Week 1 session 1: Introduction and tools

The first session is the Monday session. The second session is the Thursday session.

These are the notes for the Monday session.

Recording

Recording of Monday Week 1 session 1

Schedule and plan

• 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 (learn.nipraxis.org is now down after our CZI grant funding expired).
  • https://hub.nipraxis.org and the Jupyter notebook (hub.nipraxis.org is now down — see above).
  • 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

For homework

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 matthew.brett@gmail.com, I will point you to someone else who can help if I can’t help you.

For everyone: surviving computer, installing on your own machine

Make sure you have watched:

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 need practice with Python or Numpy

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.

Python

If you are new to Python, do the expressions and statements exercise.

• Multi-model 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.

• string1 exercise
• list1 exercise

We also suggest you try the slightly more advanced exercises:

• string2 exercise
• list2 exercise

Numpy

(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