#!/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.
# 

# # 6.3. Creating interactive Web visualizations with Bokeh

# 1. Let's import NumPy and Bokeh. We need to call `output_notebook` in order to tell Bokeh to render plots in the IPython notebook.

# In[3]:


import numpy as np
import bokeh.plotting as bkh
bkh.output_notebook()


# 2. Let's create some random data.

# In[4]:


x = np.linspace(0., 1., 100)
y = np.cumsum(np.random.randn(100))


# 3. Let's draw a curve.

# In[5]:


#bkh.line(x, y, line_width=5)
#bkh.show()

p = bkh.figure()
p.line(x=x, y=y)
bkh.show(p)


# An interactive plot is rendered in the notebook. We can pan and zoom by clicking on the buttons above the plot.

# 4. Let's move to another example. We first load a sample dataset (*Iris flowers*). We also generate some colors based on the species of the flowers.

# In[6]:


from bokeh.sampledata.iris import flowers
colormap = {'setosa': 'red',
            'versicolor': 'green',
            'virginica': 'blue'}
flowers['color'] = flowers['species'].map(lambda x: colormap[x])


# 5. Now, we render an interactive scatter plot.

# In[9]:


p = bkh.figure()
p.scatter(flowers["petal_length"], 
          flowers["petal_width"],
          color=flowers["color"], 
          fill_alpha=0.25, size=10,)
bkh.show(p)


# > 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).