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

# # <img style='float: left' src="http://lightning-viz.github.io/images/logo.png"> <br> <br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Matrix plots in <a href='http://lightning-viz.github.io/'><font color='#9175f0'>Lightning</font></a>

# ## <hr> Setup

# In[1]:


from lightning import Lightning

from numpy import random, arange, asarray, corrcoef, argsort, array
import networkx as nx
from sklearn import datasets


# ## Connect to server

# In[2]:


lgn = Lightning(ipython=True, host='http://public.lightning-viz.org')


# ## <hr> Simple matrix

# Matrices are useful ways to visualize dense tables and correlation matrices data.
# <br>
# First we show a random matrix with default styles.
# <br>
# You can us the arrow keys to change the contrast (up/down) or the colormap (left/right).

# In[3]:


mat = random.randn(10,10)
lgn.matrix(mat)


# ## <hr> Different shapes

# Rectanglular matrices will automatically size appropriately.

# In[4]:


mat = random.randn(10,20)
lgn.matrix(mat)


# In[5]:


mat = random.randn(20,10)
lgn.matrix(mat)


# ## <hr> Colors

# Matrices can be rendered using any colorbrewer colormaps.

# In[6]:


mat = random.rand(10,10)
lgn.matrix(mat, colormap='Reds')


# In[7]:


mat = random.rand(10,10)
lgn.matrix(mat, colormap='Spectral')


# ## <hr> Labels

# You can label the rows and columns of a matrix. Clicking on the text labels will highlight those rows and columns -- try it!

# In[8]:


n, m = (8, 16)
mat = arange(n*m).reshape(n,m)
rows = ['row ' + str(i) for i in range(n)]
columns = ['col ' + str(i) for i in range(m)]

lgn.matrix(mat, row_labels=rows, column_labels=columns)


# You can also turn on labeling of cells by their value.

# In[9]:


mat = arange(n*m).reshape(n,m)

lgn.matrix(mat, numbers=True)