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def matrixTeX(a, decimal=0, justifyRight=False, suppress=True, formatString=None):
	"""
	
	Returns a LaTeX bmatrix or bmatrix* environment
	
	::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
	: Input   		: 
	: a			: numpy array or matrix
	: decimal		: 0 or # of decimal digits
	:			  (default: inherit from numpy array)

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\documentclass[11pt,letterpaper]{article}
\usepackage{caption}
\usepackage{graphicx}
\pagestyle{empty}

\begin{document}

\begin{figure}
	\begin{center}
		\includegraphics[scale=0.3]{fsu}

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% toggle to ignore \pause in handout slides
%\documentclass[xcolor=dvipsnames,onlymath,12pt]{beamer}
\documentclass[xcolor=dvipsnames,onlymath,12pt,handout]{beamer}

% standard tools for graphics, math, and hyperlinks
\usepackage{graphics,amsmath,url,mathtools}
\usepackage{hyperref}

% package to pretty print code
\usepackage{minted}

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def prony(t, F, m):
  """Input  : real arrays t, F of the same size (ti, Fi)
            : integer m - the number of modes in the exponential fit
     Output : arrays a and b such that F(t) ~ sum ai exp(bi*t)"""

	import numpy as np
	import numpy.polynomial.polynomial as poly

	# Solve LLS problem in step 1

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def blockAverage(datastream, isplot=True, maxBlockSize=0):
    
	"""This program computes the block average of a potentially correlated timeseries "x", and 
	provides error bounds for the estimated mean <x>. 
	As input provide a vector or timeseries "x", and the largest block size.
	
	Check out writeup in the following blog posts for more:
	http://sachinashanbhag.blogspot.com/2013/08/block-averaging-estimating-uncertainty_14.html
	http://sachinashanbhag.blogspot.com/2013/08/block-averaging-estimating-uncertainty.html
	"""

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