earthdaily documentation

EarthDaily Python Client

Your gateway to the Earth Data Store STAC Catalog.

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The EarthDaily Python client simplifies access to the Earth Data Store STAC catalog and streamlines workflows for geospatial data analysis and Earth observation. It automates key preprocessing tasks, making it easier to work with Earth observation data.

Features

Easy Data Access: Connect directly to the Earth Data Store STAC catalog.
Automated Datacube Creation: Includes reflectance conversion and clipping to areas of interest.
Scalable Processing: Fully compatible with Dask for handling large datasets.

This package is designed to make geospatial workflows more efficient and accessible for researchers and analysts working with Earth observation data.

Prerequisites

Make sure you have valid Earth Data Store authentication credentials. These can be retrieved from the EarthDaily Account Management page.

Installation

From PyPI, using pip

pip install earthdaily

From Repository using Conda and pip

# Clone the repository and navigate inside
git clone git@github.com:earthdaily/earthdaily-python-client.git
cd earthdaily-python-client

# Create a conda environment and install dependencies
conda env create -n earthdaily -f requirements.yml
conda init
conda activate earthdaily

# Install package in editable mode
pip install -e .

Authentication from Environment Variables

Authentication credentials can be automatically parsed from environment variables. The python-dotenv package is supported for convenience.

Rename the .env.sample file in this repository to .env and enter your Earth Data Store authentication credentials. Note this file is gitingored and will not be committed.

In your script or notebook, add:

from dotenv import load_dotenv

load_dotenv(".env")  # Load environment variables from .env file

Quickstart

To help you get started quickly, we provide a quickstart.ipynb Jupyter notebook that demonstrates how to use the EarthDaily Python client. You only need your .env file with your authentication credentials to run it.

Simply open the notebook, load your environment variables as shown above, and follow the instructions to begin accessing and analyzing Earth observation data.

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