/tools/rgenetics/rgGRR.py
https://bitbucket.org/cistrome/cistrome-harvard/ · Python · 1089 lines · 869 code · 64 blank · 156 comment · 120 complexity · f2b858baca27314e3adaa7f42540fa9a MD5 · raw file
- """
- # july 2009: Need to see outliers so need to draw them last?
- # could use clustering on the zscores to guess real relationships for unrelateds
- # but definitely need to draw last
- # added MAX_SHOW_ROWS to limit the length of the main report page
- # Changes for Galaxy integration
- # added more robust knuth method for one pass mean and sd
- # no difference really - let's use scipy.mean() and scipy.std() instead...
- # fixed labels and changed to .xls for outlier reports so can open in excel
- # interesting - with a few hundred subjects, 5k gives good resolution
- # and 100k gives better but not by much
- # TODO remove non autosomal markers
- # TODO it would be best if label had the zmean and zsd as these are what matter for
- # outliers rather than the group mean/sd
- # mods to rgGRR.py from channing CVS which John Ziniti has rewritten to produce SVG plots
- # to make a Galaxy tool - we need the table of mean and SD for interesting pairs, the SVG and the log
- # so the result should be an HTML file
- # rgIBS.py
- # use a random subset of markers for a quick ibs
- # to identify sample dups and closely related subjects
- # try snpMatrix and plink and see which one works best for us?
- # abecasis grr plots mean*sd for every subject to show clusters
- # mods june 23 rml to avoid non-autosomal markers
- # we seem to be distinguishing parent-child by gender - 2 clouds!
- snpMatrix from David Clayton has:
- ibs.stats function to calculate the identity-by-state stats of a group of samples
- Description
- Given a snp.matrix-class or a X.snp.matrix-class object with N samples, calculates some statistics
- about the relatedness of every pair of samples within.
- Usage
- ibs.stats(x)
- 8 ibs.stats
- Arguments
- x a snp.matrix-class or a X.snp.matrix-class object containing N samples
- Details
- No-calls are excluded from consideration here.
- Value
- A data.frame containing N(N - 1)/2 rows, where the row names are the sample name pairs separated
- by a comma, and the columns are:
- Count count of identical calls, exclusing no-calls
- Fraction fraction of identical calls comparied to actual calls being made in both samples
- Warning
- In some applications, it may be preferable to subset a (random) selection of SNPs first - the
- calculation
- time increases as N(N - 1)M/2 . Typically for N = 800 samples and M = 3000 SNPs, the
- calculation time is about 1 minute. A full GWA scan could take hours, and quite unnecessary for
- simple applications such as checking for duplicate or related samples.
- Note
- This is mostly written to find mislabelled and/or duplicate samples.
- Illumina indexes their SNPs in alphabetical order so the mitochondria SNPs comes first - for most
- purpose it is undesirable to use these SNPs for IBS purposes.
- TODO: Worst-case S4 subsetting seems to make 2 copies of a large object, so one might want to
- subset before rbind(), etc; a future version of this routine may contain a built-in subsetting facility
- """
- import sys,os,time,random,string,copy,optparse
- try:
- set
- except NameError:
- from Sets import Set as set
- from rgutils import timenow,plinke
- import plinkbinJZ
- opts = None
- verbose = False
- showPolygons = False
- class NullDevice:
- def write(self, s):
- pass
- tempstderr = sys.stderr # save
- #sys.stderr = NullDevice()
- # need to avoid blather about deprecation and other strange stuff from scipy
- # the current galaxy job runner assumes that
- # the job is in error if anything appears on sys.stderr
- # grrrrr. James wants to keep it that way instead of using the
- # status flag for some strange reason. Presumably he doesn't use R or (in this case, scipy)
- import numpy
- import scipy
- from scipy import weave
- sys.stderr=tempstderr
- PROGNAME = os.path.split(sys.argv[0])[-1]
- X_AXIS_LABEL = 'Mean Alleles Shared'
- Y_AXIS_LABEL = 'SD Alleles Shared'
- LEGEND_ALIGN = 'topleft'
- LEGEND_TITLE = 'Relationship'
- DEFAULT_SYMBOL_SIZE = 1.0 # default symbol size
- DEFAULT_SYMBOL_SIZE = 0.5 # default symbol size
- ### Some colors for R/rpy
- R_BLACK = 1
- R_RED = 2
- R_GREEN = 3
- R_BLUE = 4
- R_CYAN = 5
- R_PURPLE = 6
- R_YELLOW = 7
- R_GRAY = 8
- ### ... and some point-styles
- ###
- PLOT_HEIGHT = 600
- PLOT_WIDTH = 1150
- #SVG_COLORS = ('black', 'darkblue', 'blue', 'deepskyblue', 'firebrick','maroon','crimson')
- #SVG_COLORS = ('cyan','dodgerblue','mediumpurple', 'fuchsia', 'red','gold','gray')
- SVG_COLORS = ('cyan','dodgerblue','mediumpurple','forestgreen', 'lightgreen','gold','gray')
- # dupe,parentchild,sibpair,halfsib,parents,unrel,unkn
- #('orange', 'red', 'green', 'chartreuse', 'blue', 'purple', 'gray')
- OUTLIERS_HEADER_list = ['Mean','Sdev','ZMean','ZSdev','FID1','IID1','FID2','IID2','RelMean_M','RelMean_SD','RelSD_M','RelSD_SD','PID1','MID1','PID2','MID2','Ped']
- OUTLIERS_HEADER = '\t'.join(OUTLIERS_HEADER_list)
- TABLE_HEADER='fid1_iid1\tfid2_iid2\tmean\tsdev\tzmean\tzsdev\tgeno\trelcode\tpid1\tmid1\tpid2\tmid2\n'
- ### Relationship codes, text, and lookups/mappings
- N_RELATIONSHIP_TYPES = 7
- REL_DUPE, REL_PARENTCHILD, REL_SIBS, REL_HALFSIBS, REL_RELATED, REL_UNRELATED, REL_UNKNOWN = range(N_RELATIONSHIP_TYPES)
- REL_LOOKUP = {
- REL_DUPE: ('dupe', R_BLUE, 1),
- REL_PARENTCHILD: ('parentchild', R_YELLOW, 1),
- REL_SIBS: ('sibpairs', R_RED, 1),
- REL_HALFSIBS: ('halfsibs', R_GREEN, 1),
- REL_RELATED: ('parents', R_PURPLE, 1),
- REL_UNRELATED: ('unrelated', R_CYAN, 1),
- REL_UNKNOWN: ('unknown', R_GRAY, 1),
- }
- OUTLIER_STDEVS = {
- REL_DUPE: 2,
- REL_PARENTCHILD: 2,
- REL_SIBS: 2,
- REL_HALFSIBS: 2,
- REL_RELATED: 2,
- REL_UNRELATED: 3,
- REL_UNKNOWN: 2,
- }
- # note now Z can be passed in
- REL_STATES = [REL_LOOKUP[r][0] for r in range(N_RELATIONSHIP_TYPES)]
- REL_COLORS = SVG_COLORS
- REL_POINTS = [REL_LOOKUP[r][2] for r in range(N_RELATIONSHIP_TYPES)]
- DEFAULT_MAX_SAMPLE_SIZE = 10000
- REF_COUNT_HOM1 = 3
- REF_COUNT_HET = 2
- REF_COUNT_HOM2 = 1
- MISSING = 0
- MAX_SHOW_ROWS = 100 # framingham has millions - delays showing output page - so truncate and explain
- MARKER_PAIRS_PER_SECOND_SLOW = 15000000.0
- MARKER_PAIRS_PER_SECOND_FAST = 70000000.0
- galhtmlprefix = """<?xml version="1.0" encoding="utf-8" ?>
- <!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
- <html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
- <head>
- <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
- <meta name="generator" content="Galaxy %s tool output - see http://g2.trac.bx.psu.edu/" />
- <title></title>
- <link rel="stylesheet" href="/static/style/base.css" type="text/css" />
- </head>
- <body>
- <div class="document">
- """
- SVG_HEADER = '''<?xml version="1.0" standalone="no"?>
- <!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.2//EN" "http://www.w3.org/Graphics/SVG/1.2/DTD/svg12.dtd">
- <svg width="1280" height="800"
- xmlns="http://www.w3.org/2000/svg" version="1.2"
- xmlns:xlink="http://www.w3.org/1999/xlink" viewBox="0 0 1280 800" onload="init()">
- <script type="text/ecmascript" xlink:href="/static/scripts/checkbox_and_radiobutton.js"/>
- <script type="text/ecmascript" xlink:href="/static/scripts/helper_functions.js"/>
- <script type="text/ecmascript" xlink:href="/static/scripts/timer.js"/>
- <script type="text/ecmascript">
- <![CDATA[
- var checkBoxes = new Array();
- var radioGroupBandwidth;
- var colours = ['%s','%s','%s','%s','%s','%s','%s'];
- function init() {
- var style = {"font-family":"Arial,Helvetica", "fill":"black", "font-size":12};
- var dist = 12;
- var yOffset = 4;
- //A checkBox for each relationship type dupe,parentchild,sibpair,halfsib,parents,unrel,unkn
- checkBoxes["dupe"] = new checkBox("dupe","checkboxes",20,40,"cbRect","cbCross",true,"Duplicate",style,dist,yOffset,undefined,hideShowLayer);
- checkBoxes["parentchild"] = new checkBox("parentchild","checkboxes",20,60,"cbRect","cbCross",true,"Parent-Child",style,dist,yOffset,undefined,hideShowLayer);
- checkBoxes["sibpairs"] = new checkBox("sibpairs","checkboxes",20,80,"cbRect","cbCross",true,"Sib-pairs",style,dist,yOffset,undefined,hideShowLayer);
- checkBoxes["halfsibs"] = new checkBox("halfsibs","checkboxes",20,100,"cbRect","cbCross",true,"Half-sibs",style,dist,yOffset,undefined,hideShowLayer);
- checkBoxes["parents"] = new checkBox("parents","checkboxes",20,120,"cbRect","cbCross",true,"Parents",style,dist,yOffset,undefined,hideShowLayer);
- checkBoxes["unrelated"] = new checkBox("unrelated","checkboxes",20,140,"cbRect","cbCross",true,"Unrelated",style,dist,yOffset,undefined,hideShowLayer);
- checkBoxes["unknown"] = new checkBox("unknown","checkboxes",20,160,"cbRect","cbCross",true,"Unknown",style,dist,yOffset,undefined,hideShowLayer);
- }
- function hideShowLayer(id, status, label) {
- var vis = "hidden";
- if (status) {
- vis = "visible";
- }
- document.getElementById(id).setAttributeNS(null, 'visibility', vis);
- }
- function showBTT(evt, rel, mm, dm, md, dd, n, mg, dg, lg, hg) {
- var x = parseInt(evt.pageX)-250;
- var y = parseInt(evt.pageY)-110;
- switch(rel) {
- case 0:
- fill = colours[rel];
- relt = "dupe";
- break;
- case 1:
- fill = colours[rel];
- relt = "parentchild";
- break;
- case 2:
- fill = colours[rel];
- relt = "sibpairs";
- break;
- case 3:
- fill = colours[rel];
- relt = "halfsibs";
- break;
- case 4:
- fill = colours[rel];
- relt = "parents";
- break;
- case 5:
- fill = colours[rel];
- relt = "unrelated";
- break;
- case 6:
- fill = colours[rel];
- relt = "unknown";
- break;
- default:
- fill = "cyan";
- relt = "ERROR_CODE: "+rel;
- }
- document.getElementById("btRel").textContent = "GROUP: "+relt;
- document.getElementById("btMean").textContent = "mean="+mm+" +/- "+dm;
- document.getElementById("btSdev").textContent = "sdev="+dm+" +/- "+dd;
- document.getElementById("btPair").textContent = "npairs="+n;
- document.getElementById("btGeno").textContent = "ngenos="+mg+" +/- "+dg+" (min="+lg+", max="+hg+")";
- document.getElementById("btHead").setAttribute('fill', fill);
- var tt = document.getElementById("btTip");
- tt.setAttribute("transform", "translate("+x+","+y+")");
- tt.setAttribute('visibility', 'visible');
- }
- function showOTT(evt, rel, s1, s2, mean, sdev, ngeno, rmean, rsdev) {
- var x = parseInt(evt.pageX)-150;
- var y = parseInt(evt.pageY)-180;
- switch(rel) {
- case 0:
- fill = colours[rel];
- relt = "dupe";
- break;
- case 1:
- fill = colours[rel];
- relt = "parentchild";
- break;
- case 2:
- fill = colours[rel];
- relt = "sibpairs";
- break;
- case 3:
- fill = colours[rel];
- relt = "halfsibs";
- break;
- case 4:
- fill = colours[rel];
- relt = "parents";
- break;
- case 5:
- fill = colours[rel];
- relt = "unrelated";
- break;
- case 6:
- fill = colours[rel];
- relt = "unknown";
- break;
- default:
- fill = "cyan";
- relt = "ERROR_CODE: "+rel;
- }
- document.getElementById("otRel").textContent = "PAIR: "+relt;
- document.getElementById("otS1").textContent = "s1="+s1;
- document.getElementById("otS2").textContent = "s2="+s2;
- document.getElementById("otMean").textContent = "mean="+mean;
- document.getElementById("otSdev").textContent = "sdev="+sdev;
- document.getElementById("otGeno").textContent = "ngenos="+ngeno;
- document.getElementById("otRmean").textContent = "relmean="+rmean;
- document.getElementById("otRsdev").textContent = "relsdev="+rsdev;
- document.getElementById("otHead").setAttribute('fill', fill);
- var tt = document.getElementById("otTip");
- tt.setAttribute("transform", "translate("+x+","+y+")");
- tt.setAttribute('visibility', 'visible');
- }
- function hideBTT(evt) {
- document.getElementById("btTip").setAttributeNS(null, 'visibility', 'hidden');
- }
- function hideOTT(evt) {
- document.getElementById("otTip").setAttributeNS(null, 'visibility', 'hidden');
- }
- ]]>
- </script>
- <defs>
- <!-- symbols for check boxes -->
- <symbol id="cbRect" overflow="visible">
- <rect x="-5" y="-5" width="10" height="10" fill="white" stroke="dimgray" stroke-width="1" cursor="pointer"/>
- </symbol>
- <symbol id="cbCross" overflow="visible">
- <g pointer-events="none" stroke="black" stroke-width="1">
- <line x1="-3" y1="-3" x2="3" y2="3"/>
- <line x1="3" y1="-3" x2="-3" y2="3"/>
- </g>
- </symbol>
- </defs>
- <desc>Developer Works Dynamic Scatter Graph Scaling Example</desc>
- <!-- Now Draw the main X and Y axis -->
- <g style="stroke-width:1.0; stroke:black; shape-rendering:crispEdges">
- <!-- X Axis top and bottom -->
- <path d="M 100 100 L 1250 100 Z"/>
- <path d="M 100 700 L 1250 700 Z"/>
- <!-- Y Axis left and right -->
- <path d="M 100 100 L 100 700 Z"/>
- <path d="M 1250 100 L 1250 700 Z"/>
- </g>
- <g transform="translate(100,100)">
- <!-- Grid Lines -->
- <g style="fill:none; stroke:#dddddd; stroke-width:1; stroke-dasharray:2,2; text-anchor:end; shape-rendering:crispEdges">
- <!-- Vertical grid lines -->
- <line x1="125" y1="0" x2="115" y2="600" />
- <line x1="230" y1="0" x2="230" y2="600" />
- <line x1="345" y1="0" x2="345" y2="600" />
- <line x1="460" y1="0" x2="460" y2="600" />
- <line x1="575" y1="0" x2="575" y2="600" style="stroke-dasharray:none;" />
- <line x1="690" y1="0" x2="690" y2="600" />
- <line x1="805" y1="0" x2="805" y2="600" />
- <line x1="920" y1="0" x2="920" y2="600" />
- <line x1="1035" y1="0" x2="1035" y2="600" />
- <!-- Horizontal grid lines -->
- <line x1="0" y1="60" x2="1150" y2="60" />
- <line x1="0" y1="120" x2="1150" y2="120" />
- <line x1="0" y1="180" x2="1150" y2="180" />
- <line x1="0" y1="240" x2="1150" y2="240" />
- <line x1="0" y1="300" x2="1150" y2="300" style="stroke-dasharray:none;" />
- <line x1="0" y1="360" x2="1150" y2="360" />
- <line x1="0" y1="420" x2="1150" y2="420" />
- <line x1="0" y1="480" x2="1150" y2="480" />
- <line x1="0" y1="540" x2="1150" y2="540" />
- </g>
- <!-- Legend -->
- <g style="fill:black; stroke:none" font-size="12" font-family="Arial" transform="translate(25,25)">
- <rect width="160" height="270" style="fill:none; stroke:black; shape-rendering:crispEdges" />
- <text x="5" y="20" style="fill:black; stroke:none;" font-size="13" font-weight="bold">Given Pair Relationship</text>
- <rect x="120" y="35" width="10" height="10" fill="%s" stroke="%s" stroke-width="1" cursor="pointer"/>
- <rect x="120" y="55" width="10" height="10" fill="%s" stroke="%s" stroke-width="1" cursor="pointer"/>
- <rect x="120" y="75" width="10" height="10" fill="%s" stroke="%s" stroke-width="1" cursor="pointer"/>
- <rect x="120" y="95" width="10" height="10" fill="%s" stroke="%s" stroke-width="1" cursor="pointer"/>
- <rect x="120" y="115" width="10" height="10" fill="%s" stroke="%s" stroke-width="1" cursor="pointer"/>
- <rect x="120" y="135" width="10" height="10" fill="%s" stroke="%s" stroke-width="1" cursor="pointer"/>
- <rect x="120" y="155" width="10" height="10" fill="%s" stroke="%s" stroke-width="1" cursor="pointer"/>
- <text x="15" y="195" style="fill:black; stroke:none" font-size="12" font-family="Arial" >Zscore gt 15</text>
- <circle cx="125" cy="192" r="6" style="stroke:red; fill:gold; fill-opacity:1.0; stroke-width:1;"/>
- <text x="15" y="215" style="fill:black; stroke:none" font-size="12" font-family="Arial" >Zscore 4 to 15</text>
- <circle cx="125" cy="212" r="3" style="stroke:gold; fill:gold; fill-opacity:1.0; stroke-width:1;"/>
- <text x="15" y="235" style="fill:black; stroke:none" font-size="12" font-family="Arial" >Zscore lt 4</text>
- <circle cx="125" cy="232" r="2" style="stroke:gold; fill:gold; fill-opacity:1.0; stroke-width:1;"/>
- <g id="checkboxes">
- </g>
- </g>
- <g style='fill:black; stroke:none' font-size="17" font-family="Arial">
- <!-- X Axis Labels -->
- <text x="480" y="660">Mean Alleles Shared</text>
- <text x="0" y="630" >1.0</text>
- <text x="277" y="630" >1.25</text>
- <text x="564" y="630" >1.5</text>
- <text x="842" y="630" >1.75</text>
- <text x="1140" y="630" >2.0</text>
- </g>
- <g transform="rotate(270)" style="fill:black; stroke:none" font-size="17" font-family="Arial">
- <!-- Y Axis Labels -->
- <text x="-350" y="-40">SD Alleles Shared</text>
- <text x="-20" y="-10" >1.0</text>
- <text x="-165" y="-10" >0.75</text>
- <text x="-310" y="-10" >0.5</text>
- <text x="-455" y="-10" >0.25</text>
- <text x="-600" y="-10" >0.0</text>
- </g>
- <!-- Plot Title -->
- <g style="fill:black; stroke:none" font-size="18" font-family="Arial">
- <text x="425" y="-30">%s</text>
- </g>
- <!-- One group/layer of points for each relationship type -->
- '''
- SVG_FOOTER = '''
- <!-- End of Data -->
- </g>
- <g id="btTip" visibility="hidden" style="stroke-width:1.0; fill:black; stroke:none;" font-size="10" font-family="Arial">
- <rect width="250" height="110" style="fill:silver" rx="2" ry="2"/>
- <rect id="btHead" width="250" height="20" rx="2" ry="2" />
- <text id="btRel" y="14" x="85">unrelated</text>
- <text id="btMean" y="40" x="4">mean=1.5 +/- 0.04</text>
- <text id="btSdev" y="60" x="4">sdev=0.7 +/- 0.03</text>
- <text id="btPair" y="80" x="4">npairs=1152</text>
- <text id="btGeno" y="100" x="4">ngenos=4783 +/- 24 (min=1000, max=5000)</text>
- </g>
- <g id="otTip" visibility="hidden" style="stroke-width:1.0; fill:black; stroke:none;" font-size="10" font-family="Arial">
- <rect width="150" height="180" style="fill:silver" rx="2" ry="2"/>
- <rect id="otHead" width="150" height="20" rx="2" ry="2" />
- <text id="otRel" y="14" x="40">sibpairs</text>
- <text id="otS1" y="40" x="4">s1=fid1,iid1</text>
- <text id="otS2" y="60" x="4">s2=fid2,iid2</text>
- <text id="otMean" y="80" x="4">mean=1.82</text>
- <text id="otSdev" y="100" x="4">sdev=0.7</text>
- <text id="otGeno" y="120" x="4">ngeno=4487</text>
- <text id="otRmean" y="140" x="4">relmean=1.85</text>
- <text id="otRsdev" y="160" x="4">relsdev=0.65</text>
- </g>
- </svg>
- '''
- DEFAULT_MAX_SAMPLE_SIZE = 5000
- REF_COUNT_HOM1 = 3
- REF_COUNT_HET = 2
- REF_COUNT_HOM2 = 1
- MISSING = 0
- MARKER_PAIRS_PER_SECOND_SLOW = 15000000
- MARKER_PAIRS_PER_SECOND_FAST = 70000000
- POLYGONS = {
- REL_UNRELATED: ((1.360, 0.655), (1.385, 0.730), (1.620, 0.575), (1.610, 0.505)),
- REL_HALFSIBS: ((1.630, 0.500), (1.630, 0.550), (1.648, 0.540), (1.648, 0.490)),
- REL_SIBS: ((1.660, 0.510), (1.665, 0.560), (1.820, 0.410), (1.820, 0.390)),
- REL_PARENTCHILD: ((1.650, 0.470), (1.650, 0.490), (1.750, 0.440), (1.750, 0.420)),
- REL_DUPE: ((1.970, 0.000), (1.970, 0.150), (2.000, 0.150), (2.000, 0.000)),
- }
- def distance(point1, point2):
- """ Calculate the distance between two points
- """
- (x1,y1) = [float(d) for d in point1]
- (x2,y2) = [float(d) for d in point2]
- dx = abs(x1 - x2)
- dy = abs(y1 - y2)
- return math.sqrt(dx**2 + dy**2)
- def point_inside_polygon(x, y, poly):
- """ Determine if a point (x,y) is inside a given polygon or not
- poly is a list of (x,y) pairs.
- Taken from: http://www.ariel.com.au/a/python-point-int-poly.html
- """
- n = len(poly)
- inside = False
- p1x,p1y = poly[0]
- for i in range(n+1):
- p2x,p2y = poly[i % n]
- if y > min(p1y,p2y):
- if y <= max(p1y,p2y):
- if x <= max(p1x,p2x):
- if p1y != p2y:
- xinters = (y-p1y)*(p2x-p1x)/(p2y-p1y)+p1x
- if p1x == p2x or x <= xinters:
- inside = not inside
- p1x,p1y = p2x,p2y
- return inside
- def readMap(pedfile):
- """
- """
- mapfile = pedfile.replace('.ped', '.map')
- marker_list = []
- if os.path.exists(mapfile):
- print 'readMap: %s' % (mapfile)
- fh = file(mapfile, 'r')
- for line in fh:
- marker_list.append(line.strip().split())
- fh.close()
- print 'readMap: %s markers' % (len(marker_list))
- return marker_list
- def calcMeanSD(useme):
- """
- A numerically stable algorithm is given below. It also computes the mean.
- This algorithm is due to Knuth,[1] who cites Welford.[2]
- n = 0
- mean = 0
- M2 = 0
- foreach x in data:
- n = n + 1
- delta = x - mean
- mean = mean + delta/n
- M2 = M2 + delta*(x - mean) // This expression uses the new value of mean
- end for
- variance_n = M2/n
- variance = M2/(n - 1)
- """
- mean = 0.0
- M2 = 0.0
- sd = 0.0
- n = len(useme)
- if n > 1:
- for i,x in enumerate(useme):
- delta = x - mean
- mean = mean + delta/(i+1) # knuth uses n+=1 at start
- M2 = M2 + delta*(x - mean) # This expression uses the new value of mean
- variance = M2/(n-1) # assume is sample so lose 1 DOF
- sd = pow(variance,0.5)
- return mean,sd
- def doIBSpy(ped=None,basename='',outdir=None,logf=None,
- nrsSamples=10000,title='title',pdftoo=0,Zcutoff=2.0):
- #def doIBS(pedName, title, nrsSamples=None, pdftoo=False):
- """ started with snpmatrix but GRR uses actual IBS counts and sd's
- """
- repOut = [] # text strings to add to the html display
- refallele = {}
- tblf = '%s_table.xls' % (title)
- tbl = file(os.path.join(outdir,tblf), 'w')
- tbl.write(TABLE_HEADER)
- svgf = '%s.svg' % (title)
- svg = file(os.path.join(outdir,svgf), 'w')
- nMarkers = len(ped._markers)
- if nMarkers < 5:
- print sys.stderr, '### ERROR - %d is too few markers for reliable estimation in %s - terminating' % (nMarkers,PROGNAME)
- sys.exit(1)
- nSubjects = len(ped._subjects)
- nrsSamples = min(nMarkers, nrsSamples)
- if opts and opts.use_mito:
- markers = range(nMarkers)
- nrsSamples = min(len(markers), nrsSamples)
- sampleIndexes = sorted(random.sample(markers, nrsSamples))
- else:
- autosomals = ped.autosomal_indices()
- nrsSamples = min(len(autosomals), nrsSamples)
- sampleIndexes = sorted(random.sample(autosomals, nrsSamples))
- print ''
- print 'Getting random.sample of %s from %s total' % (nrsSamples, nMarkers)
- npairs = (nSubjects*(nSubjects-1))/2 # total rows in table
- newfiles=[svgf,tblf]
- explanations = ['rgGRR Plot (requires SVG)','Mean by SD alleles shared - %d rows' % npairs]
- # these go with the output file links in the html file
- s = 'Reading genotypes for %s subjects and %s markers\n' % (nSubjects, nrsSamples)
- logf.write(s)
- minUsegenos = nrsSamples/2 # must have half?
- nGenotypes = nSubjects*nrsSamples
- stime = time.time()
- emptyRows = set()
- genos = numpy.zeros((nSubjects, nrsSamples), dtype=int)
- for s in xrange(nSubjects):
- nValid = 0
- #getGenotypesByIndices(self, s, mlist, format)
- genos[s] = ped.getGenotypesByIndices(s, sampleIndexes, format='ref')
- nValid = sum([1 for g in genos[s] if g])
- if not nValid:
- emptyRows.add(s)
- sub = ped.getSubject(s)
- print 'All missing for row %d (%s)' % (s, sub)
- logf.write('All missing for row %d (%s)\n' % (s, sub))
- rtime = time.time() - stime
- if verbose:
- print '@@Read %s genotypes in %s seconds' % (nGenotypes, rtime)
- ### Now the expensive part. For each pair of subjects, we get the mean number
- ### and standard deviation of shared alleles over all of the markers where both
- ### subjects have a known genotype. Identical subjects should have mean shared
- ### alleles very close to 2.0 with a standard deviation very close to 0.0.
- tot = nSubjects*(nSubjects-1)/2
- nprog = tot/10
- nMarkerpairs = tot * nrsSamples
- estimatedTimeSlow = nMarkerpairs/MARKER_PAIRS_PER_SECOND_SLOW
- estimatedTimeFast = nMarkerpairs/MARKER_PAIRS_PER_SECOND_FAST
- pairs = []
- pair_data = {}
- means = [] ## Mean IBS for each pair
- ngenoL = [] ## Count of comparable genotypes for each pair
- sdevs = [] ## Standard dev for each pair
- rels = [] ## A relationship code for each pair
- zmeans = [0.0 for x in xrange(tot)] ## zmean score for each pair for the relgroup
- zstds = [0.0 for x in xrange(tot)] ## zstd score for each pair for the relgrp
- skip = set()
- ndone = 0 ## How many have been done so far
- logf.write('Calculating %d pairs...\n' % (tot))
- logf.write('Estimated time is %2.2f to %2.2f seconds ...\n' % (estimatedTimeFast, estimatedTimeSlow))
- t1sum = 0
- t2sum = 0
- t3sum = 0
- now = time.time()
- scache = {}
- _founder_cache = {}
- C_CODE = """
- #include "math.h"
- int i;
- int sumibs = 0;
- int ssqibs = 0;
- int ngeno = 0;
- float mean = 0;
- float M2 = 0;
- float delta = 0;
- float sdev=0;
- float variance=0;
- for (i=0; i<nrsSamples; i++) {
- int a1 = g1[i];
- int a2 = g2[i];
- if (a1 != 0 && a2 != 0) {
- ngeno += 1;
- int shared = 2-abs(a1-a2);
- delta = shared - mean;
- mean = mean + delta/ngeno;
- M2 += delta*(shared-mean);
- // yes that second time, the updated mean is used see calcmeansd above;
- //printf("%d %d %d %d %d %d\\n", i, a1, a2, ngeno, shared, squared);
- }
- }
- if (ngeno > 1) {
- variance = M2/(ngeno-1);
- sdev = sqrt(variance);
- //printf("OK: %d %3.2f %3.2f\\n", ngeno, mean, sdev);
- }
- //printf("%d %d %d %1.2f %1.2f\\n", ngeno, sumibs, ssqibs, mean, sdev);
- result[0] = ngeno;
- result[1] = mean;
- result[2] = sdev;
- return_val = ngeno;
- """
- started = time.time()
- for s1 in xrange(nSubjects):
- if s1 in emptyRows:
- continue
- (fid1,iid1,did1,mid1,sex1,phe1,iid1,d_sid1,m_sid1) = scache.setdefault(s1, ped.getSubject(s1))
- isFounder1 = _founder_cache.setdefault(s1, (did1==mid1))
- g1 = genos[s1]
- for s2 in xrange(s1+1, nSubjects):
- if s2 in emptyRows:
- continue
- t1s = time.time()
- (fid2,iid2,did2,mid2,sex2,phe2,iid2,d_sid2,m_sid2) = scache.setdefault(s2, ped.getSubject(s2))
- g2 = genos[s2]
- isFounder2 = _founder_cache.setdefault(s2, (did2==mid2))
- # Determine the relationship for this pair
- relcode = REL_UNKNOWN
- if (fid2 == fid1):
- if iid1 == iid2:
- relcode = REL_DUPE
- elif (did2 == did1) and (mid2 == mid1) and did1 != mid1:
- relcode = REL_SIBS
- elif (iid1 == mid2) or (iid1 == did2) or (iid2 == mid1) or (iid2 == did1):
- relcode = REL_PARENTCHILD
- elif (str(did1) != '0' and (did2 == did1)) or (str(mid1) != '0' and (mid2 == mid1)):
- relcode = REL_HALFSIBS
- else:
- # People in the same family should be marked as some other
- # form of related. In general, these people will have a
- # pretty random spread of similarity. This distinction is
- # probably not very useful most of the time
- relcode = REL_RELATED
- else:
- ### Different families
- relcode = REL_UNRELATED
- t1e = time.time()
- t1sum += t1e-t1s
- ### Calculate sum(2-abs(a1-a2)) and sum((2-abs(a1-a2))**2) and count
- ### the number of contributing genotypes. These values are not actually
- ### calculated here, but instead are looked up in a table for speed.
- ### FIXME: This is still too slow ...
- result = [0.0, 0.0, 0.0]
- ngeno = weave.inline(C_CODE, ['g1', 'g2', 'nrsSamples', 'result'])
- if ngeno >= minUsegenos:
- _, mean, sdev = result
- means.append(mean)
- sdevs.append(sdev)
- ngenoL.append(ngeno)
- pairs.append((s1, s2))
- rels.append(relcode)
- else:
- skip.add(ndone) # signal no comparable genotypes for this pair
- ndone += 1
- t2e = time.time()
- t2sum += t2e-t1e
- t3e = time.time()
- t3sum += t3e-t2e
- logme = [ 'T1: %s' % (t1sum), 'T2: %s' % (t2sum), 'T3: %s' % (t3sum),'TOT: %s' % (t3e-now),
- '%s pairs with no (or not enough) comparable genotypes (%3.1f%%)' % (len(skip),
- float(len(skip))/float(tot)*100)]
- logf.write('%s\n' % '\t'.join(logme))
- ### Calculate mean and standard deviation of scores on a per relationship
- ### type basis, allowing us to flag outliers for each particular relationship
- ### type
- relstats = {}
- relCounts = {}
- outlierFiles = {}
- for relCode, relInfo in REL_LOOKUP.items():
- relName, relColor, relStyle = relInfo
- useme = [means[x] for x in xrange(len(means)) if rels[x] == relCode]
- relCounts[relCode] = len(useme)
- mm = scipy.mean(useme)
- ms = scipy.std(useme)
- useme = [sdevs[x] for x in xrange(len(sdevs)) if rels[x] == relCode]
- sm = scipy.mean(useme)
- ss = scipy.std(useme)
- relstats[relCode] = {'sd':(sm,ss), 'mean':(mm,ms)}
- s = 'Relstate %s (n=%d): mean(mean)=%3.2f sdev(mean)=%3.2f, mean(sdev)=%3.2f sdev(sdev)=%3.2f\n' % \
- (relName,relCounts[relCode], mm, ms, sm, ss)
- logf.write(s)
- ### now fake z scores for each subject like abecasis recommends max(|zmu|,|zsd|)
- ### within each group, for each pair, z=(groupmean-pairmean)/groupsd
- available = len(means)
- logf.write('%d pairs are available of %d\n' % (available, tot))
- ### s = '\nOutliers:\nrelationship\tzmean\tzsd\tped1\tped2\tmean\tsd\trmeanmean\trmeansd\trsdmean\trsdsd\n'
- ### logf.write(s)
- pairnum = 0
- offset = 0
- nOutliers = 0
- cexs = []
- outlierRecords = dict([(r, []) for r in range(N_RELATIONSHIP_TYPES)])
- zsdmax = 0
- for s1 in range(nSubjects):
- if s1 in emptyRows:
- continue
- (fid1,iid1,did1,mid1,sex1,aff1,ok1,d_sid1,m_sid1) = scache[s1]
- for s2 in range(s1+1, nSubjects):
- if s2 in emptyRows:
- continue
- if pairnum not in skip:
- ### Get group stats for this relationship
- (fid2,iid2,did2,mid2,sex2,aff2,ok2,d_sid2,m_sid2) = scache[s2]
- try:
- r = rels[offset]
- except IndexError:
- logf.write('###OOPS offset %d available %d pairnum %d len(rels) %d', offset, available, pairnum, len(rels))
- notfound = ('?',('?','0','0'))
- relInfo = REL_LOOKUP.get(r,notfound)
- relName, relColor, relStyle = relInfo
- rmm,rmd = relstats[r]['mean'] # group mean, group meansd alleles shared
- rdm,rdd = relstats[r]['sd'] # group sdmean, group sdsd alleles shared
- try:
- zsd = (sdevs[offset] - rdm)/rdd # distance from group mean in group sd units
- except:
- zsd = 1
- if abs(zsd) > zsdmax:
- zsdmax = zsd # keep for sort scaling
- try:
- zmean = (means[offset] - rmm)/rmd # distance from group mean
- except:
- zmean = 1
- zmeans[offset] = zmean
- zstds[offset] = zsd
- pid=(s1,s2)
- zrad = max(zsd,zmean)
- if zrad < 4:
- zrad = 2
- elif 4 < zrad < 15:
- zrad = 3 # to 9
- else: # > 15 6=24+
- zrad=zrad/4
- zrad = min(zrad,6) # scale limit
- zrad = max(2,max(zsd,zmean)) # as > 2, z grows
- pair_data[pid] = (zmean,zsd,r,zrad)
- if max(zsd,zmean) > Zcutoff: # is potentially interesting
- mean = means[offset]
- sdev = sdevs[offset]
- outlierRecords[r].append((mean, sdev, zmean, zsd, fid1, iid1, fid2, iid2, rmm, rmd, rdm, rdd,did1,mid1,did2,mid2))
- nOutliers += 1
- tbl.write('%s_%s\t%s_%s\t%f\t%f\t%f\t%f\t%d\t%s\t%s\t%s\t%s\t%s\n' % \
- (fid1, iid1, fid2, iid2, mean, sdev, zmean,zsd, ngeno, relName, did1,mid1,did2,mid2))
- offset += 1
- pairnum += 1
- logf.write( 'Outliers: %s\n' % (nOutliers))
- ### Write outlier files for each relationship type
- repOut.append('<h2>Outliers in tab delimited files linked above are also listed below</h2>')
- lzsd = round(numpy.log10(zsdmax)) + 1
- scalefactor = 10**lzsd
- for relCode, relInfo in REL_LOOKUP.items():
- relName, _, _ = relInfo
- outliers = outlierRecords[relCode]
- if not outliers:
- continue
- outliers = [(scalefactor*int(abs(x[3]))+ int(abs(x[2])),x) for x in outliers] # decorate
- outliers.sort()
- outliers.reverse() # largest deviation first
- outliers = [x[1] for x in outliers] # undecorate
- nrows = len(outliers)
- truncated = 0
- if nrows > MAX_SHOW_ROWS:
- s = '<h3>%s outlying pairs (top %d of %d) from %s</h3><table border="0" cellpadding="3">' % \
- (relName,MAX_SHOW_ROWS,nrows,title)
- truncated = nrows - MAX_SHOW_ROWS
- else:
- s = '<h3>%s outlying pairs (n=%d) from %s</h3><table border="0" cellpadding="3">' % (relName,nrows,title)
- repOut.append(s)
- fhname = '%s_rgGRR_%s_outliers.xls' % (title, relName)
- fhpath = os.path.join(outdir,fhname)
- fh = open(fhpath, 'w')
- newfiles.append(fhname)
- explanations.append('%s Outlier Pairs %s, N=%d, Cutoff SD=%f' % (relName,title,len(outliers),Zcutoff))
- fh.write(OUTLIERS_HEADER)
- s = ''.join(['<th>%s</th>' % x for x in OUTLIERS_HEADER_list])
- repOut.append('<tr align="center">%s</tr>' % s)
- for n,rec in enumerate(outliers):
- #(mean, sdev, zmean, zsd, fid1, iid1, fid2, iid2, rmm, rmd, rdm, rdd) = rec
- s = '%f\t%f\t%f\t%f\t%s\t%s\t%s\t%s\t%f\t%f\t%f\t%f\t%s\t%s\t%s\t%s\t' % tuple(rec)
- fh.write('%s%s\n' % (s,relName))
- # (mean, sdev, zmean, zsd, fid1, iid1, fid2, iid2, rmm, rmd, rdm, rdd, did1,mid1,did2,mid2))
- s = '''<td>%f</td><td>%f</td><td>%f</td><td>%f</td><td>%s</td><td>%s</td>
- <td>%s</td><td>%s</td><td>%f</td><td>%f</td><td>%f</td><td>%f</td><td>%s</td><td>%s</td><td>%s</td><td>%s</td>''' % tuple(rec)
- s = '%s<td>%s</td>' % (s,relName)
- if n < MAX_SHOW_ROWS:
- repOut.append('<tr align="center">%s</tr>' % s)
- if truncated > 0:
- repOut.append('<H2>WARNING: %d rows truncated - see outlier file for all %d rows</H2>' % (truncated,
- nrows))
- fh.close()
- repOut.append('</table><p>')
- ### Now, draw the plot in jpeg and svg formats, and optionally in the PDF format
- ### if requested
- logf.write('Plotting ...')
- pointColors = [REL_COLORS[rel] for rel in rels]
- pointStyles = [REL_POINTS[rel] for rel in rels]
- mainTitle = '%s (%s subjects, %d snp)' % (title, nSubjects, nrsSamples)
- svg.write(SVG_HEADER % (SVG_COLORS[0],SVG_COLORS[1],SVG_COLORS[2],SVG_COLORS[3],SVG_COLORS[4],
- SVG_COLORS[5],SVG_COLORS[6],SVG_COLORS[0],SVG_COLORS[0],SVG_COLORS[1],SVG_COLORS[1],
- SVG_COLORS[2],SVG_COLORS[2],SVG_COLORS[3],SVG_COLORS[3],SVG_COLORS[4],SVG_COLORS[4],
- SVG_COLORS[5],SVG_COLORS[5],SVG_COLORS[6],SVG_COLORS[6],mainTitle))
- #rpy.r.jpeg(filename='%s.jpg' % (title), width=1600, height=1200, pointsize=12, quality=100, bg='white')
- #rpy.r.par(mai=(1,1,1,0.5))
- #rpy.r('par(xaxs="i",yaxs="i")')
- #rpy.r.plot(means, sdevs, main=mainTitle, ylab=Y_AXIS_LABEL, xlab=X_AXIS_LABEL, cex=cexs, col=pointColors, pch=pointStyles, xlim=(0,2), ylim=(0,2))
- #rpy.r.legend(LEGEND_ALIGN, legend=REL_STATES, pch=REL_POINTS, col=REL_COLORS, title=LEGEND_TITLE)
- #rpy.r.grid(nx=10, ny=10, col='lightgray', lty='dotted')
- #rpy.r.dev_off()
- ### We will now go through each relationship type to partition plot points
- ### into "bulk" and "outlier" groups. Bulk points will represent common
- ### mean/sdev pairs and will cover the majority of the points in the plot --
- ### they will use generic tooltip informtion about all of the pairs
- ### represented by that point. "Outlier" points will be uncommon pairs,
- ### with very specific information in their tooltips. It would be nice to
- ### keep hte total number of plotted points in the SVG representation to
- ### ~10000 (certainly less than 100000?)
- pointMap = {}
- orderedRels = [y[1] for y in reversed(sorted([(relCounts.get(x, 0),x) for x in REL_LOOKUP.keys()]))]
- # do we really want this? I want out of zone points last and big
- for relCode in orderedRels:
- svgColor = SVG_COLORS[relCode]
- relName, relColor, relStyle = REL_LOOKUP[relCode]
- svg.write('<g id="%s" style="stroke:%s; fill:%s; fill-opacity:1.0; stroke-width:1;" cursor="pointer">\n' % (relName, svgColor, svgColor))
- pMap = pointMap.setdefault(relCode, {})
- nPoints = 0
- rpairs=[]
- rgenos=[]
- rmeans=[]
- rsdevs=[]
- rz = []
- for x,rel in enumerate(rels): # all pairs
- if rel == relCode:
- s1,s2 = pairs[x]
- pid=(s1,s2)
- zmean,zsd,r,zrad = pair_data[pid][:4]
- rpairs.append(pairs[x])
- rgenos.append(ngenoL[x])
- rmeans.append(means[x])
- rsdevs.append(sdevs[x])
- rz.append(zrad)
- ### Now add the svg point group for this relationship to the svg file
- for x in range(len(rmeans)):
- svgX = '%d' % ((rmeans[x] - 1.0) * PLOT_WIDTH) # changed so mean scale is 1-2
- svgY = '%d' % (PLOT_HEIGHT - (rsdevs[x] * PLOT_HEIGHT)) # changed so sd scale is 0-1
- s1, s2 = rpairs[x]
- (fid1,uid1,did1,mid1,sex1,phe1,iid1,d_sid1,m_sid1) = scache[s1]
- (fid2,uid2,did2,mid2,sex2,phe2,iid2,d_sid2,m_sid2) = scache[s2]
- ngenos = rgenos[x]
- nPoints += 1
- point = pMap.setdefault((svgX, svgY), [])
- point.append((rmeans[x], rsdevs[x], fid1, iid1, did1, mid1, fid2, iid2, did2, mid2, ngenos,rz[x]))
- for (svgX, svgY) in pMap:
- points = pMap[(svgX, svgY)]
- svgX = int(svgX)
- svgY = int(svgY)
- if len(points) > 1:
- mmean,dmean = calcMeanSD([p[0] for p in points])
- msdev,dsdev = calcMeanSD([p[1] for p in points])
- mgeno,dgeno = calcMeanSD([p[-1] for p in points])
- mingeno = min([p[-1] for p in points])
- maxgeno = max([p[-1] for p in points])
- svg.write("""<circle cx="%d" cy="%d" r="2"
- onmouseover="showBTT(evt, %d, %1.2f, %1.2f, %1.2f, %1.2f, %d, %d, %d, %d, %d)"
- onmouseout="hideBTT(evt)" />\n""" % (svgX, svgY, relCode, mmean, dmean, msdev, dsdev, len(points), mgeno, dgeno, mingeno, maxgeno))
- else:
- mean, sdev, fid1, iid1, did1, mid1, fid2, iid2, did2, mid2, ngenos, zrad = points[0][:12]
- rmean = float(relstats[relCode]['mean'][0])
- rsdev = float(relstats[relCode]['sd'][0])
- if zrad < 4:
- zrad = 2
- elif 4 < zrad < 9:
- zrad = 3 # to 9
- else: # > 9 5=15+
- zrad=zrad/3
- zrad = min(zrad,5) # scale limit
- if zrad <= 3:
- svg.write('<circle cx="%d" cy="%d" r="%s" onmouseover="showOTT(evt, %d, \'%s,%s,%s,%s\', \'%s,%s,%s,%s\', %1.2f, %1.2f, %s, %1.2f, %1.2f)" onmouseout="hideOTT(evt)" />\n' % (svgX, svgY, zrad, relCode, fid1, iid1, did1, mid1, fid2, iid2, did2, mid2, mean, sdev, ngenos, rmean, rsdev))
- else: # highlight pairs a long way from expectation by outlining circle in red
- svg.write("""<circle cx="%d" cy="%d" r="%s" style="stroke:red; fill:%s; fill-opacity:1.0; stroke-width:1;"
- onmouseover="showOTT(evt, %d, \'%s,%s,%s,%s\', \'%s,%s,%s,%s\', %1.2f, %1.2f, %s, %1.2f, %1.2f)"
- onmouseout="hideOTT(evt)" />\n""" % \
- (svgX, svgY, zrad, svgColor, relCode, fid1, iid1, did1, mid1, fid2, iid2, did2, mid2, mean, sdev, ngenos, rmean, rsdev))
- svg.write('</g>\n')
- ### Create a pdf as well if indicated on the command line
- ### WARNING! for framingham share, with about 50M pairs, this is a 5.5GB pdf!
- ## if pdftoo:
- ## pdfname = '%s.pdf' % (title)
- ## rpy.r.pdf(pdfname, 6, 6)
- ## rpy.r.par(mai=(1,1,1,0.5))
- ## rpy.r('par(xaxs="i",yaxs="i")')
- ## rpy.r.plot(means, sdevs, main='%s, %d snp' % (title, nSamples), ylab=Y_AXIS_LABEL, xlab=X_AXIS_LABEL, cex=cexs, col=pointColors, pch=pointStyles, xlim=(0,2), ylim=(0,2))
- ## rpy.r.legend(LEGEND_ALIGN, legend=REL_STATES, pch=REL_POINTS, col=REL_COLORS, title=LEGEND_TITLE)
- ## rpy.r.grid(nx=10, ny=10, col='lightgray', lty='dotted')
- ## rpy.r.dev_off()
- ### Draw polygons
- if showPolygons:
- svg.write('<g id="polygons" cursor="pointer">\n')
- for rel, poly in POLYGONS.items():
- points = ' '.join(['%s,%s' % ((p[0]-1.0)*float(PLOT_WIDTH), (PLOT_HEIGHT - p[1]*PLOT_HEIGHT)) for p in poly])
- svg.write('<polygon points="%s" fill="transparent" style="stroke:%s; stroke-width:1"/>\n' % (points, SVG_COLORS[rel]))
- svg.write('</g>\n')
- svg.write(SVG_FOOTER)
- svg.close()
- return newfiles,explanations,repOut
- def doIBS(n=100):
- """parse parameters from galaxy
- expect 'input pbed path' 'basename' 'outpath' 'title' 'logpath' 'n'
- <command interpreter="python">
- rgGRR.py $i.extra_files_path/$i.metadata.base_name "$i.metadata.base_name"
- '$out_file1' '$out_file1.files_path' "$title1" '$n' '$Z'
- </command>
- """
- u="""<command interpreter="python">
- rgGRR.py $i.extra_files_path/$i.metadata.base_name "$i.metadata.base_name"
- '$out_file1' '$out_file1.files_path' "$title1" '$n' '$Z'
- </command>
- """
- if len(sys.argv) < 7:
- print >> sys.stdout, 'Need pbed inpath, basename, out_htmlname, outpath, title, logpath, nSNP, Zcutoff on command line please'
- print >> sys.stdout, u
- sys.exit(1)
- ts = '%s%s' % (string.punctuation,string.whitespace)
- ptran = string.maketrans(ts,'_'*len(ts))
- inpath = sys.argv[1]
- ldinpath = os.path.split(inpath)[0]
- basename = sys.argv[2]
- outhtml = sys.argv[3]
- newfilepath = sys.argv[4]
- title = sys.argv[5].translate(ptran)
- logfname = 'Log_%s.txt' % title
- logpath = os.path.join(newfilepath,logfname) # log was a child - make part of html extra_files_path zoo
- n = int(sys.argv[6])
- try:
- Zcutoff = float(sys.argv[7])
- except:
- Zcutoff = 2.0
- try:
- os.makedirs(newfilepath)
- except:
- pass
- logf = file(logpath,'w')
- efp,ibase_name = os.path.split(inpath) # need to use these for outputs in files_path
- ped = plinkbinJZ.BPed(inpath)
- ped.parse(quick=True)
- if ped == None:
- print >> sys.stderr, '## doIBSpy problem - cannot open %s or %s - cannot run' % (ldreduced,basename)
- sys.exit(1)
- newfiles,explanations,repOut = doIBSpy(ped=ped,basename=basename,outdir=newfilepath,
- logf=logf,nrsSamples=n,title=title,pdftoo=0,Zcutoff=Zcutoff)
- logf.close()
- logfs = file(logpath,'r').readlines()
- lf = file(outhtml,'w')
- lf.write(galhtmlprefix % PROGNAME)
- # this is a mess. todo clean up - should each datatype have it's own directory? Yes
- # probably. Then titles are universal - but userId libraries are separate.
- s = '<div>Output from %s run at %s<br>\n' % (PROGNAME,timenow())
- lf.write('<h4>%s</h4>\n' % s)
- fixed = ["'%s'" % x for x in sys.argv] # add quotes just in case
- s = 'If you need to rerun this analysis, the command line was\n<pre>%s</pre>\n</div>' % (' '.join(fixed))
- lf.write(s)
- # various ways of displaying svg - experiments related to missing svg mimetype on test (!)
- #s = """<object data="%s" type="image/svg+xml" width="%d" height="%d">
- # <embed src="%s" type="image/svg+xml" width="%d" height="%d" />
- # </object>""" % (newfiles[0],PLOT_WIDTH,PLOT_HEIGHT,newfiles[0],PLOT_WIDTH,PLOT_HEIGHT)
- s = """ <embed src="%s" type="image/svg+xml" width="%d" height="%d" />""" % (newfiles[0],PLOT_WIDTH,PLOT_HEIGHT)
- #s = """ <iframe src="%s" type="image/svg+xml" width="%d" height="%d" />""" % (newfiles[0],PLOT_WIDTH,PLOT_HEIGHT)
- lf.write(s)
- lf.write('<div><h4>Click the links below to save output files and plots</h4><br><ol>\n')
- for i in range(len(newfiles)):
- if i == 0:
- lf.write('<li><a href="%s" type="image/svg+xml" >%s</a></li>\n' % (newfiles[i],explanations[i]))
- else:
- lf.write('<li><a href="%s">%s</a></li>\n' % (newfiles[i],explanations[i]))
- flist = os.listdir(newfilepath)
- for fname in flist:
- if not fname in newfiles:
- lf.write('<li><a href="%s">%s</a></li>\n' % (fname,fname))
- lf.write('</ol></div>')
- lf.write('<div>%s</div>' % ('\n'.join(repOut))) # repOut is a list of tables
- lf.write('<div><hr><h3>Log from this job (also stored in %s)</h3><pre>%s</pre><hr></div>' % (logfname,''.join(logfs)))
- lf.write('</body></html>\n')
- lf.close()
- logf.close()
- if __name__ == '__main__':
- doIBS()