Removed everything from 'web' directory except genofiles and renamed the directory to 'genotype_files'

7 files changed, 0 insertions, 5824 deletions

diff --git a/web/webqtl/correlation/CorrelationPage.py b/web/webqtl/correlation/CorrelationPage.py
deleted file mode 100755
index 0c98f032..00000000
--- a/web/webqtl/correlation/CorrelationPage.py
+++ /dev/null
@@ -1,2065 +0,0 @@
-## 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 2011/02/11
-# Last updated by Christian Fernandez 2012/04/07
-# Refactored correlation calculation into smaller functions in preparation of
-# separating html from existing code
-
-import string
-from math import *
-import cPickle
-import os
-import time
-import pyXLWriter as xl
-import pp
-import math
-
-from htmlgen import HTMLgen2 as HT
-import reaper
-
-from base import webqtlConfig
-from utility.THCell import THCell
-from utility.TDCell import TDCell
-from base.webqtlTrait import webqtlTrait
-from base.webqtlDataset import webqtlDataset
-from base.templatePage import templatePage
-from utility import webqtlUtil
-from dbFunction import webqtlDatabaseFunction
-import utility.webqtlUtil #this is for parallel computing only.
-from correlation import correlationFunction
-
-import logging
-logging.basicConfig(filename="/tmp/gn_log", level=logging.INFO)
-_log = logging.getLogger("correlation")
-
-METHOD_SAMPLE_PEARSON = "1"
-METHOD_SAMPLE_RANK = "2"
-METHOD_LIT = "3"
-METHOD_TISSUE_PEARSON = "4"
-METHOD_TISSUE_RANK = "5"
-
-TISSUE_METHODS = [METHOD_TISSUE_PEARSON, METHOD_TISSUE_RANK]
-
-TISSUE_MOUSE_DB = 1
-
-class AuthException(Exception): pass
-
-
-class Trait(object):
-    def __init__(self, name, raw_values = None, lit_corr = None, tissue_corr = None, p_tissue = None):
-        self.name = name
-        self.raw_values = raw_values
-        self.lit_corr = lit_corr
-        self.tissue_corr = tissue_corr
-        self.p_tissue = p_tissue
-        self.correlation = 0
-        self.p_value = 0
-
-    @staticmethod
-    def from_csv(line, data_start = 1):
-        name = line[0]
-        numbers = line[data_start:]
-    #	_log.info(numbers)
-        numbers = [ float(number) for number in numbers ]
-
-        return Trait(name, raw_values = numbers)
-
-    def calculate_correlation(self, values, method):
-        """Calculate the correlation value and p value according to the method specified"""
-
-        #ZS: This takes the list of values of the trait our selected trait is being correlated against and removes the values of the samples our trait has no value for
-        #There's probably a better way of dealing with this, but I'll have to ask Christian
-        updated_raw_values = []
-        updated_values = []
-        for i in range(len(values)):
-            if values[i] != "None":
-                updated_raw_values.append(self.raw_values[i])
-                updated_values.append(values[i])
-
-        self.raw_values = updated_raw_values
-        values = updated_values
-
-        if method == METHOD_SAMPLE_PEARSON or method == METHOD_LIT or method == METHOD_TISSUE_PEARSON:
-            corr,nOverlap = webqtlUtil.calCorrelation(self.raw_values, values, len(values))
-        else:
-            corr,nOverlap = webqtlUtil.calCorrelationRank(self.raw_values, values, len(values))
-
-        self.correlation = corr
-        self.overlap = nOverlap
-
-        if self.overlap < 3:
-            self.p_value = 1.0
-        else:
-            #ZS - This is probably the wrong way to deal with this. Correlation values of 1.0 definitely exist (the trait correlated against itself), so zero division needs to br prevented.
-            if abs(self.correlation) >= 1.0:
-                self.p_value = 0.0
-            else:
-                ZValue = 0.5*log((1.0+self.correlation)/(1.0-self.correlation))
-                ZValue = ZValue*sqrt(self.overlap-3)
-                self.p_value = 2.0*(1.0 - reaper.normp(abs(ZValue)))
-
-
-
-#XZ, 01/14/2009: This method is for parallel computing only.
-#XZ: It is supposed to be called when "Genetic Correlation, Pearson's r" (method 1)
-#XZ: or "Genetic Correlation, Spearman's rho" (method 2) is selected
-def compute_corr( input_nnCorr, input_trait, input_list, computing_method):
-
-    allcorrelations = []
-
-    for line in input_list:
-	tokens = line.split('","')
-	tokens[-1] = tokens[-1][:-2] #remove the last "
-	tokens[0] = tokens[0][1:] #remove the first "
-
-	traitdataName = tokens[0]
-	database_trait = tokens[1:]
-
-	if computing_method == "1": #XZ: Pearson's r
-	    corr,nOverlap = utility.webqtlUtil.calCorrelationText(input_trait, database_trait, input_nnCorr)
-	else: #XZ: Spearman's rho
-	    corr,nOverlap = utility.webqtlUtil.calCorrelationRankText(input_trait, database_trait, input_nnCorr)
-	traitinfo = [traitdataName,corr,nOverlap]
-	allcorrelations.append(traitinfo)
-
-    return allcorrelations
-
-def get_correlation_method_key(form_data):
-    #XZ, 09/28/2008: if user select "1", then display 1, 3 and 4.
-    #XZ, 09/28/2008: if user select "2", then display 2, 3 and 5.
-    #XZ, 09/28/2008: if user select "3", then display 1, 3 and 4.
-    #XZ, 09/28/2008: if user select "4", then display 1, 3 and 4.
-    #XZ, 09/28/2008: if user select "5", then display 2, 3 and 5.
-
-    method = form_data.formdata.getvalue("method")
-    if method not in ["1", "2", "3" ,"4", "5"]:
-	return "1"
-
-    return method
-
-
-def get_custom_trait(form_data, cursor):
-    """Pulls the custom trait, if it exists, out of the form data"""
-    trait_name = form_data.formdata.getvalue('fullname')
-
-    if trait_name:
-	trait = webqtlTrait(fullname=trait_name, cursor=cursor)
-	trait.retrieveInfo()
-	return trait
-    else:
-	return None
-
-
-#XZ, 09/18/2008: get the information such as value, variance of the input strain names from the form.
-def get_sample_data(form_data):
-    if form_data.allstrainlist:
-	mdpchoice = form_data.formdata.getvalue('MDPChoice')
-	#XZ, in HTML source code, it is "BXD Only" or "BXH only", and so on
-	if mdpchoice == "1":
-	    strainlist = form_data.f1list + form_data.strainlist
-	#XZ, in HTML source code, it is "MDP Only"
-	elif mdpchoice == "2":
-	    strainlist = []
-	    strainlist2 = form_data.f1list + form_data.strainlist
-	    for strain in form_data.allstrainlist:
-		if strain not in strainlist2:
-		    strainlist.append(strain)
-	    #So called MDP Panel
-	    if strainlist:
-		strainlist = form_data.f1list+form_data.parlist+strainlist
-	#XZ, in HTML source code, it is "All Cases"
-	else:
-	    strainlist = form_data.allstrainlist
-	#XZ, 09/18/2008: put the trait data into dictionary form_data.allTraitData
-	form_data.readData(form_data.allstrainlist)
-    else:
-	mdpchoice = None
-	strainlist = form_data.strainlist
-	#XZ, 09/18/2008: put the trait data into dictionary form_data.allTraitData
-	form_data.readData()
-
-    return strainlist
-
-
-def get_mdp_choice(form_data):
-    if form_data.allstrainlist:
-	return form_data.formdata.getvalue("MDPChoice")
-    else:
-	return None
-
-
-def get_species(fd, cursor):
-    #XZ, 3/16/2010: variable RISet must be pass by the form
-    RISet = fd.RISet
-    #XZ, 12/12/2008: get species infomation
-    species = webqtlDatabaseFunction.retrieveSpecies(cursor=cursor, RISet=RISet)
-    return species
-
-
-def sortTraitCorrelations(traits, method="1"):
-    if method in TISSUE_METHODS:
-        traits.sort(key=lambda trait: trait.tissue_corr != None and abs(trait.tissue_corr), reverse=True)
-    elif method == METHOD_LIT:
-        traits.sort(key=lambda trait: trait.lit_corr != None and abs(trait.lit_corr), reverse=True)
-    else:
-        traits.sort(key=lambda trait: trait.correlation != None and abs(trait.correlation), reverse=True)
-
-    return traits
-
-
-def auth_user_for_db(db, cursor, target_db_name, privilege, username):
-    """Authorize a user for access to a database if that database is
-    confidential. A db (identified by a record in ProbeSetFreeze) contains a
-    list of authorized users who may access it, as well as its confidentiality
-    level.
-
-    If the current user's privilege level is greater than 'user', ie: root or
-    admin, then they are automatically authed, otherwise, check the
-    AuthorizedUsers field for the presence of their name."""
-
-    if db.type == 'ProbeSet':
-	cursor.execute('SELECT Id, Name, FullName, confidentiality, AuthorisedUsers FROM ProbeSetFreeze WHERE Name = "%s"' %  target_db_name)
-	indId, indName, indFullName, confidential, AuthorisedUsers = cursor.fetchall()[0]
-
-	if confidential:
-	    authorized = 0
-
-	    #for the dataset that confidentiality is 1
-	    #1. 'admin' and 'root' can see all of the dataset
-	    #2. 'user' can see the dataset that AuthorisedUsers contains his id(stored in the Id field of User table)
-	    if webqtlConfig.USERDICT[privilege] > webqtlConfig.USERDICT['user']:
-		authorized = 1
-	    else:
-		if username in AuthorisedUsers.split(","):
-		    authorized = 1
-
-	    if not authorized:
-		raise AuthException("The %s database you selected is not open to the public at this time, please go back and select other database." % indFullName)
-
-
-class CorrelationPage(templatePage):
-
-    corrMinInformative = 4
-
-    PAGE_HEADING = "Correlation Table"
-    CORRELATION_METHODS = {"1" : "Genetic Correlation (Pearson's r)",
-			   "2" : "Genetic Correlation (Spearman's rho)",
-			   "3" : "SGO Literature Correlation",
-			   "4" : "Tissue Correlation (Pearson's r)",
-			   "5" : "Tissue Correlation (Spearman's rho)"}
-
-    RANK_ORDERS = {"1": 0, "2": 1, "3": 0, "4": 0, "5": 1}
-
-
-    def error(self, message, error="Error", heading = None):
-	heading = heading or self.PAGE_HEADING
-	return templatePage.error(heading = heading, detail = [message], error=error)
-
-    def __init__(self, fd):
-
-	# Call the superclass constructor
-        templatePage.__init__(self, fd)
-
-	# Connect to the database
-        if not self.openMysql():
-            return
-
-	# Read the genotype from a file
-        if not fd.genotype:
-            fd.readGenotype()
-
-        sample_list = get_sample_data(fd)
-        mdp_choice = get_mdp_choice(fd) # No idea what this is yet
-        self.species = get_species(fd, self.cursor)
-
-        #XZ, 09/18/2008: get all information about the user selected database.
-        target_db_name = fd.formdata.getvalue('database')
-        self.target_db_name = target_db_name
-
-        try:
-            self.db = webqtlDataset(target_db_name, self.cursor)
-        except:
-            detail = ["The database you just requested has not been established yet."]
-            self.error(detail)
-            return
-
-	 # Auth if needed
-        try:
-	     auth_user_for_db(self.db, self.cursor, target_db_name, self.privilege, self.userName)
-        except AuthException, e:
-            detail = [e.message]
-            return self.error(detail)
-
-        #XZ, 09/18/2008: filter out the strains that have no value.
-        self.sample_names, vals, vars, N = fd.informativeStrains(sample_list)
-
-	 #CF - If less than a minimum number of strains/cases in common, don't calculate anything
-        if len(self.sample_names) < self.corrMinInformative:
-            detail = ['Fewer than %d strain data were entered for %s data set. No calculation of correlation has been attempted.' % (self.corrMinInformative, fd.RISet)]
-            self.error(heading=PAGE_HEADING,detail=detail)
-
-
-        self.method = get_correlation_method_key(fd)
-        correlation_method = self.CORRELATION_METHODS[self.method]
-        rankOrder = self.RANK_ORDERS[self.method]
-
-	 # CF - Number of results returned
-        self.returnNumber = int(fd.formdata.getvalue('criteria'))
-
-        self.record_count = 0
-
-        myTrait = get_custom_trait(fd, self.cursor)
-
-
-        # We will not get Literature Correlations if there is no GeneId because there is nothing to look against
-        self.gene_id = int(fd.formdata.getvalue('GeneId') or 0)
-
-        # We will not get Tissue Correlations if there is no gene symbol because there is nothing to look against
-        self.trait_symbol = myTrait.symbol
-
-
-        #XZ, 12/12/2008: if the species is rat or human, translate the geneid to mouse geneid
-        self.input_trait_mouse_gene_id = self.translateToMouseGeneID(self.species, self.gene_id)
-
-        #XZ: As of Nov/13/2010, this dataset is 'UTHSC Illumina V6.2 RankInv B6 D2 average CNS GI average (May 08)'
-        self.tissue_probeset_freeze_id = 1
-
-        traitList = self.correlate(vals)
-
-        _log.info("Done doing correlation calculation")
-
-############################################################################################################################################
-
-        TD_LR = HT.TD(height=200,width="100%",bgColor='#eeeeee')
-
-        mainfmName = webqtlUtil.genRandStr("fm_")
-        form = HT.Form(cgi= os.path.join(webqtlConfig.CGIDIR, webqtlConfig.SCRIPTFILE), enctype='multipart/form-data', name= mainfmName, submit=HT.Input(type='hidden'))
-        hddn = {'FormID': 'showDatabase',
-	        'ProbeSetID': '_',
-		'database': self.target_db_name,
-		'databaseFull': self.db.fullname,
-		'CellID': '_',
-		'RISet': fd.RISet,
-		'identification': fd.identification}
-
-        if myTrait:
-            hddn['fullname']=fd.formdata.getvalue('fullname')
-        if mdp_choice:
-            hddn['MDPChoice']=mdp_choice
-
-
-        #XZ, 09/18/2008: pass the trait data to next page by hidden parameters.
-        webqtlUtil.exportData(hddn, fd.allTraitData)
-
-        if fd.incparentsf1:
-            hddn['incparentsf1']='ON'
-
-        if fd.allstrainlist:
-            hddn['allstrainlist'] = string.join(fd.allstrainlist, ' ')
-
-
-        for key in hddn.keys():
-            form.append(HT.Input(name=key, value=hddn[key], type='hidden'))
-
-        #XZ, 11/21/2008: add two parameters to form
-        form.append(HT.Input(name="X_geneSymbol", value="", type='hidden'))
-        form.append(HT.Input(name="Y_geneSymbol", value="", type='hidden'))
-
-        #XZ, 3/11/2010: add one parameter to record if the method is rank order.
-        form.append(HT.Input(name="rankOrder", value="%s" % rankOrder, type='hidden'))
-
-        form.append(HT.Input(name="TissueProbeSetFreezeId", value="%s" % self.tissue_probeset_freeze_id, type='hidden'))
-
-        ####################################
-        # generate the info on top of page #
-        ####################################
-
-        info = self.getTopInfo(myTrait=myTrait, method=self.method, db=self.db, target_db_name=self.target_db_name, returnNumber=self.returnNumber, methodDict=self.CORRELATION_METHODS, totalTraits=traitList, identification=fd.identification  )
-
-        ##############
-        # Excel file #
-        ##############
-        filename= webqtlUtil.genRandStr("Corr_")
-        xlsUrl = HT.Input(type='button', value = 'Download Table', onClick= "location.href='/tmp/%s.xls'" % filename, Class='button')
-        # Create a new Excel workbook
-        workbook = xl.Writer('%s.xls' % (webqtlConfig.TMPDIR+filename))
-        headingStyle = workbook.add_format(align = 'center', bold = 1, border = 1, size=13, fg_color = 0x1E, color="white")
-
-        #XZ, 3/18/2010: pay attention to the line number of header in this file. As of today, there are 7 lines.
-        worksheet = self.createExcelFileWithTitleAndFooter(workbook=workbook, identification=fd.identification, db=self.db, returnNumber=self.returnNumber)
-
-        newrow = 7
-
-
-#####################################################################
-
-
-        #Select All, Deselect All, Invert Selection, Add to Collection
-        mintmap = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('%s')[0], 'showIntMap');" % mainfmName)
-        mintmap_img = HT.Image("/images/multiple_interval_mapping1_final.jpg", name='mintmap', alt="Multiple Interval Mapping", title="Multiple Interval Mapping", style="border:none;")
-        mintmap.append(mintmap_img)
-        mcorr = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('%s')[0], 'compCorr');" % mainfmName)
-        mcorr_img = HT.Image("/images/compare_correlates2_final.jpg", alt="Compare Correlates", title="Compare Correlates", style="border:none;")
-        mcorr.append(mcorr_img)
-        cormatrix = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('%s')[0], 'corMatrix');" % mainfmName)
-        cormatrix_img = HT.Image("/images/correlation_matrix1_final.jpg", alt="Correlation Matrix and PCA", title="Correlation Matrix and PCA", style="border:none;")
-        cormatrix.append(cormatrix_img)
-        networkGraph = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('%s')[0], 'networkGraph');" % mainfmName)
-        networkGraph_img = HT.Image("/images/network_graph1_final.jpg", name='mintmap', alt="Network Graphs", title="Network Graphs", style="border:none;")
-        networkGraph.append(networkGraph_img)
-        heatmap = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('%s')[0], 'heatmap');" % mainfmName)
-        heatmap_img = HT.Image("/images/heatmap2_final.jpg", name='mintmap', alt="QTL Heat Map and Clustering", title="QTL Heatmap and Clustering", style="border:none;")
-        heatmap.append(heatmap_img)
-        partialCorr = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('%s')[0], 'partialCorrInput');" % mainfmName)
-        partialCorr_img = HT.Image("/images/partial_correlation_final.jpg", name='partialCorr', alt="Partial Correlation", title="Partial Correlation", style="border:none;")
-        partialCorr.append(partialCorr_img)
-        addselect = HT.Href(url="#redirect", onClick="addRmvSelection('%s', document.getElementsByName('%s')[0], 'addToSelection');" % (fd.RISet, mainfmName))
-        addselect_img = HT.Image("/images/add_collection1_final.jpg", name="addselect", alt="Add To Collection", title="Add To Collection", style="border:none;")
-        addselect.append(addselect_img)
-        selectall = HT.Href(url="#redirect", onClick="checkAll(document.getElementsByName('%s')[0]);" % mainfmName)
-        selectall_img = HT.Image("/images/select_all2_final.jpg", name="selectall", alt="Select All", title="Select All", style="border:none;")
-        selectall.append(selectall_img)
-        selectinvert = HT.Href(url="#redirect", onClick = "checkInvert(document.getElementsByName('%s')[0]);" % mainfmName)
-        selectinvert_img = HT.Image("/images/invert_selection2_final.jpg", name="selectinvert", alt="Invert Selection", title="Invert Selection", style="border:none;")
-        selectinvert.append(selectinvert_img)
-        reset = HT.Href(url="#redirect", onClick="checkNone(document.getElementsByName('%s')[0]); return false;" % mainfmName)
-        reset_img = HT.Image("/images/select_none2_final.jpg", alt="Select None", title="Select None", style="border:none;")
-        reset.append(reset_img)
-        selecttraits = HT.Input(type='button' ,name='selecttraits',value='Select Traits', onClick="checkTraits(this.form);",Class="button")
-        selectgt = HT.Input(type='text' ,name='selectgt',value='-1.0', size=6,maxlength=10,onChange="checkNumeric(this,1.0,'-1.0','gthan','greater than filed')")
-        selectlt = HT.Input(type='text' ,name='selectlt',value='1.0', size=6,maxlength=10,onChange="checkNumeric(this,-1.0,'1.0','lthan','less than field')")
-        selectandor = HT.Select(name='selectandor')
-        selectandor.append(('AND','and'))
-        selectandor.append(('OR','or'))
-        selectandor.selected.append('AND')
-
-
-        #External analysis tools
-        GCATButton = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('%s')[0], 'GCAT');" % mainfmName)
-        GCATButton_img = HT.Image("/images/GCAT_logo_final.jpg", name="GCAT", alt="GCAT", title="GCAT", style="border:none")
-        GCATButton.append(GCATButton_img)
-
-        ODE = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('%s')[0], 'ODE');" % mainfmName)
-        ODE_img = HT.Image("/images/ODE_logo_final.jpg", name="ode", alt="ODE", title="ODE", style="border:none")
-        ODE.append(ODE_img)
-
-        '''
-        #XZ, 07/07/2010: I comment out this block of code.
-        WebGestaltScript = HT.Script(language="Javascript")
-        WebGestaltScript.append("""
-setTimeout('openWebGestalt()', 2000);
-function openWebGestalt(){
-var thisForm = document['WebGestalt'];
-makeWebGestaltTree(thisForm, '%s', %d, 'edag_only.php');
-}
-        """ % (mainfmName, len(traitList)))
-        '''
-
-        self.cursor.execute('SELECT GeneChip.GO_tree_value FROM GeneChip, ProbeFreeze, ProbeSetFreeze WHERE GeneChip.Id = ProbeFreeze.ChipId and ProbeSetFreeze.ProbeFreezeId = ProbeFreeze.Id and ProbeSetFreeze.Name = "%s"' % self.db.name)
-        result = self.cursor.fetchone()
-
-        if result:
-            GO_tree_value = result[0]
-
-        if GO_tree_value:
-
-            WebGestalt = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('%s')[0], 'GOTree');" % mainfmName)
-            WebGestalt_img = HT.Image("/images/webgestalt_icon_final.jpg", name="webgestalt", alt="Gene Set Analysis Toolkit", title="Gene Set Analysis Toolkit", style="border:none")
-            WebGestalt.append(WebGestalt_img)
-
-            hddnWebGestalt = {
-                              'id_list':'',
-                              'correlation':'',
-                              'id_value':'',
-                              'llid_list':'',
-                              'id_type':GO_tree_value,
-                              'idtype':'',
-                              'species':'',
-                              'list':'',
-                              'client':''}
-
-            hddnWebGestalt['ref_type'] = hddnWebGestalt['id_type']
-            hddnWebGestalt['cat_type'] = 'GO'
-            hddnWebGestalt['significancelevel'] = 'Top10'
-
-            if self.species == 'rat':
-                hddnWebGestalt['org'] = 'Rattus norvegicus'
-            elif self.species == 'human':
-                hddnWebGestalt['org'] = 'Homo sapiens'
-            elif self.species == 'mouse':
-                hddnWebGestalt['org'] = 'Mus musculus'
-            else:
-                hddnWebGestalt['org'] = ''
-
-            for key in hddnWebGestalt.keys():
-                form.append(HT.Input(name=key, value=hddnWebGestalt[key], type='hidden'))
-
-
-        #Create tables with options, etc
-
-        pageTable = HT.TableLite(cellSpacing=0,cellPadding=0,width="100%", border=0, align="Left")
-
-        containerTable = HT.TableLite(cellSpacing=0,cellPadding=0,width="90%",border=0, align="Left")
-
-
-        if not GO_tree_value:
-            optionsTable = HT.TableLite(cellSpacing=2, cellPadding=0,width="480", height="80", border=0, align="Left")
-            optionsTable.append(HT.TR(HT.TD(selectall), HT.TD(reset), HT.TD(selectinvert), HT.TD(addselect), HT.TD(GCATButton), HT.TD(ODE), align="left"))
-            optionsTable.append(HT.TR(HT.TD("&nbsp;"*1,"Select"), HT.TD("Deselect"), HT.TD("&nbsp;"*1,"Invert"), HT.TD("&nbsp;"*3,"Add"), HT.TD("Gene Set"), HT.TD("&nbsp;"*2,"GCAT")))
-        else:
-            optionsTable = HT.TableLite(cellSpacing=2, cellPadding=0,width="560", height="80", border=0, align="Left")
-            optionsTable.append(HT.TR(HT.TD(selectall), HT.TD(reset), HT.TD(selectinvert), HT.TD(addselect), HT.TD(GCATButton), HT.TD(ODE), HT.TD(WebGestalt), align="left"))
-            optionsTable.append(HT.TR(HT.TD("&nbsp;"*1,"Select"), HT.TD("Deselect"), HT.TD("&nbsp;"*1,"Invert"), HT.TD("&nbsp;"*3,"Add"), HT.TD("Gene Set"), HT.TD("&nbsp;"*2,"GCAT"), HT.TD("&nbsp;"*3, "ODE")))
-        containerTable.append(HT.TR(HT.TD(optionsTable)))
-
-        functionTable = HT.TableLite(cellSpacing=2,cellPadding=0,width="480",height="80", border=0, align="Left")
-        functionRow = HT.TR(HT.TD(networkGraph, width="16.7%"), HT.TD(cormatrix, width="16.7%"), HT.TD(partialCorr, width="16.7%"), HT.TD(mcorr, width="16.7%"), HT.TD(mintmap, width="16.7%"), HT.TD(heatmap), align="left")
-        labelRow = HT.TR(HT.TD("&nbsp;"*1,HT.Text("Graph")), HT.TD("&nbsp;"*1,HT.Text("Matrix")), HT.TD("&nbsp;"*1,HT.Text("Partial")), HT.TD(HT.Text("Compare")), HT.TD(HT.Text("QTL Map")), HT.TD(HT.Text(text="Heat Map")))
-        functionTable.append(functionRow, labelRow)
-        containerTable.append(HT.TR(HT.TD(functionTable), HT.BR()))
-
-        #more_options = HT.Image("/images/more_options1_final.jpg", name='more_options', alt="Expand Options", title="Expand Options", style="border:none;", Class="toggleShowHide")
-
-        #containerTable.append(HT.TR(HT.TD(more_options, HT.BR(), HT.BR())))
-
-        moreOptions = HT.Input(type='button',name='options',value='More Options', onClick="",Class="toggle")
-        fewerOptions = HT.Input(type='button',name='options',value='Fewer Options', onClick="",Class="toggle")
-
-        """
-        if (fd.formdata.getvalue('showHideOptions') == 'less'):
-            containerTable.append(HT.TR(HT.TD("&nbsp;"), height="10"), HT.TR(HT.TD(HT.Div(fewerOptions, Class="toggleShowHide"))))
-            containerTable.append(HT.TR(HT.TD("&nbsp;")))
-        else:
-            containerTable.append(HT.TR(HT.TD("&nbsp;"), height="10"), HT.TR(HT.TD(HT.Div(moreOptions, Class="toggleShowHide"))))
-            containerTable.append(HT.TR(HT.TD("&nbsp;")))
-        """
-
-        containerTable.append(HT.TR(HT.TD(HT.Span(selecttraits,' with r > ',selectgt, ' ',selectandor, ' r < ',selectlt,Class="bd1 cbddf fs11")), style="display:none;", Class="extra_options"))
-
-        chrMenu = HT.Input(type='hidden',name='chromosomes',value='all')
-
-        corrHeading = HT.Paragraph('Correlation Table', Class="title")
-
-
-        tblobj = {}
-
-        if self.db.type=="Geno":
-            containerTable.append(HT.TR(HT.TD(xlsUrl, height=60)))
-
-            pageTable.append(HT.TR(HT.TD(containerTable)))
-
-            tblobj['header'], worksheet = self.getTableHeaderForGeno( method=self.method, worksheet=worksheet, newrow=newrow, headingStyle=headingStyle)
-            newrow += 1
-
-            sortby = self.getSortByValue( calculationMethod = self.method )
-
-            corrScript = HT.Script(language="Javascript")
-            corrScript.append("var corrArray = new Array();")
-
-            tblobj['body'], worksheet, corrScript = self.getTableBodyForGeno(traitList=traitList, formName=mainfmName, worksheet=worksheet, newrow=newrow, corrScript=corrScript)
-
-            workbook.close()
-            objfile = open('%s.obj' % (webqtlConfig.TMPDIR+filename), 'wb')
-            cPickle.dump(tblobj, objfile)
-            objfile.close()
-
-            div = HT.Div(webqtlUtil.genTableObj(tblobj=tblobj, file=filename, sortby=sortby, tableID = "sortable", addIndex = "1"), corrScript, Id="sortable")
-
-            pageTable.append(HT.TR(HT.TD(div)))
-
-            form.append(HT.Input(name='ShowStrains',type='hidden', value =1),
-            	 	HT.Input(name='ShowLine',type='hidden', value =1),
-            		HT.P(), HT.P(), pageTable)
-            TD_LR.append(corrHeading, info, form, HT.P())
-
-            self.dict['body'] =  str(TD_LR)
-            self.dict['js1'] = ''
-            self.dict['title'] = 'Correlation'
-
-        elif self.db.type=="Publish":
-
-            containerTable.append(HT.TR(HT.TD(xlsUrl, height=40)))
-
-            pageTable.append(HT.TR(HT.TD(containerTable)))
-
-            tblobj['header'], worksheet = self.getTableHeaderForPublish(method=self.method, worksheet=worksheet, newrow=newrow, headingStyle=headingStyle)
-            newrow += 1
-
-            sortby = self.getSortByValue( calculationMethod = self.method )
-
-            corrScript = HT.Script(language="Javascript")
-            corrScript.append("var corrArray = new Array();")
-
-            tblobj['body'], worksheet, corrScript = self.getTableBodyForPublish(traitList=traitList, formName=mainfmName, worksheet=worksheet, newrow=newrow, corrScript=corrScript, species=self.species)
-
-            workbook.close()
-
-            objfile = open('%s.obj' % (webqtlConfig.TMPDIR+filename), 'wb')
-            cPickle.dump(tblobj, objfile)
-            objfile.close()
-			# NL, 07/27/2010. genTableObj function has been moved from templatePage.py to webqtlUtil.py;
-            div = HT.Div(webqtlUtil.genTableObj(tblobj=tblobj, file=filename, sortby=sortby, tableID = "sortable", addIndex = "1"), corrScript, Id="sortable")
-
-            pageTable.append(HT.TR(HT.TD(div)))
-
-            form.append(
-            HT.Input(name='ShowStrains',type='hidden', value =1),
-            HT.Input(name='ShowLine',type='hidden', value =1),
-            HT.P(), pageTable)
-            TD_LR.append(corrHeading, info, form, HT.P())
-
-            self.dict['body'] = str(TD_LR)
-            self.dict['js1'] = ''
-            self.dict['title'] = 'Correlation'
-
-
-        elif self.db.type=="ProbeSet":
-            tblobj['header'], worksheet = self.getTableHeaderForProbeSet(method=self.method, worksheet=worksheet, newrow=newrow, headingStyle=headingStyle)
-            newrow += 1
-
-            sortby = self.getSortByValue( calculationMethod = self.method )
-
-            corrScript = HT.Script(language="Javascript")
-            corrScript.append("var corrArray = new Array();")
-
-            tblobj['body'], worksheet, corrScript = self.getTableBodyForProbeSet(traitList=traitList, primaryTrait=myTrait, formName=mainfmName, worksheet=worksheet, newrow=newrow, corrScript=corrScript, species=self.species)
-
-            workbook.close()
-            objfile = open('%s.obj' % (webqtlConfig.TMPDIR+filename), 'wb')
-            cPickle.dump(tblobj, objfile)
-            objfile.close()
-
-            #XZ: here is the table of traits
-            div = HT.Div(webqtlUtil.genTableObj(tblobj=tblobj, file=filename, sortby=sortby, tableID = "sortable", addIndex = "1", hiddenColumns=["Gene ID","Homologene ID"]), corrScript, Id="sortable")
-
-
-            #XZ, 01/12/2009: create database menu for 'Add Correlation'
-            self.cursor.execute("""
-                select
-                    ProbeSetFreeze.FullName, ProbeSetFreeze.Id, Tissue.name
-                from
-                    ProbeSetFreeze, ProbeFreeze, ProbeSetFreeze as ps2, ProbeFreeze as p2, Tissue
-                where
-                    ps2.Id = %d
-                    and ps2.ProbeFreezeId = p2.Id
-                    and ProbeSetFreeze.ProbeFreezeId = ProbeFreeze.Id
-                    and (ProbeFreeze.InbredSetId = p2.InbredSetId or (ProbeFreeze.InbredSetId in (1, 3) and p2.InbredSetId in (1, 3)))
-                    and p2.ChipId = ProbeFreeze.ChipId
-                    and ps2.Id != ProbeSetFreeze.Id
-                    and ProbeFreeze.TissueId = Tissue.Id
-                    and ProbeSetFreeze.public > %d
-                order by
-                    ProbeFreeze.TissueId, ProbeSetFreeze.CreateTime desc
-                """ % (self.db.id, webqtlConfig.PUBLICTHRESH))
-
-            results = self.cursor.fetchall()
-            dbCustomizer = HT.Select(results, name = "customizer")
-            databaseMenuSub = preTissue = ""
-            for item in results:
-                TName, TId, TTissue = item
-                if TTissue != preTissue:
-                    if databaseMenuSub:
-                        dbCustomizer.append(databaseMenuSub)
-                    databaseMenuSub = HT.Optgroup(label = '%s mRNA ------' % TTissue)
-                    preTissue = TTissue
-
-                databaseMenuSub.append(item[:2])
-            if databaseMenuSub:
-                dbCustomizer.append(databaseMenuSub)
-
-            #updated by NL. Delete function generateJavaScript, move js files to dhtml.js, webqtl.js and jqueryFunction.js
-            #variables: filename, strainIds and vals are required by getquerystring function
-            strainIds=self.getStrainIds(species=self.species, strains=self.sample_names)
-            var1 = HT.Input(name="filename", value=filename, type='hidden')
-            var2 = HT.Input(name="strainIds", value=strainIds, type='hidden')
-            var3 = HT.Input(name="vals", value=vals, type='hidden')
-            customizerButton = HT.Input(type="button", Class="button", value="Add Correlation", onClick = "xmlhttpPost('%smain.py?FormID=AJAX_table', 'sortable', (getquerystring(this.form)))" % webqtlConfig.CGIDIR)
-
-            containerTable.append(HT.TR(HT.TD(HT.Span(var1,var2,var3,customizerButton, "with", dbCustomizer, Class="bd1 cbddf fs11"), HT.BR(), HT.BR()), style="display:none;", Class="extra_options"))
-
-            containerTable.append(HT.TR(HT.TD(xlsUrl, HT.BR(), HT.BR())))
-
-            pageTable.append(HT.TR(HT.TD(containerTable)))
-
-            pageTable.append(HT.TR(HT.TD(div)))
-
-            if self.species == 'human':
-                heatmap = ""
-
-            form.append(HT.Input(name='ShowStrains',type='hidden', value =1),
-                            HT.Input(name='ShowLine',type='hidden', value =1),
-                            info, HT.BR(), pageTable, HT.BR())
-
-            TD_LR.append(corrHeading, form, HT.P())
-
-
-            self.dict['body'] = str(TD_LR)
-            self.dict['title'] = 'Correlation'
-			# updated by NL. Delete function generateJavaScript, move js files to dhtml.js, webqtl.js and jqueryFunction.js
-            self.dict['js1'] = ''
-            self.dict['js2'] = 'onLoad="pageOffset()"'
-            self.dict['layer'] = self.generateWarningLayer()
-        else:
-            self.dict['body'] = ""
-
-
-#############################
-#                           #
-# CorrelationPage Functions #
-#                           #
-#############################
-
-
-    def getSortByValue(self, calculationMethod):
-
-        if calculationMethod == "1":
-                sortby = ("Sample p(r)", "up")
-        elif calculationMethod == "2":
-                sortby = ("Sample p(rho)", "up")
-        elif calculationMethod == "3": #XZ: literature correlation
-                sortby = ("Lit Corr","down")
-        elif calculationMethod == "4": #XZ: tissue correlation
-                sortby = ("Tissue r", "down")
-        elif calculationMethod == "5":
-                sortby = ("Tissue rho", "down")
-
-        return sortby
-
-
-
-    def generateWarningLayer(self):
-
-        layerString = """
-<!-- BEGIN FLOATING LAYER CODE //-->
-<div id="warningLayer" style="padding:3px; border: 1px solid #222;
-  background-color: #fff; position:absolute;width:250px;left:100;top:100;visibility:hidden">
-<table border="0" width="250" class="cbrb" cellspacing="0" cellpadding="5">
-<tr>
-<td width="100%">
-  <table border="0" width="100%" cellspacing="0" cellpadding="0" height="36">
-  <tr>
-  <td class="cbrb cw ff15 fwb" align="Center" width="100%" style="padding:4px">
-        Sort Table
-  </td>
-  </tr>
-  <tr>
-  <td width="100%" bgcolor="#eeeeee" align="Center" style="padding:4px">
-<!-- PLACE YOUR CONTENT HERE //-->
-Resorting this table <br>
-<!-- END OF CONTENT AREA //-->
-  </td>
-  </tr>
-  </table>
-</td>
-</tr>
-</table>
-</div>
-<!-- END FLOATING LAYER CODE //-->
-
-            """
-
-        return layerString
-
-
-    #XZ, 01/07/2009: In HTML code, the variable 'database' corresponds to the column 'Name' in database table.
-    def getFileName(self, target_db_name):  ### dcrowell  August 2008
-        """Returns the name of the reference database file with which correlations are calculated.
-        Takes argument cursor which is a cursor object of any instance of a subclass of templatePage
-        Used by correlationPage"""
-
-        query = 'SELECT Id, FullName FROM ProbeSetFreeze WHERE Name = "%s"' %  target_db_name
-        self.cursor.execute(query)
-        result = self.cursor.fetchone()
-        Id = result[0]
-        FullName = result[1]
-        FullName = FullName.replace(' ','_')
-        FullName = FullName.replace('/','_')
-
-        FileName = 'ProbeSetFreezeId_' + str(Id) + '_FullName_' + FullName + '.txt'
-
-        return FileName
-
-
-    #XZ, 01/29/2009: I modified this function.
-    #XZ: Note that the type of StrainIds must be number, not string.
-    def getStrainIds(self, species=None, strains=[]):
-        StrainIds = []
-        for item in strains:
-            self.cursor.execute('''SELECT Strain.Id FROM Strain, Species WHERE
-                Strain.Name="%s" and Strain.SpeciesId=Species.Id and Species.name = "%s" ''' % (item, species))
-            Id = self.cursor.fetchone()[0]
-            StrainIds.append(Id)
-
-        return StrainIds
-
-
-    #XZ, 12/12/2008: if the species is rat or human, translate the geneid to mouse geneid
-    #XZ, 12/12/2008: if the input geneid is 'None', return 0
-    #XZ, 12/12/2008: if the input geneid has no corresponding mouse geneid, return 0
-    def translateToMouseGeneID (self, species, geneid):
-        mouse_geneid = 0
-
-        #if input geneid is None, return 0.
-        if not geneid:
-            return mouse_geneid
-
-        if species == 'mouse':
-            mouse_geneid = geneid
-        elif species == 'rat':
-            self.cursor.execute( "SELECT mouse FROM GeneIDXRef WHERE rat=%d" % int(geneid) )
-            record = self.cursor.fetchone()
-            if record:
-                mouse_geneid = record[0]
-        elif species == 'human':
-            self.cursor.execute( "SELECT mouse FROM GeneIDXRef WHERE human=%d" % int(geneid) )
-            record = self.cursor.fetchone()
-            if record:
-                mouse_geneid = record[0]
-
-        return mouse_geneid
-
-
-    #XZ, 12/16/2008: the input geneid is of mouse type
-    def checkForLitInfo(self,geneId):
-        q = 'SELECT 1 FROM LCorrRamin3 WHERE GeneId1=%s LIMIT 1' % geneId
-        self.cursor.execute(q)
-        try:
-            x = self.cursor.fetchone()
-            if x: return True
-            else: raise
-        except: return False
-
-
-    #XZ, 12/16/2008: the input geneid is of mouse type
-    def checkSymbolForTissueCorr(self, tissueProbeSetFreezeId=0, symbol=""):
-        q = "SELECT 1 FROM  TissueProbeSetXRef WHERE TissueProbeSetFreezeId=%s and Symbol='%s' LIMIT 1" % (tissueProbeSetFreezeId,symbol)
-        self.cursor.execute(q)
-        try:
-            x = self.cursor.fetchone()
-            if x: return True
-            else: raise
-        except: return False
-
-
-
-    def fetchAllDatabaseData(self, species, GeneId, GeneSymbol, strains, db, method, returnNumber, tissueProbeSetFreezeId):
-
-        StrainIds = []
-        for item in strains:
-            self.cursor.execute('''SELECT Strain.Id FROM Strain, Species WHERE Strain.Name="%s" and Strain.SpeciesId=Species.Id and Species.name = "%s" ''' % (item, species))
-            Id = self.cursor.fetchone()[0]
-            StrainIds.append('%d' % Id)
-
-        # break it into smaller chunks so we don't overload the MySql server
-        nnn = len(StrainIds) / 25
-        if len(StrainIds) % 25:
-            nnn += 1
-        oridata = []
-
-        #XZ, 09/24/2008: build one temporary table that only contains the records associated with the input GeneId
-        tempTable = None
-        if GeneId and db.type == "ProbeSet":
-            if method == "3":
-                tempTable = self.getTempLiteratureTable(species=species, input_species_geneid=GeneId, returnNumber=returnNumber)
-
-            if method == "4" or method == "5":
-                tempTable = self.getTempTissueCorrTable(primaryTraitSymbol=GeneSymbol, TissueProbeSetFreezeId=TISSUE_MOUSE_DB, method=method, returnNumber=returnNumber)
-
-        for step in range(nnn):
-            temp = []
-            StrainIdstep = StrainIds[step*25:min(len(StrainIds), (step+1)*25)]
-            for item in StrainIdstep: temp.append('T%s.value' % item)
-
-            if db.type == "Publish":
-                query = "SELECT PublishXRef.Id, "
-                dataStartPos = 1
-                query += string.join(temp,', ')
-                query += ' FROM (PublishXRef, PublishFreeze)'
-                #XZ, 03/04/2009: Xiaodong changed Data to PublishData
-                for item in StrainIdstep:
-                    query += 'left join PublishData as T%s on T%s.Id = PublishXRef.DataId and T%s.StrainId=%s\n' %(item,item,item,item)
-                query += "WHERE PublishXRef.InbredSetId = PublishFreeze.InbredSetId and PublishFreeze.Name = '%s'" % (db.name, )
-            #XZ, 09/20/2008: extract literature correlation value together with gene expression values.
-            #XZ, 09/20/2008: notice the difference between the code in next block.
-            elif tempTable:
-                # we can get a little performance out of selecting our LitCorr here
-                # but also we need to do this because we are unconcerned with probes that have no geneId associated with them
-                # as we would not have litCorr data.
-
-                if method == "3":
-                    query = "SELECT %s.Name, %s.value," %  (db.type,tempTable)
-                    dataStartPos = 2
-                if method == "4" or method == "5":
-                    query = "SELECT %s.Name, %s.Correlation, %s.PValue," %  (db.type,tempTable, tempTable)
-                    dataStartPos = 3
-
-                query += string.join(temp,', ')
-                query += ' FROM (%s, %sXRef, %sFreeze)' % (db.type, db.type, db.type)
-                if method == "3":
-                    query += ' LEFT JOIN %s ON %s.GeneId2=ProbeSet.GeneId ' % (tempTable,tempTable)
-                if method == "4" or method == "5":
-                    query += ' LEFT JOIN %s ON %s.Symbol=ProbeSet.Symbol ' % (tempTable,tempTable)
-                #XZ, 03/04/2009: Xiaodong changed Data to %sData and changed parameters from %(item,item, db.type,item,item) to %(db.type, item,item, db.type,item,item)
-                for item in StrainIdstep:
-                    query += 'left join %sData as T%s on T%s.Id = %sXRef.DataId and T%s.StrainId=%s\n' %(db.type, item,item, db.type,item,item)
-
-                if method == "3":
-                    query += "WHERE ProbeSet.GeneId IS NOT NULL AND %s.value IS NOT NULL AND %sXRef.%sFreezeId = %sFreeze.Id and %sFreeze.Name = '%s'  and %s.Id = %sXRef.%sId order by %s.Id" % (tempTable,db.type, db.type, db.type, db.type, db.name, db.type, db.type, db.type, db.type)
-                if method == "4" or method == "5":
-                    query += "WHERE ProbeSet.Symbol IS NOT NULL AND %s.Correlation IS NOT NULL AND %sXRef.%sFreezeId = %sFreeze.Id and %sFreeze.Name = '%s'  and %s.Id = %sXRef.%sId order by %s.Id" % (tempTable,db.type, db.type, db.type, db.type, db.name, db.type, db.type, db.type, db.type)
-            else:
-                query = "SELECT %s.Name," %  db.type
-                dataStartPos = 1
-                query += string.join(temp,', ')
-                query += ' FROM (%s, %sXRef, %sFreeze)' % (db.type, db.type, db.type)
-                #XZ, 03/04/2009: Xiaodong changed Data to %sData and changed parameters from %(item,item, db.type,item,item) to %(db.type, item,item, db.type,item,item)
-                for item in StrainIdstep:
-                    query += 'left join %sData as T%s on T%s.Id = %sXRef.DataId and T%s.StrainId=%s\n' %(db.type, item,item, db.type,item,item)
-                query += "WHERE %sXRef.%sFreezeId = %sFreeze.Id and %sFreeze.Name = '%s'  and %s.Id = %sXRef.%sId order by %s.Id" % (db.type, db.type, db.type, db.type, db.name, db.type, db.type, db.type, db.type)
-
-            self.cursor.execute(query)
-            results = self.cursor.fetchall()
-            oridata.append(results)
-
-        datasize = len(oridata[0])
-        traits = []
-        # put all of the separate data together into a huge list of lists
-        for j in range(datasize):
-            traitdata = list(oridata[0][j])
-            for i in range(1,nnn):
-                traitdata += list(oridata[i][j][dataStartPos:])
-
-            trait = Trait(traitdata[0], traitdata[dataStartPos:])
-
-            if method == METHOD_LIT:
-                trait.lit_corr = traitdata[1]
-
-            if method in TISSUE_METHODS:
-                trait.tissue_corr = traitdata[1]
-                trait.p_tissue = traitdata[2]
-
-            traits.append(trait)
-
-        if tempTable:
-            self.cursor.execute( 'DROP TEMPORARY TABLE %s' % tempTable )
-
-        return traits
-
-
-
-
-    # XZ, 09/20/2008: This function creates TEMPORARY TABLE tmpTableName_2 and return its name.
-    # XZ, 09/20/2008: It stores top literature correlation values associated with the input geneId.
-    # XZ, 09/20/2008: Attention: In each row, the input geneId is always in column GeneId1.
-    #XZ, 12/16/2008: the input geneid can be of mouse, rat or human type
-    def getTempLiteratureTable(self, species, input_species_geneid, returnNumber):
-        # according to mysql the TEMPORARY TABLE name should not have to be unique because
-        # it is only available to the current connection. This program will be invoked via command line, but if it
-        # were to be invoked over mod_python this could cuase problems.  mod_python will keep the connection alive
-        # in its executing threads ( i think) so there is a potential for the table not being dropped between users.
-        #XZ, 01/29/2009: To prevent the potential risk, I generate random table names and drop the tables after use them.
-
-
-        # the 'input_species_geneid' could be rat or human geneid, need to translate it to mouse geneid
-        translated_mouse_geneid = self.translateToMouseGeneID (species, input_species_geneid)
-
-        tmpTableName_1 = webqtlUtil.genRandStr(prefix="LITERATURE")
-
-        q1 = 'CREATE TEMPORARY TABLE %s (GeneId1 int(12) unsigned, GeneId2 int(12) unsigned PRIMARY KEY, value double)' % tmpTableName_1
-        q2 = 'INSERT INTO %s (GeneId1, GeneId2, value) SELECT GeneId1,GeneId2,value FROM LCorrRamin3 WHERE GeneId1=%s' % (tmpTableName_1, translated_mouse_geneid)
-        q3 = 'INSERT INTO %s (GeneId1, GeneId2, value) SELECT GeneId2,GeneId1,value FROM LCorrRamin3 WHERE GeneId2=%s AND GeneId1!=%s' % (tmpTableName_1, translated_mouse_geneid,translated_mouse_geneid)
-        for x in [q1,q2,q3]: self.cursor.execute(x)
-
-        #XZ, 09/23/2008: Just use the top records insteard of using all records
-        tmpTableName_2 = webqtlUtil.genRandStr(prefix="TOPLITERATURE")
-
-        q1 = 'CREATE TEMPORARY TABLE %s (GeneId1 int(12) unsigned, GeneId2 int(12) unsigned PRIMARY KEY, value double)' % tmpTableName_2
-        self.cursor.execute(q1)
-        q2 = 'SELECT GeneId1, GeneId2, value FROM %s ORDER BY value DESC' % tmpTableName_1
-        self.cursor.execute(q2)
-        result = self.cursor.fetchall()
-
-        counter = 0 #this is to count how many records being inserted into table
-        for one_row in result:
-            mouse_geneid1, mouse_geneid2, lit_corr_alue = one_row
-
-            #mouse_geneid1 has been tested before, now should test if mouse_geneid2 has corresponding geneid in other species
-            translated_species_geneid = 0
-            if species == 'mouse':
-                translated_species_geneid = mouse_geneid2
-            elif species == 'rat':
-                self.cursor.execute( "SELECT rat FROM GeneIDXRef WHERE mouse=%d" % int(mouse_geneid2) )
-                record = self.cursor.fetchone()
-                if record:
-                    translated_species_geneid = record[0]
-            elif species == 'human':
-                self.cursor.execute( "SELECT human FROM GeneIDXRef WHERE mouse=%d" % int(mouse_geneid2) )
-                record = self.cursor.fetchone()
-                if record:
-                    translated_species_geneid = record[0]
-
-            if translated_species_geneid:
-                self.cursor.execute( 'INSERT INTO %s (GeneId1, GeneId2, value) VALUES (%d,%d,%f)' % (tmpTableName_2, int(input_species_geneid),int(translated_species_geneid), float(lit_corr_alue)) )
-                counter = counter + 1
-
-            #pay attention to the number
-            if (counter > 2*returnNumber):
-                break
-
-        self.cursor.execute('DROP TEMPORARY TABLE %s' % tmpTableName_1)
-
-        return tmpTableName_2
-
-
-
-    #XZ, 09/23/2008: In tissue correlation tables, there is no record of GeneId1 == GeneId2
-    #XZ, 09/24/2008: Note that the correlation value can be negative.
-    def getTempTissueCorrTable(self, primaryTraitSymbol="", TissueProbeSetFreezeId=0, method="", returnNumber=0):
-
-        def cmpTissCorrAbsoluteValue(A, B):
-            try:
-                if abs(A[1]) < abs(B[1]): return 1
-                elif abs(A[1]) == abs(B[1]):
-                     return 0
-                else: return -1
-            except:
-                return 0
-
-        symbolCorrDict, symbolPvalueDict = self.calculateCorrOfAllTissueTrait(primaryTraitSymbol=primaryTraitSymbol, TissueProbeSetFreezeId=TISSUE_MOUSE_DB, method=method)
-
-        symbolCorrList = symbolCorrDict.items()
-
-        symbolCorrList.sort(cmpTissCorrAbsoluteValue)
-        symbolCorrList = symbolCorrList[0 : 2*returnNumber]
-
-        tmpTableName = webqtlUtil.genRandStr(prefix="TOPTISSUE")
-
-        q1 = 'CREATE TEMPORARY TABLE %s (Symbol varchar(100) PRIMARY KEY, Correlation float, PValue float)' % tmpTableName
-        self.cursor.execute(q1)
-
-        for one_pair in symbolCorrList:
-            one_symbol = one_pair[0]
-            one_corr = one_pair[1]
-            one_p_value = symbolPvalueDict[one_symbol]
-
-            self.cursor.execute( "INSERT INTO %s (Symbol, Correlation, PValue) VALUES ('%s',%f,%f)" % (tmpTableName, one_symbol, float(one_corr), float(one_p_value)) )
-
-        return tmpTableName
-
-
-    #XZ, 01/09/2009: This function was created by David Crowell. Xiaodong cleaned up and modified it.
-    def fetchLitCorrelations(self, species, GeneId, db, returnNumber): ### Used to generate Lit Correlations when calculations are done from text file.  dcrowell August 2008
-        """Uses getTempLiteratureTable to generate table of literatire correlations.  This function then gathers that data and
-        pairs it with the TraitID string.  Takes as its arguments a formdata instance, and a database instance.
-        Returns a dictionary of 'TraitID':'LitCorr' for the requested correlation"""
-
-        tempTable = self.getTempLiteratureTable(species=species, input_species_geneid=GeneId, returnNumber=returnNumber)
-
-        query = "SELECT %s.Name, %s.value" %  (db.type,tempTable)
-        query += ' FROM (%s, %sXRef, %sFreeze)' % (db.type, db.type, db.type)
-        query += ' LEFT JOIN %s ON %s.GeneId2=ProbeSet.GeneId ' % (tempTable,tempTable)
-        query += "WHERE ProbeSet.GeneId IS NOT NULL AND %s.value IS NOT NULL AND %sXRef.%sFreezeId = %sFreeze.Id and %sFreeze.Name = '%s'  and %s.Id = %sXRef.%sId order by %s.Id" % (tempTable, db.type, db.type, db.type, db.type, db.name, db.type, db.type, db.type, db.type)
-
-        self.cursor.execute(query)
-        results = self.cursor.fetchall()
-
-        litCorrDict = {}
-
-        for entry in results:
-            traitName,litcorr = entry
-            litCorrDict[traitName] = litcorr
-
-        self.cursor.execute('DROP TEMPORARY TABLE %s' % tempTable)
-
-        return litCorrDict
-
-
-
-    #XZ, 01/09/2009: Xiaodong created this function.
-    def fetchTissueCorrelations(self, db, primaryTraitSymbol="", TissueProbeSetFreezeId=0, method="", returnNumber = 0):
-        """Uses getTempTissueCorrTable to generate table of tissue correlations.  This function then gathers that data and
-        pairs it with the TraitID string.  Takes as its arguments a formdata instance, and a database instance.
-        Returns a dictionary of 'TraitID':(tissueCorr, tissuePValue) for the requested correlation"""
-
-
-        tempTable = self.getTempTissueCorrTable(primaryTraitSymbol=primaryTraitSymbol, TissueProbeSetFreezeId=TISSUE_MOUSE_DB, method=method, returnNumber=returnNumber)
-
-        query = "SELECT ProbeSet.Name, %s.Correlation, %s.PValue" %  (tempTable, tempTable)
-        query += ' FROM (ProbeSet, ProbeSetXRef, ProbeSetFreeze)'
-        query += ' LEFT JOIN %s ON %s.Symbol=ProbeSet.Symbol ' % (tempTable,tempTable)
-        query += "WHERE ProbeSetFreeze.Name = '%s' and ProbeSetFreeze.Id=ProbeSetXRef.ProbeSetFreezeId and ProbeSet.Id = ProbeSetXRef.ProbeSetId and ProbeSet.Symbol IS NOT NULL AND %s.Correlation IS NOT NULL" % (db.name, tempTable)
-
-        self.cursor.execute(query)
-        results = self.cursor.fetchall()
-
-        tissueCorrDict = {}
-
-        for entry in results:
-            traitName, tissueCorr, tissuePValue = entry
-            tissueCorrDict[traitName] = (tissueCorr, tissuePValue)
-
-        self.cursor.execute('DROP TEMPORARY TABLE %s' % tempTable)
-
-        return tissueCorrDict
-
-
-
-    #XZ, 01/13/2008
-    def getLiteratureCorrelationByList(self, input_trait_mouse_geneid=None, species=None, traitList=None):
-
-        tmpTableName = webqtlUtil.genRandStr(prefix="LITERATURE")
-
-        q1 = 'CREATE TEMPORARY TABLE %s (GeneId1 int(12) unsigned, GeneId2 int(12) unsigned PRIMARY KEY, value double)' % tmpTableName
-        q2 = 'INSERT INTO %s (GeneId1, GeneId2, value) SELECT GeneId1,GeneId2,value FROM LCorrRamin3 WHERE GeneId1=%s' % (tmpTableName, input_trait_mouse_geneid)
-        q3 = 'INSERT INTO %s (GeneId1, GeneId2, value) SELECT GeneId2,GeneId1,value FROM LCorrRamin3 WHERE GeneId2=%s AND GeneId1!=%s' % (tmpTableName, input_trait_mouse_geneid, input_trait_mouse_geneid)
-
-        for x in [q1,q2,q3]:
-            self.cursor.execute(x)
-
-        for thisTrait in traitList:
-            try:
-                if thisTrait.geneid:
-                    thisTrait.mouse_geneid = self.translateToMouseGeneID(species, thisTrait.geneid)
-                else:
-                    thisTrait.mouse_geneid = 0
-            except:
-                thisTrait.mouse_geneid = 0
-
-            if thisTrait.mouse_geneid and str(thisTrait.mouse_geneid).find(";") == -1:
-                try:
-                    self.cursor.execute("SELECT value FROM %s WHERE GeneId2 = %s" % (tmpTableName, thisTrait.mouse_geneid))
-                    result =  self.cursor.fetchone()
-                    if result:
-                        thisTrait.LCorr = result[0]
-                    else:
-                        thisTrait.LCorr = None
-                except:
-                    thisTrait.LCorr = None
-            else:
-                thisTrait.LCorr = None
-
-        self.cursor.execute("DROP TEMPORARY TABLE %s" % tmpTableName)
-
-        return traitList
-
-    def get_trait(self, cached, vals):
-
-        if cached:
-            _log.info("Using the fast method because the file exists")
-            lit_corrs = {}
-            tissue_corrs = {}
-            use_lit = False
-            if self.method == METHOD_LIT:
-                lit_corrs = self.fetchLitCorrelations(species=self.species, GeneId=self.gene_id, db=self.db, returnNumber=self.returnNumber)
-                use_lit = True
-
-            use_tissue_corr = False
-            if self.method in TISSUE_METHODS:
-                tissue_corrs = self.fetchTissueCorrelations(db=self.db, primaryTraitSymbol=self.trait_symbol, TissueProbeSetFreezeId=TISSUE_MOUSE_DB, method=self.method, returnNumber = self.returnNumber)
-                use_tissue_corr = True
-
-            DatabaseFileName = self.getFileName( target_db_name=self.target_db_name )
-            datasetFile = open(webqtlConfig.TEXTDIR+DatabaseFileName,'r')
-
-            #XZ, 01/08/2009: read the first line
-            line = datasetFile.readline()
-            cached_sample_names = webqtlUtil.readLineCSV(line)[1:]
-
-            #XZ, 01/08/2009: This step is critical. It is necessary for this new method.
-            #XZ: The original function fetchAllDatabaseData uses all strains stored in variable _strains to
-            #XZ: retrieve the values of each strain from database in real time.
-            #XZ: The new method uses all strains stored in variable dataset_strains to create a new variable
-            #XZ: _newvals. _newvals has the same length as dataset_strains. The items in _newvals is in
-            #XZ: the same order of items in dataset_strains. The value of each item in _newvals is either
-            #XZ: the value of correspinding strain in _vals or 'None'.
-            new_vals = []
-            for name in cached_sample_names:
-                if name in self.sample_names:
-                    new_vals.append(float(vals[self.sample_names.index(name)]))
-                else:
-                    new_vals.append('None')
-
-            nnCorr = len(new_vals)
-
-            #XZ, 01/14/2009: If literature corr or tissue corr is selected,
-            #XZ: there is no need to use parallel computing.
-
-            traits = []
-            data_start = 1
-            for line in datasetFile:
-                raw_trait = webqtlUtil.readLineCSV(line)
-                trait = Trait.from_csv(raw_trait, data_start)
-                trait.lit_corr = lit_corrs.get(trait.name)
-                trait.tissue_corr, trait.p_tissue = tissue_corrs.get(trait.name, (None, None))
-                traits.append(trait)
-
-            return traits, new_vals
-
-        else:
-            _log.info("Using the slow method for correlation")
-
-            _log.info("Fetching from database")
-            traits = self.fetchAllDatabaseData(species=self.species, GeneId=self.gene_id, GeneSymbol=self.trait_symbol, strains=self.sample_names, db=self.db, method=self.method, returnNumber=self.returnNumber, tissueProbeSetFreezeId= self.tissue_probeset_freeze_id)
-            _log.info("Done fetching from database")
-            totalTraits = len(traits) #XZ, 09/18/2008: total trait number
-
-        return traits, vals
-
-
-	def do_parallel_correlation(self):
-	    _log.info("Invoking parallel computing")
-	    input_line_list = datasetFile.readlines()
-	    _log.info("Read lines from the file")
-	    all_line_number = len(input_line_list)
-
-	    step = 1000
-	    job_number = math.ceil( float(all_line_number)/step )
-
-	    job_input_lists = []
-
-	    _log.info("Configuring jobs")
-
-	    for job_index in range( int(job_number) ):
-		starti = job_index*step
-		endi = min((job_index+1)*step, all_line_number)
-
-		one_job_input_list = []
-
-		for i in range( starti, endi ):
-		    one_job_input_list.append( input_line_list[i] )
-
-		job_input_lists.append( one_job_input_list )
-
-	    _log.info("Creating pp servers")
-
-	    ppservers = ()
-	    # Creates jobserver with automatically detected number of workers
-	    job_server = pp.Server(ppservers=ppservers)
-
-	    _log.info("Done creating servers")
-
-	    jobs = []
-	    results = []
-
-	    _log.info("Starting parallel computation, submitting jobs")
-	    for one_job_input_list in job_input_lists: #pay attention to modules from outside
-		jobs.append( job_server.submit(func=compute_corr, args=(nnCorr, _newvals, one_job_input_list, self.method), depfuncs=(), modules=("utility.webqtlUtil",)) )
-	    _log.info("Done submitting jobs")
-
-	    for one_job in jobs:
-		one_result = one_job()
-		results.append( one_result )
-
-	    _log.info("Acquiring results")
-
-	    for one_result in results:
-		for one_traitinfo in one_result:
-		    allcorrelations.append( one_traitinfo )
-
-	    _log.info("Appending the results")
-
-	datasetFile.close()
-	totalTraits = len(allcorrelations)
-	_log.info("Done correlating using the fast method")
-
-
-    def correlate(self, vals):
-
-        correlations = []
-
-        #XZ: Use the fast method only for probeset dataset, and this dataset must have been created.
-        #XZ: Otherwise, use original method
-        _log.info("Entering correlation")
-
-        db_filename = self.getFileName( target_db_name=self.target_db_name )
-
-        cache_available = db_filename in os.listdir(webqtlConfig.TEXTDIR)
-
-	 # If the cache file exists, do a cached correlation for probeset data
-        if self.db.type == "ProbeSet":
-#	    if self.method in [METHOD_SAMPLE_PEARSON, METHOD_SAMPLE_RANK] and cache_available:
-#		traits = do_parallel_correlation()
-#
-#	    else:
-
-            (traits, vals) = self.get_trait(cache_available, vals)
-
-            for trait in traits:
-                trait.calculate_correlation(vals, self.method)
-
-        self.record_count = len(traits) #ZS: This isn't a good way to get this value, so I need to change it later
-
-        #XZ, 3/31/2010: Theoretically, we should create one function 'comTissueCorr'
-        #to compare each trait by their tissue corr p values.
-        #But because the tissue corr p values are generated by permutation test,
-        #the top ones always have p value 0. So comparing p values actually does nothing.
-        #In addition, for the tissue data in our database, the N is always the same.
-        #So it's safe to compare with tissue corr statistic value.
-        #That's the same as literature corr.
-        #if self.method in [METHOD_LIT, METHOD_TISSUE_PEARSON, METHOD_TISSUE_RANK] and self.gene_id:
-        #    traits.sort(webqtlUtil.cmpLitCorr)
-        #else:
-        #if self.method in TISSUE_METHODS:
-        #    sort(traits, key=lambda A: math.fabs(A.tissue_corr))
-        #elif self.method == METHOD_LIT:
-        #    traits.sort(traits, key=lambda A: math.fabs(A.lit_corr))
-        #else:
-        traits = sortTraitCorrelations(traits, self.method)
-
-        # Strip to the top N correlations
-        traits = traits[:min(self.returnNumber, len(traits))]
-
-        addLiteratureCorr = False
-        addTissueCorr = False
-
-        trait_list = []
-        for trait in traits:
-            db_trait = webqtlTrait(db=self.db, name=trait.name, cursor=self.cursor)
-            db_trait.retrieveInfo( QTL='Yes' )
-
-            db_trait.Name = trait.name
-            db_trait.corr = trait.correlation
-            db_trait.nOverlap = trait.overlap
-            db_trait.corrPValue = trait.p_value
-
-            # NL, 07/19/2010
-            # js function changed, add a new parameter rankOrder for js function 'showTissueCorrPlot'
-            db_trait.RANK_ORDER = self.RANK_ORDERS[self.method]
-
-            #XZ, 26/09/2008: Method is 4 or 5. Have fetched tissue corr, but no literature correlation yet.
-            if self.method in TISSUE_METHODS:
-                db_trait.tissueCorr = trait.tissue_corr
-                db_trait.tissuePValue = trait.p_tissue
-                addTissueCorr = True
-
-
-            #XZ, 26/09/2008: Method is 3,  Have fetched literature corr, but no tissue corr yet.
-            elif self.method == METHOD_LIT:
-                db_trait.LCorr = trait.lit_corr
-                db_trait.mouse_geneid = self.translateToMouseGeneID(self.species, db_trait.geneid)
-                addLiteratureCorr = True
-
-            #XZ, 26/09/2008: Method is 1 or 2. Have NOT fetched literature corr and tissue corr yet.
-            # Phenotype data will not have geneid, and neither will some probes
-            # we need to handle this because we will get an attribute error
-            else:
-                if self.input_trait_mouse_gene_id and self.db.type=="ProbeSet":
-                    addLiteratureCorr = True
-                if self.trait_symbol and self.db.type=="ProbeSet":
-                    addTissueCorr = True
-
-            trait_list.append(db_trait)
-
-        if addLiteratureCorr:
-            trait_list = self.getLiteratureCorrelationByList(self.input_trait_mouse_gene_id,
-						    self.species, trait_list)
-        if addTissueCorr:
-            trait_list = self.getTissueCorrelationByList(
-			primaryTraitSymbol = self.trait_symbol,
-			traitList = trait_list,
-			TissueProbeSetFreezeId = TISSUE_MOUSE_DB,
-			method=self.method)
-
-	return trait_list
-
-
-    def calculateCorrOfAllTissueTrait(self, primaryTraitSymbol=None, TissueProbeSetFreezeId=None, method=None):
-
-        symbolCorrDict = {}
-        symbolPvalueDict = {}
-
-        primaryTraitSymbolValueDict = correlationFunction.getGeneSymbolTissueValueDictForTrait(cursor=self.cursor, GeneNameLst=[primaryTraitSymbol], TissueProbeSetFreezeId=TISSUE_MOUSE_DB)
-        primaryTraitValue = primaryTraitSymbolValueDict.values()[0]
-
-        SymbolValueDict = correlationFunction.getGeneSymbolTissueValueDictForTrait(cursor=self.cursor, GeneNameLst=[], TissueProbeSetFreezeId=TISSUE_MOUSE_DB)
-
-        if method in ["2","5"]:
-            symbolCorrDict, symbolPvalueDict = correlationFunction.batchCalTissueCorr(primaryTraitValue,SymbolValueDict,method='spearman')
-        else:
-            symbolCorrDict, symbolPvalueDict = correlationFunction.batchCalTissueCorr(primaryTraitValue,SymbolValueDict)
-
-
-        return (symbolCorrDict, symbolPvalueDict)
-
-
-
-    #XZ, 10/13/2010
-    def getTissueCorrelationByList(self, primaryTraitSymbol=None, traitList=None, TissueProbeSetFreezeId=None, method=None):
-
-        primaryTraitSymbolValueDict = correlationFunction.getGeneSymbolTissueValueDictForTrait(cursor=self.cursor, GeneNameLst=[primaryTraitSymbol], TissueProbeSetFreezeId=TISSUE_MOUSE_DB)
-
-        if primaryTraitSymbol.lower() in primaryTraitSymbolValueDict:
-            primaryTraitValue = primaryTraitSymbolValueDict[primaryTraitSymbol.lower()]
-
-            geneSymbolList = []
-
-            for thisTrait in traitList:
-                if hasattr(thisTrait, 'symbol'):
-                    geneSymbolList.append(thisTrait.symbol)
-
-            SymbolValueDict = correlationFunction.getGeneSymbolTissueValueDictForTrait(cursor=self.cursor, GeneNameLst=geneSymbolList, TissueProbeSetFreezeId=TISSUE_MOUSE_DB)
-
-            for thisTrait in traitList:
-                if hasattr(thisTrait, 'symbol') and thisTrait.symbol and thisTrait.symbol.lower() in SymbolValueDict:
-                    oneTraitValue = SymbolValueDict[thisTrait.symbol.lower()]
-                    if method in ["2","5"]:
-                        result = correlationFunction.calZeroOrderCorrForTiss( primaryTraitValue, oneTraitValue, method='spearman' )
-                    else:
-                        result = correlationFunction.calZeroOrderCorrForTiss( primaryTraitValue, oneTraitValue)
-                    thisTrait.tissueCorr = result[0]
-                    thisTrait.tissuePValue = result[2]
-                else:
-                    thisTrait.tissueCorr = None
-                    thisTrait.tissuePValue = None
-        else:
-            for thisTrait in traitList:
-                thisTrait.tissueCorr = None
-                thisTrait.tissuePValue = None
-
-        return traitList
-
-
-    def getTopInfo(self, myTrait=None, method=None, db=None, target_db_name=None, returnNumber=None, methodDict=None, totalTraits=None, identification=None  ):
-
-        if myTrait:
-            if method in ["1","2"]: #genetic correlation
-                info = HT.Paragraph("Values of Record %s in the " % myTrait.getGivenName(), HT.Href(text=myTrait.db.fullname,url=webqtlConfig.INFOPAGEHREF % myTrait.db.name,target="_blank", Class="fwn"),
-                                     " database were compared to all %d records in the " % self.record_count, HT.Href(text=db.fullname,url=webqtlConfig.INFOPAGEHREF % target_db_name,target="_blank", Class="fwn"),
-                                     ' database. The top %d correlations ranked by the %s are displayed.' % (returnNumber,methodDict[method]),
-                                     ' You can resort this list using the small arrowheads in the top row.')
-            else:
-                #myTrait.retrieveInfo()#need to know geneid and symbol
-                if method == "3":#literature correlation
-                    searchDBName = "Literature Correlation"
-                    searchDBLink = "/correlationAnnotation.html#literatureCorr"
-                else: #tissue correlation
-                    searchDBName = "Tissue Correlation"
-                    searchDBLink = "/correlationAnnotation.html#tissueCorr"
-                info = HT.Paragraph("Your input record %s in the " % myTrait.getGivenName(), HT.Href(text=myTrait.db.fullname,url=webqtlConfig.INFOPAGEHREF % myTrait.db.name,target="_blank", Class="fwn"),
-                                     " database corresponds to ",
-                                     HT.Href(text='gene Id %s, and gene symbol %s' % (myTrait.geneid, myTrait.symbol), target='_blank',url="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene&cmd=Retrieve&dopt=Graphics&list_uids=%s" % myTrait.geneid, Class="fs12 fwn"),
-                                     '. GN ranked all genes in the ', HT.Href(text=searchDBName,url=searchDBLink,target="_blank", Class="fwn"),' database by the %s.' % methodDict[method],
-                                     ' The top %d probes or probesets in the ' % returnNumber, HT.Href(text=db.fullname,url=webqtlConfig.INFOPAGEHREF % target_db_name,target="_blank", Class="fwn"),
-                                     ' database corresponding to the top genes ranked by the %s are displayed.' %( methodDict[method]),
-                                     ' You can resort this list using the small arrowheads in the top row.' )
-
-        elif identification:
-            info = HT.Paragraph('Values of %s were compared to all %d traits in '  % (identification, self.record_count),
-                                 HT.Href(text=db.fullname,url=webqtlConfig.INFOPAGEHREF % target_db_name,target="_blank",Class="fwn"),
-                                 ' database. The TOP %d correlations ranked by the %s are displayed.' % (returnNumber,methodDict[method]),
-                                 ' You can resort this list using the small arrowheads in the top row.')
-
-        else:
-            info = HT.Paragraph('Trait values were compared to all values in ',
-                                 HT.Href(text=db.fullname,url=webqtlConfig.INFOPAGEHREF % target_db_name,target="_blank",Class="fwn"),
-                                 ' database. The TOP %d correlations ranked by the %s are displayed.' % (returnNumber,methodDict[method]),
-                                 ' You can resort this list using the small arrowheads in the top row.')
-
-        if db.type=="Geno":
-            info.append(HT.BR(),HT.BR(),'Clicking on the Locus will open the  genotypes data for that locus. Click on the correlation to see a scatter plot of the trait data.')
-        elif db.type=="Publish":
-            info.append(HT.BR(),HT.BR(),'Clicking on the record ID will open the  published phenotype data for that publication. Click on the correlation to see a scatter plot of the trait data. ')
-        elif db.type=="ProbeSet":
-            info.append(HT.BR(),'Click the correlation values to generate scatter plots. Select the Record ID to open the Trait Data and Analysis form. Select the symbol to open NCBI Entrez.')
-        else:
-            pass
-
-
-        return info
-
-
-    def createExcelFileWithTitleAndFooter(self, workbook=None, identification=None, db=None, returnNumber=None):
-
-        worksheet = workbook.add_worksheet()
-
-        titleStyle = workbook.add_format(align = 'left', bold = 0, size=14, border = 1, border_color="gray")
-
-        ##Write title Info
-        # Modified by Hongqiang Li
-        worksheet.write([1, 0], "Citations: Please see %s/reference.html" % webqtlConfig.PORTADDR, titleStyle)
-        worksheet.write([1, 0], "Citations: Please see %s/reference.html" % webqtlConfig.PORTADDR, titleStyle)
-        worksheet.write([2, 0], "Trait : %s" % identification, titleStyle)
-        worksheet.write([3, 0], "Database : %s" % db.fullname, titleStyle)
-        worksheet.write([4, 0], "Date : %s" % time.strftime("%B %d, %Y", time.gmtime()), titleStyle)
-        worksheet.write([5, 0], "Time : %s GMT" % time.strftime("%H:%M ", time.gmtime()), titleStyle)
-        worksheet.write([6, 0], "Status of data ownership: Possibly unpublished data; please see %s/statusandContact.html for details on sources, ownership, and usage of these data." % webqtlConfig.PORTADDR, titleStyle)
-        #Write footer info
-        worksheet.write([9 + returnNumber, 0], "Funding for The GeneNetwork: NIAAA (U01AA13499, U24AA13513), NIDA, NIMH, and NIAAA (P20-DA21131), NCI MMHCC (U01CA105417), and NCRR (U01NR 105417)", titleStyle)
-        worksheet.write([10 + returnNumber, 0], "PLEASE RETAIN DATA SOURCE INFORMATION WHENEVER POSSIBLE", titleStyle)
-
-        return worksheet
-
-
-    def getTableHeaderForGeno(self, method=None, worksheet=None, newrow=None, headingStyle=None):
-
-        tblobj_header = []
-
-        if method in ["1","3","4"]:
-            tblobj_header = [[THCell(HT.TD(' ', Class="fs13 fwb ffl b1 cw cbrb"), sort=0),
-                              THCell(HT.TD('Record', HT.BR(), 'ID', HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text='Record ID', idx=1),
-                              THCell(HT.TD('Location', HT.BR(), 'Chr and Mb', HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text='Location (Chr and Mb)', idx=2),
-                              THCell(HT.TD(HT.Href(
-                                                   text = HT.Span('Sample',HT.BR(), 'r', HT.Sup('  ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"),
-                                                   target = '_blank',
-                                                   url = "/correlationAnnotation.html#genetic_r"),
-                                           Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Sample r", idx=3),
-                              THCell(HT.TD('N',HT.BR(),'Cases',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="N Cases", idx=4),
-                              THCell(HT.TD(HT.Href(
-                                                   text = HT.Span('Sample',HT.BR(), 'p(r)', HT.Sup('  ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"),
-                                                   target = '_blank',
-                                                   url = "/correlationAnnotation.html#genetic_p_r"),
-                                           Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Sample p(r)", idx=5)]]
-
-            for ncol, item in enumerate(['Record ID', 'Location (Chr, Mb)', 'Sample r', 'N Cases', 'Sample p(r)']):
-                worksheet.write([newrow, ncol], item, headingStyle)
-                worksheet.set_column([ncol, ncol], 2*len(item))
-        else:
-            tblobj_header = [[THCell(HT.TD(' ', Class="fs13 fwb ffl b1 cw cbrb"), sort=0),
-                              THCell(HT.TD('Record', HT.BR(), 'ID', HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text='Record ID', idx=1),
-                              THCell(HT.TD('Location', HT.BR(), 'Chr and Mb', HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text='Location (Chr and Mb)', idx=2),
-                              THCell(HT.TD(HT.Href(
-                                                   text = HT.Span('Sample',HT.BR(), 'rho', HT.Sup('  ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"),
-                                                   target = '_blank',
-                                                   url = "/correlationAnnotation.html#genetic_rho"),
-                                           Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Sample rho", idx=3),
-                              THCell(HT.TD('N',HT.BR(),'Cases',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="N Cases", idx=4),
-                              THCell(HT.TD(HT.Href(
-                                                   text = HT.Span('Sample',HT.BR(), 'p(rho)', HT.Sup('  ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"),
-                                                   target = '_blank',
-                                                   url = "/correlationAnnotation.html#genetic_p_rho"),
-                                           Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Sample p(rho)", idx=5)]]
-
-            for ncol, item in enumerate(['Record ID', 'Location (Chr, Mb)', 'Sample rho', 'N Cases', 'Sample p(rho)']):
-                worksheet.write([newrow, ncol], item, headingStyle)
-                worksheet.set_column([ncol, ncol], 2*len(item))
-
-
-        return tblobj_header, worksheet
-
-
-    def getTableBodyForGeno(self, traitList, formName=None, worksheet=None, newrow=None, corrScript=None):
-
-        tblobj_body = []
-
-        for thisTrait in traitList:
-                tr = []
-
-                trId = str(thisTrait)
-
-                corrScript.append('corrArray["%s"] = {corr:%1.4f};' % (trId, thisTrait.corr))
-
-                tr.append(TDCell(HT.TD(HT.Input(type="checkbox", Class="checkbox", name="searchResult",value=trId, onClick="highlight(this)"), nowrap="on", Class="fs12 fwn ffl b1 c222"), text=trId))
-
-                tr.append(TDCell(HT.TD(HT.Href(text=thisTrait.name,url="javascript:showTrait('%s', '%s')" % (formName, thisTrait.name), Class="fs12 fwn ffl"),align="left", Class="fs12 fwn ffl b1 c222"), text=thisTrait.name, val=thisTrait.name.upper()))
-
-                #XZ: trait_location_value is used for sorting
-                trait_location_repr = '--'
-                trait_location_value = 1000000
-
-                if thisTrait.chr and thisTrait.mb:
-                    try:
-                        trait_location_value = int(thisTrait.chr)*1000 + thisTrait.mb
-                    except:
-                        if thisTrait.chr.upper() == 'X':
-                            trait_location_value = 20*1000 + thisTrait.mb
-                        else:
-                            trait_location_value = ord(str(thisTrait.chr).upper()[0])*1000 + thisTrait.mb
-
-                    trait_location_repr = 'Chr%s: %.6f' % (thisTrait.chr, float(thisTrait.mb) )
-
-                tr.append(TDCell(HT.TD(trait_location_repr, Class="fs12 fwn b1 c222", nowrap="on"), trait_location_repr, trait_location_value))
-
-
-                repr='%3.3f' % thisTrait.corr
-                tr.append(TDCell(HT.TD(HT.Href(text=repr, url="javascript:showCorrPlot('%s', '%s')" % (formName, thisTrait.name), Class="fs12 fwn ffl"), Class="fs12 fwn ffl b1 c222", nowrap='ON', align='right'),repr,abs(thisTrait.corr)))
-
-                repr = '%d' % thisTrait.nOverlap
-                tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222",align='right'),repr,thisTrait.nOverlap))
-
-                repr = webqtlUtil.SciFloat(thisTrait.corrPValue)
-                tr.append(TDCell(HT.TD(repr,nowrap='ON', Class="fs12 fwn ffl b1 c222", align='right'),repr,thisTrait.corrPValue))
-
-                tblobj_body.append(tr)
-
-                for ncol, item in enumerate([thisTrait.name, trait_location_repr, thisTrait.corr, thisTrait.nOverlap, thisTrait.corrPValue]):
-                    worksheet.write([newrow, ncol], item)
-                newrow += 1
-
-        return tblobj_body, worksheet, corrScript
-
-
-    def getTableHeaderForPublish(self, method=None, worksheet=None, newrow=None, headingStyle=None):
-
-        tblobj_header = []
-
-        if method in ["1","3","4"]:
-            tblobj_header = [[THCell(HT.TD(' ', Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), sort=0),
-                              THCell(HT.TD('Record',HT.BR(), 'ID',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="Record ID", idx=1),
-                              THCell(HT.TD('Phenotype', HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="Phenotype", idx=2),
-                              THCell(HT.TD('Authors', HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="Authors", idx=3),
-                              THCell(HT.TD('Year', HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="Year", idx=4),
-                              THCell(HT.TD('Max',HT.BR(), 'LRS', HT.BR(), Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="Max LRS", idx=5),
-                              THCell(HT.TD('Max LRS Location',HT.BR(),'Chr and Mb',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="Max LRS Location", idx=6),
-                              THCell(HT.TD(HT.Href(
-                                                   text = HT.Span('Sample',HT.BR(), 'r', HT.Sup('  ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"),
-                                                   target = '_blank',
-                                                   url = "/correlationAnnotation.html#genetic_r"),
-                                           Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Sample r", idx=7),
-                              THCell(HT.TD('N',HT.BR(),'Cases',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="N Cases", idx=8),
-                              THCell(HT.TD(HT.Href(
-                                                   text = HT.Span('Sample',HT.BR(), 'p(r)', HT.Sup('  ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"),
-                                                   target = '_blank',
-                                                   url = "/correlationAnnotation.html#genetic_p_r"),
-                                           Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Sample p(r)", idx=9)]]
-
-            for ncol, item in enumerate(["Record", "Phenotype", "Authors", "Year", "Pubmed Id", "Max LRS", "Max LRS Location (Chr: Mb)", "Sample r", "N Cases", "Sample p(r)"]):
-                worksheet.write([newrow, ncol], item, headingStyle)
-                worksheet.set_column([ncol, ncol], 2*len(item))
-        else:
-            tblobj_header = [[THCell(HT.TD(' ', Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), sort=0),
-                              THCell(HT.TD('Record',HT.BR(), 'ID',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="Record ID", idx=1),
-                              THCell(HT.TD('Phenotype', HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="Phenotype", idx=2),
-                              THCell(HT.TD('Authors', HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="Authors", idx=3),
-                              THCell(HT.TD('Year', HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="Year", idx=4),
-                              THCell(HT.TD('Max',HT.BR(), 'LRS', HT.BR(), Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="Max LRS", idx=5),
-                              THCell(HT.TD('Max LRS Location',HT.BR(),'Chr and Mb',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="Max LRS Location", idx=6),
-                              THCell(HT.TD(HT.Href(
-                                                   text = HT.Span('Sample',HT.BR(), 'rho', HT.Sup('  ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"),
-                                                   target = '_blank',
-                                                   url = "/correlationAnnotation.html#genetic_rho"),
-                                           Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Sample rho", idx=7),
-                              THCell(HT.TD('N',HT.BR(),'Cases',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="N Cases", idx=8),
-                              THCell(HT.TD(HT.Href(
-                                                   text = HT.Span('Sample',HT.BR(), 'p(rho)', HT.Sup('  ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"),
-                                                   target = '_blank',
-                                                   url = "/correlationAnnotation.html#genetic_p_rho"),
-                                           Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Sample p(rho)", idx=9)]]
-
-            for ncol, item in enumerate(["Record", "Phenotype", "Authors", "Year", "Pubmed Id", "Max LRS", "Max LRS Location (Chr: Mb)", "Sample rho", "N Cases", "Sample p(rho)"]):
-                worksheet.write([newrow, ncol], item, headingStyle)
-                worksheet.set_column([ncol, ncol], 2*len(item))
-
-
-        return tblobj_header, worksheet
-
-
-    def getTableBodyForPublish(self, traitList, formName=None, worksheet=None, newrow=None, corrScript=None, species=''):
-
-        tblobj_body = []
-
-        for thisTrait in traitList:
-            tr = []
-
-            trId = str(thisTrait)
-
-            corrScript.append('corrArray["%s"] = {corr:%1.4f};' % (trId, thisTrait.corr))
-
-            tr.append(TDCell(HT.TD(HT.Input(type="checkbox", Class="checkbox", name="searchResult",value=trId, onClick="highlight(this)"), nowrap="on", Class="fs12 fwn ffl b1 c222"), text=trId))
-
-            tr.append(TDCell(HT.TD(HT.Href(text=thisTrait.name,url="javascript:showTrait('%s', '%s')" % (formName, thisTrait.name), Class="fs12 fwn"), nowrap="yes",align="center", Class="fs12 fwn b1 c222"),str(thisTrait.name), 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
-
-            tr.append(TDCell(HT.TD(PhenotypeString, Class="fs12 fwn b1 c222"), PhenotypeString, PhenotypeString.upper()))
-
-            tr.append(TDCell(HT.TD(thisTrait.authors, Class="fs12 fwn b1 c222 fsI"),thisTrait.authors, thisTrait.authors.strip().upper()))
-
-            try:
-                PubMedLinkText = myear = repr = int(thisTrait.year)
-            except:
-                PubMedLinkText = repr = "--"
-                myear = 0
-            if thisTrait.pubmed_id:
-                PubMedLink = HT.Href(text= repr,url= webqtlConfig.PUBMEDLINK_URL % thisTrait.pubmed_id,target='_blank', Class="fs12 fwn")
-            else:
-                PubMedLink = repr
-
-            tr.append(TDCell(HT.TD(PubMedLink, Class="fs12 fwn b1 c222", align='center'), repr, myear))
-
-            #LRS and its location
-            LRS_score_repr = '--'
-            LRS_score_value = 0
-            LRS_location_repr = '--'
-            LRS_location_value = 1000000
-            LRS_flag = 1
-
-            #Max LRS and its Locus location
-            if thisTrait.lrs and thisTrait.locus:
-                self.cursor.execute("""
-                    select Geno.Chr, Geno.Mb from Geno, Species
-                    where Species.Name = '%s' and
-                          Geno.Name = '%s' and
-                          Geno.SpeciesId = Species.Id
-                """ % (species, thisTrait.locus))
-                result = self.cursor.fetchone()
-
-                if result:
-                    if result[0] and result[1]:
-                        LRS_Chr = result[0]
-                        LRS_Mb = result[1]
-
-                        #XZ: LRS_location_value is used for sorting
-                        try:
-                            LRS_location_value = int(LRS_Chr)*1000 + float(LRS_Mb)
-                        except:
-                            if LRS_Chr.upper() == 'X':
-                                LRS_location_value = 20*1000 + float(LRS_Mb)
-                            else:
-                                LRS_location_value = ord(str(LRS_chr).upper()[0])*1000 + float(LRS_Mb)
-
-
-                        LRS_score_repr = '%3.1f' % thisTrait.lrs
-                        LRS_score_value = thisTrait.lrs
-                        LRS_location_repr = 'Chr%s: %.6f' % (LRS_Chr, float(LRS_Mb) )
-                        LRS_flag = 0
-
-                        #tr.append(TDCell(HT.TD(HT.Href(text=LRS_score_repr,url="javascript:showIntervalMapping('%s', '%s : %s')" % (formName, thisTrait.db.shortname, thisTrait.name), Class="fs12 fwn"), Class="fs12 fwn ffl b1 c222", align='right', nowrap="on"),LRS_score_repr, LRS_score_value))
-                        tr.append(TDCell(HT.TD(LRS_score_repr, Class="fs12 fwn b1 c222", align='right', nowrap="on"), LRS_score_repr, LRS_score_value))
-                        tr.append(TDCell(HT.TD(LRS_location_repr, Class="fs12 fwn b1 c222"), LRS_location_repr, LRS_location_value))
-
-            if LRS_flag:
-                tr.append(TDCell(HT.TD(LRS_score_repr, Class="fs12 fwn b1 c222"), LRS_score_repr, LRS_score_value))
-                tr.append(TDCell(HT.TD(LRS_location_repr, Class="fs12 fwn b1 c222"), LRS_location_repr, LRS_location_value))
-
-            repr = '%3.4f' % thisTrait.corr
-            tr.append(TDCell(HT.TD(HT.Href(text=repr,url="javascript:showCorrPlot('%s', '%s')" % (formName,thisTrait.name), Class="fs12 fwn"), Class="fs12 fwn b1 c222", align='right',nowrap="on"), repr, abs(thisTrait.corr)))
-
-            repr = '%d' % thisTrait.nOverlap
-            tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", align='right'),repr,thisTrait.nOverlap))
-
-            repr = webqtlUtil.SciFloat(thisTrait.corrPValue)
-            tr.append(TDCell(HT.TD(repr,nowrap='ON', Class="fs12 fwn ffl b1 c222", align='right'),repr,thisTrait.corrPValue))
-
-            tblobj_body.append(tr)
-
-            for ncol, item in enumerate([thisTrait.name, PhenotypeString, thisTrait.authors, thisTrait.year, thisTrait.pubmed_id, LRS_score_repr, LRS_location_repr, thisTrait.corr, thisTrait.nOverlap, thisTrait.corrPValue]):
-                worksheet.write([newrow, ncol], item)
-            newrow += 1
-
-        return tblobj_body, worksheet, corrScript
-
-
-    def getTableHeaderForProbeSet(self, method=None, worksheet=None, newrow=None, headingStyle=None):
-
-        tblobj_header = []
-
-        if method in ["1","3","4"]:
-                tblobj_header = [[THCell(HT.TD(' ', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), sort=0),
-                                  THCell(HT.TD('Record',HT.BR(), 'ID',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="Record ID", idx=1),
-                                  THCell(HT.TD('Gene',HT.BR(), 'ID',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="Gene ID", idx=2),
-                                  THCell(HT.TD('Homologene',HT.BR(), 'ID',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="Homologene ID", idx=3),
-                                  THCell(HT.TD('Symbol',HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="Symbol", idx=4),
-                                  THCell(HT.TD('Description',HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="Description", idx=5),
-                                  THCell(HT.TD('Location',HT.BR(), 'Chr and Mb', HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="Location (Chr: Mb)", idx=6),
-                                  THCell(HT.TD('Mean',HT.BR(),'Expr',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="Mean Expr", idx=7),
-                                  THCell(HT.TD('Max',HT.BR(),'LRS',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Max LRS", idx=8),
-                                  THCell(HT.TD('Max LRS Location',HT.BR(),'Chr and Mb',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Max LRS Location (Chr: Mb)", idx=9),
-                                  THCell(HT.TD(HT.Href(
-                                                       text = HT.Span('Sample',HT.BR(), 'r', HT.Sup('  ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"),
-                                                       target = '_blank',
-                                                       url = "/correlationAnnotation.html#genetic_r"),
-                                               Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Sample r", idx=10),
-                                  THCell(HT.TD('N',HT.BR(),'Cases',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="N Cases", idx=11),
-                                  THCell(HT.TD(HT.Href(
-                                                       text = HT.Span('Sample',HT.BR(), 'p(r)', HT.Sup('  ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"),
-                                                       target = '_blank',
-                                                       url = "/correlationAnnotation.html#genetic_p_r"),
-                                               Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Sample p(r)", idx=12),
-                                  THCell(HT.TD(HT.Href(
-                                                       text = HT.Span('Lit',HT.BR(), 'Corr', HT.Sup('  ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"),
-                                                       target = '_blank',
-                                                       url = "/correlationAnnotation.html#literatureCorr"),
-                                               Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Lit Corr", idx=13),
-                                  #XZ, 09/22/2008: tissue correlation
-                                  THCell(HT.TD(HT.Href(
-                                                       text = HT.Span('Tissue',HT.BR(), 'r', HT.Sup('  ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"),
-                                                       target = '_blank',
-                                                       url = "/correlationAnnotation.html#tissue_r"),
-                                               Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Tissue r", idx=14),
-                                  THCell(HT.TD(HT.Href(
-                                                       text = HT.Span('Tissue',HT.BR(), 'p(r)', HT.Sup('  ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"),
-                                                       target = '_blank',
-                                                       url = "/correlationAnnotation.html#tissue_p_r"),
-                                               Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Tissue p(r)", idx=15)]]
-
-                for ncol, item in enumerate(['Record', 'Gene ID', 'Homologene ID', 'Symbol', 'Description', 'Location (Chr: Mb)', 'Mean Expr', 'Max LRS', 'Max LRS Location (Chr: Mb)', 'Sample r', 'N Cases', 'Sample p(r)', 'Lit Corr', 'Tissue r', 'Tissue p(r)']):
-                    worksheet.write([newrow, ncol], item, headingStyle)
-                    worksheet.set_column([ncol, ncol], 2*len(item))
-        else:
-                tblobj_header = [[THCell(HT.TD(' ', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), sort=0),
-                                  THCell(HT.TD('Record',HT.BR(), 'ID',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="Record ID", idx=1),
-                                  THCell(HT.TD('Gene',HT.BR(), 'ID',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="Gene ID", idx=2),
-                                  THCell(HT.TD('Homologene',HT.BR(), 'ID',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="Homologene ID", idx=3),
-                                  THCell(HT.TD('Symbol',HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="Symbol", idx=4),
-                                  THCell(HT.TD('Description',HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="Description", idx=5),
-                                  THCell(HT.TD('Location',HT.BR(), 'Chr and Mb', HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="Location (Chr: Mb)", idx=6),
-                                  THCell(HT.TD('Mean',HT.BR(),'Expr',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="Mean Expr", idx=7),
-                                  THCell(HT.TD('Max',HT.BR(),'LRS',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Max LRS", idx=8),
-                                  THCell(HT.TD('Max LRS Location',HT.BR(),'Chr and Mb',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Max LRS Location (Chr: Mb)", idx=9),
-                                  THCell(HT.TD(HT.Href(
-                                                       text = HT.Span('Sample',HT.BR(), 'rho', HT.Sup('  ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"),
-                                                       target = '_blank',
-                                                       url = "/correlationAnnotation.html#genetic_rho"),
-                                               Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Sample rho", idx=10),
-                                  THCell(HT.TD('N',HT.BR(),'Cases',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="N Cases", idx=11),
-                                  THCell(HT.TD(HT.Href(
-                                                       text = HT.Span('Sample',HT.BR(), 'p(rho)', HT.Sup('  ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"),
-                                                       target = '_blank',
-                                                       url = "/correlationAnnotation.html#genetic_p_rho"),
-                                               Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Sample p(rho)", idx=12),
-                                  THCell(HT.TD(HT.Href(
-                                                       text = HT.Span('Lit',HT.BR(), 'Corr', HT.Sup('  ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"),
-                                                       target = '_blank',
-                                                       url = "/correlationAnnotation.html#literatureCorr"),
-                                               Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Lit Corr", idx=13),
-                                  #XZ, 09/22/2008: tissue correlation
-                                  THCell(HT.TD(HT.Href(
-                                                       text = HT.Span('Tissue',HT.BR(), 'rho', HT.Sup('  ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"),
-                                                       target = '_blank',
-                                                       url = "/correlationAnnotation.html#tissue_r"),
-                                               Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Tissue rho", idx=14),
-                                  THCell(HT.TD(HT.Href(
-                                                       text = HT.Span('Tissue',HT.BR(), 'p(rho)', HT.Sup('  ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"),
-                                                       target = '_blank',
-                                                       url = "/correlationAnnotation.html#tissue_p_r"),
-                                               Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="Tissue p(rho)", idx=15)]]
-
-                for ncol, item in enumerate(['Record ID', 'Gene ID', 'Homologene ID', 'Symbol', 'Description', 'Location (Chr: Mb)', 'Mean Expr', 'Max LRS', 'Max LRS Location (Chr: Mb)', 'Sample rho', 'N Cases', 'Sample p(rho)', 'Lit Corr', 'Tissue rho', 'Tissue p(rho)']):
-                    worksheet.write([newrow, ncol], item, headingStyle)
-                    worksheet.set_column([ncol, ncol], 2*len(item))
-
-        return tblobj_header, worksheet
-
-
-    def getTableBodyForProbeSet(self, traitList=[], primaryTrait=None, formName=None, worksheet=None, newrow=None, corrScript=None, species=''):
-
-        tblobj_body = []
-
-        for thisTrait in traitList:
-
-            if thisTrait.symbol:
-                pass
-            else:
-                thisTrait.symbol = "--"
-
-            if thisTrait.geneid:
-                symbolurl = HT.Href(text=thisTrait.symbol,target='_blank',url="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene&cmd=Retrieve&dopt=Graphics&list_uids=%s" % thisTrait.geneid, Class="fs12 fwn")
-            else:
-                symbolurl = HT.Href(text=thisTrait.symbol,target='_blank',url="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?CMD=search&DB=gene&term=%s" % thisTrait.symbol, Class="fs12 fwn")
-
-            tr = []
-
-            trId = str(thisTrait)
-
-            corrScript.append('corrArray["%s"] = {corr:%1.4f};' % (trId, thisTrait.corr))
-
-            #XZ, 12/08/2008: checkbox
-            tr.append(TDCell(HT.TD(HT.Input(type="checkbox", Class="checkbox", name="searchResult",value=trId, onClick="highlight(this)"), nowrap="on", Class="fs12 fwn ffl b1 c222"), text=trId))
-
-            #XZ, 12/08/2008: probeset name
-            tr.append(TDCell(HT.TD(HT.Href(text=thisTrait.name,url="javascript:showTrait('%s', '%s')" % (formName,thisTrait.name), Class="fs12 fwn"), Class="fs12 fwn b1 c222"), thisTrait.name, thisTrait.name.upper()))
-
-            #XZ, 12/08/2008: gene id
-            if thisTrait.geneid:
-                tr.append(TDCell(None, thisTrait.geneid, val=999))
-            else:
-                tr.append(TDCell(None, thisTrait.geneid, val=999))
-
-            #XZ, 12/08/2008: homologene id
-            if thisTrait.homologeneid:
-                tr.append(TDCell("", thisTrait.homologeneid, val=999))
-            else:
-                tr.append(TDCell("", thisTrait.homologeneid, val=999))
-
-            #XZ, 12/08/2008: gene symbol
-            tr.append(TDCell(HT.TD(symbolurl, Class="fs12 fwn b1 c222 fsI"),thisTrait.symbol, thisTrait.symbol.upper()))
-
-            #XZ, 12/08/2008: description
-            #XZ, 06/05/2009: Rob asked to add probe target description
-            description_string = str(thisTrait.description).strip()
-            target_string = str(thisTrait.probe_target_description).strip()
-
-            description_display = ''
-
-            if len(description_string) > 1 and description_string != 'None':
-                description_display = description_string
-            else:
-                description_display = thisTrait.symbol
-
-            if len(description_display) > 1 and description_display != 'N/A' and len(target_string) > 1 and target_string != 'None':
-                description_display = description_display + '; ' + target_string.strip()
-
-            tr.append(TDCell(HT.TD(description_display, Class="fs12 fwn b1 c222"), description_display, description_display))
-
-            #XZ: trait_location_value is used for sorting
-            trait_location_repr = '--'
-            trait_location_value = 1000000
-
-            if thisTrait.chr and thisTrait.mb:
-                try:
-                    trait_location_value = int(thisTrait.chr)*1000 + thisTrait.mb
-                except:
-                    if thisTrait.chr.upper() == 'X':
-                        trait_location_value = 20*1000 + thisTrait.mb
-                    else:
-                        trait_location_value = ord(str(thisTrait.chr).upper()[0])*1000 + thisTrait.mb
-
-                trait_location_repr = 'Chr%s: %.6f' % (thisTrait.chr, float(thisTrait.mb) )
-
-            tr.append(TDCell(HT.TD(trait_location_repr, Class="fs12 fwn b1 c222", nowrap="on"), trait_location_repr, trait_location_value))
-
-            """
-            #XZ, 12/08/2008: chromosome number
-            #XZ, 12/10/2008: use Mbvalue to sort chromosome
-            tr.append(TDCell( HT.TD(thisTrait.chr, Class="fs12 fwn b1 c222", align='right'), thisTrait.chr, Mbvalue) )
-
-            #XZ, 12/08/2008: Rob wants 6 digit precision, and we have to deal with that the mb could be None
-            if not thisTrait.mb:
-                tr.append(TDCell(HT.TD(thisTrait.mb, Class="fs12 fwn b1 c222",align='right'), thisTrait.mb, Mbvalue))
-            else:
-                tr.append(TDCell(HT.TD('%.6f' % thisTrait.mb, Class="fs12 fwn b1 c222", align='right'), thisTrait.mb, Mbvalue))
-            """
-
-
-
-            #XZ, 01/12/08: This SQL query is much faster.
-            self.cursor.execute("""
-                    select ProbeSetXRef.mean from ProbeSetXRef, ProbeSet
-                    where ProbeSetXRef.ProbeSetFreezeId = %d and
-                          ProbeSet.Id = ProbeSetXRef.ProbeSetId and
-                          ProbeSet.Name = '%s'
-            """ % (thisTrait.db.id, thisTrait.name))
-            result = self.cursor.fetchone()
-            if result:
-                if result[0]:
-                    mean = result[0]
-                else:
-                    mean=0
-            else:
-                mean = 0
-
-            #XZ, 06/05/2009: It is neccessary to turn on nowrap
-            repr = "%2.3f" % mean
-            tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", align='right', nowrap='ON'),repr, mean))
-
-            #LRS and its location
-            LRS_score_repr = '--'
-            LRS_score_value = 0
-            LRS_location_repr = '--'
-            LRS_location_value = 1000000
-            LRS_flag = 1
-
-            #Max LRS and its Locus location
-            if thisTrait.lrs and thisTrait.locus:
-                self.cursor.execute("""
-                    select Geno.Chr, Geno.Mb from Geno, Species
-                    where Species.Name = '%s' and
-                          Geno.Name = '%s' and
-                          Geno.SpeciesId = Species.Id
-                """ % (species, thisTrait.locus))
-                result = self.cursor.fetchone()
-
-                if result:
-                    if result[0] and result[1]:
-                        LRS_Chr = result[0]
-                        LRS_Mb = result[1]
-
-                        #XZ: LRS_location_value is used for sorting
-                        try:
-                            LRS_location_value = int(LRS_Chr)*1000 + float(LRS_Mb)
-                        except:
-                            if LRS_Chr.upper() == 'X':
-                                LRS_location_value = 20*1000 + float(LRS_Mb)
-                            else:
-                                LRS_location_value = ord(str(LRS_chr).upper()[0])*1000 + float(LRS_Mb)
-
-
-                        LRS_score_repr = '%3.1f' % thisTrait.lrs
-                        LRS_score_value = thisTrait.lrs
-                        LRS_location_repr = 'Chr%s: %.6f' % (LRS_Chr, float(LRS_Mb) )
-                        LRS_flag = 0
-
-                        #tr.append(TDCell(HT.TD(HT.Href(text=LRS_score_repr,url="javascript:showIntervalMapping('%s', '%s : %s')" % (formName, thisTrait.db.shortname, thisTrait.name), Class="fs12 fwn"), Class="fs12 fwn ffl b1 c222", align='right', nowrap="on"),LRS_score_repr, LRS_score_value))
-                        tr.append(TDCell(HT.TD(LRS_score_repr, Class="fs12 fwn b1 c222", align='right', nowrap="on"), LRS_score_repr, LRS_score_value))
-                        tr.append(TDCell(HT.TD(LRS_location_repr, Class="fs12 fwn b1 c222", nowrap="on"), LRS_location_repr, LRS_location_value))
-
-            if LRS_flag:
-                tr.append(TDCell(HT.TD(LRS_score_repr, Class="fs12 fwn b1 c222"), LRS_score_repr, LRS_score_value))
-                tr.append(TDCell(HT.TD(LRS_location_repr, Class="fs12 fwn b1 c222"), LRS_location_repr, LRS_location_value))
-
-
-            #XZ, 12/08/2008: generic correlation
-            repr='%3.3f' % thisTrait.corr
-            tr.append(TDCell(HT.TD(HT.Href(text=repr, url="javascript:showCorrPlot('%s', '%s')" % (formName, thisTrait.name), Class="fs12 fwn ffl"), Class="fs12 fwn ffl b1 c222", align='right'),repr,abs(thisTrait.corr)))
-
-            #XZ, 12/08/2008: number of overlaped cases
-            repr = '%d' % thisTrait.nOverlap
-            tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", align='right'),repr,thisTrait.nOverlap))
-
-            #XZ, 12/08/2008: p value of genetic correlation
-            repr = webqtlUtil.SciFloat(thisTrait.corrPValue)
-            tr.append(TDCell(HT.TD(repr,nowrap='ON', Class="fs12 fwn ffl b1 c222", align='right'),repr,thisTrait.corrPValue))
-
-            #XZ, 12/08/2008: literature correlation
-            LCorr = 0.0
-            LCorrStr = "--"
-            if hasattr(thisTrait, 'LCorr') and thisTrait.LCorr:
-                LCorr = thisTrait.LCorr
-                LCorrStr = "%2.3f" % thisTrait.LCorr
-            tr.append(TDCell(HT.TD(LCorrStr, Class="fs12 fwn b1 c222", align='right'), LCorrStr, abs(LCorr)))
-
-            #XZ, 09/22/2008: tissue correlation.
-            TCorr = 0.0
-            TCorrStr = "--"
-            #XZ, 11/20/2008: need to pass two geneids: input_trait_mouse_geneid and thisTrait.mouse_geneid
-            if hasattr(thisTrait, 'tissueCorr') and thisTrait.tissueCorr:
-                TCorr = thisTrait.tissueCorr
-                TCorrStr = "%2.3f" % thisTrait.tissueCorr
-                # NL, 07/19/2010: add a new parameter rankOrder for js function 'showTissueCorrPlot'
-                rankOrder = self.RANK_ORDERS[self.method]
-                TCorrPlotURL = "javascript:showTissueCorrPlot('%s','%s','%s',%d)" %(formName, primaryTrait.symbol, thisTrait.symbol,rankOrder)
-                tr.append(TDCell(HT.TD(HT.Href(text=TCorrStr, url=TCorrPlotURL, Class="fs12 fwn ff1"), Class="fs12 fwn ff1 b1 c222", align='right'), TCorrStr, abs(TCorr)))
-            else:
-                tr.append(TDCell(HT.TD(TCorrStr, Class="fs12 fwn b1 c222", align='right'), TCorrStr, abs(TCorr)))
-
-            #XZ, 12/08/2008: p value of tissue correlation
-            TPValue = 1.0
-            TPValueStr = "--"
-            if hasattr(thisTrait, 'tissueCorr') and thisTrait.tissuePValue: #XZ, 09/22/2008: thisTrait.tissuePValue can't be used here because it could be 0
-                TPValue = thisTrait.tissuePValue
-                TPValueStr = "%2.3f" % thisTrait.tissuePValue
-            tr.append(TDCell(HT.TD(TPValueStr, Class="fs12 fwn b1 c222", align='right'), TPValueStr, TPValue))
-
-            tblobj_body.append(tr)
-
-            for ncol, item in enumerate([thisTrait.name, thisTrait.geneid, thisTrait.homologeneid, thisTrait.symbol, thisTrait.description, trait_location_repr, mean, LRS_score_repr, LRS_location_repr, thisTrait.corr, thisTrait.nOverlap, thisTrait.corrPValue, LCorr, TCorr, TPValue]):
-                worksheet.write([newrow, ncol], item)
-
-            newrow += 1
-
-        return tblobj_body, worksheet, corrScript
diff --git a/web/webqtl/correlation/PartialCorrDBPage.py b/web/webqtl/correlation/PartialCorrDBPage.py
deleted file mode 100755
index ecd1e623..00000000
--- a/web/webqtl/correlation/PartialCorrDBPage.py
+++ /dev/null
@@ -1,1359 +0,0 @@
-import string
-import cPickle
-import os
-import pyXLWriter as xl
-
-from htmlgen import HTMLgen2 as HT
-
-from base import webqtlConfig
-#import webqtlData
-from utility.THCell import THCell
-from utility.TDCell import TDCell
-from base.webqtlTrait import webqtlTrait
-from base.webqtlDataset import webqtlDataset
-from base.templatePage import templatePage
-from utility import webqtlUtil
-from CorrelationPage import CorrelationPage
-import correlationFunction
-from dbFunction import webqtlDatabaseFunction
-
-
-#########################################
-#      Partial Correlation Dataset Page
-#########################################
-
-
-class PartialCorrDBPage(CorrelationPage):
-
-    corrMinInformative = 4
-
-    def __init__(self, fd):
-
-
-        templatePage.__init__(self, fd)
-
-        if not self.openMysql():
-            return
-
-
-        primaryTraitString = fd.formdata.getvalue('primaryTrait')
-        primaryTrait = (webqtlTrait(fullname=primaryTraitString, cursor=self.cursor))
-
-        controlTraitsString = fd.formdata.getvalue('controlTraits')
-        controlTraitsList = list(string.split(controlTraitsString,','))
-        controlTraits = []
-        for item in controlTraitsList:
-            controlTraits.append(webqtlTrait(fullname=item, cursor=self.cursor))
-
-        #XZ, 3/16/2010: variable RISet must be pass by the form
-        RISet = fd.RISet
-        #XZ, 12/12/2008: get species infomation
-        species = webqtlDatabaseFunction.retrieveSpecies(cursor=self.cursor, RISet=RISet)
-
-        #XZ, 09/18/2008: get all information about the user selected database.
-        self.target_db_name = fd.formdata.getvalue('database2')
-
-	try:
-            self.db = webqtlDataset(self.target_db_name, self.cursor)
-        except:
-            heading = "Partial Correlation Table"
-            detail = ["The database you just requested has not been established yet."]
-            self.error(heading=heading,detail=detail)
-            return
-
-        #XZ, 09/18/2008: check if user has the authority to get access to the database.
-        if self.db.type == 'ProbeSet':
-            self.cursor.execute('SELECT Id, Name, FullName, confidentiality, AuthorisedUsers FROM ProbeSetFreeze WHERE Name = "%s"' %  self.target_db_name)
-            indId, indName, indFullName, confidential, AuthorisedUsers = self.cursor.fetchall()[0]
-
-            if confidential == 1:
-                access_to_confidential_dataset = 0
-
-                #for the dataset that confidentiality is 1
-                #1. 'admin' and 'root' can see all of the dataset
-                #2. 'user' can see the dataset that AuthorisedUsers contains his id(stored in the Id field of User table)
-                if webqtlConfig.USERDICT[self.privilege] > webqtlConfig.USERDICT['user']:
-                    access_to_confidential_dataset = 1
-                else:
-                    AuthorisedUsersList=AuthorisedUsers.split(',')
-                    if AuthorisedUsersList.__contains__(self.userName):
-                        access_to_confidential_dataset = 1
-
-                if not access_to_confidential_dataset:
-                    #Error, Confidential Database
-                    heading = "Partial Correlation Table"
-                    detail = ["The %s database you selected is not open to the public at this time, please go back and select another database." % indFullName]
-                    self.error(heading=heading,detail=detail,error="Confidential Database")
-                    return
-
-
-        primaryTrait.retrieveData()
-        _primarystrains, _primaryvals, _primaryvars = primaryTrait.exportInformative()
-
-	controlTraitNames = fd.formdata.getvalue('controlTraits')
-        _controlstrains,_controlvals,_controlvars,_controlNs = correlationFunction.controlStrains(controlTraitNames,_primarystrains)
-
-        ## If the strains for which each of the control traits and the primary trait have values are not identical,
-        ## we must remove from the calculation all vlaues for strains that are not present in each. Without doing this,
-        ## undesirable biases would be introduced.
-
-        common_primary_control_strains = _primarystrains #keep _primarystrains
-        fixed_primary_vals = _primaryvals #keep _primaryvals
-        fixed_control_vals = _controlvals
-
-	allsame = True	
-	##allsame is boolean for whether or not primary and control trait have values for the same strains
-	for i in _controlstrains:
-		if _primarystrains != i:
-			allsame=False
-			break
-	
-	if not allsame:
-                common_primary_control_strains, fixed_primary_vals, fixed_control_vals, _vars, _controlvars = correlationFunction.fixStrains(_primarystrains,_controlstrains,_primaryvals,_controlvals,_primaryvars,_controlvars)
-
-        N = len(common_primary_control_strains)
-        if N < self.corrMinInformative:
-            heading = "Partial Correlation Table"
-            detail = ['Fewer than %d strain data were entered for %s data set. No calculation of correlation has been attempted.' % (self.corrMinInformative, RISet)]
-            self.error(heading=heading,detail=detail)
-            return
-
-        #XZ: We should check the value of control trait and primary trait here.
-        nameOfIdenticalTraits = correlationFunction.findIdenticalTraits ( fixed_primary_vals, primaryTraitString, fixed_control_vals, controlTraitsList )
-        if nameOfIdenticalTraits:
-            heading = "Partial Correlation Table"
-            detail = ['%s and %s have same values for the %s strains that will be used to calculate partial correlation (common for all primary and control traits). In such case, partial correlation can NOT be calculated. Please re-select your traits.' % (nameOfIdenticalTraits[0], nameOfIdenticalTraits[1], len(fixed_primary_vals))]
-            self.error(heading=heading,detail=detail)
-            return
-
-
-        #XZ, 09/28/2008: if user select "1", then display 1, 3 and 4.
-        #XZ, 09/28/2008: if user select "2", then display 2, 3 and 5.
-        #XZ, 09/28/2008: if user select "3", then display 1, 3 and 4.
-        #XZ, 09/28/2008: if user select "4", then display 1, 3 and 4.
-        #XZ, 09/28/2008: if user select "5", then display 2, 3 and 5.		
-        methodDict = {"1":"Genetic Correlation (Pearson's r)","2":"Genetic Correlation (Spearman's rho)","3":"SGO Literature Correlation","4":"Tissue Correlation (Pearson's r)", "5":"Tissue Correlation (Spearman's rho)"}
-        self.method = fd.formdata.getvalue('method')
-        if self.method not in ("1","2","3","4","5"):
-            self.method = "1"
-
-        self.returnNumber = int(fd.formdata.getvalue('criteria'))
-
-        myTrait = primaryTrait
-        myTrait.retrieveInfo()
-
-        # We will not get Literature Correlations if there is no GeneId because there is nothing to look against
-        try:
-            input_trait_GeneId = myTrait.geneid
-        except:
-            input_trait_GeneId = None
-
-        # We will not get Tissue Correlations if there is no gene symbol because there is nothing to look against
-        try:
-            input_trait_symbol = myTrait.symbol
-        except:
-            input_trait_symbol = None
-
-        
-        #XZ, 12/12/2008: if the species is rat or human, translate the geneid to mouse geneid
-        input_trait_mouse_geneid = self.translateToMouseGeneID(species, input_trait_GeneId)
-
-        #XZ: As of Nov/13/2010, this dataset is 'UTHSC Illumina V6.2 RankInv B6 D2 average CNS GI average (May 08)'
-        TissueProbeSetFreezeId = 1
-
-
-        #XZ, 09/22/2008: If we need search by GeneId, 
-        #XZ, 09/22/2008: we have to check if this GeneId is in the literature or tissue correlation table.
-        #XZ, 10/15/2008: We also to check if the selected database is probeset type.
-        if self.method == "3" or self.method == "4" or self.method == "5":
-            if self.db.type != "ProbeSet":
-               self.error(heading="Wrong correlation type",detail="It is not possible to compute the %s between your trait and data in this %s database. Please try again after selecting another type of correlation." % (methodDict[self.method],self.db.name),error="Correlation Type Error")
-               return
-
-            """
-            if not input_trait_GeneId:
-                self.error(heading="No Associated GeneId",detail="This trait has no associated GeneId, so we are not able to show any literature or tissue related information.",error="No GeneId Error")
-                return 
-            """
-
-            #XZ: We have checked geneid did exist 
-
-            if self.method == "3":
-                if not input_trait_GeneId or not self.checkForLitInfo(input_trait_mouse_geneid):
-                    self.error(heading="No Literature Info",detail="This gene does not have any associated Literature Information.",error="Literature Correlation Error")
-                    return  
-
-            if self.method == "4" or self.method == "5":
-                if not input_trait_symbol:
-                    self.error(heading="No Tissue Correlation Information",detail="This gene does not have any associated Tissue Correlation Information.",error="Tissue Correlation Error")
-                    return
-
-                if not self.checkSymbolForTissueCorr(TissueProbeSetFreezeId, myTrait.symbol):
-                    self.error(heading="No Tissue Correlation Information",detail="This gene does not have any associated Tissue Correlation Information.",error="Tissue Correlation Error")
-                    return
-
-#######################################################################################################################################
-
-        nnCorr = len(fixed_primary_vals)
-
-        #XZ: Use the fast method only for probeset dataset, and this dataset must have been created.
-        #XZ: Otherwise, use original method
-
-        useFastMethod = False
-
-        if self.db.type == "ProbeSet":
-            DatabaseFileName = self.getFileName( target_db_name=self.target_db_name )
-            DirectoryList = os.listdir(webqtlConfig.TEXTDIR)  # List of existing text files. Used to check if a text file already exists
-            if DatabaseFileName in DirectoryList:
-                useFastMethod = True
-
-        if useFastMethod:
-            totalTraits, allcorrelations = self.getPartialCorrelationsFast(common_primary_control_strains , fixed_primary_vals, fixed_control_vals, nnCorr, DatabaseFileName, species, input_trait_GeneId, input_trait_symbol, TissueProbeSetFreezeId)
-
-            if totalTraits == 0:
-                useFastMethod = False
-
-        #XZ, 01/08/2009: use the original method to retrieve from database and compute.            
-        if not useFastMethod:
-            totalTraits, allcorrelations = self.getPartialCorrelationsNormal(common_primary_control_strains, fixed_primary_vals, fixed_control_vals, nnCorr, species, input_trait_GeneId, input_trait_symbol,TissueProbeSetFreezeId)
-	
-#############################################################
-
-        if self.method == "3" and input_trait_GeneId:
-            allcorrelations.sort(self.cmpLitCorr)
-        elif self.method in ["4","5"] and input_trait_GeneId:
-            allcorrelations.sort(self.cmpLitCorr)
-        else:
-            allcorrelations.sort(self.cmpPartialCorrPValue)
-
-        #XZ, 09/20/2008: we only need the top ones.
-        self.returnNumber = min(self.returnNumber,len(allcorrelations))
-        allcorrelations = allcorrelations[:self.returnNumber]
-
-        addLiteratureCorr = False
-        addTissueCorr = False
-
-        traitList = []
-        for item in allcorrelations:
-            thisTrait = webqtlTrait(db=self.db, name=item[0], cursor=self.cursor)
-            thisTrait.retrieveInfo()
-
-            thisTrait.Name = item[0]
-            thisTrait.NOverlap = item[1]
-
-            thisTrait.partial_corr = item[2]
-            thisTrait.partial_corrPValue = item[3]
-
-            thisTrait.corr = item[4]
-            thisTrait.corrPValue = item[5] 
-            # NL, 07/19/2010
-            # js function changed, add a new parameter rankOrder for js function 'showTissueCorrPlot'		
-            rankOrder = 0;
-            if self.method in ["2","5"]:
-                rankOrder = 1;
-            thisTrait.rankOrder = rankOrder
-
-            #XZ, 26/09/2008: Method is 4 or 5. Have fetched tissue corr, but no literature correlation yet.
-            if len(item) == 8:
-                thisTrait.tissueCorr = item[6]
-                thisTrait.tissuePValue = item[7]
-                addLiteratureCorr = True
-
-            #XZ, 26/09/2008: Method is 3,  Have fetched literature corr, but no tissue corr yet.
-            elif len(item) == 7:
-                thisTrait.LCorr = item[6]
-                thisTrait.mouse_geneid = self.translateToMouseGeneID(species, thisTrait.geneid)
-                addTissueCorr = True
-
-            #XZ, 26/09/2008: Method is 1 or 2. Have NOT fetched literature corr and tissue corr yet.
-            # Phenotype data will not have geneid, and neither will some probes
-            # we need to handle this because we will get an attribute error
-            else:
-                if input_trait_mouse_geneid and self.db.type=="ProbeSet":
-                    addLiteratureCorr = True
-                if input_trait_symbol and self.db.type=="ProbeSet":
-                    addTissueCorr = True
-
-            traitList.append(thisTrait)
-
-        if addLiteratureCorr:
-            traitList = self.getLiteratureCorrelationByList(input_trait_mouse_geneid, species, traitList)
-        if addTissueCorr:
-            traitList = self.getTissueCorrelationByList(primaryTraitSymbol=input_trait_symbol, traitList=traitList,TissueProbeSetFreezeId=TissueProbeSetFreezeId, method=self.method)
-
-########################################################
-
-        TD_LR = HT.TD(height=200,width="100%",bgColor='#eeeeee')
-
-        mainfmName = webqtlUtil.genRandStr("fm_")
-        form = HT.Form(cgi= os.path.join(webqtlConfig.CGIDIR, webqtlConfig.SCRIPTFILE), enctype='multipart/form-data', name= mainfmName, submit=HT.Input(type='hidden'))
-        hddn = {'FormID':'showDatabase', 'ProbeSetID':'_','database':self.target_db_name, 'CellID':'_', 'RISet':RISet, 'identification':fd.identification}
-
-        if myTrait:
-            hddn['fullname']=str(myTrait)
-
-
-        for key in hddn.keys():
-            form.append(HT.Input(name=key, value=hddn[key], type='hidden'))
-
-        #XZ, 11/21/2008: add two parameters to form
-        form.append(HT.Input(name="X_geneSymbol", value="", type='hidden'))
-        form.append(HT.Input(name="Y_geneSymbol", value="", type='hidden'))
-
-        #XZ, 3/11/2010: add one parameter to record if the method is rank order.
-
-        form.append(HT.Input(name="rankOrder", value="%s" % rankOrder, type='hidden'))
-
-        form.append(HT.Input(name="TissueProbeSetFreezeId", value="%s" % TissueProbeSetFreezeId, type='hidden'))
-
-
-        ####################################
-        # generate the info on top of page #
-        ####################################
-
-        info_form = self.getFormForPrimaryAndControlTraits (primaryTrait, controlTraits)
-        info = self.getTopInfo(myTrait=myTrait, method=self.method, db=self.db, target_db_name=self.target_db_name, returnNumber=self.returnNumber, methodDict=methodDict, totalTraits=totalTraits, identification=fd.identification  )
-
-        ##############
-        # Excel file #
-        ##############	
-        filename= webqtlUtil.genRandStr("Corr_")
-        xlsUrl = HT.Input(type='button', value = 'Download Table', onClick= "location.href='/tmp/%s.xls'" % filename, Class='button')
-        # Create a new Excel workbook
-        workbook = xl.Writer('%s.xls' % (webqtlConfig.TMPDIR+filename))
-        headingStyle = workbook.add_format(align = 'center', bold = 1, border = 1, size=13, fg_color = 0x1E, color="white")
-
-        #XZ, 3/18/2010: pay attention to the line number of header in this file. As of today, there are 7 lines.
-        worksheet = self.createExcelFileWithTitleAndFooter(workbook=workbook, identification=fd.identification, db=self.db, returnNumber=self.returnNumber)
-
-        newrow = 7
-
-
-
-#####################################################################
-
-        mintmap = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('%s')[0], 'showIntMap');" % mainfmName)
-        mintmap_img = HT.Image("/images/multiple_interval_mapping1_final.jpg", name='mintmap', alt="Multiple Interval Mapping", title="Multiple Interval Mapping", style="border:none;")
-        mintmap.append(mintmap_img)
-        mcorr = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('%s')[0], 'compCorr');" % mainfmName)
-        mcorr_img = HT.Image("/images/compare_correlates2_final.jpg", alt="Compare Correlates", title="Compare Correlates", style="border:none;")
-        mcorr.append(mcorr_img)
-        cormatrix = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('%s')[0], 'corMatrix');" % mainfmName)
-        cormatrix_img = HT.Image("/images/correlation_matrix1_final.jpg", alt="Correlation Matrix and PCA", title="Correlation Matrix and PCA", style="border:none;")
-        cormatrix.append(cormatrix_img)
-        networkGraph = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('%s')[0], 'networkGraph');" % mainfmName)
-        networkGraph_img = HT.Image("/images/network_graph1_final.jpg", name='mintmap', alt="Network Graphs", title="Network Graphs", style="border:none;")
-        networkGraph.append(networkGraph_img)
-        heatmap = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('%s')[0], 'heatmap');" % mainfmName)
-        heatmap_img = HT.Image("/images/heatmap2_final.jpg", name='mintmap', alt="QTL Heat Map and Clustering", title="QTL Heatmap and Clustering", style="border:none;")
-        heatmap.append(heatmap_img)
-        partialCorr = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('%s')[0], 'partialCorrInput');" % mainfmName) 
-        partialCorr_img = HT.Image("/images/partial_correlation_final.jpg", name='partialCorr', alt="Partial Correlation", title="Partial Correlation", style="border:none;")
-        partialCorr.append(partialCorr_img)
-        addselect = HT.Href(url="#redirect", onClick="addRmvSelection('%s', document.getElementsByName('%s')[0], 'addToSelection');" % (RISet, mainfmName))
-        addselect_img = HT.Image("/images/add_collection1_final.jpg", name="addselect", alt="Add To Collection", title="Add To Collection", style="border:none;")
-        addselect.append(addselect_img)
-	selectall = HT.Href(url="#redirect", onClick="checkAll(document.getElementsByName('%s')[0]);" % mainfmName)
-        selectall_img = HT.Image("/images/select_all2_final.jpg", name="selectall", alt="Select All", title="Select All", style="border:none;")
-        selectall.append(selectall_img)
-        selectinvert = HT.Href(url="#redirect", onClick = "checkInvert(document.getElementsByName('%s')[0]);" % mainfmName)
-        selectinvert_img = HT.Image("/images/invert_selection2_final.jpg", name="selectinvert", alt="Invert Selection", title="Invert Selection", style="border:none;")
-        selectinvert.append(selectinvert_img)
-        reset = HT.Href(url="#redirect", onClick="checkNone(document.getElementsByName('%s')[0]); return false;" % mainfmName)
-        reset_img = HT.Image("/images/select_none2_final.jpg", alt="Select None", title="Select None", style="border:none;")
-        reset.append(reset_img)
-        selecttraits = HT.Input(type='button' ,name='selecttraits',value='Select Traits', onClick="checkTraits(this.form);",Class="button")
-        selectgt = HT.Input(type='text' ,name='selectgt',value='-1.0', size=6,maxlength=10,onChange="checkNumeric(this,1.0,'-1.0','gthan','greater than filed')")
-        selectlt = HT.Input(type='text' ,name='selectlt',value='1.0', size=6,maxlength=10,onChange="checkNumeric(this,-1.0,'1.0','lthan','less than field')")
-        selectandor = HT.Select(name='selectandor')
-        selectandor.append(('AND','and'))
-        selectandor.append(('OR','or'))
-        selectandor.selected.append('AND')
-
-        chrMenu = HT.Input(type='hidden',name='chromosomes',value='all')
-
-        corrHeading = HT.Paragraph('Partial Correlation Table', Class="title")
-
-        	
-        pageTable = HT.TableLite(cellSpacing=0,cellPadding=0,width="100%", border=0, align="Left")
-        containerTable = HT.TableLite(cellSpacing=0,cellPadding=0,width="90%",border=0, align="Left")
-
-        optionsTable = HT.TableLite(cellSpacing=2, cellPadding=0,width="320", height="80", border=0, align="Left")
-        optionsTable.append(HT.TR(HT.TD(selectall), HT.TD(reset), HT.TD(selectinvert), HT.TD(addselect), align="left"))
-        optionsTable.append(HT.TR(HT.TD("&nbsp;"*1,"Select"), HT.TD("Deselect"), HT.TD("&nbsp;"*1,"Invert"), HT.TD("&nbsp;"*3,"Add")))
-        containerTable.append(HT.TR(HT.TD(optionsTable)))
-
-        functionTable = HT.TableLite(cellSpacing=2,cellPadding=0,width="480",height="80", border=0, align="Left")
-        functionRow = HT.TR(HT.TD(networkGraph, width="16.7%"), HT.TD(cormatrix, width="16.7%"), HT.TD(partialCorr, width="16.7%"), HT.TD(mcorr, width="16.7%"), HT.TD(mintmap, width="16.7%"), HT.TD(heatmap), align="left")
-        labelRow = HT.TR(HT.TD("&nbsp;"*1,HT.Text("Graph")), HT.TD("&nbsp;"*1,HT.Text("Matrix")), HT.TD("&nbsp;"*1,HT.Text("Partial")), HT.TD(HT.Text("Compare")), HT.TD(HT.Text("QTL Map")), HT.TD(HT.Text(text="Heat Map")))
-        functionTable.append(functionRow, labelRow)
-        containerTable.append(HT.TR(HT.TD(functionTable), HT.BR()))
-
-        moreOptions = HT.Input(type='button',name='options',value='More Options', onClick="",Class="toggle")
-        fewerOptions = HT.Input(type='button',name='options',value='Fewer Options', onClick="",Class="toggle")
-
-        if (fd.formdata.getvalue('showHideOptions') == 'less'):		
-			containerTable.append(HT.TR(HT.TD("&nbsp;"), height="10"), HT.TR(HT.TD(HT.Div(fewerOptions, Class="toggleShowHide"))))
-			containerTable.append(HT.TR(HT.TD("&nbsp;")))
-        else:	
-			containerTable.append(HT.TR(HT.TD("&nbsp;"), height="10"), HT.TR(HT.TD(HT.Div(moreOptions, Class="toggleShowHide"))))	
-			containerTable.append(HT.TR(HT.TD("&nbsp;")))
-
-        containerTable.append(HT.TR(HT.TD(HT.Span(selecttraits,' with partial r > ',selectgt, ' ',selectandor, ' r < ',selectlt,Class="bd1 cbddf fs11")), style="display:none;", Class="extra_options"))
-
-
-        tblobj = {}
-
-
-        if self.db.type=="Geno":
-        	
-            containerTable.append(HT.TR(HT.TD(xlsUrl, height=40)))
-            pageTable.append(HT.TR(HT.TD(containerTable)))
-        	
-            tblobj['header'], worksheet = self.getTableHeaderForGeno( method=self.method, worksheet=worksheet, newrow=newrow, headingStyle=headingStyle)
-            newrow += 1
-            
-            corrScript = HT.Script(language="Javascript")
-            corrScript.append("var corrArray = new Array();")
-
-            sortby = self.getSortByValue( calculationMethod = self.method )
-
-            tblobj['body'], worksheet, corrScript = self.getTableBodyForGeno(traitList=traitList, formName=mainfmName, worksheet=worksheet, newrow=newrow, corrScript=corrScript)
-
-            workbook.close()
-            objfile = open('%s.obj' % (webqtlConfig.TMPDIR+filename), 'wb')
-            cPickle.dump(tblobj, objfile)
-            objfile.close()
-			# NL, 07/27/2010. genTableObj function has been moved from templatePage.py to webqtlUtil.py;	
-            div = HT.Div(webqtlUtil.genTableObj(tblobj=tblobj, file=filename, sortby=sortby, tableID = "sortable", addIndex = "1"), corrScript, Id="sortable")
-            pageTable.append(HT.TR(HT.TD(div)))
-            form.append(HT.Input(name='ShowStrains',type='hidden', value =1),
-                        HT.Input(name='ShowLine',type='hidden', value =1),
-                        HT.P(),pageTable)
-
-            TD_LR.append(corrHeading, info_form, HT.P(), info, form, HT.P())
-
-            self.dict['body'] =  str(TD_LR)
-			# updated by NL. Delete function generateJavaScript, move js files to dhtml.js, webqtl.js and jqueryFunction.js
-            self.dict['js1'] = ''
-            self.dict['title'] = 'Partial Correlation Result'
-
-        elif self.db.type=="Publish":
-        	
-            containerTable.append(HT.TR(HT.TD(xlsUrl, height=40)))
-            pageTable.append(HT.TR(HT.TD(containerTable)))
-        	
-            tblobj['header'], worksheet = self.getTableHeaderForPublish(method=self.method, worksheet=worksheet, newrow=newrow, headingStyle=headingStyle)
-            newrow += 1
-
-            sortby = self.getSortByValue( calculationMethod = self.method )
-            
-            corrScript = HT.Script(language="Javascript")
-            corrScript.append("var corrArray = new Array();")
-
-            tblobj['body'], worksheet, corrScript = self.getTableBodyForPublish(traitList=traitList, formName=mainfmName, worksheet=worksheet, newrow=newrow, corrScript=corrScript)
-
-            workbook.close()
-
-            objfile = open('%s.obj' % (webqtlConfig.TMPDIR+filename), 'wb')
-            cPickle.dump(tblobj, objfile)
-            objfile.close()
-			# NL, 07/27/2010. genTableObj function has been moved from templatePage.py to webqtlUtil.py;
-            div = HT.Div(webqtlUtil.genTableObj(tblobj=tblobj, file=filename, sortby=sortby, tableID = "sortable", addIndex = "1"), corrScript, Id="sortable")
-            pageTable.append(HT.TR(HT.TD(div)))
-
-            form.append(
-            HT.Input(name='ShowStrains',type='hidden', value =1),
-            HT.Input(name='ShowLine',type='hidden', value =1),
-            HT.P(),pageTable)
-
-            TD_LR.append(corrHeading, info_form, HT.P(), info, form, HT.P())
-
-            self.dict['body'] = str(TD_LR)
-			#updated by NL. Delete function generateJavaScript, move js files to dhtml.js, webqtl.js and jqueryFunction.js
-            self.dict['js1'] = ''
-            self.dict['title'] = 'Partial Correlation Result'
-
-        elif self.db.type=="ProbeSet":
-
-            tblobj['header'], worksheet = self.getTableHeaderForProbeSet(method=self.method, worksheet=worksheet, newrow=newrow, headingStyle=headingStyle)
-            newrow += 1
-
-            sortby = self.getSortByValue( calculationMethod = self.method )
-
-            corrScript = HT.Script(language="Javascript")
-            corrScript.append("var corrArray = new Array();")
-
-            tblobj['body'], worksheet, corrScript = self.getTableBodyForProbeSet(traitList=traitList, primaryTrait=myTrait, formName=mainfmName, worksheet=worksheet, newrow=newrow, corrScript=corrScript)
-
-            workbook.close()
-            objfile = open('%s.obj' % (webqtlConfig.TMPDIR+filename), 'wb')
-            cPickle.dump(tblobj, objfile)
-            objfile.close()	
-
-            '''
-            #XZ, 07/07/2010: I comment out this block of code.
-            WebGestaltScript = HT.Script(language="Javascript")
-            WebGestaltScript.append("""
-setTimeout('openWebGestalt()', 2000);
-function openWebGestalt(){
-	var thisForm = document['WebGestalt'];
-	makeWebGestaltTree(thisForm, '%s', %d, 'edag_only.php');
-}	
-            """ % (mainfmName, len(traitList)))	
-            '''
-
-            #XZ: here is the table of traits
-			# NL, 07/27/2010. genTableObj function has been moved from templatePage.py to webqtlUtil.py;
-            div = HT.Div(webqtlUtil.genTableObj(tblobj=tblobj, file=filename, sortby=sortby, tableID = "sortable", addIndex = "1"), corrScript, Id="sortable")
-
-            self.cursor.execute('SELECT GeneChip.GO_tree_value FROM GeneChip, ProbeFreeze, ProbeSetFreeze WHERE GeneChip.Id = ProbeFreeze.ChipId and ProbeSetFreeze.ProbeFreezeId = ProbeFreeze.Id and ProbeSetFreeze.Name = "%s"' % self.db.name)
-            result = self.cursor.fetchone()
-
-            if result:
-                GO_tree_value = result[0]
-
-            if GO_tree_value:      
-
-                hddnWebGestalt = {
-                                  'id_list':'',
-                                  'correlation':'',
-                                  'id_value':'', 
-                                  'llid_list':'',
-                                  'id_type':GO_tree_value,
-                                  'idtype':'',
-                                  'species':'',
-                                  'list':'',
-                                  'client':''}
-            
-                hddnWebGestalt['ref_type'] = hddnWebGestalt['id_type']
-                hddnWebGestalt['cat_type'] = 'GO'
-                hddnWebGestalt['significancelevel'] = 'Top10'
-
-                if species == 'rat':
-                    hddnWebGestalt['org'] = 'Rattus norvegicus'
-                elif species == 'human':
-                    hddnWebGestalt['org'] = 'Homo sapiens'
-                elif species == 'mouse':
-                    hddnWebGestalt['org'] = 'Mus musculus'
-                else:
-                    hddnWebGestalt['org'] = ''
-            
-                for key in hddnWebGestalt.keys():
-                    form.append(HT.Input(name=key, value=hddnWebGestalt[key], type='hidden'))
-
-            #XZ, 01/12/2009: create database menu for 'Add Correlation'
-            self.cursor.execute("""
-                select 
-                    ProbeSetFreeze.FullName, ProbeSetFreeze.Id, Tissue.name
-                from 
-                    ProbeSetFreeze, ProbeFreeze, ProbeSetFreeze as ps2, ProbeFreeze as p2, Tissue
-                where
-                    ps2.Id = %d
-                    and ps2.ProbeFreezeId = p2.Id
-                    and ProbeSetFreeze.ProbeFreezeId = ProbeFreeze.Id
-                    and (ProbeFreeze.InbredSetId = p2.InbredSetId or (ProbeFreeze.InbredSetId in (1, 3) and p2.InbredSetId in (1, 3)))
-                    and p2.ChipId = ProbeFreeze.ChipId
-                    and ps2.Id != ProbeSetFreeze.Id
-                    and ProbeFreeze.TissueId = Tissue.Id
-                    and ProbeSetFreeze.public > %d
-                order by
-                    ProbeFreeze.TissueId, ProbeSetFreeze.CreateTime desc
-                """ % (self.db.id, webqtlConfig.PUBLICTHRESH))
-
-            results = self.cursor.fetchall()
-            dbCustomizer = HT.Select(results, name = "customizer")
-            databaseMenuSub = preTissue = ""
-            for item in results:
-                TName, TId, TTissue = item
-                if TTissue != preTissue:
-                    if databaseMenuSub:
-                        dbCustomizer.append(databaseMenuSub)
-                    databaseMenuSub = HT.Optgroup(label = '%s mRNA ------' % TTissue)
-                    preTissue = TTissue
-
-                databaseMenuSub.append(item[:2])
-            if databaseMenuSub:
-                dbCustomizer.append(databaseMenuSub)
-			#updated by NL. Delete function generateJavaScript, move js files to dhtml.js, webqtl.js and jqueryFunction.js
-			#variables: filename, strainIds and vals are required by getquerystring function
-            strainIds=self.getStrainIds(species=species, strains=_primarystrains)
-            var1 = HT.Input(name="filename", value=filename, type='hidden')
-            var2 = HT.Input(name="strainIds", value=strainIds, type='hidden')
-            var3 = HT.Input(name="vals", value=_primaryvals, type='hidden')
-            customizerButton = HT.Input(type="button", Class="button", value="Add Correlation", onClick = "xmlhttpPost('%smain.py?FormID=AJAX_table', 'sortable', (getquerystring(this.form)))" % webqtlConfig.CGIDIR)
-
-            containerTable.append(HT.TR(HT.TD(HT.Span(var1,var2,var3,customizerButton, "with", dbCustomizer, Class="bd1 cbddf fs11"), HT.BR(), HT.BR()), style="display:none;", Class="extra_options"))
-
-            #outside analysis part
-            GCATButton = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('%s')[0], 'GCAT');" % mainfmName)
-            GCATButton_img = HT.Image("/images/GCAT_logo_final.jpg", name="GCAT", alt="GCAT", title="GCAT", style="border:none")
-            GCATButton.append(GCATButton_img)
-
-            ODE = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('%s')[0], 'ODE');" % mainfmName)
-            ODE_img = HT.Image("/images/ODE_logo_final.jpg", name="ode", alt="ODE", title="ODE", style="border:none")
-            ODE.append(ODE_img)
-            	
-            WebGestalt = HT.Href(url="#redirect", onClick="databaseFunc(document.getElementsByName('%s')[0], 'GOTree');" % mainfmName)
-            WebGestalt_img = HT.Image("/images/webgestalt_icon_final.jpg", name="webgestalt", alt="Gene Set Analysis Toolkit", title="Gene Set Analysis Toolkit", style="border:none")        
-            WebGestalt.append(WebGestalt_img)
-                
-            LinkOutTable = HT.TableLite(cellSpacing=2,cellPadding=0,width="320",height="80", border=0, align="Left")
-            if not GO_tree_value:
-                LinkOutRow = HT.TR(HT.TD(GCATButton, width="50%"), HT.TD(ODE, width="50%"), align="left")
-                LinkOutLabels = HT.TR(HT.TD("&nbsp;", HT.Text("GCAT"), width="50%"), HT.TD("&nbsp;",HT.Text("ODE"), width="50%"), align="left")
-            else:
-                LinkOutRow = HT.TR(HT.TD(WebGestalt, width="25%"), HT.TD(GCATButton, width="25%"), HT.TD(ODE, width="25%"), align="left")
-                LinkOutLabels = HT.TR(HT.TD(HT.Text("Gene Set")), HT.TD("&nbsp;"*2, HT.Text("GCAT")), HT.TD("&nbsp;"*3, HT.Text("ODE")), style="display:none;", Class="extra_options")
-            LinkOutTable.append(LinkOutRow,LinkOutLabels)
-
-            containerTable.append(HT.TR(HT.TD(LinkOutTable), Class="extra_options", style="display:none;"))                   
-        	
-            containerTable.append(HT.TR(HT.TD(xlsUrl, HT.BR(), HT.BR(), height=40)))
-
-            pageTable.append(HT.TR(HT.TD(containerTable)))
-
-            pageTable.append(HT.TR(HT.TD(div)))      
-            
-            if species == 'human':
-                heatmap = ""     	
-        	
-            form.append(HT.Input(name='ShowStrains',type='hidden', value =1),
-                        HT.Input(name='ShowLine',type='hidden', value =1),
-                        info, HT.BR(), pageTable, HT.BR())
-            		
-            TD_LR.append(corrHeading, info_form, HT.P(), form, HT.P())
-
-
-            self.dict['body'] = str(TD_LR)
-            self.dict['title'] = 'Partial Correlation Result'
-			# updated by NL. Delete function generateJavaScript, move js files to dhtml.js, webqtl.js and jqueryFunction.js
-            self.dict['js1'] = ''
-            self.dict['js2'] = 'onLoad="pageOffset()"'
-            self.dict['layer'] = self.generateWarningLayer()
-            
-        else:
-            self.dict['body'] = ""
-
-
-
-####################################
-#                                  #
-#Partial CorrelationPage Functions #
-#                                  #
-####################################
-
-
-    def getSortByValue(self, calculationMethod):
-
-        sortby = ("partial_pv", "up")
-
-        if calculationMethod == "3": #XZ: literature correlation
-                sortby = ("lcorr","down")
-        elif calculationMethod == "4" or calculationMethod == "5": #XZ: tissue correlation
-                sortby = ("tissuecorr", "down")
-
-        return sortby
-
-
-    #XZ, 3/31/2010: 
-    #A[0] holds trait name.
-    #A[1] holds partial correlation coefficient number.
-    #A[2] holds N.
-    #A[3] holds p value of partial correlation.
-    def cmpPartialCorrPValue (self, A, B):
-        try:
-            if A[3] < B[3]:
-                return -1
-            elif A[3] == B[3]:
-                return 0
-            else:
-                return 1
-        except:
-                return 0
-
-
-    #XZ, 4/1/2010:
-    #A[0] holds trait name.
-    #A[1] holds N.
-    #A[2] holds partial correlation coefficient number.
-    #A[3] holds p value of partial correlation.
-    #A[6] holds literature corr or tissue corr value.
-    #Sort by literature corr or tissue corr first, then by partial corr p value.
-    def cmpLitCorr(self, A, B):
-        try:
-            if abs(A[6]) < abs(B[6]):
-                return 1
-            elif abs(A[6]) == abs(B[6]):
-                if A[3] < B[3]:
-                    return -1
-                elif A[3] == B[3]:
-                    return 0
-                else:
-                    return 1
-            else:
-                return -1
-        except:
-            return 0
-
-
-    def getPartialCorrelationsFast(self, _strains, _vals, _controlvals, nnCorr, DatabaseFileName, species, input_trait_GeneId,gene_symbol,TissueProbeSetFreezeId ):
-	"""Calculates and returns correlation coefficients using data from a csv text file."""
-
-	try:
-		allcorrelations = []
-
-		useLit = False
-                if self.method == "3":
-                    litCorrs = self.fetchLitCorrelations(species=species, GeneId=input_trait_GeneId, db=self.db, returnNumber=self.returnNumber)
-                    useLit = True
-
-                useTissueCorr = False
-                if self.method == "4" or self.method == "5":
-                    tissueCorrs = self.fetchTissueCorrelations(db=self.db,primaryTraitSymbol=gene_symbol, TissueProbeSetFreezeId=TissueProbeSetFreezeId, method=self.method, returnNumber=self.returnNumber)
-                    useTissueCorr = True
-
-                datasetFile = open(webqtlConfig.TEXTDIR+DatabaseFileName,'r')
-
-                #XZ, 01/08/2009: read the first line
-                line = datasetFile.readline()
-                dataset_strains = webqtlUtil.readLineCSV(line)[1:]
-
-                #XZ, 3/30/2010: This step is critical.
-                good_dataset_strains_index = []
-
-                for i in range(len(_strains)):
-                    found_in_dataset_strains = 0
-                    for j, one_dataset_strain in enumerate(dataset_strains):
-                        if one_dataset_strain == _strains[i]:
-                            found_in_dataset_strains = 1
-                            good_dataset_strains_index.append(j)
-                            break
-
-                    if not found_in_dataset_strains:
-                        good_dataset_strains_index.append(-99999)
-
-                allTargetTraitNames = []
-                allTargetTraitValues = []
-
-                #XZ, 04/01/2009: If literature corr or tissue corr is selected,
-                #XZ: there is no need to compute partial correlation for all traits.
-                #XZ: If genetic corr is selected, compute partial correlation for all traits.
-                for line in datasetFile:
-                        trait_line = webqtlUtil.readLineCSV(line)
-                        trait_name = trait_line[0]
-                        trait_data = trait_line[1:]
-
-                        if useLit:
-                           if not litCorrs.has_key( trait_name ):
-                                continue
-
-                        if useTissueCorr:
-                            if not tissueCorrs.has_key( trait_name ):
-                                continue
-
-                        #XZ, 04/01/2010: If useLit or useTissueCorr, and this trait should not be added,
-                        #it will not go to the next step.
-
-                        good_dataset_vals = []
-                        for i in good_dataset_strains_index:
-                            if i == -99999:
-                                good_dataset_vals.append(None)
-                            else:
-                                good_dataset_vals.append( float(trait_data[i]) )
-
-                        allTargetTraitNames.append(trait_name)
-                        allTargetTraitValues.append(good_dataset_vals)
-
-                datasetFile.close()
-
-                if self.method in ["2", "5"]: #Spearman
-                    allcorrelations = correlationFunction.determinePartialsByR(primaryVal=_vals, controlVals=_controlvals, targetVals=allTargetTraitValues, targetNames=allTargetTraitNames, method='s')
-                else:
-                    allcorrelations = correlationFunction.determinePartialsByR(primaryVal=_vals, controlVals=_controlvals, targetVals=allTargetTraitValues, targetNames=allTargetTraitNames)
-
-                totalTraits = len(allcorrelations)
-
-                if useLit or useTissueCorr:
-                    for i, item in enumerate(allcorrelations):
-                        if useLit:
-                            allcorrelations[i].append(litCorrs[ item[0] ])
-                        if useTissueCorr:
-                            tempCorr, tempPValue = tissueCorrs[ item[0] ]
-                            allcorrelations[i].append(tempCorr)
-                            allcorrelations[i].append(tempPValue)
-
-		return totalTraits, allcorrelations
-	except:
-                return 0, 0
-
-
-    def getPartialCorrelationsNormal(self,  _strains, _vals, _controlvals, nnCorr, species, input_trait_GeneId, input_trait_symbol,TissueProbeSetFreezeId):
-	    """Calculates and returns correlation coefficients"""
-
-            traitdatabase, dataStartPos = self.fetchAllDatabaseData(species=species, GeneId=input_trait_GeneId, GeneSymbol=input_trait_symbol, strains=_strains, db=self.db, method=self.method, returnNumber=self.returnNumber, tissueProbeSetFreezeId=TissueProbeSetFreezeId)
-            totalTraits = len(traitdatabase) #XZ, 09/18/2008: total trait number
-
-	    allcorrelations = []
-
-            allTargetTraitNames = []
-            allTargetTraitValues = []
-
-	    for traitdata in traitdatabase:
-                traitdataName = traitdata[0]
-                traitvals = traitdata[dataStartPos:]
-                allTargetTraitNames.append (traitdataName)
-                allTargetTraitValues.append (traitvals)
-
-            if self.method in ["2", "5"]: #Spearman
-                allcorrelations = correlationFunction.determinePartialsByR(primaryVal=_vals, controlVals=_controlvals, targetVals=allTargetTraitValues, targetNames=allTargetTraitNames, method='s')
-            else:
-                allcorrelations = correlationFunction.determinePartialsByR(primaryVal=_vals, controlVals=_controlvals, targetVals=allTargetTraitValues, targetNames=allTargetTraitNames)
-
-            #XZ, 09/28/2008: if user select '3', then fetchAllDatabaseData would give us LitCorr in the [1] position
-            #XZ, 09/28/2008: if user select '4' or '5', then fetchAllDatabaseData would give us Tissue Corr in the [1] position
-            #XZ, 09/28/2008: and Tissue Corr P Value in the [2] position
-            if input_trait_GeneId and self.db.type == "ProbeSet" and self.method in ["3", "4", "5"]:
-                for i, item in enumerate(allcorrelations):
-                    if self.method == "3":
-                        item.append( traitdatabase[1] )
-                    if self.method == "4" or self.method == "5":
-                        item.append( traitdatabase[1] )
-                        item.append( traitdatabase[2] )	
-
-
-	    return totalTraits, allcorrelations
-
-
-    def getTableHeaderForPublish(self, method=None, worksheet=None, newrow=None, headingStyle=None):
-
-        tblobj_header = []
-
-        if method in ["1", "3", "4"]:
-            tblobj_header = [[THCell(HT.TD('', Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), sort=0), 
-                              THCell(HT.TD('Record', Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="id", idx=1),
-                              THCell(HT.TD('Phenotype', Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="pheno", idx=2),
-                              THCell(HT.TD('Authors', Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="auth", idx=3),
-                              THCell(HT.TD('Year', Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="year", idx=4),
-                              THCell(HT.TD('N', Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="nstr", idx=5),
-                              THCell(HT.TD('Partial r ', Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="partial_corr", idx=6),
-                              THCell(HT.TD('p(partial r)', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text="partial_pv", idx=7),
-                              THCell(HT.TD('r ', Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="corr", idx=8),
-                              THCell(HT.TD('p(r)', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text="pv", idx=9),
-                              THCell(HT.TD('delta r', Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="delta_corr", idx=10)]]
-
-            for ncol, item in enumerate(["Record", "Phenotype", "Authors", "Year", "PubMedID", "N", "Partial r", "p(partial r)", "r   ", "p(r)", "delta r"]):
-                worksheet.write([newrow, ncol], item, headingStyle)
-                worksheet.set_column([ncol, ncol], 2*len(item))
-        else:
-            tblobj_header = [[THCell(HT.TD('', Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), sort=0),
-                              THCell(HT.TD('Record', Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="id", idx=1),
-                              THCell(HT.TD('Phenotype', Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="pheno", idx=2),
-                              THCell(HT.TD('Authors', Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="auth", idx=3),
-                              THCell(HT.TD('Year', Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="year", idx=4),
-                              THCell(HT.TD('N', Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="nstr", idx=5),
-                              THCell(HT.TD('Partial rho ', Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="partial_corr", idx=6),
-                              THCell(HT.TD('p(partial rho)', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text="partial_pv", idx=7),
-                              THCell(HT.TD('rho ', Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="corr", idx=8),
-                              THCell(HT.TD('p(rho)', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text="pv", idx=9),
-                              THCell(HT.TD('delta rho', Class="fs13 fwb ffl b1 cw cbrb", nowrap="on"), text="delta_corr", idx=10)]]
-
-            for ncol, item in enumerate(["Record", "Phenotype", "Authors", "Year", "PubMedID", "N", "Partial rho", "p(partial rho)", "rho ", "p(rho)", "delta rho"]):
-                worksheet.write([newrow, ncol], item, headingStyle)
-                worksheet.set_column([ncol, ncol], 2*len(item))
-
-        return tblobj_header, worksheet
-
-
-    def getTableBodyForPublish(self, traitList, formName=None, worksheet=None, newrow=None, corrScript=None):
-
-        tblobj_body = []
-
-        for thisTrait in traitList:
-            tr = []
-
-            trId = str(thisTrait)
-
-            #partial corr value could be string 'NA'
-            try:
-                corrScript.append('corrArray["%s"] = {corr:%1.4f};' % (trId, thisTrait.partial_corr))
-            except:
-                corrScript.append('corrArray["%s"] = {corr:"NA"};' % (trId))
-
-            tr.append(TDCell(HT.TD(HT.Input(type="checkbox", Class="checkbox", name="searchResult",value=trId, onClick="highlight(this)"), nowrap="on", Class="fs12 fwn ffl b1 c222"), text=trId))
-
-            tr.append(TDCell(HT.TD(HT.Href(text=thisTrait.name,url="javascript:showTrait('%s', '%s')" % (formName, thisTrait.name), Class="fs12 fwn"), nowrap="yes",align="center", Class="fs12 fwn b1 c222"),str(thisTrait.name), 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
-            tr.append(TDCell(HT.TD(PhenotypeString, Class="fs12 fwn b1 c222"), PhenotypeString, PhenotypeString.upper()))
-
-            tr.append(TDCell(HT.TD(thisTrait.authors, Class="fs12 fwn b1 c222 fsI"),thisTrait.authors, thisTrait.authors.strip().upper()))
-
-            try:
-                PubMedLinkText = myear = repr = int(thisTrait.year)
-            except:
-                PubMedLinkText = repr = "N/A"
-                myear = 0
-            if thisTrait.pubmed_id:
-                PubMedLink = HT.Href(text= repr,url= webqtlConfig.PUBMEDLINK_URL % thisTrait.pubmed_id,target='_blank', Class="fs12 fwn")
-            else:
-                PubMedLink = repr
-
-            tr.append(TDCell(HT.TD(PubMedLink, Class="fs12 fwn b1 c222", align='center'), repr, myear))
-
-            repr = '%d' % thisTrait.NOverlap
-            tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", align='right'),repr,thisTrait.NOverlap))
-
-            try:
-                repr = '%3.3f' % thisTrait.partial_corr
-                tr.append(TDCell(HT.TD(repr, Class="fs12 fwn b1 c222", align='right', nowrap="on"), repr, abs(thisTrait.partial_corr)))
-            except:
-                repr = 'NA'
-                tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", align='left'), text=repr, val=0 ))
-
-            repr = webqtlUtil.SciFloat(thisTrait.partial_corrPValue)
-            tr.append(TDCell(HT.TD(repr,nowrap='ON', Class="fs12 fwn ffl b1 c222", align='right'),repr,thisTrait.partial_corrPValue))
-
-            repr = '%3.3f' % thisTrait.corr
-            tr.append(TDCell(HT.TD(repr, Class="fs12 fwn b1 c222", align='right', nowrap="on"), repr, abs(thisTrait.corr)))
-
-            repr = webqtlUtil.SciFloat(thisTrait.corrPValue)
-            tr.append(TDCell(HT.TD(repr,nowrap='ON', Class="fs12 fwn ffl b1 c222", align='right'),repr,thisTrait.corrPValue))
-
-            #delta
-            try:
-                delta = '%3.3f' % ( float(thisTrait.partial_corr) - float(thisTrait.corr) )
-                tr.append(TDCell(HT.TD(delta, Class="fs12 fwn ffl b1 c222", align='right', nowrap="on"), text=delta, val=abs(float(delta)) ))            
-            except:
-                delta = 'NA'
-                tr.append(TDCell(HT.TD(delta, Class="fs12 fwn ffl b1 c222", align='left'), text=delta, val=0 ))
-
-            tblobj_body.append(tr)
-            
-            for ncol, item in enumerate([thisTrait.name, PhenotypeString, thisTrait.authors, thisTrait.year, thisTrait.pubmed_id, thisTrait.NOverlap, thisTrait.partial_corr, thisTrait.partial_corrPValue, thisTrait.corr, thisTrait.corrPValue, delta]):
-                worksheet.write([newrow, ncol], str(item) )
-            newrow += 1
-
-        return tblobj_body, worksheet, corrScript
-
-
-    def getTableHeaderForGeno(self, method=None, worksheet=None, newrow=None, headingStyle=None):
-        tblobj_header = []
-
-        if method in ["1", "3", "4"]:
-            tblobj_header = [[THCell(HT.TD('', Class="fs13 fwb ffl b1 cw cbrb"), sort=0),
-                              THCell(HT.TD('Locus', Class="fs13 fwb ffl b1 cw cbrb",align='center'), text='locus', idx=1),
-                              THCell(HT.TD('Chr', Class="fs13 fwb ffl b1 cw cbrb"), text='chr', idx=2),
-                              THCell(HT.TD('Megabase', Class="fs13 fwb ffl b1 cw cbrb"), text='Mb', idx=3),
-                              THCell(HT.TD('N', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text='nstr', idx=4),
-                              THCell(HT.TD('Partial r ', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text='partial_corr', idx=5),
-                              THCell(HT.TD('p(partial r)', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text='partial_pv', idx=6),
-                              THCell(HT.TD('r ', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text='corr', idx=7),
-                              THCell(HT.TD('p(r)', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text='pv', idx=8),
-                              THCell(HT.TD('delta r', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text='delta_corr', idx=9)]]
-
-            for ncol, item in enumerate(['Locus', 'Chr', '  Mb  ', ' N ', 'Partial r', 'p(partial r)', 'r    ', 'p(r)', 'delta r' ]):
-                worksheet.write([newrow, ncol], item, headingStyle)
-                worksheet.set_column([ncol, ncol], 2*len(item))
-        else:
-            tblobj_header = [[THCell(HT.TD('', Class="fs13 fwb ffl b1 cw cbrb"), sort=0),
-                              THCell(HT.TD('Locus', Class="fs13 fwb ffl b1 cw cbrb",align='center'), text='locus', idx=1),
-                              THCell(HT.TD('Chr', Class="fs13 fwb ffl b1 cw cbrb"), text='chr', idx=2),
-                              THCell(HT.TD('Megabase', Class="fs13 fwb ffl b1 cw cbrb"), text='Mb', idx=3),
-                              THCell(HT.TD('N', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text='nstr', idx=4),
-                              THCell(HT.TD('Partial rho', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text='partial_corr', idx=5),
-                              THCell(HT.TD('p(partial rho)', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text='partial_pv', idx=6),
-                              THCell(HT.TD('rho ', Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text='corr', idx=7),
-                              THCell(HT.TD('p(rho)', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text='pv', idx=8),
-                              THCell(HT.TD('delta rho', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text='delta_corr', idx=9)]]
-
-            for ncol, item in enumerate(['Locus', 'Chr', '  Mb  ', ' N ', 'Partial rho', 'p(partial rho)', 'rho  ', 'p(rho)', 'delta rho' ]):
-                worksheet.write([newrow, ncol], item, headingStyle)
-                worksheet.set_column([ncol, ncol], 2*len(item))
-
-        return tblobj_header, worksheet
-
-
-
-    def getTableBodyForGeno(self, traitList, formName=None, worksheet=None, newrow=None, corrScript=None):
-
-        tblobj_body = []
-
-        for thisTrait in traitList:
-            tr = []
-
-            trId = str(thisTrait)
-
-            #partial corr value could be string 'NA'
-            try:
-                corrScript.append('corrArray["%s"] = {corr:%1.4f};' % (trId, thisTrait.partial_corr))
-            except:
-                corrScript.append('corrArray["%s"] = {corr:"NA"};' % (trId))
-
-            tr.append(TDCell(HT.TD(HT.Input(type="checkbox", Class="checkbox", name="searchResult",value=trId, onClick="highlight(this)"), nowrap="on", Class="fs12 fwn ffl b1 c222"), text=trId))
-
-            tr.append(TDCell(HT.TD(HT.Href(text=thisTrait.name,url="javascript:showTrait('%s', '%s')" % (formName, thisTrait.name), Class="fs12 fwn ffl"),align="center", Class="fs12 fwn ffl b1 c222"), text=thisTrait.name, val=thisTrait.name.upper()))
-
-            #tr.append(TDCell(HT.TD(thisTrait.chr, Class="fs12 fwn ffl b1 c222", align='right'), text=str(thisTrait.chr)))
-
-            try:
-                Mbvalue = int(thisTrait.chr)*1000 + thisTrait.mb
-            except:
-                if not thisTrait.chr or not thisTrait.mb:
-                    Mbvalue = 1000000
-                elif thisTrait.chr.upper() == 'X':
-                    Mbvalue = 20*1000 + thisTrait.mb
-                else:
-                    Mbvalue = ord(str(thisTrait.chr).upper()[0])*1000 + thisTrait.mb
-
-            tr.append(TDCell( HT.TD(thisTrait.chr, Class="fs12 fwn b1 c222", align='right'), thisTrait.chr, Mbvalue) )
-            tr.append(TDCell(HT.TD(thisTrait.mb, Class="fs12 fwn ffl b1 c222", align='right'), text=str(thisTrait.mb), val=Mbvalue))
-
-            repr = '%d' % thisTrait.NOverlap
-            tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", align='right'),repr,thisTrait.NOverlap))
-
-            try:
-                repr='%3.3f' % thisTrait.partial_corr
-                tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", align='right',nowrap='ON'),repr,abs(thisTrait.partial_corr)))
-            except:
-                repr = 'NA'
-                tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", align='left'), text=repr, val=0 ))
-
-            repr = webqtlUtil.SciFloat(thisTrait.partial_corrPValue)
-            tr.append(TDCell(HT.TD(repr,nowrap='ON', Class="fs12 fwn ffl b1 c222", align='right'),repr,thisTrait.partial_corrPValue))
-
-            repr = '%3.3f' % thisTrait.corr
-            tr.append(TDCell(HT.TD(repr, Class="fs12 fwn b1 c222", align='right',nowrap='ON'), repr, abs(thisTrait.corr)))
-
-            repr = webqtlUtil.SciFloat(thisTrait.corrPValue)
-            tr.append(TDCell(HT.TD(repr,nowrap='ON', Class="fs12 fwn ffl b1 c222", align='right'),repr,thisTrait.corrPValue))
-
-            #delta
-            try:
-                delta = '%3.3f' % ( float(thisTrait.partial_corr) - float(thisTrait.corr) )
-                tr.append(TDCell(HT.TD(delta, Class="fs12 fwn ffl b1 c222", align='right', nowrap='ON'), text=delta, val=abs(float(delta)) ))
-            except:
-                delta = 'NA'
-                tr.append(TDCell(HT.TD(delta, Class="fs12 fwn ffl b1 c222", align='left'), text=delta, val=0 ))
-
-            tblobj_body.append(tr)
-
-            for ncol, item in enumerate([thisTrait.name, thisTrait.chr, thisTrait.mb, thisTrait.NOverlap, thisTrait.partial_corr, thisTrait.partial_corrPValue, thisTrait.corr, thisTrait.corrPValue, delta]):
-                worksheet.write([newrow, ncol], item)
-            newrow += 1
-
-        return tblobj_body, worksheet, corrScript
-
-
-    def getTableHeaderForProbeSet(self, method=None, worksheet=None, newrow=None, headingStyle=None):
-
-        tblobj_header = []
-
-        if method in ["1","3","4"]:
-            tblobj_header = [[THCell(HT.TD(' ', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), sort=0),
-                              THCell(HT.TD('Record',HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="id", idx=1),
-                              THCell(HT.TD('','Symbol',HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="symbol", idx=2),
-                              THCell(HT.TD('','Description',HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="desc", idx=3),
-                              #XZ, 12/09/2008: sort chr
-                              THCell(HT.TD('','Chr',HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="chr", idx=4),
-                              THCell(HT.TD('','Mb',HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="Mb", idx=5),
-                              THCell(HT.TD('Mean',HT.BR(),'Expr',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="mean", idx=6),
-                              THCell(HT.TD('N',HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text="nstr", idx=7),
-                              THCell(HT.TD(HT.Href(
-                                                   text = HT.Span('Sample',HT.BR(), 'Partial r', HT.Sup('  ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"),
-                                                   target = '_blank',
-                                                   url = "/correlationAnnotation.html#genetic_r"), 
-                                           Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="partial_corr", idx=8),
-                              THCell(HT.TD(HT.Href(
-                                                   text = HT.Span('Sample',HT.BR(), 'p(partial r)', HT.Sup('  ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"),
-                                                   target = '_blank',
-                                                   url = "/correlationAnnotation.html#genetic_p_r"),
-                                           Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="partial_pv", idx=9),
-                              THCell(HT.TD(HT.Href(
-                                                   text = HT.Span('Sample',HT.BR(), 'r', HT.Sup('  ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"),
-                                                   target = '_blank',
-                                                   url = "/correlationAnnotation.html#genetic_r"),
-                                           Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="corr", idx=10),
-                              THCell(HT.TD(HT.Href(
-                                                   text = HT.Span('Sample',HT.BR(), 'p(r)', HT.Sup('  ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"),
-                                                   target = '_blank',
-                                                   url = "/correlationAnnotation.html#genetic_p_r"),
-                                           Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="pv", idx=11),
-                              THCell(HT.TD('delta',HT.BR(), 'r', HT.BR(), Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text="delta_corr", idx=12),
-                              THCell(HT.TD(HT.Href(
-                                                   text = HT.Span('Pubmed',HT.BR(), 'r', HT.Sup('  ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"),
-                                                   target = '_blank',
-                                                   url = "/correlationAnnotation.html#literatureCorr"),
-                                           Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="lcorr", idx=13),
-                              #XZ, 09/22/2008: tissue correlation
-                              THCell(HT.TD(HT.Href(
-                                                   text = HT.Span('Tissue',HT.BR(), 'r', HT.Sup('  ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"),
-                                                   target = '_blank',
-                                                   url = "/correlationAnnotation.html#tissue_r"),
-                                           Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="tissuecorr", idx=14),
-                              THCell(HT.TD(HT.Href(
-                                                   text = HT.Span('Tissue',HT.BR(), 'p(r)', HT.Sup('  ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"),
-                                                   target = '_blank',
-                                                   url = "/correlationAnnotation.html#tissue_p_r"),
-                                           Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="tissuepvalue", idx=15)]]
-
-            for ncol, item in enumerate(['Record', 'Gene ID', 'Symbol', 'Description', 'Chr', 'Megabase', 'Mean Expr', 'N ', 'Sample Partial r', 'Sample p(partial r)', 'Sample r', 'Sample p(r)', 'delta r', 'Lit Corr', 'Tissue r', 'Tissue p(r)']):
-                worksheet.write([newrow, ncol], item, headingStyle)
-                worksheet.set_column([ncol, ncol], 2*len(item))
-        else:
-            tblobj_header = [[THCell(HT.TD(' ', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), sort=0),
-                              THCell(HT.TD('Record',HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="id", idx=1),
-                              THCell(HT.TD('','Symbol',HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="symbol", idx=2),
-                              THCell(HT.TD('','Description',HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="desc", idx=3),
-                              THCell(HT.TD('','Chr',HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="chr", idx=4),
-                              THCell(HT.TD('','Mb',HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="Mb", idx=5),
-                              THCell(HT.TD('Mean',HT.BR(),'Expr',HT.BR(), Class="fs13 fwb ffl b1 cw cbrb"), text="mean", idx=6),
-                              THCell(HT.TD('N',HT.BR(),HT.BR(), Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text="nstr", idx=7),
-                              THCell(HT.TD(HT.Href(
-                                                   text = HT.Span('Sample',HT.BR(), 'Partial rho', HT.Sup('  ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"),
-                                                   target = '_blank',
-                                                   url = "/correlationAnnotation.html#genetic_rho"), 
-                                           Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="partial_corr", idx=8),
-                              THCell(HT.TD(HT.Href(
-                                                   text = HT.Span('Sample',HT.BR(), 'p(partial rho)', HT.Sup('  ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"),
-                                                   target = '_blank',
-                                                   url = "/correlationAnnotation.html#genetic_p_rho"),
-                                           Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="partial_pv", idx=9),
-                              THCell(HT.TD(HT.Href(
-                                                   text = HT.Span('Sample',HT.BR(), 'rho', HT.Sup('  ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"),
-                                                   target = '_blank',
-                                                   url = "/correlationAnnotation.html#genetic_r"),
-                                           Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="corr", idx=10),
-                              THCell(HT.TD(HT.Href(
-                                                   text = HT.Span('Sample',HT.BR(), 'p(rho)', HT.Sup('  ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"),
-                                                   target = '_blank',
-                                                   url = "/correlationAnnotation.html#genetic_p_r"),
-                                           Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="pv", idx=11),
-                              THCell(HT.TD('delta',HT.BR(),'rho', HT.BR(), Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text="delta_corr", idx=12),
-                              THCell(HT.TD(HT.Href(
-                                                   text = HT.Span('Pubmed',HT.BR(), 'r', HT.Sup('  ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"),
-                                                   target = '_blank',
-                                                   url = "/correlationAnnotation.html#literatureCorr"),
-                                           Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="lcorr", idx=13),
-                              #XZ, 09/22/2008: tissue correlation
-                              THCell(HT.TD(HT.Href(
-                                                   text = HT.Span('Tissue',HT.BR(), 'rho', HT.Sup('  ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"),
-                                                   target = '_blank',
-                                                   url = "/correlationAnnotation.html#tissue_rho"),
-                                           Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="tissuecorr", idx=14),
-                              THCell(HT.TD(HT.Href(
-                                                   text = HT.Span('Tissue',HT.BR(), 'p(rho)', HT.Sup('  ?', style="color:#f00"),HT.BR(), Class="fs13 fwb ffl cw"),
-                                                   target = '_blank',
-                                                   url = "/correlationAnnotation.html#tissue_p_rho"),
-                                           Class="fs13 fwb ffl b1 cw cbrb", nowrap='ON'), text="tissuepvalue", idx=15)]]
-
-            for ncol, item in enumerate(['Record', 'Gene ID', 'Symbol', 'Description', 'Chr', 'Megabase', 'Mean Expr', 'N ', 'Sample Partial rho', 'Sample p(partial rho)', 'Sample rho', 'Sample p(rho)', 'delta rho', 'Pubmed r', 'Tissue rho', 'Tissue p(rho)']):
-                worksheet.write([newrow, ncol], item, headingStyle)
-                worksheet.set_column([ncol, ncol], 2*len(item))
-
-        return tblobj_header, worksheet
-
-
-    def getTableBodyForProbeSet(self, traitList=[], primaryTrait=None, formName=None, worksheet=None, newrow=None, corrScript=None):
-
-        tblobj_body = []
-
-        for thisTrait in traitList:
-
-            if thisTrait.symbol:
-                pass
-            else:
-                thisTrait.symbol = "N/A"
-
-            if thisTrait.geneid:
-                symbolurl = HT.Href(text=thisTrait.symbol,target='_blank',url="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene&cmd=Retrieve&dopt=Graphics&list_uids=%s" % thisTrait.geneid, Class="fs12 fwn")
-            else:
-                symbolurl = HT.Href(text=thisTrait.symbol,target='_blank',url="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?CMD=search&DB=gene&term=%s" % thisTrait.symbol, Class="fs12 fwn")
-
-            tr = []
-
-            trId = str(thisTrait)
-
-            #partial corr value could be string 'NA'
-            try:
-                corrScript.append('corrArray["%s"] = {corr:%1.4f};' % (trId, thisTrait.partial_corr))
-            except:
-                corrScript.append('corrArray["%s"] = {corr:"NA"};' % (trId))
-
-            #XZ, 12/08/2008: checkbox
-            tr.append(TDCell(HT.TD(HT.Input(type="checkbox", Class="checkbox", name="searchResult",value=trId, onClick="highlight(this)"), nowrap="on", Class="fs12 fwn ffl b1 c222"), text=trId))
-
-            #XZ, 12/08/2008: probeset name
-            tr.append(TDCell(HT.TD(HT.Href(text=thisTrait.name,url="javascript:showTrait('%s', '%s')" % (formName,thisTrait.name), Class="fs12 fwn"), Class="fs12 fwn b1 c222"), thisTrait.name, thisTrait.name.upper()))
-
-            #XZ, 12/08/2008: gene symbol
-            tr.append(TDCell(HT.TD(symbolurl, Class="fs12 fwn b1 c222 fsI"),thisTrait.symbol, thisTrait.symbol.upper()))
-
-            #XZ, 12/08/2008: description
-            #XZ, 06/05/2009: Rob asked to add probe target description
-            description_string = str(thisTrait.description).strip()
-            target_string = str(thisTrait.probe_target_description).strip()
-
-            description_display = ''
-
-            if len(description_string) > 1 and description_string != 'None':
-                description_display = description_string
-            else:
-                description_display = thisTrait.symbol
-
-            if len(description_display) > 1 and description_display != 'N/A' and len(target_string) > 1 and target_string != 'None':
-                description_display = description_display + '; ' + target_string.strip()
-
-            tr.append(TDCell(HT.TD(description_display, Class="fs12 fwn b1 c222"), description_display, description_display))
-
-            #XZ, 12/08/2008: Mbvalue is used for sorting
-            try:
-                Mbvalue = int(thisTrait.chr)*1000 + thisTrait.mb
-            except:
-                if not thisTrait.chr or not thisTrait.mb:
-                    Mbvalue = 1000000
-                elif thisTrait.chr.upper() == 'X':
-                    Mbvalue = 20*1000 + thisTrait.mb
-                else:
-                    Mbvalue = ord(str(thisTrait.chr).upper()[0])*1000 + thisTrait.mb
-
-            #XZ, 12/08/2008: chromosome number
-            #XZ, 12/10/2008: use Mbvalue to sort chromosome
-            tr.append(TDCell( HT.TD(thisTrait.chr, Class="fs12 fwn b1 c222", align='right'), thisTrait.chr, Mbvalue) )
-
-            #XZ, 12/08/2008: Rob wants 6 digit precision, and we have to deal with that the mb could be None
-            if not thisTrait.mb:
-                tr.append(TDCell(HT.TD(thisTrait.mb, Class="fs12 fwn b1 c222",align='right'), thisTrait.mb, Mbvalue))
-            else:
-                tr.append(TDCell(HT.TD('%.6f' % thisTrait.mb, Class="fs12 fwn b1 c222", align='right'), thisTrait.mb, Mbvalue))
-
-            #XZ, 01/12/08: This SQL query is much faster.
-            self.cursor.execute("""
-                    select ProbeSetXRef.mean from ProbeSetXRef, ProbeSet
-                    where ProbeSetXRef.ProbeSetFreezeId = %d and
-                          ProbeSet.Id = ProbeSetXRef.ProbeSetId and
-                          ProbeSet.Name = '%s'
-            """ % (thisTrait.db.id, thisTrait.name))
-            result = self.cursor.fetchone()
-            if result:
-                if result[0]:
-                    mean = result[0]
-                else:
-                    mean=0
-            else:
-                mean = 0
-
-            #XZ, 06/05/2009: It is neccessary to turn on nowrap
-            repr = "%2.3f" % mean
-            tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", align='right', nowrap='ON'),repr, mean))
-
-            #XZ: number of overlaped cases for partial corr
-            repr = '%d' % thisTrait.NOverlap
-            tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", align='right'),repr,thisTrait.NOverlap))
-
-            #XZ: sample partial correlation
-            try:
-                repr='%3.3f' % thisTrait.partial_corr
-                tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", align='right', nowrap='ON'),repr,abs(thisTrait.partial_corr)))
-            except:
-                repr = 'NA'
-                tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", align='left'), text=repr, val=0 ))
-
-            #XZ: p value of genetic partial correlation
-            repr = webqtlUtil.SciFloat(thisTrait.partial_corrPValue)
-            tr.append(TDCell(HT.TD(repr,nowrap='ON', Class="fs12 fwn ffl b1 c222", align='right'),repr,thisTrait.partial_corrPValue))
-
-            repr = '%3.3f' % thisTrait.corr
-            tr.append(TDCell(HT.TD(repr, Class="fs12 fwn b1 c222", align='right',nowrap='ON'), repr, abs(thisTrait.corr)))
-
-            repr = webqtlUtil.SciFloat(thisTrait.corrPValue)
-            tr.append(TDCell(HT.TD(repr,nowrap='ON', Class="fs12 fwn ffl b1 c222", align='right'),repr,thisTrait.corrPValue))
-
-            #delta
-            try:
-                delta = '%3.3f' % ( float(thisTrait.partial_corr) - float(thisTrait.corr) )
-                tr.append(TDCell(HT.TD(delta, Class="fs12 fwn ffl b1 c222", align='right', nowrap='ON'), text=delta, val=abs(float(delta)) ))
-            except:
-                delta = 'NA'
-                tr.append(TDCell(HT.TD(delta, Class="fs12 fwn ffl b1 c222", align='left'), text=delta, val=0 ))
-
-            #XZ, 12/08/2008: literature correlation
-            LCorr = 0.0
-            LCorrStr = "N/A"
-            if hasattr(thisTrait, 'LCorr') and thisTrait.LCorr:
-                LCorr = thisTrait.LCorr
-                LCorrStr = "%2.3f" % thisTrait.LCorr
-            tr.append(TDCell(HT.TD(LCorrStr, Class="fs12 fwn b1 c222", align='right'), LCorrStr, abs(LCorr)))
-
-            #XZ, 09/22/2008: tissue correlation.
-            TCorr = 0.0
-            TCorrStr = "N/A"
-            #XZ, 11/18/2010: need to pass two gene symbols
-            if hasattr(thisTrait, 'tissueCorr') and thisTrait.tissueCorr:
-                TCorr = thisTrait.tissueCorr
-                TCorrStr = "%2.3f" % thisTrait.tissueCorr
-		#NL, 07/19/2010: add a new parameter rankOrder for js function 'showTissueCorrPlot'
-                rankOrder =thisTrait.rankOrder
-                TCorrPlotURL = "javascript:showTissueCorrPlot('%s','%s','%s',%d)" %(formName, primaryTrait.symbol, thisTrait.symbol,rankOrder)
-                tr.append(TDCell(HT.TD(HT.Href(text=TCorrStr, url=TCorrPlotURL, Class="fs12 fwn ff1"), Class="fs12 fwn ff1 b1 c222", align='right'), TCorrStr, abs(TCorr) ))
-            else:
-                tr.append(TDCell(HT.TD(TCorrStr, Class="fs12 fwn b1 c222", align='right'), TCorrStr, abs(TCorr)))
-
-            #XZ, 12/08/2008: p value of tissue correlation
-            TPValue = 1.0
-            TPValueStr = "N/A"
-            if hasattr(thisTrait, 'tissueCorr') and thisTrait.tissueCorr: #XZ, 09/22/2008: thisTrait.tissuePValue can't be used here because it could be 0
-                TPValue = thisTrait.tissuePValue
-                TPValueStr = "%2.3f" % thisTrait.tissuePValue
-            tr.append(TDCell(HT.TD(TPValueStr, Class="fs12 fwn b1 c222", align='right'), TPValueStr, abs(TPValue) ))
-
-            tblobj_body.append(tr)
-
-            for ncol, item in enumerate([thisTrait.name, thisTrait.geneid, thisTrait.symbol, thisTrait.description, thisTrait.chr, thisTrait.mb, mean, thisTrait.NOverlap, thisTrait.partial_corr, thisTrait.partial_corrPValue, thisTrait.corr, thisTrait.corrPValue, delta, LCorrStr, TCorrStr, TPValueStr]):
-                worksheet.write([newrow, ncol], item)
-
-            newrow += 1
-
-        return tblobj_body, worksheet, corrScript
-
-
-    def getFormForPrimaryAndControlTraits (self, primaryTrait, controlTraits):
-
-        info_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', 'database':'_', 'ProbeSetID':'_', 'CellID':'_' }#XZ: These four parameters are required by javascript function showDatabase2.
-        
-        for key in hddn.keys():
-            info_form.append(HT.Input(name=key, value=hddn[key], type='hidden'))
-
-        info_form.append(HT.Paragraph("Primary Trait", Class="subtitle"), '\n')
-
-        primaryTraitTable = HT.TableLite(cellSpacing=4,cellPadding=0,width="90%",border=0)
-        descriptionString = primaryTrait.genHTML(dispFromDatabase=1)
-        if primaryTrait.db.type == 'Publish' and primaryTrait.confidential:
-            descriptionString = primaryTrait.genHTML(dispFromDatabase=1, privilege=self.privilege, userName=self.userName, authorized_users=primaryTrait.authorized_users)
-        primaryTraitTable.append(HT.TR(HT.TD(HT.Href(text='%s' % descriptionString, url="javascript:showDatabase2('%s','%s','%s')" % (primaryTrait.db.name,primaryTrait.name,primaryTrait.cellid), Class="fs12 fwn") )))
-
-        info_form.append(primaryTraitTable)
-
-        info_form.append(HT.Paragraph("Control Traits", Class="subtitle"), '\n')
-
-        controlTraitsTable = HT.TableLite(cellSpacing=4,cellPadding=0,width="90%",border=0)
-
-        seq = 1
-
-        ## Generate the listing table for control traits
-        for thisTrait in controlTraits:
-            descriptionString = thisTrait.genHTML(dispFromDatabase=1)
-            if thisTrait.db.type == 'Publish' and thisTrait.confidential:
-                descriptionString = thisTrait.genHTML(dispFromDatabase=1, privilege=self.privilege, userName=self.userName, authorized_users=thisTrait.authorized_users)
-            controlTraitsTable.append(HT.TR(HT.TD("%d."%seq,align="right",width=10),
-                                            HT.TD(HT.Href(text='%s' % descriptionString,url="javascript:showDatabase2('%s','%s','%s')" % (thisTrait.db.name,thisTrait.name,thisTrait.cellid), Class="fs12 fwn") )))
-            seq += 1
-
-        info_form.append(controlTraitsTable)
-
-        return info_form
diff --git a/web/webqtl/correlation/PartialCorrInputPage.py b/web/webqtl/correlation/PartialCorrInputPage.py
deleted file mode 100755
index 7d32da6d..00000000
--- a/web/webqtl/correlation/PartialCorrInputPage.py
+++ /dev/null
@@ -1,484 +0,0 @@
-# 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 GeneNetwork Core Team 2010/10/20
-
-import os
-import string
-import cPickle
-
-from htmlgen import HTMLgen2 as HT
-
-from base import webqtlConfig
-from utility.THCell import THCell
-from utility.TDCell import TDCell
-from base.webqtlTrait import webqtlTrait
-from base.templatePage import templatePage
-from dbFunction import webqtlDatabaseFunction
-from utility import webqtlUtil
-
-
-
-class PartialCorrInputPage(templatePage):
-
-    def __init__(self,fd):
-
-        templatePage.__init__(self, fd)
-
-        if not self.openMysql():
-                return
-
-        searchResult = fd.formdata.getvalue('searchResult')
-
-        if not searchResult:
-            heading = 'Partial Correlation'
-            detail = ['You need to select at least three traits in order to calculate partial correlation.']
-            self.error(heading=heading,detail=detail)
-            return
-
-
-        ## Adds the Trait instance for each trait name from the collection
-        traits = []
-
-        for item in searchResult:
-            traits.append(webqtlTrait(fullname=item, cursor=self.cursor))
-
-        RISet = fd.formdata.getvalue('RISet')
-        species = webqtlDatabaseFunction.retrieveSpecies(cursor=self.cursor, RISet=RISet)
-
-        #XZ: HTML part
-        TD_LR = HT.TD(colspan=2,height=200,width="100%",bgColor='#eeeeee')
-        TD_LR.append("Please select one primary trait, one to three control traits, and at least one target trait.", HT.P() )
-
-        mainFormName = 'showDatabase'
-        mainForm = HT.Form(cgi= os.path.join(webqtlConfig.CGIDIR, webqtlConfig.SCRIPTFILE), name=mainFormName,submit=HT.Input(type='hidden'))
-
-        #XZ: Add hidden form values
-        hddn = {'FormID':'calPartialCorrTrait', 'database':'', 'ProbeSetID':'', 'CellID':'', #XZ: These four parameters are required by javascript function showDatabase2.
-                'controlTraits':'',
-                'primaryTrait':'',
-                'targetTraits':'',
-                'pcMethod':'',
-                'RISet':RISet
-               }
-
-
-        for key in hddn.keys():
-            mainForm.append(HT.Input(type='hidden', name=key, value=hddn[key]))
-
-        radioNames = []
-
-	for thisTrait in traits:
-            oneRadioName = thisTrait.getName()
-            radioNames.append(oneRadioName)
-            
-        radioNamesString = ','.join(radioNames)
-
-        # Creates the image href that runs the javascript setting all traits as target or ignored
-        setAllTarget = HT.Href(url="#redirect", onClick="setAllAsTarget(document.getElementsByName('showDatabase')[0], '%s');" % radioNamesString)
-        setAllTargetImg = HT.Image("/images/select_all.gif", alt="Select All", title="Select All", style="border:none;")
-        setAllTarget.append(setAllTargetImg)
-        setAllIgnore = HT.Href(url="#redirect", onClick="setAllAsIgnore(document.getElementsByName('showDatabase')[0], '%s');" % radioNamesString)
-        setAllIgnoreImg = HT.Image("/images/select_all.gif", alt="Select All", title="Select All", style="border:none;")
-        setAllIgnore.append(setAllIgnoreImg)
-
-
-        tblobj = {}
-        tblobj['header'] = self.getCollectionTableHeader()
-
-        sortby = self.getSortByValue()
-
-        tblobj['body'] = self.getCollectionTableBody(traitList=traits, formName=mainFormName, species=species)
-
-        filename= webqtlUtil.genRandStr("Search_")
-
-        objfile = open('%s.obj' % (webqtlConfig.TMPDIR+filename), 'wb')
-        cPickle.dump(tblobj, objfile)
-        objfile.close()
-
-        div = HT.Div(webqtlUtil.genTableObj(tblobj=tblobj, file=filename, sortby=sortby, tableID = "sortable", addIndex = "1"), Id="sortable")
-
-        mainForm.append(div)
-
-        #XZ: Add button
-        radioNamesString = ','.join(radioNames)
-        jsCommand_1 = "validateTrait(this.form, \'" + radioNamesString + "\', 0, 1);"
-        jsCommand_2 = "validateTrait(this.form, \'" + radioNamesString + "\', 0, 2);"
-        partialCorrTraitButton_1 = HT.Input(type='button', name='submitPartialCorrTrait_1', value='Pearson\'s r', onClick='%s' % jsCommand_1, Class="button")
-        partialCorrTraitButton_2 = HT.Input(type='button', name='submitPartialCorrTrait_2', value='Spearman\'s rho', onClick='%s' % jsCommand_2, Class="button")
-        mainForm.append(HT.BR(), "Compute partial correlation for target selected above:", HT.BR(),  partialCorrTraitButton_1, partialCorrTraitButton_2, HT.BR(), HT.BR(), HT.HR(color="gray",size=3) )
-
-        jsCommand = "validateTrait(this.form, \'" + radioNamesString + "\', 1);"
-        partialCorrDBButton = HT.Input(type='button', name='submitPartialCorrDB', value='Calculate', onClick='%s' % jsCommand,Class="button")
-
-        methodText = HT.Span("Calculate:", Class="ffl fwb fs12")
-
-        methodMenu = HT.Select(name='method')
-        methodMenu.append(('Genetic Correlation, Pearson\'s r','1'))
-        methodMenu.append(('Genetic Correlation, Spearman\'s rho','2'))
-        methodMenu.append(('SGO Literature Correlation','3'))
-        methodMenu.append(('Tissue Correlation, Pearson\'s r','4'))
-        methodMenu.append(('Tissue Correlation, Spearman\'s rho','5'))
-
-        databaseText = HT.Span("Choose Database:", Class="ffl fwb fs12")
-        databaseMenu = HT.Select(name='database2')
-
-        nmenu = 0
-
-        self.cursor.execute('SELECT PublishFreeze.FullName,PublishFreeze.Name FROM \
-                                PublishFreeze,InbredSet WHERE PublishFreeze.InbredSetId = InbredSet.Id \
-                                and InbredSet.Name = "%s" and PublishFreeze.public > %d' % \
-                                (RISet,webqtlConfig.PUBLICTHRESH))
-        for item in self.cursor.fetchall():
-                                databaseMenu.append(item)
-                                nmenu += 1
-
-        self.cursor.execute('SELECT GenoFreeze.FullName,GenoFreeze.Name FROM GenoFreeze,\
-                                InbredSet WHERE GenoFreeze.InbredSetId = InbredSet.Id and InbredSet.Name = \
-                                "%s" and GenoFreeze.public > %d' % (RISet,webqtlConfig.PUBLICTHRESH))
-        for item in self.cursor.fetchall():
-                                databaseMenu.append(item)
-                                nmenu += 1
-
-        #03/09/2009: Xiaodong changed the SQL query to order by Name as requested by Rob.
-        self.cursor.execute('SELECT Id, Name FROM Tissue order by Name')
-        for item in self.cursor.fetchall():
-                                TId, TName = item
-                                databaseMenuSub = HT.Optgroup(label = '%s ------' % TName)
-                                self.cursor.execute('SELECT ProbeSetFreeze.FullName,ProbeSetFreeze.Name FROM ProbeSetFreeze, ProbeFreeze, \
-                                InbredSet WHERE ProbeSetFreeze.ProbeFreezeId = ProbeFreeze.Id and ProbeFreeze.TissueId = %d and \
-                                ProbeSetFreeze.public > %d and ProbeFreeze.InbredSetId = InbredSet.Id and InbredSet.Name like "%s%%" \
-                                order by ProbeSetFreeze.CreateTime desc, ProbeSetFreeze.AvgId '  % (TId,webqtlConfig.PUBLICTHRESH, RISet))
-                                for item2 in self.cursor.fetchall():
-                                        databaseMenuSub.append(item2)
-                                        nmenu += 1
-                                databaseMenu.append(databaseMenuSub)
-
-        if nmenu:
-                                criteriaText = HT.Span("Return:", Class="ffl fwb fs12") 
-                                criteriaMenu = HT.Select(name='criteria', selected='500')
-                                criteriaMenu.append(('top 100','100'))
-                                criteriaMenu.append(('top 200','200'))
-                                criteriaMenu.append(('top 500','500'))
-                                criteriaMenu.append(('top 1000','1000'))
-                                criteriaMenu.append(('top 2000','2000'))
-				criteriaMenu.append(('top 5000','5000'))
-				criteriaMenu.append(('top 10000','10000'))
-				criteriaMenu.append(('top 15000','15000'))
-				criteriaMenu.append(('top 20000','20000'))
-
-                                self.MPDCell = HT.TD()
-                                correlationMenus = HT.TableLite( 
-                                        HT.TR(
-                                                HT.TD(databaseText,HT.BR(),databaseMenu, colspan=4)
-                                        ), 
-                                        HT.TR(
-                                                HT.TD(methodText,HT.BR(),methodMenu),
-                                                self.MPDCell, 
-                                                HT.TD(criteriaText,HT.BR(),criteriaMenu)), 
-                                        border=0, cellspacing=4, cellpadding=0)
-        else:
-            correlationMenus = ""
-
-        mainForm.append(HT.Font('or',color='red', size=4), HT.BR(), HT.BR(), "Compute partial correlation for each trait in the database selected below:", HT.BR() )
-        mainForm.append( partialCorrDBButton, HT.BR(), HT.BR(), correlationMenus)
-
-        TD_LR.append(mainForm)
-
-        self.dict['body'] = str(TD_LR)
-        self.dict['js1'] =''
-        self.dict['title'] = 'Partial Correlation Input'
-
-
-    def getCollectionTableHeader(self):
-
-                tblobj_header = []
-
-                className = "fs13 fwb ffl b1 cw cbrb"
-
-                tblobj_header = [[THCell(HT.TD('Index', Class=className, nowrap="on"), sort=0), 
-                        THCell(HT.TD("Primary (X)",align="center", Class="fs13 fwb ffl b1 cw cbrb", nowrap="ON"), text="primary", sort=0),
-                        THCell(HT.TD("Control (Z)",align="center", Class="fs13 fwb ffl b1 cw cbrb", nowrap="ON"), text="control", sort=0),
-                        THCell(HT.TD("Target (Y)",align="center", Class="fs13 fwb ffl b1 cw cbrb", nowrap="ON"), text="target", sort=0),
-                        THCell(HT.TD("Ignored",align="center", Class="fs13 fwb ffl b1 cw cbrb", nowrap="ON"), text="target", sort=0),
-                        THCell(HT.TD('Dataset', HT.BR(), HT.BR(), valign="top", Class=className, nowrap="on"), text="dataset", idx=1),
-                        THCell(HT.TD('Trait', HT.BR(), 'ID', HT.BR(), valign="top", Class=className, nowrap="on"), text="name", idx=2),
-                        THCell(HT.TD('Description', HT.BR(), HT.BR(), valign="top", Class=className, nowrap="on"), text="desc", idx=3),
-                        THCell(HT.TD('Location', HT.BR(), HT.BR(), valign="top", Class=className, nowrap="on"), text="location", idx=4),
-                        THCell(HT.TD('Mean', HT.BR(), HT.BR(), valign="top", Class=className, nowrap="on"), text="mean", idx=5),
-                        THCell(HT.TD('N', HT.BR(), 'Cases', HT.BR(), valign="top", Class=className, nowrap="on"), text="samples", idx=6),
-                        THCell(HT.TD('Max LRS', HT.BR(), HT.BR(), valign="top", Class=className, nowrap="on"), text="lrs", idx=7),
-                        THCell(HT.TD('Max LRS Location',HT.BR(),'Chr and Mb', HT.BR(), valign="top", Class=className, nowrap="on"), text="lrs_location", idx=8)]]
-
-                return tblobj_header
-
-
-
-    def getCollectionTableBody(self, traitList=None, formName=None, species=''):
-
-                tblobj_body = []
-
-                className = "fs12 fwn b1 c222"
-
-                for thisTrait in traitList:
-                        tr = []
-
-                        if not thisTrait.haveinfo:
-                                thisTrait.retrieveInfo(QTL=1)
-
-                        trId = str(thisTrait)
-
-                        oneRadioName = thisTrait.getName()
-
-                        tr.append(TDCell( HT.TD(' ',align="center",valign="center",Class=className) ))
-                        tr.append(TDCell( HT.TD(HT.Input(type="radio", name=oneRadioName, value="primary"),align="center",valign="center",Class=className) ))
-                        tr.append(TDCell( HT.TD(HT.Input(type="radio", name=oneRadioName, value="control"),align="center",valign="center",Class=className) ))
-                        tr.append(TDCell( HT.TD(HT.Input(type="radio", name=oneRadioName, value="target", checked="true"),align="center",valign="center",Class=className) ))
-                        tr.append(TDCell( HT.TD(HT.Input(type="radio", name=oneRadioName, value="ignored"),align="center",valign="center",Class=className) ))
-
-                        tr.append(TDCell(HT.TD(thisTrait.db.name, Class="fs12 fwn b1 c222"), thisTrait.db.name, thisTrait.db.name.upper()))
-
-                        tr.append(TDCell(HT.TD(HT.Href(text=thisTrait.name,url="javascript:showDatabase3('%s','%s','%s','')" % (formName, thisTrait.db.name, thisTrait.name), Class="fs12 fwn"), nowrap="yes",align="left", Class=className
-),str(thisTrait.name), thisTrait.name))
-
-                        #description column
-                        if (thisTrait.db.type == "Publish"):
-				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
-                                tr.append(TDCell(HT.TD(PhenotypeString, Class=className), PhenotypeString, PhenotypeString.upper()))
-                        elif (thisTrait.db.type == "ProbeSet" or thisTrait.db.type == "Temp"):
-                                description_string = str(thisTrait.description).strip()
-                                if (thisTrait.db.type == "ProbeSet"):
-                                        target_string = str(thisTrait.probe_target_description).strip()
-
-                                        description_display = ''
-
-                                        if len(description_string) > 1 and description_string != 'None':
-                                                description_display = description_string
-                                        else:
-                                                description_display = thisTrait.symbol
-
-                                        if len(description_display) > 1 and description_display != 'N/A' and len(target_string) > 1 and target_string != 'None':
-                                                description_display = description_display + '; ' + target_string.strip()
-
-                                        description_string = description_display
-
-                                tr.append(TDCell(HT.TD(description_string, Class=className), description_string, description_string))
-                        else:
-                                tr.append(TDCell(HT.TD("--", align="left", Class=className), "--", "Zz"))
-
-                        #location column
-                        if (thisTrait.db.type == "Publish"):
-                                tr.append(TDCell(HT.TD("--", align="left", Class=className), "--", "Zz"))
-                        else:
-                                #ZS: trait_location_value is used for sorting
-                                trait_location_repr = "N/A"
-                                trait_location_value = 1000000
-
-                                if hasattr(thisTrait, 'chr') and hasattr(thisTrait, 'mb') and thisTrait.chr and thisTrait.mb:
-                                        try:
-                                                trait_location_value = int(thisTrait.chr)*1000 + thisTrait.mb
-                                        except:
-                                                if thisTrait.chr.upper() == "X":
-                                                        trait_location_value = 20*1000 + thisTrait.mb
-                                                else:
-                                                        trait_location_value = ord(str(thisTrait.chr).upper()[0])*1000 + thisTrait.mb
-
-                                        trait_location_repr = "Chr%s: %.6f" % (thisTrait.chr, float(thisTrait.mb) )
-
-                                tr.append( TDCell(HT.TD(trait_location_repr, nowrap="yes", Class=className), trait_location_repr, trait_location_value) )
-
-                        if (thisTrait.db.type == "ProbeSet"):
-                                self.cursor.execute("""
-                                        select ProbeSetXRef.mean from ProbeSetXRef, ProbeSet
-                                        where ProbeSetXRef.ProbeSetFreezeId = %d and
-                                                ProbeSet.Id = ProbeSetXRef.ProbeSetId and
-                                                ProbeSet.Name = '%s'
-                                """ % (thisTrait.db.id, thisTrait.name))
-                                result = self.cursor.fetchone()
-                                if result:
-                                        if result[0]:
-                                                mean = result[0]
-                                        else:
-                                                mean=0
-                                else:
-                                        mean = 0
-
-                                #XZ, 06/05/2009: It is neccessary to turn on nowrap
-                                repr = "%2.3f" % mean
-                                tr.append(TDCell(HT.TD(repr, Class=className, align='right', nowrap='ON'),repr, mean))
-
-                        elif (thisTrait.db.type == "Publish"):
-                                self.cursor.execute("""
-                                select count(PublishData.value), sum(PublishData.value) from PublishData, PublishXRef, PublishFreeze
-                                where PublishData.Id = PublishXRef.DataId and 
-                                        PublishXRef.Id = %s and
-                                        PublishXRef.InbredSetId = PublishFreeze.InbredSetId and
-                                        PublishFreeze.Id = %d
-                                """ % (thisTrait.name, thisTrait.db.id))
-                                result = self.cursor.fetchone()
-
-                                if result:
-                                        if result[0] and result[1]:
-                                                mean = result[1]/result[0]
-                                        else:
-                                                mean = 0
-                                else:
-                                        mean = 0
-
-                                repr = "%2.3f" % mean
-                                tr.append(TDCell(HT.TD(repr, Class=className, align='right', nowrap='ON'),repr, mean))
-                        else:
-                                tr.append(TDCell(HT.TD("--", Class=className, align='left', nowrap='ON'),"--", 0))
-
-                        #Number of cases
-                        n_cases_value = 0
-                        n_cases_repr = "--"
-                        if (thisTrait.db.type == "Publish"):
-                                self.cursor.execute("""
-                                select count(PublishData.value) from PublishData, PublishXRef, PublishFreeze
-                                where PublishData.Id = PublishXRef.DataId and 
-                                        PublishXRef.Id = %s and
-                                        PublishXRef.InbredSetId = PublishFreeze.InbredSetId and
-                                        PublishFreeze.Id = %d
-                                """ % (thisTrait.name, thisTrait.db.id))
-                                result = self.cursor.fetchone()
-
-                                if result:
-                                        if result[0]:
-                                                n_cases_value = result[0]
-                                                n_cases_repr = result[0]
-
-                                if (n_cases_value == "--"):
-                                        tr.append(TDCell(HT.TD(n_cases_repr, Class=className, align='left', nowrap="on"), n_cases_repr, n_cases_value))
-                                else:
-                                        tr.append(TDCell(HT.TD(n_cases_repr, Class=className, align='right', nowrap="on"), n_cases_repr, n_cases_value))
-
-                        elif (thisTrait.db.type == "ProbeSet"):
-                                self.cursor.execute("""
-                                select count(ProbeSetData.value) from ProbeSet, ProbeSetXRef, ProbeSetData, ProbeSetFreeze
-                                where ProbeSet.Name='%s' and
-                                        ProbeSetXRef.ProbeSetId = ProbeSet.Id and
-                                        ProbeSetXRef.DataId = ProbeSetData.Id and
-                                        ProbeSetXRef.ProbeSetFreezeId = ProbeSetFreeze.Id and
-                                        ProbeSetFreeze.Name = '%s'
-                                """ % (thisTrait.name, thisTrait.db.name))
-                                result = self.cursor.fetchone()
-
-                                if result:
-                                        if result[0]:
-                                                n_cases_value = result[0]
-                                                n_cases_repr = result[0]
-                                if (n_cases_value == "--"):
-                                        tr.append(TDCell(HT.TD(n_cases_repr, Class=className, align='left', nowrap="on"), n_cases_repr, n_cases_value))
-                                else:
-                                        tr.append(TDCell(HT.TD(n_cases_repr, Class=className, align='right', nowrap="on"), n_cases_repr, n_cases_value))
-
-                        elif (thisTrait.db.type == "Geno"):
-                                self.cursor.execute("""
-                                select count(GenoData.value) from GenoData, GenoXRef, GenoFreeze, Geno, Strain
-                                where Geno.SpeciesId = %s and Geno.Name='%s' and
-                                        GenoXRef.GenoId = Geno.Id and
-                                        GenoXRef.DataId = GenoData.Id and
-                                        GenoXRef.GenoFreezeId = GenoFreeze.Id and
-                                        GenoData.StrainId = Strain.Id and
-                                        GenoFreeze.Name = '%s'
-                                """ % (webqtlDatabaseFunction.retrieveSpeciesId(self.cursor, thisTrait.db.riset), thisTrait.name, thisTrait.db.name))
-                                result = self.cursor.fetchone()
-
-                                if result:
-                                        if result[0]:
-                                                n_cases_value = result[0]
-                                                n_cases_repr = result[0]
-                                if (n_cases_value == "--"):
-                                        tr.append(TDCell(HT.TD(n_cases_repr, Class=className, align='left', nowrap="on"), n_cases_repr, n_cases_value))
-                                else:
-                                        tr.append(TDCell(HT.TD(n_cases_repr, Class=className, align='right', nowrap="on"), n_cases_repr, n_cases_value))
-
-                        else:
-                                tr.append(TDCell(HT.TD(n_cases_repr, Class=className, align='left', nowrap="on"), n_cases_repr, n_cases_value))
-
-
-                        if (thisTrait.db.type != "Geno"):
-                                #LRS and its location
-                                LRS_score_repr = '--'
-                                LRS_score_value = 0
-                                LRS_location_repr = '--'
-                                LRS_location_value = 1000000
-                                LRS_flag = 1
-
-                                #Max LRS and its Locus location
-                                if hasattr(thisTrait, 'lrs') and hasattr(thisTrait, 'locus') and thisTrait.lrs and thisTrait.locus:
-                                        self.cursor.execute("""
-                                        select Geno.Chr, Geno.Mb from Geno, Species
-                                        where Species.Name = '%s' and
-                                                Geno.Name = '%s' and
-                                                Geno.SpeciesId = Species.Id
-                                        """ % (species, thisTrait.locus))
-                                        result = self.cursor.fetchone()
-
-                                        if result:
-                                                if result[0] and result[1]:
-                                                        LRS_Chr = result[0]
-                                                        LRS_Mb = result[1]
-
-                                                        #XZ: LRS_location_value is used for sorting
-                                                        try:
-                                                                LRS_location_value = int(LRS_Chr)*1000 + float(LRS_Mb)
-                                                        except:
-                                                                if LRS_Chr.upper() == 'X':
-                                                                        LRS_location_value = 20*1000 + float(LRS_Mb)
-                                                                else:
-                                                                        LRS_location_value = ord(str(LRS_chr).upper()[0])*1000 + float(LRS_Mb)
-
-
-                                                        LRS_score_repr = '%3.1f' % thisTrait.lrs
-                                                        LRS_score_value = thisTrait.lrs
-                                                        LRS_location_repr = 'Chr%s: %.6f' % (LRS_Chr, float(LRS_Mb) )
-                                                        LRS_flag = 0
-
-                                                        tr.append(TDCell(HT.TD(LRS_score_repr, Class=className, align='right', nowrap="on"), LRS_score_repr, LRS_score_value))
-                                                        tr.append(TDCell(HT.TD(LRS_location_repr, Class=className), LRS_location_repr, LRS_location_value))
-
-                                if LRS_flag:
-                                        tr.append(TDCell(HT.TD(LRS_score_repr, Class=className), LRS_score_repr, LRS_score_value))
-                                        tr.append(TDCell(HT.TD(LRS_location_repr, Class=className), LRS_location_repr, LRS_location_value))
-                        else:
-                                tr.append(TDCell(HT.TD("--", align="left", Class=className), "--", 0))
-                                tr.append(TDCell(HT.TD("--", align="left", Class=className), "--", 1000000))
-
-                        tblobj_body.append(tr)
-
-                return tblobj_body
-
-
-
-    def getSortByValue(self):
-
-                sortby = ("pv", "up")
-
-                return sortby
-
diff --git a/web/webqtl/correlation/PartialCorrTraitPage.py b/web/webqtl/correlation/PartialCorrTraitPage.py
deleted file mode 100755
index 1c79e250..00000000
--- a/web/webqtl/correlation/PartialCorrTraitPage.py
+++ /dev/null
@@ -1,310 +0,0 @@
-# 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 GeneNetwork Core Team 2010/10/20
-
-import string
-import cPickle
-import os
-
-from htmlgen import HTMLgen2 as HT
-
-from base import webqtlConfig
-from utility.THCell import THCell
-from utility.TDCell import TDCell
-from base.webqtlTrait import webqtlTrait
-from base.templatePage import templatePage
-from utility import webqtlUtil
-from CorrelationPage import CorrelationPage
-import correlationFunction
-
-
-
-class PartialCorrTraitPage(CorrelationPage):
-
-    corrMinInformative = 4
-
-
-    def __init__(self,fd):
-
-        templatePage.__init__(self, fd)
-
-        if not self.openMysql():
-            return
-
-        TD_LR = HT.TD(colspan=2,height=200,width="100%",bgColor='#eeeeee')
-
-        TD_LR.append(HT.Paragraph("Partial Correlation Table", Class="title"), '\n')
-
-        pc_method = fd.formdata.getvalue('pcMethod')
-
-        primaryTraitString = fd.formdata.getvalue('primaryTrait')
-        primaryTrait = (webqtlTrait(fullname=primaryTraitString, cursor=self.cursor))
-
-        controlTraitsString = fd.formdata.getvalue('controlTraits')
-        controlTraitsList = list(string.split(controlTraitsString,','))
-        controlTraits = []
-        for item in controlTraitsList:
-            controlTraits.append(webqtlTrait(fullname=item, cursor=self.cursor))
-
-        targetTraitsString = fd.formdata.getvalue('targetTraits')
-        targetTraitsList = list(string.split(targetTraitsString,','))
-        targetTraits = []
-        _targetnames = []
-        for item in targetTraitsList:
-            oneTargetTrait = webqtlTrait(fullname=item, cursor=self.cursor)
-            oneTargetTrait.retrieveInfo()
-            targetTraits.append( oneTargetTrait )
-            _targetnames.append( oneTargetTrait.name )
-
-        #XZ: filter out the strains that have no value.
-        primaryTrait.retrieveData()
-        _strains, _vals, _vars = primaryTrait.exportInformative()
-
-        #XZ: _controlstrains, _controlvals and _controlvars are list of list [ [], [], ...]. _controlNs is number
-        _controlstrains,_controlvals,_controlvars,_controlNs = correlationFunction.controlStrains(controlTraitsString,_strains)
-
-        N = len(_strains)
-
-        allsame = True
-        ##allsame is boolean for whether or not primary and control trait have values for the same strains
-        for i in _controlstrains:
-                if _strains != i:
-                        allsame=False
-                        break
-
-        ##   If the strains for which each of the control traits and the primary trait have values are not identical, 
-        ## we must remove from the calculation all vlaues for strains that are not present in each. Without doing this,
-        ## undesirable biases would be introduced.
-        # XZ, 01/11/2010: After execution of function fixStrains, variables _vals,_controlvals,_vars,_controlvars have the same number and same order of strains as strains in variable _strains. The _controlstrains remains intact.
-        if not allsame:
-                _strains,_vals,_controlvals,_vars,_controlvars = correlationFunction.fixStrains(_strains,_controlstrains,_vals,_controlvals,_vars,_controlvars)
-                N = len(_strains)
-
-        #XZ: We should check the value of control trait and primary trait here.
-        nameOfIdenticalTraits = correlationFunction.findIdenticalTraits ( _vals, primaryTraitString, _controlvals, controlTraitsList )
-        if nameOfIdenticalTraits:
-            heading = "Partial Correlation Table"
-            detail = ['%s and %s have same values for the %s strains that will be used to calculate partial correlation (common for all primary and control traits). In such case, partial correlation can NOT be calculated. Please re-select your traits.' % (nameOfIdenticalTraits[0], nameOfIdenticalTraits[1], len(_vals))]
-            self.error(heading=heading,detail=detail)
-            return
-
-
-        if N < self.corrMinInformative:
-            heading = "Partial Correlation Table"
-            detail = ['Fewer than %d strain data were entered for %s data set. No calculation of correlation has been attempted.' % (self.corrMinInformative, fd.RISet)]
-            self.error(heading=heading,detail=detail)
-            return
-
-        #XZ, 01/11/2010: Pay attention to the target trait strain number and order!
-        #XZ 03/29/2010: need to input target trait values to this function.
-
-        _targetvals = []
-        for oneTargetTrait in targetTraits:
-            oneTargetTrait.retrieveData()
-            oneTraitVals = oneTargetTrait.exportData( _strains )
-            _targetvals.append(oneTraitVals)
-
-
-        if pc_method == 'spearman':
-            allcorrelations = correlationFunction.determinePartialsByR(primaryVal = _vals, controlVals = _controlvals, targetVals = _targetvals, targetNames = _targetnames, method='s')
-        else:
-            allcorrelations = correlationFunction.determinePartialsByR(primaryVal = _vals, controlVals = _controlvals, targetVals = _targetvals, targetNames = _targetnames)
-
-        totalTraits = len(allcorrelations)
-
-
-        info_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', 'database':'_', 'ProbeSetID':'_', 'CellID':'_' }#XZ: These four parameters are required by javascript function showDatabase2.
-        
-        for key in hddn.keys():
-            info_form.append(HT.Input(name=key, value=hddn[key], type='hidden'))
-
-        info_form.append(HT.Paragraph("Primary Trait", Class="subtitle"), '\n')
-
-        primaryTraitTable = HT.TableLite(cellSpacing=4,cellPadding=0,width="90%",border=0)
-
-        descriptionString = primaryTrait.genHTML(dispFromDatabase=1)
-        if primaryTrait.db.type == 'Publish' and primaryTrait.confidential:
-            descriptionString = primaryTrait.genHTML(dispFromDatabase=1, privilege=self.privilege, userName=self.userName, authorized_users=primaryTrait.authorized_users)
-        primaryTraitTable.append(HT.TR(HT.TD(HT.Href(text='%s' % descriptionString, url="javascript:showDatabase2('%s','%s','%s')" % (primaryTrait.db.name,primaryTrait.name,primaryTrait.cellid), Class="fs12 fwn") )))
-
-        info_form.append(primaryTraitTable)
-
-        info_form.append(HT.Paragraph("Control Traits", Class="subtitle"), '\n')
-
-        controlTraitsTable = HT.TableLite(cellSpacing=4,cellPadding=0,width="90%",border=0)
-
-        seq = 1
-
-        ## Generate the listing table for control traits
-        for thisTrait in controlTraits:
-            descriptionString = thisTrait.genHTML(dispFromDatabase=1)
-            if thisTrait.db.type == 'Publish' and thisTrait.confidential:
-                descriptionString = thisTrait.genHTML(dispFromDatabase=1, privilege=self.privilege, userName=self.userName, authorized_users=thisTrait.authorized_users)
-            controlTraitsTable.append(HT.TR(HT.TD("%d."%seq,align="left", width=10),
-                                            HT.TD(HT.Href(text='%s' % descriptionString,url="javascript:showDatabase2('%s','%s','%s')" % (thisTrait.db.name,thisTrait.name,thisTrait.cellid), Class="fs12 fwn") )))
-            seq += 1
-
-        info_form.append(controlTraitsTable)
-
-
-        TD_LR.append(info_form)        
-
-
-        mainfmName = webqtlUtil.genRandStr("fm_")
-        form = HT.Form(cgi= os.path.join(webqtlConfig.CGIDIR, webqtlConfig.SCRIPTFILE), enctype='multipart/form-data', name= mainfmName, submit=HT.Input(type='hidden'))
-
-        hddn = {'FormID':'showDatabase', 'database':'_', 'ProbeSetID':'_', 'CellID':'_' }#XZ: These four parameters are required by javascript function showDatabase2.
-
-        for key in hddn.keys():
-            form.append(HT.Input(name=key, value=hddn[key], type='hidden'))
-
-
-        filename= webqtlUtil.genRandStr("Corr_")
-
-        tblobj = {}
-
-        if pc_method == 'spearman':
-            tblobj['header'] = \
-            [[THCell(HT.TD('', Class="fs13 fwb ffl b1 cw cbrb"), sort=0),
-              THCell(HT.TD('Database', Class="fs13 fwb ffl b1 cw cbrb",align='center'), text='db', idx=1),
-              THCell(HT.TD('Record', Class="fs13 fwb ffl b1 cw cbrb",align='center'), text='id', idx=2),
-              THCell(HT.TD('Symbol', Class="fs13 fwb ffl b1 cw cbrb"), text='symbol', idx=3),
-              THCell(HT.TD('Description', Class="fs13 fwb ffl b1 cw cbrb", align='center'), text='desc', idx=4),
-              THCell(HT.TD('N ', nowrap="on", Class="fs13 fwb ffl b1 cw cbrb"), text='nstr', idx=5),
-              THCell(HT.TD('Partial rho ', nowrap="on", Class="fs13 fwb ffl b1 cw cbrb"), text='partial_corr', idx=6),
-              THCell(HT.TD('p(partial rho)', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text='partial_pv', idx=7),
-              THCell(HT.TD('rho ', nowrap="on", Class="fs13 fwb ffl b1 cw cbrb"), text='corr', idx=8),
-              THCell(HT.TD('p(rho)', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text='pv', idx=9),
-              THCell(HT.TD('delta rho', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text='delta_rho', idx=10)]]
-        else:
-            tblobj['header'] = \
-            [[THCell(HT.TD('', Class="fs13 fwb ffl b1 cw cbrb"), sort=0),
-              THCell(HT.TD('Database', Class="fs13 fwb ffl b1 cw cbrb",align='center'), text='db', idx=1),
-              THCell(HT.TD('Record', Class="fs13 fwb ffl b1 cw cbrb",align='center'), text='id', idx=2),
-              THCell(HT.TD('Symbol', Class="fs13 fwb ffl b1 cw cbrb"), text='symbol', idx=3),
-              THCell(HT.TD('Description', Class="fs13 fwb ffl b1 cw cbrb", align='center'), text='desc', idx=4),
-              THCell(HT.TD('N ', nowrap="on", Class="fs13 fwb ffl b1 cw cbrb"), text='nstr', idx=5),
-              THCell(HT.TD('Partial r ', nowrap="on", Class="fs13 fwb ffl b1 cw cbrb"), text='partial_corr', idx=6),
-              THCell(HT.TD('p(partial r)', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text='partial_pv', idx=7),
-              THCell(HT.TD('r ', nowrap="on", Class="fs13 fwb ffl b1 cw cbrb"), text='corr', idx=8),
-              THCell(HT.TD('p(r)', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text='pv', idx=9),
-              THCell(HT.TD('delta r', Class="fs13 fwb ffl b1 cw cbrb",nowrap='ON'), text='delta_r', idx=10)]]
-
-        sortby = ("partial_pv", "up")
-
-        tblobj['body'] = []
-        for i, thisTrait in enumerate(targetTraits):
-            tr = []
-
-            trId = str(thisTrait)
-            tr.append(TDCell(HT.TD(HT.Input(type="checkbox", Class="checkbox", name="searchResult",value=trId, onClick="highlight(this)"), nowrap="on", Class="fs12 fwn ffl b1 c222"), text=trId))
-            tr.append(TDCell(HT.TD(HT.Href(text=thisTrait.db.fullname,url=webqtlConfig.INFOPAGEHREF % thisTrait.db.name,target="_blank", Class="fs12 fwn"), Class="fs12 fwn ffl b1 c222"), text=thisTrait.db.name, val=thisTrait
-.db.name.upper()))
-            tr.append(TDCell(HT.TD(HT.Href(text=thisTrait.name,url="javascript:showDatabase3('%s', '%s', '%s', '%s')" % (mainfmName,thisTrait.db.name,thisTrait.name,thisTrait.cellid), Class="fs12 fwn"), Class="fs12 fwn b1 c222"), text=thisTrait.name, val=thisTrait.name))
-
-            #XZ: Symbol column
-            if thisTrait.db.type =="ProbeSet":
-                if thisTrait.symbol:
-                    tr.append(TDCell(HT.TD(thisTrait.symbol, Class="fs12 fwn ffl b1 c222"), text=thisTrait.symbol, val=thisTrait.symbol.upper()))
-                else:
-                    tr.append(TDCell(HT.TD('NA', Class="fs12 fwn ffl b1 c222"), text='NA', val='NA'))
-            elif thisTrait.db.type =="Publish":
-                AbbreviationString = "--"
-                if (thisTrait.post_publication_abbreviation != None):
-                    AbbreviationString = thisTrait.post_publication_abbreviation
-
-                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 = "--"
-
-                if AbbreviationString == "--":
-                    tr.append(TDCell(HT.TD('NA', Class="fs12 fwn ffl b1 c222"), text='NA', val='NA'))
-                else:
-                    tr.append(TDCell(HT.TD(AbbreviationString, Class="fs12 fwn ffl b1 c222"), text=AbbreviationString, val=AbbreviationString.upper()))
-            else:
-                tr.append(TDCell(HT.TD(thisTrait.name, Class="fs12 fwn ffl b1 c222"), text=thisTrait.name, val=thisTrait.name))
-
-            #XZ: Description column
-            if thisTrait.db.type =="ProbeSet" or thisTrait.db.type == "Temp":
-                tr.append(TDCell(HT.TD(thisTrait.description, Class="fs12 fwn ffl b1 c222"), text=thisTrait.description, val=thisTrait.description.upper()))
-            elif thisTrait.db.type =="Publish":
-		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
-                tr.append(TDCell(HT.TD(PhenotypeString, Class="fs12 fwn ffl b1 c222"), text=PhenotypeString, val=PhenotypeString.upper()))
-            else:
-                tr.append(TDCell(HT.TD(thisTrait.name, Class="fs12 fwn ffl b1 c222"), text=thisTrait.name, val=thisTrait.name))
-
-            tr.append(TDCell(HT.TD(allcorrelations[i][1], Class="fs12 fwn ffl b1 c222", align='right'), text=allcorrelations[i][1], val=allcorrelations[i][1]))
-
-            #partial correlation result
-            try:
-                repr = '%3.3f' % float(allcorrelations[i][2])
-                tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", align='right'), text=repr, val=abs(allcorrelations[i][2])))
-            except:
-                repr = 'NA'
-                tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", align='left'), text=repr, val=0 ))
-
-            repr = webqtlUtil.SciFloat(allcorrelations[i][3])
-            tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", nowrap='ON', align='right'), text=repr, val=allcorrelations[i][3]))
-
-            #zero order correlation result
-            repr = '%3.3f' % float(allcorrelations[i][4])
-            tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", align='right'), text=repr, val=abs(allcorrelations[i][4])))
-
-            repr = webqtlUtil.SciFloat(allcorrelations[i][5])
-            tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", nowrap='ON', align='right'), text=repr, val=allcorrelations[i][5]))
-
-            #delta
-            try:
-                repr = '%3.3f' % ( float(allcorrelations[i][2]) - float(allcorrelations[i][4]) )
-                tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", align='right'), text=repr, val=repr ))
-            except:
-                repr = 'NA'
-                tr.append(TDCell(HT.TD(repr, Class="fs12 fwn ffl b1 c222", align='left'), text=repr, val=0 ))
-
-            tblobj['body'].append(tr)
-
-        objfile = open('%s.obj' % (webqtlConfig.TMPDIR+filename), 'wb')
-        cPickle.dump(tblobj, objfile)
-        objfile.close()
-		# NL, 07/27/2010. genTableObj function has been moved from templatePage.py to webqtlUtil.py;
-        div = HT.Div(webqtlUtil.genTableObj(tblobj=tblobj, file=filename, sortby=sortby, tableID = "sortable", addIndex = "1"), Id="sortable")
-        form.append(div)
-
-
-        TD_LR.append(HT.Center(form),HT.P())
-
-        self.dict['body'] =  str(TD_LR)
-		# updated by NL, moved js function xmlhttpPost() and updatepage() to dhtml.js
-        self.dict['js1'] = ''
-        self.dict['title'] = 'Partial Correlation Result'
-
diff --git a/web/webqtl/correlation/PlotCorrelationPage.py b/web/webqtl/correlation/PlotCorrelationPage.py
deleted file mode 100755
index 23d2ccde..00000000
--- a/web/webqtl/correlation/PlotCorrelationPage.py
+++ /dev/null
@@ -1,683 +0,0 @@
-# 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 Ning Liu 2011/01/11
-
-import string
-import piddle as pid
-import os
-
-from htmlgen import HTMLgen2 as HT
-
-from utility import svg #Code using this module currently commented out
-from utility import Plot
-from base.webqtlTrait import webqtlTrait
-from base.templatePage import templatePage
-from utility import webqtlUtil
-from base import webqtlConfig
-from dbFunction import webqtlDatabaseFunction
-from correlation import correlationFunction
-
-#########################################
-#      PlotCorrelationPage
-#########################################
-class PlotCorrelationPage(templatePage):
-	corrMinInformative = 4
-   
-	def __init__(self, fd):
-
-		templatePage.__init__(self, fd)
-
-		self.initializeDisplayParameters(fd)
-
-		if not fd.genotype:
-			fd.readGenotype()
-
-		if fd.allstrainlist:
-			mdpchoice = fd.formdata.getvalue('MDPChoice')
-			if mdpchoice == "1":
-				strainlist = fd.f1list + fd.strainlist
-			elif mdpchoice == "2":
-				strainlist = []
-				strainlist2 = fd.f1list + fd.strainlist
-				for strain in fd.allstrainlist:
-					if strain not in strainlist2:
-						strainlist.append(strain)
-				#So called MDP Panel
-				if strainlist:
-					strainlist = fd.f1list+fd.parlist+strainlist
-			else:
-				strainlist = fd.allstrainlist
-			fd.readData(fd.allstrainlist)
-		else:
-			mdpchoice = None
-			strainlist = fd.strainlist
-			fd.readData()
-
-		#if fd.allstrainlist:
-		#	fd.readData(fd.allstrainlist)
-		#	strainlist = fd.allstrainlist
-		#else:
-		#	fd.readData()
-		#	strainlist = fd.strainlist
-		
-		
-		if not self.openMysql():
-			return
-
-		isSampleCorr = 0 #XZ: initial value is false
-		isTissueCorr = 0 #XZ: initial value is false
-
-		#Javascript functions (showCorrelationPlot2, showTissueCorrPlot) have made sure the correlation type is either sample correlation or tissue correlation.
-		if (self.database and (self.ProbeSetID != 'none')):
-			isSampleCorr = 1
-		elif (self.X_geneSymbol and self.Y_geneSymbol):
-			isTissueCorr = 1
-		else:
-			heading = "Correlation Type Error"
-			detail = ["For the input parameters, GN can not recognize the correlation type is sample correlation or tissue correlation."]
-			self.error(heading=heading,detail=detail)
-			return
-
-
-        	TD_LR = HT.TD(colspan=2,height=200,width="100%",bgColor='#eeeeee', align="left", wrap="off")
-
-
-		dataX=[]
-		dataY=[]
-		dataZ=[] # shortname
-		fullTissueName=[]
-		xlabel = ''
-		ylabel = ''
-
-		if isTissueCorr:
-			dataX, dataY, xlabel, ylabel, dataZ, fullTissueName = self.getTissueLabelsValues(X_geneSymbol=self.X_geneSymbol, Y_geneSymbol=self.Y_geneSymbol, TissueProbeSetFreezeId=self.TissueProbeSetFreezeId)
-			plotHeading = HT.Paragraph('Tissue Correlation Scatterplot')
-			plotHeading.__setattr__("class","title")
-
-		if isSampleCorr:
-			plotHeading = HT.Paragraph('Sample Correlation Scatterplot')
-                        plotHeading.__setattr__("class","title")
-
-			#XZ: retrieve trait 1 info, Y axis
-			trait1_data = [] #trait 1 data
-			trait1Url = ''
-
-			try:
-				Trait1 = webqtlTrait(db=self.database, name=self.ProbeSetID, cellid=self.CellID, cursor=self.cursor)
-				Trait1.retrieveInfo()
-				Trait1.retrieveData()
-			except:
-				heading = "Retrieve Data"
-				detail = ["The database you just requested has not been established yet."]
-				self.error(heading=heading,detail=detail)
-				return
-	
-			trait1_data = Trait1.exportData(strainlist)
-			if Trait1.db.type == 'Publish' and Trait1.confidential:
-				trait1Url = Trait1.genHTML(dispFromDatabase=1, privilege=self.privilege, userName=self.userName, authorized_users=Trait1.authorized_users)
-			else:
-				trait1Url = Trait1.genHTML(dispFromDatabase=1)
-			ylabel = '%s : %s' % (Trait1.db.shortname, Trait1.name)
-			if Trait1.cellid:
-				ylabel += ' : ' + Trait1.cellid
-
-
-			#XZ, retrieve trait 2 info, X axis
-			traitdata2 = [] #trait 2 data
-			_vals = [] #trait 2 data
-			trait2Url = ''
-
-			if ( self.database2 and (self.ProbeSetID2 != 'none') ):
-				try:
-					Trait2 = webqtlTrait(db=self.database2, name=self.ProbeSetID2, cellid=self.CellID2, cursor=self.cursor)
-					Trait2.retrieveInfo()
-					Trait2.retrieveData()
-				except:
-					heading = "Retrieve Data"
-					detail = ["The database you just requested has not been established yet."]
-					self.error(heading=heading,detail=detail)
-					return
-
-				if Trait2.db.type == 'Publish' and Trait2.confidential:
-					trait2Url = Trait2.genHTML(dispFromDatabase=1, privilege=self.privilege, userName=self.userName, authorized_users=Trait2.authorized_users)
-				else:
-					trait2Url = Trait2.genHTML(dispFromDatabase=1)
-				traitdata2 = Trait2.exportData(strainlist)
-				_vals = traitdata2[:]
-				xlabel = '%s : %s' % (Trait2.db.shortname, Trait2.name)
-				if Trait2.cellid:
-					xlabel += ' : ' + Trait2.cellid
-			else:
-				for item in strainlist:
-					if fd.allTraitData.has_key(item):
-						_vals.append(fd.allTraitData[item].val)
-					else:
-						_vals.append(None)
-
-				if fd.identification:
-					xlabel = fd.identification
-				else:
-					xlabel = "User Input Data"
-
-				try:
-					Trait2 = webqtlTrait(fullname=fd.formdata.getvalue('fullname'), cursor=self.cursor)
-					trait2Url = Trait2.genHTML(dispFromDatabase=1)
-				except:
-					trait2Url = xlabel
-
-			if (_vals and trait1_data):
-				if len(_vals) != len(trait1_data):
-					errors = HT.Blockquote(HT.Font('Error: ',color='red'),HT.Font('The number of traits are inconsistent, Program quit',color='black'))
-					errors.__setattr__("class","subtitle")
-					TD_LR.append(errors)
-					self.dict['body'] = str(TD_LR)
-					return
-
-				for i in range(len(_vals)):
-					if _vals[i]!= None and trait1_data[i]!= None:
-						dataX.append(_vals[i])
-						dataY.append(trait1_data[i])
-						strainName = strainlist[i]
-						if self.showstrains:
-							dataZ.append(webqtlUtil.genShortStrainName(RISet=fd.RISet, input_strainName=strainName))
-			else:
-				heading = "Correlation Plot"
-				detail = ['Empty Dataset for sample correlation, please check your data.']
-				self.error(heading=heading,detail=detail)
-				return
-
-
-		#XZ: We have gotten all data for both traits.
-		if len(dataX) >= self.corrMinInformative:
-
-			if self.rankOrder == 0:
-				rankPrimary = 0
-				rankSecondary = 1
-			else:
-				rankPrimary = 1
-				rankSecondary = 0
-
-			lineColor = self.setLineColor();
-			symbolColor = self.setSymbolColor();
-			idColor = self.setIdColor();					
-				
-			c = pid.PILCanvas(size=(self.plotSize, self.plotSize*0.90))
-			data_coordinate = Plot.plotXY(canvas=c, dataX=dataX, dataY=dataY, rank=rankPrimary, dataLabel = dataZ, labelColor=pid.black, lineSize=self.lineSize, lineColor=lineColor, idColor=idColor, idFont=self.idFont, idSize=self.idSize, symbolColor=symbolColor, symbolType=self.symbol, filled=self.filled, symbolSize=self.symbolSize, XLabel=xlabel, connectdot=0, YLabel=ylabel, title='', fitcurve=self.showline, displayR =1, offset= (90, self.plotSize/20, self.plotSize/10, 90), showLabel = self.showIdentifiers)
-				
-			if rankPrimary == 1:
-				dataXlabel, dataYlabel = webqtlUtil.calRank(xVals=dataX, yVals=dataY, N=len(dataX))
-			else:
-				dataXlabel, dataYlabel = dataX, dataY
-					
-			gifmap1 = HT.Map(name='CorrelationPlotImageMap1')
-				
-			for i, item in enumerate(data_coordinate):
-				one_rect_coordinate = "%d, %d, %d, %d" % (item[0] - 5, item[1] - 5, item[0] + 5, item[1] + 5)
-				if isTissueCorr:
-					one_rect_title = "%s (%s, %s)" % (fullTissueName[i], dataXlabel[i], dataYlabel[i])
-				else:
-					one_rect_title = "%s (%s, %s)" % (dataZ[i], dataXlabel[i], dataYlabel[i])
-				gifmap1.areas.append(HT.Area(shape='rect',coords=one_rect_coordinate, title=one_rect_title) )
-
-			filename= webqtlUtil.genRandStr("XY_")
-			c.save(webqtlConfig.IMGDIR+filename, format='gif')
-			img1=HT.Image('/image/'+filename+'.gif',border=0, usemap='#CorrelationPlotImageMap1')
-
-			mainForm_1 = 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':'_','RISet':fd.RISet, 'ProbeSetID2':'_', 'database2':'_', 'CellID2':'_', 'allstrainlist':string.join(fd.strainlist, " "), 'traitList': fd.formdata.getvalue("traitList")}
-			if fd.incparentsf1:
-				hddn['incparentsf1'] = 'ON'		
-			for key in hddn.keys():
-				mainForm_1.append(HT.Input(name=key, value=hddn[key], type='hidden'))
-			
-			if isSampleCorr:
-					mainForm_1.append(HT.P(), HT.Blockquote(HT.Strong('X axis:'),HT.Blockquote(trait2Url),HT.Strong('Y axis:'),HT.Blockquote(trait1Url), style='width: %spx;' % self.plotSize, wrap="hard"))
-			
-			graphForm = HT.Form(cgi= os.path.join(webqtlConfig.CGIDIR, webqtlConfig.SCRIPTFILE), name='MDP_Form',submit=HT.Input(type='hidden'))
-			graph_hddn = self.setHiddenParameters(fd, rankPrimary)
-			webqtlUtil.exportData(graph_hddn, fd.allTraitData) #XZ: This is necessary to replot with different groups of strains
-							
-			for key in graph_hddn.keys():
-				graphForm.append(HT.Input(name=key, value=graph_hddn[key], type='hidden'))			
-
-			options = self.createOptionsMenu(fd, mdpchoice)	
-			
-			if (self.showOptions == '0'):
-					showOptionsButton = HT.Input(type='button' ,name='optionsButton',value='Hide Options', onClick="showHideOptions();", Class="button")
-			else:
-					showOptionsButton = HT.Input(type='button' ,name='optionsButton',value='Show Options', onClick="showHideOptions();", Class="button")
-					
-			# updated by NL: 12-07-2011 add variables for tissue abbreviation page
-			if isTissueCorr: 
-				graphForm.append(HT.Input(name='shortTissueName', value='', type='hidden'))
-				graphForm.append(HT.Input(name='fullTissueName', value='', type='hidden'))
-				shortTissueNameStr=string.join(dataZ, ",")
-				fullTissueNameStr=string.join(fullTissueName, ",")
-		
-				tissueAbbrButton=HT.Input(type='button' ,name='tissueAbbrButton',value='Show Abbreviations', onClick="showTissueAbbr('MDP_Form','%s','%s')" % (shortTissueNameStr,fullTissueNameStr), Class="button")
-				graphForm.append(showOptionsButton,'&nbsp;&nbsp;&nbsp;&nbsp;',tissueAbbrButton, HT.BR(), HT.BR())
-			else:
-				graphForm.append(showOptionsButton, HT.BR(), HT.BR())
-
-			graphForm.append(options, HT.BR())				
-			graphForm.append(HT.HR(), HT.BR(), HT.P())
-						
-			TD_LR.append(plotHeading, HT.BR(),graphForm, HT.BR(), gifmap1, HT.P(), img1, HT.P(), mainForm_1)
-			TD_LR.append(HT.BR(), HT.HR(color="grey", size=5, width="100%"))
-
-
-
-			c = pid.PILCanvas(size=(self.plotSize, self.plotSize*0.90))
-			data_coordinate = Plot.plotXY(canvas=c, dataX=dataX, dataY=dataY, rank=rankSecondary, dataLabel = dataZ, labelColor=pid.black,lineColor=lineColor, lineSize=self.lineSize, idColor=idColor, idFont=self.idFont, idSize=self.idSize, symbolColor=symbolColor, symbolType=self.symbol, filled=self.filled, symbolSize=self.symbolSize, XLabel=xlabel, connectdot=0, YLabel=ylabel,title='', fitcurve=self.showline, displayR =1, offset= (90, self.plotSize/20, self.plotSize/10, 90), showLabel = self.showIdentifiers)
-
-			if rankSecondary == 1:
-				dataXlabel, dataYlabel = webqtlUtil.calRank(xVals=dataX, yVals=dataY, N=len(dataX))
-			else:
-				dataXlabel, dataYlabel = dataX, dataY
-				
-			gifmap2 = HT.Map(name='CorrelationPlotImageMap2')
-				
-			for i, item in enumerate(data_coordinate):
-				one_rect_coordinate = "%d, %d, %d, %d" % (item[0] - 6, item[1] - 6, item[0] + 6, item[1] + 6)
-				if isTissueCorr:
-					one_rect_title = "%s (%s, %s)" % (fullTissueName[i], dataXlabel[i], dataYlabel[i])
-				else:
-					one_rect_title = "%s (%s, %s)" % (dataZ[i], dataXlabel[i], dataYlabel[i])
-					
-				gifmap2.areas.append(HT.Area(shape='rect',coords=one_rect_coordinate, title=one_rect_title) )
-
-			filename= webqtlUtil.genRandStr("XY_")
-			c.save(webqtlConfig.IMGDIR+filename, format='gif')
-			img2=HT.Image('/image/'+filename+'.gif',border=0, usemap='#CorrelationPlotImageMap2')
-
-			mainForm_2 = HT.Form( cgi= os.path.join(webqtlConfig.CGIDIR, webqtlConfig.SCRIPTFILE), enctype='multipart/form-data', name='showDatabase2', submit=HT.Input(type='hidden'))
-			hddn = {'FormID':'showDatabase2','ProbeSetID':'_','database':'_','CellID':'_','RISet':fd.RISet, 'ProbeSetID2':'_', 'database2':'_', 'CellID2':'_', 'allstrainlist':string.join(fd.strainlist, " "), 'traitList': fd.formdata.getvalue("traitList")}
-			if fd.incparentsf1:
-				hddn['incparentsf1'] = 'ON'
-			for key in hddn.keys():
-				mainForm_2.append(HT.Input(name=key, value=hddn[key], type='hidden'))
-				
-			if isSampleCorr:
-				mainForm_2.append(HT.P(), HT.Blockquote(HT.Strong('X axis:'),HT.Blockquote(trait2Url),HT.Strong('Y axis:'),HT.Blockquote(trait1Url), style='width:%spx;' % self.plotSize))
-
-	
-			TD_LR.append(HT.BR(), HT.P())
-			TD_LR.append('\n', gifmap2, HT.P(), HT.P(), img2, HT.P(), mainForm_2)
-
-			self.dict['body'] = str(TD_LR)
-		else:
-			heading = "Correlation Plot"
-			detail = ['Fewer than %d strain data were entered for %s data set. No statitical analysis has been attempted.' % (self.corrMinInformative, fd.RISet)]
-			self.error(heading=heading,detail=detail)
-			return
-
-
-	
-	def initializeDisplayParameters(self, fd):
-		"""
-		Initializes all of the PlotCorrelationPage class parameters, 
-		acquiring most values from the formdata (fd)
-		"""
-		
-		rankOrderString = fd.formdata.getvalue('rankOrder')
-        	if rankOrderString == "1":
-            	    self.rankOrder = 1
-        	else:
-                    self.rankOrder = 0
-            
-                self.dict['title'] = 'Correlation X-Y Scatterplot'
-		focusScript = "onLoad=\"document.getElementsByName('plotSize')[0].focus();\";"  
-		self.dict['js2'] = focusScript        
-		
-		self.showstrains = fd.formdata.getvalue('ShowStrains')
-		self.showline = fd.formdata.getvalue('ShowLine')
-		self.X_geneSymbol = fd.formdata.getvalue('X_geneSymbol','')
-		self.Y_geneSymbol = fd.formdata.getvalue('Y_geneSymbol','')
-		self.TissueProbeSetFreezeId = fd.formdata.getvalue('TissueProbeSetFreezeId', '1')
-		
-		self.symbolColor = fd.formdata.getvalue('symbolColor', 'black')
-		self.symbol = fd.formdata.getvalue('symbol', 'circle')
-		self.filled = fd.formdata.getvalue('filled', 'yes')
-		self.symbolSize = fd.formdata.getvalue('symbolSize', 'tiny')
-		self.idColor = fd.formdata.getvalue('idColor', 'blue')
-		self.idFont = fd.formdata.getvalue('idFont', 'arial')
-		self.idSize = fd.formdata.getvalue('idSize', '14')
-		self.lineColor = fd.formdata.getvalue('lineColor', 'grey')
-		self.lineSize = fd.formdata.getvalue('lineSize', 'medium')
-		self.showOptions = fd.formdata.getvalue('showOptions', '0')
-		
-		try:
-			self.plotSize = int(fd.formdata.getvalue('plotSize', 900))
-		except:
-			self.plotSize = 900
-		try:
-            		self.showIdentifiers = int(fd.formdata.getvalue('showIdentifiers', 1))
-        	except:
-            		self.showIdentifiers = 1
-            		
-		self.database = fd.formdata.getvalue('database')
-		self.ProbeSetID = fd.formdata.getvalue('ProbeSetID', 'none')
-		self.CellID = fd.formdata.getvalue('CellID')
-		
-        	self.database2 = fd.formdata.getvalue('database2')
-		self.ProbeSetID2 = fd.formdata.getvalue('ProbeSetID2', 'none')
-		self.CellID2 = fd.formdata.getvalue('CellID2')	
-	
-	def createOptionsMenu(self, fd, mdpchoice):
-		"""
-		Create all the HTML for the options menu; the first if/else statements
-		determine whether the Div container holding all the other html is visible
-		or not.
-		"""
-		
-		if (self.showOptions == '0'):
-			options = HT.Div(name="options", id="options", style="display: none")
-			self.showOptions = '1'
-		else:
-			options = HT.Div(name="options", id="options", style="display: ''")	
-			self.showOptions = '0'
-		
-		if self.showIdentifiers:	
-			containerTable = HT.TableLite(cellspacing=1, width=730, height=150, border=1)
-		else:
-			containerTable = HT.TableLite(cellspacing=1, width=730, height=130, border=1)		
-		
-		if self.showIdentifiers:	
-			containerTable = HT.TableLite(cellspacing=1, width=730, height=150, border=1)
-		else:
-			containerTable = HT.TableLite(cellspacing=1, width=730, height=130, border=1)
-		
-		containerRow = HT.TR()
-		containerCell = HT.TD(valign="middle", align="center")
-		
-		optionsTable = HT.TableLite(Class="collap", cellspacing=2, width=700)
-
-		sizeOptions = HT.TR(align="right")
-		tagOptions = HT.TR(align="right")
-		markerOptions = HT.TR(align="right")
-		lineOptions = HT.TR(align="right")
-		replot_mdpOptions = HT.TR(align="right")
-
-		sizeOptions.append(HT.TD(HT.Bold("Size: "), "&nbsp;"*1, HT.Input(type='text' ,name='plotSize', value=self.plotSize, style="background-color: #FFFFFF; width: 50px;", onChange="checkWidth();"), align="left"))
-
-		idColorSel = HT.Select(name="idColorSel", onChange="changeIdColor(); submit();", selected=self.idColor)
-		idColorSel.append(("blue", "blue"))
-		idColorSel.append(("green", "green"))
-		idColorSel.append(("red", "red"))
-		idColorSel.append(("yellow", "yellow"))
-		idColorSel.append(("white", "white"))
-		idColorSel.append(("purple", "purple"))
-		idColorSel.append(("brown", "brown"))
-		idColorSel.append(("grey", "grey"))
-		idColorSel.append(("black","black")) 
-
-		idFontSel = HT.Select(name="idFontSel", onChange="changeIdFont(); submit();", selected=self.idFont)
-		idFontSel.append(("Arial", "arial"))
-		idFontSel.append(("Trebuchet", "trebuc"))
-		idFontSel.append(("Verdana", "verdana"))
-		idFontSel.append(("Georgia", "Georgia"))
-		idFontSel.append(("Courier", "cour"))
-		
-		idSizeSel = HT.Select(name="idSizeSel", onChange="changeIdSize(); submit();", selected=self.idSize)
-		idSizeSel.append(("10", "10"))
-		idSizeSel.append(("12", "12"))
-		idSizeSel.append(("14", "14"))
-		idSizeSel.append(("16", "16"))
-		idSizeSel.append(("18", "18"))
-
-		if self.showIdentifiers:
-			tagButton = HT.TD(HT.Input(type='button' ,name='',value='  Hide Tags  ',onClick="this.form.showIdentifiers.value=0;submit();", Class="button"), align="right")
-		else:
-			tagButton = HT.TD(HT.Input(type='button' ,name='',value='  Show Tags  ',onClick="this.form.showIdentifiers.value=1;submit();", Class="button"), align="right")
-
-        	tagOptions.append(HT.TD(HT.Text(HT.Bold("Tag Settings: ")), align="left"))
-        	tagOptions.append(HT.TD(HT.Text(text="Font: "), idFontSel))
-		tagOptions.append(HT.TD(HT.Text(text="Color: "), idColorSel))
-		tagOptions.append(HT.TD(HT.Text(text="Point: "), idSizeSel))
-		tagOptions.append(tagButton)
-		optionsTable.append(sizeOptions, tagOptions)
-		
-		if fd.allstrainlist and mdpchoice:
-			allStrainList = HT.Input(name='allstrainlist', value=string.join(fd.allstrainlist, " "), type='hidden')
-			mdpChoice = HT.Input(name='MDPChoice', value=mdpchoice, type='hidden')
-			btn0 = HT.Input(type='button' ,name='',value='All Cases',onClick="this.form.MDPChoice.value=0;submit();", Class="button")
-			btn1 = HT.Input(type='button' ,name='',value='%s Only' % fd.RISet,onClick="this.form.MDPChoice.value=1;submit();", Class="button")
-			btn2 = HT.Input(type='button' ,name='',value='MDP Only', onClick="this.form.MDPChoice.value=2;submit();", Class="button")
-                
-
-    		colorSel = HT.Select(name="colorSel", onChange="changeSymbolColor(); submit();", selected=self.symbolColor)
-    		colorSel.append(("red", "red"))
-    		colorSel.append(("green", "green"))
-    		colorSel.append(("blue", "blue"))
-    		colorSel.append(("yellow", "yellow"))
-    		colorSel.append(("purple", "purple"))
-    		colorSel.append(("brown", "brown"))
-    		colorSel.append(("grey", "grey"))
-    		colorSel.append(("black","black")) 
-    		
-    		symbolSel = HT.Select(name="symbolSel", onChange="changeSymbol(); submit();", selected=self.symbol)
-    		symbolSel.append(("4-star","4-star"))
-    		symbolSel.append(("3-star","3-star"))
-    		symbolSel.append(("cross", "cross"))
-    		symbolSel.append(("circle","circle"))
-            	symbolSel.append(("diamond", "diamond"))
-    		symbolSel.append(("square", "square"))
-    		symbolSel.append(("vert rect", "vertRect"))
-    		symbolSel.append(("hori rect", "horiRect"))
-    		
-    		sizeSel = HT.Select(name="sizeSel", onChange="changeSize(); submit();", selected=self.symbolSize)
-		sizeSel.append(("tiny","tiny"))
-    		sizeSel.append(("small","small"))
-    		sizeSel.append(("medium","medium"))
-    		sizeSel.append(("large","large"))         
-
-    		fillSel = HT.Select(name="fillSel", onChange="changeFilled(); submit();", selected=self.filled)
-    		fillSel.append(("no","no"))
-    		fillSel.append(("yes","yes"))
- 
-    		lineColorSel = HT.Select(name="lineColorSel", onChange="changeLineColor(); submit();", selected=self.lineColor)
-    		lineColorSel.append(("red", "red"))
-    		lineColorSel.append(("green", "green"))
-    		lineColorSel.append(("blue", "blue"))
-    		lineColorSel.append(("yellow", "yellow"))
-    		lineColorSel.append(("purple", "purple"))
-    		lineColorSel.append(("brown", "brown"))
-    		lineColorSel.append(("grey", "grey"))
-    		lineColorSel.append(("black","black")) 
-    		
-    		lineSizeSel = HT.Select(name="lineSizeSel", onChange="changeLineSize(); submit();", selected=self.lineSize)
-    		lineSizeSel.append(("thin", "thin"))
-    		lineSizeSel.append(("medium", "medium"))
-    		lineSizeSel.append(("thick", "thick"))
-    		
-    		
-    		markerOptions.append(HT.TD(HT.Text(HT.Bold("Marker Settings: ")), align="left"))
-    		markerOptions.append(HT.TD(HT.Text(text="Marker: "), symbolSel))
-    		markerOptions.append(HT.TD(HT.Text(text="Color: "), colorSel))
-    		markerOptions.append(HT.TD(HT.Text(text="Fill: "), fillSel))
-    		markerOptions.append(HT.TD(HT.Text(text="Size: "), sizeSel))
-    		
-    		lineOptions.append(HT.TD(HT.Text(HT.Bold("Line Settings: ")), align="left"))
-    		lineOptions.append(HT.TD(HT.Text(text="Width: "), lineSizeSel))
-    		lineOptions.append(HT.TD(HT.Text(text="Color: "), lineColorSel))
-    	
-		replotButton = HT.Input(type='button', name='', value='    Replot    ',onClick="checkWidth(); submit();", Class="button")
-	
-    		if fd.allstrainlist and mdpchoice:
-                    replot_mdpOptions.append(HT.TD(replotButton, align="left"), HT.TD(allStrainList, mdpChoice, btn0, btn1, btn2, align="center", colspan=3))
-		    optionsTable.append(markerOptions, lineOptions, HT.TR(HT.TD(HT.BR())), replot_mdpOptions )
-                else:
-                    replot_mdpOptions.append(HT.TD(replotButton, align="left"))
-		    optionsTable.append(markerOptions, lineOptions, HT.TR(HT.TD(HT.BR())), replot_mdpOptions)
-            
-    		containerCell.append(optionsTable)
-    		containerRow.append(containerCell)
-    		containerTable.append(containerRow)
-    		
-    		options.append(containerTable)
-
-		return options
-	
-	def setHiddenParameters(self, fd, rankPrimary):
-		"""
-		Create the dictionary of hidden form parameters from PlotCorrelationPage's class parameters
-		"""
-		
-		graph_hddn = {'FormID':'showCorrelationPlot','RISet':fd.RISet, 'identification':fd.identification, "incparentsf1":1, "showIdentifiers":self.showIdentifiers}
-		
-		if self.database:	graph_hddn['database']=self.database
-		if self.ProbeSetID:	graph_hddn['ProbeSetID']=self.ProbeSetID
-		if self.CellID:	graph_hddn['CellID']=self.CellID
-		if self.database2:	graph_hddn['database2']=self.database2
-		if self.ProbeSetID2:	graph_hddn['ProbeSetID2']=self.ProbeSetID2
-		if self.CellID2:	graph_hddn['CellID2']=self.CellID2
-		if self.showstrains:	graph_hddn['ShowStrains']=self.showstrains
-		if self.showline:	graph_hddn['ShowLine']=self.showline
-		if self.X_geneSymbol: graph_hddn['X_geneSymbol']=self.X_geneSymbol
-		if self.Y_geneSymbol: graph_hddn['Y_geneSymbol']=self.Y_geneSymbol
-		if self.TissueProbeSetFreezeId: graph_hddn['TissueProbeSetFreezeId']=self.TissueProbeSetFreezeId
-		if self.rankOrder:  graph_hddn['rankOrder'] = rankPrimary
-		if fd.formdata.getvalue('fullname'):	graph_hddn['fullname']=fd.formdata.getvalue('fullname')
-		if self.lineColor: graph_hddn['lineColor'] = self.lineColor
-		if self.lineSize: graph_hddn['lineSize'] = self.lineSize
-		if self.idColor: graph_hddn['idColor'] = self.idColor
-		if self.idFont: graph_hddn['idFont'] = self.idFont
-		if self.idSize: graph_hddn['idSize'] = self.idSize
-		if self.symbolColor:   graph_hddn['symbolColor'] = self.symbolColor
-		if self.symbol: graph_hddn['symbol'] = self.symbol
-		if self.filled: graph_hddn['filled'] = self.filled
-		if self.symbolSize: graph_hddn['symbolSize'] = self.symbolSize
-		if self.showOptions: graph_hddn['showOptions'] = self.showOptions
-	
-		return graph_hddn
-	
-	def setIdColor(self):
-		"""
-		Set the plot tag/ID color based upon the value of the idColor class parameter
-		"""
-		
-		if self.idColor == 'black':
-			idColor = pid.black
-		elif self.idColor == 'white':
-			idColor = pid.white
-		elif self.idColor == 'yellow':
-			idColor = pid.yellow
-		elif self.idColor == 'grey':
-			idColor = pid.grey
-		elif self.idColor == 'blue':
-			idColor = pid.blue
-		elif self.idColor == 'purple':
-			idColor = pid.purple
-		elif self.idColor == 'brown':
-			idColor = pid.brown
-		elif self.idColor == 'green':
-			idColor = pid.green		
-		else:
-			idColor = pid.red
-			
-		return idColor	
-	
-	def setSymbolColor(self):
-		"""
-		Set the plot symbol color based upon the value of the symbolColor class parameter
-		"""
-		
-		if self.symbolColor == 'black':
-			symbolColor = pid.black
-		elif self.symbolColor == 'grey':
-			symbolColor = pid.grey
-		elif self.symbolColor == 'yellow':
-			symbolColor = pid.yellow
-		elif self.symbolColor == 'blue':
-			symbolColor = pid.blue
-		elif self.symbolColor == 'purple':
-			symbolColor = pid.purple
-		elif self.symbolColor == 'brown':
-			symbolColor = pid.brown
-		elif self.symbolColor== 'green':
-			symbolColor = pid.green		
-		else:
-			symbolColor = pid.red
-			
-		return symbolColor
-
-	def setLineColor(self):
-		"""
-		Set the plot line color based upon the lineColor class parameter
-		"""
-		
-		if self.lineColor == 'black':
-			lineColor = pid.black
-		elif self.lineColor == 'grey':
-			lineColor = pid.grey
-		elif self.lineColor == 'yellow':
-			lineColor = pid.yellow
-		elif self.lineColor == 'blue':
-			lineColor = pid.blue
-		elif self.lineColor == 'purple':
-			lineColor = pid.purple
-		elif self.lineColor == 'brown':
-			lineColor = pid.brown
-		elif self.lineColor== 'green':
-			lineColor = pid.green		
-		else:
-			lineColor = pid.red
-			
-		return lineColor
-		
-
-	def getTissueLabelsValues(self, X_geneSymbol=None, Y_geneSymbol=None, TissueProbeSetFreezeId=None ):
-
-	    dataX = []
-	    dataY = []
-	    data_fullLabel = []
-	    data_shortLabel = []
-		# updated by NL, 2011-01-11 using new function getTissueProbeSetXRefInfo to get dataId value
-	    X_symbolList,X_geneIdDict,X_dataIdDict,X_ChrDict,X_MbDict,X_descDict,X_pTargetDescDict = correlationFunction.getTissueProbeSetXRefInfo(cursor=self.cursor,GeneNameLst=[X_geneSymbol],TissueProbeSetFreezeId=TissueProbeSetFreezeId)
-	    Y_symbolList,Y_geneIdDict,Y_dataIdDict,Y_ChrDict,Y_MbDict,Y_descDict,Y_pTargetDescDict = correlationFunction.getTissueProbeSetXRefInfo(cursor=self.cursor,GeneNameLst=[Y_geneSymbol],TissueProbeSetFreezeId=TissueProbeSetFreezeId)
-		# in dataIdDict, key is the lower cased geneSymbol
-	    X_DataId = X_dataIdDict[X_geneSymbol.lower()]
-	    Y_DataId = Y_dataIdDict[Y_geneSymbol.lower()]
-
-	    self.cursor.execute("SELECT TissueID,value FROM  TissueProbeSetData WHERE Id = %d ORDER BY TissueID" % int(X_DataId) )
-	    results = self.cursor.fetchall()
-	    for item in results:
-	        TissueID, Value = item
-	        dataX.append(Value)
-	        self.cursor.execute("SELECT Tissue.Name, Tissue.Short_Name FROM Tissue WHERE Id = %d" % int(TissueID) )
-	        temp = self.cursor.fetchone()
-	        data_fullLabel.append( temp[0] )
-	        data_shortLabel.append( temp[1] )
-
-	    self.cursor.execute("SELECT TissueID,value FROM  TissueProbeSetData WHERE Id = %d ORDER BY TissueID" % int(Y_DataId) )
-	    results = self.cursor.fetchall()
-	    for item in results:
-	        TissueID, Value = item
-	        dataY.append(Value)
-
-	    X_label = "%s" % X_geneSymbol
-	    Y_label = "%s" % Y_geneSymbol
-		
-	    return dataX, dataY, X_label, Y_label, data_shortLabel, data_fullLabel
diff --git a/web/webqtl/correlation/__init__.py b/web/webqtl/correlation/__init__.py
deleted file mode 100755
index e69de29b..00000000
--- a/web/webqtl/correlation/__init__.py
+++ /dev/null
diff --git a/web/webqtl/correlation/correlationFunction.py b/web/webqtl/correlation/correlationFunction.py
deleted file mode 100755
index cc19f54e..00000000
--- a/web/webqtl/correlation/correlationFunction.py
+++ /dev/null
@@ -1,923 +0,0 @@
-# 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 2011/03/23
-
-
-import math
-import rpy2.robjects
-import pp
-import string
-
-from utility import webqtlUtil
-from base.webqtlTrait import webqtlTrait
-from dbFunction import webqtlDatabaseFunction
-
-
-
-#XZ: The input 'controls' is String. It contains the full name of control traits.
-#XZ: The input variable 'strainlst' is List. It contains the strain names of primary trait.
-#XZ: The returned tcstrains is the list of list [[],[]...]. So are tcvals and tcvars. The last returned parameter is list of numbers.
-#XZ, 03/29/2010: For each returned control trait, there is no None value in it.
-def controlStrains(controls, strainlst):
-
-    controls = controls.split(',')
-
-    cvals = {}
-    for oneTraitName in controls:
-        oneTrait = webqtlTrait(fullname=oneTraitName, cursor=webqtlDatabaseFunction.getCursor() )
-        oneTrait.retrieveData()
-        cvals[oneTraitName] = oneTrait.data
-
-    tcstrains = []
-    tcvals = []
-    tcvars = []
-
-    for oneTraitName in controls:
-        strains = []
-        vals = []
-        vars = []
-
-        for _strain in strainlst:
-            if cvals[oneTraitName].has_key(_strain):
-                _val = cvals[oneTraitName][_strain].val
-                if _val != None:
-                    strains.append(_strain)
-                    vals.append(_val)
-                    vars.append(None)
-
-        tcstrains.append(strains)
-        tcvals.append(vals)
-        tcvars.append(vars)
-
-    return tcstrains, tcvals, tcvars, [len(x) for x in tcstrains]
-
-
-
-#XZ, 03/29/2010: After execution of functon "controlStrains" and "fixStrains", primary trait and control traits have the same strains and in the same order. There is no 'None' value in them.
-def fixStrains(_strains,_controlstrains,_vals,_controlvals,_vars,_controlvars):
-    """Corrects strains, vals, and vars so that all contrain only those strains common
-    to the reference trait and all control traits."""
-
-    def dictify(strains,vals,vars):
-        subdict = {}
-        for i in xrange(len(strains)):
-            subdict[strains[i]] = (vals[i],vars[i])
-        return subdict
-
-    #XZ: The 'dicts' is a list of dictionary. The first element is the dictionary of reference trait. The rest elements are for control traits.
-    dicts = []
-    dicts.append(dictify(_strains,_vals,_vars))
-
-    nCstrains = len(_controlstrains)
-    for i in xrange(nCstrains):
-        dicts.append(dictify(_controlstrains[i],_controlvals[i],_controlvars[i]))
-
-    _newstrains = []
-    _vals = []
-    _vars = []
-    _controlvals = [[] for x in xrange(nCstrains)]
-    _controlvars = [[] for x in xrange(nCstrains)]
-
-    for strain in _strains:
-        inall = True
-        for d in dicts:
-            if strain not in d: 
-                inall = False
-                break
-        if inall:
-            _newstrains.append(strain)
-            _vals.append(dicts[0][strain][0])
-            _vars.append(dicts[0][strain][1])
-            for i in xrange(nCstrains):
-                _controlvals[i].append(dicts[i+1][strain][0])
-                _controlvars[i].append(dicts[i+1][strain][1])
-
-    return _newstrains,  _vals, _controlvals, _vars, _controlvars
-
-
-#XZ, 6/15/2010: If there is no identical control traits, the returned list is empty.
-#else, the returned list has two elements of control trait name.
-def findIdenticalControlTraits ( controlVals, controlNames ):
-    nameOfIdenticalTraits = []
-
-    controlTraitNumber = len(controlVals)
-
-    if controlTraitNumber > 1:
-
-        #XZ: reset the precision of values and convert to string type
-        for oneTraitVal in controlVals:
-            for oneStrainVal in oneTraitVal:
-                oneStrainVal = '%.3f' % oneStrainVal                
-
-        for i, oneTraitVal in enumerate( controlVals ):
-            for j in range(i+1, controlTraitNumber):
-                if oneTraitVal == controlVals[j]:
-                    nameOfIdenticalTraits.append(controlNames[i])
-                    nameOfIdenticalTraits.append(controlNames[j])
-
-    return nameOfIdenticalTraits
-
-#XZ, 6/15/2010: If there is no identical control traits, the returned list is empty.
-#else, the returned list has two elements of control trait name.
-#primaryVal is of list type. It contains value of primary trait.
-#primaryName is of string type.
-#controlVals is of list type. Each element is list too. Each element contain value of one control trait.
-#controlNames is of list type.
-def findIdenticalTraits (primaryVal, primaryName, controlVals, controlNames ):
-    nameOfIdenticalTraits = []
-
-    #XZ: reset the precision of values and convert to string type
-    for oneStrainVal in primaryVal:
-        oneStrainVal = '%.3f' % oneStrainVal
-
-    for oneTraitVal in controlVals:
-        for oneStrainVal in oneTraitVal:
-            oneStrainVal = '%.3f' % oneStrainVal
-
-    controlTraitNumber = len(controlVals)
-
-    if controlTraitNumber > 1:
-        for i, oneTraitVal in enumerate( controlVals ):
-            for j in range(i+1, controlTraitNumber):
-                if oneTraitVal == controlVals[j]:
-                    nameOfIdenticalTraits.append(controlNames[i])
-                    nameOfIdenticalTraits.append(controlNames[j])
-                    break
-
-    if len(nameOfIdenticalTraits) == 0:
-        for i, oneTraitVal in enumerate( controlVals ):
-            if primaryVal == oneTraitVal:
-                nameOfIdenticalTraits.append(primaryName)
-                nameOfIdenticalTraits.append(controlNames[i])
-                break
-
-    return nameOfIdenticalTraits
-
-
-
-#XZ, 03/29/2010: The strains in primaryVal, controlVals, targetVals must be of the same number and in same order.
-#XZ: No value in primaryVal and controlVals could be None.
-
-def determinePartialsByR (primaryVal, controlVals, targetVals, targetNames, method='p'):
-
-    def compute_partial ( primaryVal, controlVals, targetVals, targetNames, method ):
-
-        rpy2.robjects.r("""
-pcor.test <- function(x,y,z,use="mat",method="p",na.rm=T){
-        # The partial correlation coefficient between x and y given z
-        #
-        # pcor.test is free and comes with ABSOLUTELY NO WARRANTY.
-        #
-        # x and y should be vectors
-        #
-        # z can be either a vector or a matrix
-        #
-        # use: There are two methods to calculate the partial correlation coefficient.
-        #        One is by using variance-covariance matrix ("mat") and the other is by using recursive formula ("rec").
-        #        Default is "mat".
-        #
-        # method: There are three ways to calculate the correlation coefficient,
-        #           which are Pearson's ("p"), Spearman's ("s"), and Kendall's ("k") methods.
-        #           The last two methods which are Spearman's and Kendall's coefficient are based on the non-parametric analysis.
-        #           Default is "p".
-        #
-        # na.rm: If na.rm is T, then all the missing samples are deleted from the whole dataset, which is (x,y,z).
-        #        If not, the missing samples will be removed just when the correlation coefficient is calculated.
-        #          However, the number of samples for the p-value is the number of samples after removing
-        #          all the missing samples from the whole dataset.
-        #          Default is "T".
-
-        x <- c(x)
-        y <- c(y)
-        z <- as.data.frame(z)
-
-        if(use == "mat"){
-                p.use <- "Var-Cov matrix"
-                pcor = pcor.mat(x,y,z,method=method,na.rm=na.rm)
-        }else if(use == "rec"){
-                p.use <- "Recursive formula"
-                pcor = pcor.rec(x,y,z,method=method,na.rm=na.rm)
-        }else{
-                stop("use should be either rec or mat!\n")
-        }
-
-        # print the method
-        if(gregexpr("p",method)[[1]][1] == 1){
-                p.method <- "Pearson"
-        }else if(gregexpr("s",method)[[1]][1] == 1){
-                p.method <- "Spearman"
-        }else if(gregexpr("k",method)[[1]][1] == 1){
-                p.method <- "Kendall"
-        }else{
-                stop("method should be pearson or spearman or kendall!\n")
-        }
-
-        # sample number
-        n <- dim(na.omit(data.frame(x,y,z)))[1]
-
-        # given variables' number
-        gn <- dim(z)[2]
-
-        # p-value
-        if(p.method == "Kendall"){
-                statistic <- pcor/sqrt(2*(2*(n-gn)+5)/(9*(n-gn)*(n-1-gn)))
-                p.value <- 2*pnorm(-abs(statistic))
-
-        }else{
-                statistic <- pcor*sqrt((n-2-gn)/(1-pcor^2))
-                p.value <- 2*pnorm(-abs(statistic))
-        }
-
-        data.frame(estimate=pcor,p.value=p.value,statistic=statistic,n=n,gn=gn,Method=p.method,Use=p.use)
-}
-
-# By using var-cov matrix
-pcor.mat <- function(x,y,z,method="p",na.rm=T){
-
-        x <- c(x)
-        y <- c(y)
-        z <- as.data.frame(z)
-
-        if(dim(z)[2] == 0){
-                stop("There should be given data\n")
-        }
-
-        data <- data.frame(x,y,z)
-
-        if(na.rm == T){
-                data = na.omit(data)
-        }
-
-        xdata <- na.omit(data.frame(data[,c(1,2)]))
-        Sxx <- cov(xdata,xdata,m=method)
-
-        xzdata <- na.omit(data)
-        xdata <- data.frame(xzdata[,c(1,2)])
-        zdata <- data.frame(xzdata[,-c(1,2)])
-        Sxz <- cov(xdata,zdata,m=method)
-
-        zdata <- na.omit(data.frame(data[,-c(1,2)]))
-        Szz <- cov(zdata,zdata,m=method)
-
-        # is Szz positive definite?
-        zz.ev <- eigen(Szz)$values
-        if(min(zz.ev)[1]<0){
-                stop("\'Szz\' is not positive definite!\n")
-        }
-
-        # partial correlation
-        Sxx.z <- Sxx - Sxz %*% solve(Szz) %*% t(Sxz)
-
-        rxx.z <- cov2cor(Sxx.z)[1,2]
-
-        rxx.z
-}
-
-# By using recursive formula
-pcor.rec <- function(x,y,z,method="p",na.rm=T){
-        #
-
-        x <- c(x)
-        y <- c(y)
-        z <- as.data.frame(z)
-
-        if(dim(z)[2] == 0){
-                stop("There should be given data\n")
-        }
-
-        data <- data.frame(x,y,z)
-
-        if(na.rm == T){
-                data = na.omit(data)
-        }
-
-        # recursive formula
-        if(dim(z)[2] == 1){
-                tdata <- na.omit(data.frame(data[,1],data[,2]))
-                rxy <- cor(tdata[,1],tdata[,2],m=method)
-
-                tdata <- na.omit(data.frame(data[,1],data[,-c(1,2)]))
-                rxz <- cor(tdata[,1],tdata[,2],m=method)
-
-                tdata <- na.omit(data.frame(data[,2],data[,-c(1,2)]))
-                ryz <- cor(tdata[,1],tdata[,2],m=method)
-
-                rxy.z <- (rxy - rxz*ryz)/( sqrt(1-rxz^2)*sqrt(1-ryz^2) )
-
-                return(rxy.z)
-        }else{
-                x <- c(data[,1])
-                y <- c(data[,2])
-                z0 <- c(data[,3])
-                zc <- as.data.frame(data[,-c(1,2,3)])
-
-                rxy.zc <- pcor.rec(x,y,zc,method=method,na.rm=na.rm)
-                rxz0.zc <- pcor.rec(x,z0,zc,method=method,na.rm=na.rm)
-                ryz0.zc <- pcor.rec(y,z0,zc,method=method,na.rm=na.rm)
-
-                rxy.z <- (rxy.zc - rxz0.zc*ryz0.zc)/( sqrt(1-rxz0.zc^2)*sqrt(1-ryz0.zc^2) )
-                return(rxy.z)
-        }
-}
-""")
-
-        R_pcorr_function = rpy2.robjects.r['pcor.test']
-        R_corr_test = rpy2.robjects.r['cor.test']
-
-        primary = rpy2.robjects.FloatVector(range(len(primaryVal)))
-        for i in range(len(primaryVal)):
-            primary[i] = primaryVal[i]
-
-        control = rpy2.robjects.r.matrix(rpy2.robjects.FloatVector( range(len(controlVals)*len(controlVals[0])) ), ncol=len(controlVals))
-        for i in range(len(controlVals)):
-            for j in range(len(controlVals[0])):
-                control[i*len(controlVals[0]) + j] = controlVals[i][j]
-
-        allcorrelations = []
-
-        for targetIndex, oneTargetVals in enumerate(targetVals):
-
-            this_primary = None
-            this_control = None
-            this_target = None
-
-            if None in oneTargetVals:
-
-                goodIndex = []
-                for i in range(len(oneTargetVals)):
-                    if oneTargetVals[i] != None:
-                        goodIndex.append(i)
-
-                this_primary = rpy2.robjects.FloatVector(range(len(goodIndex)))
-                for i in range(len(goodIndex)):
-                    this_primary[i] = primaryVal[goodIndex[i]]
-
-                this_control = rpy2.robjects.r.matrix(rpy2.robjects.FloatVector( range(len(controlVals)*len(goodIndex)) ), ncol=len(controlVals))
-                for i in range(len(controlVals)):
-                    for j in range(len(goodIndex)):
-                        this_control[i*len(goodIndex) + j] = controlVals[i][goodIndex[j]]
-
-                this_target = rpy2.robjects.FloatVector(range(len(goodIndex)))
-                for i in range(len(goodIndex)):
-                    this_target[i] = oneTargetVals[goodIndex[i]]
-
-            else:
-                this_primary = primary
-                this_control = control
-                this_target = rpy2.robjects.FloatVector(range(len(oneTargetVals)))
-                for i in range(len(oneTargetVals)):
-                    this_target[i] = oneTargetVals[i]
-
-            one_name = targetNames[targetIndex]
-            one_N = len(this_primary)
-
-            #calculate partial correlation
-            one_pc_coefficient = 'NA'
-            one_pc_p = 1
-
-            try:
-                if method == 's':
-                    result = R_pcorr_function(this_primary, this_target, this_control, method='s')
-                else:
-                    result = R_pcorr_function(this_primary, this_target, this_control)
-
-                #XZ: In very few cases, the returned coefficient is nan.
-                #XZ: One way to detect nan is to compare the number to itself. NaN is always != NaN
-                if result[0][0] == result[0][0]:
-                    one_pc_coefficient = result[0][0]
-                    #XZ: when the coefficient value is 1 (primary trait and target trait are the same),
-                    #XZ: occationally, the returned p value is nan instead of 0.
-                    if result[1][0] == result[1][0]:
-                        one_pc_p = result[1][0]
-                    elif abs(one_pc_coefficient - 1) < 0.0000001:
-                        one_pc_p = 0
-            except:
-                pass
-
-            #calculate zero order correlation
-            one_corr_coefficient = 0
-            one_corr_p = 1
-
-            try:
-                if method == 's':
-                    R_result = R_corr_test(this_primary, this_target, method='spearman')
-                else:
-                    R_result = R_corr_test(this_primary, this_target)
-
-                one_corr_coefficient = R_result[3][0]
-                one_corr_p = R_result[2][0]
-            except:
-                pass
-
-            traitinfo = [ one_name, one_N, one_pc_coefficient, one_pc_p, one_corr_coefficient, one_corr_p ]
-
-            allcorrelations.append(traitinfo)
-
-        return allcorrelations
-    #End of function compute_partial
-
-
-    allcorrelations = []
-
-    target_trait_number = len(targetVals)
-
-    if target_trait_number < 1000:
-        allcorrelations = compute_partial ( primaryVal, controlVals, targetVals, targetNames, method )
-    else:
-        step = 1000
-        job_number = math.ceil( float(target_trait_number)/step )
-
-        job_targetVals_lists = []
-        job_targetNames_lists = []
-
-        for job_index in range( int(job_number) ):
-            starti = job_index*step
-            endi = min((job_index+1)*step, target_trait_number)
-
-            one_job_targetVals_list = []
-            one_job_targetNames_list = []
-
-            for i in range( starti, endi ):
-                one_job_targetVals_list.append( targetVals[i] )
-                one_job_targetNames_list.append( targetNames[i] )
-
-            job_targetVals_lists.append( one_job_targetVals_list )
-            job_targetNames_lists.append( one_job_targetNames_list )
-
-        ppservers = ()
-        # Creates jobserver with automatically detected number of workers
-        job_server = pp.Server(ppservers=ppservers)
-
-        jobs = []
-        results = []
-
-        for i, one_job_targetVals_list in enumerate( job_targetVals_lists ):
-            one_job_targetNames_list = job_targetNames_lists[i]
-            #pay attention to modules from outside
-            jobs.append( job_server.submit(func=compute_partial, args=( primaryVal, controlVals, one_job_targetVals_list, one_job_targetNames_list, method), depfuncs=(), modules=("rpy2.robjects",)) )
-
-        for one_job in jobs:
-            one_result = one_job()
-            results.append( one_result )
-
-        for one_result in results:
-            for one_traitinfo in one_result:
-                allcorrelations.append( one_traitinfo )
-
-    return allcorrelations
-
-
-
-#XZ, April 30, 2010: The input primaryTrait and targetTrait are instance of webqtlTrait
-#XZ: The primaryTrait and targetTrait should have executed retrieveData function
-def calZeroOrderCorr (primaryTrait, targetTrait, method='pearson'):
-
-    #primaryTrait.retrieveData()
-
-    #there is no None value in primary_val
-    primary_strain, primary_val, primary_var = primaryTrait.exportInformative()
-
-    #targetTrait.retrieveData()
-
-    #there might be None value in target_val
-    target_val = targetTrait.exportData(primary_strain, type="val")
-
-    R_primary = rpy2.robjects.FloatVector(range(len(primary_val)))
-    for i in range(len(primary_val)):
-        R_primary[i] = primary_val[i]
-
-    N = len(target_val)
-
-    if None in target_val:
-        goodIndex = []
-        for i in range(len(target_val)):
-            if target_val[i] != None:
-                goodIndex.append(i)
-
-        N = len(goodIndex)
-
-        R_primary = rpy2.robjects.FloatVector(range(len(goodIndex)))
-        for i in range(len(goodIndex)):
-            R_primary[i] = primary_val[goodIndex[i]]
-
-        R_target = rpy2.robjects.FloatVector(range(len(goodIndex)))
-        for i in range(len(goodIndex)):
-            R_target[i] = target_val[goodIndex[i]]
-
-    else:
-        R_target = rpy2.robjects.FloatVector(range(len(target_val)))
-        for i in range(len(target_val)):
-            R_target[i] = target_val[i]
-
-    R_corr_test = rpy2.robjects.r['cor.test']
-
-    if method == 'spearman':
-        R_result = R_corr_test(R_primary, R_target, method='spearman')
-    else:
-        R_result = R_corr_test(R_primary, R_target)
-
-    corr_result = []
-    corr_result.append( R_result[3][0] )
-    corr_result.append( N )
-    corr_result.append( R_result[2][0] )
-    
-    return corr_result
-	
-#####################################################################################
-#Input: primaryValue(list): one list of expression values of one probeSet, 
-#       targetValue(list): one list of expression values of one probeSet, 
-#		method(string): indicate correlation method ('pearson' or 'spearman')
-#Output: corr_result(list): first item is Correlation Value, second item is tissue number,
-#                           third item is PValue
-#Function: get correlation value,Tissue quantity ,p value result by using R;
-#Note : This function is special case since both primaryValue and targetValue are from
-#the same dataset. So the length of these two parameters is the same. They are pairs.
-#Also, in the datatable TissueProbeSetData, all Tissue values are loaded based on 
-#the same tissue order
-#####################################################################################	
-
-def calZeroOrderCorrForTiss (primaryValue=[], targetValue=[], method='pearson'):
-
-    R_primary = rpy2.robjects.FloatVector(range(len(primaryValue)))
-    N = len(primaryValue)
-    for i in range(len(primaryValue)):
-        R_primary[i] = primaryValue[i]
-
-    R_target = rpy2.robjects.FloatVector(range(len(targetValue)))
-    for i in range(len(targetValue)):
-        R_target[i]=targetValue[i]
-
-    R_corr_test = rpy2.robjects.r['cor.test']
-    if method =='spearman':
-        R_result = R_corr_test(R_primary, R_target, method='spearman')
-    else:
-        R_result = R_corr_test(R_primary, R_target)
-	
-    corr_result =[]
-    corr_result.append( R_result[3][0])
-    corr_result.append( N )
-    corr_result.append( R_result[2][0])
-
-    return corr_result
-
-
-
-
-def batchCalTissueCorr(primaryTraitValue=[], SymbolValueDict={}, method='pearson'):
-
-    def cal_tissue_corr(primaryTraitValue, oneSymbolValueDict, method ):
-
-        oneSymbolCorrDict = {}
-        oneSymbolPvalueDict = {}
-
-        R_corr_test = rpy2.robjects.r['cor.test']
-
-        R_primary = rpy2.robjects.FloatVector(range(len(primaryTraitValue)))
-
-        for i in range(len(primaryTraitValue)):
-            R_primary[i] = primaryTraitValue[i]
-
-        for (oneTraitSymbol, oneTraitValue) in oneSymbolValueDict.iteritems():
-            R_target = rpy2.robjects.FloatVector(range(len(oneTraitValue)))
-            for i in range(len(oneTraitValue)):
-                R_target[i] = oneTraitValue[i]
-
-            if method =='spearman':
-                R_result = R_corr_test(R_primary, R_target, method='spearman')
-            else:
-                R_result = R_corr_test(R_primary, R_target)
-
-            oneSymbolCorrDict[oneTraitSymbol] = R_result[3][0]
-            oneSymbolPvalueDict[oneTraitSymbol] = R_result[2][0]
-
-        return(oneSymbolCorrDict, oneSymbolPvalueDict)
-
-
-
-    symbolCorrDict = {}
-    symbolPvalueDict = {}
-
-    items_number = len(SymbolValueDict)
-
-    if items_number <= 1000:
-        symbolCorrDict, symbolPvalueDict = cal_tissue_corr(primaryTraitValue, SymbolValueDict, method)
-    else:
-        items_list = SymbolValueDict.items()
-
-        step = 1000
-        job_number = math.ceil( float(items_number)/step )
-
-        job_oneSymbolValueDict_list = []
-
-        for job_index in range( int(job_number) ):
-            starti = job_index*step
-            endi = min((job_index+1)*step, items_number)
-
-            oneSymbolValueDict = {}
-
-            for i in range( starti, endi ):
-                one_item = items_list[i]
-                one_symbol = one_item[0]
-                one_value = one_item[1]
-                oneSymbolValueDict[one_symbol] = one_value
-
-            job_oneSymbolValueDict_list.append( oneSymbolValueDict )
-
-
-        ppservers = ()
-        # Creates jobserver with automatically detected number of workers
-        job_server = pp.Server(ppservers=ppservers)
-
-        jobs = []
-        results = []
-
-        for i, oneSymbolValueDict in enumerate( job_oneSymbolValueDict_list ):
-
-            #pay attention to modules from outside
-            jobs.append( job_server.submit(func=cal_tissue_corr, args=(primaryTraitValue, oneSymbolValueDict, method), depfuncs=(), modules=("rpy2.robjects",)) )
-
-        for one_job in jobs:
-            one_result = one_job()
-            results.append( one_result )
-
-        for one_result in results:
-            oneSymbolCorrDict, oneSymbolPvalueDict = one_result
-            symbolCorrDict.update( oneSymbolCorrDict )
-            symbolPvalueDict.update( oneSymbolPvalueDict )
-
-    return (symbolCorrDict, symbolPvalueDict)
-
-###########################################################################
-#Input: cursor, GeneNameLst (list), TissueProbeSetFreezeId
-#output: geneIdDict,dataIdDict,ChrDict,MbDict,descDict,pTargetDescDict (Dict)      
-#function: get multi dicts for short and long label functions, and for getSymbolValuePairDict and 
-# getGeneSymbolTissueValueDict to build dict to get CorrPvArray
-#Note: If there are multiple probesets for one gene, select the one with highest mean.
-###########################################################################	
-def getTissueProbeSetXRefInfo(cursor=None,GeneNameLst=[],TissueProbeSetFreezeId=0):
-	Symbols =""	
-	symbolList =[]
-	geneIdDict ={}
-	dataIdDict = {}
-	ChrDict = {}
-	MbDict = {}
-	descDict = {}
-	pTargetDescDict = {}
-
-	count = len(GeneNameLst)
-
-	# Added by NL 01/06/2011
-	# Note that:inner join is necessary in this query to get distinct record in one symbol group with highest mean value
-	# Duo to the limit size of TissueProbeSetFreezeId table in DB, performance of inner join is acceptable.
-	if count==0:
-		query='''		
-				select t.Symbol,t.GeneId, t.DataId,t.Chr, t.Mb,t.description,t.Probe_Target_Description 
-				from (
-					select Symbol, max(Mean) as maxmean 
-					from TissueProbeSetXRef 
-					where TissueProbeSetFreezeId=%s and Symbol!='' and Symbol Is Not Null group by Symbol) 
-				as x inner join TissueProbeSetXRef as t on t.Symbol = x.Symbol and t.Mean = x.maxmean;				
-			'''%TissueProbeSetFreezeId
-				
-	else:
-		for i, item in enumerate(GeneNameLst):
-		
-			if i == count-1:
-				Symbols += "'%s'" %item
-			else:
-				Symbols += "'%s'," %item
-				
-		Symbols = "("+ Symbols+")"
-		query='''
-				select t.Symbol,t.GeneId, t.DataId,t.Chr, t.Mb,t.description,t.Probe_Target_Description 
-				from (
-					select Symbol, max(Mean) as maxmean 
-					from TissueProbeSetXRef 
-					where TissueProbeSetFreezeId=%s and Symbol in %s group by Symbol) 
-				as x inner join TissueProbeSetXRef as t on t.Symbol = x.Symbol and t.Mean = x.maxmean;		
-			'''% (TissueProbeSetFreezeId,Symbols)
-
-	try: 
-	
-		cursor.execute(query)
-		results =cursor.fetchall()
-		resultCount = len(results)
-		# Key in all dicts is the lower-cased symbol
-		for i, item in enumerate(results):	
-			symbol = item[0]
-			symbolList.append(symbol)
-			
-			key =symbol.lower()
-			geneIdDict[key]=item[1]
-			dataIdDict[key]=item[2]			
-			ChrDict[key]=item[3]
-			MbDict[key]=item[4]
-			descDict[key]=item[5]
-			pTargetDescDict[key]=item[6]
-
-	except:
-		symbolList = None
-		geneIdDict=None
-		dataIdDict=None
-		ChrDict=None
-		MbDict=None
-		descDict=None
-		pTargetDescDict=None
-		
-	return symbolList,geneIdDict,dataIdDict,ChrDict,MbDict,descDict,pTargetDescDict
-		
-###########################################################################
-#Input: cursor, symbolList (list), dataIdDict(Dict)
-#output: symbolValuepairDict (dictionary):one dictionary of Symbol and Value Pair,
-#        key is symbol, value is one list of expression values of one probeSet;
-#function: get one dictionary whose key is gene symbol and value is tissue expression data (list type).
-#Attention! All keys are lower case!
-###########################################################################	
-def getSymbolValuePairDict(cursor=None,symbolList=None,dataIdDict={}):		
-	symbolList = map(string.lower, symbolList)
-	symbolValuepairDict={}
-	valueList=[]
-
-	for key in symbolList:
-		if dataIdDict.has_key(key):
-			DataId = dataIdDict[key]
-			
-			valueQuery = "select value from TissueProbeSetData where Id=%s" % DataId
-			try :
-				cursor.execute(valueQuery)
-				valueResults = cursor.fetchall()
-				for item in valueResults:
-					item =item[0]
-					valueList.append(item)	
-				symbolValuepairDict[key] = valueList
-				valueList=[]
-			except:
-				symbolValuepairDict[key] = None
-			
-	return symbolValuepairDict
-
-
-########################################################################################################
-#input: cursor, symbolList (list), dataIdDict(Dict): key is symbol
-#output: SymbolValuePairDict(dictionary):one dictionary of Symbol and Value Pair.
-#        key is symbol, value is one list of expression values of one probeSet.
-#function: wrapper function for getSymbolValuePairDict function
-#          build gene symbol list if necessary, cut it into small lists if necessary,
-#          then call getSymbolValuePairDict function and merge the results.
-########################################################################################################
-
-def getGeneSymbolTissueValueDict(cursor=None,symbolList=None,dataIdDict={}):
-        limitNum=1000
-        count = len(symbolList)
-
-        SymbolValuePairDict = {}
-
-        if count !=0 and count <=limitNum:
-                SymbolValuePairDict = getSymbolValuePairDict(cursor=cursor,symbolList=symbolList,dataIdDict=dataIdDict)
-
-        elif count >limitNum:
-                SymbolValuePairDict={}
-                n = count/limitNum
-                start =0
-                stop =0
-
-                for i in range(n):
-                        stop =limitNum*(i+1)
-                        gList1 = symbolList[start:stop]
-                        PairDict1 = getSymbolValuePairDict(cursor=cursor,symbolList=gList1,dataIdDict=dataIdDict)
-                        start =limitNum*(i+1)
-
-                        SymbolValuePairDict.update(PairDict1)
-
-                if stop < count:
-                        stop = count
-                        gList2 = symbolList[start:stop]
-                        PairDict2 = getSymbolValuePairDict(cursor=cursor,symbolList=gList2,dataIdDict=dataIdDict)
-                        SymbolValuePairDict.update(PairDict2)
-
-        return SymbolValuePairDict
-
-########################################################################################################
-#input: cursor, GeneNameLst (list), TissueProbeSetFreezeId(int)
-#output: SymbolValuePairDict(dictionary):one dictionary of Symbol and Value Pair.
-#        key is symbol, value is one list of expression values of one probeSet.
-#function: wrapper function of getGeneSymbolTissueValueDict function
-#          for CorrelationPage.py
-########################################################################################################
-
-def getGeneSymbolTissueValueDictForTrait(cursor=None,GeneNameLst=[],TissueProbeSetFreezeId=0):
-	SymbolValuePairDict={}
-	symbolList,geneIdDict,dataIdDict,ChrDict,MbDict,descDict,pTargetDescDict = getTissueProbeSetXRefInfo(cursor=cursor,GeneNameLst=GeneNameLst,TissueProbeSetFreezeId=TissueProbeSetFreezeId)
-	if symbolList:
-		SymbolValuePairDict = getGeneSymbolTissueValueDict(cursor=cursor,symbolList=symbolList,dataIdDict=dataIdDict)
-	return SymbolValuePairDict
-	
-########################################################################################################
-#Input: cursor(cursor): MySQL connnection cursor; 
-#       priGeneSymbolList(list): one list of gene symbol;
-#       symbolValuepairDict(dictionary): one dictionary of Symbol and Value Pair,
-#               key is symbol, value is one list of expression values of one probeSet;
-#Output: corrArray(array): array of Correlation Value, 
-#        pvArray(array): array of PValue;
-#Function: build corrArray, pvArray for display by calling  calculation function:calZeroOrderCorrForTiss
-########################################################################################################
-
-def getCorrPvArray(cursor=None,priGeneSymbolList=[],symbolValuepairDict={}):
-        # setting initial value for corrArray, pvArray equal to 0
-        Num = len(priGeneSymbolList)
-
-        corrArray = [([0] * (Num))[:] for i in range(Num)] 
-        pvArray = [([0] * (Num))[:] for i in range(Num)]
-        i = 0
-        for pkey in priGeneSymbolList:
-                j = 0
-                pkey = pkey.strip().lower()# key in symbolValuepairDict is low case
-                if symbolValuepairDict.has_key(pkey):
-                        priValue = symbolValuepairDict[pkey]
-                        for tkey in priGeneSymbolList:
-                                tkey = tkey.strip().lower()# key in symbolValuepairDict is low case
-                                if priValue and symbolValuepairDict.has_key(tkey):
-                                        tarValue = symbolValuepairDict[tkey]
-
-                                        if tarValue:
-                                                if i>j:
-                                                        # corrArray stores Pearson Correlation values
-                                                        # pvArray stores Pearson P-Values
-                                                        pcorr_result =calZeroOrderCorrForTiss(primaryValue=priValue,targetValue=tarValue)
-                                                        corrArray[i][j] =pcorr_result[0]
-                                                        pvArray[i][j] =pcorr_result[2]
-                                                elif i<j:
-                                                        # corrArray stores Spearman Correlation values
-                                                        # pvArray stores Spearman P-Values
-                                                        scorr_result =calZeroOrderCorrForTiss(primaryValue=priValue,targetValue=tarValue,method='spearman')
-                                                        corrArray[i][j] =scorr_result[0]
-                                                        pvArray[i][j] =scorr_result[2]
-                                                else:
-                                                        # on the diagonal line, correlation value is 1, P-Values is 0
-                                                        corrArray[i][j] =1
-                                                        pvArray[i][j] =0
-                                                j+=1
-                                        else:
-                                                corrArray[i][j] = None
-                                                pvArray[i][j] = None
-                                                j+=1
-                                else:
-                                        corrArray[i][j] = None
-                                        pvArray[i][j] = None
-                                        j+=1
-                else: 
-                        corrArray[i][j] = None
-                        pvArray[i][j] = None
-
-                i+=1
-
-        return corrArray, pvArray
-
-########################################################################################################
-#Input: cursor(cursor): MySQL connnection cursor; 
-#       primaryTraitSymbol(string): one gene symbol;
-#		TissueProbeSetFreezeId (int): Id of related TissueProbeSetFreeze
-#       method: '0' default value, Pearson Correlation; '1', Spearman Correlation
-#Output: symbolCorrDict(Dict): Dict of Correlation Value, key is symbol 
-#        symbolPvalueDict(Dict): Dict of PValue,key is symbol ;
-#Function: build symbolCorrDict, symbolPvalueDict for display by calling  calculation function:calZeroOrderCorrForTiss
-########################################################################################################
-def calculateCorrOfAllTissueTrait(cursor=None, primaryTraitSymbol=None, TissueProbeSetFreezeId=None,method='0'):
-
-	symbolCorrDict = {}
-	symbolPvalueDict = {}
-
-	primaryTraitSymbolValueDict = getGeneSymbolTissueValueDictForTrait(cursor=cursor, GeneNameLst=[primaryTraitSymbol], TissueProbeSetFreezeId=TissueProbeSetFreezeId)
-	primaryTraitValue = primaryTraitSymbolValueDict.values()[0]
-	
-	SymbolValueDict = getGeneSymbolTissueValueDictForTrait(cursor=cursor, GeneNameLst=[], TissueProbeSetFreezeId=TissueProbeSetFreezeId)
-	
-	if method =='1':
-		symbolCorrDict, symbolPvalueDict = batchCalTissueCorr(primaryTraitValue,SymbolValueDict,method='spearman')
-	else:
-		symbolCorrDict, symbolPvalueDict = batchCalTissueCorr(primaryTraitValue,SymbolValueDict)
-
-
-	return (symbolCorrDict, symbolPvalueDict)