Ran reindent.py recursively on wqflask directory

1 files changed, 140 insertions, 140 deletions

diff --git a/wqflask/basicStatistics/BasicStatisticsFunctions.py b/wqflask/basicStatistics/BasicStatisticsFunctions.py
index 5cbbb145..285addae 100755
--- a/wqflask/basicStatistics/BasicStatisticsFunctions.py
+++ b/wqflask/basicStatistics/BasicStatisticsFunctions.py
@@ -13,162 +13,162 @@ 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
+    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)
+    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)
+    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)
+    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')
+    filename= webqtlUtil.genRandStr("nP_")
+    c.save(webqtlConfig.IMGDIR+filename, format='gif')
 
-	img=HT.Image('/image/'+filename+'.gif',border=0)
+    img=HT.Image('/image/'+filename+'.gif',border=0)
 
-	return img
+    return img
 
 def plotBoxPlot(vals):
 
-	valsOnly = []
-	dataXZ = vals[:]
-	for i in range(len(dataXZ)):
-		valsOnly.append(dataXZ[i][1])
+    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
+    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[:])]
+    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)
+    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"))
+    plotLink = HT.Span("More about ", HT.Href(text="Box Plots", url="http://davidmlane.com/hyperstat/A37797.html", target="_blank", Class="fs13"))
 
-	return img, plotLink
+    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
+    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