--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/MicroPITA.py	Tue May 13 21:58:57 2014 -0400
@@ -0,0 +1,1145 @@
+#!/usr/bin/env python
+"""
+Author: Timothy Tickle
+Description: Class to Run analysis for the microPITA paper
+"""
+
+#####################################################################################
+#Copyright (C) <2012>
+#
+#Permission is hereby granted, free of charge, to any person obtaining a copy of
+#this software and associated documentation files (the "Software"), to deal in the
+#Software without restriction, including without limitation the rights to use, copy,
+#modify, merge, publish, distribute, sublicense, and/or sell copies of the Software,
+#and to permit persons to whom the Software is furnished to do so, subject to
+#the following conditions:
+#
+#The above copyright notice and this permission notice shall be included in all copies
+#or substantial portions of the Software.
+#
+#THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED,
+#INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A
+#PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT
+#HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
+#OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+#SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+#####################################################################################
+
+__author__ = "Timothy Tickle"
+__copyright__ = "Copyright 2012"
+__credits__ = ["Timothy Tickle"]
+__license__ = "MIT"
+__maintainer__ = "Timothy Tickle"
+__email__ = "ttickle@sph.harvard.edu"
+__status__ = "Development"
+
+import sys
+import argparse
+from src.breadcrumbs.src.AbundanceTable import AbundanceTable
+from src.breadcrumbs.src.ConstantsBreadCrumbs import ConstantsBreadCrumbs
+from src.breadcrumbs.src.Metric import Metric
+from src.breadcrumbs.src.KMedoids import Kmedoids
+from src.breadcrumbs.src.MLPYDistanceAdaptor import MLPYDistanceAdaptor
+from src.breadcrumbs.src.SVM import SVM
+from src.breadcrumbs.src.UtilityMath import UtilityMath
+
+from src.ConstantsMicropita import ConstantsMicropita
+import csv
+import logging
+import math
+import mlpy
+import numpy as np
+import operator
+import os
+import random
+import scipy.cluster.hierarchy as hcluster
+import scipy.spatial.distance
+from types import *
+
+class MicroPITA:
+	"""
+	Selects samples from a first tier of a multi-tiered study to be used in a second tier.
+	Different methods can be used for selection.
+	The expected input is an abundance table (and potentially a text file of targeted features,
+	if using the targeted features option). Output is a list of samples exhibiting the
+	characteristics of interest.
+	"""
+
+	#Constants
+	#Diversity metrics Alpha
+	c_strInverseSimpsonDiversity = Metric.c_strInvSimpsonDiversity
+	c_strChao1Diversity = Metric.c_strChao1Diversity
+
+	#Diversity metrics Beta
+	c_strBrayCurtisDissimilarity = Metric.c_strBrayCurtisDissimilarity
+
+	#Additive inverses of diversity metrics beta
+	c_strInvBrayCurtisDissimilarity = Metric.c_strInvBrayCurtisDissimilarity
+
+	#Technique Names
+	ConstantsMicropita.c_strDiversity2 = ConstantsMicropita.c_strDiversity+"_C"
+
+	#Targeted feature settings
+	c_strTargetedRanked = ConstantsMicropita.c_strTargetedRanked
+	c_strTargetedAbundance = ConstantsMicropita.c_strTargetedAbundance
+
+	#Technique groupings
+#	c_lsDiversityMethods = [ConstantsMicropita.c_strDiversity,ConstantsMicropita.c_strDiversity2]
+
+	#Converts ecology metrics into standardized method selection names
+	dictConvertAMetricDiversity = {c_strInverseSimpsonDiversity:ConstantsMicropita.c_strDiversity, c_strChao1Diversity:ConstantsMicropita.c_strDiversity2}
+#	dictConvertMicroPITAToAMetric = {ConstantsMicropita.c_strDiversity:c_strInverseSimpsonDiversity, ConstantsMicropita.c_strDiversity2:c_strChao1Diversity}
+	dictConvertBMetricToMethod = {c_strBrayCurtisDissimilarity:ConstantsMicropita.c_strRepresentative}
+	dictConvertInvBMetricToMethod = {c_strBrayCurtisDissimilarity:ConstantsMicropita.c_strExtreme}
+
+	#Linkage used in the Hierarchical clustering
+	c_strHierarchicalClusterMethod = 'average'
+
+####Group 1## Diversity
+	#Testing: Happy path Testing (8)
+	def funcGetTopRankedSamples(self, lldMatrix = None, lsSampleNames = None, iTopAmount = None):
+		"""
+		Given a list of lists of measurements, for each list the indices of the highest values are returned. If lsSamplesNames is given
+			it is treated as a list of string names that is in the order of the measurements in each list. Indices are returned or the sample
+			names associated with the indices.
+		
+		:param	lldMatrix:	List of lists [[value,value,value,value],[value,value,value,value]].
+		:type:	List of lists	List of measurements. Each list is a different measurement. Each measurement in positionally related to a sample.
+		:param	lsSampleNames:	List of sample names positionally related (the same) to each list (Optional).
+		:type:	List of strings	List of strings.
+		:param	iTopAmount:	The amount of top measured samples (assumes the higher measurements are better).
+		:type:	integer	Integer amount of sample names/ indices to return.
+		:return	List:	List of samples to be selected.
+		"""
+		topRankListRet = []
+		for rowMetrics in lldMatrix:
+			#Create 2 d array to hold value and index and sort
+			liIndexX = [rowMetrics,range(len(rowMetrics))]
+			liIndexX[1].sort(key = liIndexX[0].__getitem__,reverse = True)
+
+			if lsSampleNames:
+				topRankListRet.append([lsSampleNames[iIndex] for iIndex in liIndexX[1][:iTopAmount]])
+			else:
+				topRankListRet.append(liIndexX[1][:iTopAmount])
+
+		return topRankListRet
+	
+	####Group 2## Representative Dissimilarity
+	#Testing: Happy path tested 1
+	def funcGetCentralSamplesByKMedoids(self, npaMatrix=None, sMetric=None, lsSampleNames=None, iNumberSamplesReturned=0, istmBetaMatrix=None, istrmTree=None, istrmEnvr=None):
+		"""
+		Gets centroid samples by k-medoids clustering of a given matrix.
+		
+		:param	npaMatrix:	Numpy array where row=features and columns=samples
+		:type:	Numpy array	Abundance Data.
+		:param	sMetric:	String name of beta metric used as the distance metric.
+		:type:	String	String name of beta metric.
+		:param	lsSampleNames:	The names of the sample
+		:type:	List	List of strings
+		:param	iNumberSamplesReturned:	Number of samples to return, each will be a centroid of a sample.
+		:type:	Integer	Number of samples to return
+		:return	List:	List of selected samples.
+		:param	istmBetaMatrix: File with beta-diversity matrix
+		:type:	File stream or file path string
+		"""
+
+		#Count of how many rows
+		sampleCount = npaMatrix.shape[0]
+		if iNumberSamplesReturned > sampleCount:
+			logging.error("MicroPITA.funcGetCentralSamplesByKMedoids:: There are not enough samples to return the amount of samples specified. Return sample count = "+str(iNumberSamplesReturned)+". Sample number = "+str(sampleCount)+".")
+			return False
+
+		#If the cluster count is equal to the sample count return all samples
+		if sampleCount == iNumberSamplesReturned:
+			return list(lsSampleNames)
+
+		#Get distance matrix
+		distanceMatrix=scipy.spatial.distance.squareform(Metric.funcReadMatrixFile(istmMatrixFile=istmBetaMatrix,lsSampleOrder=lsSampleNames)[0]) if istmBetaMatrix else Metric.funcGetBetaMetric(npadAbundancies=npaMatrix, sMetric=sMetric, istrmTree=istrmTree, istrmEnvr=istrmEnvr, lsSampleOrder=lsSampleNames)
+		if type(distanceMatrix) is BooleanType:
+			logging.error("MicroPITA.funcGetCentralSamplesByKMedoids:: Could not read in the supplied distance matrix, returning false.")
+			return False
+
+		# Handle unifrac output
+		if sMetric in [Metric.c_strUnifracUnweighted,Metric.c_strUnifracWeighted]:
+			distanceMatrix = distanceMatrix[0]
+	
+		#Log distance matrix
+		logging.debug("MicroPITA.funcGetCentralSamplesByKMedoids:: Distance matrix for representative selection using metric="+str(sMetric))
+	
+		distance = MLPYDistanceAdaptor(npaDistanceMatrix=distanceMatrix, fIsCondensedMatrix=True)
+	
+		#Create object to determine clusters/medoids
+		medoidsMaker = Kmedoids(k=iNumberSamplesReturned, dist=distance)
+		#medoidsData includes(1d numpy array, medoids indexes; 
+		#			  1d numpy array, non-medoids indexes;
+		#			  1d numpy array, cluster membership for non-medoids;
+		#			  double, cost of configuration)
+		#npaMatrix is samples x rows
+		#Build a matrix of lists of indicies to pass to the distance matrix
+		lliIndicesMatrix = [[iIndexPosition] for iIndexPosition in xrange(0,len(npaMatrix))]
+		medoidsData = medoidsMaker.compute(np.array(lliIndicesMatrix))
+		logging.debug("MicroPITA.funcGetCentralSamplesByKMedoids:: Results from the kmedoid method in representative selection:")
+		logging.debug(str(medoidsData))
+	
+		#If returning the same amount of clusters and samples
+		#Return centroids
+		selectedIndexes = medoidsData[0]
+		return [lsSampleNames[selectedIndexes[index]] for index in xrange(0,iNumberSamplesReturned)]
+	
+	####Group 3## Highest Dissimilarity
+	#Testing: Happy path tested
+	def funcSelectExtremeSamplesFromHClust(self, strBetaMetric, npaAbundanceMatrix, lsSampleNames, iSelectSampleCount, istmBetaMatrix=None, istrmTree=None, istrmEnvr=None):
+		"""
+		Select extreme samples from HClustering.
+		
+		:param	strBetaMetric:	The beta metric to use for distance matrix generation.
+		:type:	String	The name of the beta metric to use.
+		:param	npaAbundanceMatrix:	Numpy array where row=samples and columns=features.
+		:type:	Numpy Array	Abundance data.
+		:param	lsSampleNames:	The names of the sample.
+		:type:	List	List of strings.
+		:param	iSelectSampleCount:	Number of samples to select (return).
+		:type:	Integer	Integer number of samples returned.
+		:return	Samples:	List of samples.
+		:param	istmBetaMatrix: File with beta-diversity matrix
+		:type:	File stream or file path string
+		"""
+	
+		#If they want all the sample count, return all sample names
+		iSampleCount=len(npaAbundanceMatrix[:,0])
+		if iSelectSampleCount==iSampleCount:
+		  return lsSampleNames
+	
+		#Holds the samples to be returned
+		lsReturnSamplesRet = []
+	
+		#Generate beta matrix
+		#Returns condensed matrix
+		tempDistanceMatrix = scipy.spatial.distance.squareform(Metric.funcReadMatrixFile(istmMatrixFile=istmBetaMatrix,lsSampleOrder=lsSampleNames)[0]) if istmBetaMatrix else Metric.funcGetBetaMetric(npadAbundancies=npaAbundanceMatrix, sMetric=strBetaMetric, istrmTree=istrmTree, istrmEnvr=istrmEnvr, lsSampleOrder=lsSampleNames, fAdditiveInverse = True)
+
+		if strBetaMetric in [Metric.c_strUnifracUnweighted,Metric.c_strUnifracWeighted]:
+			tempDistanceMatrix = tempDistanceMatrix[0]
+
+		if type(tempDistanceMatrix) is BooleanType:
+			logging.error("MicroPITA.funcSelectExtremeSamplesFromHClust:: Could not read in the supplied distance matrix, returning false.")
+			return False
+
+		if istmBetaMatrix:
+			tempDistanceMatrix = 1-tempDistanceMatrix
+
+		#Feed beta matrix to linkage to cluster
+		#Send condensed matrix
+		linkageMatrix = hcluster.linkage(tempDistanceMatrix, method=self.c_strHierarchicalClusterMethod)
+	
+		#Extract cluster information from dendrogram
+		#The linakge matrix is of the form
+		#[[int1 int2 doube int3],...]
+		#int1 and int1 are the paired samples indexed at 0 and up.
+		#each list is an entry for a branch that is number starting with the first
+		#list being sample count index + 1
+		#each list is then named by an increment as they appear
+		#this means that if a number is in the list and is = sample count or greater it is not
+		#terminal and is instead a branch.
+		#This method just takes the lowest metric measurement (highest distance pairs/clusters)
+		#Works much better than the original technique
+		#get total number of samples
+	
+		iCurrentSelectCount = 0
+		for row in linkageMatrix:
+			#Get nodes ofthe lowest pairing (so the furthest apart pair)
+			iNode1 = int(row[0])
+			iNode2 = int(row[1])
+			#Make sure the nodes are a terminal node (sample) and not a branch in the dendrogram
+			#The branching in the dendrogram will start at the number of samples and increment higher.
+			#Add each of the pair one at a time breaking when enough samples are selected.
+			if iNode1<iSampleCount:
+				lsReturnSamplesRet.append(lsSampleNames[iNode1])
+				iCurrentSelectCount = iCurrentSelectCount + 1
+			if iCurrentSelectCount == iSelectSampleCount:
+				break
+			if iNode2<iSampleCount:
+				lsReturnSamplesRet.append(lsSampleNames[iNode2])
+				iCurrentSelectCount = iCurrentSelectCount + 1
+			if iCurrentSelectCount == iSelectSampleCount:
+				break
+	
+		#Return selected samples
+		return lsReturnSamplesRet
+	
+	####Group 4## Rank Average of user Defined Taxa
+		#Testing: Happy Path Tested
+	def funcGetAverageAbundanceSamples(self, abndTable, lsTargetedFeature, fRank=False):
+		"""
+		Averages feature abundance or ranked abundance. Expects a column 0 of taxa id that is skipped.
+		
+		:param	abndTable:	Abundance Table to analyse
+		:type:	AbundanceTable	Abundance Table
+		:param	lsTargetedFeature:	String names
+		:type:	list	list of string names of features (bugs) which are measured after ranking against the full sample
+		:param  fRank:	Indicates to rank the abundance before getting the average abundance of the features (default false)
+		:type:   boolean	Flag indicating ranking abundance before calculating average feature measurement (false= no ranking)
+		:return	List of lists or boolean:	List of lists or False on error. One internal list per sample indicating the sample,
+				feature average abundance or ranked abundance. Lists will already be sorted.
+				For not Ranked [[sample,average abundance of selected feature,1]]
+				For Ranked [[sample,average ranked abundance, average abundance of selected feature]]
+				Error Returns false
+		"""
+		
+		llAbundance = abndTable.funcGetAverageAbundancePerSample(lsTargetedFeature)
+		if not llAbundance:
+			logging.error("MicroPITA.funcGetAverageAbundanceSamples:: Could not get average abundance, returned false. Make sure the features (bugs) are spelled correctly and in the abundance table.")
+			return False
+		#Add a space for ranking if needed
+		#Not ranked will be [[sSample,average abundance,1]]
+		#(where 1 will not discriminant ties if used in later functions, so this generalizes)
+		#Ranked will be [[sSample, average rank, average abundance]]
+		llRetAbundance = [[llist[0],-1,llist[1]] for llist in llAbundance]
+		#Rank if needed
+		if fRank:
+			abndRanked = abndTable.funcRankAbundance()
+			if abndRanked == None:
+				logging.error("MicroPITA.funcGetAverageAbundanceSamples:: Could not rank the abundance table, returned false.")
+				return False
+			llRetRank = abndRanked.funcGetAverageAbundancePerSample(lsTargetedFeature)
+			if not llRetRank:
+				logging.error("MicroPITA.funcGetAverageAbundanceSamples:: Could not get average ranked abundance, returned false. Make sure the features (bugs) are spelled correctly and in the abundance table.")
+				return False
+			dictRanks = dict(llRetRank)
+			llRetAbundance = [[a[0],dictRanks[a[0]],a[2]] for a in llRetAbundance]
+			
+		#Sort first for ties and then for the main feature
+ 		if not fRank or ConstantsMicropita.c_fBreakRankTiesByDiversity:
+			llRetAbundance = sorted(llRetAbundance, key = lambda sampleData: sampleData[2], reverse = not fRank)
+		if fRank:
+			llRetAbundance = sorted(llRetAbundance, key = lambda sampleData: sampleData[1], reverse = not fRank)
+		return llRetAbundance
+	
+	#Testing: Happy Path Tested
+	def funcSelectTargetedTaxaSamples(self, abndMatrix, lsTargetedTaxa, iSampleSelectionCount, sMethod = ConstantsMicropita.lsTargetedFeatureMethodValues[0]):
+	  """
+	  Selects samples with the highest ranks or abundance of targeted features.
+	  If ranked, select the highest abundance for tie breaking
+	
+	  :param	abndMatrix:	Abundance table to analyse 
+	  :type:	AbundanceTable	Abundance table
+	  :param	lsTargetedTaxa:	List of features
+	  :type:	list	list of strings
+	  :param	iSampleSelectionCount:	Number of samples to select
+	  :type:	integer	integer
+	  :param	sMethod:	Method to select targeted features
+	  :type:	string	String (Can be values found in ConstantsMicropita.lsTargetedFeatureMethodValues)
+	  :return	List of strings:	List of sample names which were selected
+	  List of strings	Empty list is returned on an error.
+	  """
+	
+	  #Check data
+	  if(len(lsTargetedTaxa) < 1):
+		logging.error("MicroPITA.funcSelectTargetedTaxaSamples. Taxa defined selection was requested but no features were given.")
+		return []
+
+	  lsTargetedSamples = self.funcGetAverageAbundanceSamples(abndTable=abndMatrix, lsTargetedFeature=lsTargetedTaxa,
+	  	fRank=sMethod.lower() == self.c_strTargetedRanked.lower())
+	  #If an error occured or the key word for the method was not recognized
+	  if lsTargetedSamples == False: 
+		  logging.error("MicroPITA.funcSelectTargetedTaxaSamples:: Was not able to select for the features given. So targeted feature selection was performed. Check to make sure the features are spelled correctly and exist in the abundance file.")
+		  return []
+	
+	  #Select from results
+	  return [sSample[0] for sSample in lsTargetedSamples[:iSampleSelectionCount]]
+	
+	####Group 5## Random
+	#Testing: Happy path Tested
+	def funcGetRandomSamples(self, lsSamples=None, iNumberOfSamplesToReturn=0):
+		"""
+		Returns random sample names of the number given. No replacement.
+		
+		:param	lsSamples:	List of sample names 
+		:type:	list	list of strings
+		:param	iNumberOfSamplesToReturn:	Number of samples to select
+		:type:	integer	integer.
+		:return	List:	List of selected samples (strings).
+		"""
+
+		#Input matrix sample count
+		sampleCount = len(lsSamples)
+
+		#Return the full matrix if they ask for a return matrix where length == original
+		if(iNumberOfSamplesToReturn >= sampleCount):
+			return lsSamples
+	
+		#Get the random indices for the sample (without replacement)
+		liRandomIndices = random.sample(range(sampleCount), iNumberOfSamplesToReturn)
+	
+		#Create a boolean array of if indexes are to be included in the reduced array
+                return [sSample for iIndex, sSample in enumerate(lsSamples) if iIndex in liRandomIndices]
+
+	#Happy path tested (case 3)
+	def funcGetAveragePopulation(self, abndTable, lfCompress):
+		"""
+		Get the average row per column in the abndtable.
+
+		:param abndTable: AbundanceTable of data to be averaged
+		:type: AbudanceTable
+		:param lfCompress: List of boolean flags (false means to remove sample before averaging
+		:type: List of floats
+		:return List of doubles: 
+		"""
+		if sum(lfCompress) == 0:
+			return []
+
+		#Get the average populations
+		lAverageRet = []
+
+		for sFeature in abndTable.funcGetAbundanceCopy():
+			sFeature = list(sFeature)[1:]
+			sFeature=np.compress(lfCompress,sFeature,axis=0)
+			lAverageRet.append(sum(sFeature)/float(len(sFeature)))
+		return lAverageRet
+
+	#Happy path tested (2 cases)
+	def funcGetDistanceFromAverage(self, abndTable,ldAverage,lsSamples,lfSelected):
+		"""
+		Given an abundance table and an average sample, this returns the distance of each sample
+		(measured using brays-curtis dissimilarity) from the average.
+		The distances are reduced by needing to be in the lsSamples and being a true in the lfSelected
+		(which is associated with the samples in the order of the samples in the abundance table;
+		use abundancetable.funcGetSampleNames() to see the order if needed).
+
+		:param abndTable: Abundance table holding the data to be analyzed.
+		:type: AbundanceTable
+		:param ldAverage: Average population (Average features of the abundance table of samples)
+		:type: List of doubles which represent the average population
+		:param lsSamples: These are the only samples used in the analysis
+		:type: List of strings (sample ids)
+		:param lfSelected: Samples to be included in the analysis
+		:type: List of boolean (true means include)
+		:return: List of distances (doubles)
+		"""
+		#Get the distance from label 1 of all samples in label0 splitting into selected and not selected lists
+		ldSelectedDistances = []
+
+		for sSampleName in [sSample for iindex, sSample in enumerate(lsSamples) if lfSelected[iindex]]:
+			#Get the sample measurements
+			ldSelectedDistances.append(Metric.funcGetBrayCurtisDissimilarity(np.array([abndTable.funcGetSample(sSampleName),ldAverage]))[0])
+		return ldSelectedDistances
+
+	#Happy path tested (1 case)
+	def funcMeasureDistanceFromLabelToAverageOtherLabel(self, abndTable, lfGroupOfInterest, lfGroupOther):
+		"""
+		Get the distance of samples from one label from the average sample of not the label.
+		Note: This assumes 2 classes.  
+
+		:param abndTable: Table of data to work out of.
+		:type: Abundace Table
+		:param lfGroupOfInterest: Boolean indicator of the sample being in the first group.
+		:type: List of floats, true indicating an individual in the group of interest.
+		:param lfGroupOther:	Boolean indicator of the sample being in the other group.
+		:type:	List of floats, true indicating an individual in the 
+		:return List of List of doubles: [list of tuples (string sample name,double distance) for the selected population, list of tuples for the not selected population]
+		"""
+		#Get all sample names
+		lsAllSamples = abndTable.funcGetSampleNames()
+
+		#Get average populations
+		lAverageOther = self.funcGetAveragePopulation(abndTable=abndTable, lfCompress=lfGroupOther)
+
+		#Get the distance from the average of the other label (label 1)
+		ldSelectedDistances = self.funcGetDistanceFromAverage(abndTable=abndTable, ldAverage=lAverageOther,
+			lsSamples=lsAllSamples, lfSelected=lfGroupOfInterest)
+
+		return zip([lsAllSamples[iindex] for iindex, fGroup in enumerate(lfGroupOfInterest) if fGroup],ldSelectedDistances)
+
+	#Happy path tested (1 test case)
+	def funcPerformDistanceSelection(self, abndTable, iSelectionCount, sLabel, sValueOfInterest):
+		"""
+		Given metadata, metadata of one value (sValueOfInterest) is measured from the average (centroid) value of another label group.
+		An iSelectionCount of samples is selected from the group of interest closest to and furthest from the centroid of the other group.
+
+		:params  abndTable: Abundance of measurements
+		:type: AbundanceTable
+		:params iSelectionCount: The number of samples selected per sample.
+		:type: Integer Integer greater than 0
+		:params sLabel: ID of the metadata which is the supervised label
+		:type: String
+		:params sValueOfInterest: Metadata value in the sLabel metadta row of the abundance table which defines the group of interest.
+		:type: String found in the abundance table metadata row indicated by sLabel.
+		:return list list of tuples (samplename, distance) [[iSelectionCount of tuples closest to the other centroid], [iSelectionCount of tuples farthest from the other centroid], [all tuples of samples not selected]]
+		"""
+
+		lsMetadata = abndTable.funcGetMetadata(sLabel)
+		#Other metadata values
+		lsUniqueOtherValues = list(set(lsMetadata)-set(sValueOfInterest))
+
+		#Get boolean indicator of values of interest
+		lfLabelsInterested = [sValueOfInterest == sValue for sValue in lsMetadata]
+
+                #Get the distances of the items of interest from the other metadata values
+		dictDistanceAverages = {}
+                for sOtherLabel in lsUniqueOtherValues:
+			#Get boolean indicator of labels not of interest 
+			lfLabelsOther = [sOtherLabel == sValue for sValue in lsMetadata]
+
+			#Get the distances of data from two different groups to the average of the other
+			ldValueDistances = dict(self.funcMeasureDistanceFromLabelToAverageOtherLabel(abndTable, lfLabelsInterested, lfLabelsOther))
+
+			for sKey in ldValueDistances:
+				dictDistanceAverages[sKey] = ldValueDistances[sKey] + dictDistanceAverages[sKey] if sKey in dictDistanceAverages else ldValueDistances[sKey]
+
+		#Finish average by dividing by length of lsUniqueOtherValues
+		ltpleAverageDistances = [(sKey, dictDistanceAverages[sKey]/float(len(lsUniqueOtherValues))) for sKey in dictDistanceAverages]
+
+                #Sort to extract extremes
+                ltpleAverageDistances = sorted(ltpleAverageDistances,key=operator.itemgetter(1))
+
+		#Get the closest and farthest distances
+		ltupleDiscriminantSamples = ltpleAverageDistances[:iSelectionCount]
+		ltupleDistinctSamples = ltpleAverageDistances[iSelectionCount*-1:]
+
+		#Remove the selected samples from the larger population of distances (better visualization)
+		ldSelected = [tpleSelected[0] for tpleSelected in ltupleDiscriminantSamples+ltupleDistinctSamples]
+
+		#Return discriminant tuples, distinct tuples, other tuples
+		return [ltupleDiscriminantSamples, ltupleDistinctSamples,
+			   [tplData for tplData in ltpleAverageDistances if tplData[0] not in ldSelected]]
+
+	#Run the supervised method surrounding distance from centroids
+	#Happy path tested (3 test cases)
+	def funcRunSupervisedDistancesFromCentroids(self, abundanceTable, fRunDistinct, fRunDiscriminant,
+						xOutputSupFile, xPredictSupFile, strSupervisedMetadata,
+						iSampleSupSelectionCount, lsOriginalSampleNames, lsOriginalLabels, fAppendFiles = False):
+		"""
+		Runs supervised methods based on measuring distances of one label from the centroid of another. NAs are evaluated as theirown group.
+
+		:param	abundanceTable:	AbundanceTable
+		:type:	AbudanceTable	Data to analyze
+		:param	fRunDistinct:	Run distinct selection method
+		:type:	Boolean	boolean (true runs method)
+		:param	fRunDiscriminant:	Run discriminant method
+		:type:	Boolean	boolean (true runs method)
+		:param	xOutputSupFile:	File output from supervised methods detailing data going into the method.
+		:type:	String or FileStream
+		:param	xPredictSupFile:	File output from supervised methods distance results from supervised methods.
+		:type:	String or FileStream
+		:param strSupervisedMetadata:	The metadata that will be used to group samples.
+		:type:	String
+		:param	iSampleSupSelectionCount:	Number of samples to select
+		:type:	Integer	int sample selection count
+		:param lsOriginalSampleNames:	List of the sample names, order is important and should be preserved from the abundanceTable.
+		:type:	List of samples	
+		:param	fAppendFiles:	Indicates that output files already exist and appending is occuring.
+		:type:	Boolean
+		:return	Selected Samples:	A dictionary of selected samples by selection ID
+		Dictionary	{"Selection Method":["SampleID","SampleID"...]}
+		"""
+		#Get labels and run one label against many
+		lstrMetadata = abundanceTable.funcGetMetadata(strSupervisedMetadata)
+		dictlltpleDistanceMeasurements = {}
+		for sMetadataValue in set(lstrMetadata):
+
+			#For now perform the selection here for the label of interest against the other labels
+			dictlltpleDistanceMeasurements.setdefault(sMetadataValue,[]).extend(self.funcPerformDistanceSelection(abndTable=abundanceTable,
+				iSelectionCount=iSampleSupSelectionCount, sLabel=strSupervisedMetadata, sValueOfInterest=sMetadataValue))
+
+		#Make expected output files for supervised methods
+		#1. Output file which is similar to an input file for SVMs
+		#2. Output file that is similar to the probabilitic output of a SVM (LibSVM)
+		#Manly for making output of supervised methods (Distance from Centroid) similar
+		#MicropitaVis needs some of these files
+		if xOutputSupFile:
+			if fAppendFiles:
+				SVM.funcUpdateSVMFileWithAbundanceTable(abndAbundanceTable=abundanceTable, xOutputSVMFile=xOutputSupFile,
+					lsOriginalLabels=lsOriginalLabels, lsSampleOrdering=lsOriginalSampleNames)
+			else:
+				SVM.funcConvertAbundanceTableToSVMFile(abndAbundanceTable=abundanceTable, xOutputSVMFile=xOutputSupFile,
+					sMetadataLabel=strSupervisedMetadata, lsOriginalLabels=lsOriginalLabels, lsSampleOrdering=lsOriginalSampleNames)
+
+		#Will contain the samples selected to return
+		#One or more of the methods may be active so this is why I am extending instead of just returning the result of each method type
+		dictSelectedSamplesRet = dict()
+		for sKey, ltplDistances in dictlltpleDistanceMeasurements.items():
+			if fRunDistinct:
+				dictSelectedSamplesRet.setdefault(ConstantsMicropita.c_strDistinct,[]).extend([ltple[0] for ltple in ltplDistances[1]])
+			if fRunDiscriminant:
+				dictSelectedSamplesRet.setdefault(ConstantsMicropita.c_strDiscriminant,[]).extend([ltple[0] for ltple in ltplDistances[0]])
+
+		if xPredictSupFile:
+			dictFlattenedDistances = dict()
+			[dictFlattenedDistances.setdefault(sKey, []).append(tple)
+				for sKey, lltple in dictlltpleDistanceMeasurements.items()
+				for ltple in lltple for tple in ltple]
+			if fAppendFiles:
+				self._updatePredictFile(xPredictSupFile=xPredictSupFile, xInputLabelsFile=xOutputSupFile,
+					dictltpleDistanceMeasurements=dictFlattenedDistances, abundanceTable=abundanceTable, lsOriginalSampleNames=lsOriginalSampleNames)
+			else:
+				self._writeToPredictFile(xPredictSupFile=xPredictSupFile, xInputLabelsFile=xOutputSupFile,
+					dictltpleDistanceMeasurements=dictFlattenedDistances, abundanceTable=abundanceTable, lsOriginalSampleNames=lsOriginalSampleNames)
+		return dictSelectedSamplesRet
+
+	#Two happy path test cases
+	def _updatePredictFile(self, xPredictSupFile, xInputLabelsFile, dictltpleDistanceMeasurements, abundanceTable, lsOriginalSampleNames):
+		"""
+		Manages updating the predict file.
+
+		:param	xPredictSupFile: File that has predictions (distances) from the supervised method.
+		:type:	FileStream or String file path
+		:param	xInputLabelsFile: File that as input to the supervised methods.
+		:type:	FileStream or String file path
+		:param	dictltpleDistanceMeasurements: 
+		:type:	Dictionary of lists of tuples {"labelgroup":[("SampleName",dDistance)...], "labelgroup":[("SampleName",dDistance)...]}
+		"""
+
+		if not isinstance(xPredictSupFile, str):
+			xPredictSupFile.close()
+			xPredictSupFile = xPredictSupFile.name
+		csvr = open(xPredictSupFile,'r')
+
+		f = csv.reader(csvr,delimiter=ConstantsBreadCrumbs.c_strBreadCrumbsSVMSpace)
+		lsHeader = f.next()[1:]
+		dictlltpleRead = dict([(sHeader,[]) for sHeader in lsHeader])
+
+		#Read data in 
+		iSampleIndex = 0
+		for sRow in f:
+			sLabel = sRow[0]
+			[dictlltpleRead[lsHeader[iDistanceIndex]].append((lsOriginalSampleNames[iSampleIndex],dDistance)) for iDistanceIndex, dDistance in enumerate(sRow[1:])
+				if not dDistance == ConstantsMicropita.c_sEmptyPredictFileValue]
+			iSampleIndex += 1
+
+		#Combine dictltpleDistanceMeasurements with new data
+		#If they share a key then merge keeping parameter data
+		#If they do not share the key, keep the full data
+		dictNew = {}
+		for sKey in dictltpleDistanceMeasurements.keys():
+			lsSamples = [tple[0] for tple in dictltpleDistanceMeasurements[sKey]]
+			dictNew[sKey] = dictltpleDistanceMeasurements[sKey]+[tple for tple in dictlltpleRead[sKey] if tple[0] not in lsSamples] if sKey in dictlltpleRead.keys() else dictltpleDistanceMeasurements[sKey]
+                for sKey in dictlltpleRead:
+			if sKey not in dictltpleDistanceMeasurements.keys():
+				dictNew[sKey] = dictlltpleRead[sKey]
+
+		#Call writer
+		self._writeToPredictFile(xPredictSupFile=xPredictSupFile, xInputLabelsFile=xInputLabelsFile,
+			dictltpleDistanceMeasurements=dictNew, abundanceTable=abundanceTable,
+			lsOriginalSampleNames=lsOriginalSampleNames, fFromUpdate=True)
+
+	#2 happy path test cases
+        def _writeToPredictFile(self, xPredictSupFile, xInputLabelsFile, dictltpleDistanceMeasurements, abundanceTable, lsOriginalSampleNames, fFromUpdate=False):
+		"""
+		Write to the predict file.
+
+		:param	xPredictSupFile: File that has predictions (distances) from the supervised method.
+		:type:	FileStream or String file path
+		:param	xInputLabelsFile: File that as input to the supervised methods.
+		:type:	FileStream or String file path
+		:param	dictltpleDistanceMeasurements: 
+		:type:	Dictionary of lists of tuples {"labelgroup":[("SampleName",dDistance)...], "labelgroup":[("SampleName",dDistance)...]}
+		:param	abundanceTable: An abundance table of the sample data.
+		:type:	AbundanceTable
+		:param	lsOriginalSampleNames: Used if the file is being updated as the sample names so that it may be passed in and consistent with other writing.
+			Otherwise will use the sample names from the abundance table.
+		:type:	List of strings
+		:param	fFromUpdate:	Indicates if this is part of an update to the file or not.
+		:type:	Boolean
+		"""
+
+		xInputLabelsFileName = xInputLabelsFile
+		if not isinstance(xInputLabelsFile,str):
+			xInputLabelsFileName = xInputLabelsFile.name
+		f = csv.writer(open(xPredictSupFile,"w") if isinstance(xPredictSupFile, str) else xPredictSupFile,delimiter=ConstantsBreadCrumbs.c_strBreadCrumbsSVMSpace)
+
+		lsAllSampleNames = abundanceTable.funcGetSampleNames()
+		lsLabels = SVM.funcReadLabelsFromFile(xSVMFile=xInputLabelsFileName, lsAllSampleNames= lsOriginalSampleNames if fFromUpdate else lsAllSampleNames,
+						isPredictFile=False)
+		dictLabels = dict([(sSample,sLabel) for sLabel in lsLabels.keys() for sSample in lsLabels[sLabel]])
+
+		#Dictionay keys will be used to order the predict file
+		lsMeasurementKeys = dictltpleDistanceMeasurements.keys()
+		#Make header
+		f.writerow(["labels"]+lsMeasurementKeys)
+
+		#Reformat dictionary to make it easier to use
+		for sKey in dictltpleDistanceMeasurements:
+			dictltpleDistanceMeasurements[sKey] = dict([ltpl for ltpl in dictltpleDistanceMeasurements[sKey]])
+
+		for sSample in lsOriginalSampleNames:
+			#Make body of file
+			f.writerow([dictLabels.get(sSample,ConstantsMicropita.c_sEmptyPredictFileValue)]+
+				[str(dictltpleDistanceMeasurements[sKey].get(sSample,ConstantsMicropita.c_sEmptyPredictFileValue))
+				for sKey in lsMeasurementKeys])
+
+	def _funcRunNormalizeSensitiveMethods(self, abndData, iSampleSelectionCount, dictSelectedSamples, lsAlphaMetrics, lsBetaMetrics, lsInverseBetaMetrics,
+												fRunDiversity, fRunRepresentative, fRunExtreme, strAlphaMetadata=None,
+												istmBetaMatrix=None, istrmTree=None, istrmEnvr=None, fInvertDiversity=False):
+		"""
+		Manages running methods that are sensitive to normalization. This is called twice, once for the set of methods which should not be normalized and the other
+		for the set that should be normalized.
+	
+		:param	abndData:	Abundance table object holding the samples to be measured.
+		:type:	AbundanceTable
+		:param	iSampleSelectionCount	The number of samples to select per method.
+		:type:	Integer
+		:param	dictSelectedSamples	Will be added to as samples are selected {"Method:["strSelectedSampleID","strSelectedSampleID"...]}.
+		:type:	Dictionary
+		:param	lsAlphaMetrics:	List of alpha metrics to use on alpha metric dependent assays (like highest diversity).
+		:type:	List of strings
+		:param	lsBetaMetrics:	List of beta metrics to use on beta metric dependent assays (like most representative).
+		:type:	List of strings
+		:param	lsInverseBetaMetrics:	List of inverse beta metrics to use on inverse beta metric dependent assays (like most dissimilar).
+		:type:	List of strings
+		:param	fRunDiversity:	Run Diversity based methods (true indicates run).
+		:type:	Boolean	
+		:param	fRunRepresentative:	Run Representative based methods (true indicates run).
+		:type:	Boolean	
+		:param	fRunExtreme:	Run Extreme based methods (true indicates run).
+		:type:	Boolean	
+		:param	istmBetaMatrix:	File that has a precalculated beta matrix
+		:type:	File stream or File path string
+		:return	Selected Samples:	Samples selected by methods.
+				Dictionary	{"Selection Method":["SampleID","SampleID","SampleID",...]}
+		"""
+
+		#Sample ids/names
+		lsSampleNames = abndData.funcGetSampleNames()
+	
+		#Generate alpha metrics and get most diverse
+		if fRunDiversity:
+
+			#Get Alpha metrics matrix
+			internalAlphaMatrix = None
+			#Name of technique
+			strMethod = [strAlphaMetadata] if strAlphaMetadata else lsAlphaMetrics
+
+			#If given an alpha-diversity metadata
+			if strAlphaMetadata:
+				internalAlphaMatrix = [[float(strNum) for strNum in abndData.funcGetMetadata(strAlphaMetadata)]]
+			else:
+				#Expects Observations (Taxa (row) x sample (column))
+				#Returns [[metric1-sample1, metric1-sample2, metric1-sample3],[metric1-sample1, metric1-sample2, metric1-sample3]]
+				internalAlphaMatrix = Metric.funcBuildAlphaMetricsMatrix(npaSampleAbundance = abndData.funcGetAbundanceCopy()
+							if not abndData.funcIsSummed()
+							else abndData.funcGetFeatureAbundanceTable(abndData.funcGetTerminalNodes()).funcGetAbundanceCopy(),
+							lsSampleNames = lsSampleNames, lsDiversityMetricAlpha = lsAlphaMetrics)
+	
+			if internalAlphaMatrix:
+				#Invert measurments
+				if fInvertDiversity:
+					lldNewDiversity = []
+					for lsLine in internalAlphaMatrix:
+						lldNewDiversity.append([1/max(dValue,ConstantsMicropita.c_smallNumber) for dValue in lsLine])
+					internalAlphaMatrix = lldNewDiversity
+				#Get top ranked alpha diversity by most diverse
+				#Expects [[sample1,sample2,sample3...],[sample1,sample2,sample3..],...]
+				#Returns [[sampleName1, sampleName2, sampleNameN],[sampleName1, sampleName2, sampleNameN]]
+				mostDiverseAlphaSamplesIndexes = self.funcGetTopRankedSamples(lldMatrix=internalAlphaMatrix, lsSampleNames=lsSampleNames, iTopAmount=iSampleSelectionCount)
+
+				#Add to results
+				for index in xrange(0,len(strMethod)):
+					strSelectionMethod = self.dictConvertAMetricDiversity.get(strMethod[index],ConstantsMicropita.c_strDiversity+"="+strMethod[index])
+					dictSelectedSamples.setdefault(strSelectionMethod,[]).extend(mostDiverseAlphaSamplesIndexes[index])
+
+		logging.info("MicroPITA.funcRunNormalizeSensitiveMethods:: Selected Samples 1b")
+		logging.info(dictSelectedSamples)
+	
+		#Generate beta metrics and 
+		if fRunRepresentative or fRunExtreme:
+
+			#Abundance matrix transposed
+			npaTransposedAbundance = UtilityMath.funcTransposeDataMatrix(abndData.funcGetAbundanceCopy(), fRemoveAdornments=True)
+	
+			#Get center selection using clusters/tiling
+			#This will be for beta metrics in normalized space
+			if fRunRepresentative:
+
+				if istmBetaMatrix:
+					#Get representative dissimilarity samples
+					medoidSamples=self.funcGetCentralSamplesByKMedoids(npaMatrix=npaTransposedAbundance, sMetric=ConstantsMicropita.c_custom, lsSampleNames=lsSampleNames, iNumberSamplesReturned=iSampleSelectionCount, istmBetaMatrix=istmBetaMatrix, istrmTree=istrmTree, istrmEnvr=istrmEnvr)
+
+					if medoidSamples:
+						dictSelectedSamples.setdefault(ConstantsMicropita.c_strRepresentative+"="+ConstantsMicropita.c_custom,[]).extend(medoidSamples)
+				else:
+					logging.info("MicroPITA.funcRunNormalizeSensitiveMethods:: Performing representative selection on normalized data.")
+					for bMetric in lsBetaMetrics:
+
+						#Get representative dissimilarity samples
+						medoidSamples=self.funcGetCentralSamplesByKMedoids(npaMatrix=npaTransposedAbundance, sMetric=bMetric, lsSampleNames=lsSampleNames, iNumberSamplesReturned=iSampleSelectionCount, istmBetaMatrix=istmBetaMatrix, istrmTree=istrmTree, istrmEnvr=istrmEnvr)
+
+						if medoidSamples:
+							dictSelectedSamples.setdefault(self.dictConvertBMetricToMethod.get(bMetric,ConstantsMicropita.c_strRepresentative+"="+bMetric),[]).extend(medoidSamples)
+
+			#Get extreme selection using clusters, tiling
+			if fRunExtreme:
+				logging.info("MicroPITA.funcRunNormalizeSensitiveMethods:: Performing extreme selection on normalized data.")
+				if istmBetaMatrix:
+
+					#Samples for representative dissimilarity
+					#This involves inverting the distance metric,
+					#Taking the dendrogram level of where the number cluster == the number of samples to select
+					#Returning a repersentative sample from each cluster
+					extremeSamples = self.funcSelectExtremeSamplesFromHClust(strBetaMetric=ConstantsMicropita.c_custom, npaAbundanceMatrix=npaTransposedAbundance, lsSampleNames=lsSampleNames, iSelectSampleCount=iSampleSelectionCount, istmBetaMatrix=istmBetaMatrix, istrmTree=istrmTree, istrmEnvr=istrmEnvr)
+	
+					#Add selected samples
+					if extremeSamples:
+						dictSelectedSamples.setdefault(ConstantsMicropita.c_strExtreme+"="+ConstantsMicropita.c_custom,[]).extend(extremeSamples)
+
+				else:
+					#Run KMedoids with inverse custom distance metric in normalized space
+					for bMetric in lsInverseBetaMetrics:
+
+						#Samples for representative dissimilarity
+						#This involves inverting the distance metric,
+						#Taking the dendrogram level of where the number cluster == the number of samples to select
+						#Returning a repersentative sample from each cluster
+						extremeSamples = self.funcSelectExtremeSamplesFromHClust(strBetaMetric=bMetric, npaAbundanceMatrix=npaTransposedAbundance, lsSampleNames=lsSampleNames, iSelectSampleCount=iSampleSelectionCount, istmBetaMatrix=istmBetaMatrix, istrmTree=istrmTree, istrmEnvr=istrmEnvr)
+	
+						#Add selected samples
+						if extremeSamples:
+							dictSelectedSamples.setdefault(self.dictConvertInvBMetricToMethod.get(bMetric,ConstantsMicropita.c_strExtreme+"="+bMetric),[]).extend(extremeSamples)
+
+		logging.info("MicroPITA.funcRunNormalizeSensitiveMethods:: Selected Samples 2,3b")
+		logging.info(dictSelectedSamples)
+		return dictSelectedSamples
+
+	def funcRun(self, strIDName, strLastMetadataName, istmInput,
+					  ostmInputPredictFile, ostmPredictFile, ostmCheckedFile, ostmOutput,
+					  cDelimiter, cFeatureNameDelimiter, strFeatureSelection,
+					  istmFeatures, iCount, lstrMethods, strLastRowMetadata = None, strLabel = None, strStratify = None,
+					  strCustomAlpha = None, strCustomBeta = None, strAlphaMetadata = None, istmBetaMatrix = None, istrmTree = None, istrmEnvr = None, 
+					  iMinSeqs = ConstantsMicropita.c_liOccurenceFilter[0], iMinSamples = ConstantsMicropita.c_liOccurenceFilter[1], fInvertDiversity = False):
+		"""
+		Manages the selection of samples given different metrics.
+
+		:param	strIDName: Sample Id metadata row
+		:type:	String
+		:param	strLastMetadataName: The id of the metadata positioned last in the abundance table.
+		:type:	String	String metadata id.
+		:param	istmInput: File to store input data to supervised methods.
+		:type:	FileStream of String file path
+		:param	ostmInputPredictFile: File to store distances from supervised methods.
+		:type:	FileStream or String file path
+		:param	ostmCheckedFile: File to store the AbundanceTable data after it is being checked.
+		:type:	FileStream or String file path
+		:param	ostmOutPut: File to store sample selection by methods of interest.
+		:type:	FileStream or String file path
+		:param	cDelimiter: Delimiter of abundance table.
+		:type:	Character Char (default TAB).
+		:param	cFeatureNameDelimiter: Delimiter of the name of features (for instance if they contain consensus lineages indicating clades).
+		:type:	Character (default |).
+		:param	stFeatureSelectionMethod: Which method to use to select features in a targeted manner (Using average ranked abundance or average abundance).
+		:type:	String (specific values indicated in ConstantsMicropita.lsTargetedFeatureMethodValues).
+		:param	istmFeatures: File which holds the features of interest if using targeted feature methodology.
+		:type:	FileStream or String file path
+		:param	iCount:	Number of samples to select in each methods, supervised methods select this amount per label if possible.
+		:type:	Integer	integer.
+		:param	lstrMethods: List of strings indicating selection techniques.
+		:type:	List of string method names
+		:param	strLabel: The metadata used for supervised labels.
+		:type:	String
+		:param	strStratify: The metadata used to stratify unsupervised data.
+		:type:	String
+		:param	strCustomAlpha: Custom alpha diversity metric
+		:type:	String
+		:param	strCustomBeta: Custom beta diversity metric
+		:type:	String
+		:param	strAlphaMetadata: Metadata id which is a diveristy metric to use in highest diversity sampling
+		:type:	String
+		:param	istmBetaMatrix: File containing precalculated beta-diversity matrix for representative sampling
+		:type:	FileStream or String file path
+		:param	istrmTree: File containing tree for phylogentic beta-diversity analysis
+		:type:	FileStream or String file path
+		:param	istrmEnvr: File containing environment for phylogentic beta-diversity analysis
+		:type:	FileStream or String file path
+		:param	iMinSeqs: Minimum sequence in the occurence filter which filters all features not with a minimum number of sequences in each of a minimum number of samples.
+		:type:	Integer
+		:param	iMinSamples: Minimum sample count for the occurence filter.
+		:type:	Integer
+		:param	fInvertDiversity: When true will invert diversity measurements before using.
+		:type:	boolean
+		:return	Selected Samples:	Samples selected by methods.
+				Dictionary	{"Selection Method":["SampleID","SampleID","SampleID",...]}
+		"""
+
+		#Holds the top ranked samples from different metrics
+		#dict[metric name] = [samplename,samplename...]
+		selectedSamples = dict()
+	
+		#If a target feature file is given make sure that targeted feature is in the selection methods, if not add
+		if ConstantsMicropita.c_strFeature in lstrMethods:
+		  if not istmFeatures:
+			logging.error("MicroPITA.funcRun:: Did not receive both the Targeted feature file and the feature selection method. MicroPITA did not run.")
+			return False
+
+		#Diversity metrics to run
+		#Use custom metrics if specified
+                #Custom beta metrics set to normalized only, custom alpha metrics set to count only
+		diversityMetricsAlpha = [] if strCustomAlpha or strAlphaMetadata else [MicroPITA.c_strInverseSimpsonDiversity]
+		diversityMetricsBeta = [] if istmBetaMatrix else [strCustomBeta] if strCustomBeta else [MicroPITA.c_strBrayCurtisDissimilarity]
+#		inverseDiversityMetricsBeta = [MicroPITA.c_strInvBrayCurtisDissimilarity]
+		diversityMetricsAlphaNoNormalize = [strAlphaMetadata] if strAlphaMetadata else [strCustomAlpha] if strCustomAlpha else []
+		diversityMetricsBetaNoNormalize = []
+#		inverseDiversityMetricsBetaNoNormalize = []
+
+		#Targeted taxa
+		userDefinedTaxa = []
+	
+		#Perform different flows flags
+		c_RUN_MAX_DIVERSITY_1 = ConstantsMicropita.c_strDiversity in lstrMethods
+		c_RUN_REPRESENTIVE_DISSIMILARITY_2 = ConstantsMicropita.c_strRepresentative in lstrMethods
+		c_RUN_MAX_DISSIMILARITY_3 = ConstantsMicropita.c_strExtreme in lstrMethods
+		c_RUN_RANK_AVERAGE_USER_4 = False
+		if ConstantsMicropita.c_strFeature in lstrMethods:
+			c_RUN_RANK_AVERAGE_USER_4 = True
+			if not istmFeatures:
+				logging.error("MicroPITA.funcRun:: No taxa file was given for taxa selection.") 
+				return False
+			#Read in taxa list, break down to lines and filter out empty strings
+			userDefinedTaxa = filter(None,(s.strip( ) for s in istmFeatures.readlines()))
+		c_RUN_RANDOM_5 = ConstantsMicropita.c_strRandom in lstrMethods
+		c_RUN_DISTINCT = ConstantsMicropita.c_strDistinct in lstrMethods
+		c_RUN_DISCRIMINANT = ConstantsMicropita.c_strDiscriminant in lstrMethods
+
+		#Read in abundance data
+		#Abundance is a structured array. Samples (column) by Taxa (rows) with the taxa id row included as the column index=0
+		#Abundance table object to read in and manage data
+		totalAbundanceTable = AbundanceTable.funcMakeFromFile(xInputFile=istmInput, lOccurenceFilter = [iMinSeqs, iMinSamples],
+								cDelimiter=cDelimiter, sMetadataID=strIDName, sLastMetadataRow=strLastRowMetadata,
+								sLastMetadata=strLastMetadataName, cFeatureNameDelimiter=cFeatureNameDelimiter, xOutputFile=ostmCheckedFile)
+		if not totalAbundanceTable:
+			logging.error("MicroPITA.funcRun:: Could not read in the abundance table. Analysis was not performed."+
+				" This often occurs when the Last Metadata is not specified correctly."+
+				" Please check to make sure the Last Metadata selection is the row of the last metadata,"+
+				" all values after this selection should be microbial measurements and should be numeric.")
+			return False
+
+		lsOriginalLabels = SVM.funcMakeLabels(totalAbundanceTable.funcGetMetadata(strLabel)) if strLabel else strLabel
+
+		dictTotalMetadata = totalAbundanceTable.funcGetMetadataCopy()
+		logging.debug("MicroPITA.funcRun:: Received metadata=" + str(dictTotalMetadata))
+		#If there is only 1 unique value for the labels, do not run the Supervised methods
+		if strLabel and ( len(set(dictTotalMetadata.get(strLabel,[]))) < 2 ):
+			logging.error("The label " + strLabel + " did not have 2 or more values. Labels found=" + str(dictTotalMetadata.get(strLabel,[])))
+			return False
+
+		#Run unsupervised methods###
+		#Stratify the data if need be and drop the old data
+		lStratifiedAbundanceTables = totalAbundanceTable.funcStratifyByMetadata(strStratify) if strStratify else [totalAbundanceTable]
+
+		#For each stratified abundance block or for the unstratfified abundance
+		#Run the unsupervised blocks
+		fAppendSupFiles = False
+		for stratAbundanceTable in lStratifiedAbundanceTables:
+			logging.info("MicroPITA.funcRun:: Running abundance block:"+stratAbundanceTable.funcGetName())
+
+ 			###NOT SUMMED, NOT NORMALIZED			
+			#Only perform if the data is not yet normalized
+			if not stratAbundanceTable.funcIsNormalized( ):
+				#Need to first work with unnormalized data
+				if c_RUN_MAX_DIVERSITY_1 or c_RUN_REPRESENTIVE_DISSIMILARITY_2 or c_RUN_MAX_DISSIMILARITY_3:
+
+					self._funcRunNormalizeSensitiveMethods(abndData=stratAbundanceTable, iSampleSelectionCount=iCount,
+													 dictSelectedSamples=selectedSamples, lsAlphaMetrics=diversityMetricsAlphaNoNormalize,
+													 lsBetaMetrics=diversityMetricsBetaNoNormalize,
+													 lsInverseBetaMetrics=diversityMetricsBetaNoNormalize,
+													 fRunDiversity=c_RUN_MAX_DIVERSITY_1,fRunRepresentative=c_RUN_REPRESENTIVE_DISSIMILARITY_2,
+													 fRunExtreme=c_RUN_MAX_DISSIMILARITY_3, strAlphaMetadata=strAlphaMetadata, 
+                                                                                                         istrmTree=istrmTree, istrmEnvr=istrmEnvr, fInvertDiversity=fInvertDiversity)
+
+
+			#Generate selection by the rank average of user defined taxa
+			#Expects (Taxa (row) by Samples (column))
+			#Expects a column 0 of taxa id that is skipped
+			#Returns [(sample name,average,rank)]
+			#SUMMED AND NORMALIZED
+			stratAbundanceTable.funcSumClades()
+			#Normalize data at this point
+			stratAbundanceTable.funcNormalize()
+			if c_RUN_RANK_AVERAGE_USER_4:
+				selectedSamples[ConstantsMicropita.c_strFeature] = self.funcSelectTargetedTaxaSamples(abndMatrix=stratAbundanceTable,
+						lsTargetedTaxa=userDefinedTaxa, iSampleSelectionCount=iCount, sMethod=strFeatureSelection)
+				logging.info("MicroPITA.funcRun:: Selected Samples Rank")
+				logging.info(selectedSamples)
+
+ 			###SUMMED AND NORMALIZED analysis block
+			#Diversity based metric will move reduce to terminal taxa as needed
+			if c_RUN_MAX_DIVERSITY_1 or c_RUN_REPRESENTIVE_DISSIMILARITY_2 or c_RUN_MAX_DISSIMILARITY_3:
+
+				self._funcRunNormalizeSensitiveMethods(abndData=stratAbundanceTable, iSampleSelectionCount=iCount,
+												 dictSelectedSamples=selectedSamples, lsAlphaMetrics=diversityMetricsAlpha,
+												 lsBetaMetrics=diversityMetricsBeta,
+												 lsInverseBetaMetrics=diversityMetricsBeta,
+												 fRunDiversity=c_RUN_MAX_DIVERSITY_1,fRunRepresentative=c_RUN_REPRESENTIVE_DISSIMILARITY_2,
+												 fRunExtreme=c_RUN_MAX_DISSIMILARITY_3,
+                                                                                                 istmBetaMatrix=istmBetaMatrix, istrmTree=istrmTree, istrmEnvr=istrmEnvr, fInvertDiversity=fInvertDiversity)
+
+			#5::Select randomly
+			#Expects sampleNames = List of sample names [name, name, name...]
+			if(c_RUN_RANDOM_5):
+				#Select randomly from sample names
+				selectedSamples[ConstantsMicropita.c_strRandom] = self.funcGetRandomSamples(lsSamples=stratAbundanceTable.funcGetSampleNames(), iNumberOfSamplesToReturn=iCount)
+				logging.info("MicroPITA.funcRun:: Selected Samples Random")
+				logging.info(selectedSamples)
+
+			#Perform supervised selection
+			if c_RUN_DISTINCT or c_RUN_DISCRIMINANT:
+ 				if strLabel:
+					dictSelectionRet = self.funcRunSupervisedDistancesFromCentroids(abundanceTable=stratAbundanceTable,
+								fRunDistinct=c_RUN_DISTINCT, fRunDiscriminant=c_RUN_DISCRIMINANT,
+								xOutputSupFile=ostmInputPredictFile,xPredictSupFile=ostmPredictFile,
+								strSupervisedMetadata=strLabel, iSampleSupSelectionCount=iCount,
+								lsOriginalSampleNames = totalAbundanceTable.funcGetSampleNames(),
+								lsOriginalLabels = lsOriginalLabels,
+								fAppendFiles=fAppendSupFiles)
+
+					[selectedSamples.setdefault(sKey,[]).extend(lValue) for sKey,lValue in dictSelectionRet.items()]
+
+					if not fAppendSupFiles:
+						fAppendSupFiles = True
+					logging.info("MicroPITA.funcRun:: Selected Samples Unsupervised")
+					logging.info(selectedSamples)
+		return selectedSamples
+	
+	#Testing: Happy path tested
+	@staticmethod
+	def funcWriteSelectionToFile(dictSelection,xOutputFilePath):
+		"""
+		Writes the selection of samples by method to an output file.
+		
+		:param	dictSelection:	The dictionary of selections by method to be written to a file.
+		:type:	Dictionary	The dictionary of selections by method {"method":["sample selected","sample selected"...]}
+		:param	xOutputFilePath:	FileStream or String path to file inwhich the dictionary is written.
+		:type:	String	FileStream or String path to file
+		"""
+	
+		if not dictSelection:
+			return
+
+		#Open file
+		f = csv.writer(open(xOutputFilePath,"w") if isinstance(xOutputFilePath, str) else xOutputFilePath, delimiter=ConstantsMicropita.c_outputFileDelim )
+
+		#Create output content from dictionary
+		for sKey in dictSelection:
+			f.writerow([sKey]+dictSelection[sKey])
+			logging.debug("MicroPITA.funcRun:: Selected samples output to file:"+str(dictSelection[sKey]))
+	
+	#Testing: Happy Path tested
+	@staticmethod
+	def funcReadSelectionFileToDictionary(xInputFile):
+		"""
+		Reads in an output selection file from micropita and formats it into a dictionary.
+		
+		:param	xInputFile:	String path to file or file stream to read and translate into a dictionary.
+									{"method":["sample selected","sample selected"...]}
+		:type:	FileStream or String Path to file
+		:return	Dictionary:	Samples selected by methods.
+					Dictionary	{"Selection Method":["SampleID","SampleID","SampleID",...]}
+		"""
+
+		#Open file
+		istmReader = csv.reader(open(xInputFile,'r') if isinstance(xInputFile, str) else xInputFile, delimiter = ConstantsMicropita.c_outputFileDelim)
+
+		#Dictionary to hold selection data
+		return dict([(lsLine[0], lsLine[1:]) for lsLine in istmReader])
+
+#Set up arguments reader
+argp = argparse.ArgumentParser( prog = "MicroPITA.py", 
+	description = """Selects samples from abundance tables based on various selection schemes.""" )
+
+args = argp.add_argument_group( "Common", "Commonly modified options" )
+args.add_argument(ConstantsMicropita.c_strCountArgument,"--num", dest="iCount", metavar = "samples", default = 10, type = int, help = ConstantsMicropita.c_strCountHelp)
+args.add_argument("-m","--method", dest = "lstrMethods", metavar = "method", default = [], help = ConstantsMicropita.c_strSelectionTechniquesHelp,
+	choices = ConstantsMicropita.c_lsAllMethods, action = "append")
+
+args = argp.add_argument_group( "Custom", "Selecting and inputing custom metrics" )
+args.add_argument("-a","--alpha", dest = "strAlphaDiversity", metavar = "AlphaDiversity", default = None, help = ConstantsMicropita.c_strCustomAlphaDiversityHelp,  choices = Metric.setAlphaDiversities)
+args.add_argument("-b","--beta", dest = "strBetaDiversity", metavar = "BetaDiversity", default = None, help = ConstantsMicropita.c_strCustomBetaDiversityHelp,  choices = list(Metric.setBetaDiversities)+[Metric.c_strUnifracUnweighted,Metric.c_strUnifracWeighted])
+args.add_argument("-q","--alphameta", dest = "strAlphaMetadata", metavar = "AlphaDiversityMetadata", default = None, help = ConstantsMicropita.c_strCustomAlphaDiversityMetadataHelp)
+args.add_argument("-x","--betamatrix", dest = "istmBetaMatrix", metavar = "BetaDiversityMatrix", default = None, help = ConstantsMicropita.c_strCustomBetaDiversityMatrixHelp)
+args.add_argument("-o","--tree", dest = "istrmTree", metavar = "PhylogeneticTree", default = None, help = ConstantsMicropita.c_strCustomPhylogeneticTreeHelp)
+args.add_argument("-i","--envr", dest = "istrmEnvr", metavar = "EnvironmentFile", default = None, help = ConstantsMicropita.c_strCustomEnvironmentFileHelp)
+args.add_argument("-f","--invertDiversity", dest = "fInvertDiversity", action="store_true", default = False, help = ConstantsMicropita.c_strInvertDiversityHelp)
+
+args = argp.add_argument_group( "Miscellaneous", "Row/column identifiers and feature targeting options" )
+args.add_argument("-d",ConstantsMicropita.c_strIDNameArgument, dest="strIDName", metavar="sample_id", help= ConstantsMicropita.c_strIDNameHelp)
+args.add_argument("-l",ConstantsMicropita.c_strLastMetadataNameArgument, dest="strLastMetadataName", metavar = "metadata_id", default = None,
+				  help= ConstantsMicropita.c_strLastMetadataNameHelp)
+args.add_argument("-r",ConstantsMicropita.c_strTargetedFeatureMethodArgument, dest="strFeatureSelection", metavar="targeting_method", default=ConstantsMicropita.lsTargetedFeatureMethodValues[0],
+				  choices=ConstantsMicropita.lsTargetedFeatureMethodValues, help= ConstantsMicropita.c_strTargetedFeatureMethodHelp)
+args.add_argument("-t",ConstantsMicropita.c_strTargetedSelectionFileArgument, dest="istmFeatures", metavar="feature_file", type=argparse.FileType("rU"), help=ConstantsMicropita.c_strTargetedSelectionFileHelp)
+args.add_argument("-w",ConstantsMicropita.c_strFeatureMetadataArgument, dest="strLastFeatureMetadata", metavar="Last_Feature_Metadata", default=None, help=ConstantsMicropita.c_strFeatureMetadataHelp)
+
+args = argp.add_argument_group( "Data labeling", "Metadata IDs for strata and supervised label values" )
+args.add_argument("-e",ConstantsMicropita.c_strSupervisedLabelArgument, dest="strLabel", metavar= "supervised_id", help=ConstantsMicropita.c_strSupervisedLabelHelp)
+args.add_argument("-s",ConstantsMicropita.c_strUnsupervisedStratifyMetadataArgument, dest="strUnsupervisedStratify", metavar="stratify_id", 
+				  help= ConstantsMicropita.c_strUnsupervisedStratifyMetadataHelp)
+
+args = argp.add_argument_group( "File formatting", "Rarely modified file formatting options" )
+args.add_argument("-j",ConstantsMicropita.c_strFileDelimiterArgument, dest="cFileDelimiter", metavar="column_delimiter", default="\t", help=ConstantsMicropita.c_strFileDelimiterHelp) 
+args.add_argument("-k",ConstantsMicropita.c_strFeatureNameDelimiterArgument, dest="cFeatureNameDelimiter", metavar="taxonomy_delimiter", default="|", help=ConstantsMicropita.c_strFeatureNameDelimiterHelp) 
+
+args = argp.add_argument_group( "Debugging", "Debugging options - modify at your own risk!" )
+args.add_argument("-v",ConstantsMicropita.c_strLoggingArgument, dest="strLogLevel", metavar = "log_level", default="WARNING", 
+				  choices=ConstantsMicropita.c_lsLoggingChoices, help= ConstantsMicropita.c_strLoggingHelp)
+args.add_argument("-c",ConstantsMicropita.c_strCheckedAbundanceFileArgument, dest="ostmCheckedFile", metavar = "output_qc", type = argparse.FileType("w"), help = ConstantsMicropita.c_strCheckedAbundanceFileHelp)
+args.add_argument("-g",ConstantsMicropita.c_strLoggingFileArgument, dest="ostmLoggingFile", metavar = "output_log", type = argparse.FileType("w"), help = ConstantsMicropita.c_strLoggingFileHelp)
+args.add_argument("-u",ConstantsMicropita.c_strSupervisedInputFile, dest="ostmInputPredictFile", metavar = "output_scaled", type = argparse.FileType("w"), help = ConstantsMicropita.c_strSupervisedInputFileHelp)
+args.add_argument("-p",ConstantsMicropita.c_strSupervisedPredictedFile, dest="ostmPredictFile", metavar = "output_labels", type = argparse.FileType("w"), help = ConstantsMicropita.c_strSupervisedPredictedFileHelp)
+
+argp.add_argument("istmInput", metavar = "input.pcl/biome", type = argparse.FileType("rU"), help = ConstantsMicropita.c_strAbundanceFileHelp,
+	default = sys.stdin)
+argp.add_argument("ostmOutput", metavar = "output.txt", type = argparse.FileType("w"), help = ConstantsMicropita.c_strGenericOutputDataFileHelp,
+	default = sys.stdout)
+
+__doc__ = "::\n\n\t" + argp.format_help( ).replace( "\n", "\n\t" ) + __doc__
+
+def _main( ):
+	args = argp.parse_args( )
+
+	#Set up logger
+	iLogLevel = getattr(logging, args.strLogLevel.upper(), None)
+	logging.basicConfig(stream = args.ostmLoggingFile if args.ostmLoggingFile else sys.stderr, filemode = 'w', level=iLogLevel)
+
+	#Run micropita
+	logging.info("MicroPITA:: Start microPITA")
+	microPITA = MicroPITA()
+
+	#Argparse will append to the default but will not remove the default so I do this here
+	if not len(args.lstrMethods):
+		args.lstrMethods = [ConstantsMicropita.c_strRepresentative]
+
+	dictSelectedSamples = microPITA.funcRun(
+		strIDName		= args.strIDName,
+		strLastMetadataName	= args.strLastMetadataName,
+		istmInput		= args.istmInput,
+		ostmInputPredictFile	= args.ostmInputPredictFile,
+		ostmPredictFile		= args.ostmPredictFile,
+		ostmCheckedFile		= args.ostmCheckedFile,
+		ostmOutput		= args.ostmOutput,
+		cDelimiter		= args.cFileDelimiter,
+		cFeatureNameDelimiter	= args.cFeatureNameDelimiter,
+		istmFeatures		= args.istmFeatures,
+		strFeatureSelection	= args.strFeatureSelection,
+		iCount			= args.iCount,
+		strLastRowMetadata	= args.strLastFeatureMetadata,
+		strLabel		= args.strLabel,
+		strStratify		= args.strUnsupervisedStratify,
+		strCustomAlpha		= args.strAlphaDiversity,
+		strCustomBeta		= args.strBetaDiversity,
+		strAlphaMetadata	= args.strAlphaMetadata,
+		istmBetaMatrix		= args.istmBetaMatrix,
+		istrmTree		= args.istrmTree,
+		istrmEnvr		= args.istrmEnvr,
+		lstrMethods		= args.lstrMethods,
+		fInvertDiversity	= args.fInvertDiversity
+	)
+
+	if not dictSelectedSamples:
+		logging.error("MicroPITA:: Error, did not get a result from analysis.")
+		return -1
+	logging.info("End microPITA")
+
+	#Log output for debugging
+	logging.debug("MicroPITA:: Returned the following samples:"+str(dictSelectedSamples))
+
+	#Write selection to file
+	microPITA.funcWriteSelectionToFile(dictSelection=dictSelectedSamples, xOutputFilePath=args.ostmOutput)
+
+if __name__ == "__main__":
+	_main( )