#!/usr/bin/env python

#

# Quick script to visualize a correlation matrix

#

# How to use:

# Pipe in tabbed separated data such that:

# -each row is an instance/point/dataum

# -each column is a variable/feature

#

from numpy import corrcoef, transpose, arange

import matplotlib.pyplot as plt

import fileinput

#default plot filename

filename = "corr_matrix_plot"

data = []

# read in data

for line in fileinput.input():

row = [float(item) for item in line.strip().split("\t")]

data.append(row)

data_t = transpose(data)

features = len(data_t)

data = None # Clear information in data, just in case it is large

# plot the correlation matrix

fig = plt.figure()

R = corrcoef(data_t)

fig = plt.pcolor(R)

plt.colorbar()

plt.yticks(arange(0.5,float(features)+0.5),range(0,features))

plt.xticks(arange(0.5,float(features)+0.5),range(0,features))

plt.savefig(filename+'.svg', bbox_inches='tight')