Ran reindent.py recursively on wqflask directory

1 files changed, 299 insertions, 303 deletions

diff --git a/wqflask/basicStatistics/BasicStatisticsPage_alpha.py b/wqflask/basicStatistics/BasicStatisticsPage_alpha.py
index 4ba9d54a..365143db 100755
--- a/wqflask/basicStatistics/BasicStatisticsPage_alpha.py
+++ b/wqflask/basicStatistics/BasicStatisticsPage_alpha.py
@@ -43,306 +43,302 @@ from dbFunction import webqtlDatabaseFunction
 
 class BasicStatisticsPage_alpha(templatePage):
 
-	plotMinInformative = 4
-
-	def __init__(self, fd):
-
-		templatePage.__init__(self, fd)
-
-		if not fd.genotype:
-			fd.readGenotype()
-			strainlist2 = fd.strainlist
-
-		if fd.allstrainlist:
-			strainlist2 = fd.allstrainlist
-
-		fd.readData(strainlist2)
-
-		specialStrains = []
-		setStrains = []
-		for item in strainlist2:
-			if item not in fd.strainlist and item.find('F1') < 0:
-				specialStrains.append(item)
-			else:
-				setStrains.append(item)
-		specialStrains.sort()
-		#So called MDP Panel
-		if specialStrains:
-			specialStrains = fd.f1list+fd.parlist+specialStrains
-
-		self.plotType = fd.formdata.getvalue('ptype', '0')
-		plotStrains = strainlist2
-		if specialStrains:
-			if self.plotType == '1':
-				plotStrains = setStrains
-			if self.plotType == '2':
-				plotStrains = specialStrains
-
-		self.dict['title'] = 'Basic Statistics'
-		if not self.openMysql():
-			return
-
-		self.showstrains = 1
-		self.identification = "unnamed trait"
-
-		self.fullname = fd.formdata.getvalue('fullname', '')
-		if self.fullname:
-			self.Trait = webqtlTrait(fullname=self.fullname, cursor=self.cursor)
-			self.Trait.retrieveInfo()
-		else:
-			self.Trait = None
-
-		if fd.identification:
-			self.identification = fd.identification
-			self.dict['title'] = self.identification + ' / '+self.dict['title']
-		TD_LR = HT.TD(height=200,width="100%",bgColor='#eeeeee')
-
-		##should not display Variance, but cannot convert Variance to SE
-		#print plotStrains, fd.allTraitData.keys()
-		if len(fd.allTraitData) > 0:
-			vals=[]
-			InformData = []
-			for _strain in plotStrains:
-				if fd.allTraitData.has_key(_strain):
-					_val, _var = fd.allTraitData[_strain].val, fd.allTraitData[_strain].var
-					if _val != None:
-						vals.append([_strain, _val, _var])
-						InformData.append(_val)
-
-			if len(vals) >= self.plotMinInformative:
-				supertable2 = HT.TableLite(border=0, cellspacing=0, cellpadding=5,width="800")
-
-				staIntro1 = HT.Paragraph("The table and plots below list the basic statistical analysis result of trait",HT.Strong(" %s" % self.identification))
-
-				#####
-				#anova
-				#####
-				traitmean, traitmedian, traitvar, traitstdev, traitsem, N = reaper.anova(InformData)
-				TDStatis = HT.TD(width="360", valign="top")
-				tbl2 = HT.TableLite(cellpadding=5, cellspacing=0, Class="collap")
-				dataXZ = vals[:]
-				dataXZ.sort(self.cmpValue)
-				tbl2.append(HT.TR(HT.TD("Statistic",align="center", Class="fs14 fwb ffl b1 cw cbrb", width = 200),
-						HT.TD("Value", align="center", Class="fs14 fwb ffl b1 cw cbrb", width = 140)))
-				tbl2.append(HT.TR(HT.TD("N of Cases",align="center", Class="fs13 b1 cbw c222"),
-						HT.TD(N,nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
-				tbl2.append(HT.TR(HT.TD("Mean",align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
-						HT.TD("%2.3f" % traitmean,nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
-				tbl2.append(HT.TR(HT.TD("Median",align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
-						HT.TD("%2.3f" % traitmedian,nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
-				#tbl2.append(HT.TR(HT.TD("Variance",align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
-				#		HT.TD("%2.3f" % traitvar,nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
-				tbl2.append(HT.TR(HT.TD("SEM",align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
-						HT.TD("%2.3f" % traitsem,nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
-				tbl2.append(HT.TR(HT.TD("SD",align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
-						HT.TD("%2.3f" % traitstdev,nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
-				tbl2.append(HT.TR(HT.TD("Minimum",align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
-						HT.TD("%s" % dataXZ[0][1],nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
-				tbl2.append(HT.TR(HT.TD("Maximum",align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
-						HT.TD("%s" % dataXZ[-1][1],nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
-				if self.Trait and self.Trait.db.type == 'ProbeSet':
-					#IRQuest = HT.Href(text="Interquartile Range", url=webqtlConfig.glossaryfile +"#Interquartile",target="_blank", Class="fs14")
-					#IRQuest.append(HT.BR())
-					#IRQuest.append(" (fold difference)")
-					tbl2.append(HT.TR(HT.TD("Range (log2)",align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
-						HT.TD("%2.3f" % (dataXZ[-1][1]-dataXZ[0][1]),nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
-					tbl2.append(HT.TR(HT.TD(HT.Span("Range (fold)"),align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
-						HT.TD("%2.2f" % pow(2.0,(dataXZ[-1][1]-dataXZ[0][1])), nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
-					tbl2.append(HT.TR(HT.TD(HT.Span("Quartile Range",HT.BR()," (fold difference)"),align="center", 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",align="center", Class="fs13 b1 cbw c222")))
-
-					# (Lei Yan)
-					# 2008/12/19
-					self.Trait.retrieveData()
-					#XZ, 04/01/2009: don't try to get H2 value for probe.
-					if self.Trait.cellid:
-						pass
-					else:
-						self.cursor.execute("SELECT DataId, h2 from ProbeSetXRef WHERE DataId = %d" % self.Trait.mysqlid)
-						dataid, heritability = self.cursor.fetchone()
-						if heritability:
-							tbl2.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:
-							tbl2.append(HT.TR(HT.TD(HT.Span("Heritability"),align="center", Class="fs13 b1 cbw c222",nowrap="yes"),HT.TD("NaN", nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
-
-					# Lei Yan
-					# 2008/12/19
-
-				TDStatis.append(tbl2)
-
-				plotHeight = 220
-				plotWidth = 120
-				xLeftOffset = 60
-				xRightOffset = 25
-				yTopOffset = 20
-				yBottomOffset = 53
-
-				canvasHeight = plotHeight + yTopOffset + yBottomOffset
-				canvasWidth = plotWidth + xLeftOffset + xRightOffset
-				canvas = pid.PILCanvas(size=(canvasWidth,canvasHeight))
-				XXX = [('', InformData[:])]
-
-				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)
-
-				#supertable2.append(HT.TR(HT.TD(staIntro1, colspan=3 )))
-				tb = HT.TableLite(border=0, cellspacing=0, cellpadding=0)
-				tb.append(HT.TR(HT.TD(img, align="left", style="border: 1px solid #999999; padding:0px;")))
-				supertable2.append(HT.TR(TDStatis, HT.TD(tb)))
-
-				dataXZ = vals[:]
-				tvals = []
-				tnames = []
-				tvars = []
-				for i in range(len(dataXZ)):
-					tvals.append(dataXZ[i][1])
-					tnames.append(webqtlUtil.genShortStrainName(fd, 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='%s by Case (sorted by name)' % self.identification, sLabel = sLabel, barSpace = sw)
-
-				filename= webqtlUtil.genRandStr("Bar_")
-				c.save(webqtlConfig.IMGDIR+filename, format='gif')
-				img0=HT.Image('/image/'+filename+'.gif',border=0)
-
-				dataXZ = vals[:]
-				dataXZ.sort(self.cmpValue)
-				tvals = []
-				tnames = []
-				tvars = []
-				for i in range(len(dataXZ)):
-					tvals.append(dataXZ[i][1])
-					tnames.append(webqtlUtil.genShortStrainName(fd, dataXZ[i][0]))
-					tvars.append(dataXZ[i][2])
-
-				c = pid.PILCanvas(size=(plotWidth,plotHeight))
-				Plot.plotBarText(c, tvals, tnames, variance=tvars, YLabel='Value', title='%s by Case (ranked)' % self.identification, sLabel = sLabel, barSpace = sw)
-
-				filename= webqtlUtil.genRandStr("Bar_")
-				c.save(webqtlConfig.IMGDIR+filename, format='gif')
-				img1=HT.Image('/image/'+filename+'.gif',border=0)
-				
-				# Lei Yan
-				# 05/18/2009
-				# report
-				
-				title = HT.Paragraph('REPORT on the variation of Shh (or PCA Composite Trait XXXX) (sonic hedgehog) in the (insert Data set name) of (insert Species informal name, e.g., Mouse, Rat, Human, Barley, Arabidopsis)', Class="title")
-				header = HT.Paragraph('''This report was generated by GeneNetwork on May 11, 2009, at 11.20 AM using the Basic Statistics module (v 1.0) and data from the Hippocampus Consortium M430v2 (Jun06) PDNN data set. For more details and updates on this data set please link to URL:get Basic Statistics''')
-				hr = HT.HR()
-				p1 = HT.Paragraph('''Trait values for Shh were taken from the (insert Database name, Hippocampus Consortium M430v2 (Jun06) PDNN). GeneNetwork contains data for NN (e.g., 99) cases. In general, data are averages for each case. A summary of mean, median, and the range of these data are provided in Table 1 and in the box plot (Figure 1). Data for individual cases are provided in Figure 2A and 2B, often with error bars (SEM). ''')
-				p2 = HT.Paragraph('''Trait values for Shh range 5.1-fold: from a low of 8.2 (please round value) in 129S1/SvImJ to a high of 10.6 (please round value) in BXD9.  The interquartile range (the difference between values closest to the 25% and 75% levels) is a more modest 1.8-fold. The mean value is XX. ''')
-				t1 = HT.Paragraph('''Table 1.  Summary of Shh data from the Hippocampus Consortium M430v2 (june06) PDNN data set''')
-				f1 = HT.Paragraph('''Figure 1. ''')
-				f1.append(HT.Href(text="Box plot", url="http://davidmlane.com/hyperstat/A37797.html", target="_blank", Class="fs14"))
-				f1.append(HT.Text(''' of Shh data from the Hippocampus Consortium M430v2 (june06) PDNN data set'''))
-				f2A = HT.Paragraph('''Figure 2A: Bar chart of Shh data ordered by case from the Hippocampus Consortium M430v2 (june06) PDNN data set''')
-				f2B = HT.Paragraph('''Figure 2B: Bar chart of Shh values ordered by from the Hippocampus Consortium M430v2 (june06) PDNN data set''')
-				TD_LR.append(HT.Blockquote(title, HT.P(), header, hr, p1, HT.P(), p2, HT.P(), supertable2, t1, f1, HT.P(), img0, f2A, HT.P(), img1, f2B))
-				self.dict['body'] = str(TD_LR)
-			else:
-				heading = "Basic Statistics"
-				detail = ['Fewer than %d case data were entered for %s data set. No statitical analysis has been attempted.' % (self.plotMinInformative, fd.RISet)]
-				self.error(heading=heading,detail=detail)
-				return
-		else:
-			heading = "Basic Statistics"
-			detail = ['Empty data set, please check your data.']
-			self.error(heading=heading,detail=detail)
-			return
-
-	def traitInfo(self, fd, specialStrains = None):
-		species = webqtlDatabaseFunction.retrieveSpecies(cursor=self.cursor, RISet=fd.RISet)
-		heading2 = HT.Paragraph(HT.Strong('Population: '), "%s %s" % (species.title(), fd.RISet) , HT.BR())
-		if self.Trait:
-			trait_url = HT.Href(text=self.Trait.name, url = os.path.join(webqtlConfig.CGIDIR, webqtlConfig.SCRIPTFILE) + \
-					"?FormID=showDatabase&incparentsf1=1&database=%s&ProbeSetID=%s" % (self.Trait.db.name, self.Trait.name), \
-					target='_blank', Class="fs13 fwn")
-			heading2.append(HT.Strong("Database: "),
-				HT.Href(text=self.Trait.db.fullname, url = webqtlConfig.INFOPAGEHREF % self.Trait.db.name ,
-					target='_blank',Class="fs13 fwn"),HT.BR())
-			if self.Trait.db.type == 'ProbeSet':
-				heading2.append(HT.Strong('Trait ID: '), trait_url, HT.BR(),
-					HT.Strong("Gene Symbol: "), HT.Italic('%s' % self.Trait.symbol,id="green"),HT.BR())
-				if self.Trait.chr and self.Trait.mb:
-					heading2.append(HT.Strong("Location: "), 'Chr %s @ %s Mb' % (self.Trait.chr, self.Trait.mb))
-			elif self.Trait.db.type == 'Geno':
-				heading2.append(HT.Strong('Locus : '), trait_url, HT.BR())
-				#heading2.append(HT.Strong("Gene Symbol: "), HT.Italic('%s' % self.Trait.Symbol,id="green"),HT.BR())
-				if self.Trait.chr and self.Trait.mb:
-					heading2.append(HT.Strong("Location: "), 'Chr %s @ %s Mb' % (self.Trait.chr, self.Trait.mb))
-			elif self.Trait.db.type == 'Publish':
-				heading2.append(HT.Strong('Record ID: '), trait_url, HT.BR())
-				heading2.append(HT.Strong('Phenotype: '), self.Trait.phenotype, HT.BR())
-				heading2.append(HT.Strong('Author: '), self.Trait.authors, HT.BR())
-			elif self.Trait.db.type == 'Temp':
-				heading2.append(HT.Strong('Description: '), self.Trait.description, HT.BR())
-				#heading2.append(HT.Strong('Author: '), self.Trait.authors, HT.BR())
-			else:
-				pass
-		else:
-			heading2.append(HT.Strong("Trait Name: "), fd.identification)
-
-		if specialStrains:
-			mdpform = HT.Form(cgi= os.path.join(webqtlConfig.CGIDIR, webqtlConfig.SCRIPTFILE), name='MDP_Form',submit=HT.Input(type='hidden'))
-			mdphddn = {'FormID':'dataEditing', 'submitID':'basicStatistics','RISet':fd.RISet, "allstrainlist":string.join(fd.allstrainlist, " "), "ptype":self.plotType, 'identification':fd.identification, "incparentsf1":1}
-			if self.fullname:  mdphddn['fullname'] = self.fullname
-			webqtlUtil.exportData(mdphddn, fd.allTraitData)
-			for key in mdphddn.keys():
-				mdpform.append(HT.Input(name=key, value=mdphddn[key], type='hidden'))
-			btn0 = HT.Input(type='button' ,name='',value='All Cases',onClick="this.form.ptype.value=0;submit();", Class="button")
-			btn1 = HT.Input(type='button' ,name='',value='%s Only' % fd.RISet,onClick="this.form.ptype.value=1;submit();", Class="button")
-			btn2 = HT.Input(type='button' ,name='',value='MDP Only', onClick="this.form.ptype.value=2;submit();", Class="button")
-			mdpform.append(btn0)
-			mdpform.append(btn1)
-			mdpform.append(btn2)
-			heading2.append(HT.P(), mdpform)
-
-		return HT.Span(heading2)
-
-	def calSD(self,var):
-		try:
-			return sqrt(abs(var))
-		except:
-			return None
-
-
-	def cmpValue(self,A,B):
-		try:
-			if A[1] < B[1]:
-				return -1
-			elif A[1] == B[1]:
-				return 0
-			else:
-				return 1
-		except:
-			return 0
-
-
-
-
+    plotMinInformative = 4
+
+    def __init__(self, fd):
+
+        templatePage.__init__(self, fd)
+
+        if not fd.genotype:
+            fd.readGenotype()
+            strainlist2 = fd.strainlist
+
+        if fd.allstrainlist:
+            strainlist2 = fd.allstrainlist
+
+        fd.readData(strainlist2)
+
+        specialStrains = []
+        setStrains = []
+        for item in strainlist2:
+            if item not in fd.strainlist and item.find('F1') < 0:
+                specialStrains.append(item)
+            else:
+                setStrains.append(item)
+        specialStrains.sort()
+        #So called MDP Panel
+        if specialStrains:
+            specialStrains = fd.f1list+fd.parlist+specialStrains
+
+        self.plotType = fd.formdata.getvalue('ptype', '0')
+        plotStrains = strainlist2
+        if specialStrains:
+            if self.plotType == '1':
+                plotStrains = setStrains
+            if self.plotType == '2':
+                plotStrains = specialStrains
+
+        self.dict['title'] = 'Basic Statistics'
+        if not self.openMysql():
+            return
+
+        self.showstrains = 1
+        self.identification = "unnamed trait"
+
+        self.fullname = fd.formdata.getvalue('fullname', '')
+        if self.fullname:
+            self.Trait = webqtlTrait(fullname=self.fullname, cursor=self.cursor)
+            self.Trait.retrieveInfo()
+        else:
+            self.Trait = None
+
+        if fd.identification:
+            self.identification = fd.identification
+            self.dict['title'] = self.identification + ' / '+self.dict['title']
+        TD_LR = HT.TD(height=200,width="100%",bgColor='#eeeeee')
+
+        ##should not display Variance, but cannot convert Variance to SE
+        #print plotStrains, fd.allTraitData.keys()
+        if len(fd.allTraitData) > 0:
+            vals=[]
+            InformData = []
+            for _strain in plotStrains:
+                if fd.allTraitData.has_key(_strain):
+                    _val, _var = fd.allTraitData[_strain].val, fd.allTraitData[_strain].var
+                    if _val != None:
+                        vals.append([_strain, _val, _var])
+                        InformData.append(_val)
+
+            if len(vals) >= self.plotMinInformative:
+                supertable2 = HT.TableLite(border=0, cellspacing=0, cellpadding=5,width="800")
+
+                staIntro1 = HT.Paragraph("The table and plots below list the basic statistical analysis result of trait",HT.Strong(" %s" % self.identification))
+
+                #####
+                #anova
+                #####
+                traitmean, traitmedian, traitvar, traitstdev, traitsem, N = reaper.anova(InformData)
+                TDStatis = HT.TD(width="360", valign="top")
+                tbl2 = HT.TableLite(cellpadding=5, cellspacing=0, Class="collap")
+                dataXZ = vals[:]
+                dataXZ.sort(self.cmpValue)
+                tbl2.append(HT.TR(HT.TD("Statistic",align="center", Class="fs14 fwb ffl b1 cw cbrb", width = 200),
+                                HT.TD("Value", align="center", Class="fs14 fwb ffl b1 cw cbrb", width = 140)))
+                tbl2.append(HT.TR(HT.TD("N of Cases",align="center", Class="fs13 b1 cbw c222"),
+                                HT.TD(N,nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
+                tbl2.append(HT.TR(HT.TD("Mean",align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
+                                HT.TD("%2.3f" % traitmean,nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
+                tbl2.append(HT.TR(HT.TD("Median",align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
+                                HT.TD("%2.3f" % traitmedian,nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
+                #tbl2.append(HT.TR(HT.TD("Variance",align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
+                #               HT.TD("%2.3f" % traitvar,nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
+                tbl2.append(HT.TR(HT.TD("SEM",align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
+                                HT.TD("%2.3f" % traitsem,nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
+                tbl2.append(HT.TR(HT.TD("SD",align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
+                                HT.TD("%2.3f" % traitstdev,nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
+                tbl2.append(HT.TR(HT.TD("Minimum",align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
+                                HT.TD("%s" % dataXZ[0][1],nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
+                tbl2.append(HT.TR(HT.TD("Maximum",align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
+                                HT.TD("%s" % dataXZ[-1][1],nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
+                if self.Trait and self.Trait.db.type == 'ProbeSet':
+                    #IRQuest = HT.Href(text="Interquartile Range", url=webqtlConfig.glossaryfile +"#Interquartile",target="_blank", Class="fs14")
+                    #IRQuest.append(HT.BR())
+                    #IRQuest.append(" (fold difference)")
+                    tbl2.append(HT.TR(HT.TD("Range (log2)",align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
+                            HT.TD("%2.3f" % (dataXZ[-1][1]-dataXZ[0][1]),nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
+                    tbl2.append(HT.TR(HT.TD(HT.Span("Range (fold)"),align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
+                            HT.TD("%2.2f" % pow(2.0,(dataXZ[-1][1]-dataXZ[0][1])), nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
+                    tbl2.append(HT.TR(HT.TD(HT.Span("Quartile Range",HT.BR()," (fold difference)"),align="center", 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",align="center", Class="fs13 b1 cbw c222")))
+
+                    # (Lei Yan)
+                    # 2008/12/19
+                    self.Trait.retrieveData()
+                    #XZ, 04/01/2009: don't try to get H2 value for probe.
+                    if self.Trait.cellid:
+                        pass
+                    else:
+                        self.cursor.execute("SELECT DataId, h2 from ProbeSetXRef WHERE DataId = %d" % self.Trait.mysqlid)
+                        dataid, heritability = self.cursor.fetchone()
+                        if heritability:
+                            tbl2.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:
+                            tbl2.append(HT.TR(HT.TD(HT.Span("Heritability"),align="center", Class="fs13 b1 cbw c222",nowrap="yes"),HT.TD("NaN", nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
+
+                    # Lei Yan
+                    # 2008/12/19
+
+                TDStatis.append(tbl2)
+
+                plotHeight = 220
+                plotWidth = 120
+                xLeftOffset = 60
+                xRightOffset = 25
+                yTopOffset = 20
+                yBottomOffset = 53
+
+                canvasHeight = plotHeight + yTopOffset + yBottomOffset
+                canvasWidth = plotWidth + xLeftOffset + xRightOffset
+                canvas = pid.PILCanvas(size=(canvasWidth,canvasHeight))
+                XXX = [('', InformData[:])]
+
+                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)
+
+                #supertable2.append(HT.TR(HT.TD(staIntro1, colspan=3 )))
+                tb = HT.TableLite(border=0, cellspacing=0, cellpadding=0)
+                tb.append(HT.TR(HT.TD(img, align="left", style="border: 1px solid #999999; padding:0px;")))
+                supertable2.append(HT.TR(TDStatis, HT.TD(tb)))
+
+                dataXZ = vals[:]
+                tvals = []
+                tnames = []
+                tvars = []
+                for i in range(len(dataXZ)):
+                    tvals.append(dataXZ[i][1])
+                    tnames.append(webqtlUtil.genShortStrainName(fd, 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='%s by Case (sorted by name)' % self.identification, sLabel = sLabel, barSpace = sw)
+
+                filename= webqtlUtil.genRandStr("Bar_")
+                c.save(webqtlConfig.IMGDIR+filename, format='gif')
+                img0=HT.Image('/image/'+filename+'.gif',border=0)
+
+                dataXZ = vals[:]
+                dataXZ.sort(self.cmpValue)
+                tvals = []
+                tnames = []
+                tvars = []
+                for i in range(len(dataXZ)):
+                    tvals.append(dataXZ[i][1])
+                    tnames.append(webqtlUtil.genShortStrainName(fd, dataXZ[i][0]))
+                    tvars.append(dataXZ[i][2])
+
+                c = pid.PILCanvas(size=(plotWidth,plotHeight))
+                Plot.plotBarText(c, tvals, tnames, variance=tvars, YLabel='Value', title='%s by Case (ranked)' % self.identification, sLabel = sLabel, barSpace = sw)
+
+                filename= webqtlUtil.genRandStr("Bar_")
+                c.save(webqtlConfig.IMGDIR+filename, format='gif')
+                img1=HT.Image('/image/'+filename+'.gif',border=0)
+
+                # Lei Yan
+                # 05/18/2009
+                # report
+
+                title = HT.Paragraph('REPORT on the variation of Shh (or PCA Composite Trait XXXX) (sonic hedgehog) in the (insert Data set name) of (insert Species informal name, e.g., Mouse, Rat, Human, Barley, Arabidopsis)', Class="title")
+                header = HT.Paragraph('''This report was generated by GeneNetwork on May 11, 2009, at 11.20 AM using the Basic Statistics module (v 1.0) and data from the Hippocampus Consortium M430v2 (Jun06) PDNN data set. For more details and updates on this data set please link to URL:get Basic Statistics''')
+                hr = HT.HR()
+                p1 = HT.Paragraph('''Trait values for Shh were taken from the (insert Database name, Hippocampus Consortium M430v2 (Jun06) PDNN). GeneNetwork contains data for NN (e.g., 99) cases. In general, data are averages for each case. A summary of mean, median, and the range of these data are provided in Table 1 and in the box plot (Figure 1). Data for individual cases are provided in Figure 2A and 2B, often with error bars (SEM). ''')
+                p2 = HT.Paragraph('''Trait values for Shh range 5.1-fold: from a low of 8.2 (please round value) in 129S1/SvImJ to a high of 10.6 (please round value) in BXD9.  The interquartile range (the difference between values closest to the 25% and 75% levels) is a more modest 1.8-fold. The mean value is XX. ''')
+                t1 = HT.Paragraph('''Table 1.  Summary of Shh data from the Hippocampus Consortium M430v2 (june06) PDNN data set''')
+                f1 = HT.Paragraph('''Figure 1. ''')
+                f1.append(HT.Href(text="Box plot", url="http://davidmlane.com/hyperstat/A37797.html", target="_blank", Class="fs14"))
+                f1.append(HT.Text(''' of Shh data from the Hippocampus Consortium M430v2 (june06) PDNN data set'''))
+                f2A = HT.Paragraph('''Figure 2A: Bar chart of Shh data ordered by case from the Hippocampus Consortium M430v2 (june06) PDNN data set''')
+                f2B = HT.Paragraph('''Figure 2B: Bar chart of Shh values ordered by from the Hippocampus Consortium M430v2 (june06) PDNN data set''')
+                TD_LR.append(HT.Blockquote(title, HT.P(), header, hr, p1, HT.P(), p2, HT.P(), supertable2, t1, f1, HT.P(), img0, f2A, HT.P(), img1, f2B))
+                self.dict['body'] = str(TD_LR)
+            else:
+                heading = "Basic Statistics"
+                detail = ['Fewer than %d case data were entered for %s data set. No statitical analysis has been attempted.' % (self.plotMinInformative, fd.RISet)]
+                self.error(heading=heading,detail=detail)
+                return
+        else:
+            heading = "Basic Statistics"
+            detail = ['Empty data set, please check your data.']
+            self.error(heading=heading,detail=detail)
+            return
+
+    def traitInfo(self, fd, specialStrains = None):
+        species = webqtlDatabaseFunction.retrieveSpecies(cursor=self.cursor, RISet=fd.RISet)
+        heading2 = HT.Paragraph(HT.Strong('Population: '), "%s %s" % (species.title(), fd.RISet) , HT.BR())
+        if self.Trait:
+            trait_url = HT.Href(text=self.Trait.name, url = os.path.join(webqtlConfig.CGIDIR, webqtlConfig.SCRIPTFILE) + \
+                            "?FormID=showDatabase&incparentsf1=1&database=%s&ProbeSetID=%s" % (self.Trait.db.name, self.Trait.name), \
+                            target='_blank', Class="fs13 fwn")
+            heading2.append(HT.Strong("Database: "),
+                    HT.Href(text=self.Trait.db.fullname, url = webqtlConfig.INFOPAGEHREF % self.Trait.db.name ,
+                            target='_blank',Class="fs13 fwn"),HT.BR())
+            if self.Trait.db.type == 'ProbeSet':
+                heading2.append(HT.Strong('Trait ID: '), trait_url, HT.BR(),
+                        HT.Strong("Gene Symbol: "), HT.Italic('%s' % self.Trait.symbol,id="green"),HT.BR())
+                if self.Trait.chr and self.Trait.mb:
+                    heading2.append(HT.Strong("Location: "), 'Chr %s @ %s Mb' % (self.Trait.chr, self.Trait.mb))
+            elif self.Trait.db.type == 'Geno':
+                heading2.append(HT.Strong('Locus : '), trait_url, HT.BR())
+                #heading2.append(HT.Strong("Gene Symbol: "), HT.Italic('%s' % self.Trait.Symbol,id="green"),HT.BR())
+                if self.Trait.chr and self.Trait.mb:
+                    heading2.append(HT.Strong("Location: "), 'Chr %s @ %s Mb' % (self.Trait.chr, self.Trait.mb))
+            elif self.Trait.db.type == 'Publish':
+                heading2.append(HT.Strong('Record ID: '), trait_url, HT.BR())
+                heading2.append(HT.Strong('Phenotype: '), self.Trait.phenotype, HT.BR())
+                heading2.append(HT.Strong('Author: '), self.Trait.authors, HT.BR())
+            elif self.Trait.db.type == 'Temp':
+                heading2.append(HT.Strong('Description: '), self.Trait.description, HT.BR())
+                #heading2.append(HT.Strong('Author: '), self.Trait.authors, HT.BR())
+            else:
+                pass
+        else:
+            heading2.append(HT.Strong("Trait Name: "), fd.identification)
+
+        if specialStrains:
+            mdpform = HT.Form(cgi= os.path.join(webqtlConfig.CGIDIR, webqtlConfig.SCRIPTFILE), name='MDP_Form',submit=HT.Input(type='hidden'))
+            mdphddn = {'FormID':'dataEditing', 'submitID':'basicStatistics','RISet':fd.RISet, "allstrainlist":string.join(fd.allstrainlist, " "), "ptype":self.plotType, 'identification':fd.identification, "incparentsf1":1}
+            if self.fullname:  mdphddn['fullname'] = self.fullname
+            webqtlUtil.exportData(mdphddn, fd.allTraitData)
+            for key in mdphddn.keys():
+                mdpform.append(HT.Input(name=key, value=mdphddn[key], type='hidden'))
+            btn0 = HT.Input(type='button' ,name='',value='All Cases',onClick="this.form.ptype.value=0;submit();", Class="button")
+            btn1 = HT.Input(type='button' ,name='',value='%s Only' % fd.RISet,onClick="this.form.ptype.value=1;submit();", Class="button")
+            btn2 = HT.Input(type='button' ,name='',value='MDP Only', onClick="this.form.ptype.value=2;submit();", Class="button")
+            mdpform.append(btn0)
+            mdpform.append(btn1)
+            mdpform.append(btn2)
+            heading2.append(HT.P(), mdpform)
+
+        return HT.Span(heading2)
+
+    def calSD(self,var):
+        try:
+            return sqrt(abs(var))
+        except:
+            return None
+
+
+    def cmpValue(self,A,B):
+        try:
+            if A[1] < B[1]:
+                return -1
+            elif A[1] == B[1]:
+                return 0
+            else:
+                return 1
+        except:
+            return 0