Skip to content
Switch branches/tags

Latest commit


Git stats


Failed to load latest commit information.
Latest commit message
Commit time

Mathematical Cartography 2021

These Jupyter notebooks support the Mathematical Cartography LOOM.02.007 MSc course and focus on the capabilities of the Python mathematical cartography and visualization library Cartopy.

The first tutorial gives an overview of creating a map window, using projections and constructing basic map elements (grid, text)

The second tutorial focuses on additional map elements, such as point symbols and different cartographic line objects (orthodrome, rhumb line, almucantar)

Lab session

During the live lab session we will go through the aforementioned notebooks in the Binder computing environment. Binder will create a Python environment based on the file environment.yml, which lists the libraries used in this session. It will also provide the computational resources needed to run the notebooks online. We can then run and edit the notebooks in the browser without setting up an Anaconda environment ourselves.

Click the Binder icon below to launch the environment and access the notebooks


NB As Binder works online, edits made in the notebooks will not be saved if you close the browser or quit the environment. You might also get a timeout message if the notebook stays inactive, so Binder will ask to restart the kernel, which also resets your progress.

If you wish to save your task solution progress then you should either

  • download the notebook (.ipynb) and then continue working using your own Anaconda setup (see below)
  • copy the cell content of your solution somewhere (e.g. Notepad), so you could paste it when you restart Binder next time


Setting up Anaconda on your machine

We will use Anaconda for these exercises, which should already be installed in the computer class. For installing it on your own machine you can use the tutorial provided by Alex Kmoch.

After installing Anaconda, download and unzip this repository into a folder you can find on your machine.

Code -> Download ZIP

Find and launch the Anaconda Prompt using the search bar.

Now move to the folder where you unzipped the GitHub repository.

cd C:\Users\Holger\mcarto2021-main\mcarto2021-main

You should be able to see the content of this folder with the ls command.


We will start by creating an Anaconda environment using the existing environment file environment.yml, which installs all Python libraries needed for the two exercises.

conda env create -f environment.yml

The next command will activate the Anaconda environment we just created.

conda activate mcarto2021

Before we start Python coding we will make our newly created conda Python environment known to the Jupyter notebook system by installing the kernel, basically the execution engine link from Jupyter web notebook to our Python environment.

python -m ipykernel install --user --name mcarto2021

Finally, we can activate the Jupyter environment.

jupyter lab

A browser window will open where clicking on a Jupyter notebook file with the extension .ipynb allows you to start editing the notebook.

In order to make sure that Jupyter uses the correct Anaconda environment select Kernel -> Change Kernel and choose mcarto2021 as the preferred kernel.


Materials for the Python lab session in the Mathematical Cartography course in Department of Geography at University of Tartu





No releases published


No packages published