PeakLocator Application

This is a web application built with Python and Streamlit to analyze and compare spatial grid files (e.g., Surfer .grd format). The application allows users to upload multiple grid files, define parameters, and compute a resulting grid that identifies common peak or trough regions across the datasets.

This project is a modern migration of an original Flask-based application, now featuring an interactive, user-friendly interface powered by Plotly and Streamlit.

The original tool is described in:

Granieri, Domenico, Mattia de’Michieli Vitturi, and Marina Bisson. "PeakLocator 1.0, a web tool to compare extreme value areas among maps." Annals of Geophysics 61, no. 5 (2018).

Features

• Dynamic File Inputs: Start with two input slots and add up to 10 dynamically.
• Interactive Plots: All data visualizations are rendered with Plotly, allowing for zoom, pan, and hover-to-inspect data points.
• Live Input Preview: A dedicated "Input Data Viewer" tab lets you inspect uploaded files before running the full computation.
• Configurable Parameters: Set Min/Max and a numeric Sigma value for each input file.
• Advanced Options: Fine-tune the analysis with options for interpolation method (nearest, linear, cubic) and result map colorscale.
• Result Caching: The st.cache_data feature ensures that computations are not re-run unnecessarily, providing instant results for repeated analyses.
• Download Results: Download the final computed grid as a standard Surfer .grd file.
• Reproducible Environment: Uses a Conda environment.yml file for easy and reliable setup.

Prerequisites

Before you begin, ensure you have Anaconda or Miniconda installed on your system. This will be used to manage the project environment and its dependencies.

• Download Anaconda Distribution

Installation and Setup

Follow these steps to set up the project environment and get the application running.

1. Clone the Repository

First, clone this repository to your local machine or download the source files.

bash git clone https://github.com/demichie/peakLocator_1.0 cd peakLocator_1.0

2. Create the Conda Environment

Use the provided environment.yml file to create a new Conda environment with all the necessary dependencies. This command will create an environment named peakLocator-env.

bash conda env create -f environment.yml

This process may take a few minutes as Conda resolves and installs all the packages.

3. Activate the Conda Environment

Once the environment is created, you must activate it. All subsequent commands should be run inside this environment.

bash conda activate peakLocator-env

Your terminal prompt should now be prefixed with (peakLocator-env).

Running the Application

With the Conda environment activated, you can launch the Streamlit application with a single command:

bash streamlit run app_streamlit.py

Streamlit will start a local web server, and the application should automatically open in your default web browser. If it doesn't, the terminal will provide a local URL (usually http://localhost:8501) that you can navigate to.

How to Use

1. Input Configuration Tab:

   • Upload at least two .grd files using the file uploaders.
   • Adjust the "Min/Max" and "Sigma" parameters for each file.
   • Use the "Add/Remove Slot" buttons to manage the number of inputs.
   • Optionally, expand "Advanced Options" to change the interpolation method or result colorscale.
   • Click the "Run Computation" button to start the analysis.

2. Input Data Viewer Tab:

   • After uploading files on the first tab, you can come here to preview them.
   • Use the dropdown menu to select which uploaded file you want to inspect.

3. Results Tab:

   • After a successful computation, this tab will display the interactive result map.
   • Calculated metrics like "Fitting Index" and "Area" are shown below the map.
   • Use the "Download Result" button to save the output grid to your computer.

Project Structure

. ├── app_streamlit_final.py # The main Streamlit application file. ├── compute.py # Core computation and plotting logic. ├── environment.yml # Conda environment definition. └── README.md # This file.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Authors

This tool has been developed by M. de' Michieli Vitturi and D. Granieri