#!/usr/bin/env python
# coding: utf-8

# Welcome to PSYC.161-WI19
# # Introduction to Programming for Psychologists & Neuroscientists
# by Yaroslav O. Halchenko 
# 
# **email:** class-psyc161@onerussian.com
# 
# **Where:** here in Moore 453, later might move to 303
# 
# **When:** T/Th either 10:30am-12:00pm (need to find some 3h slots - TODO).
# 
# **Details:** https://github.com/dartmouth-pbs/psyc161
# 
# **Canvas:** Maybe

# # Why me?

# # Why you?

# # Why not just a "Programming 101"?
# 
# **Best Practices for Scientific Computing**
# by Greg Wilson et al., PLOS Biology 2014
# 
# http://dx.doi.org/10.1371/journal.pbio.1001745
# 
# Box 1. Summary of Best Practices
# 1. **Write programs for people, not computers**
# 2. **Let the computer do the work**
# 3. **Make incremental changes**
# 4. **Don't repeat yourself (or others)**
# 5. **Plan for mistakes**
# 6. Optimize software only after it works correctly
# 7. Document design and purpose, not mechanics
# 8. **Collaborate**
# 
# 

# # Why "Programming?"

# ## Automation
# 
# <img src=https://imgs.xkcd.com/comics/is_it_worth_the_time.png>
# <a href=https://xkcd.com/1205/>xkcd.com/1205</a>

# ## Automation
# <img src=https://imgs.xkcd.com/comics/automation.png>
# <a href=https://xkcd.com/1319/>xkcd.com/1319</a>

# # Why "Programming?"
# ## Automation
# 
# <img src=https://lh4.googleusercontent.com/-0mb8ktQfQjE/TwU72nysQaI/AAAAAAAAI1g/EVOnQXhQsuM/w720-h513-no/geeks-vs-nongeeks-repetitive-tasks.png  width=600px> by Bruno Oliveira
# 
# - **Reusable** (fix-rerun, or might even benefit others)
# - **Flexible** (could be generalized/extended -- apply accross studies)
# - **Scalable** (worked for >1 subjects, will work for any #)
# 
# - **Exact description** of performed actions (was any "Methods" section was sufficient?) 

# # Why "Programming?"
# ## Programming == Problem solving
# 
# Given a problem
# 
# 1. **Understand** what the problem is
# 2. **Conceptualize** approach to solve it
# 3. **Implement** the approach
# 4. **Verify** that the problem is solved, if not goto 1
# 

# # Why "Programming?"
# ## Programming =(historically)= Computer Science
# 
# I hoped we will touch upon topics from
# 
# - Data structures and algorithms ([CS 31](http://www.cs.dartmouth.edu/~cs31/))
# - Computability and complexity ([CS 39](https://ssl.cs.dartmouth.edu/~ac/Teach/CS39-Winter15), CS49)
# - Software engineering
# - Problem solving (CS 10)
# - Numerical and computational tools (CS 70/170)

# # How will we do it?
# 
# - Classes once/twice a week for a total of 3h/week
#   - (In-Class participation counts!)
# 
# - Hands-on sessions through out the classes
#   - Codescope within [FOPP-PBS-WI2019](https://runestone.academy/runestone/static/fopp-pbs-wi2019/index.html) online text book. Note that online built-in Python is not a complete Python
#   - Python IDE (Pycharm)/editors
#   - Git and GitHub (Travis CI and friends)
#   - IPython notebooks
#   - tests (pytest)
# 
# - Homework excercises due every week (but the last 4 weeks)
#   - Peer-review (?)
# 
# - Final project
#  

# # Final project
# 
# - It must be a new development (i.e. not something you did before this
#   course), although you can choose to improve upon your prior code
# 
# - Ideally should be something **you** or someone would end up re-using
#   later on
# 
# - Contributions to existing scientific projects will be strongly
#   encouraged (benefit from established QA, feedback, distribution
#   channels, etc)
# 
# - Possible projects will be suggested and discussed in the class on
#   4-5th week and possible domains are
# 
#     - Data structures/algorithms implementation
#     - Stimuli/experiment
#     - Data Analysis
#     - Establishing/improving quality assurance (tests, etc) of an
#       existing scientific FOSS project
#       
# - Team work is encouraged (but contribution ratio will be assessed)

# # My goals
# 
# ## 1. Teach you programming
# 
# - be able to use programming concepts (conditionals, iterations, functions, modules)
# - solve problems alogirithmically so you have a generic and scalable solution not a list of copy-pasted and tuned lines
# - get a healthy amount of paranoia - all code has bugs. Learn how to avoid/detect via in-line assertions and (unit-)tests
# 
# ## 2. Teach you to efficiently use tools
# 
# - to become at least a few times more efficient in the tools you had touched (shell, git, github, ipython/jupyter)
# - to know and know how to use existing offerings
# - to be able to collaborate
# - to be sure you have done *it* correctly: validate and (unit-)test
# 
# ## 3. Teach you to become part of the scientific Python ecosystem
# 
# - contribute back

# # Lecture #1: Python for sciences, fun and profit
# (AKA Introduction)

# # Why Python?

# # Why not?

# - Free, Open, Cross-platform - **scale and share without limits**
# - Generic programming language - **avoid learning a new software for each new problem**
#   - Stimuli delivery
#   - Numerical (Data) analysis
#   - Simulations
#   - Web platforms
#   - Web scraping/Data mining
#   - Systems automation
#   - ...
# - "Friendly" to other ecosystems - **it is ok to use other software on an occasion**
#   - Interface data/functionality from other languages (R, Matlab) and libraries
# - “Python is executable pseudo-code.” (Python lore (often attributed to Bruce Eckel)) - **code of others is readable**
# - "Batteries included" includes testings framework(s) - **testing is now integral part of nearly any Python project**
# 
# 

# # No, why "Python" name?

# ## Has nothing to do with the snake
# <img src=https://pbs.twimg.com/profile_images/659095086051889152/FZL2eeOL_400x400.png>

# # (Monty) Python
# <img src=https://ticketing.musicboxtheatre.com/CDN/media/entity/get/FilmPosterGraphic/h-HO00000040>

# # (Scientific) Python ecosystem is
# 
# - [Python](http://python.org) (programming language/interpreter): ~~2.7.15,~~ 3.7.2
#   - [The Python standard library](https://docs.python.org/3/library/) (AKA "batteries"): io, sys, re, copy, math, random, glob, csv, httplib, argparse, unittest, ...
# - [IPython](http://ipython.org) improved Shell and Notebook
# - Python IDEs - Integrated Development Environment
#   - [Pycharm](https://www.jetbrains.com/pycharm/), [Spyder](https://pythonhosted.org/spyder), ...
# - [NumPy](http://numpy.org) - core datastructure object for efficient numeric arrays manipulations
# - "Numeric batteries": 
#   - [SciPy](http://scipy.org) - high-level data processing routines. Optimization, regression, interpolation, statistics, etc...
#   - [Matplotlib](http://matplotlib.sourceforge.net), [seaborn](http://stanford.edu/~mwaskom/software/seaborn/), ... - plotting, visualization
#   - [Pandas](http://pandas.pydata.org) - efficient manipulation/analysis of tabular data
#   - [statsmodels](http://statsmodels.sourceforge.net) - statistical models
# - "Brainy ones":
#   - [PyMVPA](http://pymvpa.org), [NiLearn](http://nilearn.github.io), ... - statistical learning analysis of neural data
#   - [PsychoPy](http://www.psychopy.org), [OpenSesame](http://osdoc.cogsci.nl), ... - stimuli delivery
#   - [dipy](http://nipy.org/dipy/), [nipype](http://nipy.org/nipype), [nitime](http://nipy.org/nitime/), ...
# - Data management/distribution
#   - [DataLad](http://datalad.org)

# # How to install Python
# - Linux distributions: package managers
#   - Debian/Ubuntu: recommend enabling [NeuroDebian](http://neuro.debian.net) for recent versions of many useful Python modules (psychopy, pandas, seaborn, sklearn, pymvpa, dipy, nipype, nitime, ...)
# - OSX/Windows:
#   - OS X is coming with some Python
#   - NeuroDebian VM
#   - "Free" (but not fully open) [Anaconda](https://store.continuum.io/cshop/anaconda/)

# In[ ]: