# Programming Psychology Experiments

## Objectives

- learn to build experimental psychology experiments, that is, how to create visual and auditory stimuli, to present them at precise times and possibly record participants' responses and analyse them
- learn tools to do reproducible science (github, markdown, ...)
- improve  your programming skills (clean code, real-time, ...)


## Pedagogy

* The lectures essentially consist of **Practical tutorials**: 
  After we introduce a topic, you have to solve challenges (with help from us if necessary). 
  
* We will use mostly Python, but also Javascript for online experiments.

* Bring your computer and have the software installed in advance !!! 


## Expectations/Evaluations

* Attendance is expected (warn me by email if you cannot attend for a good reason).

* Working during the course is also expected. At the end of lectures, I will asked to you to send me by email a copy of your work folder, with your attempts.


## Resources:

### Our Github repositories: 
   - https://github.com/chrplr/programming-psychology-experiments
   - https://github.com/chrplr/PCBS

### Python

*  Books:

   -  *Automate the boring stuff with Python* <https://automatetheboringstuff.com/> (highly
      recommended!)
   -  *Apprendre à Programmer avec Python3* <https://inforef.be/swi/python.htm>
   -  *Think Python* <http://greenteapress.com/thinkpython2/>


*  MOOCs:

   -  Udemy’s Python programming for absolute
      beginners <https://www.udemy.com/python-programming-for-absolute-beginners/>
   -  Code Academy’s Learn Python
      module <https://www.codecademy.com/learn/learn-python>
   -  Openclassrooms’ Apprendre à programmer en
      Python <https://openclassrooms.com/fr/courses/235344-apprenez-a-programmer-en-python>
   -  Python 3 : des fondamentaux aux concepts avancés du
      langage <https://www.fun-mooc.fr/en/cours/python-3-des-fondamentaux-aux-concepts-avances-du-langage/>


### Programming skills


*  Software Carpentry <https://software-carpentry.org/lessons/>
   provides nice lessons about writing software for science and do reproducible science.


### Unix Command line (shell)


-  The Linux Command Line <http://linuxcommand.org/tlcl.php> by Williams Shotts.
-  Openclassrooms MOOC <https://openclassrooms.com/courses/reprenez-le-controle-a-l-aide-de-linux>



### Git

*  Openclassrooms’ MOOC Manage your code with Git and Github <https://openclassrooms.com/en/courses/5671626-manage-your-code-project-with-git-github>
*  https://product.hubspot.com/blog/git-and-github-tutorial-for-beginners
*  https://git-scm.com/book/en/v2/Getting-Started-Git-Basics
*  The Git Book <https://git-scm.com/book/en/v2>
*  My own git cheat page <http://www.pallier.org/version-control-at-your-fingertips-a-quick-start-with-git.html#version-control-at-your-fingertips-a-quick-start-with-git>



### Cognitive and Brain Sciences Programming

*  *Programming Visual Illusions for Everyone* <https://www.programmingvisualillusionsforeveryone.online/>
   by Marco Bertamini.
*  *Neural Data Science: A Primer with MATLAB and Python* by von Erik Lee Nylen and Pascal Wallisch
*  *Matlab for Brain and Cognitive Scientists* and *Analyzing neural time series data* by Mike X Cohen
*  Python in Neuroscience <https://www.frontiersin.org/research-topics/8/python-in-neuroscience>
*  *Modeling Psychophysical Data in R* by Kenneth Knoblauch & Laurence T. Maloney



### Stimulus/Experiment generation modules
--------------------------------------

-  http://www.expyriment.org (See Get started with Expyriment <https://docs.expyriment.org/Tutorial.html>
-  http://psychopy.org (See Programming with PsychoPy <https://www.socsci.ru.nl/wilberth/nocms/psychopy/print.php>)
-  http://psychtoolbox.org/ (See Psychtoolbox demos <http://peterscarfe.com/ptbtutorials.html>)
-  https://www.jspsych.org/ (See intro at https://blog.s-m.ac/using-jspsych/)
-  https://dialoguetoolkit.github.io/chattool/



### Data analyses, Statistics in Python
-----------------------------------

-  Modules: numpy, scipy, pandas, seaborn, statsmodel, sklearn

   -  Data manipulation:

      -  http://pandas.pydata.org/pandas-docs/stable/tutorials.html

   -  Plotting:

      -  http://matplotlib.org/users/pyplot_tutorial.html
      -  https://seaborn.pydata.org/tutorial.html

-  *Scipy Lecture Notes*: http://www.scipy-lectures.org/
-  *Think Stats* by Allen B. Downey:  http://greenteapress.com/thinkstats2/
-  *Python Data Science Handbook* by Jake VanderPlas:  https://jakevdp.github.io/PythonDataScienceHandbook
-  *Introduction to Data Science in Python*: notebook from a 2 day workshop organized by the Paris-Saclay Center for Data Science: https://github.com/paris-saclay-cds/data-science-workshop-2019
-  *Machine Learning with scikit-learn" MOOC*: https://www.fun-mooc.fr/en/courses/machine-learning-python-scikit-learn/  (on github: https://inria.github.io/scikit-learn-mooc/)