A set of tutorials on scientific computing with Python, using jupyter notebooks.
You can run these python codes by installing the requisite software in your computer, or online through Google Colab.
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Suggested Approach: If you're unable to set up Python on your local machine, you can execute the code using Google Colab. Simply follow the links to the Jupyter notebooks provided below and press the "Open in Colab" button at the top of the notebook. This method is compatible with any device (computer, mobile, or tablet) that has internet connectivity and a standard web browser such as Google Chrome. Please note that the code will be executed on Colab's servers, not on your local machine. Usually, this doesn't cause any issues, but be aware that the servers might sometimes be slow.
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In order to run these programs locally in your computer (instead of Google Colab), perform the following steps.
- Install GitHub Desktop after downloading it from its website @ desktop.github.com
- Then, download this repository by cloning it using GitHub Desktop (see this doc for details).
- Finally, download and install the anaconda python distribution (anaconda @ https://www.anaconda.com/). Anaconda includes Jupyter notebooks and the Spyder IDE, either of which can be readily used for designing and running python code. Also, see this blog entry on how to install anaconda.
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For a quick introduction to the Python programming language, as well as Numerical Python, Scientific Python and Matplotlib, see this tutorial
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For a more detailed introduction to the abovementioned topics, see Scientific Python Lectures.
Use the following links:
This is a fork of jrjohansson/scientific-python-lectures. The copy has some updates to the codes, and the pedagogocal material has been curated for content that is relevant to the course which this material supplements. This work is licensed under a Creative Commons Attribution 3.0 Unported License.