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galaxy/tools/rgenetics/rgGRR.py
T
Ross Lazarus 1fe808274d Fixes to some rgenetics tests and test outputs 
Some odd bugs in the new version of ggplot2 in R
Fixes to the Rrun function in rgutils
2010-08-31 02:27:33 -04:00

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"""
# 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()
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