# 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 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 []