excludelist = excludestr.split( "--" ) excludelist.remove( "" ) color = form.getvalue( "color", "none" ) shape = form.getvalue( "shape", "none" ) labels = form.getvalue( "labels", "none" ) resolution = form.getvalue( "resolution", "low" ) pc1 = form.getvalue( "pc1", "1" ) pc2 = form.getvalue( "pc2", "2" ) ptype = "species" form = cgi.FieldStorage() d3bf.chdir( form.getvalue( "datapath" ) ) ( data, volumes, mn, ml ) = d3bf.loaddata( "emap.txt" ) ilevel = ml + 1 excludelist = [] ( kdict, kdnames, kgnames, knorder, kdata ) = d3bf.loadtaxonomy( data, ml, excludelist, ilevel ) treedict = {} nodecnt = 0 knindex = {} for d in data[1:]: cs = "".join( d[0:ilevel] ) if not cs in kdnames: continue cd = treedict for i in range( ilevel ): val = d[i] if not val in cd: cd[ val ] = { "node": "n" + str( nodecnt ) } nodecnt = nodecnt + 1
ukeys = [ "*", "unassigned", "unclassified", "uncultured" ] fkeys = [ "gotu_", "fatu_", "ortu_", "cltu_" ] if len( s ) == 0 or ( s.lower() in ukeys ): return True if s.lower().find( "unclassified" ) != -1 : return True if s[0:5] in fkeys : return True return False for rilevel in range( maxlevel + 1 ): ilevel = maxlevel + 1 - rilevel if level != "all" and ilevel != int( level ): continue ( kdict, kdnames, kgnames, knorder, kdata ) = d3bf.loadtaxonomy( data, ml, spfilter, ilevel ) undef_list = [] for ckey in kdata: if is_undef( kdata[ ckey ][ -1 ] ): undef_list.append( kdict[ ckey ] ) edata = d3bf.load_edata( data, ilevel, ml, kdict, findex, gtags ) print "<p><br><br><span class=\"levellabel\">Level %d</span><br>" % ilevel print "<table class=\"indextable\"><tr><td>*" for cgtag in d3bf.sorted_alnum( gtags.keys() ): print "<td class=\"columnheader\">" + cgtag krange = range( len( slist ) - 1 ) if mmethod != "mm-fit" else [ slist.index( "mm-fit" ) ] for k in krange: print "<tr><td class=\"rowheader\">" + slnames[k]
dmethod = form.getvalue("dmethod", "Pearson") dfilter = form.getvalue("dfilter", "none") spfilter = d3bf.loadfilters("emap_filters.txt", form.getvalue("spfilter", "none")) level = form.getvalue("level") fmethod = form.getvalue("fmethod", "PCA") color = form.getvalue("color", "none") shape = form.getvalue("shape", "none") labels = form.getvalue("labels", "none") resolution = form.getvalue("resolution", "low") ilevel = int(level) (data, volumes, mn, ml) = d3bf.loaddata("emap.txt") (tags, tkeys) = d3bf.loadtags("emap_tags.txt", volumes) (kdict, kdnames, kgnames, knorder, kdata) = d3bf.loadtaxonomy(data, ml, spfilter, ilevel) (findex, mtags) = d3bf.processtags_m(volumes, tags, dfilter) (edata, site_ids, species_ids) = d3bf.load_edata_m(data, ilevel, mn, ml, kdict, volumes, findex, kdnames) aedata = np.array(edata, dtype=float) aenorm = np.sum(aedata, axis=1) aedata /= aenorm.reshape(len(edata), 1) rev = 0 if fmethod == "PCA (sklearn)": #or fmethod == "MDS (sklearn)": rev = 1 cdata = d3bf.calc_distances(edata, aedata, dmethod, kdata, knorder, rev) adist = np.array(cdata)
excludelist.remove("") color = form.getvalue("color", "none") shape = form.getvalue("shape", "none") labels = form.getvalue("labels", "none") resolution = form.getvalue("resolution", "low") pc1 = form.getvalue("pc1", "1") pc2 = form.getvalue("pc2", "2") ptype = "species" form = cgi.FieldStorage() d3bf.chdir(form.getvalue("datapath")) (data, volumes, mn, ml) = d3bf.loaddata("emap.txt") ilevel = ml + 1 excludelist = [] (kdict, kdnames, kgnames, knorder, kdata) = d3bf.loadtaxonomy(data, ml, excludelist, ilevel) treedict = {} nodecnt = 0 knindex = {} for d in data[1:]: cs = "".join(d[0:ilevel]) if not cs in kdnames: continue cd = treedict for i in range(ilevel): val = d[i] if not val in cd: cd[val] = {"node": "n" + str(nodecnt)} nodecnt = nodecnt + 1