# coding: utf-8

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#            <a href="http://bokeh.pydata.org/">     
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#            <h1>Bokeh Tutorial</h1>
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# <div style="float:right;"><h2>00. Introduction and Setup</h2></div>

# # Table of Contents
# 
# * [Tuturial Overview](#Tutorial-Overview)
# * [What is Bokeh](#What-is-Bokeh) 
# * [What can I *do* with Bokeh](#What-can-I-do-with-Bokeh) 
# * [How does it work](#How-does-it-work)
# * [Getting set up](#Getting-set-up)

# ## Tutorial Overview
# 
# The tutorial is broken into several sections, which are each presented in their own notebook:
# 
# 1. [Basic Plotting Interface](01 - plotting.ipynb)
# 
# 2. [Column Data Sources](02 - column data source.ipynb)
# 
# 3. [Layouts, Widgets, and Interactions](03 - interactions.ipynb) 
# 
# 4. [Styling Visual Attributes](04 - styling.ipynb) 
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# 5. [Data Transformations](05 - data transformations.ipynb)
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# 6. [Bokeh Applications](06 - server.ipynb) 
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# 7. [Sharing and Embedding](07 - sharing.ipynb) 
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# 8. [Annotations](08 - annotations.ipynb) 
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# 9. [Models and Primitives](09 - models.ipynb) 
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# 10. [High Level Charts](10 - charts.ipynb) 
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# 11. [Geographic Data](11 - geo.ipynb)
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# 12. [Datashader: Visualizaing Large Data](12 - datashader.ipynb)
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# 13. [HoloViews and Bokeh](13- holoviews.ipynb)
# 
# Appendices
# 
# 1. [Extra Bokeh Topics](A1 - Extra Resources.ipynb) 

# ## What is Bokeh
# 
# Bokeh is a Data Visualization library for
# 
# * interactive visualization in modern browsers
# * standalone HTML documents, or server-backed apps
# * capable of expressive and verstatile graphics
# * can handle large, dynamic or streaming data
# * available from python (or Scala, or R, or Julia, or...)
# 
# And most importantly:
# 
# ## <center>NO JAVASCRIPT REQUIRED </center>

# ## What can you *do* with Bokeh

# In[1]:


# Standard imports 

from bokeh.io import output_notebook, show
output_notebook()


# In[2]:


# Plot a complex chart in a single line

from bokeh.charts import Histogram
from bokeh.sampledata.iris import flowers as data

hist = Histogram(data, values="petal_length", color="species", legend="top_right", bins=12)

show(hist)


# In[3]:


# Build and serve beautiful web-ready interactive visualizations

import utils
p = utils.get_gapminder_plot()
show(p)


# In[4]:


# Create and deploy interactive data applications

from IPython.display import IFrame
IFrame('http://demo.bokehplots.com/apps/sliders', width=900, height=500)


# # How does it work

# <img src="assets/images/arch.png" style="width:800px">

# # Getting set up

# In[5]:


from IPython.core.display import Markdown
Markdown(open("README.md").read())


# Setup-test, run the next cell. Hopefully you should see output that looks something like this:
# 
#     IPython - 5.1.0
#     Pandas - 0.18.1
#     Bokeh - 0.12.2
#     
# If this isn't working for you, see the [`README.md`](README.md) in this directory.

# In[6]:


from IPython import __version__ as ipython_version
from pandas import __version__ as pandas_version
from bokeh import __version__ as bokeh_version
print("IPython - %s" % ipython_version)
print("Pandas - %s" % pandas_version)
print("Bokeh - %s" % bokeh_version)