Precipitation Cookbook

This Project Pythia Cookbook covers an extremely basic precipitation classification project. This notebook will introduce learners to the scikit-learn API, basic exploratory data analysis (EDA), and evaluations. It is meant to be a very early and basic introduction to these concepts, it is not meant to be an in-depth intorduction to machine learning. It could be the first introduction to machine learning for learners familiar with weather data.

Motivation

This cookbook is meant to be a companion to Unidata's CyberTraining project.

Authors

First Author, Second Author

Contributors

Structure

(State one or more sections that will comprise the notebook. E.g., This cookbook is broken up into two main sections - "Foundations" and "Example Workflows." Then, describe each section below.)

Section 1 ( Replace with the title of this section, e.g. "Foundations" )

(Add content for this section, e.g., "The foundational content includes ... ")

Section 2 ( Replace with the title of this section, e.g. "Example workflows" )

(Add content for this section, e.g., "Example workflows include ... ")

Running the Notebooks

You can either run the notebook using Binder or on your local machine.

Running on Binder

The simplest way to interact with a Jupyter Notebook is through Binder, which enables the execution of a Jupyter Book in the cloud. The details of how this works are not important for now. All you need to know is how to launch a Pythia Cookbooks chapter via Binder. Simply navigate your mouse to the top right corner of the book chapter you are viewing and click on the rocket ship icon, (see figure below), and be sure to select “launch Binder”. After a moment you should be presented with a notebook that you can interact with. I.e. you’ll be able to execute and even change the example programs. You’ll see that the code cells have no output at first, until you execute them by pressing {kbd}Shift+{kbd}Enter. Complete details on how to interact with a live Jupyter notebook are described in Getting Started with Jupyter.

Running on Your Own Machine

If you are interested in running this material locally on your computer, you will need to follow this workflow:

(Replace "cookbook-example" with the title of your cookbooks)

1. Clone the https://github.com/ProjectPythia/cookbook-example repository:

    git clone https://github.com/ProjectPythia/cookbook-example.git

2. Move into the cookbook-example directory

   cd cookbook-example

3. Create and activate your conda environment from the environment.yml file

   conda env create -f environment.yml
   conda activate cookbook-example

4. Move into the notebooks directory and start up Jupyterlab

   cd notebooks/
   jupyter lab