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

# # Foundations of Computational Economics #16
# 
# by Fedor Iskhakov, ANU
# 
# <img src="_static/img/dag3logo.png" style="width:256px;">

# ## Visualization of data and solutions
# 
# <img src="_static/img/lecture.png" style="width:64px;">

# <img src="_static/img/youtube.png" style="width:65px;">
# 
# [https://youtu.be/dJdWVkSNNpc](https://youtu.be/dJdWVkSNNpc)
# 
# Description: Principles and functions of graphics. Examples of visualization of economic models.

# ### Why visualize?
# 
# 1. **Convey ideas to others**
#   Ability to efficiently explain your idea/work to other busy people is crucial element of success in many fields  
# 1. **Check your own work**
#   Creating of new knowledge using computational tools requires absolute certainty in the code  
# 1. **Aggregate large amounts of information**
#   Makes it possible to get the big picture and the message behind it  

# <img src="_static/img/viz/strava-heatmap-north-and-south-korea.png" style="width:900px;">
# 
# [https://www.strava.com/heatmap#5.13/120.47297/37.61174/hot/all](https://www.strava.com/heatmap#5.13/120.47297/37.61174/hot/all)

# ### Matplotlib and other libraries
# 
# - `matplotlib` - Python library that abstracts from the graphical backbone
#   of each system and ensures *code mobility*  
# 
# 
# **Matplotlib thumbnail gallery** [https://matplotlib.org/gallery.html](https://matplotlib.org/gallery.html)
# 
# - `seaborn` - pretty plots geared towards statistical applications
#   [https://seaborn.pydata.org/examples/index.html](https://seaborn.pydata.org/examples/index.html)  
# - `bokeh` is a library for creating interactive plots
#   [http://bokeh.pydata.org/en/latest/docs/gallery.html](http://bokeh.pydata.org/en/latest/docs/gallery.html)  

# #### Plan
# 
# 1. Visualization examples from my own research projects  
# 1. Links to compulsory online learning resources  

# #### Graphical objects
# 
# Extensive collection of objects to modify all aspects of the graphics
# 
# - figure - axes - subplots  
# - lines - polygons (patches)  
# - fill color and edge color  
# - annotations and other text  

# #### Types of plots
# 
# - **bar** - categorical data, histograms  
# - **scatter** - individual data points  
# - **line** - continuous measure  
# - **area** - dynamics of composition  
# - **pie** - static composition  
# - **Sankey** - flow diagram  

# #### How to choose the plot type
# 
# 1. Number of variables to be represented  
# 1. Type of variables  
#   - continuous  
#   - categorical  
#   - ordered  
# 1. What is the message of the graphics?  

# ### General tips
# 
# 1. **Less visual clutter!**  
#   Every dot, line, shape and label has to convey useful information  
# 1. **Read the the manual and change the options**  
#   Defaults are good for quick and dirty preliminary runs only  
# 1. **Careful with 3D**  
#   Much harder to make clear  
# 1. **“Animations”**  
#   May be useful in cases when there are one too many dimensions in the data to visualize  

# #### Less visual clutter
# 
# <img src="_static/img/viz/dcegm_value.png" style="width:800px;">

# #### Choice of appropriate plot type

# <img src="_static/img/viz/cpu_vs_N.png" style="width:800px;">

# <img src="_static/img/viz/plot1-super.png" style="width:900px;">

# <img src="_static/img/viz/distribution_earnings.png" style="width:800px;">

# <img src="_static/img/viz/octane95_price_composition.png" style="width:900px;">

# <img src="_static/img/viz/waves_full_data.png" style="width:900px;">

# <img src="_static/img/viz/map.jpg" style="height:600px;">

# <img src="_static/img/viz/trpr2_diversification.png" style="width:900px;">

# <img src="_static/img/viz/CDF_2.png" style="width:700px;">

# #### Using many dimensions in one plot
# 
# - location (x,y,z)  
# - color  
# - line or marker style  
# - size  
# - animation  
# - multiple plots in a figure  

# <img src="_static/img/viz/eqb_n5_randtech-eps-converted-to.png" style="width:900px;">

# #### Visual debugging and dashboards
# 
# Using visual representation to verify the code
# - *Seeing* a bug in a plot is easier than in the code!
# 
# Dashboards are ideal for aggregation of large amounts of information
# 
# 1. Calibration/estimation an moment matching  
# 1. Monitoring computing resources  

# <img src="_static/img/viz/phelps4.png" style="width:900px;">

# <img src="_static/img/viz/ConsT3NumQuad_sigma0IncVar0.01.png" style="width:800px;">

# <img src="_static/img/viz/fig_moments_lastrun.png" style="width:1000px;">

# <img src="_static/img/viz/fig_moments2.png" style="width:1000px;">

# #### Visualizing economic model for new insights

# <img src="_static/img/viz/5stage_eqb.png" style="width:1200px;">

# <img src="_static/img/viz/eqb_map.png" style="width:1000px;">

# <img src="_static/img/viz/eqb2bw_pureA.png" style="width:1000px;">

# <img src="_static/img/viz/eqb2bw_symm.png" style="width:1000px;">

# ### Tutorials for compulsory self-study
# 
# - Excellent tutorial on Matplotlib on QuantEcon DataScience
#   [https://datascience.quantecon.org/applications/visualization_rules.html](https://datascience.quantecon.org/applications/visualization_rules.html)  
# - Presentation by Hans Rosling (1948-2017, Swedish physician, academic, statistician, and public speaker)
#   [https://youtu.be/hVimVzgtD6w?t=159](https://youtu.be/hVimVzgtD6w?t=159)  

# ### Further learning resources
# 
# - Excellent beginner tutorial for Matplotlib by the authors (3h)
#   [https://www.youtube.com/watch?v=6gdNUDs6QPc&t=2843s](https://www.youtube.com/watch?v=6gdNUDs6QPc&t=2843s)  
# - Playlist of lectures and tutorials
#   [https://www.youtube.com/user/EnthoughtMedia/search?query=matplotlib](https://www.youtube.com/user/EnthoughtMedia/search?query=matplotlib)  
# - Visualization of sorting algorithms
#   [https://www.youtube.com/watch?v=kPRA0W1kECg](https://www.youtube.com/watch?v=kPRA0W1kECg)