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diff --git a/wqflask/basicStatistics/BasicStatisticsFunctions.py b/wqflask/basicStatistics/BasicStatisticsFunctions.py
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+++ b/wqflask/basicStatistics/BasicStatisticsFunctions.py
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+#import string
+from math import *
+#import piddle as pid
+#import os
+
+#import reaper
+from htmlgen import HTMLgen2 as HT
+
+#from utility import Plot
+from utility import webqtlUtil
+from base import webqtlConfig
+from dbFunction import webqtlDatabaseFunction
+
+def basicStatsTable(vals, trait_type=None, cellid=None, heritability=None):
+
+	valsOnly = []
+	dataXZ = vals[:]
+	for i in range(len(dataXZ)):
+		valsOnly.append(dataXZ[i][1])
+
+	traitmean, traitmedian, traitvar, traitstdev, traitsem, N = reaper.anova(valsOnly) #ZS: Should convert this from reaper to R in the future
+
+	tbl = HT.TableLite(cellpadding=20, cellspacing=0)
+	dataXZ = vals[:]
+	dataXZ.sort(webqtlUtil.cmpOrder)
+	tbl.append(HT.TR(HT.TD("Statistic",align="left", Class="fs14 fwb ffl b1 cw cbrb", width = 180),
+			HT.TD("Value", align="right", Class="fs14 fwb ffl b1 cw cbrb", width = 60)))
+	tbl.append(HT.TR(HT.TD("N of Samples",align="left", Class="fs13 b1 cbw c222"),
+			HT.TD(N,nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
+	tbl.append(HT.TR(HT.TD("Mean",align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
+			HT.TD("%2.3f" % traitmean,nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
+	tbl.append(HT.TR(HT.TD("Median",align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
+			HT.TD("%2.3f" % traitmedian,nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
+	#tbl.append(HT.TR(HT.TD("Variance",align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
+	#		HT.TD("%2.3f" % traitvar,nowrap="yes",align="left", Class="fs13 b1 cbw c222")))
+	tbl.append(HT.TR(HT.TD("Standard Error (SE)",align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
+			HT.TD("%2.3f" % traitsem,nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
+	tbl.append(HT.TR(HT.TD("Standard Deviation (SD)", align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
+			HT.TD("%2.3f" % traitstdev,nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
+	tbl.append(HT.TR(HT.TD("Minimum", align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
+			HT.TD("%s" % dataXZ[0][1],nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
+	tbl.append(HT.TR(HT.TD("Maximum", align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
+			HT.TD("%s" % dataXZ[-1][1],nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
+	if (trait_type != None and trait_type == 'ProbeSet'):
+			#IRQuest = HT.Href(text="Interquartile Range", url=webqtlConfig.glossaryfile +"#Interquartile",target="_blank", Class="fs14")
+			#IRQuest.append(HT.BR())
+			#IRQuest.append(" (fold difference)")
+			tbl.append(HT.TR(HT.TD("Range (log2)",align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
+				HT.TD("%2.3f" % (dataXZ[-1][1]-dataXZ[0][1]),nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
+			tbl.append(HT.TR(HT.TD(HT.Span("Range (fold)"),align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
+				HT.TD("%2.2f" % pow(2.0,(dataXZ[-1][1]-dataXZ[0][1])), nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
+			tbl.append(HT.TR(HT.TD(HT.Span(HT.Href(url="/glossary.html#Interquartile", target="_blank", text="Interquartile Range", Class="non_bold")), align="left", Class="fs13 b1 cbw c222",nowrap="yes"),
+				HT.TD("%2.2f" % pow(2.0,(dataXZ[int((N-1)*3.0/4.0)][1]-dataXZ[int((N-1)/4.0)][1])), nowrap="yes", Class="fs13 b1 cbw c222"), align="right"))
+
+			#XZ, 04/01/2009: don't try to get H2 value for probe.
+			if cellid:
+				pass
+			else:
+				if heritability:
+					tbl.append(HT.TR(HT.TD(HT.Span("Heritability"),align="center", Class="fs13 b1 cbw c222",nowrap="yes"),HT.TD("%s" % heritability, nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
+				else:
+					pass
+			# Lei Yan
+			# 2008/12/19
+
+	return tbl
+
+def plotNormalProbability(vals=None, RISet='', title=None, showstrains=0, specialStrains=[None], size=(750,500)):
+
+	dataXZ = vals[:]
+	dataXZ.sort(webqtlUtil.cmpOrder)
+	dataLabel = []
+	dataX = map(lambda X: X[1], dataXZ)
+
+	showLabel = showstrains
+	if len(dataXZ) > 50:
+		showLabel = 0
+	for item in dataXZ:
+		strainName = webqtlUtil.genShortStrainName(RISet=RISet, input_strainName=item[0])
+		dataLabel.append(strainName)
+
+	dataY=Plot.U(len(dataX))
+	dataZ=map(Plot.inverseCumul,dataY)
+	c = pid.PILCanvas(size=(750,500))
+	Plot.plotXY(c, dataZ, dataX, dataLabel = dataLabel, XLabel='Expected Z score', connectdot=0, YLabel='Trait value', title=title, specialCases=specialStrains, showLabel = showLabel)
+
+	filename= webqtlUtil.genRandStr("nP_")
+	c.save(webqtlConfig.IMGDIR+filename, format='gif')
+
+	img=HT.Image('/image/'+filename+'.gif',border=0)
+
+	return img
+
+def plotBoxPlot(vals):
+
+	valsOnly = []
+	dataXZ = vals[:]
+	for i in range(len(dataXZ)):
+		valsOnly.append(dataXZ[i][1])
+
+	plotHeight = 320
+	plotWidth = 220
+	xLeftOffset = 60
+	xRightOffset = 40
+	yTopOffset = 40
+	yBottomOffset = 60
+
+	canvasHeight = plotHeight + yTopOffset + yBottomOffset
+	canvasWidth = plotWidth + xLeftOffset + xRightOffset
+	canvas = pid.PILCanvas(size=(canvasWidth,canvasHeight))
+	XXX = [('', valsOnly[:])]
+
+	Plot.plotBoxPlot(canvas, XXX, offset=(xLeftOffset, xRightOffset, yTopOffset, yBottomOffset), XLabel= "Trait")
+	filename= webqtlUtil.genRandStr("Box_")
+	canvas.save(webqtlConfig.IMGDIR+filename, format='gif')
+	img=HT.Image('/image/'+filename+'.gif',border=0)
+
+	plotLink = HT.Span("More about ", HT.Href(text="Box Plots", url="http://davidmlane.com/hyperstat/A37797.html", target="_blank", Class="fs13"))
+
+	return img, plotLink
+
+def plotBarGraph(identification='', RISet='', vals=None, type="name"):
+
+	this_identification = "unnamed trait"
+	if identification:
+		this_identification = identification
+
+	if type=="rank":
+		dataXZ = vals[:]
+		dataXZ.sort(webqtlUtil.cmpOrder)
+		title='%s' % this_identification
+	else:
+		dataXZ = vals[:]
+		title='%s' % this_identification
+
+	tvals = []
+	tnames = []
+	tvars = []
+	for i in range(len(dataXZ)):
+		tvals.append(dataXZ[i][1])
+		tnames.append(webqtlUtil.genShortStrainName(RISet=RISet, input_strainName=dataXZ[i][0]))
+		tvars.append(dataXZ[i][2])
+	nnStrain = len(tnames)
+
+	sLabel = 1
+
+	###determine bar width and space width
+	if nnStrain < 20:
+		sw = 4
+	elif nnStrain < 40:
+		sw = 3
+	else:
+		sw = 2
+
+	### 700 is the default plot width minus Xoffsets for 40 strains
+	defaultWidth = 650
+	if nnStrain > 40:
+		defaultWidth += (nnStrain-40)*10
+	defaultOffset = 100
+	bw = int(0.5+(defaultWidth - (nnStrain-1.0)*sw)/nnStrain)
+	if bw < 10:
+		bw = 10
+
+	plotWidth = (nnStrain-1)*sw + nnStrain*bw + defaultOffset
+	plotHeight = 500
+	#print [plotWidth, plotHeight, bw, sw, nnStrain]
+	c = pid.PILCanvas(size=(plotWidth,plotHeight))
+	Plot.plotBarText(c, tvals, tnames, variance=tvars, YLabel='Value', title=title, sLabel = sLabel, barSpace = sw)
+
+	filename= webqtlUtil.genRandStr("Bar_")
+	c.save(webqtlConfig.IMGDIR+filename, format='gif')
+	img=HT.Image('/image/'+filename+'.gif',border=0)
+
+	return img