diff options
Diffstat (limited to 'wqflask/basicStatistics/BasicStatisticsFunctions.py')
| -rwxr-xr-x | wqflask/basicStatistics/BasicStatisticsFunctions.py | 280 |
1 files changed, 140 insertions, 140 deletions
diff --git a/wqflask/basicStatistics/BasicStatisticsFunctions.py b/wqflask/basicStatistics/BasicStatisticsFunctions.py index 5cbbb145..285addae 100755 --- a/wqflask/basicStatistics/BasicStatisticsFunctions.py +++ b/wqflask/basicStatistics/BasicStatisticsFunctions.py @@ -13,162 +13,162 @@ from dbFunction import webqtlDatabaseFunction def basicStatsTable(vals, trait_type=None, cellid=None, heritability=None): - valsOnly = [] - dataXZ = vals[:] - for i in range(len(dataXZ)): - valsOnly.append(dataXZ[i][1]) - - traitmean, traitmedian, traitvar, traitstdev, traitsem, N = reaper.anova(valsOnly) #ZS: Should convert this from reaper to R in the future - - tbl = HT.TableLite(cellpadding=20, cellspacing=0) - dataXZ = vals[:] - dataXZ.sort(webqtlUtil.cmpOrder) - tbl.append(HT.TR(HT.TD("Statistic",align="left", Class="fs14 fwb ffl b1 cw cbrb", width = 180), - HT.TD("Value", align="right", Class="fs14 fwb ffl b1 cw cbrb", width = 60))) - tbl.append(HT.TR(HT.TD("N of Samples",align="left", Class="fs13 b1 cbw c222"), - HT.TD(N,nowrap="yes", Class="fs13 b1 cbw c222"), align="right")) - tbl.append(HT.TR(HT.TD("Mean",align="left", Class="fs13 b1 cbw c222",nowrap="yes"), - HT.TD("%2.3f" % traitmean,nowrap="yes", Class="fs13 b1 cbw c222"), align="right")) - tbl.append(HT.TR(HT.TD("Median",align="left", Class="fs13 b1 cbw c222",nowrap="yes"), - HT.TD("%2.3f" % traitmedian,nowrap="yes", Class="fs13 b1 cbw c222"), align="right")) - #tbl.append(HT.TR(HT.TD("Variance",align="left", Class="fs13 b1 cbw c222",nowrap="yes"), - # HT.TD("%2.3f" % traitvar,nowrap="yes",align="left", Class="fs13 b1 cbw c222"))) - tbl.append(HT.TR(HT.TD("Standard Error (SE)",align="left", Class="fs13 b1 cbw c222",nowrap="yes"), - HT.TD("%2.3f" % traitsem,nowrap="yes", Class="fs13 b1 cbw c222"), align="right")) - tbl.append(HT.TR(HT.TD("Standard Deviation (SD)", align="left", Class="fs13 b1 cbw c222",nowrap="yes"), - HT.TD("%2.3f" % traitstdev,nowrap="yes", Class="fs13 b1 cbw c222"), align="right")) - tbl.append(HT.TR(HT.TD("Minimum", align="left", Class="fs13 b1 cbw c222",nowrap="yes"), - HT.TD("%s" % dataXZ[0][1],nowrap="yes", Class="fs13 b1 cbw c222"), align="right")) - tbl.append(HT.TR(HT.TD("Maximum", align="left", Class="fs13 b1 cbw c222",nowrap="yes"), - HT.TD("%s" % dataXZ[-1][1],nowrap="yes", Class="fs13 b1 cbw c222"), align="right")) - if (trait_type != None and trait_type == 'ProbeSet'): - #IRQuest = HT.Href(text="Interquartile Range", url=webqtlConfig.glossaryfile +"#Interquartile",target="_blank", Class="fs14") - #IRQuest.append(HT.BR()) - #IRQuest.append(" (fold difference)") - tbl.append(HT.TR(HT.TD("Range (log2)",align="left", Class="fs13 b1 cbw c222",nowrap="yes"), - HT.TD("%2.3f" % (dataXZ[-1][1]-dataXZ[0][1]),nowrap="yes", Class="fs13 b1 cbw c222"), align="right")) - tbl.append(HT.TR(HT.TD(HT.Span("Range (fold)"),align="left", Class="fs13 b1 cbw c222",nowrap="yes"), - HT.TD("%2.2f" % pow(2.0,(dataXZ[-1][1]-dataXZ[0][1])), nowrap="yes", Class="fs13 b1 cbw c222"), align="right")) - tbl.append(HT.TR(HT.TD(HT.Span(HT.Href(url="/glossary.html#Interquartile", target="_blank", text="Interquartile Range", Class="non_bold")), align="left", 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", Class="fs13 b1 cbw c222"), align="right")) - - #XZ, 04/01/2009: don't try to get H2 value for probe. - if cellid: - pass - else: - if heritability: - tbl.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: - pass - # Lei Yan - # 2008/12/19 - - return tbl + valsOnly = [] + dataXZ = vals[:] + for i in range(len(dataXZ)): + valsOnly.append(dataXZ[i][1]) + + traitmean, traitmedian, traitvar, traitstdev, traitsem, N = reaper.anova(valsOnly) #ZS: Should convert this from reaper to R in the future + + tbl = HT.TableLite(cellpadding=20, cellspacing=0) + dataXZ = vals[:] + dataXZ.sort(webqtlUtil.cmpOrder) + tbl.append(HT.TR(HT.TD("Statistic",align="left", Class="fs14 fwb ffl b1 cw cbrb", width = 180), + HT.TD("Value", align="right", Class="fs14 fwb ffl b1 cw cbrb", width = 60))) + tbl.append(HT.TR(HT.TD("N of Samples",align="left", Class="fs13 b1 cbw c222"), + HT.TD(N,nowrap="yes", Class="fs13 b1 cbw c222"), align="right")) + tbl.append(HT.TR(HT.TD("Mean",align="left", Class="fs13 b1 cbw c222",nowrap="yes"), + HT.TD("%2.3f" % traitmean,nowrap="yes", Class="fs13 b1 cbw c222"), align="right")) + tbl.append(HT.TR(HT.TD("Median",align="left", Class="fs13 b1 cbw c222",nowrap="yes"), + HT.TD("%2.3f" % traitmedian,nowrap="yes", Class="fs13 b1 cbw c222"), align="right")) + #tbl.append(HT.TR(HT.TD("Variance",align="left", Class="fs13 b1 cbw c222",nowrap="yes"), + # HT.TD("%2.3f" % traitvar,nowrap="yes",align="left", Class="fs13 b1 cbw c222"))) + tbl.append(HT.TR(HT.TD("Standard Error (SE)",align="left", Class="fs13 b1 cbw c222",nowrap="yes"), + HT.TD("%2.3f" % traitsem,nowrap="yes", Class="fs13 b1 cbw c222"), align="right")) + tbl.append(HT.TR(HT.TD("Standard Deviation (SD)", align="left", Class="fs13 b1 cbw c222",nowrap="yes"), + HT.TD("%2.3f" % traitstdev,nowrap="yes", Class="fs13 b1 cbw c222"), align="right")) + tbl.append(HT.TR(HT.TD("Minimum", align="left", Class="fs13 b1 cbw c222",nowrap="yes"), + HT.TD("%s" % dataXZ[0][1],nowrap="yes", Class="fs13 b1 cbw c222"), align="right")) + tbl.append(HT.TR(HT.TD("Maximum", align="left", Class="fs13 b1 cbw c222",nowrap="yes"), + HT.TD("%s" % dataXZ[-1][1],nowrap="yes", Class="fs13 b1 cbw c222"), align="right")) + if (trait_type != None and trait_type == 'ProbeSet'): + #IRQuest = HT.Href(text="Interquartile Range", url=webqtlConfig.glossaryfile +"#Interquartile",target="_blank", Class="fs14") + #IRQuest.append(HT.BR()) + #IRQuest.append(" (fold difference)") + tbl.append(HT.TR(HT.TD("Range (log2)",align="left", Class="fs13 b1 cbw c222",nowrap="yes"), + HT.TD("%2.3f" % (dataXZ[-1][1]-dataXZ[0][1]),nowrap="yes", Class="fs13 b1 cbw c222"), align="right")) + tbl.append(HT.TR(HT.TD(HT.Span("Range (fold)"),align="left", Class="fs13 b1 cbw c222",nowrap="yes"), + HT.TD("%2.2f" % pow(2.0,(dataXZ[-1][1]-dataXZ[0][1])), nowrap="yes", Class="fs13 b1 cbw c222"), align="right")) + tbl.append(HT.TR(HT.TD(HT.Span(HT.Href(url="/glossary.html#Interquartile", target="_blank", text="Interquartile Range", Class="non_bold")), align="left", 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", Class="fs13 b1 cbw c222"), align="right")) + + #XZ, 04/01/2009: don't try to get H2 value for probe. + if cellid: + pass + else: + if heritability: + tbl.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: + pass + # Lei Yan + # 2008/12/19 + + return tbl def plotNormalProbability(vals=None, RISet='', title=None, showstrains=0, specialStrains=[None], size=(750,500)): - dataXZ = vals[:] - dataXZ.sort(webqtlUtil.cmpOrder) - dataLabel = [] - dataX = map(lambda X: X[1], dataXZ) + dataXZ = vals[:] + dataXZ.sort(webqtlUtil.cmpOrder) + dataLabel = [] + dataX = map(lambda X: X[1], dataXZ) - showLabel = showstrains - if len(dataXZ) > 50: - showLabel = 0 - for item in dataXZ: - strainName = webqtlUtil.genShortStrainName(RISet=RISet, input_strainName=item[0]) - dataLabel.append(strainName) + showLabel = showstrains + if len(dataXZ) > 50: + showLabel = 0 + for item in dataXZ: + strainName = webqtlUtil.genShortStrainName(RISet=RISet, input_strainName=item[0]) + dataLabel.append(strainName) - dataY=Plot.U(len(dataX)) - dataZ=map(Plot.inverseCumul,dataY) - c = pid.PILCanvas(size=(750,500)) - Plot.plotXY(c, dataZ, dataX, dataLabel = dataLabel, XLabel='Expected Z score', connectdot=0, YLabel='Trait value', title=title, specialCases=specialStrains, showLabel = showLabel) + dataY=Plot.U(len(dataX)) + dataZ=map(Plot.inverseCumul,dataY) + c = pid.PILCanvas(size=(750,500)) + Plot.plotXY(c, dataZ, dataX, dataLabel = dataLabel, XLabel='Expected Z score', connectdot=0, YLabel='Trait value', title=title, specialCases=specialStrains, showLabel = showLabel) - filename= webqtlUtil.genRandStr("nP_") - c.save(webqtlConfig.IMGDIR+filename, format='gif') + filename= webqtlUtil.genRandStr("nP_") + c.save(webqtlConfig.IMGDIR+filename, format='gif') - img=HT.Image('/image/'+filename+'.gif',border=0) + img=HT.Image('/image/'+filename+'.gif',border=0) - return img + return img def plotBoxPlot(vals): - valsOnly = [] - dataXZ = vals[:] - for i in range(len(dataXZ)): - valsOnly.append(dataXZ[i][1]) + 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 + 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[:])] + 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) + 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")) + plotLink = HT.Span("More about ", HT.Href(text="Box Plots", url="http://davidmlane.com/hyperstat/A37797.html", target="_blank", Class="fs13")) - return img, plotLink + return img, plotLink def plotBarGraph(identification='', RISet='', vals=None, type="name"): - this_identification = "unnamed trait" - if identification: - this_identification = identification - - if type=="rank": - dataXZ = vals[:] - dataXZ.sort(webqtlUtil.cmpOrder) - title='%s' % this_identification - else: - dataXZ = vals[:] - title='%s' % this_identification - - tvals = [] - tnames = [] - tvars = [] - for i in range(len(dataXZ)): - tvals.append(dataXZ[i][1]) - tnames.append(webqtlUtil.genShortStrainName(RISet=RISet, input_strainName=dataXZ[i][0])) - tvars.append(dataXZ[i][2]) - nnStrain = len(tnames) - - sLabel = 1 - - ###determine bar width and space width - if nnStrain < 20: - sw = 4 - elif nnStrain < 40: - sw = 3 - else: - sw = 2 - - ### 700 is the default plot width minus Xoffsets for 40 strains - defaultWidth = 650 - if nnStrain > 40: - defaultWidth += (nnStrain-40)*10 - defaultOffset = 100 - bw = int(0.5+(defaultWidth - (nnStrain-1.0)*sw)/nnStrain) - if bw < 10: - bw = 10 - - plotWidth = (nnStrain-1)*sw + nnStrain*bw + defaultOffset - plotHeight = 500 - #print [plotWidth, plotHeight, bw, sw, nnStrain] - c = pid.PILCanvas(size=(plotWidth,plotHeight)) - Plot.plotBarText(c, tvals, tnames, variance=tvars, YLabel='Value', title=title, sLabel = sLabel, barSpace = sw) - - filename= webqtlUtil.genRandStr("Bar_") - c.save(webqtlConfig.IMGDIR+filename, format='gif') - img=HT.Image('/image/'+filename+'.gif',border=0) - - return img + this_identification = "unnamed trait" + if identification: + this_identification = identification + + if type=="rank": + dataXZ = vals[:] + dataXZ.sort(webqtlUtil.cmpOrder) + title='%s' % this_identification + else: + dataXZ = vals[:] + title='%s' % this_identification + + tvals = [] + tnames = [] + tvars = [] + for i in range(len(dataXZ)): + tvals.append(dataXZ[i][1]) + tnames.append(webqtlUtil.genShortStrainName(RISet=RISet, input_strainName=dataXZ[i][0])) + tvars.append(dataXZ[i][2]) + nnStrain = len(tnames) + + sLabel = 1 + + ###determine bar width and space width + if nnStrain < 20: + sw = 4 + elif nnStrain < 40: + sw = 3 + else: + sw = 2 + + ### 700 is the default plot width minus Xoffsets for 40 strains + defaultWidth = 650 + if nnStrain > 40: + defaultWidth += (nnStrain-40)*10 + defaultOffset = 100 + bw = int(0.5+(defaultWidth - (nnStrain-1.0)*sw)/nnStrain) + if bw < 10: + bw = 10 + + plotWidth = (nnStrain-1)*sw + nnStrain*bw + defaultOffset + plotHeight = 500 + #print [plotWidth, plotHeight, bw, sw, nnStrain] + c = pid.PILCanvas(size=(plotWidth,plotHeight)) + Plot.plotBarText(c, tvals, tnames, variance=tvars, YLabel='Value', title=title, sLabel = sLabel, barSpace = sw) + + filename= webqtlUtil.genRandStr("Bar_") + c.save(webqtlConfig.IMGDIR+filename, format='gif') + img=HT.Image('/image/'+filename+'.gif',border=0) + + return img |