def GenReport(self, fd, _genotype, _strains, _vals, _vars= []):
'Create an HTML division which reports any loci which are significantly associated with the submitted trait data.'
if webqtlUtil.ListNotNull(_vars):
qtlresults = _genotype.regression(strains = _strains, trait = _vals, variance = _vars, control = self.controlLocus)
LRSArray = _genotype.permutation(strains = _strains, trait = _vals, variance = _vars, nperm=fd.nperm)
else:
qtlresults = _genotype.regression(strains = _strains, trait = _vals, control = self.controlLocus)
LRSArray = _genotype.permutation(strains = _strains, trait = _vals,nperm=fd.nperm)
myCanvas = pid.PILCanvas(size=(400,300))
#plotBar(myCanvas,10,10,390,290,LRSArray,XLabel='LRS',YLabel='Frequency',title=' Histogram of Permutation Test',identification=fd.identification)
Plot.plotBar(myCanvas, LRSArray,XLabel='LRS',YLabel='Frequency',title=' Histogram of Permutation Test')
filename= webqtlUtil.genRandStr("Reg_")
myCanvas.save(webqtlConfig.IMGDIR+filename, format='gif')
img=HT.Image('/image/'+filename+'.gif',border=0,alt='Histogram of Permutation Test')
if fd.suggestive == None:
fd.suggestive = LRSArray[int(fd.nperm*0.37-1)]
else:
fd.suggestive = float(fd.suggestive)
if fd.significance == None:
fd.significance = LRSArray[int(fd.nperm*0.95-1)]
else:
fd.significance = float(fd.significance)
#########################################
# Permutation Graph
#########################################
permutationHeading = HT.Paragraph('Histogram of Permutation Test')
permutationHeading.__setattr__("class","title")
lrs = HT.Blockquote('Total of %d permutations' % fd.nperm,HT.P(),'Suggestive LRS = %2.2f' % LRSArray[int(fd.nperm*0.37-1)],\
HT.BR(),'Significant LRS = %2.2f' % LRSArray[int(fd.nperm*0.95-1)],HT.BR(),'Highly Significant LRS =%2.2f' % LRSArray[int(fd.nperm*0.99-1)])
permutation = HT.TableLite()
permutation.append(HT.TR(HT.TD(img)),HT.TR(HT.TD(lrs)))
_dispAllLRS = 0
if fd.formdata.getvalue('displayAllLRS'):
_dispAllLRS = 1
qtlresults2 = []
if _dispAllLRS:
filtered = qtlresults[:]
else:
filtered = filter(lambda x, y=fd.suggestive: x.lrs > y, qtlresults)
if len(filtered) == 0:
qtlresults2 = qtlresults[:]
qtlresults2.sort()
filtered = qtlresults2[-10:]
#########################################
# Marker regression report
#########################################
locusFormName = webqtlUtil.genRandStr("fm_")
locusForm = HT.Form(cgi = os.path.join(webqtlConfig.CGIDIR, webqtlConfig.SCRIPTFILE), \
enctype='multipart/form-data', name=locusFormName, submit=HT.Input(type='hidden'))
hddn = {'FormID':'showDatabase','ProbeSetID':'_','database':fd.RISet+"Geno",'CellID':'_', \
'RISet':fd.RISet, 'incparentsf1':'on'}
for key in hddn.keys():
locusForm.append(HT.Input(name=key, value=hddn[key], type='hidden'))
regressionHeading = HT.Paragraph('Marker Regression Report')
regressionHeading.__setattr__("class","title")
if qtlresults2 != []:
report = HT.Blockquote(HT.Font('No association ',color="#FF0000"),HT.Font('with a likelihood ratio statistic greater than %3.1f was found. Here are the top 10 LRSs.' % fd.suggestive,color="#000000"))
else:
report = HT.Blockquote('The following loci in the %s data set have associations with the above trait data.\n' % fd.RISet, HT.P())
report.__setattr__("class","normalsize")
fpText = open('%s.txt' % (webqtlConfig.TMPDIR+filename), 'wb')
textUrl = HT.Href(text = 'Download', url= '/tmp/'+filename+'.txt', target = "_blank", Class='fs12 fwn')
bottomInfo = HT.Paragraph(textUrl, ' result in tab-delimited text format.', HT.BR(), HT.BR(),'LRS values marked with',HT.Font(' * ',color="red"), 'are greater than the significance threshold (specified by you or by permutation test). ' , HT.BR(), HT.BR(), HT.Strong('Additive Effect'), ' is half the difference in the mean phenotype of all cases that are homozygous for one parental allel at this marker minus the mean of all cases that are homozygous for the other parental allele at this marker. ','In the case of %s strains, for example,' % fd.RISet,' A positive additive effect indicates that %s alleles increase trait values. Negative additive effect indicates that %s alleles increase trait values.'% (fd.ppolar,fd.mpolar),Class="fs12 fwn")
c1 = HT.TD('LRS',Class="fs14 fwb ffl b1 cw cbrb")
c2 = HT.TD('Chr',Class="fs14 fwb ffl b1 cw cbrb")
c3 = HT.TD('Mb',Class="fs14 fwb ffl b1 cw cbrb")
c4 = HT.TD('Locus',Class="fs14 fwb ffl b1 cw cbrb")
c5 = HT.TD('Additive Effect',Class="fs14 fwb ffl b1 cw cbrb")
fpText.write('LRS\tChr\tMb\tLocus\tAdditive Effect\n')
hr = HT.TR(c1, c2, c3, c4, c5)
tbl = HT.TableLite(border=0, width="90%", cellpadding=0, cellspacing=0, Class="collap")
tbl.append(hr)
for ii in filtered:
#add by NL 06-22-2011: set LRS to 460 when LRS is infinite,
if ii.lrs==float('inf') or ii.lrs>webqtlConfig.MAXLRS:
LRS=webqtlConfig.MAXLRS #maximum LRS value
else:
LRS=ii.lrs
fpText.write('%2.3f\t%s\t%s\t%s\t%2.3f\n' % (LRS, ii.locus.chr, ii.locus.Mb, ii.locus.name, ii.additive))
if LRS > fd.significance:
c1 = HT.TD('%3.3f*' % LRS, Class="fs13 b1 cbw cr")
else:
c1 = HT.TD('%3.3f' % LRS,Class="fs13 b1 cbw c222")
tbl.append(HT.TR(c1, HT.TD(ii.locus.chr,Class="fs13 b1 cbw c222"), HT.TD(ii.locus.Mb,Class="fs13 b1 cbw c222"), HT.TD(HT.Href(text=ii.locus.name, url = "javascript:showTrait('%s','%s');" % (locusFormName, ii.locus.name), Class='normalsize'), Class="fs13 b1 cbw c222"), HT.TD('%3.3f' % ii.additive,Class="fs13 b1 cbw c222"),bgColor='#eeeeee'))
locusForm.append(tbl)
tbl2 = HT.TableLite(border=0, cellspacing=0, cellpadding=0,width="90%")
tbl2.append(HT.TR(HT.TD(bottomInfo)))
rv=HT.TD(permutationHeading,HT.Center(permutation),regressionHeading,report, HT.Center(locusForm,HT.P(),tbl2,HT.P()),width='55%',valign='top', bgColor='#eeeeee')
return rv
def __init__(self, fd):
LRSFullThresh = 30
LRSInteractThresh = 25
templatePage.__init__(self, fd)
if not fd.genotype:
fd.readData()
incVars = 0
_genotype = fd.genotype_1
_strains, _vals, _vars, N = fd.informativeStrains(_genotype.prgy, incVars)
self.dict['title'] = 'Pair-Scan Plot'
if not self.openMysql():
return
iPermuCheck = fd.formdata.getvalue('directPermuCheckbox')
try:
graphtype = int(fd.formdata.getvalue('graphtype'))
except:
graphtype = 1
try:
graphsort = int(fd.formdata.getvalue('graphSort'))
except:
graphsort = 1
try:
returnIntervalPairNum = int(fd.formdata.getvalue('pairScanReturn'))
except:
returnIntervalPairNum = 50
pairIntro = HT.Blockquote("The graph below displays pair-scan results for the trait ",HT.Strong(" %s" % fd.identification))
if not graphsort:
tblIntro = HT.Blockquote('This table lists LRS scores for the top %d pairs of intervals (Interval 1 on the left and Interval 2 on the right). Pairs are sorted by the "LRS Full" column. Both intervals are defined by proximal and distal markers that flank the single best position.' % returnIntervalPairNum)
else:
tblIntro = HT.Blockquote('This table lists LRS scores for the top %d pairs of intervals (Interval 1 on the left and Interval 2 on the right). Pairs are sorted by the "LRS Interaction" column. Both intervals are defined by proximal and distal markers that flank the single best position.' % returnIntervalPairNum)
try:
thisTrait = webqtlTrait(fullname=fd.formdata.getvalue("fullname"), cursor=self.cursor)
pairIntro.append(' from the database ' , thisTrait.db.genHTML())
except:
pass
pairIntro.append('. The upper left half of the plot highlights any epistatic interactions (corresponding to the column labeled "LRS Interact"). In contrast, the lower right half provides a summary of LRS of the full model, representing cumulative effects of linear and non-linear terms (column labeled "LRS Full"). The WebQTL implementation of the scan for 2-locus epistatic interactions is based on the DIRECT global optimization algorithm developed by ',HT.Href(text ="Ljungberg",url='http://user.it.uu.se/~kl/qtl_software.html',target="_blank", Class = "fs14 fwn"),', Holmgren, and Carlborg (',HT.Href(text = "2004",url='http://bioinformatics.oupjournals.org/cgi/content/abstract/bth175?ijkey=21Pp0pgOuBL6Q&keytype=ref', Class = "fs14 fwn"),').')
main_title = HT.Paragraph("Pair-Scan Results: An Analysis of Epistatic Interactions")
main_title.__setattr__("class","title")
subtitle1 = HT.Paragraph("Pair-Scan Graph")
subtitle3 = HT.Paragraph("Pair-Scan Top LRS")
subtitle1.__setattr__("class","subtitle")
subtitle3.__setattr__("class","subtitle")
self.identification = "unnamed trait"
if fd.identification:
self.identification = fd.identification
self.dict['title'] = self.identification + ' / '+self.dict['title']
#####################################
#
# Remove the Parents & F1 data
#
#####################################
if _vals:
if len(_vals) > webqtlConfig.KMININFORMATIVE:
ResultFull = []
ResultInteract = []
ResultAdd = []
#permutation test
subtitle2 = ''
permuTbl = ''
permuIntro = ''
if iPermuCheck:
subtitle2 = HT.Paragraph("Pair-Scan Permutation Results")
subtitle2.__setattr__("class","subtitle")
permuIntro = HT.Blockquote("Phenotypes were randomly permuted 500 times among strains or individuals and reanalyzed using the pair-scan algorithm. We extracted the single highest LRS for the full model for each of these permuted data sets. The histograms of these highest LRS values provide an empirical way to estimate the probability of obtaining an LRS above suggestive or significant thresholds.")
prtmuTblIntro1 = HT.Paragraph("The following table gives threshold values for Suggestive (P=0.63) and Significant associations (P=0.05) defined by Lander & Kruglyak and for the slightly more stringent P=0.01 level. (The Highly Significant level of Lander & Kruglyak corresponds to P=0.001 and cannot be estimated with 500 permutations.)")
prtmuTblIntro2 = HT.Paragraph("If the full model exceeds the permutation-based Significant threshold, then different models for those locations can be tested by conventional chi-square tests at P<0.01. Interaction is significant if LRS Interact exceeds 6.64 for RI strains or 13.28 for an F2. If interaction is not significant, the two-QTL model is better than a one-QTL model if LRS Additive exceeds LRS 1 or LRS 2 by 6.64 for RI strains or 9.21 for an F2.")
ResultFull, ResultInteract, ResultAdd = direct.permu(webqtlConfig.GENODIR, _vals, _strains, fd.RISet, 500) #XZ, 08/14/2008: add module name webqtlConfig
ResultFull.sort()
ResultInteract.sort()
ResultAdd.sort()
nPermuResult = len(ResultFull)
# draw Histogram
cFull = pid.PILCanvas(size=(400,300))
Plot.plotBar(cFull, ResultFull,XLabel='LRS',YLabel='Frequency',title=' Histogram of LRS Full')
#plotBar(cFull,10,10,390,290,ResultFull,XLabel='LRS',YLabel='Frequency',title=' Histogram of LRS Full')
filename= webqtlUtil.genRandStr("Pair_")
cFull.save(webqtlConfig.IMGDIR+filename, format='gif')
imgFull=HT.Image('/image/'+filename+'.gif',border=0,alt='Histogram of LRS Full')
superPermuTbl = HT.TableLite(border=0, cellspacing=0, cellpadding=0,bgcolor ='#999999')
permuTbl2 = HT.TableLite(border=0, cellspacing= 1, cellpadding=5)
permuTbl2.append(HT.TR(HT.TD(HT.Font('LRS', color = '#FFFFFF')), HT.TD(HT.Font('p = 0.63', color = '#FFFFFF'), width = 150, align='Center'), HT.TD(HT.Font('p = 0.05', color = '#FFFFFF'), width = 150, align='Center'), HT.TD(HT.Font('p = 0.01', color = '#FFFFFF'), width = 150, align='Center'),bgColor='royalblue'))
permuTbl2.append(HT.TR(HT.TD('Full'), HT.TD('%2.1f' % ResultFull[int(nPermuResult*0.37 -1)], align="Center"), HT.TD('%2.1f' % ResultFull[int(nPermuResult*0.95 -1)], align="Center"), HT.TD('%2.1f' % ResultFull[int(nPermuResult*0.99 -1)], align="Center"),bgColor="#eeeeee"))
superPermuTbl.append(HT.TD(HT.TD(permuTbl2)))
permuTbl1 = HT.TableLite(border=0, cellspacing= 0, cellpadding=5,width='100%')
permuTbl1.append(HT.TR(HT.TD(imgFull, align="Center", width = 410), HT.TD(prtmuTblIntro1, superPermuTbl, prtmuTblIntro2, width = 490)))
permuTbl = HT.Center(permuTbl1, HT.P())
#permuTbl.append(HT.TR(HT.TD(HT.BR(), 'LRS Full = %2.1f, ' % ResultFull[int(nPermuResult*0.37 -1)], 'LRS Full = %2.1f, ' % ResultFull[int(nPermuResult*0.95 -1)], 'LRS Full highly significant (p=0.001) = %2.1f, ' % ResultFull[int(nPermuResult*0.999 -1)] , HT.BR(), 'LRS Interact suggestive (p=0.63) = %2.1f, ' % ResultInteract[int(nPermuResult*0.37 -1)], 'LRS Interact significant (p=0.05) = %2.1f, ' % ResultInteract[int(nPermuResult*0.95 -1)], 'LRS Interact = %2.1f, ' % ResultInteract[int(nPermuResult*0.999 -1)] , HT.BR(),'LRS Additive suggestive (p=0.63) = %2.1f, ' % ResultAdd[int(nPermuResult*0.37 -1)], 'LRS Additive significant (p=0.05) = %2.1f, ' % ResultAdd[int(nPermuResult*0.95 -1)], 'LRS Additive highly significant (p=0.001) = %2.1f, ' % ResultAdd[int(nPermuResult*0.999 -1)], HT.BR(), 'Total number of permutation is %d' % nPermuResult, HT.BR(), HT.BR(),colspan=2)))
#tblIntro.append(HT.P(), HT.Center(permuTbl))
#print vals, strains, fd.RISet
d = direct.direct(webqtlConfig.GENODIR, _vals, _strains, fd.RISet, 8000)#XZ, 08/14/2008: add module name webqtlConfig
chrsInfo = d[2]
sum = 0
offsets = [0]
i = 0
for item in chrsInfo:
if i > 0:
offsets.append(sum)
sum += item[0]
i += 1
offsets.append(sum)
#print sum,offset,d[2]
canvasWidth = 880
canvasHeight = 880
if graphtype:
colorAreaWidth = 230
else:
colorAreaWidth = 0
c = pid.PILCanvas(size=(canvasWidth + colorAreaWidth ,canvasHeight))
xoffset = 40
yoffset = 40
width = canvasWidth - xoffset*2
height = canvasHeight - yoffset*2
xscale = width/sum
yscale = height/sum
rectInfo = d[1]
rectInfo.sort(webqtlUtil.cmpLRSFull)
finecolors = Plot.colorSpectrum(250)
finecolors.reverse()
regLRS = [0]*height
#draw LRS Full
for item in rectInfo:
LRSFull,LRSInteract,LRSa,LRSb,chras,chram,chrae,chrbs,chrbm,chrbe,chra,chrb,flanka,flankb = item
if LRSFull > 30:
dcolor = pid.red
elif LRSFull > 20:
dcolor = pid.orange
elif LRSFull > 10:
dcolor = pid.olivedrab
elif LRSFull > 0:
dcolor = pid.grey
else:
LRSFull = 0
dcolor = pid.grey
chras += offsets[chra]
chram += offsets[chra]
chrae += offsets[chra]
chrbs += offsets[chrb]
chrbm += offsets[chrb]
chrbe += offsets[chrb]
regLRSD = int(chram*yscale)
if regLRS[regLRSD] < LRSa:
regLRS[regLRSD] = LRSa
regLRSD = int(chrbm*yscale)
if regLRS[regLRSD] < LRSb:
regLRS[regLRSD] = LRSb
if graphtype:
colorIndex = int(LRSFull *250 /LRSFullThresh)
if colorIndex >= 250:
colorIndex = 249
dcolor = finecolors[colorIndex]
if chra != chrb or ((chrbe - chrae) > 10 and (chrbs - chras) > 10):
c.drawRect(xoffset+chrbs*xscale,yoffset+height-chras*yscale,xoffset+chrbe*xscale,yoffset+height-chrae*yscale,edgeColor=dcolor,fillColor=dcolor,edgeWidth = 0)
else:
c.drawPolygon([(xoffset+chrbs*xscale,yoffset+height-chras*yscale),(xoffset+chrbe*xscale,yoffset+height-chras*yscale),(xoffset+chrbe*xscale,yoffset+height-chrae*yscale)],edgeColor=dcolor,fillColor=dcolor,edgeWidth = 0,closed =1)
else:
c.drawCross(xoffset+chrbm*xscale,yoffset+height-chram*yscale,color=dcolor,size=2)
#draw Marker Regression LRS
if graphtype:
"""
maxLRS = max(regLRS)
pts = []
i = 0
for item in regLRS:
pts.append((xoffset+width+35+item*50/maxLRS, yoffset+height-i))
i += 1
c.drawPolygon(pts,edgeColor=pid.blue,edgeWidth=1,closed=0)
"""
LRS1Thresh = 16.2
i = 0
for item in regLRS:
colorIndex = int(item *250 /LRS1Thresh)
if colorIndex >= 250:
colorIndex = 249
dcolor = finecolors[colorIndex]
c.drawLine(xoffset+width+35,yoffset+height-i,xoffset+width+55,yoffset+height-i,color=dcolor)
i += 1
labelFont=pid.Font(ttf="arial",size=20,bold=0)
c.drawString('Single Locus Regression',xoffset+width+90,yoffset+height, font = labelFont,color=pid.dimgray,angle=90)
#draw LRS Interact
rectInfo.sort(webqtlUtil.cmpLRSInteract)
for item in rectInfo:
LRSFull,LRSInteract,LRSa,LRSb,chras,chram,chrae,chrbs,chrbm,chrbe,chra,chrb,flanka,flankb = item
if LRSInteract > 30:
dcolor = pid.red
elif LRSInteract > 20:
dcolor = pid.orange
elif LRSInteract > 10:
dcolor = pid.olivedrab
elif LRSInteract > 0:
dcolor = pid.grey
else:
LRSInteract = 0
dcolor = pid.grey
chras += offsets[chra]
chram += offsets[chra]
chrae += offsets[chra]
chrbs += offsets[chrb]
chrbm += offsets[chrb]
chrbe += offsets[chrb]
if graphtype:
colorIndex = int(LRSInteract *250 / LRSInteractThresh )
if colorIndex >= 250:
colorIndex = 249
dcolor = finecolors[colorIndex]
if chra != chrb or ((chrbe - chrae) > 10 and (chrbs - chras) > 10):
c.drawRect(xoffset+chras*xscale,yoffset+height-chrbs*yscale,xoffset+chrae*xscale,yoffset+height-chrbe*yscale,edgeColor=dcolor,fillColor=dcolor,edgeWidth = 0)
else:
c.drawPolygon([(xoffset+chras*xscale,yoffset+height-chrbs*yscale),(xoffset+chras*xscale,yoffset+height-chrbe*yscale),(xoffset+chrae*xscale,yoffset+height-chrbe*yscale)],edgeColor=dcolor,fillColor=dcolor,edgeWidth = 0,closed =1)
else:
c.drawCross(xoffset+chram*xscale,yoffset+height-chrbm*yscale,color=dcolor,size=2)
#draw chromosomes label
labelFont=pid.Font(ttf="tahoma",size=24,bold=0)
i = 0
for item in chrsInfo:
strWidth = c.stringWidth(item[1],font=labelFont)
c.drawString(item[1],xoffset+offsets[i]*xscale +(item[0]*xscale-strWidth)/2,canvasHeight -15,font = labelFont,color=pid.dimgray)
c.drawString(item[1],xoffset+offsets[i]*xscale +(item[0]*xscale-strWidth)/2,yoffset-10,font = labelFont,color=pid.dimgray)
c.drawString(item[1],xoffset-strWidth-5,yoffset+height - offsets[i]*yscale -(item[0]*yscale-22)/2,font = labelFont,color=pid.dimgray)
c.drawString(item[1],canvasWidth-xoffset+5,yoffset+height - offsets[i]*yscale -(item[0]*yscale-22)/2,font = labelFont,color=pid.dimgray)
i += 1
c.drawRect(xoffset,yoffset,xoffset+width,yoffset+height)
for item in offsets:
c.drawLine(xoffset,yoffset+height-item*yscale,xoffset+width,yoffset+height-item*yscale)
c.drawLine(xoffset+item*xscale,yoffset,xoffset+item*xscale,yoffset+height)
#draw pngMap
pngMap = HT.Map(name='pairPlotMap')
#print offsets, len(offsets)
for i in range(len(offsets)-1):
for j in range(len(offsets)-1):
COORDS = "%d,%d,%d,%d" %(xoffset+offsets[i]*xscale, yoffset+height-offsets[j+1]*yscale, xoffset+offsets[i+1]*xscale, yoffset+height-offsets[j]*yscale)
HREF = "javascript:showPairPlot(%d,%d);" % (i,j)
Areas = HT.Area(shape='rect',coords=COORDS,href=HREF)
pngMap.areas.append(Areas)
#draw spectrum
if graphtype:
i = 0
labelFont=pid.Font(ttf="tahoma",size=14,bold=0)
middleoffsetX = 180
for dcolor in finecolors:
if i % 50 == 0:
c.drawLine(xoffset+ width +middleoffsetX-15 , height + yoffset -i, xoffset+ width +middleoffsetX-20,height + yoffset -i, color=pid.black)
c.drawString('%d' % int(LRSInteractThresh*i/250.0),xoffset+ width+ middleoffsetX-40,height + yoffset -i +5, font = labelFont,color=pid.black)
c.drawLine(xoffset+ width +middleoffsetX+15 , height + yoffset -i, xoffset+ width +middleoffsetX+20 ,height + yoffset -i, color=pid.black)
c.drawString('%d' % int(LRSFullThresh*i/250.0),xoffset+ width + middleoffsetX+25,height + yoffset -i +5, font = labelFont,color=pid.black)
c.drawLine(xoffset+ width +middleoffsetX-15 , height + yoffset -i, xoffset+ width +middleoffsetX+15 ,height + yoffset -i, color=dcolor)
i += 1
if i % 50 == 0:
i -= 1
c.drawLine(xoffset+ width +middleoffsetX-15 , height + yoffset -i, xoffset+ width +middleoffsetX-20,height + yoffset -i, color=pid.black)
c.drawString('%d' % ceil(LRSInteractThresh*i/250.0),xoffset+ width + middleoffsetX-40,height + yoffset -i +5, font = labelFont,color=pid.black)
c.drawLine(xoffset+ width +middleoffsetX+15 , height + yoffset -i, xoffset+ width +middleoffsetX+20 ,height + yoffset -i, color=pid.black)
c.drawString('%d' % ceil(LRSFullThresh*i/250.0),xoffset+ width + middleoffsetX+25,height + yoffset -i +5, font = labelFont,color=pid.black)
labelFont=pid.Font(ttf="verdana",size=20,bold=0)
c.drawString('LRS Interaction',xoffset+ width + middleoffsetX-50,height + yoffset, font = labelFont,color=pid.dimgray,angle=90)
c.drawString('LRS Full',xoffset+ width + middleoffsetX+50,height + yoffset, font = labelFont,color=pid.dimgray,angle=90)
filename= webqtlUtil.genRandStr("Pair_")
c.save(webqtlConfig.IMGDIR+filename, format='png')
img2=HT.Image('/image/'+filename+'.png',border=0,usemap='#pairPlotMap')
form0 = HT.Form( cgi= os.path.join(webqtlConfig.CGIDIR, webqtlConfig.SCRIPTFILE), enctype='multipart/form-data', name='showPairPlot', submit=HT.Input(type='hidden'))
hddn0 = {'FormID':'pairPlot','Chr_A':'_','Chr_B':'','idata':string.join(map(str, _vals), ','),'istrain':string.join(_strains, ','),'RISet':fd.RISet}
for key in hddn0.keys():
form0.append(HT.Input(name=key, value=hddn0[key], type='hidden'))
form0.append(img2, pngMap)
mainfmName = webqtlUtil.genRandStr("fm_")
txtform = HT.Form( cgi= os.path.join(webqtlConfig.CGIDIR, webqtlConfig.SCRIPTFILE), enctype='multipart/form-data', name=mainfmName, submit=HT.Input(type='hidden'))
hddn = {'FormID':'showDatabase','ProbeSetID':'_','database':fd.RISet+"Geno",'CellID':'_','RISet':fd.RISet}
#XZ, Aug 11, 2010: The variable traitStrains is not assigned right values before (should not be assigned fd.strainlist).
#hddn['traitStrains'] = string.join(fd.strainlist, ',')
hddn['traitStrains'] = string.join(_strains, ',')
hddn['traitValues'] = string.join(map(str, _vals), ',')
hddn['interval1'] = ''
hddn['interval2'] = ''
if fd.incparentsf1:
hddn['incparentsf1']='ON'
for key in hddn.keys():
txtform.append(HT.Input(name=key, value=hddn[key], type='hidden'))
tbl = HT.TableLite(Class="collap", cellspacing=1, cellpadding=5,width=canvasWidth + colorAreaWidth)
c1 = HT.TD('Interval 1',colspan=3,align="Center", Class="fs13 fwb ffl b1 cw cbrb")
c2 = HT.TD('Interval 2',colspan=3,align="Center", Class="fs13 fwb ffl b1 cw cbrb")
c11 = HT.TD('Position',rowspan=2,align="Center", Class="fs13 fwb ffl b1 cw cbrb")
c12 = HT.TD('Flanking Markers',colspan=2,align="Center", Class="fs13 fwb ffl b1 cw cbrb")
c111 = HT.TD('Proximal',align="Center", Class="fs13 fwb ffl b1 cw cbrb")
c112 = HT.TD('Distal',align="Center", Class="fs13 fwb ffl b1 cw cbrb")
c3 = HT.TD('LRS Full',rowspan=3,align="Center", Class="fs13 fwb ffl b1 cw cbrb")
c4 = HT.TD('LRS Additive',rowspan=3,align="Center", Class="fs13 fwb ffl b1 cw cbrb")
c5 = HT.TD('LRS Interact',rowspan=3,align="Center", Class="fs13 fwb ffl b1 cw cbrb")
c6 = HT.TD('LRS 1',rowspan=3,align="Center", Class="fs13 fwb ffl b1 cw cbrb")
c7 = HT.TD('LRS 2',rowspan=3,align="Center", Class="fs13 fwb ffl b1 cw cbrb")
tbl.append(HT.TR(c1,c3,c4,c5,c6,c7,c2))
tbl.append(HT.TR(c11,c12,c11,c12))
tbl.append(HT.TR(c111,c112,c111,c112))
if not graphsort: #Sort by LRS Full
rectInfo.sort(webqtlUtil.cmpLRSFull)
rectInfoReturned = rectInfo[len(rectInfo) - returnIntervalPairNum:]
rectInfoReturned.reverse()
for item in rectInfoReturned:
LRSFull,LRSInteract,LRSa,LRSb,chras,chram,chrae,chrbs,chrbm,chrbe,chra,chrb,flanka,flankb = item
LRSAdditive = LRSFull - LRSInteract
flanka1,flanka2 = string.split(flanka)
flankb1,flankb2 = string.split(flankb)
urla1 = HT.Href(text = flanka1, url = "javascript:showTrait('%s','%s');" % (mainfmName, flanka1),Class= "fs12 fwn")
urla2 = HT.Href(text = flanka2, url = "javascript:showTrait('%s','%s');" % (mainfmName, flanka2),Class= "fs12 fwn")
urlb1 = HT.Href(text = flankb1, url = "javascript:showTrait('%s','%s');" % (mainfmName, flankb1),Class= "fs12 fwn")
urlb2 = HT.Href(text = flankb2, url = "javascript:showTrait('%s','%s');" % (mainfmName, flankb2),Class= "fs12 fwn")
urlGenGraph = HT.Href(text = "Plot", url = "javascript:showCateGraph('%s', '%s %s %2.3f', '%s %s %2.3f');" % (mainfmName, flanka1, flanka2, chram, flankb1, flankb2, chrbm),Class= "fs12 fwn")
tr1 = HT.TR(
HT.TD('Chr %s @ %2.1f cM ' % (chrsInfo[chra][1],chram),Class= "fs12 b1 fwn"),
HT.TD(urla1,Class= "fs12 b1 fwn"),
HT.TD(urla2,Class= "fs12 b1 fwn"),
HT.TD('%2.3f ' % LRSFull, urlGenGraph,Class= "fs12 b1 fwn"),
HT.TD('%2.3f' % LRSAdditive,Class= "fs12 b1 fwn"),
HT.TD('%2.3f' % LRSInteract,Class= "fs12 b1 fwn"),
HT.TD('%2.3f' % LRSa,Class= "fs12 b1 fwn"),
HT.TD('%2.3f' % LRSb,Class= "fs12 b1 fwn"),
HT.TD('Chr %s @ %2.1f cM' % (chrsInfo[chrb][1],chrbm),Class= "fs12 b1 fwn"),
HT.TD(urlb1,Class= "fs12 b1 fwn"),
HT.TD(urlb2,Class= "fs12 b1 fwn"))
tbl.append(tr1)
plotType1 = HT.Input(type="radio", name="plotType", value ="Dot", checked=1)
plotType2 = HT.Input(type="radio", name="plotType", value ="Box")
plotText = HT.Paragraph("Plot Type : ", plotType1, " Dot ", plotType2, " Box", )
txtform.append(plotText, tbl)
TD_LR = HT.TD(colspan=2,height=200,width="100%",bgColor='#eeeeee')
TD_LR.append(main_title,HT.Blockquote(subtitle1, pairIntro, HT.P(), HT.Center(form0,HT.P())),HT.Blockquote(subtitle2, permuIntro,HT.P(), HT.Center(permuTbl)), HT.Blockquote(subtitle3, tblIntro, HT.P(),HT.Center(txtform), HT.P()))
self.dict['body'] = str(TD_LR)
else:
heading = "Direct Plot"
detail = ['Fewer than %d strain data were entered for %s data set. No statitical analysis has been attempted.' % (webqtlConfig.KMININFORMATIVE, fd.RISet)]
self.error(heading=heading,detail=detail)
return
else:
heading = "Direct Plot"
detail = ['Empty data set, please check your data.']
self.error(heading=heading,detail=detail)
return
