#!/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.
# 

# # 6.5. Converting matplotlib figures to d3.js visualizations with mpld3

# 1. First, we load NumPy and matplotlib as usual.

# In[ ]:


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


# 2. Then, we enable the mpld3 figures in the notebook with a single function call.

# In[ ]:


from mpld3 import enable_notebook
enable_notebook()


# 3. Now, let's create a scatter plot with matplotlib.

# In[ ]:


X = np.random.normal(0, 1, (100, 3))
color = np.random.random(100)
size = 500 * np.random.random(100)
plt.figure(figsize=(6,4))
plt.scatter(X[:,0], X[:,1], c=color,
            s=size, alpha=0.5, linewidths=2)
plt.grid(color='lightgray', alpha=0.7)


# The matplotlib figure is rendered with d3.js instead of the standard matplotlib backend. In particular, the figure is interactive (pan and zoom).

# 4. Now, we create a more complex example with multiple subplots representing different 2D projections of a 3D dataset. We use the `sharex` and `sharey` keywords in matplotlib's `subplots` function to automatically bind the x and y axes of the different figures. Panning and zooming in any of the subplots automatically updates all the other subplots.

# In[6]:


fig, ax = plt.subplots(3, 3, figsize=(6, 6),
                       sharex=True, sharey=True)
fig.subplots_adjust(hspace=0.3)
X[::2,2] += 3
for i in range(3):
    for j in range(3):
        ax[i,j].scatter(X[:,i], X[:,j], c=color,
            s=.1*size, alpha=0.5, linewidths=2)
        ax[i,j].grid(color='lightgray', alpha=0.7)


# This use case is perfectly handled by mpld3: the d3.js subplots are also dynamically linked together.

# > 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).