CDIAC at AppState Dashboard

Author: M. W. Hefner

This is an interactive dashboard designed to explore detailed carbon emission data from the Carbon Dioxide Information Analysis Center (CDIAC) at Appalachian State University. It uses Dash, a Python web application framework, to render the user interface in a web browser.

For more information about CDIAC at AppState, please see the CDIAC at AppState webpage and the "about" markdown tab of this application. For more details on contributing to this project, refer to the RIEEE environmental data scientist.

Table of Contents

• Prerequisites
• Installation
  • Cloning the Repository
  • Setting Up the Environment
• Application Structure
• Running the Application Locally
• Known Issues
• Updating the Dashboard Annually

Prerequisites

Before you start, make sure you have Python 3.11 installed on your system. You can download Python from python.org.

Installation

Cloning the Repository

To get started with the CDIAC Dashboard, you'll first need to clone the repository to your local machine:

git clone https://github.com/your-repository-url/cdiac-dashboard.git
cd cdiac-dashboard

Setting Up the Environment

It's recommended to use a virtual environment to manage the dependencies and isolate the project:

python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`
pip install -r requirements.txt

For pip to install properly, it is necessary to have Visual Studio developer tools installed. Otherwise, requirements can be installed effectively with conda or miniconda, which is recommended for virtual environment management for this very reason.

Application Structure

The application is structured as follows:

• application.py: The main Python script to run the Dash app. It initializes the server and layouts.
• components/: Contains Python modules for different parts of the application like figures, tables, and utility functions.
• assets/: Stores static files like stylesheets, JavaScript files, images, and markdown files.
• Dockerfile: Contains commands to build a Docker image for the application.
• requirements.txt: Lists all Python libraries that the application depends on.

Key Components

• components/main_container.py: The main container that encapsulates the entire Dash layout.
• components/figures/: This directory contains scripts to generate the interactive charts and figures used in the dashboard.
• components/tables/: Handles the table views and data interactions within the dashboard.
• components/control_panel/: Manages the interactive control elements like dropdowns and buttons, allowing users to filter and manipulate the data displayed in the dashboard.
• components/content_display/: Responsible for rendering the main content views such as graphs and data tables based on user interactions with the control panel.
• components/utils/: Contains application constants, links to local data, database connectivity, and other useful functionality.

Running the Application Locally

To run the application locally, ensure you are in the project's root directory and have activated your virtual environment. Start the app by running:

python application.py

This will start the Dash server on http://127.0.0.1:8050/.

Known Issues

• Both assets/markdown/methodology.md and assets/markdown/about.md pages need to be re-written and updated, respectively. Until they are, these options have been commented out in the navigation dropdown options.
• There is a bug with the time series by source code when deployed onto the server - nations are not showing, only regions; consider mapping line-marker attributes differently. Until this is fixed, the option has been commented out in the navigation dropdown options.

Updating the Dashboard Annually

To update the dashboard data annually:

1. Modify components/utils/constants.py with the new year and data file name.
2. Replace the data file in assets/data/ with the updated dataset.
3. Update assets/markdown/download.md with new download information.
4. After releasing on GitHub and publishing the updated application to Zonodo, update the application's Zonodo DOI badge in components/utils/constants.py.

After these steps, your application should reflect the latest data and be ready for use.