The first session is the Monday session. The second session is the Thursday session.
These are the notes for the Monday session.
We will update them before and after the session, to please keep checking back for updates. Just refresh the page in your browser to get updates.
The Zoom link for all sessions is https://numfocus-org.zoom.us/j/89458437731?pwd=eTF0V252bHlKOXBucnBUWlVBSm9uUT09.
Recording of Monday Week 1 session 1
- We will be referring often to the textbook.
- Hello and welcome.
- Thanks to the Chan Zuckerberg Initiative.
- Surviving the computer. See video link below.
- Machinery:
- https://textbook.nipraxis.org
- https://learn.nipraxis.org
- https://hub.nipraxis.org and the Jupyter notebook.
- https://github.com/nipraxis
- About the project.
- We will gradually move away from notebooks.
- Using Jupyter notebooks and more on Jupyter notebooks
- For reference: Very fast introduction to Python
If you need to catch up on Python and Numpy, you have a double-dose of homework this week. In particular, you really will need to do the second section here, to get fluent in basic Python and Numpy. If you are already fluent, you only need to do the first section.
Any problems, email [email protected], I will point you to someone else who can help if I can't help you.
Make sure you have watched:
<iframe title="Surviving the computer" src="https://player.vimeo.com/video/693542789?h=63ccfc6dfa" width="640" height="360" frameborder="0" allowfullscreen></iframe>Next:
- Check your knowledge with the fast introduction to Python.
- Install Python and required libraries on your computer. Make sure you have run the install check at the end of that page.
That's all for those of you who are comfortable with Python and Numpy. See below for those of you who need to catch up.
If you don't know Python well, we strongly suggest you do these exercises to catch up, otherwise the rest of the course will be too fast. Make sure, too, that you've read the fast introduction to Python above.
If you are new to Python, do the expressions and statements exercise, to get into the swing of things.
Next, do these exercises from the Google Python class. There are links in the exercises to the sections to read to revise the ideas you need.
We also suggest you try the slightly more advanced exercises:
(Re-)Read the introduction to Numpy.
For a fuller introduction to Numpy, please see the Scientific Python Numpy tutorial.
Specifically, see:
- The numpy array object;
- Array operations.
- If you know Matlab, you might want to look over this page on Numpy for Matlab users.
Make sure you can do the arrays exercise
That's it for this session.