""" Here's a list of potential social behaviors of termites and how they can be analyzed using data from the .h5 file:

1. Proximity and Interaction Duration:

• Description: The duration during which two termites remain within a certain distance from each other. This can indicate social interactions or cooperative behaviors such as feeding or nest building.
• Calculation: Use the coordinates of tracked points from the .h5 file to compute distances between termites and determine how long they stay within a defined threshold.

2. Grooming Behavior:

• Description: Grooming is an essential social behavior in termite colonies for maintaining hygiene and communication. Detecting when one termite approaches and stays near the abdomen or head of another for a set duration can indicate grooming.
• Calculation: Analyze the distance between the mandible of one termite and the abdomen or head of another, checking if this distance stays within a defined range for a specific number of frames.

3. Antennal Contact:

• Description: Termites often use their antennae to touch and sense each other, which can be a form of communication.
• Calculation: Identify when the antennae (tracked as specific nodes) come close to the body or head of another termite, staying within a short distance range.

4. Following Behavior:

• Description: This behavior occurs when one termite follows the movement path of another.
• Calculation: Track the paths of individual termites over time to identify cases where one termite maintains a consistent distance behind another for a series of frames.

5. Alignment and Group Movement:

• Description: Termites moving in the same direction or aligned in a group can indicate cooperative tasks or foraging.
• Calculation: Compare the angles of movement vectors between multiple termites and identify consistent directional alignment.

6. Collision or Physical Contact:

• Description: Instances where two termites come into direct contact.
• Calculation: Check when the distance between two tracked termite points falls below a minimal threshold (indicating touch).

7. Stationary Clustering:

• Description: A group of termites remaining within a close area for an extended period could indicate colony defense or nest-related behavior.
• Calculation: Use clustering algorithms to find groups of termites that do not move significantly over time.

Which Data from the .h5 File Can Be Used?

• Coordinates (tracks): The 3D array of termite positions over time, which is essential for calculating distances, angles, and movements.
• Node Names (node_names): Helps identify specific body parts like mandibles, antennae, and abdomens.
• Track Names (track_names): Used to differentiate between individual termites in the analysis.

With these analyses in mind, we can now create the social_behaviors.py file to define these behaviors and make them accessible for integration into sosyal.py. Shall we proceed with drafting this file? """

Termite Social Behavior Analysis

This repository contains Python scripts for analyzing the social behavior of termites using .h5 data files. The project focuses on loading, cleaning, and analyzing termite tracking data to detect interactions and grooming events.

Project Structure

• sosyal.py: Main script for analyzing termite social interactions.
• fillmissing.py: Script for handling missing data using interpolation.
• cleaning.py: Contains functions for cleaning and validating data.
• connect_broken_tracks.py: Connects broken tracks and computes distances.
• groom.py: Detects grooming events and visualizes them.
• loadh5.py: Utility for loading and inspecting .h5 data files.

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/termite-analysis.git
   cd termite-analysis

2. Install dependencies: Make sure you have Python 3.x installed. You may need the following libraries:

   pip install numpy h5py matplotlib scipy seaborn

Usage

1. Analyzing Termite Interactions (sosyal.py)

This script loads .h5 data and identifies social interactions among termites based on distance, angle, and duration criteria.

Run the script:

python sosyal.py

2. Detecting Grooming Events (groom.py)

Detect grooming events using the following:

python groom.py

3. Filling Missing Data (fillmissing.py)

The fill_missing function is used to fill missing data in termite locations:

from fillmissing import fill_missing

4. Connecting Broken Tracks (connect_broken_tracks.py)

Connect broken tracks in the dataset:

from connect_broken_tracks import connect_broken_tracks

File Descriptions

• sosyal.py: Main code for analyzing interactions with functions for angle calculation and proximity analysis.
• fillmissing.py: Provides fill_missing for data interpolation.
• cleaning.py: Validates data quality and handles missing or invalid data points.
• connect_broken_tracks.py: Connects broken tracks and calculates distances.
• groom.py: Detects and visualizes grooming behavior among termites.
• loadh5.py: Loads and inspects .h5 files.

Example Output

Example of detected interactions printed by sosyal.py:

Detected Interactions (with distance and time filters):
Termites 0 and 1 interacted between frames 100 and 200
Termites 2 and 3 interacted between frames 350 and 450

Contributing

Feel free to contribute by forking the repository and submitting pull requests.

License

This project is licensed under the MIT License.