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
