root/galaxy-central/tools/rgenetics/rgGRR.py @ 2

リビジョン 2, 45.2 KB (コミッタ: hatakeyama, 14 年 前)

import galaxy-central
行番号 
1"""
2# july 2009: Need to see outliers so need to draw them last?
3# could use clustering on the zscores to guess real relationships for unrelateds
4# but definitely need to draw last
5# added MAX_SHOW_ROWS to limit the length of the main report page
6# Changes for Galaxy integration
7# added more robust knuth method for one pass mean and sd
8# no difference really - let's use scipy.mean() and scipy.std() instead...
9# fixed labels and changed to .xls for outlier reports so can open in excel
10# interesting - with a few hundred subjects, 5k gives good resolution
11# and 100k gives better but not by much
12# TODO remove non autosomal markers
13# TODO it would be best if label had the zmean and zsd as these are what matter for
14# outliers rather than the group mean/sd
15# mods to rgGRR.py from channing CVS which John Ziniti has rewritten to produce SVG plots
16# to make a Galaxy tool - we need the table of mean and SD for interesting pairs, the SVG and the log
17# so the result should be an HTML file
18
19# rgIBS.py
20# use a random subset of markers for a quick ibs
21# to identify sample dups and closely related subjects
22# try snpMatrix and plink and see which one works best for us?
23# abecasis grr plots mean*sd for every subject to show clusters
24# mods june 23 rml to avoid non-autosomal markers
25# we seem to be distinguishing parent-child by gender - 2 clouds!
26
27
28snpMatrix from David Clayton has:
29ibs.stats function to calculate the identity-by-state stats of a group of samples
30Description
31Given a snp.matrix-class or a X.snp.matrix-class object with N samples, calculates some statistics
32about the relatedness of every pair of samples within.
33
34Usage
35ibs.stats(x)
368 ibs.stats
37Arguments
38x a snp.matrix-class or a X.snp.matrix-class object containing N samples
39Details
40No-calls are excluded from consideration here.
41Value
42A data.frame containing N(N - 1)/2 rows, where the row names are the sample name pairs separated
43by a comma, and the columns are:
44Count count of identical calls, exclusing no-calls
45Fraction fraction of identical calls comparied to actual calls being made in both samples
46Warning
47In some applications, it may be preferable to subset a (random) selection of SNPs first - the
48calculation
49time increases as N(N - 1)M/2 . Typically for N = 800 samples and M = 3000 SNPs, the
50calculation time is about 1 minute. A full GWA scan could take hours, and quite unnecessary for
51simple applications such as checking for duplicate or related samples.
52Note
53This is mostly written to find mislabelled and/or duplicate samples.
54Illumina indexes their SNPs in alphabetical order so the mitochondria SNPs comes first - for most
55purpose it is undesirable to use these SNPs for IBS purposes.
56TODO: Worst-case S4 subsetting seems to make 2 copies of a large object, so one might want to
57subset before rbind(), etc; a future version of this routine may contain a built-in subsetting facility
58"""
59import sys,os,time,random,string,copy,optparse
60
61try:
62  set
63except NameError:
64  from Sets import Set as set
65
66from rgutils import timenow,plinke
67
68import plinkbinJZ
69
70
71opts = None
72verbose = False
73
74showPolygons = False
75
76class NullDevice:
77  def write(self, s):
78    pass
79
80tempstderr = sys.stderr # save
81#sys.stderr = NullDevice()
82# need to avoid blather about deprecation and other strange stuff from scipy
83# the current galaxy job runner assumes that
84# the job is in error if anything appears on sys.stderr
85# grrrrr. James wants to keep it that way instead of using the
86# status flag for some strange reason. Presumably he doesn't use R or (in this case, scipy)
87import numpy
88import scipy
89from scipy import weave
90
91
92sys.stderr=tempstderr
93
94
95PROGNAME = os.path.split(sys.argv[0])[-1]
96X_AXIS_LABEL = 'Mean Alleles Shared'
97Y_AXIS_LABEL = 'SD Alleles Shared'
98LEGEND_ALIGN = 'topleft'
99LEGEND_TITLE = 'Relationship'
100DEFAULT_SYMBOL_SIZE = 1.0 # default symbol size
101DEFAULT_SYMBOL_SIZE = 0.5 # default symbol size
102
103### Some colors for R/rpy
104R_BLACK  = 1
105R_RED    = 2
106R_GREEN  = 3
107R_BLUE   = 4
108R_CYAN   = 5
109R_PURPLE = 6
110R_YELLOW = 7
111R_GRAY   = 8
112
113### ... and some point-styles
114
115###
116PLOT_HEIGHT = 600
117PLOT_WIDTH = 1150
118
119
120#SVG_COLORS = ('black', 'darkblue', 'blue', 'deepskyblue', 'firebrick','maroon','crimson')
121#SVG_COLORS = ('cyan','dodgerblue','mediumpurple', 'fuchsia', 'red','gold','gray')
122SVG_COLORS = ('cyan','dodgerblue','mediumpurple','forestgreen', 'lightgreen','gold','gray')
123# dupe,parentchild,sibpair,halfsib,parents,unrel,unkn
124#('orange', 'red', 'green', 'chartreuse', 'blue', 'purple', 'gray')
125
126OUTLIERS_HEADER_list = ['Mean','Sdev','ZMean','ZSdev','FID1','IID1','FID2','IID2','RelMean_M','RelMean_SD','RelSD_M','RelSD_SD','PID1','MID1','PID2','MID2','Ped']
127OUTLIERS_HEADER = '\t'.join(OUTLIERS_HEADER_list)
128TABLE_HEADER='fid1_iid1\tfid2_iid2\tmean\tsdev\tzmean\tzsdev\tgeno\trelcode\tpid1\tmid1\tpid2\tmid2\n'
129
130
131### Relationship codes, text, and lookups/mappings
132N_RELATIONSHIP_TYPES = 7
133REL_DUPE, REL_PARENTCHILD, REL_SIBS, REL_HALFSIBS, REL_RELATED, REL_UNRELATED, REL_UNKNOWN = range(N_RELATIONSHIP_TYPES)
134REL_LOOKUP = {
135    REL_DUPE:        ('dupe',        R_BLUE,   1),
136    REL_PARENTCHILD: ('parentchild', R_YELLOW, 1),
137    REL_SIBS:        ('sibpairs',    R_RED,    1),
138    REL_HALFSIBS:    ('halfsibs',    R_GREEN,  1),
139    REL_RELATED:     ('parents',     R_PURPLE, 1),
140    REL_UNRELATED:   ('unrelated',   R_CYAN,   1),
141    REL_UNKNOWN:     ('unknown',     R_GRAY,   1),
142    }
143OUTLIER_STDEVS = {
144    REL_DUPE:        2,
145    REL_PARENTCHILD: 2,
146    REL_SIBS:        2,
147    REL_HALFSIBS:    2,
148    REL_RELATED:     2,
149    REL_UNRELATED:   3,
150    REL_UNKNOWN:     2,
151    }
152# note now Z can be passed in
153
154REL_STATES = [REL_LOOKUP[r][0] for r in range(N_RELATIONSHIP_TYPES)]
155REL_COLORS = SVG_COLORS
156REL_POINTS = [REL_LOOKUP[r][2] for r in range(N_RELATIONSHIP_TYPES)]
157
158DEFAULT_MAX_SAMPLE_SIZE = 10000
159
160REF_COUNT_HOM1 = 3
161REF_COUNT_HET  = 2
162REF_COUNT_HOM2 = 1
163MISSING        = 0
164MAX_SHOW_ROWS = 100 # framingham has millions - delays showing output page - so truncate and explain
165MARKER_PAIRS_PER_SECOND_SLOW = 15000000.0
166MARKER_PAIRS_PER_SECOND_FAST = 70000000.0
167
168
169galhtmlprefix = """<?xml version="1.0" encoding="utf-8" ?>
170<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
171<html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en">
172<head>
173<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
174<meta name="generator" content="Galaxy %s tool output - see http://g2.trac.bx.psu.edu/" />
175<title></title>
176<link rel="stylesheet" href="/static/style/base.css" type="text/css" />
177</head>
178<body>
179<div class="document">
180"""
181
182
183SVG_HEADER = '''<?xml version="1.0" standalone="no"?>
184<!DOCTYPE svg PUBLIC "-//W3C//DTD SVG 1.2//EN" "http://www.w3.org/Graphics/SVG/1.2/DTD/svg12.dtd">
185
186<svg width="1280" height="800"
187     xmlns="http://www.w3.org/2000/svg" version="1.2"
188     xmlns:xlink="http://www.w3.org/1999/xlink" viewBox="0 0 1280 800" onload="init()">
189
190  <script type="text/ecmascript" xlink:href="/static/scripts/checkbox_and_radiobutton.js"/>
191  <script type="text/ecmascript" xlink:href="/static/scripts/helper_functions.js"/>
192  <script type="text/ecmascript" xlink:href="/static/scripts/timer.js"/>
193  <script type="text/ecmascript">
194    <![CDATA[
195      var checkBoxes = new Array();
196      var radioGroupBandwidth;
197      var colours = ['%s','%s','%s','%s','%s','%s','%s'];
198      function init() {
199          var style = {"font-family":"Arial,Helvetica", "fill":"black", "font-size":12};
200          var dist = 12;
201          var yOffset = 4;
202
203          //A checkBox for each relationship type dupe,parentchild,sibpair,halfsib,parents,unrel,unkn
204          checkBoxes["dupe"] = new checkBox("dupe","checkboxes",20,40,"cbRect","cbCross",true,"Duplicate",style,dist,yOffset,undefined,hideShowLayer);
205          checkBoxes["parentchild"] = new checkBox("parentchild","checkboxes",20,60,"cbRect","cbCross",true,"Parent-Child",style,dist,yOffset,undefined,hideShowLayer);
206          checkBoxes["sibpairs"] = new checkBox("sibpairs","checkboxes",20,80,"cbRect","cbCross",true,"Sib-pairs",style,dist,yOffset,undefined,hideShowLayer);
207          checkBoxes["halfsibs"] = new checkBox("halfsibs","checkboxes",20,100,"cbRect","cbCross",true,"Half-sibs",style,dist,yOffset,undefined,hideShowLayer);
208          checkBoxes["parents"] = new checkBox("parents","checkboxes",20,120,"cbRect","cbCross",true,"Parents",style,dist,yOffset,undefined,hideShowLayer);
209          checkBoxes["unrelated"] = new checkBox("unrelated","checkboxes",20,140,"cbRect","cbCross",true,"Unrelated",style,dist,yOffset,undefined,hideShowLayer);
210          checkBoxes["unknown"] = new checkBox("unknown","checkboxes",20,160,"cbRect","cbCross",true,"Unknown",style,dist,yOffset,undefined,hideShowLayer);
211
212      }
213
214      function hideShowLayer(id, status, label) {
215          var vis = "hidden";
216          if (status) {
217              vis = "visible";
218          }
219          document.getElementById(id).setAttributeNS(null, 'visibility', vis);
220      }
221
222      function showBTT(evt, rel, mm, dm, md, dd, n, mg, dg, lg, hg) {
223    var x = parseInt(evt.pageX)-250;
224    var y = parseInt(evt.pageY)-110;
225        switch(rel) {
226        case 0:
227        fill = colours[rel];
228        relt = "dupe";
229        break;
230        case 1:
231        fill = colours[rel];
232        relt = "parentchild";
233        break;
234        case 2:
235        fill = colours[rel];
236        relt = "sibpairs";
237        break;
238        case 3:
239        fill = colours[rel];
240        relt = "halfsibs";
241        break;
242        case 4:
243        fill = colours[rel];
244        relt = "parents";
245        break;
246        case 5:
247        fill = colours[rel];
248        relt = "unrelated";
249        break;
250        case 6:
251        fill = colours[rel];
252        relt = "unknown";
253        break;
254        default:
255        fill = "cyan";
256        relt = "ERROR_CODE: "+rel;
257    }
258
259    document.getElementById("btRel").textContent = "GROUP: "+relt;
260    document.getElementById("btMean").textContent = "mean="+mm+" +/- "+dm;
261        document.getElementById("btSdev").textContent = "sdev="+dm+" +/- "+dd;
262        document.getElementById("btPair").textContent = "npairs="+n;
263        document.getElementById("btGeno").textContent = "ngenos="+mg+" +/- "+dg+" (min="+lg+", max="+hg+")";
264        document.getElementById("btHead").setAttribute('fill', fill);
265
266        var tt = document.getElementById("btTip");
267    tt.setAttribute("transform", "translate("+x+","+y+")");
268    tt.setAttribute('visibility', 'visible');
269      }
270
271      function showOTT(evt, rel, s1, s2, mean, sdev, ngeno, rmean, rsdev) {
272    var x = parseInt(evt.pageX)-150;
273    var y = parseInt(evt.pageY)-180;
274
275        switch(rel) {
276        case 0:
277        fill = colours[rel];
278        relt = "dupe";
279        break;
280        case 1:
281        fill = colours[rel];
282        relt = "parentchild";
283        break;
284        case 2:
285        fill = colours[rel];
286        relt = "sibpairs";
287        break;
288        case 3:
289        fill = colours[rel];
290        relt = "halfsibs";
291        break;
292        case 4:
293        fill = colours[rel];
294        relt = "parents";
295        break;
296        case 5:
297        fill = colours[rel];
298        relt = "unrelated";
299        break;
300        case 6:
301        fill = colours[rel];
302        relt = "unknown";
303        break;
304        default:
305        fill = "cyan";
306        relt = "ERROR_CODE: "+rel;
307    }
308
309    document.getElementById("otRel").textContent = "PAIR: "+relt;
310    document.getElementById("otS1").textContent = "s1="+s1;
311    document.getElementById("otS2").textContent = "s2="+s2;
312    document.getElementById("otMean").textContent = "mean="+mean;
313        document.getElementById("otSdev").textContent = "sdev="+sdev;
314        document.getElementById("otGeno").textContent = "ngenos="+ngeno;
315        document.getElementById("otRmean").textContent = "relmean="+rmean;
316        document.getElementById("otRsdev").textContent = "relsdev="+rsdev;
317    document.getElementById("otHead").setAttribute('fill', fill);
318
319        var tt = document.getElementById("otTip");
320    tt.setAttribute("transform", "translate("+x+","+y+")");
321    tt.setAttribute('visibility', 'visible');
322      }
323
324      function hideBTT(evt) {
325        document.getElementById("btTip").setAttributeNS(null, 'visibility', 'hidden');
326      }
327
328      function hideOTT(evt) {
329        document.getElementById("otTip").setAttributeNS(null, 'visibility', 'hidden');
330      }
331
332     ]]>
333  </script>
334  <defs>
335    <!-- symbols for check boxes -->
336    <symbol id="cbRect" overflow="visible">
337        <rect x="-5" y="-5" width="10" height="10" fill="white" stroke="dimgray" stroke-width="1" cursor="pointer"/>
338    </symbol>
339    <symbol id="cbCross" overflow="visible">
340        <g pointer-events="none" stroke="black" stroke-width="1">
341            <line x1="-3" y1="-3" x2="3" y2="3"/>
342            <line x1="3" y1="-3" x2="-3" y2="3"/>
343        </g>
344    </symbol>
345  </defs>
346
347<desc>Developer Works Dynamic Scatter Graph Scaling Example</desc>
348
349<!-- Now Draw the main X and Y axis -->
350<g style="stroke-width:1.0; stroke:black; shape-rendering:crispEdges">
351   <!-- X Axis top and bottom -->
352   <path d="M 100 100 L 1250 100 Z"/>
353   <path d="M 100 700 L 1250 700 Z"/>
354
355   <!-- Y Axis left and right -->
356   <path d="M 100  100 L 100  700 Z"/>
357   <path d="M 1250 100 L 1250 700 Z"/>
358</g>
359
360<g transform="translate(100,100)">
361
362  <!-- Grid Lines -->
363  <g style="fill:none; stroke:#dddddd; stroke-width:1; stroke-dasharray:2,2; text-anchor:end; shape-rendering:crispEdges">
364
365    <!-- Vertical grid lines -->
366    <line x1="125" y1="0" x2="115" y2="600" />
367    <line x1="230" y1="0" x2="230" y2="600" />
368    <line x1="345" y1="0" x2="345" y2="600" />
369    <line x1="460" y1="0" x2="460" y2="600" />
370    <line x1="575" y1="0" x2="575" y2="600" style="stroke-dasharray:none;" />
371    <line x1="690" y1="0" x2="690" y2="600"   />
372    <line x1="805" y1="0" x2="805" y2="600"   />
373    <line x1="920" y1="0" x2="920" y2="600"   />
374    <line x1="1035" y1="0" x2="1035" y2="600" />
375
376    <!-- Horizontal grid lines -->
377    <line x1="0" y1="60" x2="1150" y2="60"   />
378    <line x1="0" y1="120" x2="1150" y2="120" />
379    <line x1="0" y1="180" x2="1150" y2="180" />
380    <line x1="0" y1="240" x2="1150" y2="240" />
381    <line x1="0" y1="300" x2="1150" y2="300" style="stroke-dasharray:none;" />
382    <line x1="0" y1="360" x2="1150" y2="360" />
383    <line x1="0" y1="420" x2="1150" y2="420" />
384    <line x1="0" y1="480" x2="1150" y2="480" />
385    <line x1="0" y1="540" x2="1150" y2="540" />
386  </g>
387
388  <!-- Legend -->
389  <g style="fill:black; stroke:none" font-size="12" font-family="Arial" transform="translate(25,25)">
390    <rect width="160" height="270" style="fill:none; stroke:black; shape-rendering:crispEdges" />
391    <text x="5" y="20" style="fill:black; stroke:none;" font-size="13" font-weight="bold">Given Pair Relationship</text>
392    <rect x="120" y="35" width="10" height="10" fill="%s" stroke="%s" stroke-width="1" cursor="pointer"/>
393    <rect x="120" y="55" width="10" height="10" fill="%s" stroke="%s" stroke-width="1" cursor="pointer"/>
394    <rect x="120" y="75" width="10" height="10" fill="%s" stroke="%s" stroke-width="1" cursor="pointer"/>
395    <rect x="120" y="95" width="10" height="10" fill="%s" stroke="%s" stroke-width="1" cursor="pointer"/>
396    <rect x="120" y="115" width="10" height="10" fill="%s" stroke="%s" stroke-width="1" cursor="pointer"/>
397    <rect x="120" y="135" width="10" height="10" fill="%s" stroke="%s" stroke-width="1" cursor="pointer"/>
398    <rect x="120" y="155" width="10" height="10" fill="%s" stroke="%s" stroke-width="1" cursor="pointer"/>
399    <text x="15"  y="195" style="fill:black; stroke:none" font-size="12" font-family="Arial" >Zscore gt 15</text>
400    <circle cx="125" cy="192" r="6" style="stroke:red; fill:gold; fill-opacity:1.0; stroke-width:1;"/>
401    <text x="15" y="215" style="fill:black; stroke:none" font-size="12" font-family="Arial" >Zscore 4 to 15</text>
402    <circle cx="125" cy="212" r="3" style="stroke:gold; fill:gold; fill-opacity:1.0; stroke-width:1;"/>
403    <text x="15" y="235" style="fill:black; stroke:none" font-size="12" font-family="Arial" >Zscore lt 4</text>
404    <circle cx="125" cy="232" r="2" style="stroke:gold; fill:gold; fill-opacity:1.0; stroke-width:1;"/>
405    <g id="checkboxes">
406    </g>
407  </g>
408
409
410   <g style='fill:black; stroke:none' font-size="17" font-family="Arial">
411    <!-- X Axis Labels -->
412    <text x="480" y="660">Mean Alleles Shared</text>
413    <text x="0"    y="630" >1.0</text>
414    <text x="277"  y="630" >1.25</text>
415    <text x="564"  y="630" >1.5</text>
416    <text x="842" y="630" >1.75</text>
417    <text x="1140" y="630" >2.0</text>
418  </g>
419
420  <g transform="rotate(270)" style="fill:black; stroke:none" font-size="17" font-family="Arial">
421    <!-- Y Axis Labels -->
422    <text x="-350" y="-40">SD Alleles Shared</text>
423    <text x="-20" y="-10" >1.0</text>
424    <text x="-165" y="-10" >0.75</text>
425    <text x="-310" y="-10" >0.5</text>
426    <text x="-455" y="-10" >0.25</text>
427    <text x="-600" y="-10" >0.0</text>
428  </g>
429
430<!-- Plot Title -->
431<g style="fill:black; stroke:none" font-size="18" font-family="Arial">
432    <text x="425" y="-30">%s</text>
433</g>
434
435<!-- One group/layer of points for each relationship type -->
436'''
437
438SVG_FOOTER = '''
439<!-- End of Data -->
440</g>
441<g id="btTip" visibility="hidden" style="stroke-width:1.0; fill:black; stroke:none;" font-size="10" font-family="Arial">
442  <rect width="250" height="110" style="fill:silver" rx="2" ry="2"/>
443  <rect id="btHead" width="250" height="20" rx="2" ry="2" />
444  <text id="btRel" y="14" x="85">unrelated</text>
445  <text id="btMean" y="40" x="4">mean=1.5 +/- 0.04</text>
446  <text id="btSdev" y="60" x="4">sdev=0.7 +/- 0.03</text>
447  <text id="btPair" y="80" x="4">npairs=1152</text>
448  <text id="btGeno" y="100" x="4">ngenos=4783 +/- 24 (min=1000, max=5000)</text>
449</g>
450
451<g id="otTip" visibility="hidden" style="stroke-width:1.0; fill:black; stroke:none;" font-size="10" font-family="Arial">
452  <rect width="150" height="180" style="fill:silver" rx="2" ry="2"/>
453  <rect id="otHead" width="150" height="20" rx="2" ry="2" />
454  <text id="otRel" y="14" x="40">sibpairs</text>
455  <text id="otS1" y="40" x="4">s1=fid1,iid1</text>
456  <text id="otS2" y="60" x="4">s2=fid2,iid2</text>
457  <text id="otMean" y="80" x="4">mean=1.82</text>
458  <text id="otSdev" y="100" x="4">sdev=0.7</text>
459  <text id="otGeno" y="120" x="4">ngeno=4487</text>
460  <text id="otRmean" y="140" x="4">relmean=1.85</text>
461  <text id="otRsdev" y="160" x="4">relsdev=0.65</text>
462</g>
463</svg>
464'''
465
466
467DEFAULT_MAX_SAMPLE_SIZE = 5000
468
469REF_COUNT_HOM1 = 3
470REF_COUNT_HET  = 2
471REF_COUNT_HOM2 = 1
472MISSING        = 0
473
474MARKER_PAIRS_PER_SECOND_SLOW = 15000000
475MARKER_PAIRS_PER_SECOND_FAST = 70000000
476
477POLYGONS = {
478    REL_UNRELATED:   ((1.360, 0.655), (1.385, 0.730), (1.620, 0.575), (1.610, 0.505)),
479    REL_HALFSIBS:    ((1.630, 0.500), (1.630, 0.550), (1.648, 0.540), (1.648, 0.490)),
480    REL_SIBS:        ((1.660, 0.510), (1.665, 0.560), (1.820, 0.410), (1.820, 0.390)),
481    REL_PARENTCHILD: ((1.650, 0.470), (1.650, 0.490), (1.750, 0.440), (1.750, 0.420)),
482    REL_DUPE:        ((1.970, 0.000), (1.970, 0.150), (2.000, 0.150), (2.000, 0.000)),
483    }
484
485def distance(point1, point2):
486    """ Calculate the distance between two points
487    """
488    (x1,y1) = [float(d) for d in point1]
489    (x2,y2) = [float(d) for d in point2]
490    dx = abs(x1 - x2)
491    dy = abs(y1 - y2)
492    return math.sqrt(dx**2 + dy**2)
493
494def point_inside_polygon(x, y, poly):
495    """ Determine if a point (x,y) is inside a given polygon or not
496        poly is a list of (x,y) pairs.
497
498        Taken from: http://www.ariel.com.au/a/python-point-int-poly.html
499    """
500
501    n = len(poly)
502    inside = False
503
504    p1x,p1y = poly[0]
505    for i in range(n+1):
506        p2x,p2y = poly[i % n]
507        if y > min(p1y,p2y):
508            if y <= max(p1y,p2y):
509                if x <= max(p1x,p2x):
510                    if p1y != p2y:
511                        xinters = (y-p1y)*(p2x-p1x)/(p2y-p1y)+p1x
512                    if p1x == p2x or x <= xinters:
513                        inside = not inside
514        p1x,p1y = p2x,p2y
515    return inside
516
517def readMap(pedfile):
518    """
519    """
520    mapfile = pedfile.replace('.ped', '.map')
521    marker_list = []
522    if os.path.exists(mapfile):
523        print 'readMap: %s' % (mapfile)
524        fh = file(mapfile, 'r')
525    for line in fh:
526        marker_list.append(line.strip().split())
527    fh.close()
528    print 'readMap: %s markers' % (len(marker_list))
529    return marker_list
530
531def calcMeanSD(useme):
532    """
533    A numerically stable algorithm is given below. It also computes the mean.
534    This algorithm is due to Knuth,[1] who cites Welford.[2]
535    n = 0
536    mean = 0
537    M2 = 0
538
539    foreach x in data:
540      n = n + 1
541      delta = x - mean
542      mean = mean + delta/n
543      M2 = M2 + delta*(x - mean)      // This expression uses the new value of mean
544    end for
545
546    variance_n = M2/n
547    variance = M2/(n - 1)
548    """
549    mean = 0.0
550    M2 = 0.0
551    sd = 0.0
552    n = len(useme)
553    if n > 1:
554        for i,x in enumerate(useme):
555            delta = x - mean
556            mean = mean + delta/(i+1) # knuth uses n+=1 at start
557            M2 = M2 + delta*(x - mean)      # This expression uses the new value of mean
558        variance = M2/(n-1) # assume is sample so lose 1 DOF
559        sd = pow(variance,0.5)
560    return mean,sd
561
562
563def doIBSpy(ped=None,basename='',outdir=None,logf=None,
564            nrsSamples=10000,title='title',pdftoo=0,Zcutoff=2.0):
565    #def doIBS(pedName, title, nrsSamples=None, pdftoo=False):
566    """ started with snpmatrix but GRR uses actual IBS counts and sd's
567    """
568    repOut = [] # text strings to add to the html display
569    refallele = {}
570    tblf = '%s_table.xls' % (title)
571    tbl = file(os.path.join(outdir,tblf), 'w')
572    tbl.write(TABLE_HEADER)
573    svgf = '%s.svg' % (title)
574    svg = file(os.path.join(outdir,svgf), 'w')
575
576    nMarkers = len(ped._markers)
577    if nMarkers < 5:
578        print sys.stderr, '### ERROR - %d is too few markers for reliable estimation in %s - terminating' % (nMarkers,PROGNAME)
579        sys.exit(1)
580    nSubjects = len(ped._subjects)
581    nrsSamples = min(nMarkers, nrsSamples)
582    if opts and opts.use_mito:
583        markers = range(nMarkers)
584        nrsSamples = min(len(markers), nrsSamples)
585        sampleIndexes = sorted(random.sample(markers, nrsSamples))
586    else:
587        autosomals = ped.autosomal_indices()
588        nrsSamples = min(len(autosomals), nrsSamples)
589        sampleIndexes = sorted(random.sample(autosomals, nrsSamples))
590
591    print ''
592    print 'Getting random.sample of %s from %s total' % (nrsSamples, nMarkers)
593    npairs = (nSubjects*(nSubjects-1))/2 # total rows in table
594    newfiles=[svgf,tblf]
595    explanations = ['rgGRR Plot (requires SVG)','Mean by SD alleles shared - %d rows' % npairs]
596    # these go with the output file links in the html file
597    s = 'Reading genotypes for %s subjects and %s markers\n' % (nSubjects, nrsSamples)
598    logf.write(s)
599    minUsegenos = nrsSamples/2 # must have half?
600    nGenotypes = nSubjects*nrsSamples
601    stime = time.time()
602    emptyRows = set()
603    genos = numpy.zeros((nSubjects, nrsSamples), dtype=int)
604    for s in xrange(nSubjects):
605        nValid = 0
606        #getGenotypesByIndices(self, s, mlist, format)
607        genos[s] = ped.getGenotypesByIndices(s, sampleIndexes, format='ref')
608        nValid = sum([1 for g in genos[s] if g])
609        if not nValid:
610            emptyRows.add(s)
611            sub = ped.getSubject(s)
612            print 'All missing for row %d (%s)' % (s, sub)
613            logf.write('All missing for row %d (%s)\n' % (s, sub))
614    rtime = time.time() - stime
615    if verbose:
616        print '@@Read %s genotypes in %s seconds' % (nGenotypes, rtime)
617
618
619    ### Now the expensive part.  For each pair of subjects, we get the mean number
620    ### and standard deviation of shared alleles over all of the markers where both
621    ### subjects have a known genotype.  Identical subjects should have mean shared
622    ### alleles very close to 2.0 with a standard deviation very close to 0.0.
623    tot = nSubjects*(nSubjects-1)/2
624    nprog = tot/10
625    nMarkerpairs = tot * nrsSamples
626    estimatedTimeSlow = nMarkerpairs/MARKER_PAIRS_PER_SECOND_SLOW
627    estimatedTimeFast = nMarkerpairs/MARKER_PAIRS_PER_SECOND_FAST
628
629    pairs = []
630    pair_data = {}
631    means = []    ## Mean IBS for each pair
632    ngenoL = []   ## Count of comparable genotypes for each pair
633    sdevs = []    ## Standard dev for each pair
634    rels  = []    ## A relationship code for each pair
635    zmeans  = [0.0 for x in xrange(tot)]    ## zmean score for each pair for the relgroup
636    zstds  = [0.0 for x in xrange(tot)]   ## zstd score for each pair for the relgrp
637    skip = set()
638    ndone = 0     ## How many have been done so far
639
640    logf.write('Calculating %d pairs...\n' % (tot))
641    logf.write('Estimated time is %2.2f to %2.2f seconds ...\n' % (estimatedTimeFast, estimatedTimeSlow))
642
643    t1sum = 0
644    t2sum = 0
645    t3sum = 0
646    now = time.time()
647    scache = {}
648    _founder_cache = {}
649    C_CODE = """
650    #include "math.h"
651    int i;
652    int sumibs = 0;
653    int ssqibs = 0;
654    int ngeno  = 0;
655    float mean = 0;
656    float M2 = 0;
657    float delta = 0;
658    float sdev=0;
659    float variance=0;
660    for (i=0; i<nrsSamples; i++) {
661        int a1 = g1[i];
662        int a2 = g2[i];
663        if (a1 != 0 && a2 != 0) {
664            ngeno += 1;
665            int shared = 2-abs(a1-a2);
666            delta = shared - mean;
667            mean = mean + delta/ngeno;
668            M2 += delta*(shared-mean);
669            // yes that second time, the updated mean is used see calcmeansd above;
670            //printf("%d %d %d %d %d %d\\n", i, a1, a2, ngeno, shared, squared);
671            }
672    }
673    if (ngeno > 1) {
674        variance = M2/(ngeno-1);
675        sdev = sqrt(variance);
676        //printf("OK: %d %3.2f %3.2f\\n", ngeno, mean, sdev);
677    }
678    //printf("%d %d %d %1.2f %1.2f\\n", ngeno, sumibs, ssqibs, mean, sdev);
679    result[0] = ngeno;
680    result[1] = mean;
681    result[2] = sdev;
682    return_val = ngeno;
683    """
684    started = time.time()
685    for s1 in xrange(nSubjects):
686        if s1 in emptyRows:
687            continue
688        (fid1,iid1,did1,mid1,sex1,phe1,iid1,d_sid1,m_sid1) = scache.setdefault(s1, ped.getSubject(s1))
689
690        isFounder1 = _founder_cache.setdefault(s1, (did1==mid1))
691        g1 = genos[s1]
692
693        for s2 in xrange(s1+1, nSubjects):
694            if s2 in emptyRows:
695                continue
696            t1s = time.time()
697
698            (fid2,iid2,did2,mid2,sex2,phe2,iid2,d_sid2,m_sid2) = scache.setdefault(s2, ped.getSubject(s2))
699
700            g2 = genos[s2]
701            isFounder2 = _founder_cache.setdefault(s2, (did2==mid2))
702
703            # Determine the relationship for this pair
704            relcode = REL_UNKNOWN
705            if (fid2 == fid1):
706                if iid1 == iid2:
707                    relcode = REL_DUPE
708                elif (did2 == did1) and (mid2 == mid1) and did1 != mid1:
709                    relcode = REL_SIBS
710                elif (iid1 == mid2) or (iid1 == did2) or (iid2 == mid1) or (iid2 == did1):
711                    relcode = REL_PARENTCHILD
712                elif (str(did1) != '0' and (did2 == did1)) or (str(mid1) != '0' and (mid2 == mid1)):
713                    relcode = REL_HALFSIBS
714                else:
715                    # People in the same family should be marked as some other
716                    # form of related.  In general, these people will have a
717                    # pretty random spread of similarity. This distinction is
718                    # probably not very useful most of the time
719                    relcode = REL_RELATED
720            else:
721                ### Different families
722                relcode = REL_UNRELATED
723
724            t1e = time.time()
725            t1sum += t1e-t1s
726
727
728            ### Calculate sum(2-abs(a1-a2)) and sum((2-abs(a1-a2))**2) and count
729            ### the number of contributing genotypes.  These values are not actually
730            ### calculated here, but instead are looked up in a table for speed.
731            ### FIXME: This is still too slow ...
732            result = [0.0, 0.0, 0.0]
733            ngeno = weave.inline(C_CODE, ['g1', 'g2', 'nrsSamples', 'result'])
734            if ngeno >= minUsegenos:
735                _, mean, sdev = result
736                means.append(mean)
737                sdevs.append(sdev)
738                ngenoL.append(ngeno)
739                pairs.append((s1, s2))
740                rels.append(relcode)
741            else:
742                skip.add(ndone) # signal no comparable genotypes for this pair
743            ndone += 1
744            t2e = time.time()
745            t2sum += t2e-t1e
746            t3e = time.time()
747            t3sum += t3e-t2e
748
749    logme = [ 'T1:  %s' % (t1sum), 'T2:  %s' % (t2sum), 'T3:  %s' % (t3sum),'TOT: %s' % (t3e-now),
750             '%s pairs with no (or not enough) comparable genotypes (%3.1f%%)' % (len(skip),
751                                                            float(len(skip))/float(tot)*100)]
752    logf.write('%s\n' % '\t'.join(logme))
753    ### Calculate mean and standard deviation of scores on a per relationship
754    ### type basis, allowing us to flag outliers for each particular relationship
755    ### type
756    relstats = {}
757    relCounts = {}
758    outlierFiles = {}
759    for relCode, relInfo in REL_LOOKUP.items():
760        relName, relColor, relStyle = relInfo
761        useme = [means[x] for x in xrange(len(means)) if rels[x] == relCode]
762        relCounts[relCode] = len(useme)
763        mm = scipy.mean(useme)
764        ms = scipy.std(useme)
765        useme = [sdevs[x] for x in xrange(len(sdevs)) if rels[x] == relCode]
766        sm = scipy.mean(useme)
767        ss = scipy.std(useme)
768        relstats[relCode] = {'sd':(sm,ss), 'mean':(mm,ms)}
769        s = 'Relstate %s (n=%d): mean(mean)=%3.2f sdev(mean)=%3.2f, mean(sdev)=%3.2f sdev(sdev)=%3.2f\n' % \
770          (relName,relCounts[relCode], mm, ms, sm, ss)
771        logf.write(s)
772
773    ### now fake z scores for each subject like abecasis recommends max(|zmu|,|zsd|)
774    ### within each group, for each pair, z=(groupmean-pairmean)/groupsd
775    available = len(means)
776    logf.write('%d pairs are available of %d\n' % (available, tot))
777    ### s = '\nOutliers:\nrelationship\tzmean\tzsd\tped1\tped2\tmean\tsd\trmeanmean\trmeansd\trsdmean\trsdsd\n'
778    ### logf.write(s)
779    pairnum   = 0
780    offset    = 0
781    nOutliers = 0
782    cexs      = []
783    outlierRecords = dict([(r, []) for r in range(N_RELATIONSHIP_TYPES)])
784    zsdmax = 0
785    for s1 in range(nSubjects):
786        if s1 in emptyRows:
787            continue
788        (fid1,iid1,did1,mid1,sex1,aff1,ok1,d_sid1,m_sid1) = scache[s1]
789        for s2 in range(s1+1, nSubjects):
790            if s2 in emptyRows:
791                continue
792            if pairnum not in skip:
793                ### Get group stats for this relationship
794                (fid2,iid2,did2,mid2,sex2,aff2,ok2,d_sid2,m_sid2) = scache[s2]
795                try:
796                    r = rels[offset]
797                except IndexError:
798                    logf.write('###OOPS offset %d available %d  pairnum %d  len(rels) %d', offset, available, pairnum, len(rels))
799                notfound = ('?',('?','0','0'))
800                relInfo = REL_LOOKUP.get(r,notfound)
801                relName, relColor, relStyle = relInfo
802                rmm,rmd = relstats[r]['mean'] # group mean, group meansd alleles shared
803                rdm,rdd = relstats[r]['sd'] # group sdmean, group sdsd alleles shared
804
805                try:
806                    zsd = (sdevs[offset] - rdm)/rdd # distance from group mean in group sd units
807                except:
808                    zsd = 1
809                if abs(zsd) > zsdmax:
810                    zsdmax = zsd # keep for sort scaling
811                try:
812                    zmean = (means[offset] - rmm)/rmd # distance from group mean
813                except:
814                    zmean = 1
815                zmeans[offset] = zmean
816                zstds[offset] = zsd
817                pid=(s1,s2)
818                zrad = max(zsd,zmean)
819                if zrad < 4:
820                    zrad = 2
821                elif 4 < zrad < 15:
822                    zrad = 3 # to 9
823                else: # > 15 6=24+
824                    zrad=zrad/4
825                    zrad = min(zrad,6) # scale limit
826                zrad = max(2,max(zsd,zmean)) # as > 2, z grows
827                pair_data[pid] = (zmean,zsd,r,zrad)
828                if max(zsd,zmean) > Zcutoff: # is potentially interesting
829                    mean = means[offset]
830                    sdev = sdevs[offset]
831                    outlierRecords[r].append((mean, sdev, zmean, zsd, fid1, iid1, fid2, iid2, rmm, rmd, rdm, rdd,did1,mid1,did2,mid2))
832                    nOutliers += 1
833                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' % \
834                          (fid1, iid1, fid2, iid2, mean, sdev, zmean,zsd, ngeno, relName, did1,mid1,did2,mid2))
835                offset += 1
836            pairnum += 1
837    logf.write( 'Outliers: %s\n' % (nOutliers))
838
839    ### Write outlier files for each relationship type
840    repOut.append('<h2>Outliers in tab delimited files linked above are also listed below</h2>')
841    lzsd = round(numpy.log10(zsdmax)) + 1
842    scalefactor = 10**lzsd
843    for relCode, relInfo in REL_LOOKUP.items():
844        relName, _, _ = relInfo
845        outliers = outlierRecords[relCode]
846        if not outliers:
847            continue
848        outliers = [(scalefactor*int(abs(x[3]))+ int(abs(x[2])),x) for x in outliers] # decorate
849        outliers.sort()
850        outliers.reverse() # largest deviation first
851        outliers = [x[1] for x in outliers] # undecorate
852        nrows = len(outliers)
853        truncated = 0
854        if nrows > MAX_SHOW_ROWS:
855            s = '<h3>%s outlying pairs (top %d of %d) from %s</h3><table border="0" cellpadding="3">' % \
856               (relName,MAX_SHOW_ROWS,nrows,title)
857            truncated = nrows - MAX_SHOW_ROWS
858        else:
859            s = '<h3>%s outlying pairs (n=%d) from %s</h3><table border="0" cellpadding="3">' % (relName,nrows,title)
860        repOut.append(s)
861        fhname = '%s_rgGRR_%s_outliers.xls' % (title, relName)
862        fhpath = os.path.join(outdir,fhname)
863        fh = open(fhpath, 'w')
864        newfiles.append(fhname)
865        explanations.append('%s Outlier Pairs %s, N=%d, Cutoff SD=%f' % (relName,title,len(outliers),Zcutoff))
866        fh.write(OUTLIERS_HEADER)
867        s = ''.join(['<th>%s</th>' % x for x in OUTLIERS_HEADER_list])
868        repOut.append('<tr align="center">%s</tr>' % s)
869        for n,rec in enumerate(outliers):
870            #(mean, sdev, zmean, zsd, fid1, iid1, fid2, iid2, rmm, rmd, rdm, rdd) = rec
871            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)
872            fh.write('%s%s\n' % (s,relName))
873            # (mean, sdev, zmean, zsd, fid1, iid1, fid2, iid2, rmm, rmd, rdm, rdd, did1,mid1,did2,mid2))
874            s = '''<td>%f</td><td>%f</td><td>%f</td><td>%f</td><td>%s</td><td>%s</td>
875            <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)
876            s = '%s<td>%s</td>' % (s,relName)
877            if n < MAX_SHOW_ROWS:
878                repOut.append('<tr align="center">%s</tr>' % s)
879        if truncated > 0:
880            repOut.append('<H2>WARNING: %d rows truncated - see outlier file for all %d rows</H2>' % (truncated,
881                                                                                            nrows))
882        fh.close()
883        repOut.append('</table><p>')
884
885    ### Now, draw the plot in jpeg and svg formats, and optionally in the PDF format
886    ### if requested
887    logf.write('Plotting ...')
888    pointColors = [REL_COLORS[rel] for rel in rels]
889    pointStyles = [REL_POINTS[rel] for rel in rels]
890
891    mainTitle = '%s (%s subjects, %d snp)' % (title, nSubjects, nrsSamples)
892    svg.write(SVG_HEADER % (SVG_COLORS[0],SVG_COLORS[1],SVG_COLORS[2],SVG_COLORS[3],SVG_COLORS[4],
893        SVG_COLORS[5],SVG_COLORS[6],SVG_COLORS[0],SVG_COLORS[0],SVG_COLORS[1],SVG_COLORS[1],
894        SVG_COLORS[2],SVG_COLORS[2],SVG_COLORS[3],SVG_COLORS[3],SVG_COLORS[4],SVG_COLORS[4],
895        SVG_COLORS[5],SVG_COLORS[5],SVG_COLORS[6],SVG_COLORS[6],mainTitle))
896    #rpy.r.jpeg(filename='%s.jpg' % (title), width=1600, height=1200, pointsize=12, quality=100, bg='white')
897    #rpy.r.par(mai=(1,1,1,0.5))
898    #rpy.r('par(xaxs="i",yaxs="i")')
899    #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))
900    #rpy.r.legend(LEGEND_ALIGN, legend=REL_STATES, pch=REL_POINTS, col=REL_COLORS, title=LEGEND_TITLE)
901    #rpy.r.grid(nx=10, ny=10, col='lightgray', lty='dotted')
902    #rpy.r.dev_off()
903
904    ### We will now go through each relationship type to partition plot points
905    ### into "bulk" and "outlier" groups.  Bulk points will represent common
906    ### mean/sdev pairs and will cover the majority of the points in the plot --
907    ### they will use generic tooltip informtion about all of the pairs
908    ### represented by that point.  "Outlier" points will be uncommon pairs,
909    ### with very specific information in their tooltips.  It would be nice to
910    ### keep hte total number of plotted points in the SVG representation to
911    ### ~10000 (certainly less than 100000?)
912    pointMap = {}
913    orderedRels = [y[1] for y in reversed(sorted([(relCounts.get(x, 0),x) for x in REL_LOOKUP.keys()]))]
914    # do we really want this? I want out of zone points last and big
915    for relCode in orderedRels:
916        svgColor = SVG_COLORS[relCode]
917        relName, relColor, relStyle = REL_LOOKUP[relCode]
918        svg.write('<g id="%s" style="stroke:%s; fill:%s; fill-opacity:1.0; stroke-width:1;" cursor="pointer">\n' % (relName, svgColor, svgColor))
919        pMap = pointMap.setdefault(relCode, {})
920        nPoints = 0
921        rpairs=[]
922        rgenos=[]
923        rmeans=[]
924        rsdevs=[]
925        rz = []
926        for x,rel in enumerate(rels): # all pairs
927            if rel == relCode:
928                s1,s2 = pairs[x]
929                pid=(s1,s2)
930                zmean,zsd,r,zrad = pair_data[pid][:4]
931                rpairs.append(pairs[x])
932                rgenos.append(ngenoL[x])
933                rmeans.append(means[x])
934                rsdevs.append(sdevs[x])
935                rz.append(zrad)
936        ### Now add the svg point group for this relationship to the svg file
937        for x in range(len(rmeans)):
938            svgX = '%d' % ((rmeans[x] - 1.0) * PLOT_WIDTH) # changed so mean scale is 1-2
939            svgY = '%d' % (PLOT_HEIGHT - (rsdevs[x] * PLOT_HEIGHT)) # changed so sd scale is 0-1
940            s1, s2 = rpairs[x]
941            (fid1,uid1,did1,mid1,sex1,phe1,iid1,d_sid1,m_sid1) = scache[s1]
942            (fid2,uid2,did2,mid2,sex2,phe2,iid2,d_sid2,m_sid2) = scache[s2]
943            ngenos = rgenos[x]
944            nPoints += 1
945            point = pMap.setdefault((svgX, svgY), [])
946            point.append((rmeans[x], rsdevs[x], fid1, iid1, did1, mid1, fid2, iid2, did2, mid2, ngenos,rz[x]))
947        for (svgX, svgY) in pMap:
948            points = pMap[(svgX, svgY)]
949            svgX = int(svgX)
950            svgY = int(svgY)
951            if len(points) > 1:
952                mmean,dmean = calcMeanSD([p[0] for p in points])
953                msdev,dsdev = calcMeanSD([p[1] for p in points])
954                mgeno,dgeno = calcMeanSD([p[-1] for p in points])
955                mingeno = min([p[-1] for p in points])
956                maxgeno = max([p[-1] for p in points])
957                svg.write("""<circle cx="%d" cy="%d" r="2"
958                onmouseover="showBTT(evt, %d, %1.2f, %1.2f, %1.2f, %1.2f, %d, %d, %d, %d, %d)"
959                onmouseout="hideBTT(evt)" />\n""" % (svgX, svgY, relCode, mmean, dmean, msdev, dsdev, len(points), mgeno, dgeno, mingeno, maxgeno))
960            else:
961                mean, sdev, fid1, iid1, did1, mid1, fid2, iid2, did2, mid2, ngenos, zrad = points[0][:12]
962                rmean = float(relstats[relCode]['mean'][0])
963                rsdev = float(relstats[relCode]['sd'][0])
964                if zrad < 4:
965                    zrad = 2
966                elif 4 < zrad < 9:
967                    zrad = 3 # to 9
968                else: # > 9 5=15+
969                    zrad=zrad/3
970                    zrad = min(zrad,5) # scale limit
971                if zrad <= 3:
972                    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))
973                else: # highlight pairs a long way from expectation by outlining circle in red
974                    svg.write("""<circle cx="%d" cy="%d" r="%s" style="stroke:red; fill:%s; fill-opacity:1.0; stroke-width:1;"
975                    onmouseover="showOTT(evt, %d, \'%s,%s,%s,%s\', \'%s,%s,%s,%s\', %1.2f, %1.2f, %s, %1.2f, %1.2f)"
976                    onmouseout="hideOTT(evt)" />\n""" % \
977                    (svgX, svgY, zrad, svgColor, relCode, fid1, iid1, did1, mid1, fid2, iid2, did2, mid2, mean, sdev, ngenos, rmean, rsdev))
978        svg.write('</g>\n')
979
980    ### Create a pdf as well if indicated on the command line
981    ### WARNING! for framingham share, with about 50M pairs, this is a 5.5GB pdf!
982##    if pdftoo:
983##        pdfname = '%s.pdf' % (title)
984##        rpy.r.pdf(pdfname, 6, 6)
985##        rpy.r.par(mai=(1,1,1,0.5))
986##        rpy.r('par(xaxs="i",yaxs="i")')
987##        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))
988##        rpy.r.legend(LEGEND_ALIGN, legend=REL_STATES, pch=REL_POINTS, col=REL_COLORS, title=LEGEND_TITLE)
989##        rpy.r.grid(nx=10, ny=10, col='lightgray', lty='dotted')
990##        rpy.r.dev_off()
991
992    ### Draw polygons
993    if showPolygons:
994        svg.write('<g id="polygons" cursor="pointer">\n')
995        for rel, poly in POLYGONS.items():
996            points = ' '.join(['%s,%s' % ((p[0]-1.0)*float(PLOT_WIDTH), (PLOT_HEIGHT - p[1]*PLOT_HEIGHT)) for p in poly])
997            svg.write('<polygon points="%s" fill="transparent" style="stroke:%s; stroke-width:1"/>\n' % (points, SVG_COLORS[rel]))
998        svg.write('</g>\n')
999
1000
1001    svg.write(SVG_FOOTER)
1002    svg.close()
1003    return newfiles,explanations,repOut
1004
1005def doIBS(n=100):
1006    """parse parameters from galaxy
1007    expect 'input pbed path' 'basename' 'outpath' 'title' 'logpath' 'n'
1008    <command interpreter="python">
1009         rgGRR.py $i.extra_files_path/$i.metadata.base_name "$i.metadata.base_name"
1010        '$out_file1' '$out_file1.files_path' "$title1"  '$n' '$Z'
1011    </command>
1012
1013    """
1014    u="""<command interpreter="python">
1015         rgGRR.py $i.extra_files_path/$i.metadata.base_name "$i.metadata.base_name"
1016        '$out_file1' '$out_file1.files_path' "$title1"  '$n' '$Z'
1017         </command>
1018      """
1019
1020
1021    if len(sys.argv) < 7:
1022        print >> sys.stdout, 'Need pbed inpath, basename, out_htmlname, outpath, title, logpath, nSNP, Zcutoff on command line please'
1023        print >> sys.stdout, u
1024        sys.exit(1)
1025    ts = '%s%s' % (string.punctuation,string.whitespace)
1026    ptran =  string.maketrans(ts,'_'*len(ts))
1027    inpath = sys.argv[1]
1028    ldinpath = os.path.split(inpath)[0]
1029    basename = sys.argv[2]
1030    outhtml = sys.argv[3]
1031    newfilepath = sys.argv[4]
1032    title = sys.argv[5].translate(ptran)
1033    logfname = 'Log_%s.txt' % title
1034    logpath = os.path.join(newfilepath,logfname) # log was a child - make part of html extra_files_path zoo
1035    n = int(sys.argv[6])
1036    try:
1037        Zcutoff = float(sys.argv[7])
1038    except:
1039        Zcutoff = 2.0
1040    try:
1041        os.makedirs(newfilepath)
1042    except:
1043        pass
1044    logf = file(logpath,'w')
1045    efp,ibase_name = os.path.split(inpath) # need to use these for outputs in files_path
1046    ped = plinkbinJZ.BPed(inpath)
1047    ped.parse(quick=True)       
1048    if ped == None:
1049        print >> sys.stderr, '## doIBSpy problem - cannot open %s or %s - cannot run' % (ldreduced,basename)
1050        sys.exit(1)
1051    newfiles,explanations,repOut = doIBSpy(ped=ped,basename=basename,outdir=newfilepath,
1052                                    logf=logf,nrsSamples=n,title=title,pdftoo=0,Zcutoff=Zcutoff)
1053    logf.close()
1054    logfs = file(logpath,'r').readlines()
1055    lf = file(outhtml,'w')
1056    lf.write(galhtmlprefix % PROGNAME)
1057    # this is a mess. todo clean up - should each datatype have it's own directory? Yes
1058    # probably. Then titles are universal - but userId libraries are separate.
1059    s = '<div>Output from %s run at %s<br>\n' % (PROGNAME,timenow())
1060    lf.write('<h4>%s</h4>\n' % s)
1061    fixed = ["'%s'" % x for x in sys.argv] # add quotes just in case
1062    s = 'If you need to rerun this analysis, the command line was\n<pre>%s</pre>\n</div>' % (' '.join(fixed))
1063    lf.write(s)
1064    # various ways of displaying svg - experiments related to missing svg mimetype on test (!)
1065    #s = """<object data="%s" type="image/svg+xml"  width="%d" height="%d">
1066    #       <embed src="%s" type="image/svg+xml" width="%d" height="%d" />
1067    #       </object>""" % (newfiles[0],PLOT_WIDTH,PLOT_HEIGHT,newfiles[0],PLOT_WIDTH,PLOT_HEIGHT)
1068    s = """ <embed src="%s" type="image/svg+xml" width="%d" height="%d" />""" % (newfiles[0],PLOT_WIDTH,PLOT_HEIGHT)
1069    #s = """ <iframe src="%s" type="image/svg+xml" width="%d" height="%d" />""" % (newfiles[0],PLOT_WIDTH,PLOT_HEIGHT)
1070    lf.write(s)
1071    lf.write('<div><h4>Click the links below to save output files and plots</h4><br><ol>\n')
1072    for i in range(len(newfiles)):
1073       if i == 0:
1074            lf.write('<li><a href="%s" type="image/svg+xml" >%s</a></li>\n' % (newfiles[i],explanations[i]))
1075       else:
1076             lf.write('<li><a href="%s">%s</a></li>\n' % (newfiles[i],explanations[i]))
1077    flist = os.listdir(newfilepath)
1078    for fname in flist:
1079        if not fname in newfiles:
1080             lf.write('<li><a href="%s">%s</a></li>\n' % (fname,fname))
1081    lf.write('</ol></div>')
1082    lf.write('<div>%s</div>' % ('\n'.join(repOut))) # repOut is a list of tables
1083    lf.write('<div><hr><h3>Log from this job (also stored in %s)</h3><pre>%s</pre><hr></div>' % (logfname,''.join(logfs)))
1084    lf.write('</body></html>\n')
1085    lf.close()
1086    logf.close()
1087
1088if __name__ == '__main__':
1089    doIBS()
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