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

# In[1]:


get_ipython().run_line_magic('matplotlib', 'inline')
import sys
sys.path.insert(0,'..')
from IPython.display import HTML
from helpers import header

HTML(header())


# # bokeh experimental support

# Some illustration available in this course use the `bokeh` visualization framework.
# 
# [Bokeh](http://bokeh.pydata.org/) is an interactive visualization library that enhence the viewing capabilities of complex data. It allows interactive data view (as opposed of the standard embedded matplotlib figures). 
# 
# In order to have bokeh active, please read the [installation procedure](http://bokeh.pydata.org/en/latest/docs/installation.html).
# An easy installation can be achieved using [Anaconda Python Distribution](http://continuum.io/anaconda):
# 
# > conda install bokeh
# 
# Below a simple test using bokeh to display a binary image, user can zoom in/out and have pixel value. 

# In[2]:


import bokeh.plotting as bk
from helpers import bk_image,bk_image_hoover,bk_compare_image

bk.output_notebook()


# In[3]:


from skimage.morphology import disk
from skimage.data import camera
from skimage.filters.rank import median

g = camera()[-1:2:-1,::2]
f = median(g,disk(3))

bk_compare_image(g,f)


# In[4]:


from skimage.morphology import disk

bk_image_hoover(disk(4)*128.)