# Groundwater Analysis Project

## Overview

This project contains the code and data necessary to perform a comprehensive analysis of groundwater levels using the Lagging Theory and a Transfer Function-Noise (TFN) model. The analysis is centered around the examination of groundwater time series data in an unconfined aquifer with limited pumping information.

## Contents

- `laggingTFN_2024.ipynb`: A Jupyter notebook containing Python code to execute the analysis of the paper entitled "Analysis of Groundwater Time Series with Limited Pumping Information in Unconfined Aquifer: Response Function Based on Lagging Theory". This notebook includes all necessary computations, model fitting, and visualization of results.

- `Stainfo.pickle`: A Python pickle file containing information about the stations where groundwater levels were measured. This includes location data, station IDs, and other relevant metadata.

- `WL2019.pickle`: A Python pickle file with groundwater level data collected in 2019. 

## Getting Started

To use these files, you will need a Python environment capable of running Jupyter notebooks and handling `.pickle` files. We recommend setting up an environment with Anaconda or Miniconda and installing the necessary libraries such as `numpy`, `pandas`, `matplotlib`, and `pickle`.

### Installation

1. If you haven't already, install Anaconda or Miniconda on your system.
2. Clone this repository or download the ZIP file and extract it to your local machine.
3. Navigate to the project directory in your terminal or command prompt.
4. Create a new Conda environment:

```
conda create --name groundwater python=3.8
```

5. Activate the environment:

```
conda activate groundwater
```

6. Install required packages:

```
pip install notebook pandas numpy matplotlib
```

7. Launch Jupyter Notebook:

```
jupyter notebook
```

8. Open `laggingTFN_2024.ipynb` from the Jupyter Notebook interface to start the analysis.

## Usage

- Follow the instructions within `laggingTFN_2024.ipynb` to perform the analysis. The notebook is annotated with comments to guide you through the process.
- The `.pickle` files are automatically loaded in the notebook. Ensure they are in the same directory as the notebook for seamless integration.