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authorSam Ockman2012-06-05 00:24:44 -0400
committerSam Ockman2012-06-05 00:24:44 -0400
commit8ac39ead1014953c634e85d0ce340497ecfe2934 (patch)
treef69bef8650f64bdfa5093c39fe7dc6a8b5ffac82 /wqflask/basicStatistics/BasicStatisticsFunctions.py
parent8abd879e71f492ce61e0b8d3eab53fcb43c34681 (diff)
downloadgenenetwork2-8ac39ead1014953c634e85d0ce340497ecfe2934.tar.gz
Ran reindent.py recursively on wqflask directory
Diffstat (limited to 'wqflask/basicStatistics/BasicStatisticsFunctions.py')
-rwxr-xr-xwqflask/basicStatistics/BasicStatisticsFunctions.py280
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