Add all the source codes into the github.

1 files changed, 595 insertions, 0 deletions

diff --git a/web/webqtl/correlationMatrix/CorrelationMatrixPage.py b/web/webqtl/correlationMatrix/CorrelationMatrixPage.py
new file mode 100755
index 00000000..a01111f5
--- /dev/null
+++ b/web/webqtl/correlationMatrix/CorrelationMatrixPage.py
@@ -0,0 +1,595 @@
+# Copyright (C) University of Tennessee Health Science Center, Memphis, TN.
+#
+# This program is free software: you can redistribute it and/or modify it
+# under the terms of the GNU Affero General Public License
+# as published by the Free Software Foundation, either version 3 of the
+# License, or (at your option) any later version.
+#
+# This program is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
+# See the GNU Affero General Public License for more details.
+#
+# This program is available from Source Forge: at GeneNetwork Project
+# (sourceforge.net/projects/genenetwork/).
+#
+# Contact Drs. Robert W. Williams and Xiaodong Zhou (2010)
+# at rwilliams@uthsc.edu and xzhou15@uthsc.edu
+#
+#
+#
+# This module is used by GeneNetwork project (www.genenetwork.org)
+#
+# Created by GeneNetwork Core Team 2010/08/10
+#
+# Last updated by NL 2011/02/14
+
+import os
+import string
+from htmlgen import HTMLgen2 as HT
+import sys
+import time
+import numarray
+import numarray.linear_algebra as la
+import piddle as pid
+import math
+
+from base.templatePage import templatePage
+from base import webqtlConfig
+from base.webqtlTrait import webqtlTrait
+from utility import webqtlUtil
+from utility import Plot
+
+
+
+# XZ, 09/09/2008: After adding several traits to collection, click "Correlation Matrix" button,
+# XZ, 09/09/2008: This class will generate what you see.
+#########################################
+#      Correlation Matrix Page
+#########################################
+
+class CorrelationMatrixPage(templatePage):
+
+    def __init__(self,fd,InputData=None):
+
+        templatePage.__init__(self, fd)
+
+        self.dict['title'] = 'Correlation Matrix'
+
+        if not self.openMysql():
+            return
+            
+        if not fd.genotype:
+            fd.readGenotype()
+            fd.strainlist = fd.f1list + fd.strainlist
+            
+        #self.searchResult = fd.formdata.getvalue('searchResult')
+        self.oldSearchResult = fd.formdata.getvalue('oldSearchResult')
+        
+        if self.oldSearchResult:
+            try:
+                self.searchResult = fd.formdata.getvalue('oldSearchResult')
+            except:
+            	self.searchResult = fd.formdata.getvalue('searchResult')
+        
+        else:
+        	self.searchResult = fd.formdata.getvalue('searchResult')
+        
+        if not self.searchResult:
+            heading = 'Correlation Matrix'
+            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 = [self.searchResult]
+        
+        if self.searchResult:
+            #testvals,names,dbInfos = self.getAllSearchResult(fd,self.searchResult)
+            if len(self.searchResult) > webqtlConfig.MAXCORR:
+                heading = 'Correlation Matrix'
+                detail = ['In order to display Correlation Matrix properly, Do not select more than %d traits for Correlation Matrix.' % webqtlConfig.MAXCORR]
+                self.error(heading=heading,detail=detail)
+                return
+
+            #XZ, 7/22/2009: this block is not necessary
+            #elif len(self.searchResult) > 40:
+            #    noPCA = 1
+            #else:
+            #    noPCA = 0
+    
+            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 = 'Correlation Matrix'
+            detail = [HT.Font('Error : ',color='red'),HT.Font('Error occurs while retrieving data FROM database.',color='black')]
+            self.error(heading=heading,detail=detail)
+            return
+
+        NNN = len(traitList)
+        
+        if NNN == 0:
+            heading = "Correlation Matrix"
+            detail = ['No trait was selected for %s data set. No matrix generated.' % self.data.RISet]
+            self.error(heading=heading,detail=detail)
+            return
+        elif NNN < 2:
+            heading = 'Correlation Matrix'
+            detail = ['You need to select at least two traits in order to generate correlation matrix.']
+            self.error(heading=heading,detail=detail)
+            return
+        else:
+        	
+        	
+        	
+            corArray = [([0] * (NNN+1))[:] for i in range(NNN+1)]
+            pearsonArray = [([0] * (NNN))[:] for i in range(NNN)]
+            spearmanArray = [([0] * (NNN))[:] for i in range(NNN)]
+            corArray[0][0] = 'Correlation'
+            TD_LR = HT.TD(colspan=2,width="100%",bgColor='#eeeeee')
+            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':'_',
+            'CellID':'_','ProbeSetID2':'_','database2':'_','CellID2':'_',
+            'newNames':fd.formdata.getvalue("newNames", "_"), 
+            'RISet':fd.RISet,'ShowStrains':'ON','ShowLine':'ON', 'rankOrder':'_', 
+            "allstrainlist":string.join(fd.strainlist, " "), 'traitList':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'))
+           
+            for item in self.searchResult:
+            	form.append(HT.Input(name='oldSearchResult', value=str(item), type='hidden'))
+            
+            traiturls = []
+            traiturls2 = []
+            shortNames = []
+            verboseNames = []
+            verboseNames2 = []
+            verboseNames3 = []
+            abbreviation = ''
+            
+            #dbInfo.ProbeSetID = ProbeSetID
+            #dbInfo.CellID = CellID
+            for i, thisTrait in enumerate(traitList):
+                _url = "javascript:showDatabase2('%s','%s','%s');" % (thisTrait.db.name, thisTrait.name, thisTrait.cellid)
+                #_text = 'Trait%d: ' % (i+1)+str(thisTrait)
+                _text = 'Trait %d: ' % (i+1)+thisTrait.displayName()
+                                
+                if thisTrait.db.type == 'Geno':
+                    _shortName = 'Genotype'
+                    abbreviation = 'Genotype'
+                    _verboseName = 'Locus %s' % (thisTrait.name)
+                    _verboseName2 = 'Chr %s @ %s Mb' % (thisTrait.chr, '%2.3f' % thisTrait.mb)
+                    _verboseName3 = ''
+                elif thisTrait.db.type == 'Publish':
+                    if thisTrait.post_publication_abbreviation:
+                        AbbreviationString = thisTrait.post_publication_abbreviation
+                    else:
+                        AbbreviationString = ''
+                    if thisTrait.confidential:
+                        if not webqtlUtil.hasAccessToConfidentialPhenotypeTrait(privilege=self.privilege, userName=self.userName, authorized_users=thisTrait.authorized_users):
+                            if thisTrait.pre_publication_abbreviation:
+                                AbbreviationString = thisTrait.pre_publication_abbreviation
+                            else:
+                                AbbreviationString = ''
+                    _shortName = 'Phenotype: %s' % (AbbreviationString)  
+                    _verboseName2 = ''
+                    _verboseName3 = ''
+                    if thisTrait.pubmed_id:
+                        _verboseName = 'PubMed %d: ' % thisTrait.pubmed_id
+                    else: 
+                        _verboseName = 'Unpublished '
+                    _verboseName += 'RecordID/%s' % (thisTrait.name)
+                    PhenotypeString = thisTrait.post_publication_description
+                    if thisTrait.confidential:
+                        if not webqtlUtil.hasAccessToConfidentialPhenotypeTrait(privilege=self.privilege, userName=self.userName, authorized_users=thisTrait.authorized_users):
+                            PhenotypeString = thisTrait.pre_publication_description
+                    _verboseName2 = 'Phenotype: %s' % (PhenotypeString)
+                    if thisTrait.authors:
+                        a1 = string.split(thisTrait.authors,',')[0]
+                        while a1[0] == '"' or a1[0] == "'" :
+                            a1 = a1[1:]
+                            _verboseName += ' by '
+                            _verboseName += HT.Italic('%s, and colleagues' % (a1))
+                elif thisTrait.db.type == 'Temp':
+                    abbreviation = ''
+                    _shortName = thisTrait.name
+                    if thisTrait.description:
+                        _verboseName = thisTrait.description
+                    else:
+                        _verboseName = 'Temp'
+                    _verboseName2 = ''
+                    _verboseName3 = ''
+                else:
+                    abbreviation = thisTrait.symbol
+                    _shortName = 'Symbol: %s ' % thisTrait.symbol
+                    _verboseName = thisTrait.symbol
+                    _verboseName2 = ''
+                    _verboseName3 = ''
+                    if thisTrait.chr and thisTrait.mb:
+                        _verboseName += ' on Chr %s @ %s Mb' % (thisTrait.chr,thisTrait.mb)
+                    if thisTrait.description:
+                        _verboseName2 = '%s' % (thisTrait.description)
+                    if thisTrait.probe_target_description:
+                        _verboseName3 = '%s' % (thisTrait.probe_target_description)         
+                                
+                cururl = HT.Href(text=_text, url=_url,Class='fs12') 
+                cururl2 = HT.Href(text='Trait%d' % (i+1),url=_url,Class='fs12')
+                traiturls.append(cururl)
+                traiturls2.append(cururl2)
+                shortName = HT.Div(id="shortName_" + str(i), style="display:none")
+                shortName.append(_shortName)
+                shortNames.append(shortName)
+                verboseName = HT.Div(id="verboseName_" + str(i), style="display:none")
+                verboseName.append(_verboseName)
+                verboseNames.append(verboseName)
+                verboseName2 = HT.Div(id="verboseName2_" + str(i), style="display:none")
+                verboseName2.append(_verboseName2)
+                verboseNames2.append(verboseName2)
+                verboseName3 = HT.Div(id="verboseName3_" + str(i), style="display:none")
+                verboseName3.append(_verboseName3)
+                verboseNames3.append(verboseName3)
+                    
+
+                                
+                corArray[i+1][0] = 'Trait%d: ' % (i+1)+str(thisTrait) + '/' + str(thisTrait) + ': ' + abbreviation + '/' + str(thisTrait) + ': ' + str(_verboseName) + ' : ' + str(_verboseName2) + ' : ' + str(_verboseName3)
+                corArray[0][i+1] = 'Trait%d: ' % (i+1)+str(thisTrait)
+                
+            corMatrixHeading = HT.Paragraph('Correlation Matrix', Class="title")
+            
+            tbl = HT.TableLite(Class="collap", border=0, cellspacing=1, 
+                cellpadding=5, width='100%')
+            row1 = HT.TR(HT.TD(Class="fs14 fwb ffl b1 cw cbrb"),
+                HT.TD('Spearman Rank Correlation (rho)', Class="fs14 fwb ffl b1 cw cbrb", colspan= NNN+1,align="center")
+                )
+            row2 = HT.TR(
+                HT.TD("P e a r s o n &nbsp;&nbsp;&nbsp; r", rowspan= NNN+1,Class="fs14 fwb ffl b1 cw cbrb", width=10,align="center"),
+                HT.TD(Class="b1", width=300))
+            for i in range(NNN):
+                row2.append(HT.TD(traiturls2[i], Class="b1", align="center"))
+            tbl.append(row1,row2)
+
+            nOverlapTrait =9999            
+            nnCorr = len(fd.strainlist)
+            for i, thisTrait in enumerate(traitList):
+                newrow = HT.TR()
+                newrow.append(HT.TD(traiturls[i], shortNames[i], verboseNames[i], verboseNames2[i], 
+                                    verboseNames3[i], Class="b1"))
+                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)
+                        
+                        rank = fd.formdata.getvalue("rankOrder", "0")
+                        
+                        if nOverlap < nOverlapTrait:
+                            nOverlapTrait = nOverlap
+                        if corr > 0.7:
+                            fontcolor="red"
+                        elif corr > 0.5:
+                            fontcolor="#FF6600"
+                        elif corr < -0.7:
+                            fontcolor="blue"
+                        elif corr < -0.5:
+                            fontcolor="#009900"
+                        else:
+                            fontcolor ="#000000"
+
+                        pearsonArray[i][j] = corr
+                        pearsonArray[j][i] = corr
+                        if corr!= 0.0:
+                            corArray[i+1][j+1] = '%2.3f/%d' % (corr,nOverlap)
+                            thisurl = HT.Href(text=HT.Font('%2.3f'% corr,HT.BR(),'%d' % nOverlap ,color=fontcolor, Class="fs11 fwn"),url = "javascript:showCorrelationPlot2(db='%s',ProbeSetID='%s',CellID='%s',db2='%s',ProbeSetID2='%s',CellID2='%s',rank='%s')" % (names1[0], names1[1], names1[2], names2[0], names2[1], names2[2], rank))
+                        else:
+                            corArray[i+1][j+1] = '---/%d' % nOverlap
+                            thisurl = HT.Font('---',HT.BR(), '%d' % nOverlap)
+                        
+                        newrow.append(HT.TD(thisurl,Class="b1",NOWRAP="ON",align="middle"))
+                    elif j == i:
+                        corr,nOverlap = webqtlUtil.calCorrelation(traitDataList[i],traitDataList[j],nnCorr)
+                        pearsonArray[i][j] = 1.0
+                        spearmanArray[i][j] = 1.0
+                        corArray[i+1][j+1] = '%2.3f/%d' % (corr,nOverlap)
+                        nOverlap = webqtlUtil.calCorrelation(traitDataList[i],traitDataList[j],nnCorr)[1]
+                        newrow.append(HT.TD(HT.Href(text=HT.Font(HT.Italic("n"),HT.BR(),str(nOverlap),Class="fs11 fwn b1",align="center", color="000000"), url="javascript:showDatabase2('%s','%s','%s')" % (thisTrait.db.name, thisTrait.name, thisTrait.cellid)), bgColor='#cccccc', align="center", Class="b1", NOWRAP="ON"))
+                    else:       
+                        corr,nOverlap = webqtlUtil.calCorrelationRank(traitDataList[i],traitDataList[j],nnCorr)
+                        
+                        rank = fd.formdata.getvalue("rankOrder", "1")
+                        
+                        if corr > 0.7:
+                            fontcolor="red"
+                        elif corr > 0.5:
+                            fontcolor="#FF6600"
+                        elif corr < -0.7:
+                            fontcolor="blue"
+                        elif corr < -0.5:
+                            fontcolor="#009900"
+                        else:
+                            fontcolor ="#000000"
+                        spearmanArray[i][j] = corr
+                        spearmanArray[j][i] = corr
+                        if corr!= 0.0:
+                            corArray[i+1][j+1] = '%2.3f/%d' % (corr,nOverlap)
+                            thisurl = HT.Href(text=HT.Font('%2.3f'% corr,HT.BR(),'%d' % nOverlap ,color=fontcolor, Class="fs11 fwn"),url = "javascript:showCorrelationPlot2(db='%s',ProbeSetID='%s',CellID='%s',db2='%s',ProbeSetID2='%s',CellID2='%s',rank='%s')" % (names1[0], names1[1], names1[2], names2[0], names2[1], names2[2], rank))
+                        else:
+                            corArray[i+1][j+1] = '---/%d' % nOverlap
+                            thisurl = HT.Span('---',HT.BR(), '%d' % nOverlap, Class="fs11 fwn")
+                        newrow.append(HT.TD(thisurl,Class="b1", NOWRAP="ON",align="middle"))
+                tbl.append(newrow)
+                
+            info = HT.Blockquote('Lower left cells list Pearson product-moment correlations; upper right cells list Spearman rank order correlations. Each cell also contains the n of cases. Values higher than 0.7 are displayed in ',HT.Font('red', color='red'),'; those between 0.5 and 0.7 in  ',HT.Font('orange', color='#FF6600'),'; Values lower than -0.7 are in ',HT.Font('blue', color='blue'),'; between -0.5 and -0.7 in ',HT.Font('green', color='#009900'),'. Select any cell to generate a scatter plot. Select trait labels for more information.', Class="fs13 fwn")
+            
+            exportbutton = HT.Input(type='button',  name='export', value='Export', onClick="exportText(allCorrelations);",Class="button")
+            shortButton = HT.Input(type='button' ,name='dispShort',value=' Short Labels ', onClick="displayShortName();",Class="button")
+            verboseButton = HT.Input(type='button' ,name='dispVerbose',value=' Long Labels ', onClick="displayVerboseName();", Class="button")
+            form.append(HT.Blockquote(tbl,HT.P(),shortButton,verboseButton,exportbutton))
+            TD_LR.append(corMatrixHeading,info,form,HT.P())
+
+            #if noPCA:
+            #    TD_LR.append(HT.Blockquote('No PCA is computed if more than 32 traits are selected.'))
+
+            #print corArray
+            exportScript = """
+                <SCRIPT language=JavaScript>
+                var allCorrelations = %s;
+                </SCRIPT>
+
+            """
+            exportScript = exportScript % str(corArray)
+            self.dict['js1'] = exportScript+'<SCRIPT SRC="/javascript/correlationMatrix.js"></SCRIPT><BR>'
+            self.dict['body'] = str(TD_LR)
+            
+            #don't calculate PCA while number exceed 32
+            #if noPCA:
+            #    return
+
+            #XZ, 7/22/2009: deal with PCA stuff
+            #Only for Array Data
+            
+            if NNN > 2:
+    
+                traitname = map(lambda X:str(X.name), traitList)
+                
+                #generate eigenvalues
+                
+                # import sys
+                sys.argv=[" "]
+                # import numarray
+                # import numarray.linear_algebra as la
+                #spearmanEigen = eigenvectors(array(spearmanArray))
+                pearsonEigen = la.eigenvectors(numarray.array(pearsonArray))
+                #spearmanEigenValue,spearmanEigenVectors = self.sortEigenVectors(spearmanEigen)
+                pearsonEigenValue,pearsonEigenVectors = self.sortEigenVectors(pearsonEigen)
+		
+				
+		"""
+		for i in range(len(pearsonEigenValue)):
+			if type(pearsonEigenValue[i]).__name__ == 'complex':
+				pearsonEigenValue[i] = pearsonEigenValue[i].real
+		for i in range(len(pearsonEigenVectors)):
+			for j in range(len(pearsonEigenVectors[i])):
+				if type(pearsonEigenVectors[i][j]).__name__ == 'complex':
+					pearsonEigenVectors[i][j] = pearsonEigenVectors[i][j].real
+				if type(pearsonEigenVectors[i][j]).__name__ == 'complex':
+					pearsonEigenVectors[i][j] = pearsonEigenVectors[i][j].real		      
+		"""
+        
+		if type(pearsonEigenValue[0]).__name__ == 'complex':
+		   pass
+		else:	
+            	   traitHeading = HT.Paragraph('PCA Traits',align='left', Class="title")  
+                   
+        	   tbl2 = self.calcPCATraits(traitDataList=traitDataList, nnCorr=nnCorr, NNN=NNN, pearsonEigenValue=pearsonEigenValue, 
+                                         pearsonEigenVectors=pearsonEigenVectors, form=form, fd=fd)
+                   #Buttons on search page
+                   #mintmap = HT.Input(type='button' ,name='mintmap',value='Multiple Mapping', onClick="databaseFunc(this.form,'showIntMap');",Class="button")
+                   addselect = HT.Input(type='button' ,name='addselect',value='Add to Collection', onClick="addRmvSelection('%s', this.form, 'addToSelection');"  % fd.RISet,Class="button")
+                   selectall = HT.Input(type='button' ,name='selectall',value='Select All', onClick="checkAll(this.form);",Class="button")
+                   reset = HT.Input(type='reset',name='',value='Select None',Class="button")
+                   updateNames = HT.Input(type='button', name='updateNames',value='Update Trait Names', onClick="editPCAName(this.form);", Class="button")
+                   chrMenu = HT.Input(type='hidden',name='chromosomes',value='all')
+                   
+                   """
+                   #need to be refined
+                   if fd.genotype.Mbmap:
+                       scaleMenu = HT.Select(name='scale')
+                       scaleMenu.append(tuple(["Genetic Map",'morgan']))
+                       scaleMenu.append(tuple(["Physical Map",'physic']))
+                   else:
+                       scaleMenu = ""
+                   """    
+                   
+                   tbl2.append(HT.TR(HT.TD(HT.P(),chrMenu,updateNames,selectall,reset,addselect,colspan=3)))
+        	   form.append(HT.P(),traitHeading,HT.Blockquote(tbl2))
+                
+                   plotHeading1 = HT.Paragraph('Scree Plot', Class="title") 
+                   TD_LR.append(plotHeading1)
+                   img1 = self.screePlot(NNN=NNN, pearsonEigenValue=pearsonEigenValue)
+    
+                   TD_LR.append(HT.Blockquote(img1))
+                   
+                   plotHeading2 = HT.Paragraph('Factor Loadings Plot', Class="title")
+                   TD_LR.append(plotHeading2)
+                   img2 = self.factorLoadingsPlot(pearsonEigenVectors=pearsonEigenVectors, traitList=traitList)
+                   
+                   TD_LR.append(HT.Blockquote(img2))                      
+
+        self.dict['body'] = str(TD_LR)
+    
+    def screePlot(self, NNN=0, pearsonEigenValue=None):
+
+        c1 = pid.PILCanvas(size=(700,500))
+        Plot.plotXY(canvas=c1, dataX=range(1,NNN+1), dataY=pearsonEigenValue, rank=0, labelColor=pid.blue,plotColor=pid.red, symbolColor=pid.blue, XLabel='Factor Number', connectdot=1,YLabel='Percent of Total Variance %', title='Pearson\'s R Scree Plot')
+        filename= webqtlUtil.genRandStr("Scree_")
+        c1.save(webqtlConfig.IMGDIR+filename, format='gif')
+        img=HT.Image('/image/'+filename+'.gif',border=0)
+        
+        return img
+    
+    def factorLoadingsPlot(self, pearsonEigenVectors=None, traitList=None):
+        
+        traitname = map(lambda X:str(X.name), traitList)
+        c2 = pid.PILCanvas(size=(700,500))
+        Plot.plotXY(c2, pearsonEigenVectors[0],pearsonEigenVectors[1], 0, dataLabel = traitname, labelColor=pid.blue, plotColor=pid.red, symbolColor=pid.blue,XLabel='Factor (1)', connectdot=1, YLabel='Factor (2)', title='Factor Loadings Plot (Pearson)', loadingPlot=1)
+        filename= webqtlUtil.genRandStr("FacL_")
+        c2.save(webqtlConfig.IMGDIR+filename, format='gif')
+        img = HT.Image('/image/'+filename+'.gif',border=0)
+     
+        return img
+    
+    def calcPCATraits(self, traitDataList=None, nnCorr=0, NNN=0, pearsonEigenValue=None, pearsonEigenVectors=None, form=None, fd=None):  
+       """
+       This function currently returns the html to be displayed instead of the traits themselves. Need to fix later.
+       """
+    
+       detailInfo = string.split(self.searchResult[0],':')
+    
+       self.sameProbeSet = 'yes'
+       for item in self.searchResult[1:]:
+           detailInfo2 = string.split(item,':')
+           if detailInfo[0] != detailInfo2[0] or detailInfo[1] != detailInfo2[1]:
+               self.sameProbeSet = None
+               break
+                
+       for item in traitDataList:
+           if len(item) != nnCorr:
+               return
+       infoStrains = []
+       infoStrainsPos = []
+       dataArray  = [[] for i in range(NNN)]
+    
+       for i in range(len(traitDataList[0])):
+           currentStrain = 1
+           for j in range(NNN):
+               if not traitDataList[j][i]:
+                   currentStrain = 0
+                   break
+           if currentStrain == 1:
+               infoStrains.append(fd.strainlist[i])
+               infoStrainsPos.append(i)
+               for j in range(NNN): 
+                   dataArray[j].append(traitDataList[j][i])
+
+    
+       self.cursor.execute('delete Temp, TempData FROM Temp, TempData WHERE Temp.DataId = TempData.Id and UNIX_TIMESTAMP()-UNIX_TIMESTAMP(CreateTime)>%d;' % webqtlConfig.MAXLIFE)
+
+       StrainIds = []
+       for item in infoStrains:
+           self.cursor.execute('SELECT Strain.Id FROM Strain,StrainXRef, InbredSet WHERE Strain.Name="%s" and Strain.Id = StrainXRef.StrainId and StrainXRef.InbredSetId = InbredSet.Id and InbredSet.Name = "%s"' % (item, fd.RISet))
+           StrainIds.append('%d' % self.cursor.fetchone()[0])
+    
+       """
+       #minimal 12 overlapping strains      
+       if len(dataArray[0]) < 12:
+           form.append(HT.P(),traitHeading,HT.Blockquote(HT.Paragraph('The number of overlapping strains is less than 12, no PCA scores computed.',align='left')))
+           self.dict['body'] = str(TD_LR)
+           return
+       """
+       dataArray = self.zScore(dataArray)
+       dataArray = numarray.array(dataArray)
+       dataArray2 = numarray.dot(pearsonEigenVectors,dataArray)
+    
+       tbl2 = HT.TableLite(cellSpacing=2,cellPadding=0,border=0, width="100%")
+    
+       ct0 = time.localtime(time.time())
+       ct = time.strftime("%B/%d %H:%M:%S",ct0)
+       if self.sameProbeSet:
+           newDescription = 'PCA Traits generated at %s from %s' % (ct,detailInfo[1])
+       else:
+           newDescription = 'PCA Traits generated at %s from traits selected' % ct
+    
+
+       j = 1
+       self.cursor.execute('SELECT Id  FROM InbredSet WHERE Name = "%s"' % fd.RISet)
+       InbredSetId = self.cursor.fetchall()[0][0]
+       user_ip = fd.remote_ip
+       if fd.formdata.getvalue("newNames"):
+           newNames = fd.formdata.getvalue("newNames").split(",")
+       else:
+       	   newNames = 0
+       
+       for item in dataArray2:
+           if pearsonEigenValue[j-1] < 100.0/NNN:
+               break
+           
+           if (newNames == 0):
+               description  = '%s : PC%02d' % (newDescription, j)              
+           else:    
+               description = '%s : %s' % (newDescription, newNames[j-1])           	   
+                  
+           self.cursor.execute('SELECT max(id) FROM TempData')
+           try:
+               DataId = self.cursor.fetchall()[0][0] + 1
+           except:
+               DataId = 1
+           newProbeSetID = webqtlUtil.genRandStr("PCA_Tmp_")
+           self.cursor.execute('insert into Temp(Name,description, createtime,DataId,InbredSetId,IP) values(%s,%s,Now(),%s,%s,%s)' ,(newProbeSetID, description, DataId,InbredSetId,user_ip))
+        
+           k = 0    
+           for StrainId in StrainIds:
+               self.cursor.execute('insert into TempData(Id, StrainId, value) values(%s,%s,%s)' % (DataId, StrainId, item[k]*(-1.0)))
+               k += 1
+           setDescription = HT.Div(id="pcaTrait%s" % j)
+           descriptionLink = HT.Href(text=description, url="javascript:showDatabase2('Temp','%s','')" % newProbeSetID, Class="fwn")
+           descriptionEdit = HT.Input(type='text', value='', name='editName%s' % j)
+           
+           #onBlur='editPDAName(this.form, %s);' % j
+           
+           setDescription.append(descriptionLink)
+           setDescription.append(descriptionEdit)
+           
+           traitName = "%s:%s" % ('Temp',newProbeSetID)
+           tbl2.append(HT.TR(HT.TD("%d."%j,align="right",valign="top"),HT.TD(HT.Input(type="checkbox", Class="checkbox", name="searchResult",value=traitName),valign="top",width=50),HT.TD(setDescription)))
+           j += 1
+    
+       return tbl2
+    
+    def zScore(self,dataArray):
+        NN = len(dataArray[0])
+        if NN < 10:
+            return dataArray
+        else:
+            i = 0
+            for data in dataArray:
+                N = len(data)
+                S = reduce(lambda x,y: x+y, data, 0.)
+                SS = reduce(lambda x,y: x+y*y, data, 0.)
+                mean = S/N
+                var = SS - S*S/N
+                stdev = math.sqrt(var/(N-1))
+                data2 = map(lambda x:(x-mean)/stdev,data)
+                dataArray[i] = data2
+                i += 1
+            return dataArray
+    
+    def sortEigenVectors(self,vector):
+        try:
+            eigenValues = vector[0].tolist()
+            eigenVectors = vector[1].tolist()
+            combines = []
+            i = 0
+            for item in eigenValues:
+                combines.append([eigenValues[i],eigenVectors[i]])
+                i += 1
+            combines.sort(webqtlUtil.cmpEigenValue)
+            A = []
+            B = []
+            for item in combines:
+                A.append(item[0])
+                B.append(item[1])
+            sum = reduce(lambda x,y: x+y, A, 0.0)
+            A = map(lambda x:x*100.0/sum, A) 
+            return [A,B]
+        except:
+            return []
+