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

# <!--BOOK_INFORMATION-->
# <img align="left" style="padding-right:10px;" src="figures/PDSH-cover-small.png">
# 
# *This notebook contains an excerpt from the [Python Data Science Handbook](http://shop.oreilly.com/product/0636920034919.do) by Jake VanderPlas; the content is available [on GitHub](https://github.com/jakevdp/PythonDataScienceHandbook).*
# 
# *The text is released under the [CC-BY-NC-ND license](https://creativecommons.org/licenses/by-nc-nd/3.0/us/legalcode), and code is released under the [MIT license](https://opensource.org/licenses/MIT). If you find this content useful, please consider supporting the work by [buying the book](http://shop.oreilly.com/product/0636920034919.do)!*

# <!--NAVIGATION-->
# < [Customizing Ticks](04.10-Customizing-Ticks.ipynb) | [Contents](Index.ipynb) | [Three-Dimensional Plotting in Matplotlib](04.12-Three-Dimensional-Plotting.ipynb) >
# 
# <a href="https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.11-Settings-and-Stylesheets.ipynb"><img align="left" src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open in Colab" title="Open and Execute in Google Colaboratory"></a>
# 

# # Customizing Matplotlib: Configurations and Stylesheets

# Matplotlib's default plot settings are often the subject of complaint among its users.
# While much is slated to change in the 2.0 Matplotlib release in late 2016, the ability to customize default settings helps bring the package inline with your own aesthetic preferences.
# 
# Here we'll walk through some of Matplotlib's runtime configuration (rc) options, and take a look at the newer *stylesheets* feature, which contains some nice sets of default configurations.

# ## Plot Customization by Hand
# 
# Through this chapter, we've seen how it is possible to tweak individual plot settings to end up with something that looks a little bit nicer than the default.
# It's possible to do these customizations for each individual plot.
# For example, here is a fairly drab default histogram:

# In[1]:


import matplotlib.pyplot as plt
plt.style.use('classic')
import numpy as np

get_ipython().run_line_magic('matplotlib', 'inline')


# In[2]:


x = np.random.randn(1000)
plt.hist(x);


# We can adjust this by hand to make it a much more visually pleasing plot:

# In[3]:


# use a gray background
ax = plt.axes(axisbg='#E6E6E6')
ax.set_axisbelow(True)

# draw solid white grid lines
plt.grid(color='w', linestyle='solid')

# hide axis spines
for spine in ax.spines.values():
    spine.set_visible(False)
    
# hide top and right ticks
ax.xaxis.tick_bottom()
ax.yaxis.tick_left()

# lighten ticks and labels
ax.tick_params(colors='gray', direction='out')
for tick in ax.get_xticklabels():
    tick.set_color('gray')
for tick in ax.get_yticklabels():
    tick.set_color('gray')
    
# control face and edge color of histogram
ax.hist(x, edgecolor='#E6E6E6', color='#EE6666');


# This looks better, and you may recognize the look as inspired by the look of the R language's ggplot visualization package.
# But this took a whole lot of effort!
# We definitely do not want to have to do all that tweaking each time we create a plot.
# Fortunately, there is a way to adjust these defaults once in a way that will work for all plots.

# ## Changing the Defaults: ``rcParams``
# 
# Each time Matplotlib loads, it defines a runtime configuration (rc) containing the default styles for every plot element you create.
# This configuration can be adjusted at any time using the ``plt.rc`` convenience routine.
# Let's see what it looks like to modify the rc parameters so that our default plot will look similar to what we did before.
# 
# We'll start by saving a copy of the current ``rcParams`` dictionary, so we can easily reset these changes in the current session:

# In[4]:


IPython_default = plt.rcParams.copy()


# Now we can use the ``plt.rc`` function to change some of these settings:

# In[5]:


from matplotlib import cycler
colors = cycler('color',
                ['#EE6666', '#3388BB', '#9988DD',
                 '#EECC55', '#88BB44', '#FFBBBB'])
plt.rc('axes', facecolor='#E6E6E6', edgecolor='none',
       axisbelow=True, grid=True, prop_cycle=colors)
plt.rc('grid', color='w', linestyle='solid')
plt.rc('xtick', direction='out', color='gray')
plt.rc('ytick', direction='out', color='gray')
plt.rc('patch', edgecolor='#E6E6E6')
plt.rc('lines', linewidth=2)


# With these settings defined, we can now create a plot and see our settings in action:

# In[6]:


plt.hist(x);


# Let's see what simple line plots look like with these rc parameters:

# In[7]:


for i in range(4):
    plt.plot(np.random.rand(10))


# I find this much more aesthetically pleasing than the default styling.
# If you disagree with my aesthetic sense, the good news is that you can adjust the rc parameters to suit your own tastes!
# These settings can be saved in a *.matplotlibrc* file, which you can read about in the [Matplotlib documentation](http://Matplotlib.org/users/customizing.html).
# That said, I prefer to customize Matplotlib using its stylesheets instead.

# ## Stylesheets
# 
# The version 1.4 release of Matplotlib in August 2014 added a very convenient ``style`` module, which includes a number of new default stylesheets, as well as the ability to create and package your own styles. These stylesheets are formatted similarly to the *.matplotlibrc* files mentioned earlier, but must be named with a *.mplstyle* extension.
# 
# Even if you don't create your own style, the stylesheets included by default are extremely useful.
# The available styles are listed in ``plt.style.available``—here I'll list only the first five for brevity:

# In[8]:


plt.style.available[:5]


# The basic way to switch to a stylesheet is to call
# 
# ``` python
# plt.style.use('stylename')
# ```
# 
# But keep in mind that this will change the style for the rest of the session!
# Alternatively, you can use the style context manager, which sets a style temporarily:
# 
# ``` python
# with plt.style.context('stylename'):
#     make_a_plot()
# ```
# 

# Let's create a function that will make two basic types of plot:

# In[9]:


def hist_and_lines():
    np.random.seed(0)
    fig, ax = plt.subplots(1, 2, figsize=(11, 4))
    ax[0].hist(np.random.randn(1000))
    for i in range(3):
        ax[1].plot(np.random.rand(10))
    ax[1].legend(['a', 'b', 'c'], loc='lower left')


# We'll use this to explore how these plots look using the various built-in styles.

# ### Default style
# 
# The default style is what we've been seeing so far throughout the book; we'll start with that.
# First, let's reset our runtime configuration to the notebook default:

# In[10]:


# reset rcParams
plt.rcParams.update(IPython_default);


# Now let's see how it looks:

# In[11]:


hist_and_lines()


# ### FiveThiryEight style
# 
# The ``fivethirtyeight`` style mimics the graphics found on the popular [FiveThirtyEight website](https://fivethirtyeight.com).
# As you can see here, it is typified by bold colors, thick lines, and transparent axes:

# In[12]:


with plt.style.context('fivethirtyeight'):
    hist_and_lines()


# ### ggplot
# 
# The ``ggplot`` package in the R language is a very popular visualization tool.
# Matplotlib's ``ggplot`` style mimics the default styles from that package:

# In[13]:


with plt.style.context('ggplot'):
    hist_and_lines()


# ### *Bayesian Methods for Hackers( style
# 
# There is a very nice short online book called [*Probabilistic Programming and Bayesian Methods for Hackers*](http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/); it features figures created with Matplotlib, and uses a nice set of rc parameters to create a consistent and visually-appealing style throughout the book.
# This style is reproduced in the ``bmh`` stylesheet:

# In[14]:


with plt.style.context('bmh'):
    hist_and_lines()


# ### Dark background
# 
# For figures used within presentations, it is often useful to have a dark rather than light background.
# The ``dark_background`` style provides this:

# In[15]:


with plt.style.context('dark_background'):
    hist_and_lines()


# ### Grayscale
# 
# Sometimes you might find yourself preparing figures for a print publication that does not accept color figures.
# For this, the ``grayscale`` style, shown here, can be very useful:

# In[16]:


with plt.style.context('grayscale'):
    hist_and_lines()


# ### Seaborn style
# 
# Matplotlib also has stylesheets inspired by the Seaborn library (discussed more fully in [Visualization With Seaborn](04.14-Visualization-With-Seaborn.ipynb)).
# As we will see, these styles are loaded automatically when Seaborn is imported into a notebook.
# I've found these settings to be very nice, and tend to use them as defaults in my own data exploration.

# In[17]:


import seaborn
hist_and_lines()


# With all of these built-in options for various plot styles, Matplotlib becomes much more useful for both interactive visualization and creation of figures for publication.
# Throughout this book, I will generally use one or more of these style conventions when creating plots.

# <!--NAVIGATION-->
# < [Customizing Ticks](04.10-Customizing-Ticks.ipynb) | [Contents](Index.ipynb) | [Three-Dimensional Plotting in Matplotlib](04.12-Three-Dimensional-Plotting.ipynb) >
# 
# <a href="https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.11-Settings-and-Stylesheets.ipynb"><img align="left" src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open in Colab" title="Open and Execute in Google Colaboratory"></a>
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