def plotBoxPlot(vals):
valsOnly = []
dataXZ = vals[:]
for i in range(len(dataXZ)):
valsOnly.append(dataXZ[i][1])
plotHeight = 320
plotWidth = 220
xLeftOffset = 60
xRightOffset = 40
yTopOffset = 40
yBottomOffset = 60
canvasHeight = plotHeight + yTopOffset + yBottomOffset
canvasWidth = plotWidth + xLeftOffset + xRightOffset
canvas = pid.PILCanvas(size=(canvasWidth,canvasHeight))
XXX = [('', valsOnly[:])]
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)
plotLink = HT.Span("More about ", HT.Href(text="Box Plots", url="http://davidmlane.com/hyperstat/A37797.html", target="_blank", Class="fs13"))
return img, plotLink
def __init__(self, fd):
templatePage.__init__(self, fd)
if not fd.genotype:
fd.readGenotype()
strainlist2 = fd.strainlist
if fd.allstrainlist:
strainlist2 = fd.allstrainlist
fd.readData(strainlist2)
specialStrains = []
setStrains = []
for item in strainlist2:
if item not in fd.strainlist and item.find('F1') < 0:
specialStrains.append(item)
else:
setStrains.append(item)
specialStrains.sort()
#So called MDP Panel
if specialStrains:
specialStrains = fd.f1list+fd.parlist+specialStrains
self.plotType = fd.formdata.getvalue('ptype', '0')
plotStrains = strainlist2
if specialStrains:
if self.plotType == '1':
plotStrains = setStrains
if self.plotType == '2':
plotStrains = specialStrains
self.dict['title'] = 'Basic Statistics'
if not self.openMysql():
return
self.showstrains = 1
self.identification = "unnamed trait"
self.fullname = fd.formdata.getvalue('fullname', '')
if self.fullname:
self.Trait = webqtlTrait(fullname=self.fullname, cursor=self.cursor)
self.Trait.retrieveInfo()
else:
self.Trait = None
if fd.identification:
self.identification = fd.identification
self.dict['title'] = self.identification + ' / '+self.dict['title']
TD_LR = HT.TD(height=200,width="100%",bgColor='#eeeeee')
##should not display Variance, but cannot convert Variance to SE
#print plotStrains, fd.allTraitData.keys()
if len(fd.allTraitData) > 0:
vals=[]
InformData = []
for _strain in plotStrains:
if fd.allTraitData.has_key(_strain):
_val, _var = fd.allTraitData[_strain].val, fd.allTraitData[_strain].var
if _val != None:
vals.append([_strain, _val, _var])
InformData.append(_val)
if len(vals) >= self.plotMinInformative:
supertable2 = HT.TableLite(border=0, cellspacing=0, cellpadding=5,width="800")
staIntro1 = HT.Paragraph("The table and plots below list the basic statistical analysis result of trait",HT.Strong(" %s" % self.identification))
#####
#anova
#####
traitmean, traitmedian, traitvar, traitstdev, traitsem, N = reaper.anova(InformData)
TDStatis = HT.TD(width="360", valign="top")
tbl2 = HT.TableLite(cellpadding=5, cellspacing=0, Class="collap")
dataXZ = vals[:]
dataXZ.sort(self.cmpValue)
tbl2.append(HT.TR(HT.TD("Statistic",align="center", Class="fs14 fwb ffl b1 cw cbrb", width = 200),
HT.TD("Value", align="center", Class="fs14 fwb ffl b1 cw cbrb", width = 140)))
tbl2.append(HT.TR(HT.TD("N of Cases",align="center", Class="fs13 b1 cbw c222"),
HT.TD(N,nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
tbl2.append(HT.TR(HT.TD("Mean",align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
HT.TD("%2.3f" % traitmean,nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
tbl2.append(HT.TR(HT.TD("Median",align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
HT.TD("%2.3f" % traitmedian,nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
#tbl2.append(HT.TR(HT.TD("Variance",align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
# HT.TD("%2.3f" % traitvar,nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
tbl2.append(HT.TR(HT.TD("SEM",align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
HT.TD("%2.3f" % traitsem,nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
tbl2.append(HT.TR(HT.TD("SD",align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
HT.TD("%2.3f" % traitstdev,nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
tbl2.append(HT.TR(HT.TD("Minimum",align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
HT.TD("%s" % dataXZ[0][1],nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
tbl2.append(HT.TR(HT.TD("Maximum",align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
HT.TD("%s" % dataXZ[-1][1],nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
if self.Trait and self.Trait.db.type == 'ProbeSet':
#IRQuest = HT.Href(text="Interquartile Range", url=webqtlConfig.glossaryfile +"#Interquartile",target="_blank", Class="fs14")
#IRQuest.append(HT.BR())
#IRQuest.append(" (fold difference)")
tbl2.append(HT.TR(HT.TD("Range (log2)",align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
HT.TD("%2.3f" % (dataXZ[-1][1]-dataXZ[0][1]),nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
tbl2.append(HT.TR(HT.TD(HT.Span("Range (fold)"),align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
HT.TD("%2.2f" % pow(2.0,(dataXZ[-1][1]-dataXZ[0][1])), nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
tbl2.append(HT.TR(HT.TD(HT.Span("Quartile Range",HT.BR()," (fold difference)"),align="center", Class="fs13 b1 cbw c222",nowrap="yes"),
HT.TD("%2.2f" % pow(2.0,(dataXZ[int((N-1)*3.0/4.0)][1]-dataXZ[int((N-1)/4.0)][1])), nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
# (Lei Yan)
# 2008/12/19
self.Trait.retrieveData()
#XZ, 04/01/2009: don't try to get H2 value for probe.
if self.Trait.cellid:
pass
else:
self.cursor.execute("SELECT DataId, h2 from ProbeSetXRef WHERE DataId = %d" % self.Trait.mysqlid)
dataid, heritability = self.cursor.fetchone()
if heritability:
tbl2.append(HT.TR(HT.TD(HT.Span("Heritability"),align="center", Class="fs13 b1 cbw c222",nowrap="yes"),HT.TD("%s" % heritability, nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
else:
tbl2.append(HT.TR(HT.TD(HT.Span("Heritability"),align="center", Class="fs13 b1 cbw c222",nowrap="yes"),HT.TD("NaN", nowrap="yes",align="center", Class="fs13 b1 cbw c222")))
# Lei Yan
# 2008/12/19
TDStatis.append(tbl2)
plotHeight = 220
plotWidth = 120
xLeftOffset = 60
xRightOffset = 25
yTopOffset = 20
yBottomOffset = 53
canvasHeight = plotHeight + yTopOffset + yBottomOffset
canvasWidth = plotWidth + xLeftOffset + xRightOffset
canvas = pid.PILCanvas(size=(canvasWidth,canvasHeight))
XXX = [('', InformData[:])]
Plot.plotBoxPlot(canvas, XXX, offset=(xLeftOffset, xRightOffset, yTopOffset, yBottomOffset), XLabel= "Trait")
filename= webqtlUtil.genRandStr("Box_")
canvas.save(webqtlConfig.IMGDIR+filename, format='gif')
img=HT.Image('/image/'+filename+'.gif',border=0)
#supertable2.append(HT.TR(HT.TD(staIntro1, colspan=3 )))
tb = HT.TableLite(border=0, cellspacing=0, cellpadding=0)
tb.append(HT.TR(HT.TD(img, align="left", style="border: 1px solid #999999; padding:0px;")))
supertable2.append(HT.TR(TDStatis, HT.TD(tb)))
dataXZ = vals[:]
tvals = []
tnames = []
tvars = []
for i in range(len(dataXZ)):
tvals.append(dataXZ[i][1])
tnames.append(webqtlUtil.genShortStrainName(fd, dataXZ[i][0]))
tvars.append(dataXZ[i][2])
nnStrain = len(tnames)
sLabel = 1
###determine bar width and space width
if nnStrain < 20:
sw = 4
elif nnStrain < 40:
sw = 3
else:
sw = 2
### 700 is the default plot width minus Xoffsets for 40 strains
defaultWidth = 650
if nnStrain > 40:
defaultWidth += (nnStrain-40)*10
defaultOffset = 100
bw = int(0.5+(defaultWidth - (nnStrain-1.0)*sw)/nnStrain)
if bw < 10:
bw = 10
plotWidth = (nnStrain-1)*sw + nnStrain*bw + defaultOffset
plotHeight = 500
#print [plotWidth, plotHeight, bw, sw, nnStrain]
c = pid.PILCanvas(size=(plotWidth,plotHeight))
Plot.plotBarText(c, tvals, tnames, variance=tvars, YLabel='Value', title='%s by Case (sorted by name)' % self.identification, sLabel = sLabel, barSpace = sw)
filename= webqtlUtil.genRandStr("Bar_")
c.save(webqtlConfig.IMGDIR+filename, format='gif')
img0=HT.Image('/image/'+filename+'.gif',border=0)
dataXZ = vals[:]
dataXZ.sort(self.cmpValue)
tvals = []
tnames = []
tvars = []
for i in range(len(dataXZ)):
tvals.append(dataXZ[i][1])
tnames.append(webqtlUtil.genShortStrainName(fd, dataXZ[i][0]))
tvars.append(dataXZ[i][2])
c = pid.PILCanvas(size=(plotWidth,plotHeight))
Plot.plotBarText(c, tvals, tnames, variance=tvars, YLabel='Value', title='%s by Case (ranked)' % self.identification, sLabel = sLabel, barSpace = sw)
filename= webqtlUtil.genRandStr("Bar_")
c.save(webqtlConfig.IMGDIR+filename, format='gif')
img1=HT.Image('/image/'+filename+'.gif',border=0)
# Lei Yan
# 05/18/2009
# report
title = HT.Paragraph('REPORT on the variation of Shh (or PCA Composite Trait XXXX) (sonic hedgehog) in the (insert Data set name) of (insert Species informal name, e.g., Mouse, Rat, Human, Barley, Arabidopsis)', Class="title")
header = HT.Paragraph('''This report was generated by GeneNetwork on May 11, 2009, at 11.20 AM using the Basic Statistics module (v 1.0) and data from the Hippocampus Consortium M430v2 (Jun06) PDNN data set. For more details and updates on this data set please link to URL:get Basic Statistics''')
hr = HT.HR()
p1 = HT.Paragraph('''Trait values for Shh were taken from the (insert Database name, Hippocampus Consortium M430v2 (Jun06) PDNN). GeneNetwork contains data for NN (e.g., 99) cases. In general, data are averages for each case. A summary of mean, median, and the range of these data are provided in Table 1 and in the box plot (Figure 1). Data for individual cases are provided in Figure 2A and 2B, often with error bars (SEM). ''')
p2 = HT.Paragraph('''Trait values for Shh range 5.1-fold: from a low of 8.2 (please round value) in 129S1/SvImJ to a high of 10.6 (please round value) in BXD9. The interquartile range (the difference between values closest to the 25% and 75% levels) is a more modest 1.8-fold. The mean value is XX. ''')
t1 = HT.Paragraph('''Table 1. Summary of Shh data from the Hippocampus Consortium M430v2 (june06) PDNN data set''')
f1 = HT.Paragraph('''Figure 1. ''')
f1.append(HT.Href(text="Box plot", url="http://davidmlane.com/hyperstat/A37797.html", target="_blank", Class="fs14"))
f1.append(HT.Text(''' of Shh data from the Hippocampus Consortium M430v2 (june06) PDNN data set'''))
f2A = HT.Paragraph('''Figure 2A: Bar chart of Shh data ordered by case from the Hippocampus Consortium M430v2 (june06) PDNN data set''')
f2B = HT.Paragraph('''Figure 2B: Bar chart of Shh values ordered by from the Hippocampus Consortium M430v2 (june06) PDNN data set''')
TD_LR.append(HT.Blockquote(title, HT.P(), header, hr, p1, HT.P(), p2, HT.P(), supertable2, t1, f1, HT.P(), img0, f2A, HT.P(), img1, f2B))
self.dict['body'] = str(TD_LR)
else:
heading = "Basic Statistics"
detail = ['Fewer than %d case data were entered for %s data set. No statitical analysis has been attempted.' % (self.plotMinInformative, fd.RISet)]
self.error(heading=heading,detail=detail)
return
else:
heading = "Basic Statistics"
detail = ['Empty data set, please check your data.']
self.error(heading=heading,detail=detail)
return
def __init__(self, fd):
LRSFullThresh = 30
LRSInteractThresh = 25
maxPlotSize = 800
mainfmName = webqtlUtil.genRandStr("fm_")
templatePage.__init__(self, fd)
if not fd.genotype:
fd.readData()
##Remove F1 and Parents
fd.genotype = fd.genotype_1
plotType = fd.formdata.getvalue('plotType')
self.dict['title'] = '%s Plot' % plotType
main_title = HT.Paragraph("%s Plot" % plotType)
main_title.__setattr__("class","title")
interval1 = fd.formdata.getvalue('interval1')
interval2 = fd.formdata.getvalue('interval2')
flanka1, flanka2, chram = string.split(interval1)
flankb1, flankb2, chrbm = string.split(interval2)
traitValues = string.split(fd.formdata.getvalue('traitValues'), ',')
traitValues = map(webqtlUtil.StringAsFloat, traitValues)
traitStrains = string.split(fd.formdata.getvalue('traitStrains'), ',')
flankaGeno = []
flankbGeno = []
for chr in fd.genotype:
for locus in chr:
if locus.name in (flanka1, flankb1):
if locus.name == flanka1:
flankaGeno = locus.genotype[:]
else:
flankbGeno = locus.genotype[:]
if flankaGeno and flankbGeno:
break
flankaDict = {}
flankbDict = {}
for i in range(len(fd.genotype.prgy)):
flankaDict[fd.genotype.prgy[i]] = flankaGeno[i]
flankbDict[fd.genotype.prgy[i]] = flankbGeno[i]
BB = []
BD = []
DB = []
DD = []
iValues = []
for i in range(len(traitValues)):
if traitValues[i] != None:
iValues.append(traitValues[i])
thisstrain = traitStrains[i]
try:
a1 = flankaDict[thisstrain]
b1 = flankbDict[thisstrain]
except:
continue
if a1 == -1.0:
if b1 == -1.0:
BB.append((thisstrain, traitValues[i]))
elif b1 == 1.0:
BD.append((thisstrain, traitValues[i]))
elif a1 == 1.0:
if b1 == -1.0:
DB.append((thisstrain, traitValues[i]))
elif b1 == 1.0:
DD.append((thisstrain, traitValues[i]))
else:
pass
#print BB, BD, DB, DD, max(iValues), min(iValues)
plotHeight = 400
plotWidth = 600
xLeftOffset = 60
xRightOffset = 40
yTopOffset = 40
yBottomOffset = 60
canvasHeight = plotHeight + yTopOffset + yBottomOffset
canvasWidth = plotWidth + xLeftOffset + xRightOffset
canvas = pid.PILCanvas(size=(canvasWidth,canvasHeight))
XXX = [('Mat/Mat', BB), ('Mat/Pat', BD), ('Pat/Mat', DB), ('Pat/Pat', DD)]
XLabel = "Interval 1 / Interval 2"
if plotType == "Box":
Plot.plotBoxPlot(canvas, XXX, offset=(xLeftOffset, xRightOffset, yTopOffset, yBottomOffset), XLabel = XLabel)
else:
#Could be a separate function, but seems no other uses
max_Y = max(iValues)
min_Y = min(iValues)
scaleY = Plot.detScale(min_Y, max_Y)
Yll = scaleY[0]
Yur = scaleY[1]
nStep = scaleY[2]
stepY = (Yur - Yll)/nStep
stepYPixel = plotHeight/(nStep)
canvas.drawRect(plotWidth+xLeftOffset, plotHeight + yTopOffset, xLeftOffset, yTopOffset)
##draw Y Scale
YYY = Yll
YCoord = plotHeight + yTopOffset
scaleFont=pid.Font(ttf="cour",size=11,bold=1)
for i in range(nStep+1):
strY = Plot.cformat(d=YYY, rank=0)
YCoord = max(YCoord, yTopOffset)
canvas.drawLine(xLeftOffset,YCoord,xLeftOffset-5,YCoord)
canvas.drawString(strY, xLeftOffset -30,YCoord +5,font=scaleFont)
YYY += stepY
YCoord -= stepYPixel
##draw X Scale
stepX = plotWidth/len(XXX)
XCoord = xLeftOffset + 0.5*stepX
YCoord = plotHeight + yTopOffset
scaleFont = pid.Font(ttf="tahoma",size=12,bold=0)
labelFont = pid.Font(ttf="tahoma",size=13,bold=0)
for item in XXX:
itemname, itemvalue = item
canvas.drawLine(XCoord, YCoord,XCoord, YCoord+5, color=pid.black)
canvas.drawString(itemname, XCoord - canvas.stringWidth(itemname,font=labelFont)/2.0,YCoord +20,font=labelFont)
itemvalue.sort(webqtlUtil.cmpOrder2)
j = 0
for item2 in itemvalue:
tstrain, tvalue = item2
canvas.drawCross(XCoord, plotHeight + yTopOffset - (tvalue-Yll)*plotHeight/(Yur - Yll), color=pid.red,size=5)
if j % 2 == 0:
canvas.drawString(tstrain, XCoord+5, plotHeight + yTopOffset - \
(tvalue-Yll)*plotHeight/(Yur - Yll) +5, font=scaleFont, color=pid.blue)
else:
canvas.drawString(tstrain, XCoord-canvas.stringWidth(tstrain,font=scaleFont)-5, \
plotHeight + yTopOffset - (tvalue-Yll)*plotHeight/(Yur - Yll) +5, font=scaleFont, color=pid.blue)
j += 1
XCoord += stepX
labelFont=pid.Font(ttf="verdana",size=18,bold=0)
canvas.drawString(XLabel, xLeftOffset + (plotWidth -canvas.stringWidth(XLabel,font=labelFont))/2.0, YCoord +40, font=labelFont)
canvas.drawString("Value",xLeftOffset-40, YCoord-(plotHeight -canvas.stringWidth("Value",font=labelFont))/2.0, font=labelFont, angle =90)
filename= webqtlUtil.genRandStr("Cate_")
canvas.save(webqtlConfig.IMGDIR+filename, format='gif')
img=HT.Image('/image/'+filename+'.gif',border=0)
TD_LR = HT.TD(height=200,width="100%",bgColor='#eeeeee',valign='top')
TD_LR.append(main_title, HT.Center(img))#, traitValues , len(traitValues), traitStrains, len(traitStrains), len(fd.genotype.prgy))
#TD_LR.append(main_title, HT.BR(), flanka1, flanka2, chram, HT.BR(), flankb1, flankb2, chrbm)
self.dict['body'] = str(TD_LR)