示例#1
0
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
示例#2
0
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
	def __init__(self, fd):

		templatePage.__init__(self, fd)

		self.initializeDisplayParameters(fd)

		if not fd.genotype:
			fd.readGenotype()

		if fd.allstrainlist:
			mdpchoice = fd.formdata.getvalue('MDPChoice')
			if mdpchoice == "1":
				strainlist = fd.f1list + fd.strainlist
			elif mdpchoice == "2":
				strainlist = []
				strainlist2 = fd.f1list + fd.strainlist
				for strain in fd.allstrainlist:
					if strain not in strainlist2:
						strainlist.append(strain)
				#So called MDP Panel
				if strainlist:
					strainlist = fd.f1list+fd.parlist+strainlist
			else:
				strainlist = fd.allstrainlist
			fd.readData(fd.allstrainlist)
		else:
			mdpchoice = None
			strainlist = fd.strainlist
			fd.readData()

		#if fd.allstrainlist:
		#	fd.readData(fd.allstrainlist)
		#	strainlist = fd.allstrainlist
		#else:
		#	fd.readData()
		#	strainlist = fd.strainlist
		
		
		if not self.openMysql():
			return

		isSampleCorr = 0 #XZ: initial value is false
		isTissueCorr = 0 #XZ: initial value is false

		#Javascript functions (showCorrelationPlot2, showTissueCorrPlot) have made sure the correlation type is either sample correlation or tissue correlation.
		if (self.database and (self.ProbeSetID != 'none')):
			isSampleCorr = 1
		elif (self.X_geneSymbol and self.Y_geneSymbol):
			isTissueCorr = 1
		else:
			heading = "Correlation Type Error"
			detail = ["For the input parameters, GN can not recognize the correlation type is sample correlation or tissue correlation."]
			self.error(heading=heading,detail=detail)
			return


        	TD_LR = HT.TD(colspan=2,height=200,width="100%",bgColor='#eeeeee', align="left", wrap="off")


		dataX=[]
		dataY=[]
		dataZ=[] # shortname
		fullTissueName=[]

		if isTissueCorr:
			dataX, dataY, xlabel, ylabel, dataZ, fullTissueName = self.getTissueLabelsValues(X_geneSymbol=self.X_geneSymbol, Y_geneSymbol=self.Y_geneSymbol, TissueProbeSetFreezeId=self.TissueProbeSetFreezeId)
			plotHeading = HT.Paragraph('Tissue Correlation Scatterplot')
			plotHeading.__setattr__("class","title")

			if self.xAxisLabel == '':
				self.xAxisLabel = xlabel

			if self.yAxisLabel == '':
				self.yAxisLabel = ylabel
			
		if isSampleCorr:
			plotHeading = HT.Paragraph('Sample Correlation Scatterplot')
                        plotHeading.__setattr__("class","title")

			#XZ: retrieve trait 1 info, Y axis
			trait1_data = [] #trait 1 data
			trait1Url = ''

			try:
				Trait1 = webqtlTrait(db=self.database, name=self.ProbeSetID, cellid=self.CellID, cursor=self.cursor)
				Trait1.retrieveInfo()
				Trait1.retrieveData()
			except:
				heading = "Retrieve Data"
				detail = ["The database you just requested has not been established yet."]
				self.error(heading=heading,detail=detail)
				return
	
			trait1_data = Trait1.exportData(strainlist)
			if Trait1.db.type == 'Publish' and Trait1.confidential:
				trait1Url = Trait1.genHTML(dispFromDatabase=1, privilege=self.privilege, userName=self.userName, authorized_users=Trait1.authorized_users)
			else:
				trait1Url = Trait1.genHTML(dispFromDatabase=1)
			
			if self.yAxisLabel == '':
				self.yAxisLabel= '%s : %s' % (Trait1.db.shortname, Trait1.name)
				if Trait1.cellid:
					self.yAxisLabel += ' : ' + Trait1.cellid


			#XZ, retrieve trait 2 info, X axis
			traitdata2 = [] #trait 2 data
			_vals = [] #trait 2 data
			trait2Url = ''

			if ( self.database2 and (self.ProbeSetID2 != 'none') ):
				try:
					Trait2 = webqtlTrait(db=self.database2, name=self.ProbeSetID2, cellid=self.CellID2, cursor=self.cursor)
					Trait2.retrieveInfo()
					Trait2.retrieveData()
				except:
					heading = "Retrieve Data"
					detail = ["The database you just requested has not been established yet."]
					self.error(heading=heading,detail=detail)
					return

				if Trait2.db.type == 'Publish' and Trait2.confidential:
					trait2Url = Trait2.genHTML(dispFromDatabase=1, privilege=self.privilege, userName=self.userName, authorized_users=Trait2.authorized_users)
				else:
					trait2Url = Trait2.genHTML(dispFromDatabase=1)
				traitdata2 = Trait2.exportData(strainlist)
				_vals = traitdata2[:]
				
				if self.xAxisLabel == '':
					self.xAxisLabel = '%s : %s' % (Trait2.db.shortname, Trait2.name)
					if Trait2.cellid:
						self.xAxisLabel += ' : ' + Trait2.cellid
			else:
				for item in strainlist:
					if fd.allTraitData.has_key(item):
						_vals.append(fd.allTraitData[item].val)
					else:
						_vals.append(None)

				if self.xAxisLabel == '':
					if fd.identification:
						self.xAxisLabel = fd.identification
					else:
						self.xAxisLabel = "User Input Data"

				try:
					Trait2 = webqtlTrait(fullname=fd.formdata.getvalue('fullname'), cursor=self.cursor)
					trait2Url = Trait2.genHTML(dispFromDatabase=1)
				except:
					trait2Url = self.xAxisLabel

			if (_vals and trait1_data):
				if len(_vals) != len(trait1_data):
					errors = HT.Blockquote(HT.Font('Error: ',color='red'),HT.Font('The number of traits are inconsistent, Program quit',color='black'))
					errors.__setattr__("class","subtitle")
					TD_LR.append(errors)
					self.dict['body'] = str(TD_LR)
					return

				for i in range(len(_vals)):
					if _vals[i]!= None and trait1_data[i]!= None:
						dataX.append(_vals[i])
						dataY.append(trait1_data[i])
						strainName = strainlist[i]
						if self.showstrains:
							dataZ.append(webqtlUtil.genShortStrainName(RISet=fd.RISet, input_strainName=strainName))
			else:
				heading = "Correlation Plot"
				detail = ['Empty Dataset for sample correlation, please check your data.']
				self.error(heading=heading,detail=detail)
				return


		#XZ: We have gotten all data for both traits.
		if len(dataX) >= self.corrMinInformative:

			if self.rankOrder == 0:
				rankPrimary = 0
				rankSecondary = 1
			else:
				rankPrimary = 1
				rankSecondary = 0

			lineColor = self.setLineColor();
			symbolColor = self.setSymbolColor();
			idColor = self.setIdColor();					
				
			c = pid.PILCanvas(size=(self.plotSize, self.plotSize*0.90))
			data_coordinate = Plot.plotXY(canvas=c, dataX=dataX, dataY=dataY, rank=rankPrimary, dataLabel = dataZ, labelColor=pid.black, lineSize=self.lineSize, lineColor=lineColor, idColor=idColor, idFont=self.idFont, idSize=self.idSize, symbolColor=symbolColor, symbolType=self.symbol, filled=self.filled, symbolSize=self.symbolSize, XLabel=self.xAxisLabel, connectdot=0, YLabel=self.yAxisLabel, title='', fitcurve=self.showline, displayR =1, offset= (90, self.plotSize/20, self.plotSize/10, 90), showLabel = self.showIdentifiers)
				
			if rankPrimary == 1:
				dataXlabel, dataYlabel = webqtlUtil.calRank(xVals=dataX, yVals=dataY, N=len(dataX))
			else:
				dataXlabel, dataYlabel = dataX, dataY
					
			gifmap1 = HT.Map(name='CorrelationPlotImageMap1')
				
			for i, item in enumerate(data_coordinate):
				one_rect_coordinate = "%d, %d, %d, %d" % (item[0] - 5, item[1] - 5, item[0] + 5, item[1] + 5)
				if isTissueCorr:
					one_rect_title = "%s (%s, %s)" % (fullTissueName[i], dataXlabel[i], dataYlabel[i])
				else:
					one_rect_title = "%s (%s, %s)" % (dataZ[i], dataXlabel[i], dataYlabel[i])
				gifmap1.areas.append(HT.Area(shape='rect',coords=one_rect_coordinate, title=one_rect_title) )

			filename= webqtlUtil.genRandStr("XY_")
			c.save(webqtlConfig.IMGDIR+filename, format='gif')
			img1=HT.Image('/image/'+filename+'.gif',border=0, usemap='#CorrelationPlotImageMap1')

			mainForm_1 = HT.Form( cgi= os.path.join(webqtlConfig.CGIDIR, webqtlConfig.SCRIPTFILE), enctype='multipart/form-data', name='showDatabase', submit=HT.Input(type='hidden'))
			hddn = {'FormID':'showDatabase','ProbeSetID':'_','database':'_','CellID':'_','RISet':fd.RISet, 'ProbeSetID2':'_', 'database2':'_', 'CellID2':'_', 'allstrainlist':string.join(fd.strainlist, " "), 'traitList': fd.formdata.getvalue("traitList")}
			if fd.incparentsf1:
				hddn['incparentsf1'] = 'ON'		
			for key in hddn.keys():
				mainForm_1.append(HT.Input(name=key, value=hddn[key], type='hidden'))
			
			if isSampleCorr:
					mainForm_1.append(HT.P(), HT.Blockquote(HT.Strong('X axis:'),HT.Blockquote(trait2Url),HT.Strong('Y axis:'),HT.Blockquote(trait1Url), style='width: %spx;' % self.plotSize, wrap="hard"))
			
			graphForm = HT.Form(cgi= os.path.join(webqtlConfig.CGIDIR, webqtlConfig.SCRIPTFILE), name='MDP_Form',submit=HT.Input(type='hidden'))
			graph_hddn = self.setHiddenParameters(fd, rankPrimary)
			webqtlUtil.exportData(graph_hddn, fd.allTraitData) #XZ: This is necessary to replot with different groups of strains
							
			for key in graph_hddn.keys():
				graphForm.append(HT.Input(name=key, value=graph_hddn[key], type='hidden'))			

			options = self.createOptionsMenu(fd, mdpchoice)	
			
			if (self.showOptions == '0'):
					showOptionsButton = HT.Input(type='button' ,name='optionsButton',value='Hide Options', onClick="showHideOptions();", Class="button")
			else:
					showOptionsButton = HT.Input(type='button' ,name='optionsButton',value='Show Options', onClick="showHideOptions();", Class="button")
					
			# updated by NL: 12-07-2011 add variables for tissue abbreviation page
			if isTissueCorr: 
				graphForm.append(HT.Input(name='shortTissueName', value='', type='hidden'))
				graphForm.append(HT.Input(name='fullTissueName', value='', type='hidden'))
				shortTissueNameStr=string.join(dataZ, ",")
				fullTissueNameStr=string.join(fullTissueName, ",")
		
				tissueAbbrButton=HT.Input(type='button' ,name='tissueAbbrButton',value='Show Abbreviations', onClick="showTissueAbbr('MDP_Form','%s','%s')" % (shortTissueNameStr,fullTissueNameStr), Class="button")
				graphForm.append(showOptionsButton,'&nbsp;&nbsp;&nbsp;&nbsp;',tissueAbbrButton, HT.BR(), HT.BR())
			else:
				graphForm.append(showOptionsButton, HT.BR(), HT.BR())

			graphForm.append(options, HT.BR())				
			graphForm.append(HT.HR(), HT.BR(), HT.P())
						
			TD_LR.append(plotHeading, HT.BR(),graphForm, HT.BR(), gifmap1, HT.P(), img1, HT.P(), mainForm_1)
			TD_LR.append(HT.BR(), HT.HR(color="grey", size=5, width="100%"))



			c = pid.PILCanvas(size=(self.plotSize, self.plotSize*0.90))
			data_coordinate = Plot.plotXY(canvas=c, dataX=dataX, dataY=dataY, rank=rankSecondary, dataLabel = dataZ, labelColor=pid.black,lineColor=lineColor, lineSize=self.lineSize, idColor=idColor, idFont=self.idFont, idSize=self.idSize, symbolColor=symbolColor, symbolType=self.symbol, filled=self.filled, symbolSize=self.symbolSize, XLabel=self.xAxisLabel, connectdot=0, YLabel=self.yAxisLabel,title='', fitcurve=self.showline, displayR =1, offset= (90, self.plotSize/20, self.plotSize/10, 90), showLabel = self.showIdentifiers)

			if rankSecondary == 1:
				dataXlabel, dataYlabel = webqtlUtil.calRank(xVals=dataX, yVals=dataY, N=len(dataX))
			else:
				dataXlabel, dataYlabel = dataX, dataY
				
			gifmap2 = HT.Map(name='CorrelationPlotImageMap2')
				
			for i, item in enumerate(data_coordinate):
				one_rect_coordinate = "%d, %d, %d, %d" % (item[0] - 6, item[1] - 6, item[0] + 6, item[1] + 6)
				if isTissueCorr:
					one_rect_title = "%s (%s, %s)" % (fullTissueName[i], dataXlabel[i], dataYlabel[i])
				else:
					one_rect_title = "%s (%s, %s)" % (dataZ[i], dataXlabel[i], dataYlabel[i])
					
				gifmap2.areas.append(HT.Area(shape='rect',coords=one_rect_coordinate, title=one_rect_title) )

			filename= webqtlUtil.genRandStr("XY_")
			c.save(webqtlConfig.IMGDIR+filename, format='gif')
			img2=HT.Image('/image/'+filename+'.gif',border=0, usemap='#CorrelationPlotImageMap2')

			mainForm_2 = HT.Form( cgi= os.path.join(webqtlConfig.CGIDIR, webqtlConfig.SCRIPTFILE), enctype='multipart/form-data', name='showDatabase2', submit=HT.Input(type='hidden'))
			hddn = {'FormID':'showDatabase2','ProbeSetID':'_','database':'_','CellID':'_','RISet':fd.RISet, 'ProbeSetID2':'_', 'database2':'_', 'CellID2':'_', 'allstrainlist':string.join(fd.strainlist, " "), 'traitList': fd.formdata.getvalue("traitList")}
			if fd.incparentsf1:
				hddn['incparentsf1'] = 'ON'
			for key in hddn.keys():
				mainForm_2.append(HT.Input(name=key, value=hddn[key], type='hidden'))
				
			if isSampleCorr:
				mainForm_2.append(HT.P(), HT.Blockquote(HT.Strong('X axis:'),HT.Blockquote(trait2Url),HT.Strong('Y axis:'),HT.Blockquote(trait1Url), style='width:%spx;' % self.plotSize))

	
			TD_LR.append(HT.BR(), HT.P())
			TD_LR.append('\n', gifmap2, HT.P(), HT.P(), img2, HT.P(), mainForm_2)

			self.dict['body'] = str(TD_LR)
		else:
			heading = "Correlation Plot"
			detail = ['Fewer than %d strain data were entered for %s data set. No statitical analysis has been attempted.' % (self.corrMinInformative, fd.RISet)]
			self.error(heading=heading,detail=detail)
			return
示例#4
0
    def __init__(self, fd):
        templatePage.__init__(self, fd)
        self.initializeDisplayParameters(fd)
        if not fd.genotype:
            fd.readGenotype()
        mdpchoice = None
        strainlist = fd.strainlist
        fd.readData()
        if not self.openMysql():
            return
        isSampleCorr = 1
        isTissueCorr = 0
        TD_LR = HT.TD(colspan=2, height=200, width="100%", bgColor="#eeeeee", align="left", wrap="off")
        dataX = []
        dataY = []
        dataZ = []  # shortname
        fullTissueName = []
        xlabel = ""
        ylabel = ""
        if isSampleCorr:
            plotHeading = HT.Paragraph("Sample Correlation Scatterplot")
            plotHeading.__setattr__("class", "title")

            # XZ: retrieve trait 1 info, Y axis
            trait1_data = []  # trait 1 data
            trait1Url = ""
            try:
                Trait1 = webqtlTrait(db=self.database, name=self.ProbeSetID, cellid=self.CellID, cursor=self.cursor)
                Trait1.retrieveInfo()
                Trait1.retrieveData()
            except:
                heading = "Retrieve Data"
                detail = ["The database you just requested has not been established yet."]
                self.error(heading=heading, detail=detail)
                return
            trait1_data = Trait1.exportData(strainlist)
            trait1Url = Trait1.genHTML(dispFromDatabase=1)
            ylabel = "%s : %s" % (Trait1.db.shortname, Trait1.name)
            if Trait1.cellid:
                ylabel += " : " + Trait1.cellid

                # XZ, retrieve trait 2 info, X axis
            trait2_data = []  # trait 2 data
            trait2Url = ""
            try:
                Trait2 = webqtlTrait(db=self.database2, name=self.ProbeSetID2, cellid=self.CellID2, cursor=self.cursor)
                Trait2.retrieveInfo()
                Trait2.retrieveData()
            except:
                heading = "Retrieve Data"
                detail = ["The database you just requested has not been established yet."]
                self.error(heading=heading, detail=detail)
                return
            trait2_data = Trait2.exportData(strainlist)
            trait2Url = Trait2.genHTML(dispFromDatabase=1)
            xlabel = "%s : %s" % (Trait2.db.shortname, Trait2.name)
            if Trait2.cellid:
                xlabel += " : " + Trait2.cellid

            for strain in Trait1.data.keys():
                if Trait2.data.has_key(strain):
                    dataX.append(Trait2.data[strain].val)
                    dataY.append(Trait1.data[strain].val)
                    if self.showstrains:
                        dataZ.append(webqtlUtil.genShortStrainName(RISet=fd.RISet, input_strainName=strain))

                # XZ: We have gotten all data for both traits.
        if len(dataX) >= self.corrMinInformative:

            if self.rankOrder == 0:
                rankPrimary = 0
                rankSecondary = 1
            else:
                rankPrimary = 1
                rankSecondary = 0

            lineColor = self.setLineColor()
            symbolColor = self.setSymbolColor()
            idColor = self.setIdColor()

            c = pid.PILCanvas(size=(self.plotSize, self.plotSize * 0.90))
            data_coordinate = Plot.plotXY(
                canvas=c,
                dataX=dataX,
                dataY=dataY,
                rank=rankPrimary,
                dataLabel=dataZ,
                labelColor=pid.black,
                lineSize=self.lineSize,
                lineColor=lineColor,
                idColor=idColor,
                idFont=self.idFont,
                idSize=self.idSize,
                symbolColor=symbolColor,
                symbolType=self.symbol,
                filled=self.filled,
                symbolSize=self.symbolSize,
                XLabel=xlabel,
                connectdot=0,
                YLabel=ylabel,
                title="",
                fitcurve=self.showline,
                displayR=1,
                offset=(90, self.plotSize / 20, self.plotSize / 10, 90),
                showLabel=self.showIdentifiers,
            )

            if rankPrimary == 1:
                dataXlabel, dataYlabel = webqtlUtil.calRank(xVals=dataX, yVals=dataY, N=len(dataX))
            else:
                dataXlabel, dataYlabel = dataX, dataY

            gifmap1 = HT.Map(name="CorrelationPlotImageMap1")

            for i, item in enumerate(data_coordinate):
                one_rect_coordinate = "%d, %d, %d, %d" % (item[0] - 5, item[1] - 5, item[0] + 5, item[1] + 5)
                if isTissueCorr:
                    one_rect_title = "%s (%s, %s)" % (fullTissueName[i], dataXlabel[i], dataYlabel[i])
                else:
                    one_rect_title = "%s (%s, %s)" % (dataZ[i], dataXlabel[i], dataYlabel[i])
                gifmap1.areas.append(HT.Area(shape="rect", coords=one_rect_coordinate, title=one_rect_title))

            filename = webqtlUtil.genRandStr("XY_")
            c.save(webqtlConfig.IMGDIR + filename, format="gif")
            img1 = HT.Image("/image/" + filename + ".gif", border=0, usemap="#CorrelationPlotImageMap1")

            mainForm_1 = HT.Form(
                cgi=os.path.join(webqtlConfig.CGIDIR, webqtlConfig.SCRIPTFILE),
                enctype="multipart/form-data",
                name="showDatabase",
                submit=HT.Input(type="hidden"),
            )
            hddn = {
                "FormID": "showDatabase",
                "ProbeSetID": "_",
                "database": "_",
                "CellID": "_",
                "RISet": fd.RISet,
                "ProbeSetID2": "_",
                "database2": "_",
                "CellID2": "_",
                "allstrainlist": string.join(fd.strainlist, " "),
                "traitList": fd.formdata.getvalue("traitList"),
            }
            if fd.incparentsf1:
                hddn["incparentsf1"] = "ON"
            for key in hddn.keys():
                mainForm_1.append(HT.Input(name=key, value=hddn[key], type="hidden"))

            if isSampleCorr:
                mainForm_1.append(
                    HT.P(),
                    HT.Blockquote(
                        HT.Strong("X axis:"),
                        HT.Blockquote(trait2Url),
                        HT.Strong("Y axis:"),
                        HT.Blockquote(trait1Url),
                        style="width: %spx;" % self.plotSize,
                        wrap="hard",
                    ),
                )

            graphForm = HT.Form(
                cgi=os.path.join(webqtlConfig.CGIDIR, webqtlConfig.SCRIPTFILE),
                name="MDP_Form",
                submit=HT.Input(type="hidden"),
            )
            graph_hddn = self.setHiddenParameters(fd, rankPrimary)
            webqtlUtil.exportData(
                graph_hddn, fd.allTraitData
            )  # XZ: This is necessary to replot with different groups of strains

            for key in graph_hddn.keys():
                graphForm.append(HT.Input(name=key, value=graph_hddn[key], type="hidden"))

            options = self.createOptionsMenu(fd, mdpchoice)

            if self.showOptions == "0":
                showOptionsButton = HT.Input(
                    type="button",
                    name="optionsButton",
                    value="Hide Options",
                    onClick="showHideOptions();",
                    Class="button",
                )
            else:
                showOptionsButton = HT.Input(
                    type="button",
                    name="optionsButton",
                    value="Show Options",
                    onClick="showHideOptions();",
                    Class="button",
                )

                # updated by NL: 12-07-2011 add variables for tissue abbreviation page
            if isTissueCorr:
                graphForm.append(HT.Input(name="shortTissueName", value="", type="hidden"))
                graphForm.append(HT.Input(name="fullTissueName", value="", type="hidden"))
                shortTissueNameStr = string.join(dataZ, ",")
                fullTissueNameStr = string.join(fullTissueName, ",")

                tissueAbbrButton = HT.Input(
                    type="button",
                    name="tissueAbbrButton",
                    value="Show Abbreviations",
                    onClick="showTissueAbbr('MDP_Form','%s','%s')" % (shortTissueNameStr, fullTissueNameStr),
                    Class="button",
                )
                graphForm.append(showOptionsButton, "&nbsp;&nbsp;&nbsp;&nbsp;", tissueAbbrButton, HT.BR(), HT.BR())
            else:
                graphForm.append(showOptionsButton, HT.BR(), HT.BR())

            graphForm.append(options, HT.BR())
            graphForm.append(HT.HR(), HT.BR(), HT.P())

            TD_LR.append(plotHeading, HT.BR(), graphForm, HT.BR(), gifmap1, HT.P(), img1, HT.P(), mainForm_1)
            TD_LR.append(HT.BR(), HT.HR(color="grey", size=5, width="100%"))

            c = pid.PILCanvas(size=(self.plotSize, self.plotSize * 0.90))
            data_coordinate = Plot.plotXY(
                canvas=c,
                dataX=dataX,
                dataY=dataY,
                rank=rankSecondary,
                dataLabel=dataZ,
                labelColor=pid.black,
                lineColor=lineColor,
                lineSize=self.lineSize,
                idColor=idColor,
                idFont=self.idFont,
                idSize=self.idSize,
                symbolColor=symbolColor,
                symbolType=self.symbol,
                filled=self.filled,
                symbolSize=self.symbolSize,
                XLabel=xlabel,
                connectdot=0,
                YLabel=ylabel,
                title="",
                fitcurve=self.showline,
                displayR=1,
                offset=(90, self.plotSize / 20, self.plotSize / 10, 90),
                showLabel=self.showIdentifiers,
            )

            if rankSecondary == 1:
                dataXlabel, dataYlabel = webqtlUtil.calRank(xVals=dataX, yVals=dataY, N=len(dataX))
            else:
                dataXlabel, dataYlabel = dataX, dataY

            gifmap2 = HT.Map(name="CorrelationPlotImageMap2")

            for i, item in enumerate(data_coordinate):
                one_rect_coordinate = "%d, %d, %d, %d" % (item[0] - 6, item[1] - 6, item[0] + 6, item[1] + 6)
                if isTissueCorr:
                    one_rect_title = "%s (%s, %s)" % (fullTissueName[i], dataXlabel[i], dataYlabel[i])
                else:
                    one_rect_title = "%s (%s, %s)" % (dataZ[i], dataXlabel[i], dataYlabel[i])

                gifmap2.areas.append(HT.Area(shape="rect", coords=one_rect_coordinate, title=one_rect_title))

            filename = webqtlUtil.genRandStr("XY_")
            c.save(webqtlConfig.IMGDIR + filename, format="gif")
            img2 = HT.Image("/image/" + filename + ".gif", border=0, usemap="#CorrelationPlotImageMap2")

            mainForm_2 = HT.Form(
                cgi=os.path.join(webqtlConfig.CGIDIR, webqtlConfig.SCRIPTFILE),
                enctype="multipart/form-data",
                name="showDatabase2",
                submit=HT.Input(type="hidden"),
            )
            hddn = {
                "FormID": "showDatabase2",
                "ProbeSetID": "_",
                "database": "_",
                "CellID": "_",
                "RISet": fd.RISet,
                "ProbeSetID2": "_",
                "database2": "_",
                "CellID2": "_",
                "allstrainlist": string.join(fd.strainlist, " "),
                "traitList": fd.formdata.getvalue("traitList"),
            }
            if fd.incparentsf1:
                hddn["incparentsf1"] = "ON"
            for key in hddn.keys():
                mainForm_2.append(HT.Input(name=key, value=hddn[key], type="hidden"))

            if isSampleCorr:
                mainForm_2.append(
                    HT.P(),
                    HT.Blockquote(
                        HT.Strong("X axis:"),
                        HT.Blockquote(trait2Url),
                        HT.Strong("Y axis:"),
                        HT.Blockquote(trait1Url),
                        style="width:%spx;" % self.plotSize,
                    ),
                )

            TD_LR.append(HT.BR(), HT.P())
            TD_LR.append("\n", gifmap2, HT.P(), HT.P(), img2, HT.P(), mainForm_2)

            self.dict["body"] = str(TD_LR)
        else:
            heading = "Correlation Plot"
            detail = [
                "Fewer than %d strain data were entered for %s data set. No statitical analysis has been attempted."
                % (self.corrMinInformative, fd.RISet)
            ]
            self.error(heading=heading, detail=detail)
            return
    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