Sample notebooks
Sample code maintained to use and learn from.
To quickly become familiar with the Python client, you can explore some sample notebooks. Each notebook can be executed standalone from top to bottom.
Sample notebooks
You can access the sample notebooks on Google Drive.
Right click a notebook in Google Drive and select Open with -> Google Colaboratory
to open it directly in the browser using Google Colab.
More examples can be found throughout the docs.
Notebook overview
Tilebox S5P Tropomi Methane Data Access
Tilebox S5P Tropomi Methane Data Access
This notebook illustrates how to use the Python client to query the S5P Tropomi Opendata dataset for methane products. It also explains how to filter results based on geographical location and download product data for a specific granule.
MODIS-based Custom Dataset Ingestion Demo
MODIS-based Custom Dataset Ingestion Demo
This notebook demonstrates how to ingest data into a Custom Dataset. In this case it’s using a sample dataset from the MODIS instrument which is already prepared.
Sentinel-2 Cloud-free Mosaic
Sentinel-2 Cloud-free Mosaic
Created with Tilebox Workflows, this 10m resolution mosaic highlights distributed, auto-parallelizing capabilities.
Data from Copernicus Dataspace
was reprojected on CloudFerro
(intermediate products on AWS S3), and the final composite was built locally using auto-parallelized team notebooks.
Execute cells one by one using Shift+Enter
. Most commonly used libraries are pre-installed.
Interactive environments
Jupyter, Google Colab, and JetBrains Datalore are interactive environments that simplify the development and sharing of algorithmic code. They allow users to work with notebooks, which combine code and rich text elements like figures, links, and equations. Notebooks require no setup and can be easily shared.
Jupyter
Jupyter
Jupyter notebooks are the original interactive environment for Python. They are useful but require local installation.
Google Colab
Google Colab
Google Colab is a free tool that provides a hosted interactive Python environment. It easily connects to local Jupyter instances and allows code sharing using Google credentials or within organizations using Google Workspace.
JetBrains Datalore
JetBrains Datalore
JetBrains Datalore is a free platform for collaborative testing, development, and sharing of Python code and algorithms. It has built-in secret management for storing credentials. Datalore also features advanced JetBrains syntax highlighting and autocompletion. Currently, it only supports Python 3.8, which is not compatible with the Tilebox Python client.
Since Colab is a hosted free tool that meets all requirements, including Python ≥3.10, it’s recommended for use.
Installing packages
Within your interactive environment, you can install missing packages using pip in “magic” cells, which start with an exclamation mark.
All APIs or commands that require authentication can be accessed through client libraries that hide tokens, allowing notebooks to be shared without exposing personal credentials.
Executing code
Execute code by clicking the play button in the top left corner of the cell or by pressing Shift + Enter
. While the code is running, a spinning icon appears. When the execution finishes, the icon changes to a number, indicating the order of execution. The output displays below the code.
Authorization
When sharing notebooks, avoid directly sharing your Tilebox API key. Instead, use one of two methods to authenticate the Tilebox Python client in interactive environments: through environment variables or interactively.
Interactive authorization is possible using the built-in getpass
module. This prompts the user for the API key when running the code, storing it in memory without sharing it when the notebook is shared.