# 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 # graphviz: # a library for sending trait data to the graphviz utilities to get # graphed # ParamDict: a dictionary of strings that map to strings where the keys are # valid parameters and the values are validated versions of those parameters # # The list below also works for visualize.py; different parameters apply to different # functions in the pipeline. See visualize.py for more details. # # parameters: # filename: an input file with comma-delimited data to visualize # kValue: # how to filter the edges; edges with correlation coefficents in # [-k, k] are not drawn # whichValue: which of the two correlation coefficents are used; # 0 means the top half (pearson) and # 1 means the bottom half (spearman) # width: the width of the graph in inches # height: the height of the graph in inches # --scale: an amount to multiply the length factors by to space out the nodes # spline: whether to use splines instead of straight lines to draw graphs # tune: whether to automatically pick intelligent default values for # kValue and spline based on the number of edges in the input data # whichVersion: whether to display the graph zoomed or fullscreen # 0 means zoom # 1 means fullscreen # printIslands: whether to display nodes with no visible edges # # DataMatrix: a one-dimensional array of DataPoints in sorted order by i first import copy import os #import os.path import math import string from base import webqtlConfig from utility import webqtlUtil #import trait from nGraphException import nGraphException from ProcessedPoint import ProcessedPoint # processDataMatrix: DataMatrix -> ParamDict -> void # this is the second part after filterDataMatrix # To process the set of points in a DataMatrix as follows # 1) choose an appropriate color for the data point # 2) filter those between k values # 3) to use an r-to-Z transform to spread out the correlation # values from [-1,1] to (-inf, inf) # 4) to invert the values so that higher correlations result in # shorter edges # # Note: this function modifies the matrix in-place. My functional # programming instincts tell me that this is a bad idea. def processDataMatrix(matrix, p): for pt2 in matrix: # filter using k if (-p["kValue"] <= pt2.value) and (pt2.value <= p["kValue"]): pt2.value = 0.00 # Lei Yan # 05/28/2009 # fix color # pick a color if pt2.value >= 0.7: pt2.color = p["cL6Name"] pt2.style = p["L6style"] elif pt2.value >= 0.5: pt2.color = p["cL5Name"] pt2.style = p["L5style"] elif pt2.value >= 0.0: pt2.color = p["cL4Name"] pt2.style = p["L4style"] elif pt2.value >= -0.5: pt2.color = p["cL3Name"] pt2.style = p["L3style"] elif pt2.value >= -0.7: pt2.color = p["cL2Name"] pt2.style = p["L2style"] else: pt2.color = p["cL1Name"] pt2.style = p["L1style"] # r to Z transform to generate the length # 0 gets transformed to infinity, which we can't # represent here, and 1 gets transformed to 0 if p["lock"] == "no": if -0.01 < pt2.value and pt2.value < 0.01: pt2.length = 1000 elif pt2.value > 0.99 or pt2.value < -0.99: pt2.length = 0 else: pt2.length = pt2.value pt2.length = 0.5 * math.log((1 + pt2.length)/(1 - pt2.length)) # invert so higher correlations mean closer edges #pt2.length = abs(p["scale"] * 1/pt2.length) pt2.length = abs(1/pt2.length) else: pt2.length = 2 # tuneParamDict: ParamDict -> Int -> Int -> ParamDict # to adjust the parameter dictionary for a first-time run # so that the graphing doesn't take so long, especially since # small parameter changes can make a big performance difference # note: you can pass this function an empty dictionary and # get back a good set of default parameters for your # particular graph def tuneParamDict(p, nodes, edges): newp = copy.deepcopy(p) if nodes > 50: newp["splines"] = "no" else: newp["splines"] = "yes" if edges > 1000: newp["printIslands"] = 0 else: newp["printIslands"] = 1 if edges > 1000: newp["kValue"] = 0.8 elif edges > 500: newp["kValue"] = 0.7 elif edges > 250: newp["kValue"] = 0.6 if nodes > 50: # there's no magic here; this formula # just seems to work dim = 3*math.sqrt(nodes) newp["width"] = round(dim,2) newp["height"] = round(dim,2) # the two values below shouldn't change # newp["scale"] = round(dim/10.0,2) # newp["fontsize"] = round(14*newp["scale"],0) else: newp["width"] = 40.0 newp["height"] = 40.0 return newp # fixLabel : string -> string def fixLabel(lbl): """ To split a label with newlines so it looks a bit better Note: we send the graphing program literal '\n' strings and it converts these into newlines """ lblparts = lbl.split(" ") newlbl = "" i = 0 for part in lblparts: if 10*(i+1) < len(newlbl): i += 1 newlbl = newlbl + r"\n" + part else: newlbl = newlbl + " " + part return newlbl #return "\N" def writeGraphFile(matrix, traits, filename, p): """ Expresses the same information as the neato file, only in eXtensible Graph Markup and Modeling Language (XGMML) so the user can develop his/her own graph in a program such as Cytoscape """ inputFile1 = open(filename + "_xgmml_symbol.txt", "w") inputFile2 = open(filename + "_xgmml_name.txt", "w") inputFile3 = open(filename + "_plain_symbol.txt", "w") inputFile4 = open(filename + "_plain_name.txt", "w") inputFile1.write("<graph directed=\"1\" label=\"Network Graph\">\n") inputFile2.write("<graph directed=\"1\" label=\"Network Graph\">\n") #Write out nodes traitEdges = [] for i in range(0, len(traits)): traitEdges.append(0) for i in range(0, len(traits)): labelName = traits[i].symbol inputFile1.write("\t<node id=\"%s\" label=\"%s\"></node>\n" % (i, labelName)) for i in range(0, len(traits)): labelName = traits[i].name inputFile2.write("\t<node id=\"%s\" label=\"%s\"></node>\n" % (i, labelName)) #Write out edges for point in matrix: traitEdges[point.i] = 1 traitEdges[point.j] = 1 if p["edges"] == "complex": _traitValue = "%.3f" % point.value inputFile1.write("\t<edge source=\"%s\" target=\"%s\" label=\"%s\"></edge>\n" % (point.i, point.j, _traitValue)) inputFile2.write("\t<edge source=\"%s\" target=\"%s\" label=\"%s\"></edge>\n" % (point.i, point.j, _traitValue)) inputFile1.write("</graph>") inputFile2.write("</graph>") for edge in matrix: inputFile3.write("%s\t%s\t%s\n" % (traits[edge.i].symbol, edge.value, traits[edge.j].symbol)) for edge in matrix: inputFile4.write("%s\t%s\t%s\n" % (traits[edge.i].name, edge.value, traits[edge.j].name)) inputFile1.close() inputFile2.close() inputFile3.close() inputFile4.close() return (os.path.split(filename))[1] # writeNeatoFile : DataMatrix -> arrayof Traits -> String -> ParamDict -> String def writeNeatoFile(matrix, traits, filename, GeneIdArray, p): """ Given input data, to write a valid input file for neato, optionally writing entries for nodes that have no edges. NOTE: There is a big difference between removing an edge and zeroing its value. Because writeNeatoFile is edge-driven, zeroing an edge's value will still result in its node being written. """ inputFile = open(filename, "w") """ This file (inputFile_pdf) is rotated 90 degrees. This is because of a bug in graphviz that causes pdf output onto a non-landscape layout to often be cut off at the edge of the page. This second filename (which is just the first + "_pdf" is then read in the "visualizePage" class in networkGraph.py and used to generate the postscript file that is converted to pdf. """ inputFile_pdf = open(filename + "_pdf", "w") if p["splines"] == "yes": splines = "true" else: splines = "false" # header inputFile.write('''graph webqtlGraph { overlap="false"; start="regular"; splines="%s"; ratio="auto"; fontpath = "%s"; node [fontname="%s", fontsize=%s, shape="%s"]; edge [fontname="%s", fontsize=%s]; ''' % (splines, webqtlConfig.PIDDLE_FONT_PATH, p["nfont"], p["nfontsize"], p["nodeshapeType"], p["cfont"], p["cfontsize"])) inputFile_pdf.write('''graph webqtlGraph { overlap="false"; start="regular"; splines="%s"; rotate="90"; center="true"; size="11,8.5"; margin="0"; ratio="fill"; fontpath = "%s"; node [fontname="%s", fontsize=%s, shape="%s"]; edge [fontname="%s", fontsize=%s]; ''' % (splines, webqtlConfig.PIDDLE_FONT_PATH, p["nfont"], p["nfontsize"], p["nodeshapeType"], p["cfont"], p["cfontsize"])) # traitEdges stores whether a particular trait has edges traitEdges = [] for i in range(0, len(traits)): traitEdges.append(0) if p["dispcorr"] == "yes": _dispCorr = 1 else: _dispCorr = 0 # print edges first while keeping track of nodes for point in matrix: if point.value != 0: traitEdges[point.i] = 1 traitEdges[point.j] = 1 if p["edges"] == "complex": if _dispCorr: _traitValue = "%.3f" % point.value else: _traitValue = "" if p["correlationName"] == "Pearson": inputFile.write('%s -- %s [len=%s, weight=%s, label=\"%s\", color=\"%s\", style=\"%s\", edgeURL=\"javascript:showCorrelationPlot2(db=\'%s\',ProbeSetID=\'%s\',CellID=\'\',db2=\'%s\',ProbeSetID2=\'%s\',CellID2=\'\',rank=\'%s\');\", edgetooltip="%s"];\n' % (point.i, point.j, point.length, point.length, _traitValue, point.color, point.style, str(traits[point.i].datasetName()), str(traits[point.i].nameNoDB()), str(traits[point.j].datasetName()), str(traits[point.j].nameNoDB()), "0", "Pearson Correlation Plot between " + str(traits[point.i].symbol) + " and " + str(traits[point.j].symbol))) elif p["correlationName"] == "Spearman": inputFile.write('%s -- %s [len=%s, weight=%s, label=\"%s\", color=\"%s\", style=\"%s\", edgeURL=\"javascript:showCorrelationPlot2(db=\'%s\',ProbeSetID=\'%s\',CellID=\'\',db2=\'%s\',ProbeSetID2=\'%s\',CellID2=\'\',rank=\'%s\');\", edgetooltip="%s"];\n' % (point.i, point.j, point.length, point.length, _traitValue, point.color, point.style, str(traits[point.j].datasetName()), str(traits[point.j].nameNoDB()), str(traits[point.i].datasetName()), str(traits[point.i].nameNoDB()), "1", "Spearman Correlation Plot between " + str(traits[point.i].symbol) + " and " + str(traits[point.j].symbol))) elif p["correlationName"] == "Tissue": inputFile.write('%s -- %s [len=%s, weight=%s, label=\"%s\", color=\"%s\", style=\"%s\", edgeURL=\"javascript:showTissueCorrPlot(fmName=\'showDatabase\', X_geneSymbol=\'%s\', Y_geneSymbol=\'%s\', rank=\'0\');\", edgetooltip="%s"];\n' % (point.i, point.j, point.length, point.length, _traitValue, point.color, point.style, str(traits[point.i].symbol), str(traits[point.j].symbol), "Tissue Correlation Plot between " + str(traits[point.i].symbol) + " and " + str(traits[point.j].symbol))) else: inputFile.write('%s -- %s [len=%s, weight=%s, label=\"%s\", color=\"%s\", style=\"%s\", edgeURL=\"javascript:showCorrelationPlot2(db=\'%s\',ProbeSetID=\'%s\',CellID=\'\',db2=\'%s\',ProbeSetID2=\'%s\',CellID2=\'\',rank=\'%s\');\", edgetooltip="%s"];\n' % (point.i, point.j, point.length, point.length, _traitValue, point.color, point.style, str(traits[point.i].datasetName()), str(traits[point.i].nameNoDB()), str(traits[point.j].datasetName()), str(traits[point.j].nameNoDB()), "0", "Correlation Plot between " + str(traits[point.i].symbol) + " and " + str(traits[point.j].symbol))) inputFile_pdf.write('%s -- %s [len=%s, weight=%s, label=\"%s\", color=\"%s\", style=\"%s\", edgetooltip="%s"];\n' % (point.i, point.j, point.length, point.length, _traitValue, point.color, point.style, "Correlation Plot between " + str(traits[point.i].symbol) + " and " + str(traits[point.j].symbol))) else: inputFile.write('%s -- %s [color="%s", style="%s"];\n' % (point.i, point.j, point.color, point.style)) inputFile_pdf.write('%s -- %s [color="%s", style="%s"];\n' % (point.i, point.j, point.color, point.style)) # now print nodes # the target attribute below is undocumented; I found it by looking # in the neato code for i in range(0, len(traits)): if traitEdges[i] == 1 or p["printIslands"] == 1: _tname = str(traits[i]) if _tname.find("Publish") > 0: plotColor = p["cPubName"] elif _tname.find("Geno") > 0: plotColor = p["cGenName"] else: plotColor = p["cMicName"] if p['nodelabel'] == 'yes': labelName = _tname else: labelName = traits[i].symbol inputFile.write('%s [label="%s", href="javascript:showDatabase2(\'%s\',\'%s\',\'\');", color="%s", style = "filled"];\n' % (i, labelName, traits[i].datasetName(), traits[i].nameNoDB(), plotColor))# traits[i].color inputFile_pdf.write('%s [label="%s", href="javascript:showDatabase2(\'%s\',\'%s\',\'\');", color="%s", style = "filled"];\n' % (i, labelName, traits[i].datasetName(), traits[i].nameNoDB(), plotColor))# traits[i].color # footer inputFile.write("}\n") inputFile_pdf.write("]\n") inputFile.close() inputFile_pdf.close() # return only the filename portion, omitting the directory return (os.path.split(filename))[1] # runNeato : string -> string -> string def runNeato(filename, extension, format, gType): """ to run neato on the dataset in the given filename and produce an image file in the given format whose name we will return. Right now we assume that format is a valid neato output (see graphviz docs) and a valid extension for the source datafile. For example, runNeato('input1', 'png') will produce a file called 'input1.png' by invoking 'neato input1 -Tpng -o input1.png' """ # trim extension off of filename before adding output extension if filename.find(".") > 0: filenameBase = filename[:filename.find(".")] else: filenameBase = filename imageFilename = filenameBase + "." + extension #choose which algorithm to run depended upon parameter gType #neato: energy based algorithm #circular: nodes given circular structure determined by which nodes are most closely correlated #radial: first node listed (when you search) is center of the graph, all other nodes are in a circular structure around it #fdp: force based algorithm if gType == "none": # to keep the output of neato from going to stdout, we open a pipe # and then wait for it to terminate if format in ('gif', 'cmapx', 'ps'): neatoExit = os.spawnlp(os.P_WAIT, "/usr/local/bin/neato", "/usr/local/bin/neato", "-s", "-T", format, webqtlConfig.IMGDIR + filename, "-o", webqtlConfig.IMGDIR + imageFilename) else: neatoExit = os.spawnlp(os.P_WAIT, "/usr/local/bin/neato", "/usr/local/bin/neato", webqtlConfig.IMGDIR + filename, "-T", format, "-o", webqtlConfig.IMGDIR + imageFilename) if neatoExit == 0: return imageFilename return imageFilename elif gType == "neato": # to keep the output of neato from going to stdout, we open a pipe # and then wait for it to terminate if format in ('gif', 'cmapx', 'ps'): neatoExit = os.spawnlp(os.P_WAIT, "/usr/local/bin/neato", "/usr/local/bin/neato", "-s", "-T", format, webqtlConfig.IMGDIR + filename, "-o", webqtlConfig.IMGDIR + imageFilename) else: neatoExit = os.spawnlp(os.P_WAIT, "/usr/local/bin/neato", "/usr/local/bin/neato", webqtlConfig.IMGDIR + filename, "-T", format, "-o", webqtlConfig.IMGDIR + imageFilename) if neatoExit == 0: return imageFilename return imageFilename elif gType == "circular": if format in ('gif', 'cmapx', 'ps'): neatoExit = os.spawnlp(os.P_WAIT, "/usr/local/bin/circo", "/usr/local/bin/circo", "-s", "-T", format, webqtlConfig.IMGDIR + filename, "-o", webqtlConfig.IMGDIR + imageFilename) else: neatoExit = os.spawnlp(os.P_WAIT, "/usr/local/bin/circo", "/usr/local/bin/circo", webqtlConfig.IMGDIR + filename, "-T", format, "-o", webqtlConfig.IMGDIR + imageFilename) if neatoExit == 0: return imageFilename return imageFilename elif gType == "radial": if format in ('gif', 'cmapx', 'ps'): neatoExit = os.spawnlp(os.P_WAIT, "/usr/local/bin/twopi", "/usr/local/bin/twopi", "-s", "-T", format, webqtlConfig.IMGDIR + filename, "-o", webqtlConfig.IMGDIR + imageFilename) else: neatoExit = os.spawnlp(os.P_WAIT, "/usr/local/bin/twopi", "/usr/local/bin/twopi", webqtlConfig.IMGDIR + filename, "-T", format, "-o", webqtlConfig.IMGDIR + imageFilename) if neatoExit == 0: return imageFilename return imageFilename elif gType == "fdp": if format in ('gif', 'cmapx', 'ps'): neatoExit = os.spawnlp(os.P_WAIT, "/usr/local/bin/fdp", "/usr/local/bin/fdp", "-s", "-T", format, webqtlConfig.IMGDIR + filename, "-o", webqtlConfig.IMGDIR + imageFilename) else: neatoExit = os.spawnlp(os.P_WAIT, "/usr/local/bin/fdp", "/usr/local/bin/fdp", webqtlConfig.IMGDIR + filename, "-T", format, "-o", webqtlConfig.IMGDIR + imageFilename) if neatoExit == 0: return imageFilename return imageFilename return imageFilename # runPsToPdf: string -> int -> intstring # to run Ps2Pdf to convert the given input postscript file to an 8.5 by 11 # pdf file The width and height should be specified in inches. We assume # that the PS files output by GraphViz are 72 dpi. def runPsToPdf(psfile, width, height): # we add 1 for padding b/c sometimes a small part of the graph gets # cut off newwidth = int((width + 1) * 720) newheight = int((height + 1) * 720) # replace the ps extension with a pdf one pdffile = psfile[:-2] + "pdf" os.spawnlp(os.P_WAIT, "ps2pdf", "-g%sx%s" % (newwidth, newheight), webqtlConfig.IMGDIR + psfile, webqtlConfig.IMGDIR + pdffile) return pdffile # buildParamDict: void -> ParamDict # to process and validate CGI arguments, # looking up human-readable names where necessary # see the comment at the top of the file for valid cgi parameters def buildParamDict(fs, sessionfile): params = {} params["inputFile"] = fs.formdata.getvalue("inputFile", "") params["progress"] = fs.formdata.getvalue("progress", "1") params["filename"] = fs.formdata.getvalue("filename", "") params["session"] = sessionfile if type("1") != type(fs.formdata.getvalue("searchResult")): params["searchResult"] = string.join(fs.formdata.getvalue("searchResult"),'\t') else: params["searchResult"] = fs.formdata.getvalue("searchResult") params["riset"] = fs.formdata.getvalue("RISet", "") #if params["filename"] == "": # raise nGraphException("Required parameter filename missing") #parameter determining whether export button returns an xgmml graph file or plain text file params["exportFormat"] = fs.formdata.getvalue("exportFormat", "xgmml") #parameter determining whether or not traits in the graph file are listed by their symbol or name params["traitType"] = fs.formdata.getvalue("traitType", "symbol") #parameter saying whether or not graph structure should be locked when you redraw the graph params["lock"] = fs.formdata.getvalue("lock", "no") #parameter saying what algorithm should be used to draw the graph params["gType"] = fs.formdata.getvalue("gType", "none") params["kValue"] = webqtlUtil.safeFloat(fs.formdata.getvalue("kValue", "0.5"), 0.5) params["whichValue"] = webqtlUtil.safeInt(fs.formdata.getvalue("whichValue","0"),0) # 1 inch = 2.54 cm # 1 cm = 0.3937 inch params["width"] = webqtlUtil.safeFloat(fs.formdata.getvalue("width", "40.0"), 40.0) params["height"] = webqtlUtil.safeFloat(fs.formdata.getvalue("height", "40.0"), 40.0) yesno = ["yes", "no"] params["tune"] = webqtlUtil.safeString(fs.formdata.getvalue("tune", "yes"), yesno, "yes") params["printIslands"] = webqtlUtil.safeInt(fs.formdata.getvalue("printIslands", "1"),1) params["nodeshape"] = webqtlUtil.safeString(fs.formdata.getvalue("nodeshape","yes"), yesno, "yes") params["nodelabel"] = webqtlUtil.safeString(fs.formdata.getvalue("nodelabel","no"), yesno, "no") params["nfont"] = fs.formdata.getvalue("nfont","Arial") params["nfontsize"] = webqtlUtil.safeFloat(fs.formdata.getvalue("nfontsize", "10.0"), 10.0) params["splines"] = webqtlUtil.safeString(fs.formdata.getvalue("splines","yes"), yesno, "yes") params["dispcorr"] = webqtlUtil.safeString(fs.formdata.getvalue("dispcorr","no"), yesno, "no") params["cfont"] = fs.formdata.getvalue("cfont","Arial") params["cfontsize"] = webqtlUtil.safeFloat(fs.formdata.getvalue("cfontsize", "10.0"), 10.0) params["cPubName"] = fs.formdata.getvalue("cPubName","palegreen") params["cMicName"] = fs.formdata.getvalue("cMicName","lightblue") params["cGenName"] = fs.formdata.getvalue("cGenName","lightcoral") params["cPubColor"] = fs.formdata.getvalue("cPubColor","98fb98") params["cMicColor"] = fs.formdata.getvalue("cMicColor","add8e6") params["cGenColor"] = fs.formdata.getvalue("cGenColor","f08080") params["cL1Name"] = fs.formdata.getvalue("cL1Name","blue") params["cL2Name"] = fs.formdata.getvalue("cL2Name","green") params["cL3Name"] = fs.formdata.getvalue("cL3Name","black") params["cL4Name"] = fs.formdata.getvalue("cL4Name","pink") params["cL5Name"] = fs.formdata.getvalue("cL5Name","orange") params["cL6Name"] = fs.formdata.getvalue("cL6Name","red") params["cL1Color"] = fs.formdata.getvalue("cL1Color","0000ff") params["cL2Color"] = fs.formdata.getvalue("cL2Color","00ff00") params["cL3Color"] = fs.formdata.getvalue("cL3Color","000000") params["cL4Color"] = fs.formdata.getvalue("cL4Color","ffc0cb") params["cL5Color"] = fs.formdata.getvalue("cL5Color","ffa500") params["cL6Color"] = fs.formdata.getvalue("cL6Color","ff0000") params["L1style"] = fs.formdata.getvalue("L1style","bold") params["L2style"] = fs.formdata.getvalue("L2style","") params["L3style"] = fs.formdata.getvalue("L3style","dashed") params["L4style"] = fs.formdata.getvalue("L4style","dashed") params["L5style"] = fs.formdata.getvalue("L5style","") params["L6style"] = fs.formdata.getvalue("L6style","bold") if params["splines"] == "yes": params["splineName"] = "curves" else: params["splineName"] = "lines" if params["nodeshape"] == "yes": params["nodeshapeType"] = "box" else: params["nodeshapeType"] = "ellipse" if params["whichValue"] == 0: params["correlationName"] = "Pearson" elif params["whichValue"] == 1: params["correlationName"] = "Spearman" elif params["whichValue"] == 2: params["correlationName"] = "Literature" else: params["correlationName"] = "Tissue" # see graphviz::writeNeatoFile to find out what this done params["edges"] = "complex" return params def optimalRadialNode(matrix): """ Automatically determines the node with the most/strongest correlations with other nodes. If the user selects "radial" for Graph Type and then "Auto" for the central node then this node is used as the central node. The algorithm is simply a sum of each node's correlations that fall above the threshold set by the user. """ optMatrix = [0]*(len(matrix)+1) for pt in matrix: if abs(pt.value) > 0.5: optMatrix[pt.i] += abs(pt.value) optMatrix[pt.j] += abs(pt.value) optPoint = 0 optCorrTotal = 0 j = 0 for point in optMatrix: if (float(point) > float(optCorrTotal)): optPoint = j optCorrTotal = point j += 1 return optPoint # filterDataMatrix : DataMatrix -> ParamDict -> DataMatrix def filterDataMatrix(matrix, p): """ To convert a set of input RawPoints to a set of ProcessedPoints and to choose the appropriate correlation coefficent. """ newmatrix = [] for pt in matrix: pt2 = ProcessedPoint(pt.i, pt.j) # XZ, 09/11/2008: add module name # pick right value if p["whichValue"] == 0: pt2.value = pt.pearson elif p["whichValue"] == 1: pt2.value = pt.spearman elif p["whichValue"] == 2: pt2.value = pt.literature elif p["whichValue"] == 3: pt2.value = pt.tissue else: raise nGraphException("whichValue should be either 0, 1, 2 or 3") try: pt2.value = float(pt2.value) except: pt2.value = 0.00 newmatrix.append(pt2) return newmatrix def generateSymbolList(traits): """ Generates a list of trait symbols to be displayed in the central node selection drop-down menu when plotting a radial graph """ traitList = traits symbolList = [None]*len(traitList) i=0 for trait in traitList: symbolList[i] = str(trait.symbol) i = i+1 symbolListString = "\t".join(symbolList) return symbolListString