tideHunter

tideHunter is a Streamlit-based application designed to process and analyze NAMRIA tide data. It provides a user-friendly interface for both single and multiple file processing, allowing users to generate statistical inferences and visualizations.

Features

• Single Processor: Process individual NAMRIA tide data files.
• Multiple Processor: Process and compare multiple NAMRIA tide data files spanning over 10 years.
• Data Visualization: Generate various plots and statistical summaries.
• Date Filtering: Filter data by specific date ranges.
• Monthly and Yearly Averages: Calculate and visualize monthly and yearly tide level averages.
• Export Functionality: Export processed data and visualizations to various formats.

Installation

1. Clone the repository:

   git clone https://github.com/junealexissantos/tideHunter.git
   cd tideHunter

2. Create a virtual environment and activate it:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install the required packages:

   pip install -r requirements.txt

Usage

To run this locally, the Streamlit library is required.

Run the Streamlit application:

streamlit run main.py

You can also view the app version at: tideHunterApp(https://tidehunter.streamlit.app)

Functionalities

tideHunter provides the following core functionalities:

1. Tide Station Locator: Identify and locate tide stations based on user input or uploaded data.
2. Single NAMRIA Tide Data Processor: Process individual NAMRIA tide data files to generate statistical summaries and visualizations.
3. Multiple NAMRIA Tide Data Processor: Process and compare multiple NAMRIA tide data files spanning over multiple years or data points to identify trends and others.
4. WXTide Processor: Integrate and process WXTide data for additional analysis and comparison.
5. NEW! Windrose Processor: Works on PAG-ASA Datasets. Processes Wind Data to generate a Windrose map with Automated calculation of Wind Vector Components.

Project Structure

• main.py: The main entry point of the application.
• local_classes/variables.py: Contains enumerations for accepted upload formats and key codes.
• local_classes/utils.py: Utility functions for data processing and validation.
• appcore.py: Core logic for parsing and processing tide data.
• page_design.py: Contains the Streamlit widgets and layout for single and multiple file processing.
• exporter.py: Handles exporting processed data and visualizations.

Developed and Designed By

VISIT @ June Alexis(https://junealexis.vercel.app)

Dedication

tideHunter is a passion project by June Alexis Santos for the Coastal Assessment Team of the Mines and Geosciences Bureau - Regional Office 3

Coastal Assessment Team 2025

• Maam Weng
• Sir Carlo
• Dara
• June
• Georgette