• 1. Prerequisites
  • 1.1 Software prerequisites
  • 1.2 Clone this git repo to your local machine
  • 1.3 Set up Conda environment and install Python packages
  • 1.4 Setting up VS Code
  • 1.5 Opening our first Jupyter Notebook in VS Code
  • (Optional) 1.x Setting up the Windows Terminal

• Install git for Windows
• Install Miniconda
• Install VSCode
• (Optional) Install Windows Terminal for a nicer command line experience in Windows

Open the following link in a web browser: https://github.com/felio92/python-workshop

Copy the HTTPS link to clone the repository to your machine:

Open a terminal (e.g. Windows Terminal or Git Bash or Anaconda Prompt)

Change into the directory where you would like to clone this repository (using the cd C:\...\your-directory command within the terminal)

Clone the github repository to your local machine:

git clone https://github.com/felio92/python-workshop.git

If the cloning was successful, the output in the terminal should look something like:

In another terminal (e.g. Windows Terminal or Anaconda Prompt), create a new conda environment using the following command (confirm by typing 'Y' when prompted):

conda create -n python-workshop -c conda-forge python=3.9 jupyter matplotlib seaborn numpy scipy pandas geopandas cartopy

The command above will create a conda environment with the name "python-workshop", using the channel "conda-forge" to look up the list of packages:

• Python=3.9 Install the latest Python 3 version inside the environment.
• Jupyter Engine to run web-based Jupyter notebooks
• Matplotlib Plotting library to create static and dynamic plots
• Seaborn Data visualization library based on matplotlib, but adds some extra functionality, especially useful for creating statistical graphics.
• NumPy A library that adds support for large, multi-dimensional arrays to Python. A dependency for many scientific computing Python libraries.
• SciPy A widely used library for scientific computing. Contains modules for interpolation, linear alebra, signal and image processing, among many others.
• pandas A library for data analysis and manipulation. Offers data structures and operations, such as dataframes, for manipulating numerical tables and time series.
• geopandas A library based on pandas that extends its datatypes to make working with geospatial data easier, by allowing spatial operations on geometric types.

Install mamba which allows for a much smoother experience when installing python packages via conda on an ad-hoc basis, which we will do in the next step for a number of packages.

conda install -n base conda-forge::mamba

Lets activate the environment that we just created:

conda activate python-workshop

Let us now install a number of packages using the just installed mamba utility:

mamba install -c conda-forge -c pytorch earthengine-api geemap xarray xesmf cartopy pytorch torchvision

• earthengine-api A Python API to Google's proprietary Planetary-scale geospatial analysis engine.
• geemap A Python package for interactive geospatial analysis and visualization with Google Earth Engine.
• xarray A library that offers data types and operations to make working with multi-dimension, labelled data, such as climate data, easier.
• xesmf A library that can be used to regrid xarray arrays / datasets in a number of ways.
• cartopy A library to easily create maps and other geospatial data analyses.
• PyTorch A machine learning library for Python based on Tensors.
• torchvision A library that adds a number of classes and datatypes to use / transform images for use in PyTorch.

Further reading:

• Python Resource for Earth Sciences
• Earth Engine Python Notebook Examples
• A curated list of GEE resources (includes section for the GEE API for Python)

Open Visual Studio Code and open the workshop directory, where you previously cloned this repository to:

After navigating to the project directory and opening it, the resulting window should look something like this:

Visual Studio Code is a general purpose code editor. We'll want to install some Python-specific extensions to make working with Python within VS Code easier. To install the extensions, click (shortcut Ctrl+Shift+X) the extensions icon on the toolbar on the left:

We'll want to search and install the following extensions:

• ms-python.python Python extension for Visual Studio Code
• ms-toolsai.jupyter Adds support for jupyter notebooks within VS Code

Open the 01_getting_started.ipynb notebook in VS Code, which is found in the notebooks directory. You should be prompted to select a Python Environment:

Select the python-workshop environment, where we have installed all packages that we'll be using in this workshop.