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

# <img src="files/bokeh-transparent.png" width="64px" height="64px">
# 
# # Bokeh 5-minute Overview
# 
# Bokeh is an interactive web visualization library for Python 
# (and other languages). It provides d3-like novel graphics, over
# large datasets, all without requiring any knowledge of Javascript. It 
# has a Matplotlib compatibility layer, and it works great with
# the IPython Notebook, but can also be used to generate standalone HTML.
# 
# ## Simple Example
# 
# Here is a simple first example.  First we'll import the `bokeh.plotting`
# module, which defines the graphical functions and primitives.

# In[1]:


from bokeh.plotting import figure, output_notebook, show


# Next, we'll tell Bokeh to display its plots directly into the notebook.
# This will cause all of the Javascript and data to be embedded directly
# into the HTML of the notebook itself.
# (Bokeh can output straight to HTML files, or use a server, which we'll
# look at later.)

# In[2]:


output_notebook()


# Next, we'll import NumPy and create some simple data.

# In[3]:


from numpy import cos, linspace
x = linspace(-6, 6, 100)
y = cos(x)


# Now we'll call Bokeh's `circle()` function to render a red circle at
# each of the points in x and y.
# 
# We can immediately interact with the plot:
# 
#   * click-drag will pan the plot around.
#   * mousewheel will zoom in and out
#   
# (The toolbar is simply a default one that is available for all plots;
# this can be configured dynamically via the `tools` keyword argument.)

# In[4]:


p = figure(width=500, height=500)
p.circle(x, y, size=7, color="firebrick", alpha=0.5)
show(p)


# # Bar Plot Example
# 
# 
# Bokeh's core display model relies on *composing graphical primitives* which
# are bound to data series.  This is similar in spirit to Protovis and D3,
# and different than most other Python plotting libraries (except for perhaps
# Vincent and other, newer libraries).
# 
# A slightly more sophisticated example demonstrates this idea.
# 
# Bokeh ships with a small set of interesting "sample data" in the `bokeh.sampledata`
# package.  We'll load up some historical automobile mileage data, which is returned
# as a Pandas `DataFrame`.

# In[5]:


from bokeh.sampledata.autompg import autompg
from numpy import array

grouped = autompg.groupby("yr")
mpg = grouped["mpg"]
avg = mpg.mean()
std = mpg.std()
years = array(list(grouped.groups.keys()))
american = autompg[autompg["origin"]==1]
japanese = autompg[autompg["origin"]==3]


# For each year, we want to plot the distribution of MPG within that year.

# In[6]:


p = figure()

p.quad(left=years-0.4, right=years+0.4, bottom=avg-std, top=avg+std, fill_alpha=0.4)

p.circle(x=japanese["yr"], y=japanese["mpg"], size=8,
         alpha=0.4, line_color="red", fill_color=None, line_width=2)

p.triangle(x=american["yr"], y=american["mpg"], size=8, 
           alpha=0.4, line_color="blue", fill_color=None, line_width=2)

show(p)


# # This kind of approach can be used to generate other kinds of interesting plots, like some of the following which are available on the [Bokeh web page](http://bokeh.pydata.org/en/latest).  
# 
# *(Click on any of the thumbnails to open the interactive version.)*
# 
# <table><tr>
# <td><a href="http://bokeh.pydata.org/docs/gallery/burtin.html" target="_blank"><img width=50% src="files/burtin.png"></a></td>
# <td><a href="http://bokeh.pydata.org/docs/gallery/periodic.html" target="_blank"><img width=50% src="files/periodic.png"></a></td>
# <td><a href="http://bokeh.pydata.org/docs/gallery/boxplot.html" target="_blank"><img width=50% src="files/boxplot.png"></a></td>
# </tr>
# <tr>
# <td><a href="http://bokeh.pydata.org/docs/gallery/texas.html" target="_blank"><img width=50% src="files/texas.png"></a></td>
# <td><a href="http://bokeh.pydata.org/docs/gallery/chord_chart.html" target="_blank"><img width=50% src="files/chord_chart.png"></a></td>
# <td><a href="http://bokeh.pydata.org/docs/gallery/les_mis.html" target="_blank"><img width=50% src="files/les_mis.png"></a></td>
# </tr></table>

# ## Linked Brushing
# 
# To link plots together at a data level, we can explicitly wrap the data in a ColumnDataSource.
# This allows us to reference columns by name.
# 
# We can use the "select" tool to select points on one plot, and the linked points
# on the other plots will highlight.

# In[7]:


from bokeh.models import ColumnDataSource
from bokeh.layouts import gridplot

source = ColumnDataSource(autompg.to_dict("list"))
source.add(autompg["yr"], name="yr")

plot_config = dict(plot_width=300, plot_height=300,
                   tools="pan,wheel_zoom,box_zoom,box_select,lasso_select")

p1 = figure(title="MPG by Year", **plot_config)
p1.circle("yr", "mpg", color="blue", source=source)

p2 = figure(title="HP vs. Displacement", **plot_config)
p2.circle("hp", "displ", color="green", source=source)

p3 = figure(title="MPG vs. Displacement", **plot_config)
p3.circle("mpg", "displ", size="cyl", line_color="red", fill_color=None, source=source)

p = gridplot([[ p1, p2, p3]], toolbar_location="right")

show(p)


# ## Standalone HTML
# 
# In addition to working well with the Notebook, Bokeh can also
# save plots out into their own HTML files.  Here is the bar plot
# example from above, but saving into its own standalone file.
# 
# Note that when we call `show()`, a new browser tab is opened.
# (If we just wanted to save the file, we would use `save()` instead.)

# In[8]:


from bokeh.plotting import output_file

output_file("barplot.html")

p = figure()

p.quad(left=years-0.4, right=years+0.4, bottom=avg-std, top=avg+std, fill_alpha=0.4)

p.circle(x=japanese["yr"], y=japanese["mpg"], size=8, 
         alpha=0.4, line_color="red", fill_color=None, line_width=2)

p.triangle(x=american["yr"], y=american["mpg"], size=8, 
           alpha=0.4, line_color="blue", fill_color=None, line_width=2)

show(p)


# # Bokeh Apps
# 
# When the linked brushing and server-based operation are combined,
# you can build graphical "applets", which resemble things like
# what Crossfilter and others do. However, Bokeh provides the
# reactive object model across client and server, so these sorts
# of selections and interactions can trigger server-side code,
# which is implemented in Python.
# 
# *(Click to launch the live app.)*
# 
# <a href="https://demo.bokehplots.com/apps/movies" target="_blank">
#   <img width="40%" height="40%" style="display: inline; padding: 20px" src="http://bokeh.pydata.org/static/movies.png" >
# </a>
# <a href="https://demo.bokehplots.com/apps/crossfilter" target="_blank" >
#   <img width="40%" height="40%" style="display: inline" src="http://bokeh.pydata.org/static/crossfilter.png">
# </a>
# 

# ## BokehJS
# 
# At its core, Bokeh consists of a Javascript library, [BokehJS](https://github.com/bokeh/bokeh/tree/master/bokehjs), and a Python binding which provides classes and objects that ultimately generate a JSON representation of the plot structure.
# 
# You can read more about design and usage in the [Developing with JavaScript](http://bokeh.pydata.org/en/latest/docs/user_guide/bokehjs.html) section of the Bokeh User's Guide.

# More Information
# ----------------
# 
# Full documentation and live examples: http://bokeh.pydata.org/en/latest
# 
# GitHub: https://github.com/bokeh/bokeh
# 
# Mailing list: [bokeh@continuum.io](mailto:bokeh@continuum.io)
# 
# Gitter: https://gitter.im/bokeh/bokeh
# 
# Be sure to follow us on Twitter [@bokehplots](http://twitter.com/BokehPlots>), as well as on [Youtube](https://www.youtube.com/channel/UCK0rSk29mmg4UT4bIOvPYhw) and [Vine](https://vine.co/bokehplots)!
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