- AKA: PSYC161, call# ????
- When: Winter 2019
- Instructor: Yaroslav Halchenko, [email protected]
- Venue: Moore Hall 453 (computer lab + TBA), Dartmouth College, Hanover, NH, USA, Planet Earth
- Times: Tue 10:00-11:30, Thu 10:30-12:00
- Need help?
- Google it up
- Office Hours: just email me to set time convenient for both of us
- Email me
Neuroimaging, computational neuroscience and other fields of brain research directions are becoming more and more data driven and employing sophisticated computational methods and paradigms, such as distributed and cloud computing. That is why it becomes critical for new researchers to develop at least basic skills in programming, software engineering, code and data management. This course intends to provide wide in scope and introductory in depth coverage of many important topics in programming, software engineering and some data structures and algorithms to streamline students current and future research projects. It will be practice and interaction oriented in its format: lectures will be interleaved within practice hands-on sessions. Although some topics and home exercises will involve using established data analysis frameworks, primary accent will be made on getting a good grasp of good coding and software developing practices: collaboration, testing, code review, etc. Thus, this course is intended to serve as a prerequisite to possible follow-up course(s) which would concentrate on in-depth learning of specific neuroimaging (and/or computational neuroscience) frameworks (from stimuli delivery to advanced analysis pipelines).
Neuroimaging could be considered a somewhat exemplar field of science since nearly all scientific software for fMRI and EEG/MEG data analysis was developed as a free and open source software (FOSS). Moreover, with recent developments of stimuli delivery toolboxes (e.g., psychtoolbox-3, PsychoPy, OpenSesame) it became possible to provide a complete free and open toolbelt for carrying out research in this domain. Therefore, in this course we will use/cover available FOSS tools (e.g., bash, git and may be developed in-house DataLad) to improve our skills not only in programming itself, but also to learn basics of code management, data structures and algorithms, and even some software engineering. Python, version 3, will be our primary programming language. Besides Python we might learn to compose little snippets in shell (bash).
Python itself and all the tools we will use/learn are available for any major operating
system (Linux, OS X, MS Windows). If you are a Debian or Ubuntu user,
you are encouraged to use a turnkey platform for neuroscience --
NeuroDebian -- where all
aforementioned (and thousands of other) tools are conveniently
available. Otherwise, use of a conda distribution is highly recommended - please visit
https://www.anaconda.com/download/ and download/install Anaconda
(Python 3.7 version) for your OS which will provide you not only Python
itself, but also access to thousand of other Python (and not only)
software packages.
Conda cheatsheet
provides a quick overview of available commands to work with conda.
To establish efficient hands-on sessions and code development in Python, we will use and recommend you to use in your research/coding practice:
- git Is a Version Control System
which we will (learn to) use. Debian/Ubuntu:
sudo apt install git. conda:conda install git. - Jupyter notebooks which will be used for
interactive work with Python. Available
for all platforms. Debian/Ubuntu:
sudo apt install python3-notebook. conda:conda install ipython-notebook - PyCharm community edition is a
very versatile Integrated Development Environment (IDE)
for Python.
For Debian/Ubuntu systems - NeuroDebian
provides now
pycharm-community-sloppypackage you can install. Otherwise, download from the project's website
We might possibly use some publicly available dataset as an example for hands-on sessions. For your project(s), you are welcome to use any publicly available dataset. Consider choosing datasets available through DataLad from datasets.datalad.org.
This course is planned as a graduate level seminar course for the Winter 2019 semester -- Jan 3rd to March 13th -- so we will have roughly 11 weeks. Each week we will have either one 3h or two 1h:30m classes, which would be a mix of small lectures and practice sessions. Practice sessions would include “pair programming” and “code review” sessions. Homeworks will consist of exercises in the "online textbook" be submitted via Git (further instructions TBA). Students will learn how to use Git/GitHub and will be encouraged to contribute to existing free and open-source projects.
The main sources for lesson materials are:
Note: Local urls with secretserver.dartmouth.edu should be rewritten with
secretserver replaced with the address to the secret server (not disclosed publically).
Otherwise, original version(s) of the books could be found on https://runestone.academy .
-
FOPP-PBS Brad Miller, Paul Resnick, Lauren Murphy, Jeffrey Elkner, Peter Wentworth, Allen B. Downey, Chris Meyers, and Dario Mitchell Foundations of Python Programming (PBS-WI19 edition) An interactive online textbook. Heavily based on THP
-
THP Downey, A. (2015). Think Python: How to think like a computer scientist (2nd edition). Needham, MA: Green Tea Press PDF Amazon (dead-trees version)
-
PS Miller, B.N. and Ranum, D.L. (2011) Problem Solving with Algorithms and Data Structures using Python (2nd + PBS-WI19 edition) PDF Amazon (dead-trees version)
We will start with FOPP-PBS and then pick up some sections from PS. If you need an offline good-night-sleep read, get yourself a copy of THP
-
PT Python tutorial - formally informal introduction to basic concepts and features of Python. Includes cursory overview of "batteries included", i.e. of the Python standard library - a rich collection of modules which come with any Python installation
-
PSL Haenel, V., Gouillart, E., & Varoquaux, G. (Eds.) (2011). Python Scientific Lecture Notes - a collection of tutorials for
-
SC Software carpentry - a number of lessons on Python, intended to quickly bootstrap hacking in Python using NumPy, pandas etc.
All above are available online (free and open), and you can fetch PDF
directly using git-annex (git annex get books/).
In addition, we will make use of a number of other online resources, including documentation and user manuals for the various libraries and packages that you will be learning to use.
This is a preliminary plan, which can (and probably will) change depending on our progress and students' preferences
-
Weeks 1-5: Get to know Python language fundamentals (from variables to modules and classes). FOPP-PBS. And become efficient with the tools (environments, VCS, IDE, QA).
-
Weeks 6-8: Introduction to the exciting Data Structures and Algorithms (sorting, binary search, etc) PS
-
Weeks 9-end: Fundamentals of the scientific Python core (NumPy, SciPy, matplotlib, pandas) PSL
| Date | Times | Lecture | Reading | HW |
|---|---|---|---|---|
| Th 1/03 | 10:30-12:00 | Intro/FOPP-PBS (1) | FOPP-PBS (2-5), BPPfSC | FOPP-PBS Assignment 1 |
| Tu 1/15 | 10:00-11:30 | FOPP-PBS (2-5(rehearse)), PEP8, SHELL | FOPP-PBS (6) | FOPP-PBS Assignment 2 |
| Th 1/17 | 10:30-12:00 | FOPP-PBS (6(rehearse)), Stupid Content Trackers: GIT1, GIT2, GIT CHEATSHEET | FOPP-PBS (7, 8) | FOPP-PBS Assignment 3 |
| Tu 1/22 | 10:00-11:30 | FOPP-PBS (7(rehearse)), Git rehearse, PyCharm | FOPP-PBS (14.{1,2,5,6}) | FOPP-PBS Assignment 4 |
| Th 1/22 | 10:30-12:00 | FOPP-PBS (8, 14.{1,2,5,6}(rehearse)), HW-FACTORIAL | FOPP-PBS (9) | FOPP-PBS Assignment 5 |
| Tu 1/29 | 10:00-11:30 | FOPP-PBS (Antipatterns, 7, 8 (no turtles) (rehearse)), Monte Carlo PI | FOPP-PBS (10 - Files, no .16 or .17) | FOPP-PBS Assignment 6 |
| Th 1/31 | 10:30-12:00 | FOPP-PBS (Review of some turtles and stats), n-back balanced sequences mini-project | FOPP-PBS (11. Dictionaries, SO Survey - very optional) | FOPP-PBS Assignment 7 |
| Tu 2/05 | 10:00-11:30 | FOPP-PBS (Review of solutions), more on n-back mini-proj | None (server maintenance) | Questionnaire |
| Th 2/07 | 10:30-12:00 | FOPP-PBS (Reviews of materials/solutions), Sets, HOWTO WHINE | FOPP-PBS (12. Functions) | FOPP-PBS Assignment 8 |
| Tu 2/12 | 10:00-11:30 | FOPP-PBS (Reviews of solutions for iterations/Images and Files) | None | FOPP-PBS Assignment 9: Project |
| Th 2/14 | 10:30-12:00 | FOPP-PBS (Reviews of solutions for Dictionaries) | FOPP-PBS (13. Tuple (un)packing, 14. More Iterations) | FOPP-PBS Assignment |
| Tu 2/19 | 10:00-11:30 | FOPP-PBS (Reviews of solutions for Dictionaries (Roman)/Functions), Shablona | Shablona, Student Pack | FOPP-PBS Assignment 11: Building programs/Use Shablona for your project |
| Th 2/21 | 10:30-12:00 | FOPP-PBS (Reviews of Tuple (un)packing etc), Jupyter notebook, Numerical Python (Numpy) ls-06b | FOPP-PBS (15. Advanced Functions) | FOPP-PBS Assignment 12: Advanced Functions, Project Progress |
| Tu 2/26 | 10:00-11:30 | Numerical Python (Numpy) ls-07b, Reviews of Project/GitHub/... | FOPP-PBS (16. Sorting) | FOPP-PBS Assignment 13: Sorting |
| Th 2/28 | 10:30-12:00 | Reviews of Adv. Functions/Keyword args (THKWA), Project etc, FOPP-PBS (23. More on accumulation/Comprehensions), FOPP-PBS (19. Exceptions) | FOPP-PBS (20. Defining your own Classes) | FOPP-PBS Assignment 14: Classes, Project Progress |
| Tu 3/05 | 10:00-11:30 | Reviews of Sorting, Project/GitHub/..., FOPP-PBS (17. Nested Data and Nested Iteration) | FOPP-PBS (22. Inheritance) | Project "Final" Progress Report (due end of Sun 10th) |
| Th 3/07 | 10:30-12:00 | Some review of Classes, TBD (more numpy/scipy?) | - | - |
| Tu 3/12 | 10:00-11:30 | pandas Getting Started, Day 2 of Python Bootcamp for Neuroscientists | - | - |
Students will be evaluated on the basis of:
-
Lesson exercises / class participation. Every week there will be a set of small exercises to be submitted for the first class in the upcoming week. Initially they all should be done within the interactive FOPP-PBS textbook. You must be logged in!
-
Final project, given 3-4 final weeks to work on.
Students must complete all homeworks (which will be lighter as we get closer to the end), and show measurable progress toward the project goal -- project might be more ambitious than what could be accomplished within the time left. Accomplished work toward the project will be scored based on project's code/materials review and their reflection of principles learned in the class
- code should be well documented
- code should be easy to read (largely PEP8 compliant), and free of anti-patterns
- functionality should be (unit)tested, integration with Continuous Integration (CI) platforms will be a plus
Students have two possibilities: self-proposed or a suggested project. For a self-proposed project:
-
It must be a new development or contribution (i.e. not something you did before this course).
-
Ideally should be something you or someone would end up re-using later on, so not a throw-away.
-
Contribution to an existing scientific projects is strongly encouraged (benefit from established QA, feedback, distribution channels, etc)
-
Possible domains can be
- Data structures/algorithms implementation
- Stimuli/experiment
- Data Analysis
- Establishing/improving quality assurance (tests, etc) of an existing scientific FOSS project
- Contribution of a chapter to a free and open programming textbook (like the [FOPP]), e.g. a missing (e.g., sets, asserts/tests) or puzzle/exercise (e.g., 8 queens, balanced sequences) section
-
Team work is encouraged (but contribution ratio will be assessed,
git logandgit shortlog -snis handy for that)
You are most welcome to use materials from previous homeworks, and even review and grab ideas (not cut/pasted code) from code of others. You can either clone a blank psyc161-prj against which you would submit your solution in private, or create a new public git repository on github. For any of those projects you will need to provide me with a link to your project on GitHub: could be an existing or new repository, a pull request (or collection of them).
In non-official words: you must not re-use/cut-paste complete solutions found online. You are strongly encouraged to do your best to try figuring out the solution yourself. If you are stuck on some technical detail, it is perfectly fine to consult documentation, even "ask google", and/or email me for a hint/help. But if you are fully stuck ad researched/found a complete solution online and saw/read it (aren't you a fast reader of Python code by now) -- it is not the end of the world -- close that page and try recreating that solution yourself without looking up there again.
At Dartmouth, we value integrity, responsibility, and respect for the rights and interests of others, all central to our Principles of Community. We are dedicated to establishing and maintaining a safe and inclusive campus where all have equal access to the educational and employment opportunities Dartmouth offers. We strive to promote an environment of sexual respect, safety, and well-being. In its policies and standards, Dartmouth demonstrates unequivocally that sexual assault, gender-based harassment, domestic violence, dating violence, and stalking are not tolerated in our community.
The Sexual Respect Website at Dartmouth provides a wealth of information on your rights with regard to sexual respect and resources that are available to all in our community.
Please note that, as a faculty member, I am obligated to share disclosures regarding conduct under Title IX with Dartmouth's Title IX Coordinator. Confidential resources are also available, and include licensed medical or counseling professionals (e.g., a licensed psychologist), staff members of organizations recognized as rape crisis centers under state law (such as WISE), and ordained clergy (see https://sexual-respect.dartmouth.edu/reporting-support/all-resources/confidential-resources).
Should you have any questions, please feel free to contact Dartmouth’s Title IX Coordinator or the Deputy Title IX Coordinator for the Guarini School. Their contact information can be found on the sexual respect website at: https://sexual-respect.dartmouth.edu/reporting-support/all-resources/campus-resources
This work and other related to the course materials under https://github.com/dartmouth-pbs are licensed under a Creative Commons Attribution Share-Alike 4.0 International License.
The textbooks materials are distributed under their own terms.
Some portions of the materials within this repository are borrowed from other sources distributed under compatible terms from
- Intro CS at NYC iSchool under CC Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
- Computational Methods for Psychology and Neuroscience @ OSU
- Anatomy of Matplotlib by Ben Root under the CC Attribution-3.0 Unported license
- Practical Neuroimaging by M.Brett largely copyright Matthew Brett 2015, licensed under the Creative Commons attribution 2.0 generic license (CC-by 2.0): see http://creativecommons.org/licenses/by/2.0/
- PsychoPy Course by Jonas Lindeløv, licensed under GPL v.2