Date: 8am on Monday 9th July, 2018
Download: scipy-2018 git tag (available from around 6th July), see also releases
On the 1st of October 1872, Jules Verne’s character Phileas Fogg embarks on an adventure to circumnavigate the world in just 80 days. Travelling exclusively by sea and rail, Fogg voyages from the misty alleys of Victorian London to the exotic subcontinent and the Wild West in a race against the clock. Despite fastidiously counting each sunrise, he famously neglects the effect of crossing the dateline and thus arrives on a different day to the one he calculated.
The mistreatment of the dateline is far from unique to Phileas Fogg; it is not just the 24hr time discontinuity that we struggle with - the antimeridian is also a longitudinal discontinuity of 360 degrees for coordinate systems centered at the Greenwich Prime meridian. Whilst Cartesian treatment of geospatial data is a reasonable assumption with an appropriately transformed coordinate system and at large scale (small area), the discontinuities of the antimeridian and the poles frequently wreak havoc on our Cartesian tools when we treat larger area (global / small scale) data.
In this tutorial, join Phileas Elson on our own epic adventure by tracking Fogg’s travel log with careful non-Cartesian treatment as we go. Along the way, we will discover how many of the python libraries we may already know and love can be used in conjunction with cartopy to provide a powerful suite of open source geospatial tools.
We will cover many of the core concepts in cartopy, including topics such as: coordinate reference systems and projections; the matplotlib interface; geospatial image, data (NetCDF) and raster treatment; as well as geometry predicates and transformations. The knowledge developed from this tutorial will be applicable to a broad range of geospatial analyses (both raster and vector), and will provide hands-on experience of tools such as numpy, proj.4, matplotlib and shapely to name a few.
The tutorial will be applicable to a broad range of experiences, but familiarity with numpy and matplotlib will be essential to enable progress through the exercises. A prior awareness of tools such as shapely, proj.4, geopandas, and xarray/Iris will help, though are by no means essential. The tutorial will be made up of a number of exercises with each being designed to accommodate a broad range of expertise.
Please see the dependency requirements for a list of packages needed to participate in this tutorial.
Of significant note is that this tutorial is only designed for Python 3.6 or greater. Whilst most of the tools are available for earlier versions of Python, the tutorial itself isn't (and will never be).
Conda is the recommended tool for installing the dependencies of this tutorial. Users of conda or Anaconda can install the dependencies into a clean environment with:
conda create --file=requirements.txt --channel=conda-forge --name=scipy-cartopy-tutorial
There are a number of large packages in the requirements.txt, and this could take a
few minutes to download.
Once installed, the environment can be activated as usual with:
conda activate scipy-cartopy-tutorial
Or for users of older versions of conda source activate scipy-cartopy-tutorial.
pip is not recommended for installing this tutorial's dependencies.
This is mostly because there are a number of non-trivial (non-python) packages
that need to be installed. These are the kinds of packages that a tool like conda
was designed for.
The conda environment under which the tutorial was originally written is available
in env-exports/${platform}.txt. This may be useful in ascertaining specific
dependency versions were used when developing.
These were produced with conda list --explicit > env-exports/${platform}.txt.
To create a conda environment based on one of the appropriate conda environment files:
conda create --name scipy-cartopy-tutorial --file env-exports/${platform}.txt
First, if you have not already done so, activate the tutorial conda environment:
conda activate scipy-cartopy-tutorial
Or for users of older versions of conda source activate scipy-cartopy-tutorial.
Now, run the check_env.py script in the root of this repository.
If all the required packages say [ OK ], then you're ready to start your journey around the world!
Once you have installed and checked the necessary dependencies, run jupyter notebook
from the root of this repository, and start with introduction.ipynb.