This repository contains the laboratory portion of a split upper level undergraduate / graduate class in Python on data visualization and statistics for geo & space scientists.
Labs were updated in April at the conclusion of the Winter 2021 term as led by Dr. Michael Liemohn and supported by Alex Shane.
The previous version, Winter 2020, may be found under tags, 2018 and 2019 may be found on github.com/abbyazari.
The labs uploaded are in Jupyter primarily and coded in Python 3.5+. However, most of the functions and methods should work in Python 2.7. If you do not have/know what Jupyter is - you can download Python/Jupyter from Anaconda or Canopy as a fully installed and interactive environment. This will support most if not all of the packages used in the labs and then you can navigate to Jupyter through Anaconda/Canopy. Also see the Jupyter website for additional help with Jupyter.
Make sure you check out the Frequently Asked Questions section as you go through the labs.
Please see our license at the end of this readme. This class was developed in collaboration between Dr. Michael Liemohn of the Climate & Space Sciences & Engineering department and Dr. Abby Azari in 2018.
If you find it useful and end up using in your work please cite this version as:
- Liemohn, M. W., Shane, A. D., Swiger, B. M., and Azari, A. R. "Data Visualization and Statistics in the Geosciences". Accessed on insert date. https://github.com/clasp423/data_vis_statistics_geosciences. 2021.
Grouping of frequently asked questions on programming, visualization, and statistics.
Temperature Changes: Looking at Global Temperature Variances in the last century.
Ecosystem Impacts: Looking at Audobon Society data on bird wintering patterns.
Planetary Magnetic Fields: Jupiter data with Jupyter; investigating how to use and interpret interplanetary spacecraft mission data from the recent Juno mission.
Health Impacts: Normal distributions of temperature and boxplots of extremes.
Arctic Indicators: Calculating rates of change of sea ice extent and mapping Note: this lab uses cartopy and netcdf4 python packages, which are not part of the base anaconda distribution.
Space Weather: Solar impacts on the geospace environment; using the bootstrap method to determine uncertainty in calculated stats
Zoo of Metrics: Calculating and interpreting several Fit Performance & Event Detection metrics for comparing model output to observations.
Jupiter Images with Jupyter: Looking at spacecraft image data from the JunoCam instrument from the Juno mission.
Discussions with climate scientist Samantha Basile were critical in developing these materials. We also thank planetary scientist Camilla Harris for discussions of Juno data, Gabriel Harp the Research Director of ArtsEngin at University of Michigan for pointing us toward climate health impact data, space scientist Doga Ozturk for expertise in space weather informatics, solar physicist Yeimy Rivera for expertise in solar surface data, and Jeff Lockhart of the Computational Social Scientists (CSS) for offering resources through CSS.
The content and lessons of this repository itself is licensed under the Creative Commons Attribution-Non Commercial 4.0 license. However, the specific code used within the notebooks taken out of context and to format and display that content is licensed under the MIT license.