Add all the source codes into the github.

1 files changed, 522 insertions, 0 deletions

diff --git a/web/webqtl/heatmap/heatmapPage_GN.py b/web/webqtl/heatmap/heatmapPage_GN.py
new file mode 100755
index 00000000..abc5d8aa
--- /dev/null
+++ b/web/webqtl/heatmap/heatmapPage_GN.py
@@ -0,0 +1,522 @@
+# Copyright (C) University of Tennessee Health Science Center, Memphis, TN.
+#
+# This program is free software: you can redistribute it and/or modify it
+# under the terms of the GNU Affero General Public License
+# as published by the Free Software Foundation, either version 3 of the
+# License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+# See the GNU Affero General Public License for more details.
+#
+# This program is available from Source Forge: at GeneNetwork Project
+# (sourceforge.net/projects/genenetwork/).
+#
+# Contact Drs. Robert W. Williams and Xiaodong Zhou (2010)
+# at rwilliams@uthsc.edu and xzhou15@uthsc.edu
+#
+#
+#
+# This module is used by GeneNetwork project (www.genenetwork.org)
+#
+# Created by GeneNetwork Core Team 2010/08/10
+#
+# Last updated by GeneNetwork Core Team 2010/10/20
+
+import os
+import string
+import piddle as pid
+import cPickle
+from htmlgen import HTMLgen2 as HT
+
+from base.templatePage import templatePage
+from base import webqtlConfig
+from base.webqtlTrait import webqtlTrait
+from utility import webqtlUtil 
+from utility import Plot
+import slink
+
+
+# XZ, 09/09/2008: After adding several traits to collection, click "QTL Heatmap" button,
+# XZ, 09/09/2008: This class will generate what you see.
+#########################################
+#      QTL heatmap Page
+#########################################
+class heatmapPage(templatePage):
+
+        labelFont=pid.Font(ttf="tahoma",size=14,bold=0)
+
+        topHeight = 0
+
+        def __init__(self,fd):
+
+                templatePage.__init__(self, fd)
+
+                if not self.openMysql():
+                        return
+                if not fd.genotype:
+                        fd.readGenotype()
+
+
+                self.searchResult = fd.formdata.getvalue('searchResult')
+
+                if not self.searchResult:
+                        templatePage.__init__(self, fd)
+                        heading = 'QTL Heatmap'
+                        detail = ['You need to select at least two traits in order to generate correlation matrix.']
+                        self.error(heading=heading,detail=detail)
+                        return
+                if type("1") == type(self.searchResult):
+                        self.searchResult = string.split(self.searchResult,'\t')
+
+
+                if self.searchResult:
+                        if len(self.searchResult) > webqtlConfig.MAXCORR:
+                                heading = 'QTL Heatmap'
+                                detail = ['In order to display the QTL heat map properly, do not select more than %d traits for analysis.' % webqtlConfig.MAXCORR]
+                                self.error(heading=heading,detail=detail)
+                                return
+
+                        traitList = []
+                        traitDataList = []
+                        for item in self.searchResult:
+                                thisTrait = webqtlTrait(fullname=item, cursor=self.cursor)
+                                thisTrait.retrieveInfo()
+                                thisTrait.retrieveData(fd.strainlist)
+                                traitList.append(thisTrait)
+                                traitDataList.append(thisTrait.exportData(fd.strainlist))
+                else:
+                        heading = 'QTL Heatmap'
+                        detail = [HT.Font('Error : ',color='red'),HT.Font('Error occurs while retrieving data from database.',color='black')]
+                        self.error(heading=heading,detail=detail)
+                        return
+
+                self.colorScheme = fd.formdata.getvalue('colorScheme')
+                if not self.colorScheme:
+                        self.colorScheme = '1'
+
+                self.dict['title'] = 'QTL heatmap'
+
+                NNN = len(traitList)
+
+                if NNN == 0:
+                        heading = "QTL Heatmap"
+                        detail = ['No trait was selected for %s data set. No QTL heatmap was generated.' % fd.RISet]
+                        self.error(heading=heading,detail=detail)
+                        return
+                elif NNN < 2:
+                        templatePage.__init__(self, fd)
+                        heading = 'QTL Heatmap'
+                        detail = ['You need to select at least two traits in order to generate QTL heatmap.']
+                        self.error(heading=heading,detail=detail)
+                        return
+                else:
+                        #XZ: It's necessory to define canvas here
+                        canvas = pid.PILCanvas(size=(80+NNN*20,880))
+
+                        names = map(webqtlTrait.displayName, traitList)
+
+                        self.targetDescriptionChecked = fd.formdata.getvalue('targetDescriptionCheck', '')
+
+                        #XZ, 7/29/2009: create trait display and find max strWidth
+                        strWidth = 0
+                        for j in range(len(names)):
+                                thisTrait = traitList[j]
+
+                                if self.targetDescriptionChecked:
+                                    if thisTrait.db.type == 'ProbeSet':
+                                        if thisTrait.probe_target_description:
+                                                names[j] += ' [%s at Chr %s @ %2.3fMB, %s]' % (thisTrait.symbol, thisTrait.chr, thisTrait.mb, thisTrait.probe_target_description)
+                                        else:
+                                                names[j] += ' [%s at Chr %s @ %2.3fMB]' % (thisTrait.symbol, thisTrait.chr, thisTrait.mb)
+                                    elif thisTrait.db.type == 'Geno':
+                                        names[j] += ' [Chr %s @ %2.3fMB]' % (thisTrait.chr, thisTrait.mb)
+                                    elif thisTrait.db.type == 'Publish':
+                                        if thisTrait.abbreviation:
+                                            names[j] += ' [%s]' % (thisTrait.abbreviation)
+                                        else:
+                                            pass
+                                    else:
+                                        pass
+
+                                i = canvas.stringWidth(names[j],font=self.labelFont)
+                                if i > strWidth:
+                                        strWidth = i
+
+                        width = NNN*20
+                        xoffset = 40
+                        yoffset = 40
+                        cellHeight = 3
+                        nLoci = reduce(lambda x,y: x+y, map(lambda x: len(x),fd.genotype),0)
+
+                        if nLoci > 2000:
+                                cellHeight = 1
+                        elif nLoci > 1000:
+                                cellHeight = 2
+                        elif nLoci < 200:
+                                cellHeight = 10
+                        else:
+                                pass
+
+                        pos = range(NNN)
+                        neworder = []
+                        BWs = Plot.BWSpectrum()
+                        colors100 = Plot.colorSpectrum()
+                        colors = Plot.colorSpectrum(130)
+                        finecolors = Plot.colorSpectrum(250)
+                        colors100.reverse()
+                        colors.reverse()
+                        finecolors.reverse()
+
+                        scaleFont=pid.Font(ttf="tahoma",size=10,bold=0)
+
+                        self.clusterChecked = fd.formdata.getvalue('clusterCheck', '')
+
+
+                        if not self.clusterChecked: #XZ: this part is for original order
+                                for i in range(len(names)):
+                                        neworder.append((xoffset+20*(i+1), i))
+
+                                canvas = pid.PILCanvas(size=(80+NNN*20+240,80+ self.topHeight +5+5+strWidth+nLoci*cellHeight+80+20*cellHeight))
+
+                                self.drawTraitNameBottom(canvas,names,yoffset,neworder,strWidth)
+                        else: #XZ: this part is to cluster traits
+                                self.topHeight = 400
+                                canvas = pid.PILCanvas(size=(80+NNN*20+240,80+ self.topHeight +5+5+strWidth+nLoci*cellHeight+80+20*cellHeight))
+
+                                corArray = [([0] * (NNN))[:] for i in range(NNN)]
+
+                                nnCorr = len(fd.strainlist)
+
+                                #XZ, 08/04/2009: I commented out pearsonArray, spearmanArray
+                                for i, thisTrait in enumerate(traitList):
+                                    names1 = [thisTrait.db.name, thisTrait.name, thisTrait.cellid]
+                                    for j, thisTrait2 in enumerate(traitList):
+                                            names2 = [thisTrait2.db.name, thisTrait2.name, thisTrait2.cellid]
+                                            if j < i:
+                                                    corr,nOverlap = webqtlUtil.calCorrelation(traitDataList[i],traitDataList[j],nnCorr)
+                                                    if (1-corr) < 0:
+                                                            distance = 0.0
+                                                    else:
+                                                            distance = 1-corr
+                                                    corArray[i][j] = distance
+                                                    corArray[j][i] = distance
+                                            elif j == i:
+                                                    corArray[i][j] = 0.0
+                                            else:
+                                                    pass
+
+                                #XZ, 7/29/2009: The parameter d has info of cluster (group member and distance). The format of d is tricky. Print it out to see it's format.
+                                d = slink.slink(corArray)
+
+                                #XZ, 7/29/2009: Attention: The 'neworder' is changed by the 'draw' function
+                                #XZ, 7/30/2009: Only toppos[1][0] and top[1][1] are used later. Then what toppos[0] is used for? 
+                                toppos = self.draw(canvas,names,d,xoffset,yoffset,neworder)
+                                self.drawTraitNameTop(canvas,names,yoffset,neworder,strWidth)
+
+                                #XZ, 7/29/2009: draw the top vertical line
+                                canvas.drawLine(toppos[1][0],toppos[1][1],toppos[1][0],yoffset)
+
+                                #XZ: draw string 'distance = 1-r'
+                                canvas.drawString('distance = 1-r',neworder[-1][0] + 50, self.topHeight*3/4,font=self.labelFont,angle=90)
+
+                                #draw Scale
+                                scaleFont=pid.Font(ttf="tahoma",size=10,bold=0)
+                                x = neworder[-1][0]
+                                canvas.drawLine(x+5, self.topHeight+yoffset, x+5, yoffset, color=pid.black)
+                                y = 0
+                                while y <=2:
+                                        canvas.drawLine(x+5, self.topHeight*y/2.0+yoffset, x+10, self.topHeight*y/2.0+yoffset)
+                                        canvas.drawString('%2.1f' % (2-y), x+12, self.topHeight*y/2.0+yoffset, font=scaleFont)
+                                        y += 0.5
+
+
+                        chrname = 0
+                        chrnameFont=pid.Font(ttf="tahoma",size=24,bold=0)
+                        Ncol = 0
+
+                        gifmap = HT.Map(name='traitMap')
+
+                        nearestMarkers = self.getNearestMarker(traitList, fd.genotype)
+
+                        # import cPickle
+                        sessionfile = fd.formdata.getvalue("session")
+
+                        if sessionfile:
+                                fp = open(os.path.join(webqtlConfig.TMPDIR, sessionfile + '.session'), 'rb')
+                                permData = cPickle.load(fp)
+                                fp.close()
+                        else:
+                                permData = {}
+
+                        #XZ, 7/31/2009: This for loop is to generate the heatmap
+                        #XZ: draw trait by trait instead of marker by marker
+                        for order in neworder:
+                                #startHeight = 40+400+5+5+strWidth
+                                startHeight = self.topHeight + 40+5+5+strWidth
+                                startWidth = order[0]-5
+                                if Ncol and Ncol % 5 == 0:
+                                        drawStartPixel = 8
+                                else:
+                                        drawStartPixel = 9
+
+                                tempVal = traitDataList[order[1]]
+                                _vals = []
+                                _strains = [] 
+                                for i in range(len(fd.strainlist)):
+                                        if tempVal[i] != None:
+                                                _strains.append(fd.strainlist[i])
+                                                _vals.append(tempVal[i])
+
+                                qtlresult = fd.genotype.regression(strains = _strains, trait = _vals)
+
+                                if sessionfile:
+                                        LRSArray = permData[str(traitList[order[1]])]
+                                else:
+                                        LRSArray = fd.genotype.permutation(strains = _strains, trait = _vals, nperm = 1000)
+                                        permData[str(traitList[order[1]])] = LRSArray
+
+                                sugLRS = LRSArray[369]
+                                sigLRS = LRSArray[949]
+                                prechr = 0
+                                chrstart = 0
+                                nearest = nearestMarkers[order[1]]
+                                midpoint = []
+
+                                for item in qtlresult:
+                                        if item.lrs > webqtlConfig.MAXLRS:
+                                                adjustlrs = webqtlConfig.MAXLRS
+                                        else:
+                                                adjustlrs = item.lrs
+
+                                        if item.locus.chr != prechr:
+                                                if prechr:
+                                                        canvas.drawRect(startWidth-drawStartPixel, startHeight, startWidth+10, startHeight+3,edgeColor=pid.white, edgeWidth=0, fillColor=pid.white)
+                                                        startHeight+= 3
+                                                        if not chrname:
+                                                                canvas.drawString(prechr,xoffset-20,(chrstart+startHeight)/2,font = chrnameFont,color=pid.dimgray)
+                                                prechr = item.locus.chr
+                                                chrstart = startHeight
+                                        if self.colorScheme == '0':
+                                                if adjustlrs <= sugLRS:
+                                                        colorIndex = int(65*adjustlrs/sugLRS)
+                                                else:
+                                                        colorIndex = int(65 + 35*(adjustlrs-sugLRS)/(sigLRS-sugLRS))
+                                                if colorIndex > 99:
+                                                        colorIndex = 99
+                                                colorIndex = colors100[colorIndex]
+                                        elif self.colorScheme == '1':
+                                                sugLRS = LRSArray[369]/2.0
+                                                if adjustlrs <= sugLRS:
+                                                        colorIndex = BWs[20+int(50*adjustlrs/sugLRS)]
+                                                else:
+                                                        if item.additive > 0:
+                                                                colorIndex = int(80 + 50*(adjustlrs-sugLRS)/(sigLRS-sugLRS))
+                                                        else:
+                                                                colorIndex = int(50 - 50*(adjustlrs-sugLRS)/(sigLRS-sugLRS))
+                                                        if colorIndex > 129:
+                                                                colorIndex = 129
+                                                        if colorIndex < 0:
+                                                                colorIndex = 0
+                                                        colorIndex = colors[colorIndex]
+                                        elif self.colorScheme == '2':
+                                                if item.additive > 0:
+                                                        colorIndex = int(150 + 100*(adjustlrs/sigLRS))
+                                                else:
+                                                        colorIndex = int(100 - 100*(adjustlrs/sigLRS))
+                                                if colorIndex > 249:
+                                                        colorIndex = 249
+                                                if colorIndex < 0:
+                                                                colorIndex = 0
+                                                colorIndex = finecolors[colorIndex]
+                                        else:
+                                                colorIndex = pid.white
+
+                                        if startHeight > 1:
+                                                canvas.drawRect(startWidth-drawStartPixel, startHeight, startWidth+10, startHeight+cellHeight,edgeColor=colorIndex, edgeWidth=0, fillColor=colorIndex)
+                                        else:
+                                                canvas.drawLine(startWidth-drawStartPixel, startHeight, startWidth+10, startHeight, Color=colorIndex)
+
+                                        if item.locus.name == nearest:
+                                                midpoint = [startWidth,startHeight-5]
+                                        startHeight+=cellHeight
+
+                                #XZ, map link to trait name and band
+                                COORDS = "%d,%d,%d,%d" %(startWidth-drawStartPixel,self.topHeight+40,startWidth+10,startHeight)
+                                HREF = "javascript:showDatabase2('%s','%s','%s');" % (traitList[order[1]].db.name, traitList[order[1]].name, traitList[order[1]].cellid)
+                                Areas = HT.Area(shape='rect',coords=COORDS,href=HREF, title='%s' % names[order[1]])
+                                gifmap.areas.append(Areas)
+
+                                if midpoint:
+                                        traitPixel = ((midpoint[0],midpoint[1]),(midpoint[0]-6,midpoint[1]+12),(midpoint[0]+6,midpoint[1]+12))
+                                        canvas.drawPolygon(traitPixel,edgeColor=pid.black,fillColor=pid.orange,closed=1)
+
+                                if not chrname:
+                                        canvas.drawString(prechr,xoffset-20,(chrstart+startHeight)/2,font = chrnameFont,color=pid.dimgray)
+                                chrname = 1
+                                Ncol += 1
+
+
+                        #draw Spectrum
+                        startSpect = neworder[-1][0] + 30
+                        startHeight = self.topHeight + 40+5+5+strWidth
+
+                        if self.colorScheme == '0':
+                                for i in range(100):
+                                        canvas.drawLine(startSpect+i,startHeight+20,startSpect+i,startHeight+40,color=colors100[i])
+                                scaleFont=pid.Font(ttf="tahoma",size=10,bold=0)
+                                canvas.drawLine(startSpect,startHeight+45,startSpect,startHeight+39,color=pid.black)
+                                canvas.drawString('LRS = 0',startSpect,startHeight+55,font=scaleFont)
+                                canvas.drawLine(startSpect+64,startHeight+45,startSpect+64,startHeight+39,color=pid.black)
+                                canvas.drawString('Suggestive LRS',startSpect+64,startHeight+55,font=scaleFont)
+                                canvas.drawLine(startSpect+99,startHeight+45,startSpect+99,startHeight+39,color=pid.black)
+                                canvas.drawString('Significant LRS',startSpect+105,startHeight+40,font=scaleFont)
+                        elif self.colorScheme == '1':
+                                for i in range(50):
+                                        canvas.drawLine(startSpect+i,startHeight,startSpect+i,startHeight+40,color=BWs[20+i])
+                                for i in range(50,100):
+                                        canvas.drawLine(startSpect+i,startHeight,startSpect+i,startHeight+20,color=colors[100-i])
+                                        canvas.drawLine(startSpect+i,startHeight+20,startSpect+i,startHeight+40,color=colors[30+i])
+
+                                canvas.drawLine(startSpect,startHeight+45,startSpect,startHeight+39,color=pid.black)
+                                canvas.drawString('LRS = 0',startSpect,startHeight+60,font=scaleFont)
+                                canvas.drawLine(startSpect+50,startHeight+45,startSpect+50,startHeight+39,color=pid.black)
+                                canvas.drawString('0.5*Suggestive LRS',startSpect+50,startHeight+ 60,font=scaleFont)
+                                canvas.drawLine(startSpect+99,startHeight+45,startSpect+99,startHeight+39,color=pid.black)
+                                canvas.drawString('Significant LRS',startSpect+105,startHeight+50,font=scaleFont)
+                                textFont=pid.Font(ttf="verdana",size=18,bold=0)
+                                canvas.drawString('%s +' % fd.ppolar,startSpect+120,startHeight+ 35,font=textFont,color=pid.red)
+                                canvas.drawString('%s +' % fd.mpolar,startSpect+120,startHeight+ 15,font=textFont,color=pid.blue)
+                        elif self.colorScheme == '2':
+                                for i in range(100):
+                                        canvas.drawLine(startSpect+i,startHeight,startSpect+i,startHeight+20,color=finecolors[100-i])
+                                        canvas.drawLine(startSpect+i,startHeight+20,startSpect+i,startHeight+40,color=finecolors[150+i])
+
+                                canvas.drawLine(startSpect,startHeight+45,startSpect,startHeight+39,color=pid.black)
+                                canvas.drawString('LRS = 0',startSpect,startHeight+60,font=scaleFont)
+                                canvas.drawLine(startSpect+99,startHeight+45,startSpect+99,startHeight+39,color=pid.black)
+                                canvas.drawString('Significant LRS',startSpect+105,startHeight+50,font=scaleFont)
+                                textFont=pid.Font(ttf="verdana",size=18,bold=0)
+                                canvas.drawString('%s +' % fd.ppolar,startSpect+120,startHeight+ 35,font=textFont,color=pid.red)
+                                canvas.drawString('%s +' % fd.mpolar,startSpect+120,startHeight+ 15,font=textFont,color=pid.blue)
+
+
+                        filename= webqtlUtil.genRandStr("Heatmap_")
+                        canvas.save(webqtlConfig.IMGDIR+filename, format='png')
+                        img2=HT.Image('/image/'+filename+'.png',border=0,usemap='#traitMap')
+                        imgUrl = 'Right-click or control-click on the link to download this graph as a <a href="/image/%s.png" class="normalsize" target="_blank">PNG file</a>' % filename
+
+                        form = 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':fd.RISet+"Geno",'CellID':'_','RISet':fd.RISet,'searchResult':string.join(self.searchResult,'\t')}
+                        if fd.incparentsf1:
+                                hddn['incparentsf1']='ON'
+                        for key in hddn.keys():
+                                form.append(HT.Input(name=key, value=hddn[key], type='hidden'))
+
+                        heatmap = HT.Input(type='button' ,name='mintmap',value='Redraw QTL Heatmap', onClick="databaseFunc(this.form,'heatmap');",Class="button")
+                        spects = {'0':'Single Spectrum','1':'Grey + Blue + Red','2':'Blue + Red'}
+                        schemeMenu = HT.Select(name='colorScheme')
+                        schemeMenu.append(('Single Spectrum',0))
+                        schemeMenu.append(('Grey + Blue + Red',1))
+                        schemeMenu.append(('Blue + Red',2))
+                        schemeMenu.selected.append(spects[self.colorScheme])
+
+                        clusterCheck= HT.Input(type='checkbox', Class='checkbox', name='clusterCheck',checked=0)
+                        targetDescriptionCheck = HT.Input(type='checkbox', Class='checkbox', name='targetDescriptionCheck',checked=0)
+
+                        form.append(gifmap,schemeMenu, heatmap, HT.P(), clusterCheck, '  Cluster traits  ', targetDescriptionCheck, '  Add description', HT.P(),img2, HT.P(), imgUrl)
+
+                        if not sessionfile:
+                                filename = webqtlUtil.generate_session()
+                                webqtlUtil.dump_session(permData, os.path.join(webqtlConfig.TMPDIR, filename +'.session'))
+                                sessionfile=filename
+
+                        form.append(HT.Input(name='session', value=sessionfile, type='hidden'))
+
+                        heatmapHelp = HT.Input(type='button' ,name='heatmapHelpButton',value='Info', onClick="openNewWin('/heatmap.html');",Class="button")
+
+                        heatmapHeading = HT.Paragraph('QTL Heatmap ', heatmapHelp, Class="title")
+
+                        TD_LR = HT.TD(colspan=2,height=200,width="100%",bgColor='#eeeeee')
+                        TD_LR.append(heatmapHeading, HT.P(),HT.P(),HT.P(),HT.P(),HT.P(),form)
+
+                        self.dict['body'] = str(TD_LR)
+
+        #XZ, 7/31/2009: This function put the order of traits into parameter neworder,
+        #XZ: return the position of the top vertical line of the hierarchical tree, draw the hierarchical tree.
+        def draw(self,canvas,names,d,xoffset,yoffset,neworder):
+                maxDistance = self.topHeight
+                fontoffset = 4    #XZ, 7/31/2009: used only for drawing tree
+
+                if type(d[0]) == type(1) and type(d[1]) == type(1):
+                        neworder.append((xoffset+20,d[0]))
+                        neworder.append((xoffset+40,d[1]))
+                        height = d[2]*maxDistance/2
+                        canvas.drawLine(xoffset+20-fontoffset,maxDistance+yoffset,xoffset+20-fontoffset,maxDistance-height+yoffset)
+                        canvas.drawLine(xoffset+40-fontoffset,maxDistance+yoffset,xoffset+40-fontoffset,maxDistance-height+yoffset)
+                        canvas.drawLine(xoffset+40-fontoffset,maxDistance+yoffset-height,xoffset+20-fontoffset,maxDistance-height+yoffset)
+                        return (40,(xoffset+30-fontoffset,maxDistance-height+yoffset))
+                elif type(d[0]) == type(1):
+                        neworder.append((xoffset+20,d[0]))
+                        d2 = self.draw(canvas,names,d[1],xoffset+20,yoffset,neworder)
+                        height = d[2]*maxDistance/2
+                        canvas.drawLine(xoffset+20-fontoffset,maxDistance+yoffset,xoffset+20-fontoffset,maxDistance-height+yoffset)
+                        canvas.drawLine(d2[1][0],d2[1][1],d2[1][0],maxDistance-height+yoffset)
+                        canvas.drawLine(d2[1][0],maxDistance-height+yoffset,xoffset+20-fontoffset,maxDistance-height+yoffset)
+                        return (20+d2[0],((d2[1][0]+xoffset+20-fontoffset)/2,maxDistance-height+yoffset))
+                elif type(d[1]) == type(1):
+                        d1 = self.draw(canvas,names,d[0],xoffset,yoffset,neworder)
+                        neworder.append((xoffset+d1[0]+20,d[1]))
+                        height = d[2]*maxDistance/2
+                        canvas.drawLine(xoffset+d1[0]+20-fontoffset,maxDistance+yoffset,xoffset+d1[0]+20-fontoffset,maxDistance-height+yoffset)
+                        canvas.drawLine(d1[1][0],d1[1][1],d1[1][0],maxDistance-height+yoffset)
+                        canvas.drawLine(d1[1][0],maxDistance-height+yoffset,xoffset+d1[0]+20-fontoffset,maxDistance-height+yoffset)
+                        return (d1[0]+20,((d1[1][0]+xoffset+d1[0]+20-fontoffset)/2,maxDistance-height+yoffset))
+                else:
+                        d1 = self.draw(canvas,names,d[0],xoffset,yoffset,neworder)
+                        d2 = self.draw(canvas,names,d[1],xoffset+d1[0],yoffset,neworder)
+                        height = d[2]*maxDistance/2
+                        canvas.drawLine(d2[1][0],d2[1][1],d2[1][0],maxDistance-height+yoffset)
+                        canvas.drawLine(d1[1][0],d1[1][1],d1[1][0],maxDistance-height+yoffset)
+                        canvas.drawLine(d1[1][0],maxDistance-height+yoffset,d2[1][0],maxDistance-height+yoffset)
+                        return (d1[0]+d2[0],((d1[1][0]+d2[1][0])/2,maxDistance-height+yoffset))
+
+        #XZ, 7/31/2009: dras trait names
+        def drawTraitNameBottom (self,canvas,names,yoffset,neworder,strWidth):
+                maxDistance = self.topHeight
+
+                for oneOrder in neworder:
+                        canvas.drawString(names[oneOrder[1]],oneOrder[0]-5,maxDistance+yoffset+5+strWidth-canvas.stringWidth(names[oneOrder[1]],font=self.labelFont),font=self.labelFont,color=pid.black,angle=270)
+
+        def drawTraitNameTop (self,canvas,names,yoffset,neworder,strWidth):
+                maxDistance = self.topHeight
+
+                for oneOrder in neworder:
+                        canvas.drawString(names[oneOrder[1]],oneOrder[0]-5,maxDistance+yoffset+5,font=self.labelFont,color=pid.black,angle=270)
+
+
+        def getNearestMarker(self,traitList, genotype):
+                out = []
+                if not genotype.Mbmap:
+                        return [None]* len(traitList)
+                for item in traitList:
+                        try:
+                                nearest = None
+                                for _chr in genotype:
+                                        if _chr.name != item.chr:
+                                                continue
+                                        distance = 1e30
+                                        for _locus in _chr:
+                                                if abs(_locus.Mb-item.mb) < distance:
+                                                        distance = abs(_locus.Mb-item.mb)
+                                                        nearest = _locus.name
+                                out.append(nearest)
+                        except:
+                                out.append(None)
+
+                return out
+
+
+
+