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

# > This is one of the 100 recipes of the [IPython Cookbook](http://ipython-books.github.io/), the definitive guide to high-performance scientific computing and data science in Python.
# 

# # 6.1. Making nicer matplotlib figures with prettyplotlib

# 1. Let's first import NumPy and matplotlib.

# In[1]:


import numpy as np
import matplotlib.pyplot as plt
get_ipython().run_line_magic('matplotlib', 'inline')


# 2. We first draw several curves with matplotlib.

# In[2]:


plt.figure(figsize=(6,4));
np.random.seed(12)
for i in range(8):
    x = np.arange(1000)
    y = np.random.randn(1000).cumsum()
    plt.plot(x, y, label=str(i))
plt.legend();


# 3. Now, we create the exact same plot with prettplotlib. We can basically replace the `matplotlib.pyplot` namespace with `prettyplotlib`.

# In[3]:


import prettyplotlib as ppl
plt.figure(figsize=(6,4));
np.random.seed(12)
for i in range(8):
    x = np.arange(1000)
    y = np.random.randn(1000).cumsum()
    ppl.plot(x, y, label=str(i))
ppl.legend();


# The figure appears clearer, and the colors are more visually pleasant.

# 4. Let's show another example with an image. We first use matplotlib's `pcolormesh` function to display a 2D array as an image.

# In[4]:


plt.figure(figsize=(4,3));
np.random.seed(12)
plt.pcolormesh(np.random.rand(16, 16));
plt.colorbar();


# The default *rainbow* color map is known to mislead data visualization.

# 5. Now, we use prettyplotlib to display the exact same image.

# In[5]:


plt.figure(figsize=(4,3));
np.random.seed(12);
ppl.pcolormesh(np.random.rand(16, 16));


# This visualization is much clearer, in that high or low values are better brought out than with the rainbow color map.

# > You'll find all the explanations, figures, references, and much more in the book (to be released later this summer).
# 
# > [IPython Cookbook](http://ipython-books.github.io/), by [Cyrille Rossant](http://cyrille.rossant.net), Packt Publishing, 2014 (500 pages).