demianw / x_exed_10_2018

Introduction to scientific computing

Instructors

• Guillaume Lemaitre @glemaitre - Inria, Université Paris-Saclay

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Obtaining the Tutorial Material

If you have a GitHub account, it is probably most convenient if you clone or fork the GitHub repository. You can clone the repository by running:

git clone https://github.com/glemaitre/x_exed_10_2018.git

If you are not familiar with git or don’t have an GitHub account, you can download the repository as a .zip file by heading over to the GitHub repository (https://github.com/glemaitre/x_exed_10_2018) in your browser and click the green “Download” button in the upper right.

Please note that we may add and improve the material until shortly before the tutorial session, and we recommend you to update your copy of the materials one day before the tutorials. If you have an GitHub account and cloned the repository via GitHub, you can sync your existing local repository with:

git pull origin master

If you don’t have a GitHub account, you may have to re-download the .zip archive from GitHub.

Installation Notes

This tutorial will require recent installations of

• NumPy
• SciPy
• matplotlib
• seaborn
• pandas
• pillow
• scikit-learn
• IPython
• Jupyter Notebook

For users who do not yet have the required packages installed, a relatively painless way to install all the requirements is to use a Python distribution such as Anaconda, which includes the most relevant Python packages for science, math, engineering, and data analysis; Anaconda can be downloaded and installed for free including commercial use and redistribution. The code examples in this tutorial should be compatible to Python 2.7, Python 3.4-3.6.

We provided an environment.yml file allowing to specifically install the required packages into a specific conda environment. You can create this environment by entering the following command:

conda env create -f environment.yml

Once the packages installed, you can activate the environment:

conda activate tutorial-visualization

You will be able to use Jupyter Lab or Notebook by executing either:

jupyter notebook

or

jupyter lab

References

• The NumPy introduction 01_numpy was originally presented in the Python workshop by the CDS by Bartosz Telenczuk.
• The Pandas introduction 02_pandas was originally presented in the Python workshop by the CDS by Joris Van den Bossche.