def buildCanvas(self, colorScheme='', targetDescriptionChecked='', clusterChecked='', sessionfile='', genotype=None, strainlist=None, ppolar=None, mpolar=None, traitList=None, traitDataList=None, userPrivilege=None, userName=None): labelFont = pid.Font(ttf="tahoma",size=14,bold=0) topHeight = 0 NNN = len(traitList) #XZ: It's necessory to define canvas here canvas = pid.PILCanvas(size=(80+NNN*20,880)) names = map(webqtlTrait.displayName, traitList) #XZ, 7/29/2009: create trait display and find max strWidth strWidth = 0 for j in range(len(names)): thisTrait = traitList[j] if 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.confidential: if webqtlUtil.hasAccessToConfidentialPhenotypeTrait(privilege=userPrivilege, userName=userName, authorized_users=thisTrait.authorized_users): if thisTrait.post_publication_abbreviation: names[j] += ' [%s]' % (thisTrait.post_publication_abbreviation) else: if thisTrait.pre_publication_abbreviation: names[j] += ' [%s]' % (thisTrait.pre_publication_abbreviation) else: if thisTrait.post_publication_abbreviation: names[j] += ' [%s]' % (thisTrait.post_publication_abbreviation) else: pass i = canvas.stringWidth(names[j], font=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),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) if not 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+ topHeight +5+5+strWidth+nLoci*cellHeight+80+20*cellHeight)) self.drawTraitNameBottom(canvas,names,yoffset,neworder,strWidth,topHeight,labelFont) else: #XZ: this part is to cluster traits topHeight = 400 canvas = pid.PILCanvas(size=(80+NNN*20+240,80+ topHeight +5+5+strWidth+nLoci*cellHeight+80+20*cellHeight)) corArray = [([0] * (NNN))[:] for i in range(NNN)] nnCorr = len(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,topHeight) self.drawTraitNameTop(canvas,names,yoffset,neworder,strWidth,topHeight,labelFont) #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, topHeight*3/4,font=labelFont,angle=90) #draw Scale scaleFont=pid.Font(ttf="tahoma",size=10,bold=0) x = neworder[-1][0] canvas.drawLine(x+5, topHeight+yoffset, x+5, yoffset, color=pid.black) y = 0 while y <=2: canvas.drawLine(x+5, topHeight*y/2.0+yoffset, x+10, topHeight*y/2.0+yoffset) canvas.drawString('%2.1f' % (2-y), x+12, topHeight*y/2.0+yoffset, font=scaleFont) y += 0.5 chrname = 0 chrnameFont=pid.Font(ttf="tahoma",size=24,bold=0) Ncol = 0 nearestMarkers = self.getNearestMarker(traitList, genotype) # import cPickle if sessionfile: fp = open(os.path.join(webqtlConfig.TMPDIR, sessionfile + '.session'), 'rb') permData = cPickle.load(fp) fp.close() else: permData = {} areas = [] #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 = 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(strainlist)): if tempVal[i] != None: _strains.append(strainlist[i]) _vals.append(tempVal[i]) qtlresult = genotype.regression(strains = _strains, trait = _vals) if sessionfile: LRSArray = permData[str(traitList[order[1]])] else: LRSArray = 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 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 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 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,topHeight+40,startWidth+10,startHeight) HREF = "javascript:showDatabase2('%s','%s','%s');" % (traitList[order[1]].db.name, traitList[order[1]].name, traitList[order[1]].cellid) area = (COORDS, HREF, '%s' % names[order[1]]) areas.append(area) 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 = topHeight + 40+5+5+strWidth if 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 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 +' % ppolar,startSpect+120,startHeight+ 35,font=textFont,color=pid.red) canvas.drawString('%s +' % mpolar,startSpect+120,startHeight+ 15,font=textFont,color=pid.blue) elif 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 +' % ppolar,startSpect+120,startHeight+ 35,font=textFont,color=pid.red) canvas.drawString('%s +' % mpolar,startSpect+120,startHeight+ 15,font=textFont,color=pid.blue) filename= webqtlUtil.genRandStr("Heatmap_") canvas.save(webqtlConfig.IMGDIR+filename, format='png') if not sessionfile: sessionfile = webqtlUtil.generate_session() webqtlUtil.dump_session(permData, os.path.join(webqtlConfig.TMPDIR, sessionfile +'.session')) self.filename=filename self.areas=areas self.sessionfile=sessionfile
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)