausing and resuming the simulation.
Restart SimulationRRestarts the simulation from the beginning.
Toggle Configuration PanelLOpens or closes the configuration selector panel for adjusting simulation settings.

Additional Controls ​

Enhance your interaction with the simulation using these additional keybinds:

ActionKeybindDescription
Toggle Auto-RotatePEnables or disables the camera's auto-rotate feature.
Quit ApplicationEscExits the simulation application.

Preset Views ​

Quickly switch to predefined camera angles for different perspectives:

ActionKeybindDescription
Preset View 11Switches to the first predefined camera view.
Preset View 22Switches to the second predefined camera view.
Preset View 33Switches to the third predefined camera view.
Preset View 44Switches to the fourth predefined camera view.
Preset View 55Switches to the fifth predefined camera view.
Preset View 66Switches to the sixth predefined camera view.

Note: The implementation for preset views may be added in future updates.

Tips for Effective Navigation ​

  • Combining Controls: Feel free to combine mouse and keyboard controls for a more dynamic navigation experience. For example, hold the Left Shift key while moving the camera forward with the W key for faster traversal.
  • Pausing for Adjustments: Use the Spacebar to pause the simulation, allowing you to adjust camera angles or settings without interruption.
  • Exploring Views: Utilize the preset views to quickly jump to different perspectives, making it easier to explore various aspects of the simulation.

Happy simming!

Released under the BSD-3-Clause License.

Copyright © 2024-present, The Regents of the University of California (Regents). All Rights Reserved.

+ + + + \ No newline at end of file diff --git a/Markdown_Examples.html b/Markdown_Examples.html new file mode 100644 index 0000000..1411189 --- /dev/null +++ b/Markdown_Examples.html @@ -0,0 +1,56 @@ + + + + + + Markdown Extension Examples | micromissiles-unity + + + + + + + + + + + + + +
micromissiles-unity

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Markdown Extension Examples ​

This page demonstrates some of the built-in markdown extensions provided by VitePress.

Syntax Highlighting ​

VitePress provides Syntax Highlighting powered by Shiki, with additional features like line-highlighting:

Input

md
```js{4}
+export default {
+  data () {
+    return {
+      msg: 'Highlighted!'
+    }
+  }
+}
+```

Output

js
export default {
+  data () {
+    return {
+      msg: 'Highlighted!'
+    }
+  }
+}

Custom Containers ​

Input

md
::: info
+This is an info box.
+:::
+
+::: tip
+This is a tip.
+:::
+
+::: warning
+This is a warning.
+:::
+
+::: danger
+This is a dangerous warning.
+:::
+
+::: details
+This is a details block.
+:::

Output

INFO

This is an info box.

TIP

This is a tip.

WARNING

This is a warning.

DANGER

This is a dangerous warning.

Details

This is a details block.

More ​

Check out the documentation for the full list of markdown extensions.

Released under the BSD-3-Clause License.

Copyright © 2024-present, The Regents of the University of California (Regents). All Rights Reserved.

+ + + + \ No newline at end of file diff --git a/Simulation_Config_Guide.html b/Simulation_Config_Guide.html new file mode 100644 index 0000000..c313aa2 --- /dev/null +++ b/Simulation_Config_Guide.html @@ -0,0 +1,166 @@ + + + + + + Simulation Configuration Guide | micromissiles-unity + + + + + + + + + + + + + +
micromissiles-unity

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Simulation Configuration Guide ​

This guide provides instructions on how to configure the simulation by editing the configuration files. You can customize interceptor and threat behaviors, simulation parameters, and more to suit your needs.

Configuration Files ​

The main configuration files you will work with are located in the Assets/StreamingAssets/Configs/ directory. In a deployment context, these files are located in the micromissiles_Data/StreamingAssets/Configs/ directory.

  • Simulation Configurations:

    • 1_salvo_1_hydra_7_drones.json: A simple, barebones example of a simulation configuration featuring a single salvo in a 7-on-7 scenario.
    • 3_salvo_10_hydra_200_drones.json: A more complex example with three salvos, illustrating a 210-on-200 scenario. This demonstrates how to set up multiple salvos within the simulation.
    • C# Script: SimulationConfig.cs

  • Model Configurations (found in Assets/StreamingAssets/Configs/Models/):

File Locations ​

Development context:

  • Simulation Configurations: Assets/StreamingAssets/Configs/
  • Model Configurations: Assets/StreamingAssets/Configs/Models/

Deployment context:

  • Simulation Configurations: micromissiles_Data/StreamingAssets/Configs/
  • Model Configurations: micromissiles_Data/StreamingAssets/Configs/Models/

Overview of Simulation Configurations ​

Simulation Configuration Examples ​

The simulation configurations are defined in JSON files that specify the initial setup for missiles and targets.

1_salvo_1_hydra_7_drones.json ​

This is a basic configuration featuring a single salvo with one interceptor type (HYDRA_70) and seven threat drones.

json
{
+  "timeScale": 1,
+  "interceptor_swarm_configs": [
+    {
+      "num_agents": 1,
+      "agent_config": {
+        "interceptor_type": "HYDRA_70",
+        "initial_state": {
+          "position": { "x": 0, "y": 20, "z": 0 },
+          "rotation": { "x": -45, "y": 0, "z": 0 },
+          "velocity": { "x": 0, "y": 10, "z": 10 }
+        },
+        "dynamic_config": {
+          "launch_config": { "launch_time": 0 },
+          "sensor_config": {
+            "type": "IDEAL",
+            "frequency": 100
+          }
+        },
+        "submunitions_config": {
+          "num_submunitions": 7,
+          "launch_config": { "launch_time": 4 },
+          "agent_config": {
+            "interceptor_type": "MICROMISSILE",
+            // Submunition configuration...
+          }
+        }
+      }
+    }
+  ],
+  "threat_swarm_configs": [
+    {
+      "num_agents": 7,
+      "agent_config": {
+        "threat_type": "DRONE",
+        "initial_state": {
+          "position": { "x": 0, "y": 600, "z": 6000 },
+          "rotation": { "x": 90, "y": 0, "z": 0 },
+          "velocity": { "x": 0, "y": 0, "z": -50 }
+        },
+        // Other threat configurations...
+      }
+    }
+  ]
+}

3_salvo_10_hydra_200_drones.json ​

This configuration demonstrates a more complex scenario with three salvos, each launching ten HYDRA_70 missiles at different times against 200 threat drones. This results in a total of 210 missiles (including submunitions) engaging 200 targets.

json
{
+  "timeScale": 1,
+  "interceptor_swarm_configs": [
+    {
+      "num_agents": 10,
+      "agent_config": {
+        "interceptor_type": "HYDRA_70",
+        "initial_state": {
+          "position": { "x": 0, "y": 20, "z": 0 },
+          "rotation": { "x": -45, "y": 0, "z": 0 },
+          "velocity": { "x": 0, "y": 10, "z": 10 }
+        },
+        "dynamic_config": {
+          "launch_config": { "launch_time": 0 },
+          "sensor_config": {
+            "type": "IDEAL",
+            "frequency": 100
+          }
+        },
+        "submunitions_config": {
+          "num_submunitions": 7,
+          "launch_config": { "launch_time": 4 },
+          "agent_config": {
+            "interceptor_type": "MICROMISSILE",
+            // Submunition configuration...
+          }
+        }
+      }
+    },
+    // Two more similar interceptor_swarm_configs with different launch times...
+  ],
+  "threat_swarm_configs": [
+    {
+      "num_agents": 200,
+      "agent_config": {
+        "threat_type": "DRONE",
+        "initial_state": {
+          "position": { "x": 0, "y": 600, "z": 6000 },
+          "rotation": { "x": 90, "y": 0, "z": 0 },
+          "velocity": { "x": 0, "y": 0, "z": -50 }
+        },
+        // Other threat configurations...
+      }
+    }
+  ]
+}

Key Differences Between the Examples:

The key difference between the examples is that the Number of Salvos in 3_salvo_10_hydra_200_drones.json file includes multiple salvos by adding multiple entries in the interceptor_swarm_configs array, each with its own launch_time.

Achieving Multiple Salvos:

Multiple salvos are achieved by:

  • Adding multiple configurations in the interceptor_swarm_configs array.
  • Specifying different launch_time values in the dynamic_config for each salvo to control when they launch.

Key Configuration Parameters ​

  • timeScale: Adjusts the speed of the simulation.
  • interceptor_swarm_configs: Contains settings for interceptor swarms. Each entry represents a salvo.
  • threat_swarm_configs: Contains settings for threat swarms.

Within Each Swarm Configuration ​

  • num_agents: Number of agents (missiles or targets) in the swarm.

  • agent_config: Settings for each agent, including:

    • interceptor_type / threat_type: Defines the type of interceptor or threat.
    • initial_state: Sets the starting position, rotation, and velocity.
    • standard_deviation: Adds random noise to initial states for variability.
    • dynamic_config: Time-dependent settings like launch_time and sensor configurations.
    • submunitions_config: Details for any submunitions (e.g., micromissiles deployed by a larger interceptor).

Adding or Modifying Agents ​

  1. Add a New Swarm Configuration:

    To introduce a new interceptor or threat swarm (or an additional salvo), create a new entry in interceptor_swarm_configs or threat_swarm_configs.

    json
    {
+  "num_agents": 5,
+  "agent_config": {
+    "interceptor_type": "MICROMISSILE",
+    // Additional configurations...
+    "dynamic_config": {
+      "launch_config": { "launch_time": 15 },
+      // Other dynamic settings...
+    }
+  }
+}
    • launch_time in dynamic_config controls when this swarm (or salvo) is deployed.

  2. Modify Existing Configurations:

    Adjust parameters like num_agents, initial_state, or dynamic_config to change the behavior of existing agents or salvos.

Model Configurations ​

The model configurations define the physical and performance characteristics of interceptor and threat models. The default models provided can be customized to suit your research needs.

Available Models ​

The Models directory contains the following default model configurations:

  • micromissile.json
  • hydra70.json
  • drone.json

These JSON files serve as templates and can be tweaked to modify the behavior of the corresponding models.

Editing Model Configurations ​

Example: micromissile.json ​

This file defines parameters for the micromissile model.

json:Assets/StreamingAssets/Configs/Models/micromissile.json
{
+  "accelerationConfig": {
+    "maxReferenceAcceleration": 300,
+    "referenceSpeed": 1000
+  },
+  "boostConfig": {
+    "boostTime": 0.3,
+    "boostAcceleration": 350
+  },
+  "liftDragConfig": {
+    "liftCoefficient": 0.2,
+    "dragCoefficient": 0.7,
+    "liftDragRatio": 5
+  },
+  // Other configurations...
+}

Configurable Parameters:

  • accelerationConfig: Controls acceleration characteristics.
  • boostConfig: Settings for the boost phase of the craft.
  • liftDragConfig: Aerodynamic properties.
  • bodyConfig: Physical attributes like mass and area.
  • hitConfig: Collision detection and damage properties.

Modifying Parameters ​

You can tweak the parameters in these model files to adjust performance. For example:

  • Increase Acceleration: Modify maxReferenceAcceleration in accelerationConfig.
  • Change Mass: Adjust the mass value in bodyConfig.
  • Alter Aerodynamics: Tweak liftCoefficient and dragCoefficient in liftDragConfig.

Adding New Models ​

To define a new inte or threat model:

  1. Create a New JSON File in Assets/StreamingAssets/Configs/Models/.

  2. Define Model Parameters similar to the existing model files.

  3. Update the Code to recognize and load the new model if necessary.

Note: Ensure that any new parameters added to the model configuration are reflected in the corresponding C# classes.

Relevant C# Scripts ​

SimulationConfig.cs ​

This script defines the data structures used to interpret the JSON simulation configuration files.

Assets/Scripts/Config/SimulationConfig.cs

Classes:

  • SimulationConfig: Contains all simulation settings.
  • SwarmConfig: Represents a group of agents (missiles or targets).
  • AgentConfig: Configuration for individual agents.

Enums:

  • InterceptorType, ThreatType, and SensorType define available types.

StaticConfig.cs ​

This script defines the classes corresponding to the model configuration JSON structure.

Assets/Scripts/Config/StaticConfig.cs

For example:

csharp
[Serializable]
+public class StaticConfig {
+  [Serializable]
+  public class AccelerationConfig {
+    public float maxReferenceAcceleration = 300f;
+    public float referenceSpeed = 1000f;
+  }
+
+  [Serializable]
+  public class BoostConfig {
+    public float boostTime = 0.3f;
+    public float boostAcceleration = 350f;
+  }
+
+  [Serializable]
+  public class LiftDragConfig {
+    public float liftCoefficient = 0.2f;
+    public float dragCoefficient = 0.7f;
+    public float liftDragRatio = 5f;
+  }
+
+  // Other configuration classes...
+
+  public AccelerationConfig accelerationConfig;
+  public BoostConfig boostConfig;
+  public LiftDragConfig liftDragConfig;
+  public BodyConfig bodyConfig;
+  public HitConfig hitConfig;
+}

Updating Classes:

If you add new parameters to the JSON model files, ensure the corresponding classes in StaticConfig.cs are updated to include these new fields.

Using the Deployment Build ​

When using the deployment build:

  • Include Required Configuration Files: Ensure all necessary JSON configuration files are present in the StreamingAssets/Configs/ directory.
  • Adjust Simulations Without Rebuilding: Modify the JSON files to change simulation parameters without needing to rebuild the application.

While the simulation is running, you can load a new Simulation Configuration JSON file at runtime by opening the panel with 'L'.

Note: Always back up configuration files before making significant changes. Incorrect configurations can lead to simulation errors.

For further assistance, refer to the comments and documentation within the code files:

This guide aims to help you set up and customize the simulation project effectively. If you encounter any issues or have questions, please reach out to the project maintainers.

Released under the BSD-3-Clause License.

Copyright © 2024-present, The Regents of the University of California (Regents). All Rights Reserved.

+ + + + \ No newline at end of file diff --git a/Simulation_Logging.html b/Simulation_Logging.html new file mode 100644 index 0000000..b9a0125 --- /dev/null +++ b/Simulation_Logging.html @@ -0,0 +1,83 @@ + + + + + + Simulation Logging | micromissiles-unity + + + + + + + + + + + + + +
micromissiles-unity

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Simulation Logging ​

This guide provides instructions on how to access and interpret the simulation logs, how they are structured by the SimMonitor class, and how to utilize the provided visualize_log.py script to analyze simulation data. Additionally, it offers guidance on creating your own scripts for custom analysis.

Overview ​

Python simulation log visualizer

Simulation logs capture detailed telemetry and event data from each simulation run. These logs are essential for debugging, performance analysis, and understanding the behavior of agents within the simulation.

Logs are exported to the Telemetry/Logs folder in your operating system's persistent data path.

For example, on Windows, the logs are exported to:

C:\Users\<user>\AppData\LocalLow\BAMLAB\micromissiles\Telemetry\Logs

On macOS, the logs are exported to:

~/Library/Application Support/BAMLAB/micromissiles/Telemetry/Logs

visualize_log.py is an example script provided to help visualize and interpret the simulation logs. It is included in the Tools directory of the release download.

Understanding Log Files and Directory Structure ​

Log Directory Structure ​

Simulation logs are organized into timestamped directories within the Logs folder. Each simulation run generates a new directory named with the timestamp of the run.

For example:

Telemetry/
+└── Logs/
+    ├── 20241002_101305/
+    │   ├── sim_telemetry_20241002_101311.csv
+    │   ├── sim_events_20241002_101311.csv
+    │   │
+    │   ├── sim_telemetry_20241002_101306.csv
+    │   └── sim_events_20241002_101306.csv
+    ├── 20241002_012122/
+    │   ├── sim_telemetry_20241002_012122.csv
+    │   └── sim_events_20241002_012122.csv
+    └── ...

Each simulation run produces two main CSV files:

  • Telemetry Log (sim_telemetry_*.csv): Contains detailed state information for each agent at each time step.
  • Event Log (sim_events_*.csv): Records significant events such as hits, misses, agent creation, and destruction.

Log Files Generated by SimMonitor ​

The logging system is managed by the SimMonitor class in the simulation codebase.

csharp
public class SimMonitor : MonoBehaviour
+{
+    // Responsible for logging simulation data
+    // ...
+}

Key Responsibilities of SimMonitor:

  • Collecting agent state data at each simulation step.
  • Writing telemetry data to sim_telemetry_*.csv.
  • Recording significant events to sim_events_*.csv.
  • Organizing logs into timestamped directories for each simulation run.

Telemetry Log Structure ​

The telemetry log provides a snapshot of the simulation at each time step for every agent. Key columns include:

  • Time: Simulation time at the log entry.
  • AgentID: Unique identifier for each agent.
  • AgentType: Type of the agent (e.g., interceptor, threat).
  • AgentX, AgentY, AgentZ: Position coordinates of the agent.
  • AgentVelocityX, AgentVelocityY, AgentVelocityZ: Velocity components.
  • AgentStatus: Current status of the agent (e.g., active, destroyed).

Event Log Structure ​

The event log records significant occurrences within the simulation. Key columns include:

  • Time: Time when the event occurred.
  • PositionX, PositionY, PositionZ: Position where the event occurred.
  • EventType: Type of event (e.g., HIT, MISS, NEW_THREAT, NEW_INTERCEPTOR).
  • Details: Additional details about the event.

Running the visualize_log.py Script ​

The visualize_log.py script helps visualize agent trajectories and significant events in a 3D plot.

Locating the Script ​

After downloading and extracting the release package, you can find the script at:

Tools/visualize_log.py

Make sure you have Python 3 installed on your system, along with the required libraries to run the script.

Required Python Libraries ​

The script depends on the following Python libraries:

  • pandas
  • matplotlib
  • numpy

You can install them using pip:

bash
pip install pandas matplotlib numpy

Usage ​

Open a terminal or command prompt and navigate to the Tools directory:

bash
cd path/to/Tools/

Run the Script ​

To visualize the most recent simulation logs:

bash
python visualize_log.py

What the Script Does:

  • Automatically Finds the Latest Logs: If no arguments are provided, it locates the most recent sim_telemetry_*.csv and sim_events_*.csv files.
  • Prints a Summary: Outputs a summary of events, including total counts and timing of hits and misses.
  • Generates a 3D Plot: Displays agent trajectories and marks events such as hits and misses.

Specifying Log Files Manually ​

You can also provide specific file paths as arguments:

bash
python visualize_log.py path/to/sim_telemetry_file.csv path/to/sim_events_file.csv

Example Output ​

Total number of events: 150
+
+Event Counts:
+  HIT: 120
+  MISS: 30
+
+First hit at 5.00 seconds, last hit at 15.00 seconds
+
+[3D plot window opens showing trajectories and events]

Interpreting the Plot ​

The 3D plot displays:

  • Agent Trajectories: Lines representing the paths of interceptors and threats.
    • Colors indicate agent types (e.g., blue for interceptors, red for threats).
  • Event Markers: Symbols marking where events occurred.
    • Hits: Marked with green circles.
    • Misses: Marked with red crosses.

Adjusting the Visualization ​

  • View Angle: You can rotate the 3D plot by clicking and dragging to view the simulation from different perspectives.
  • Zoom: Use the scroll wheel to zoom in and out.

Writing Your Own Scripts ​

The simulation logs are in CSV format, making them accessible for custom analysis and visualization.

Getting Started ​

  • Choose a Programming Language: Python or MATLAB are recommended for ease-of-use and data analysis capabilities.

For example, using Python and the pandas library, you can load the telemetry and event data like this:

python
import pandas as pd
+
+telemetry_df = pd.read_csv('path/to/sim_telemetry_*.csv')
+event_df = pd.read_csv('path/to/sim_events_*.csv')

Visualization ​

  • 2D Plots: Use matplotlib to create time-series plots:
python
import matplotlib.pyplot as plt
+
+plt.plot(telemetry_df['Time'], telemetry_df['AgentY'])
+plt.xlabel('Time (s)')
+plt.ylabel('Altitude (m)')
+plt.title('Agent Altitude Over Time')
+plt.show()
  • 3D Plots: Use mpl_toolkits.mplot3d for 3D trajectory plots.

Sample Script: Calculating Miss Distances ​

python
import pandas as pd
+import numpy as np
+
+# Load telemetry and event data
+telemetry_df = pd.read_csv('path/to/sim_telemetry_*.csv')
+event_df = pd.read_csv('path/to/sim_events_*.csv')
+
+# Filter miss events
+miss_events = event_df[event_df['Event'] == 'MISS']
+
+# Calculate miss distances
+miss_distances = []
+for idx, miss in miss_events.iterrows():
+    agent_id = miss['AgentID']
+    time = miss['Time']
+    # Get agent position at the time of miss
+    agent_state = telemetry_df[(telemetry_df['AgentID'] == agent_id) & (telemetry_df['Time'] == time)]
+    if not agent_state.empty:
+        x = agent_state['AgentX'].values[0]
+        y = agent_state['AgentY'].values[0]
+        z = agent_state['AgentZ'].values[0]
+        # Calculate distance to target or predefined point
+        distance = np.sqrt(x**2 + y**2 + z**2)
+        miss_distances.append(distance)
+
+# Output average miss distance
+average_miss_distance = np.mean(miss_distances)
+print(f'Average Miss Distance: {average_miss_distance:.2f} meters')

Suggestions for Analysis ​

  • Performance Metrics: Determine interception success rates, average time to intercept, or hit accuracy.
  • Behavioral Analysis: Examine how changes in simulation configurations affect agent behavior.
  • Batch Processing: Automate analysis over multiple simulation runs to compare different scenarios.

Additional Resources ​

Released under the BSD-3-Clause License.

Copyright © 2024-present, The Regents of the University of California (Regents). All Rights Reserved.

+ + + + \ No newline at end of file diff --git a/assets/Development_Guide.md.Wx7fNEd3.js b/assets/Development_Guide.md.Wx7fNEd3.js new file mode 100644 index 0000000..ad643d8 --- /dev/null +++ b/assets/Development_Guide.md.Wx7fNEd3.js @@ -0,0 +1 @@ +import{_ as t,c as i,a2 as n,o}from"./chunks/framework.DYF6f1aH.js";const a="/micromissiles-unity/assets/unity_hub.cJHZjOmp.png",b=JSON.parse('{"title":"Development Guide","description":"","frontmatter":{},"headers":[],"relativePath":"Development_Guide.md","filePath":"Development_Guide.md"}'),l={name:"Development_Guide.md"};function s(r,e,h,c,u,p){return o(),i("div",null,e[0]||(e[0]=[n('

Development Guide ​

This guide will help you set up and run the project in development mode. You'll learn how to install Unity Hub, open the project, and navigate the main scene.

Table of Contents ​

Prerequisites ​

Installation Steps ​

1. Install Unity Hub ​

Unity Hub is a desktop application that manages your Unity projects and installations. It simplifies the process of installing different Unity versions and launching projects.

Steps to Install Unity Hub:

  1. Visit the Unity Download Page.
  2. Click on "Download Unity Hub".
  3. Run the downloaded installer and follow the on-screen instructions to complete the installation.

2. Clone the Project Repository ​

Obtain the project source code by cloning the repository from GitHub.

bash
git clone https://github.com/PisterLab/micromissiles-unity.git

Alternatively, you can download the repository as a ZIP file and extract it to a preferred location.

3. Launch the Project via Unity Hub ​

Unity Hub

  1. Open Unity Hub.

  2. Add the Project to Unity Hub:

    • Navigate to the "Projects" tab.
    • Click on the "ADD" button.
    • Browse to the folder where you cloned or extracted the project.
    • Select the folder containing the Assets folder and click on "Select Folder".

  3. Install the Required Unity Version:

    • Unity Hub will detect if the project requires a Unity version that is not currently installed.
    • A notification or warning icon may appear next to the project name.
    • Click on the notification and select "Install Unity [version]".
    • Unity Hub will download and install the required Unity version automatically.

  4. Open the Project:

    • Once the required Unity version is installed, click on the project name in Unity Hub to open it.

4. Open the Main Scene ​

After the project opens in Unity:

  1. In the Project window (usually located at the bottom), navigate to:

    Assets/Scenes/

  2. Find the main scene file, usually named MainScene.unity.

  3. Double-click on MainScene.unity to open it.

  4. The scene will load in the Scene view. You can now run the simulation by clicking the Play button at the top of the Unity Editor.

Additional Resources ​

',23)]))}const y=t(l,[["render",s]]);export{b as __pageData,y as default}; diff --git a/assets/Development_Guide.md.Wx7fNEd3.lean.js b/assets/Development_Guide.md.Wx7fNEd3.lean.js new file mode 100644 index 0000000..ad643d8 --- /dev/null +++ b/assets/Development_Guide.md.Wx7fNEd3.lean.js @@ -0,0 +1 @@ +import{_ as t,c as i,a2 as n,o}from"./chunks/framework.DYF6f1aH.js";const a="/micromissiles-unity/assets/unity_hub.cJHZjOmp.png",b=JSON.parse('{"title":"Development Guide","description":"","frontmatter":{},"headers":[],"relativePath":"Development_Guide.md","filePath":"Development_Guide.md"}'),l={name:"Development_Guide.md"};function s(r,e,h,c,u,p){return o(),i("div",null,e[0]||(e[0]=[n('

Development Guide ​

This guide will help you set up and run the project in development mode. You'll learn how to install Unity Hub, open the project, and navigate the main scene.

Table of Contents ​

Prerequisites ​

Installation Steps ​

1. Install Unity Hub ​

Unity Hub is a desktop application that manages your Unity projects and installations. It simplifies the process of installing different Unity versions and launching projects.

Steps to Install Unity Hub:

  1. Visit the Unity Download Page.
  2. Click on "Download Unity Hub".
  3. Run the downloaded installer and follow the on-screen instructions to complete the installation.

2. Clone the Project Repository ​

Obtain the project source code by cloning the repository from GitHub.

bash
git clone https://github.com/PisterLab/micromissiles-unity.git

Alternatively, you can download the repository as a ZIP file and extract it to a preferred location.

3. Launch the Project via Unity Hub ​

Unity Hub

  1. Open Unity Hub.

  2. Add the Project to Unity Hub:

    • Navigate to the "Projects" tab.
    • Click on the "ADD" button.
    • Browse to the folder where you cloned or extracted the project.
    • Select the folder containing the Assets folder and click on "Select Folder".

  3. Install the Required Unity Version:

    • Unity Hub will detect if the project requires a Unity version that is not currently installed.
    • A notification or warning icon may appear next to the project name.
    • Click on the notification and select "Install Unity [version]".
    • Unity Hub will download and install the required Unity version automatically.

  4. Open the Project:

    • Once the required Unity version is installed, click on the project name in Unity Hub to open it.

4. Open the Main Scene ​

After the project opens in Unity:

  1. In the Project window (usually located at the bottom), navigate to:

    Assets/Scenes/

  2. Find the main scene file, usually named MainScene.unity.

  3. Double-click on MainScene.unity to open it.

  4. The scene will load in the Scene view. You can now run the simulation by clicking the Play button at the top of the Unity Editor.

Additional Resources ​

',23)]))}const y=t(l,[["render",s]]);export{b as __pageData,y as default}; diff --git a/assets/Keybinds_and_Controls.md.CAuuuHw7.js b/assets/Keybinds_and_Controls.md.CAuuuHw7.js new file mode 100644 index 0000000..b46cbb1 --- /dev/null +++ b/assets/Keybinds_and_Controls.md.CAuuuHw7.js @@ -0,0 +1 @@ +import{_ as e,c as o,a2 as r,o as n}from"./chunks/framework.DYF6f1aH.js";const m=JSON.parse('{"title":"Keybinds and Controls","description":"","frontmatter":{},"headers":[],"relativePath":"Keybinds_and_Controls.md","filePath":"Keybinds_and_Controls.md"}'),a={name:"Keybinds_and_Controls.md"};function s(d,t,i,l,h,g){return n(),o("div",null,t[0]||(t[0]=[r('

Keybinds and Controls ​

This guide will help you navigate and interact with the environment using your mouse and keyboard. Below are the keybindings organized into easy-to-read tables, along with detailed descriptions to enhance your experience.

Table of Contents ​

Mouse Controls ​

Use your mouse to control the camera's orientation and zoom level.

ActionKeybindDescription
Orbit CameraLeft Mouse Button (Hold)Rotate the camera around a focal point by holding the left mouse button and moving the mouse.
Rotate CameraRight Mouse Button (Hold)Adjust the camera's rotation by holding the right mouse button and moving the mouse.
Zoom In/OutMouse Scroll WheelScroll the wheel up to zoom in and scroll down to zoom out.

Keyboard Controls ​

Camera Movement ​

Navigate the camera through the simulation using the following keys:

ActionKeysDescription
Move ForwardW or Up ArrowMoves the camera forward in the simulation space.
Move LeftA or Left ArrowMoves the camera to the left side.
Move BackwardS or Down ArrowMoves the camera backward.
Move RightD or Right ArrowMoves the camera to the right side.
Move UpwardQRaises the camera upward.
Move DownwardELowers the camera downward.

Camera Speed ​

Adjust the speed of the camera movement to suit your preference:

ActionKeybindDescription
Increase SpeedLeft Shift (Hold)Hold down the Left Shift key to move the camera faster.
Normal SpeedRelease Left ShiftReleases the Left Shift key to return to normal movement speed.

Simulation Controls ​

Manage the simulation state with these keybinds:

ActionKeybindDescription
Pause/ResumeSpacebarToggles between pausing and resuming the simulation.
Restart SimulationRRestarts the simulation from the beginning.
Toggle Configuration PanelLOpens or closes the configuration selector panel for adjusting simulation settings.

Additional Controls ​

Enhance your interaction with the simulation using these additional keybinds:

ActionKeybindDescription
Toggle Auto-RotatePEnables or disables the camera's auto-rotate feature.
Quit ApplicationEscExits the simulation application.

Preset Views ​

Quickly switch to predefined camera angles for different perspectives:

ActionKeybindDescription
Preset View 11Switches to the first predefined camera view.
Preset View 22Switches to the second predefined camera view.
Preset View 33Switches to the third predefined camera view.
Preset View 44Switches to the fourth predefined camera view.
Preset View 55Switches to the fifth predefined camera view.
Preset View 66Switches to the sixth predefined camera view.

Note: The implementation for preset views may be added in future updates.

Tips for Effective Navigation ​

Happy simming!

',27)]))}const u=e(a,[["render",s]]);export{m as __pageData,u as default}; diff --git a/assets/Keybinds_and_Controls.md.CAuuuHw7.lean.js b/assets/Keybinds_and_Controls.md.CAuuuHw7.lean.js new file mode 100644 index 0000000..b46cbb1 --- /dev/null +++ b/assets/Keybinds_and_Controls.md.CAuuuHw7.lean.js @@ -0,0 +1 @@ +import{_ as e,c as o,a2 as r,o as n}from"./chunks/framework.DYF6f1aH.js";const m=JSON.parse('{"title":"Keybinds and Controls","description":"","frontmatter":{},"headers":[],"relativePath":"Keybinds_and_Controls.md","filePath":"Keybinds_and_Controls.md"}'),a={name:"Keybinds_and_Controls.md"};function s(d,t,i,l,h,g){return n(),o("div",null,t[0]||(t[0]=[r('

Keybinds and Controls ​

This guide will help you navigate and interact with the environment using your mouse and keyboard. Below are the keybindings organized into easy-to-read tables, along with detailed descriptions to enhance your experience.

Table of Contents ​

Mouse Controls ​

Use your mouse to control the camera's orientation and zoom level.

ActionKeybindDescription
Orbit CameraLeft Mouse Button (Hold)Rotate the camera around a focal point by holding the left mouse button and moving the mouse.
Rotate CameraRight Mouse Button (Hold)Adjust the camera's rotation by holding the right mouse button and moving the mouse.
Zoom In/OutMouse Scroll WheelScroll the wheel up to zoom in and scroll down to zoom out.

Keyboard Controls ​

Camera Movement ​

Navigate the camera through the simulation using the following keys:

ActionKeysDescription
Move ForwardW or Up ArrowMoves the camera forward in the simulation space.
Move LeftA or Left ArrowMoves the camera to the left side.
Move BackwardS or Down ArrowMoves the camera backward.
Move RightD or Right ArrowMoves the camera to the right side.
Move UpwardQRaises the camera upward.
Move DownwardELowers the camera downward.

Camera Speed ​

Adjust the speed of the camera movement to suit your preference:

ActionKeybindDescription
Increase SpeedLeft Shift (Hold)Hold down the Left Shift key to move the camera faster.
Normal SpeedRelease Left ShiftReleases the Left Shift key to return to normal movement speed.

Simulation Controls ​

Manage the simulation state with these keybinds:

ActionKeybindDescription
Pause/ResumeSpacebarToggles between pausing and resuming the simulation.
Restart SimulationRRestarts the simulation from the beginning.
Toggle Configuration PanelLOpens or closes the configuration selector panel for adjusting simulation settings.

Additional Controls ​

Enhance your interaction with the simulation using these additional keybinds:

ActionKeybindDescription
Toggle Auto-RotatePEnables or disables the camera's auto-rotate feature.
Quit ApplicationEscExits the simulation application.

Preset Views ​

Quickly switch to predefined camera angles for different perspectives:

ActionKeybindDescription
Preset View 11Switches to the first predefined camera view.
Preset View 22Switches to the second predefined camera view.
Preset View 33Switches to the third predefined camera view.
Preset View 44Switches to the fourth predefined camera view.
Preset View 55Switches to the fifth predefined camera view.
Preset View 66Switches to the sixth predefined camera view.

Note: The implementation for preset views may be added in future updates.

Tips for Effective Navigation ​

Happy simming!

',27)]))}const u=e(a,[["render",s]]);export{m as __pageData,u as default}; diff --git a/assets/Markdown_Examples.md.BJC7tN3K.js b/assets/Markdown_Examples.md.BJC7tN3K.js new file mode 100644 index 0000000..0ad6644 --- /dev/null +++ b/assets/Markdown_Examples.md.BJC7tN3K.js @@ -0,0 +1,33 @@ +import{_ as a,c as i,a2 as n,o as t}from"./chunks/framework.DYF6f1aH.js";const E=JSON.parse('{"title":"Markdown Extension Examples","description":"","frontmatter":{},"headers":[],"relativePath":"Markdown_Examples.md","filePath":"Markdown_Examples.md"}'),l={name:"Markdown_Examples.md"};function e(p,s,h,k,r,d){return t(),i("div",null,s[0]||(s[0]=[n(`

Markdown Extension Examples ​

This page demonstrates some of the built-in markdown extensions provided by VitePress.

Syntax Highlighting ​

VitePress provides Syntax Highlighting powered by Shiki, with additional features like line-highlighting:

Input

md
\`\`\`js{4}
+export default {
+  data () {
+    return {
+      msg: 'Highlighted!'
+    }
+  }
+}
+\`\`\`

Output

js
export default {
+  data () {
+    return {
+      msg: 'Highlighted!'
+    }
+  }
+}

Custom Containers ​

Input

md
::: info
+This is an info box.
+:::
+
+::: tip
+This is a tip.
+:::
+
+::: warning
+This is a warning.
+:::
+
+::: danger
+This is a dangerous warning.
+:::
+
+::: details
+This is a details block.
+:::

Output

INFO

This is an info box.

TIP

This is a tip.

WARNING

This is a warning.

DANGER

This is a dangerous warning.

Details

This is a details block.

More ​

Check out the documentation for the full list of markdown extensions.

`,19)]))}const c=a(l,[["render",e]]);export{E as __pageData,c as default}; diff --git a/assets/Markdown_Examples.md.BJC7tN3K.lean.js b/assets/Markdown_Examples.md.BJC7tN3K.lean.js new file mode 100644 index 0000000..0ad6644 --- /dev/null +++ b/assets/Markdown_Examples.md.BJC7tN3K.lean.js @@ -0,0 +1,33 @@ +import{_ as a,c as i,a2 as n,o as t}from"./chunks/framework.DYF6f1aH.js";const E=JSON.parse('{"title":"Markdown Extension Examples","description":"","frontmatter":{},"headers":[],"relativePath":"Markdown_Examples.md","filePath":"Markdown_Examples.md"}'),l={name:"Markdown_Examples.md"};function e(p,s,h,k,r,d){return t(),i("div",null,s[0]||(s[0]=[n(`

Markdown Extension Examples ​

This page demonstrates some of the built-in markdown extensions provided by VitePress.

Syntax Highlighting ​

VitePress provides Syntax Highlighting powered by Shiki, with additional features like line-highlighting:

Input

md
\`\`\`js{4}
+export default {
+  data () {
+    return {
+      msg: 'Highlighted!'
+    }
+  }
+}
+\`\`\`

Output

js
export default {
+  data () {
+    return {
+      msg: 'Highlighted!'
+    }
+  }
+}

Custom Containers ​

Input

md
::: info
+This is an info box.
+:::
+
+::: tip
+This is a tip.
+:::
+
+::: warning
+This is a warning.
+:::
+
+::: danger
+This is a dangerous warning.
+:::
+
+::: details
+This is a details block.
+:::

Output

INFO

This is an info box.

TIP

This is a tip.

WARNING

This is a warning.

DANGER

This is a dangerous warning.

Details

This is a details block.

More ​

Check out the documentation for the full list of markdown extensions.

`,19)]))}const c=a(l,[["render",e]]);export{E as __pageData,c as default}; diff --git a/assets/Simulation_Config_Guide.md.BZQcs5Eb.js b/assets/Simulation_Config_Guide.md.BZQcs5Eb.js new file mode 100644 index 0000000..cb1c408 --- /dev/null +++ b/assets/Simulation_Config_Guide.md.BZQcs5Eb.js @@ -0,0 +1,143 @@ +import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DYF6f1aH.js";const E=JSON.parse('{"title":"Simulation Configuration Guide","description":"","frontmatter":{},"headers":[],"relativePath":"Simulation_Config_Guide.md","filePath":"Simulation_Config_Guide.md"}'),e={name:"Simulation_Config_Guide.md"};function l(h,s,p,k,o,r){return t(),a("div",null,s[0]||(s[0]=[n(`

Simulation Configuration Guide ​

This guide provides instructions on how to configure the simulation by editing the configuration files. You can customize interceptor and threat behaviors, simulation parameters, and more to suit your needs.

Configuration Files ​

The main configuration files you will work with are located in the Assets/StreamingAssets/Configs/ directory. In a deployment context, these files are located in the micromissiles_Data/StreamingAssets/Configs/ directory.

File Locations ​

Development context:

Deployment context:

Overview of Simulation Configurations ​

Simulation Configuration Examples ​

The simulation configurations are defined in JSON files that specify the initial setup for missiles and targets.

1_salvo_1_hydra_7_drones.json ​

This is a basic configuration featuring a single salvo with one interceptor type (HYDRA_70) and seven threat drones.

json
{
+  "timeScale": 1,
+  "interceptor_swarm_configs": [
+    {
+      "num_agents": 1,
+      "agent_config": {
+        "interceptor_type": "HYDRA_70",
+        "initial_state": {
+          "position": { "x": 0, "y": 20, "z": 0 },
+          "rotation": { "x": -45, "y": 0, "z": 0 },
+          "velocity": { "x": 0, "y": 10, "z": 10 }
+        },
+        "dynamic_config": {
+          "launch_config": { "launch_time": 0 },
+          "sensor_config": {
+            "type": "IDEAL",
+            "frequency": 100
+          }
+        },
+        "submunitions_config": {
+          "num_submunitions": 7,
+          "launch_config": { "launch_time": 4 },
+          "agent_config": {
+            "interceptor_type": "MICROMISSILE",
+            // Submunition configuration...
+          }
+        }
+      }
+    }
+  ],
+  "threat_swarm_configs": [
+    {
+      "num_agents": 7,
+      "agent_config": {
+        "threat_type": "DRONE",
+        "initial_state": {
+          "position": { "x": 0, "y": 600, "z": 6000 },
+          "rotation": { "x": 90, "y": 0, "z": 0 },
+          "velocity": { "x": 0, "y": 0, "z": -50 }
+        },
+        // Other threat configurations...
+      }
+    }
+  ]
+}

3_salvo_10_hydra_200_drones.json ​

This configuration demonstrates a more complex scenario with three salvos, each launching ten HYDRA_70 missiles at different times against 200 threat drones. This results in a total of 210 missiles (including submunitions) engaging 200 targets.

json
{
+  "timeScale": 1,
+  "interceptor_swarm_configs": [
+    {
+      "num_agents": 10,
+      "agent_config": {
+        "interceptor_type": "HYDRA_70",
+        "initial_state": {
+          "position": { "x": 0, "y": 20, "z": 0 },
+          "rotation": { "x": -45, "y": 0, "z": 0 },
+          "velocity": { "x": 0, "y": 10, "z": 10 }
+        },
+        "dynamic_config": {
+          "launch_config": { "launch_time": 0 },
+          "sensor_config": {
+            "type": "IDEAL",
+            "frequency": 100
+          }
+        },
+        "submunitions_config": {
+          "num_submunitions": 7,
+          "launch_config": { "launch_time": 4 },
+          "agent_config": {
+            "interceptor_type": "MICROMISSILE",
+            // Submunition configuration...
+          }
+        }
+      }
+    },
+    // Two more similar interceptor_swarm_configs with different launch times...
+  ],
+  "threat_swarm_configs": [
+    {
+      "num_agents": 200,
+      "agent_config": {
+        "threat_type": "DRONE",
+        "initial_state": {
+          "position": { "x": 0, "y": 600, "z": 6000 },
+          "rotation": { "x": 90, "y": 0, "z": 0 },
+          "velocity": { "x": 0, "y": 0, "z": -50 }
+        },
+        // Other threat configurations...
+      }
+    }
+  ]
+}

Key Differences Between the Examples:

The key difference between the examples is that the Number of Salvos in 3_salvo_10_hydra_200_drones.json file includes multiple salvos by adding multiple entries in the interceptor_swarm_configs array, each with its own launch_time.

Achieving Multiple Salvos:

Multiple salvos are achieved by:

Key Configuration Parameters ​

Within Each Swarm Configuration ​

Adding or Modifying Agents ​

  1. Add a New Swarm Configuration:

    To introduce a new interceptor or threat swarm (or an additional salvo), create a new entry in interceptor_swarm_configs or threat_swarm_configs.

    json
    {
+  "num_agents": 5,
+  "agent_config": {
+    "interceptor_type": "MICROMISSILE",
+    // Additional configurations...
+    "dynamic_config": {
+      "launch_config": { "launch_time": 15 },
+      // Other dynamic settings...
+    }
+  }
+}
    • launch_time in dynamic_config controls when this swarm (or salvo) is deployed.

  2. Modify Existing Configurations:

    Adjust parameters like num_agents, initial_state, or dynamic_config to change the behavior of existing agents or salvos.

Model Configurations ​

The model configurations define the physical and performance characteristics of interceptor and threat models. The default models provided can be customized to suit your research needs.

Available Models ​

The Models directory contains the following default model configurations:

These JSON files serve as templates and can be tweaked to modify the behavior of the corresponding models.

Editing Model Configurations ​

Example: micromissile.json ​

This file defines parameters for the micromissile model.

json:Assets/StreamingAssets/Configs/Models/micromissile.json
{
+  "accelerationConfig": {
+    "maxReferenceAcceleration": 300,
+    "referenceSpeed": 1000
+  },
+  "boostConfig": {
+    "boostTime": 0.3,
+    "boostAcceleration": 350
+  },
+  "liftDragConfig": {
+    "liftCoefficient": 0.2,
+    "dragCoefficient": 0.7,
+    "liftDragRatio": 5
+  },
+  // Other configurations...
+}

Configurable Parameters:

Modifying Parameters ​

You can tweak the parameters in these model files to adjust performance. For example:

Adding New Models ​

To define a new inte or threat model:

  1. Create a New JSON File in Assets/StreamingAssets/Configs/Models/.

  2. Define Model Parameters similar to the existing model files.

  3. Update the Code to recognize and load the new model if necessary.

Note: Ensure that any new parameters added to the model configuration are reflected in the corresponding C# classes.

Relevant C# Scripts ​

SimulationConfig.cs ​

This script defines the data structures used to interpret the JSON simulation configuration files.

Assets/Scripts/Config/SimulationConfig.cs

Classes:

Enums:

StaticConfig.cs ​

This script defines the classes corresponding to the model configuration JSON structure.

Assets/Scripts/Config/StaticConfig.cs

For example:

csharp
[Serializable]
+public class StaticConfig {
+  [Serializable]
+  public class AccelerationConfig {
+    public float maxReferenceAcceleration = 300f;
+    public float referenceSpeed = 1000f;
+  }
+
+  [Serializable]
+  public class BoostConfig {
+    public float boostTime = 0.3f;
+    public float boostAcceleration = 350f;
+  }
+
+  [Serializable]
+  public class LiftDragConfig {
+    public float liftCoefficient = 0.2f;
+    public float dragCoefficient = 0.7f;
+    public float liftDragRatio = 5f;
+  }
+
+  // Other configuration classes...
+
+  public AccelerationConfig accelerationConfig;
+  public BoostConfig boostConfig;
+  public LiftDragConfig liftDragConfig;
+  public BodyConfig bodyConfig;
+  public HitConfig hitConfig;
+}

Updating Classes:

If you add new parameters to the JSON model files, ensure the corresponding classes in StaticConfig.cs are updated to include these new fields.

Using the Deployment Build ​

When using the deployment build:

While the simulation is running, you can load a new Simulation Configuration JSON file at runtime by opening the panel with 'L'.

Note: Always back up configuration files before making significant changes. Incorrect configurations can lead to simulation errors.

For further assistance, refer to the comments and documentation within the code files:

This guide aims to help you set up and customize the simulation project effectively. If you encounter any issues or have questions, please reach out to the project maintainers.

`,75)]))}const g=i(e,[["render",l]]);export{E as __pageData,g as default}; diff --git a/assets/Simulation_Config_Guide.md.BZQcs5Eb.lean.js b/assets/Simulation_Config_Guide.md.BZQcs5Eb.lean.js new file mode 100644 index 0000000..cb1c408 --- /dev/null +++ b/assets/Simulation_Config_Guide.md.BZQcs5Eb.lean.js @@ -0,0 +1,143 @@ +import{_ as i,c as a,a2 as n,o as t}from"./chunks/framework.DYF6f1aH.js";const E=JSON.parse('{"title":"Simulation Configuration Guide","description":"","frontmatter":{},"headers":[],"relativePath":"Simulation_Config_Guide.md","filePath":"Simulation_Config_Guide.md"}'),e={name:"Simulation_Config_Guide.md"};function l(h,s,p,k,o,r){return t(),a("div",null,s[0]||(s[0]=[n(`

Simulation Configuration Guide ​

This guide provides instructions on how to configure the simulation by editing the configuration files. You can customize interceptor and threat behaviors, simulation parameters, and more to suit your needs.

Configuration Files ​

The main configuration files you will work with are located in the Assets/StreamingAssets/Configs/ directory. In a deployment context, these files are located in the micromissiles_Data/StreamingAssets/Configs/ directory.

File Locations ​

Development context:

Deployment context:

Overview of Simulation Configurations ​

Simulation Configuration Examples ​

The simulation configurations are defined in JSON files that specify the initial setup for missiles and targets.

1_salvo_1_hydra_7_drones.json ​

This is a basic configuration featuring a single salvo with one interceptor type (HYDRA_70) and seven threat drones.

json
{
+  "timeScale": 1,
+  "interceptor_swarm_configs": [
+    {
+      "num_agents": 1,
+      "agent_config": {
+        "interceptor_type": "HYDRA_70",
+        "initial_state": {
+          "position": { "x": 0, "y": 20, "z": 0 },
+          "rotation": { "x": -45, "y": 0, "z": 0 },
+          "velocity": { "x": 0, "y": 10, "z": 10 }
+        },
+        "dynamic_config": {
+          "launch_config": { "launch_time": 0 },
+          "sensor_config": {
+            "type": "IDEAL",
+            "frequency": 100
+          }
+        },
+        "submunitions_config": {
+          "num_submunitions": 7,
+          "launch_config": { "launch_time": 4 },
+          "agent_config": {
+            "interceptor_type": "MICROMISSILE",
+            // Submunition configuration...
+          }
+        }
+      }
+    }
+  ],
+  "threat_swarm_configs": [
+    {
+      "num_agents": 7,
+      "agent_config": {
+        "threat_type": "DRONE",
+        "initial_state": {
+          "position": { "x": 0, "y": 600, "z": 6000 },
+          "rotation": { "x": 90, "y": 0, "z": 0 },
+          "velocity": { "x": 0, "y": 0, "z": -50 }
+        },
+        // Other threat configurations...
+      }
+    }
+  ]
+}

3_salvo_10_hydra_200_drones.json ​

This configuration demonstrates a more complex scenario with three salvos, each launching ten HYDRA_70 missiles at different times against 200 threat drones. This results in a total of 210 missiles (including submunitions) engaging 200 targets.

json
{
+  "timeScale": 1,
+  "interceptor_swarm_configs": [
+    {
+      "num_agents": 10,
+      "agent_config": {
+        "interceptor_type": "HYDRA_70",
+        "initial_state": {
+          "position": { "x": 0, "y": 20, "z": 0 },
+          "rotation": { "x": -45, "y": 0, "z": 0 },
+          "velocity": { "x": 0, "y": 10, "z": 10 }
+        },
+        "dynamic_config": {
+          "launch_config": { "launch_time": 0 },
+          "sensor_config": {
+            "type": "IDEAL",
+            "frequency": 100
+          }
+        },
+        "submunitions_config": {
+          "num_submunitions": 7,
+          "launch_config": { "launch_time": 4 },
+          "agent_config": {
+            "interceptor_type": "MICROMISSILE",
+            // Submunition configuration...
+          }
+        }
+      }
+    },
+    // Two more similar interceptor_swarm_configs with different launch times...
+  ],
+  "threat_swarm_configs": [
+    {
+      "num_agents": 200,
+      "agent_config": {
+        "threat_type": "DRONE",
+        "initial_state": {
+          "position": { "x": 0, "y": 600, "z": 6000 },
+          "rotation": { "x": 90, "y": 0, "z": 0 },
+          "velocity": { "x": 0, "y": 0, "z": -50 }
+        },
+        // Other threat configurations...
+      }
+    }
+  ]
+}

Key Differences Between the Examples:

The key difference between the examples is that the Number of Salvos in 3_salvo_10_hydra_200_drones.json file includes multiple salvos by adding multiple entries in the interceptor_swarm_configs array, each with its own launch_time.

Achieving Multiple Salvos:

Multiple salvos are achieved by:

Key Configuration Parameters ​

Within Each Swarm Configuration ​

Adding or Modifying Agents ​

  1. Add a New Swarm Configuration:

    To introduce a new interceptor or threat swarm (or an additional salvo), create a new entry in interceptor_swarm_configs or threat_swarm_configs.

    json
    {
+  "num_agents": 5,
+  "agent_config": {
+    "interceptor_type": "MICROMISSILE",
+    // Additional configurations...
+    "dynamic_config": {
+      "launch_config": { "launch_time": 15 },
+      // Other dynamic settings...
+    }
+  }
+}
    • launch_time in dynamic_config controls when this swarm (or salvo) is deployed.

  2. Modify Existing Configurations:

    Adjust parameters like num_agents, initial_state, or dynamic_config to change the behavior of existing agents or salvos.

Model Configurations ​

The model configurations define the physical and performance characteristics of interceptor and threat models. The default models provided can be customized to suit your research needs.

Available Models ​

The Models directory contains the following default model configurations:

These JSON files serve as templates and can be tweaked to modify the behavior of the corresponding models.

Editing Model Configurations ​

Example: micromissile.json ​

This file defines parameters for the micromissile model.

json:Assets/StreamingAssets/Configs/Models/micromissile.json
{
+  "accelerationConfig": {
+    "maxReferenceAcceleration": 300,
+    "referenceSpeed": 1000
+  },
+  "boostConfig": {
+    "boostTime": 0.3,
+    "boostAcceleration": 350
+  },
+  "liftDragConfig": {
+    "liftCoefficient": 0.2,
+    "dragCoefficient": 0.7,
+    "liftDragRatio": 5
+  },
+  // Other configurations...
+}

Configurable Parameters:

Modifying Parameters ​

You can tweak the parameters in these model files to adjust performance. For example:

Adding New Models ​

To define a new inte or threat model:

  1. Create a New JSON File in Assets/StreamingAssets/Configs/Models/.

  2. Define Model Parameters similar to the existing model files.

  3. Update the Code to recognize and load the new model if necessary.

Note: Ensure that any new parameters added to the model configuration are reflected in the corresponding C# classes.

Relevant C# Scripts ​

SimulationConfig.cs ​

This script defines the data structures used to interpret the JSON simulation configuration files.

Assets/Scripts/Config/SimulationConfig.cs

Classes:

Enums:

StaticConfig.cs ​

This script defines the classes corresponding to the model configuration JSON structure.

Assets/Scripts/Config/StaticConfig.cs

For example:

csharp
[Serializable]
+public class StaticConfig {
+  [Serializable]
+  public class AccelerationConfig {
+    public float maxReferenceAcceleration = 300f;
+    public float referenceSpeed = 1000f;
+  }
+
+  [Serializable]
+  public class BoostConfig {
+    public float boostTime = 0.3f;
+    public float boostAcceleration = 350f;
+  }
+
+  [Serializable]
+  public class LiftDragConfig {
+    public float liftCoefficient = 0.2f;
+    public float dragCoefficient = 0.7f;
+    public float liftDragRatio = 5f;
+  }
+
+  // Other configuration classes...
+
+  public AccelerationConfig accelerationConfig;
+  public BoostConfig boostConfig;
+  public LiftDragConfig liftDragConfig;
+  public BodyConfig bodyConfig;
+  public HitConfig hitConfig;
+}

Updating Classes:

If you add new parameters to the JSON model files, ensure the corresponding classes in StaticConfig.cs are updated to include these new fields.

Using the Deployment Build ​

When using the deployment build:

While the simulation is running, you can load a new Simulation Configuration JSON file at runtime by opening the panel with 'L'.

Note: Always back up configuration files before making significant changes. Incorrect configurations can lead to simulation errors.

For further assistance, refer to the comments and documentation within the code files:

This guide aims to help you set up and customize the simulation project effectively. If you encounter any issues or have questions, please reach out to the project maintainers.

`,75)]))}const g=i(e,[["render",l]]);export{E as __pageData,g as default}; diff --git a/assets/Simulation_Logging.md.t33KQhfo.js b/assets/Simulation_Logging.md.t33KQhfo.js new file mode 100644 index 0000000..5ab07f5 --- /dev/null +++ b/assets/Simulation_Logging.md.t33KQhfo.js @@ -0,0 +1,60 @@ +import{_ as i,c as a,a2 as e,o as t}from"./chunks/framework.DYF6f1aH.js";const n="/micromissiles-unity/assets/sim_visualizer.CwuQ1zYH.png",c=JSON.parse('{"title":"Simulation Logging","description":"","frontmatter":{},"headers":[],"relativePath":"Simulation_Logging.md","filePath":"Simulation_Logging.md"}'),l={name:"Simulation_Logging.md"};function p(h,s,r,o,d,k){return t(),a("div",null,s[0]||(s[0]=[e('

Simulation Logging ​

This guide provides instructions on how to access and interpret the simulation logs, how they are structured by the SimMonitor class, and how to utilize the provided visualize_log.py script to analyze simulation data. Additionally, it offers guidance on creating your own scripts for custom analysis.

Overview ​

Python simulation log visualizer

Simulation logs capture detailed telemetry and event data from each simulation run. These logs are essential for debugging, performance analysis, and understanding the behavior of agents within the simulation.

Logs are exported to the Telemetry/Logs folder in your operating system's persistent data path.

For example, on Windows, the logs are exported to:

C:\\Users\\<user>\\AppData\\LocalLow\\BAMLAB\\micromissiles\\Telemetry\\Logs

On macOS, the logs are exported to:

~/Library/Application Support/BAMLAB/micromissiles/Telemetry/Logs

visualize_log.py is an example script provided to help visualize and interpret the simulation logs. It is included in the Tools directory of the release download.

Understanding Log Files and Directory Structure ​

Log Directory Structure ​

Simulation logs are organized into timestamped directories within the Logs folder. Each simulation run generates a new directory named with the timestamp of the run.

For example:

Telemetry/
+└── Logs/
+    ├── 20241002_101305/
+    │   ├── sim_telemetry_20241002_101311.csv
+    │   ├── sim_events_20241002_101311.csv
+    │   │
+    │   ├── sim_telemetry_20241002_101306.csv
+    │   └── sim_events_20241002_101306.csv
+    ├── 20241002_012122/
+    │   ├── sim_telemetry_20241002_012122.csv
+    │   └── sim_events_20241002_012122.csv
+    └── ...

Each simulation run produces two main CSV files:

Log Files Generated by SimMonitor ​

The logging system is managed by the SimMonitor class in the simulation codebase.

csharp
public class SimMonitor : MonoBehaviour
+{
+    // Responsible for logging simulation data
+    // ...
+}

Key Responsibilities of SimMonitor:

Telemetry Log Structure ​

The telemetry log provides a snapshot of the simulation at each time step for every agent. Key columns include:

Event Log Structure ​

The event log records significant occurrences within the simulation. Key columns include:

Running the visualize_log.py Script ​

The visualize_log.py script helps visualize agent trajectories and significant events in a 3D plot.

Locating the Script ​

After downloading and extracting the release package, you can find the script at:

Tools/visualize_log.py

Make sure you have Python 3 installed on your system, along with the required libraries to run the script.

Required Python Libraries ​

The script depends on the following Python libraries:

You can install them using pip:

bash
pip install pandas matplotlib numpy

Usage ​

Open a terminal or command prompt and navigate to the Tools directory:

bash
cd path/to/Tools/

Run the Script ​

To visualize the most recent simulation logs:

bash
python visualize_log.py

What the Script Does:

Specifying Log Files Manually ​

You can also provide specific file paths as arguments:

bash
python visualize_log.py path/to/sim_telemetry_file.csv path/to/sim_events_file.csv

Example Output ​

Total number of events: 150
+
+Event Counts:
+  HIT: 120
+  MISS: 30
+
+First hit at 5.00 seconds, last hit at 15.00 seconds
+
+[3D plot window opens showing trajectories and events]

Interpreting the Plot ​

The 3D plot displays:

Adjusting the Visualization ​

Writing Your Own Scripts ​

The simulation logs are in CSV format, making them accessible for custom analysis and visualization.

Getting Started ​

For example, using Python and the pandas library, you can load the telemetry and event data like this:

python
import pandas as pd
+
+telemetry_df = pd.read_csv('path/to/sim_telemetry_*.csv')
+event_df = pd.read_csv('path/to/sim_events_*.csv')

Visualization ​

python
import matplotlib.pyplot as plt
+
+plt.plot(telemetry_df['Time'], telemetry_df['AgentY'])
+plt.xlabel('Time (s)')
+plt.ylabel('Altitude (m)')
+plt.title('Agent Altitude Over Time')
+plt.show()

Sample Script: Calculating Miss Distances ​

python
import pandas as pd
+import numpy as np
+
+# Load telemetry and event data
+telemetry_df = pd.read_csv('path/to/sim_telemetry_*.csv')
+event_df = pd.read_csv('path/to/sim_events_*.csv')
+
+# Filter miss events
+miss_events = event_df[event_df['Event'] == 'MISS']
+
+# Calculate miss distances
+miss_distances = []
+for idx, miss in miss_events.iterrows():
+    agent_id = miss['AgentID']
+    time = miss['Time']
+    # Get agent position at the time of miss
+    agent_state = telemetry_df[(telemetry_df['AgentID'] == agent_id) & (telemetry_df['Time'] == time)]
+    if not agent_state.empty:
+        x = agent_state['AgentX'].values[0]
+        y = agent_state['AgentY'].values[0]
+        z = agent_state['AgentZ'].values[0]
+        # Calculate distance to target or predefined point
+        distance = np.sqrt(x**2 + y**2 + z**2)
+        miss_distances.append(distance)
+
+# Output average miss distance
+average_miss_distance = np.mean(miss_distances)
+print(f'Average Miss Distance: {average_miss_distance:.2f} meters')

Suggestions for Analysis ​

Additional Resources ​

`,75)]))}const u=i(l,[["render",p]]);export{c as __pageData,u as default}; diff --git a/assets/Simulation_Logging.md.t33KQhfo.lean.js b/assets/Simulation_Logging.md.t33KQhfo.lean.js new file mode 100644 index 0000000..5ab07f5 --- /dev/null +++ b/assets/Simulation_Logging.md.t33KQhfo.lean.js @@ -0,0 +1,60 @@ +import{_ as i,c as a,a2 as e,o as t}from"./chunks/framework.DYF6f1aH.js";const n="/micromissiles-unity/assets/sim_visualizer.CwuQ1zYH.png",c=JSON.parse('{"title":"Simulation Logging","description":"","frontmatter":{},"headers":[],"relativePath":"Simulation_Logging.md","filePath":"Simulation_Logging.md"}'),l={name:"Simulation_Logging.md"};function p(h,s,r,o,d,k){return t(),a("div",null,s[0]||(s[0]=[e('

Simulation Logging ​

This guide provides instructions on how to access and interpret the simulation logs, how they are structured by the SimMonitor class, and how to utilize the provided visualize_log.py script to analyze simulation data. Additionally, it offers guidance on creating your own scripts for custom analysis.

Overview ​

Python simulation log visualizer

Simulation logs capture detailed telemetry and event data from each simulation run. These logs are essential for debugging, performance analysis, and understanding the behavior of agents within the simulation.

Logs are exported to the Telemetry/Logs folder in your operating system's persistent data path.

For example, on Windows, the logs are exported to:

C:\\Users\\<user>\\AppData\\LocalLow\\BAMLAB\\micromissiles\\Telemetry\\Logs

On macOS, the logs are exported to:

~/Library/Application Support/BAMLAB/micromissiles/Telemetry/Logs

visualize_log.py is an example script provided to help visualize and interpret the simulation logs. It is included in the Tools directory of the release download.

Understanding Log Files and Directory Structure ​

Log Directory Structure ​

Simulation logs are organized into timestamped directories within the Logs folder. Each simulation run generates a new directory named with the timestamp of the run.

For example:

Telemetry/
+└── Logs/
+    ├── 20241002_101305/
+    │   ├── sim_telemetry_20241002_101311.csv
+    │   ├── sim_events_20241002_101311.csv
+    │   │
+    │   ├── sim_telemetry_20241002_101306.csv
+    │   └── sim_events_20241002_101306.csv
+    ├── 20241002_012122/
+    │   ├── sim_telemetry_20241002_012122.csv
+    │   └── sim_events_20241002_012122.csv
+    └── ...

Each simulation run produces two main CSV files:

Log Files Generated by SimMonitor ​

The logging system is managed by the SimMonitor class in the simulation codebase.

csharp
public class SimMonitor : MonoBehaviour
+{
+    // Responsible for logging simulation data
+    // ...
+}

Key Responsibilities of SimMonitor:

Telemetry Log Structure ​

The telemetry log provides a snapshot of the simulation at each time step for every agent. Key columns include:

Event Log Structure ​

The event log records significant occurrences within the simulation. Key columns include:

Running the visualize_log.py Script ​

The visualize_log.py script helps visualize agent trajectories and significant events in a 3D plot.

Locating the Script ​

After downloading and extracting the release package, you can find the script at:

Tools/visualize_log.py

Make sure you have Python 3 installed on your system, along with the required libraries to run the script.

Required Python Libraries ​

The script depends on the following Python libraries:

You can install them using pip:

bash
pip install pandas matplotlib numpy

Usage ​

Open a terminal or command prompt and navigate to the Tools directory:

bash
cd path/to/Tools/

Run the Script ​

To visualize the most recent simulation logs:

bash
python visualize_log.py

What the Script Does:

Specifying Log Files Manually ​

You can also provide specific file paths as arguments:

bash
python visualize_log.py path/to/sim_telemetry_file.csv path/to/sim_events_file.csv

Example Output ​

Total number of events: 150
+
+Event Counts:
+  HIT: 120
+  MISS: 30
+
+First hit at 5.00 seconds, last hit at 15.00 seconds
+
+[3D plot window opens showing trajectories and events]

Interpreting the Plot ​

The 3D plot displays:

Adjusting the Visualization ​

Writing Your Own Scripts ​

The simulation logs are in CSV format, making them accessible for custom analysis and visualization.

Getting Started ​

For example, using Python and the pandas library, you can load the telemetry and event data like this:

python
import pandas as pd
+
+telemetry_df = pd.read_csv('path/to/sim_telemetry_*.csv')
+event_df = pd.read_csv('path/to/sim_events_*.csv')

Visualization ​

python
import matplotlib.pyplot as plt
+
+plt.plot(telemetry_df['Time'], telemetry_df['AgentY'])
+plt.xlabel('Time (s)')
+plt.ylabel('Altitude (m)')
+plt.title('Agent Altitude Over Time')
+plt.show()

Sample Script: Calculating Miss Distances ​

python
import pandas as pd
+import numpy as np
+
+# Load telemetry and event data
+telemetry_df = pd.read_csv('path/to/sim_telemetry_*.csv')
+event_df = pd.read_csv('path/to/sim_events_*.csv')
+
+# Filter miss events
+miss_events = event_df[event_df['Event'] == 'MISS']
+
+# Calculate miss distances
+miss_distances = []
+for idx, miss in miss_events.iterrows():
+    agent_id = miss['AgentID']
+    time = miss['Time']
+    # Get agent position at the time of miss
+    agent_state = telemetry_df[(telemetry_df['AgentID'] == agent_id) & (telemetry_df['Time'] == time)]
+    if not agent_state.empty:
+        x = agent_state['AgentX'].values[0]
+        y = agent_state['AgentY'].values[0]
+        z = agent_state['AgentZ'].values[0]
+        # Calculate distance to target or predefined point
+        distance = np.sqrt(x**2 + y**2 + z**2)
+        miss_distances.append(distance)
+
+# Output average miss distance
+average_miss_distance = np.mean(miss_distances)
+print(f'Average Miss Distance: {average_miss_distance:.2f} meters')

Suggestions for Analysis ​

Additional Resources ​

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e)this.discard(s)}finally{this._options.autoVacuum=t}this.maybeAutoVacuum()}replace(e){const{idField:t,extractField:s}=this._options,n=s(e,t);this.discard(n),this.add(e)}vacuum(e={}){return this.conditionalVacuum(e)}conditionalVacuum(e,t){return this._currentVacuum?(this._enqueuedVacuumConditions=this._enqueuedVacuumConditions&&t,this._enqueuedVacuum!=null?this._enqueuedVacuum:(this._enqueuedVacuum=this._currentVacuum.then(()=>{const s=this._enqueuedVacuumConditions;return this._enqueuedVacuumConditions=We,this.performVacuuming(e,s)}),this._enqueuedVacuum)):this.vacuumConditionsMet(t)===!1?Promise.resolve():(this._currentVacuum=this.performVacuuming(e),this._currentVacuum)}performVacuuming(e,t){return Te(this,void 0,void 0,function*(){const s=this._dirtCount;if(this.vacuumConditionsMet(t)){const n=e.batchSize||Be.batchSize,r=e.batchWait||Be.batchWait;let i=1;for(const[o,l]of this._index){for(const[c,h]of l)for(const[v]of h)this._documentIds.has(v)||(h.size<=1?l.delete(c):h.delete(v));this._index.get(o).size===0&&this._index.delete(o),i%n===0&&(yield new Promise(c=>setTimeout(c,r))),i+=1}this._dirtCount-=s}yield null,this._currentVacuum=this._enqueuedVacuum,this._enqueuedVacuum=null})}vacuumConditionsMet(e){if(e==null)return!0;let{minDirtCount:t,minDirtFactor:s}=e;return t=t||Pe.minDirtCount,s=s||Pe.minDirtFactor,this.dirtCount>=t&&this.dirtFactor>=s}get isVacuuming(){return this._currentVacuum!=null}get dirtCount(){return this._dirtCount}get dirtFactor(){return this._dirtCount/(1+this._documentCount+this._dirtCount)}has(e){return this._idToShortId.has(e)}getStoredFields(e){const t=this._idToShortId.get(e);if(t!=null)return this._storedFields.get(t)}search(e,t={}){const s=this.executeQuery(e,t),n=[];for(const[r,{score:i,terms:o,match:l}]of s){const c=o.length||1,h={id:this._documentIds.get(r),score:i*c,terms:Object.keys(l),queryTerms:o,match:l};Object.assign(h,this._storedFields.get(r)),(t.filter==null||t.filter(h))&&n.push(h)}return e===le.wildcard&&t.boostDocument==null&&this._options.searchOptions.boostDocument==null||n.sort(ft),n}autoSuggest(e,t={}){t=Object.assign(Object.assign({},this._options.autoSuggestOptions),t);const s=new Map;for(const{score:r,terms:i}of this.search(e,t)){const o=i.join(" "),l=s.get(o);l!=null?(l.score+=r,l.count+=1):s.set(o,{score:r,terms:i,count:1})}const n=[];for(const[r,{score:i,terms:o,count:l}]of s)n.push({suggestion:r,terms:o,score:i/l});return n.sort(ft),n}get documentCount(){return this._documentCount}get termCount(){return this._index.size}static loadJSON(e,t){if(t==null)throw new Error("MiniSearch: loadJSON should be given the same options used when serializing the index");return this.loadJS(JSON.parse(e),t)}static loadJSONAsync(e,t){return Te(this,void 0,void 0,function*(){if(t==null)throw 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c=0;for(const[h,v]of s){const p=new Map;for(const b of Object.keys(v)){let y=v[b];o===1&&(y=y.ds),p.set(parseInt(b,10),yield Ee(y))}++c%1e3===0&&(yield kt(0)),l._index.set(h,p)}return l})}static instantiateMiniSearch(e,t){const{documentCount:s,nextId:n,fieldIds:r,averageFieldLength:i,dirtCount:o,serializationVersion:l}=e;if(l!==1&&l!==2)throw new Error("MiniSearch: cannot deserialize an index created with an incompatible version");const c=new le(t);return c._documentCount=s,c._nextId=n,c._idToShortId=new Map,c._fieldIds=r,c._avgFieldLength=i,c._dirtCount=o||0,c._index=new Z,c}executeQuery(e,t={}){if(e===le.wildcard)return this.executeWildcardQuery(t);if(typeof e!="string"){const p=Object.assign(Object.assign(Object.assign({},t),e),{queries:void 0}),b=e.queries.map(y=>this.executeQuery(y,p));return this.combineResults(b,p.combineWith)}const{tokenize:s,processTerm:n,searchOptions:r}=this._options,i=Object.assign(Object.assign({tokenize:s,processTerm:n},r),t),{tokenize:o,processTerm:l}=i,v=o(e).flatMap(p=>l(p)).filter(p=>!!p).map(Vs(i)).map(p=>this.executeQuerySpec(p,i));return this.combineResults(v,i.combineWith)}executeQuerySpec(e,t){const s=Object.assign(Object.assign({},this._options.searchOptions),t),n=(s.fields||this._options.fields).reduce((x,w)=>Object.assign(Object.assign({},x),{[w]:De(s.boost,w)||1}),{}),{boostDocument:r,weights:i,maxFuzzy:o,bm25:l}=s,{fuzzy:c,prefix:h}=Object.assign(Object.assign({},dt.weights),i),v=this._index.get(e.term),p=this.termResults(e.term,e.term,1,e.termBoost,v,n,r,l);let b,y;if(e.prefix&&(b=this._index.atPrefix(e.term)),e.fuzzy){const x=e.fuzzy===!0?.2:e.fuzzy,w=x<1?Math.min(o,Math.round(e.term.length*x)):x;w&&(y=this._index.fuzzyGet(e.term,w))}if(b)for(const[x,w]of b){const O=x.length-e.term.length;if(!O)continue;y==null||y.delete(x);const R=h*x.length/(x.length+.3*O);this.termResults(e.term,x,R,e.termBoost,w,n,r,l,p)}if(y)for(const x of y.keys()){const[w,O]=y.get(x);if(!O)continue;const R=c*x.length/(x.length+O);this.termResults(e.term,x,R,e.termBoost,w,n,r,l,p)}return p}executeWildcardQuery(e){const t=new Map,s=Object.assign(Object.assign({},this._options.searchOptions),e);for(const[n,r]of this._documentIds){const i=s.boostDocument?s.boostDocument(r,"",this._storedFields.get(n)):1;t.set(n,{score:i,terms:[],match:{}})}return t}combineResults(e,t=Je){if(e.length===0)return new Map;const s=t.toLowerCase(),n=zs[s];if(!n)throw new Error(`Invalid combination operator: ${t}`);return e.reduce(n)||new Map}toJSON(){const e=[];for(const[t,s]of this._index){const n={};for(const[r,i]of s)n[r]=Object.fromEntries(i);e.push([t,n])}return{documentCount:this._documentCount,nextId:this._nextId,documentIds:Object.fromEntries(this._documentIds),fieldIds:this._fieldIds,fieldLength:Object.fromEntries(this._fieldLength),averageFieldLength:this._avgFieldLength,storedFields:Object.fromEntries(this._storedFields),dirtCount:this._dirtCount,index:e,serializationVersion:2}}termResults(e,t,s,n,r,i,o,l,c=new Map){if(r==null)return c;for(const h of Object.keys(i)){const v=i[h],p=this._fieldIds[h],b=r.get(p);if(b==null)continue;let y=b.size;const x=this._avgFieldLength[p];for(const w of b.keys()){if(!this._documentIds.has(w)){this.removeTerm(p,w,t),y-=1;continue}const O=o?o(this._documentIds.get(w),t,this._storedFields.get(w)):1;if(!O)continue;const R=b.get(w),K=this._fieldLength.get(w)[p],G=js(R,y,this._documentCount,K,x,l),W=s*n*v*O*G,V=c.get(w);if(V){V.score+=W,Bs(V.terms,e);const $=De(V.match,t);$?$.push(h):V.match[t]=[h]}else c.set(w,{score:W,terms:[e],match:{[t]:[h]}})}}return c}addTerm(e,t,s){const n=this._index.fetch(s,pt);let r=n.get(e);if(r==null)r=new Map,r.set(t,1),n.set(e,r);else{const i=r.get(t);r.set(t,(i||0)+1)}}removeTerm(e,t,s){if(!this._index.has(s)){this.warnDocumentChanged(t,e,s);return}const n=this._index.fetch(s,pt),r=n.get(e);r==null||r.get(t)==null?this.warnDocumentChanged(t,e,s):r.get(t)<=1?r.size<=1?n.delete(e):r.delete(t):r.set(t,r.get(t)-1),this._index.get(s).size===0&&this._index.delete(s)}warnDocumentChanged(e,t,s){for(const n of Object.keys(this._fieldIds))if(this._fieldIds[n]===t){this._options.logger("warn",`MiniSearch: document with ID ${this._documentIds.get(e)} has changed before removal: term "${s}" was not present in field "${n}". 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Quick Start ​

We generate pre-built standalone binaries for Windows and Mac users from the release branch. These binaries are intended for non-development users who just want to run the application and modify a few configurations along the way.

You can find the latest release here.

Windows ​

  1. Download the zip file for Windows: micromissiles-<version>-windows_x86_64.zip.
  2. Unzip the zip file. The zip file should contain a single directory called micromissiles-<version>-windows_x86_64.
  3. In the micromissiles-<version>-windows_x86_64 directory, run micromissiles-<version>-StandaloneWindows64.exe.

Mac ​

  1. Download the tarball file for Darwin: micromissiles-<version>-darwin.tar.gz.
  2. Untar the tarball. The tarball should contain a single directory called micromissiles-<version>-darwin.
  3. In the micromissiles-<version>-darwin directory, run the app file.

Next Steps ​

',9)]))}const f=i(n,[["render",r]]);export{u as __pageData,f as default}; diff --git a/assets/index.md.CVOWUTll.lean.js b/assets/index.md.CVOWUTll.lean.js new file mode 100644 index 0000000..79c6147 --- /dev/null +++ b/assets/index.md.CVOWUTll.lean.js @@ -0,0 +1 @@ +import{_ as i,c as t,a2 as a,o}from"./chunks/framework.DYF6f1aH.js";const u=JSON.parse('{"title":"Quick Start","description":"","frontmatter":{"layout":"home","hero":{"name":"micromissiles-unity","tagline":"Swarm-on-swarm simulator using micromissiles for point defense","actions":[{"theme":"brand","text":"Documentation","link":"/Keybinds_and_Controls"},{"theme":"alt","text":"Development Guide","link":"/Development_Guide"}]}},"headers":[],"relativePath":"index.md","filePath":"index.md"}'),n={name:"index.md"};function r(s,e,l,d,c,h){return o(),t("div",null,e[0]||(e[0]=[a('

Quick Start ​

We generate pre-built standalone binaries for Windows and Mac users from the release branch. These binaries are intended for non-development users who just want to run the application and modify a few configurations along the way.

You can find the latest release here.

Windows ​

  1. Download the zip file for Windows: micromissiles-<version>-windows_x86_64.zip.
  2. Unzip the zip file. The zip file should contain a single directory called micromissiles-<version>-windows_x86_64.
  3. In the micromissiles-<version>-windows_x86_64 directory, run micromissiles-<version>-StandaloneWindows64.exe.

Mac ​

  1. Download the tarball file for Darwin: micromissiles-<version>-darwin.tar.gz.
  2. Untar the tarball. The tarball should contain a single directory called micromissiles-<version>-darwin.
  3. In the micromissiles-<version>-darwin directory, run the app file.

Next Steps ​

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micromissiles-unity

Appearance

micromissiles-unity

Swarm-on-swarm simulator using micromissiles for point defense

Documentation
Development Guide

Quick Start ​

We generate pre-built standalone binaries for Windows and Mac users from the release branch. These binaries are intended for non-development users who just want to run the application and modify a few configurations along the way.

You can find the latest release here.

Windows ​

  1. Download the zip file for Windows: micromissiles-<version>-windows_x86_64.zip.
  2. Unzip the zip file. The zip file should contain a single directory called micromissiles-<version>-windows_x86_64.
  3. In the micromissiles-<version>-windows_x86_64 directory, run micromissiles-<version>-StandaloneWindows64.exe.

Mac ​

  1. Download the tarball file for Darwin: micromissiles-<version>-darwin.tar.gz.
  2. Untar the tarball. The tarball should contain a single directory called micromissiles-<version>-darwin.
  3. In the micromissiles-<version>-darwin directory, run the app file.

Next Steps ​

Released under the BSD-3-Clause License.

Copyright © 2024-present, The Regents of the University of California (Regents). All Rights Reserved.

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