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

# # Advanced Customization and Configuration

# ## Table of Contents

# * Introduction
# * Customization
#   * matplotlib Styles
#   * Subplots
#     * Making a Plan
#     * Revisiting Pandas
#     * Individual Plots
#     * Combined Plots
# * Configuration
#   * Run Control
# 
# Warm-up proceedures:

# In[1]:


import matplotlib
matplotlib.use('nbagg')
get_ipython().run_line_magic('matplotlib', 'inline')

import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from IPython.display import Image


# Note that we're not using Seaborn for styling like we did previously -- that's beccause the first thing we're going to tackle is creating a custom matplotlib style :-)

# ## Customization

# ### Creating a custom style

# In the previous notebook, we saw that we could list the available styles with the following call:

# In[2]:


print(plt.style.available)


# You can create custom styles and use them by calling ``style.use`` with the path or URL to the style sheet. Alternatively, if you save your ``<style-name>.mplstyle`` file to the ``~/.matplotlib/stylelib`` directory (you may need to create it), you can reuse your custom style sheet with a call to ``style.use(<style-name>)``. Note that a custom style sheet in ``~/.matplotlib/stylelib`` will override a style sheet defined by matplotlib if the styles have the same name.
# 
# We've created a style sheet for you to use in this repository for this notebook, but before we go further, let's create a function that will generate a demo plot for us. Then we'll render it, using the default style -- thus having a baseline to compare our work to:

# In[3]:


def make_plot ():
    x = np.random.randn(5000, 6)
    (figure, axes) = plt.subplots(figsize=(16,10))
    (n, bins, patches) = axes.hist(x, 12, normed=1, histtype='bar',
                                   label=['Color 1', 'Color 2', 'Color 3',
                                          'Color 4', 'Color 5', 'Color 6'])
    axes.set_title("Histogram\nfor a\nNormal Distribution", fontsize=24)
    axes.set_xlabel("Data Points", fontsize=16)
    axes.set_ylabel("Counts", fontsize=16)
    axes.legend()
    plt.show()


# In[4]:


make_plot()


# Okay, we've got our sample plot. Now let's look at the style.
# 
# We've created a style called "Superheroine", based on [Thomas Park](http://thomaspark.me/)'s excellent Bootstrap theme, [Superhero](http://bootswatch.com/superhero/). Here's a screenshot of the Boostrap theme:

# In[5]:


Image(filename="superhero.png")


# We've saved captured some of the colors from this screenshot and saved them in a couple of plot style files to the "styles" directory in this notebook repo:

# In[6]:


ls -l ../styles/


# Basically, we couldn't make up our mind about whether we liked the light text (style 1) or the orange text (style 2). So we kept both :-)
# 
# Let's take a look at the second one's contents which show the hexadecimal colors we copied from the Boostrap theme:

# In[7]:


cat ../styles/superheroine-2.mplstyle


# Now let's load it:

# In[8]:


plt.style.use("../styles/superheroine-2.mplstyle")


# And then re-render our plot:

# In[9]:


make_plot()


# A full list of styles available for customization is in given in the [matplotlib run control file](http://matplotlib.org/users/customizing.html). We'll be discussing this more in the next section.

# ### Subplots

# #### Making a Plan

# In this next section, we'll be creating a sophisticated subplot to give you a sense of what's possible with matplotlib's layouts. We'll be ingesting data from the [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml/index.html), in particular the 1985 [Automobile Data Set](https://archive.ics.uci.edu/ml/datasets/Automobile), an example of data which can be used to assess the insurance risks for different vehicles.
# 
# We will use it in an effort to compare 21 automobile manufacturers (using 1985 data) along the following dimensions:
# * mean price
# * mean city MPG
# * mean highway MPG
# * mean horsepower
# * mean curb-weight
# * mean relative average loss payment
# * mean insurance riskiness
# 
# We will limit ourselves to automobile manufacturers that have data for losses as well as 6 or more data rows.
# 
# Our subplot will be comprised of the following sections:
#  * An overall title 
#  * Line plots for max, mean, and min prices
#  * Stacked bar chart for combined riskiness/losses
#  * Stacked bar chart for riskiness
#  * Stacked bar chart for losses
#  * Radar charts for each automobile manufacturer
#  * Combined scatter plot for city and highway MPG
# 
# These will be composed as subplots in the following manner:

# ```
# --------------------------------------------
# |               overall title              |
# --------------------------------------------
# |               price ranges               |
# --------------------------------------------
# | combined loss/risk |                     |
# |                    |        radar        |
# ----------------------        plots        |
# |  risk   |   loss   |                     |
# --------------------------------------------
# |                   mpg                    |
# --------------------------------------------
# ```

# #### Revisiting Pandas

# In[10]:


import sys
sys.path.append("../lib")
import demodata, demoplot, radar

raw_data = demodata.get_raw_data()
raw_data.head()


# In[11]:


limited_data = demodata.get_limited_data()
limited_data.head()


# In[12]:


demodata.get_all_auto_makes()


# In[13]:


(makes, counts) = demodata.get_make_counts(limited_data)
counts


# In[14]:


(makes, counts) = demodata.get_make_counts(limited_data, lower_bound=6)


# In[15]:


counts


# In[16]:


data = demodata.get_limited_data(lower_bound=6)
data.head()


# In[17]:


len(data.index)


# In[18]:


sum([x[1] for x in counts])


# In[19]:


normed_data = data.copy()
normed_data.rename(columns={"horsepower": "power"}, inplace=True)


# Higher values are better for these:

# In[20]:


demodata.norm_columns(["city mpg", "highway mpg", "power"], normed_data)
normed_data.head()


# Lower values are better for these:

# In[21]:


demodata.invert_norm_columns(["price", "weight", "riskiness", "losses"], normed_data)
normed_data.head()


# #### Individual Plots

# In[22]:


figure = plt.figure(figsize=(15, 5))
prices_gs = mpl.gridspec.GridSpec(1, 1)
prices_axes = demoplot.make_autos_price_plot(figure, prices_gs, data)
plt.show()


# In[23]:


figure = plt.figure(figsize=(15, 5))
mpg_gs = mpl.gridspec.GridSpec(1, 1)
mpg_axes = demoplot.make_autos_mpg_plot(figure, mpg_gs, data)
plt.show()


# In[24]:


figure = plt.figure(figsize=(15, 5))
risk_gs = mpl.gridspec.GridSpec(1, 1)
risk_axes = demoplot.make_autos_riskiness_plot(figure, risk_gs, normed_data)
plt.show()


# In[25]:


figure = plt.figure(figsize=(15, 5))
loss_gs = mpl.gridspec.GridSpec(1, 1)
loss_axes = demoplot.make_autos_losses_plot(figure, loss_gs, normed_data)
plt.show()


# In[26]:


figure = plt.figure(figsize=(15, 5))
risk_loss_gs = mpl.gridspec.GridSpec(1, 1)
risk_loss_axes = demoplot.make_autos_loss_and_risk_plot(figure, risk_loss_gs, normed_data)
plt.show()


# In[27]:


figure = plt.figure(figsize=(15, 5))
radar_gs = mpl.gridspec.GridSpec(3, 7, height_ratios=[1, 10, 10], wspace=0.50, hspace=0.60, top=0.95, bottom=0.25)
radar_axes = demoplot.make_autos_radar_plot(figure, radar_gs, normed_data)
plt.show()


# #### Combined Plots

# Here's a refresher on the plot layout we're aiming for:

# ```
# --------------------------------------------
# |               overall title              |
# --------------------------------------------
# |               price ranges               |
# --------------------------------------------
# | combined loss/risk |                     |
# |                    |        radar        |
# ----------------------        plots        |
# |  risk   |   loss   |                     |
# --------------------------------------------
# |                   mpg                    |
# --------------------------------------------
# ```

# Let's try that now with just empty graphs, to get a sense of things:

# In[28]:


figure = plt.figure(figsize=(10, 8))
gs_master = mpl.gridspec.GridSpec(4, 2, height_ratios=[1, 2, 8, 2])
# Layer 1 - Title
gs_1 = mpl.gridspec.GridSpecFromSubplotSpec(1, 1, subplot_spec=gs_master[0, :])
title_axes = figure.add_subplot(gs_1[0])
# Layer 2 - Price
gs_2 = mpl.gridspec.GridSpecFromSubplotSpec(1, 1, subplot_spec=gs_master[1, :])
price_axes = figure.add_subplot(gs_2[0])
# Layer 3 - Risks & Radar
gs_31 = mpl.gridspec.GridSpecFromSubplotSpec(2, 2, height_ratios=[2, 1], subplot_spec=gs_master[2, :1])
risk_and_loss_axes = figure.add_subplot(gs_31[0, :])
risk_axes = figure.add_subplot(gs_31[1, :1])
loss_axes = figure.add_subplot(gs_31[1:, 1])
gs_32 = mpl.gridspec.GridSpecFromSubplotSpec(1, 1, subplot_spec=gs_master[2, 1])
radar_axes = figure.add_subplot(gs_32[0])
# Layer 4 - MPG
gs_4 = mpl.gridspec.GridSpecFromSubplotSpec(1, 1, subplot_spec=gs_master[3, :])
mpg_axes = figure.add_subplot(gs_4[0])
# Tidy up
gs_master.tight_layout(figure)
plt.show()


# In[29]:


figure = plt.figure(figsize=(15, 15))
gs_master = mpl.gridspec.GridSpec(4, 2, height_ratios=[1, 24, 128, 32], hspace=0, wspace=0)

# Layer 1 - Title
gs_1 = mpl.gridspec.GridSpecFromSubplotSpec(1, 1, subplot_spec=gs_master[0, :])
title_axes = figure.add_subplot(gs_1[0])
title_axes.set_title("Demo Plots for 1985 Auto Maker Data", fontsize=30, color="#cdced1")
demoplot.hide_axes(title_axes)

# Layer 2 - Price
gs_2 = mpl.gridspec.GridSpecFromSubplotSpec(1, 1, subplot_spec=gs_master[1, :])
price_axes = figure.add_subplot(gs_2[0])
demoplot.make_autos_price_plot(figure, pddata=data, axes=price_axes)

# Layer 3, Part I - Risks
gs_31 = mpl.gridspec.GridSpecFromSubplotSpec(2, 2, height_ratios=[2, 1], hspace=0.4, subplot_spec=gs_master[2, :1])
risk_and_loss_axes = figure.add_subplot(gs_31[0, :])
demoplot.make_autos_loss_and_risk_plot(
    figure, pddata=normed_data, axes=risk_and_loss_axes, x_label=False, rotate_ticks=True)
risk_axes = figure.add_subplot(gs_31[1, :1])
demoplot.make_autos_riskiness_plot(figure, pddata=normed_data, axes=risk_axes, legend=False, labels=False)
loss_axes = figure.add_subplot(gs_31[1:, 1])
demoplot.make_autos_losses_plot(figure, pddata=normed_data, axes=loss_axes, legend=False, labels=False)

# Layer 3, Part II - Radar
gs_32 = mpl.gridspec.GridSpecFromSubplotSpec(
    5, 3, height_ratios=[1, 20, 20, 20, 20], hspace=0.6, wspace=0, subplot_spec=gs_master[2, 1])
(rows, cols) = geometry = gs_32.get_geometry()
title_axes = figure.add_subplot(gs_32[0, :])
inner_axes = []
projection = radar.RadarAxes(spoke_count=len(normed_data.groupby("make").mean().columns))
[inner_axes.append(figure.add_subplot(m, projection=projection)) for m in [n for n in gs_32][cols:]]
demoplot.make_autos_radar_plot(
    figure, pddata=normed_data, title_axes=title_axes, inner_axes=inner_axes, legend_axes=False, 
    geometry=geometry)

# Layer 4 - MPG
gs_4 = mpl.gridspec.GridSpecFromSubplotSpec(1, 1, subplot_spec=gs_master[3, :])
mpg_axes = figure.add_subplot(gs_4[0])
demoplot.make_autos_mpg_plot(figure, pddata=data, axes=mpg_axes)

# Tidy up
gs_master.tight_layout(figure)
plt.show()


# ## Configuration

# Get the directory for the matplotlib config files and cache:

# In[30]:


mpl.get_configdir()


# In[31]:


mpl.matplotlib_fname()


# matplotlib's ``rcParams`` configuration dictionary holds a great many options for tweaking your use of matplotlib the way you want to:

# In[32]:


len(mpl.rcParams.keys())


# The first 10 configuration options in ``rcParams`` are:

# In[33]:


dict(list(mpl.rcParams.items())[:10])


# In[34]:


mpl.rcParams['savefig.jpeg_quality'] = 72
mpl.rcParams['axes.formatter.limits'] = [-5, 5]


# In[35]:


mpl.rcParams['axes.formatter.limits']


# In[36]:


mpl.rcdefaults() 


# In[37]:


mpl.rcParams['axes.formatter.limits']