11/1/22, 9:24 PM
Working with Spatio-temporal data in Python
Working with Spatio-temporal data in Python (./)
With Software Carpentry lessons (https://software-carpentry.org/lessons/) and Data Carpentry lessons (http://www.datacarpentry.org/lessons/) you learn
the fundamental data skills needed to conduct research in your field and learn to write simple programs.
This one-day workshop will introduce you to Python for analyzing and visualizing spatial-temporal data. We will be using datasets from the
environmental sciences that are freely available.
We will learn:
how to identify some of the most common data formats (raster formats) in environmental Sciences i.e. netCDF
(http://www.unidata.ucar.edu/software/netcdf/docs/netcdf_introduction.html) and HDF (https://support.hdfgroup.org/) (HDF-EOS (http://hdfeos.org/)
and HDF5 (https://support.hdfgroup.org/HDF5/)), GeoTIFF (https://trac.osgeo.org/geotiff/).
How to view the content of these binary files
How to identify the provenance and explore metadata
How to organise your data and develop a simple data management plan to ease your work
How to access and process data in commonly used raster formats such as GeoTIFF, HDF-EOS, HDF5 and netCDF.
How to combine them with vector datasets such as ESRI Shapefile and GeoJSON.
The meaning of these terms will become clear as we work through the python notebooks.
 Prerequisites
1. Learners need to understand what files and directories are and what a working directory is. These concepts are covered in the Unix Shell
(http://swcarpentry.github.io/shell-novice/) lesson.
2. Learners need to have some prior knowledge of Python. For instance, what is covered in the Software Carpentry lesson Programming with Python
(http://swcarpentry.github.io/python-novice-inflammation/) is more than sufficient.
3. Learners must install Python and a few additional python libraries before the class starts. See the setup instructions (./setup/)
4. Learners must get the metos data before class starts: please download and unzip the file metos-python-data.tar
(https://zenodo.org/record/995709/files/metos-python-data.tar).
Please see the setup instructions (./setup/) for details.
Schedule
Setup (./setup)
Download files and install packages required for the lesson
00:00
1. Introduction (./01-intro/)
Where to start?
Why using common data formats?
00:00
2. Data Formats in Environmental
Sciences (./02-formats/)
What are the most common data formats in Environmental Sciences?
raster vs vector formats?
What are the most common python packages to read/write netCDF, HDF, GeoTIFF
data files?
00:00
3. Intro to Coordinate Reference
Systems & Spatial Projections
(./03-crs_proj/)
What are Coordinate Reference Systems?
00:00
4. Plotting spatio-temporal data
with Python (./04-plotting/)
How can I create maps with python?
00:00
5. Data analysis with python (./05scipy/)
What is SciPy?
How can I use Scipy?
00:00
6. Visualize and Publish with
Python (./06-visualization/)
How to create animation plots and publish them on the web?
00:00
7. Handling very large files in
Python (./07-LargeFiles/)
How to manipulate large netCDF or HDF files?
How to optimize my workflow?
What are xarray and dask?
00:00
Finish
The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.
Copyright © 2016–2019 Data Carpentry
(http://datacarpentry.org)
https://annefou.github.io/metos_python/
Edit on GitHub
(https://github.com/annefou/metos_python/edit/ghpages/index.md) / Contributing
(https://github.com/annefou/metos_python/blob/ghpages/CONTRIBUTING.md) / Source
(https://github.com/annefou/metos_python/) / Cite
(https://github.com/annefou/metos_python/blob/ghpages/CITATION) / Contact
(mailto:annefou@geo.uio.no)
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