def create(self):
fd, self.mFilename = tempfile.mkstemp()
outfile = SegmentedFile.openfile( self.mFilename, "w", slice="00-10" )
for x in range(10): outfile.write( "%i\n" % x )
outfile.close()
outfile = SegmentedFile.openfile( self.mFilename, "w", slice="10-20" )
for x in range(10,20): outfile.write( "%i\n" % x )
outfile.close()
def create(self):
fd, self.mFilename = tempfile.mkstemp()
outfile = SegmentedFile.openfile(self.mFilename, "w", slice="00-10")
for x in range(10):
outfile.write("%i\n" % x)
outfile.close()
outfile = SegmentedFile.openfile(self.mFilename, "w", slice="10-20")
for x in range(10, 20):
outfile.write("%i\n" % x)
outfile.close()
def outputSummaryGraph( self ):
"""analyse the alignments."""
return {}
infile = SegmentedFile.openfile( self.mFilenameGraph, "r" )
nlinks = 0
queries, sbjcts = set(), set()
for line in infile:
if line[0] == "#": continue
if line.startswith( "query_nid"): continue
nlinks += 1
query, sbjct = line[:-1].split("\t")[:2]
queries.add( query )
sbjcts.add( sbjct )
infile.close()
self.mOutfile.write( ">%s\n" % self.mFilenameGraph )
self.mOutfile.write( "nlinks\t%i\n" % nlinks )
self.mOutfile.write( "nqueries\t%i\t%5.2f\n" % (len(queries), 100.0 * len(queries) / self.mNNids ) )
self.mOutfile.write( "nsbjcts\t%i\t%5.2f\n" % (len(sbjcts), 100.0 * len(sbjcts) / self.mNNids ) )
nids = queries.union( sbjcts )
self.mOutfile.write( "nnids\t%i\t%5.2f\n" % (len(nids), 100.0 * len(nids) / self.mNNids ) )
return { 'nids' : len(nids), 'links' : nlinks }
def outputSummaryAlignments(self):
"""analyse the alignments."""
infile = SegmentedFile.openfile(self.mFilenameAlignments, "r")
ninput, naccepted = 0, 0
nids, domains = set(), set()
for line in infile:
if line[0] == "#": continue
if line.startswith("passed"): continue
ninput += 1
(code, query, sbjct, estimate,
qstart, qend, qali, sstart, send, sali,
score, naligned, ngaps, zscore) =\
line[:-1].split("\t")
nids.add(query.split("_")[0])
nids.add(sbjct.split("_")[0])
domains.add(query)
domains.add(sbjct)
if code == "+": naccepted += 1
infile.close()
self.mOutfile.write(">%s\n" % self.mFilenameAlignments)
self.mOutfile.write("ntotal\t%i\n" % ninput)
self.mOutfile.write("naccepted\t%i\n" % naccepted)
self.mOutfile.write("nrejected\t%i\n" % (ninput - naccepted))
return {'nids': len(nids), 'domains': len(domains)}
def outputSummaryMst(self):
"""analyse the alignments."""
infile = SegmentedFile.openfile(self.mFilenameMst, "r")
nlinks = 0
nids, domains = set(), set()
for line in infile:
if line[0] == "#": continue
if line.startswith("nid"): continue
nlinks += 1
query, sbjct = line[:-1].split("\t")[:2]
nids.add(query.split("_")[0])
nids.add(sbjct.split("_")[0])
domains.add(query)
domains.add(sbjct)
infile.close()
self.mOutfile.write(">%s\n" % self.mFilenameMst)
self.mOutfile.write("nlinks\t%i\n" % nlinks)
self.mOutfile.write("ndomains\t%i\n" % len(domains))
self.mOutfile.write("nnids\t%i\t%5.2f\n" %
(len(nids), 100.0 * len(nids) / self.mNNids))
return {'nids': len(nids), 'domains': len(domains)}
def outputSummaryGraph(self):
"""analyse the alignments."""
return {}
infile = SegmentedFile.openfile(self.mFilenameGraph, "r")
nlinks = 0
queries, sbjcts = set(), set()
for line in infile:
if line[0] == "#": continue
if line.startswith("query_nid"): continue
nlinks += 1
query, sbjct = line[:-1].split("\t")[:2]
queries.add(query)
sbjcts.add(sbjct)
infile.close()
self.mOutfile.write(">%s\n" % self.mFilenameGraph)
self.mOutfile.write("nlinks\t%i\n" % nlinks)
self.mOutfile.write("nqueries\t%i\t%5.2f\n" %
(len(queries), 100.0 * len(queries) / self.mNNids))
self.mOutfile.write("nsbjcts\t%i\t%5.2f\n" %
(len(sbjcts), 100.0 * len(sbjcts) / self.mNNids))
nids = queries.union(sbjcts)
self.mOutfile.write("nnids\t%i\t%5.2f\n" %
(len(nids), 100.0 * len(nids) / self.mNNids))
return {'nids': len(nids), 'links': nlinks}
def outputSummaryResult(self):
"""analyse the alignments."""
infile = SegmentedFile.openfile(self.mFilenameResult, "r")
ndomains = 0
nids, families = set(), set()
for line in infile:
if line[0] == "#": continue
if line.startswith("nid"): continue
ndomains += 1
nid, start, end, family = line[:-1].split("\t")
nids.add(nid)
families.add(family)
infile.close()
self.mOutfile.write(">%s\n" % self.mFilenameResult)
self.mOutfile.write("ndomains\t%i\n" % ndomains)
self.mOutfile.write("nfamilies\t%i\n" % len(families))
self.mOutfile.write("nnids\t%i\t%5.2f\n" %
(len(nids), 100.0 * len(nids) / self.mNNids))
return {
'nids': len(nids),
'domains': ndomains,
'families': len(families)
}
def outputSummaryMst( self ):
"""analyse the alignments."""
infile = SegmentedFile.openfile( self.mFilenameMst, "r" )
nlinks = 0
nids, domains = set(), set()
for line in infile:
if line[0] == "#": continue
if line.startswith( "nid"): continue
nlinks += 1
query, sbjct = line[:-1].split("\t")[:2]
nids.add( query.split("_")[0])
nids.add( sbjct.split("_")[0])
domains.add( query )
domains.add( sbjct )
infile.close()
self.mOutfile.write( ">%s\n" % self.mFilenameMst )
self.mOutfile.write( "nlinks\t%i\n" % nlinks )
self.mOutfile.write( "ndomains\t%i\n" % len(domains) )
self.mOutfile.write( "nnids\t%i\t%5.2f\n" % (len(nids), 100.0 * len(nids) / self.mNNids ) )
return { 'nids' : len(nids), 'domains' : len(domains) }
def openOutputStream(self, filename, register = False ):
"""opens an output stream.
If the output filename exists an error is raised unless
1. mForce is set: the existing file will be overwritten
2. mAppend is set: data will be appended. The registerExistingOutput
method is called to give the module the chance to advance
the input stream to the appropriate point for continuation.
If mSlice is set, the name will be mangled to reflect the slice.
If register is true, registerExistingOutput will be called.
"""
if self.mAppend:
mode = "a"
else:
mode = "w"
self.debug( "%s%s opening with mode %s" % (filename, self.getSlice(), mode ))
return SegmentedFile.openfile( filename,
mode,
slice = self.getSlice(),
force = self.mForce,
append_callback = self.readPreviousData,
)
def outputSummaryAlignments( self ):
"""analyse the alignments."""
infile = SegmentedFile.openfile( self.mFilenameAlignments, "r" )
ninput, naccepted = 0, 0
nids, domains = set(), set()
for line in infile:
if line[0] == "#": continue
if line.startswith( "passed"): continue
ninput += 1
(code, query, sbjct, estimate,
qstart, qend, qali, sstart, send, sali,
score, naligned, ngaps, zscore) =\
line[:-1].split("\t")
nids.add( query.split("_")[0])
nids.add( sbjct.split("_")[0])
domains.add( query )
domains.add( sbjct )
if code == "+": naccepted += 1
infile.close()
self.mOutfile.write( ">%s\n" % self.mFilenameAlignments )
self.mOutfile.write( "ntotal\t%i\n" % ninput )
self.mOutfile.write( "naccepted\t%i\n" % naccepted )
self.mOutfile.write( "nrejected\t%i\n" % (ninput - naccepted) )
return { 'nids' : len(nids), 'domains' : len(domains) }
def checkContents(self):
self.checkToken(self.mFilename)
infile = SegmentedFile.openfile(self.mFilename,
"r",
has_header=self.mHasHeader)
data = [int(x) for x in infile]
self.assertEqual(data, range(20))
def checkContents(self):
self.create()
self.assertEqual( SegmentedFile.merge( self.mFilename ), True )
self.checkToken( self.mFilename )
infile = SegmentedFile.openfile( self.mFilename, "r" )
data = [ x for x in infile ]
self.assertEqual( data[1], "header1\n" )
self.assertEqual( data[0], "#comment1\n" )
self.assertEqual( data[12], "#comment2\n" )
self.assertEqual( [int(x) for x in data[2:12] + data[13:]], range( 20 ) )
def checkContents(self):
self.create()
self.assertEqual(SegmentedFile.merge(self.mFilename), True)
self.checkToken(self.mFilename)
infile = SegmentedFile.openfile(self.mFilename, "r")
data = [x for x in infile]
self.assertEqual(data[1], "header1\n")
self.assertEqual(data[0], "#comment1\n")
self.assertEqual(data[12], "#comment2\n")
self.assertEqual([int(x) for x in data[2:12] + data[13:]], range(20))
def getComponents( self ):
'''return components.'''
componentor = Components.SComponents()
infile = SegmentedFile.openfile( self.mFilenameInput, "r" )
ninput = 0
for line in infile:
if line[0] == "#": continue
qdomain, sdomain = line[:-1].split("\t")[:2]
componentor.add( qdomain, sdomain )
ninput += 1
self.info( "computing components with %i links" % ninput)
return componentor.getComponents()
def getComponents(self):
'''return components.'''
componentor = Components.SComponents()
infile = SegmentedFile.openfile(self.mFilenameInput, "r")
ninput = 0
for line in infile:
if line[0] == "#": continue
qdomain, sdomain = line[:-1].split("\t")[:2]
componentor.add(qdomain, sdomain)
ninput += 1
self.info("computing components with %i links" % ninput)
return componentor.getComponents()
def outputSummarySegments( self ):
"""analyse the alignments."""
infile = SegmentedFile.openfile( self.mFilenameSegments, "r" )
ndomains = 0
nids = set()
for line in infile:
if line[0] == "#": continue
if line.startswith( "nid"): continue
ndomains += 1
nid, node, parent, level, start, end = line[:-1].split("\t")
nids.add(nid)
infile.close()
self.mOutfile.write( ">%s\n" % self.mFilenameSegments )
self.mOutfile.write( "ndomains\t%i\n" % ndomains )
self.mOutfile.write( "nnids\t%i\t%5.2f\n" % (len(nids), 100.0 * len(nids) / self.mNNids ) )
return { 'nids' : len(nids), 'domains' : ndomains }
def outputSummaryNids( self ):
infile = SegmentedFile.openfile( self.mFilenameNids, "r" )
ndomains = 0
nids = set()
for line in infile:
if line[0] == "#": continue
if line.startswith( "nid"): continue
nid, pid, hid, length, sequence = line[:-1].split("\t")
nids.add(nid)
infile.close()
self.mNids = nids
self.mNNids = len(self.mNids)
self.mOutfile.write( ">%s\n" % self.mFilenameNids )
self.mOutfile.write( "nnids\t%i\t%5.2f\n" % (len(nids), len(nids) / self.mNNids ) )
return { 'nids' : len(nids) }
def outputSummaryNids(self):
infile = SegmentedFile.openfile(self.mFilenameNids, "r")
ndomains = 0
nids = set()
for line in infile:
if line[0] == "#": continue
if line.startswith("nid"): continue
nid, pid, hid, length, sequence = line[:-1].split("\t")
nids.add(nid)
infile.close()
self.mNids = nids
self.mNNids = len(self.mNids)
self.mOutfile.write(">%s\n" % self.mFilenameNids)
self.mOutfile.write("nnids\t%i\t%5.2f\n" %
(len(nids), len(nids) / self.mNNids))
return {'nids': len(nids)}
def outputSummarySegments(self):
"""analyse the alignments."""
infile = SegmentedFile.openfile(self.mFilenameSegments, "r")
ndomains = 0
nids = set()
for line in infile:
if line[0] == "#": continue
if line.startswith("nid"): continue
ndomains += 1
nid, node, parent, level, start, end = line[:-1].split("\t")
nids.add(nid)
infile.close()
self.mOutfile.write(">%s\n" % self.mFilenameSegments)
self.mOutfile.write("ndomains\t%i\n" % ndomains)
self.mOutfile.write("nnids\t%i\t%5.2f\n" %
(len(nids), 100.0 * len(nids) / self.mNNids))
return {'nids': len(nids), 'domains': ndomains}
def outputSummaryClusters( self ):
"""analyse the alignments."""
infile = SegmentedFile.openfile( self.mFilenameClusters, "r" )
ndomains = 0
nids, families = set(), set()
for line in infile:
if line[0] == "#": continue
if line.startswith( "nid"): continue
ndomains += 1
nid, start, end, family = line[:-1].split("\t")
nids.add(nid)
families.add(family)
infile.close()
self.mOutfile.write( ">%s\n" % self.mFilenameClusters )
self.mOutfile.write( "ndomains\t%i\n" % ndomains )
self.mOutfile.write( "nfamilies\t%i\n" % len(families) )
self.mOutfile.write( "nnids\t%i\t%5.2f\n" % (len(nids), 100.0 * len(nids) / self.mNNids ) )
return { 'nids' : len(nids), 'domains' : ndomains, 'families': len(families) }
def checkContents(self):
infile = SegmentedFile.openfile(self.mFilename, "r")
data = [int(x) for x in infile]
self.assertEqual(data, range(10))
def checkContents( self ):
infile = SegmentedFile.openfile( self.mFilename, "r" )
data = [int(x) for x in infile ]
self.assertEqual( data, range( 10 ) )
def checkContents(self):
self.checkToken(self.mFilename)
infile = SegmentedFile.openfile(self.mFilename, "r")
data = [x for x in infile]
self.assertEqual(data[0], "header\n")
self.assertEqual([int(x) for x in data[1:]], range(20))
def create(self):
outfile = SegmentedFile.openfile(self.mFilename, "w")
for x in range(10):
outfile.write("%i\n" % x)
outfile.close()
def checkContents( self ):
self.checkToken( self.mFilename )
infile = SegmentedFile.openfile( self.mFilename, "r", has_header = self.mHasHeader )
data = [ x for x in infile ]
self.assertEqual( data[0], "header\n" )
self.assertEqual( [int(x) for x in data[1:]], range( 20 ) )
def applyMethod(self ):
"""apply the method.
"""
infile = SegmentedFile.openfile( self.mFilenameClusters, "r" )
family2domains = collections.defaultdict( list )
nid2domains = collections.defaultdict( list )
ndomains = 0
for line in infile:
if line[0] == "#": continue
if line.startswith("nid"): continue
nid, start, end, family = line[:-1].split("\t")
nid = int(nid)
nid2domains[nid].append( (int(start),int(end),family) )
family2domains[family].append( (nid,int(end)-int(start) ) )
ndomains += 1
self.info( "collected: nsequences=%i, ndomains=%i, nfamilies=%i" %\
(len(nid2domains), ndomains, len(family2domains) ) )
family_id = len(family2domains)
self.mOutfile.write( "nid\tstart\tend\tfamily\n" )
# output domains per nid
seqs = self.mFasta.getContigSizes()
nids = sorted(seqs.keys())
nfull_singletons = 0
npartial_singletons = 0
ndomains = 0
# compute stats at the same time
seq_lengths = seqs.values()
max_length = max(seq_lengths)
# compute summary per family
# and compute full histograms of length distributions
hist_domains_mst = numpy.zeros( max_length + 1, numpy.float)
hist_domains_full_singletons = numpy.zeros( max_length + 1, numpy.float)
hist_domains_partial_singletons = numpy.zeros( max_length + 1, numpy.float)
hist_sequences = numpy.zeros( max_length + 1, numpy.float)
for x in seq_lengths: hist_sequences[x] += 1
for nid in nids:
length = self.mFasta.getLength( nid )
id = self.mMapNid2Id[ nid ]
if nid not in nid2domains:
family_id += 1
self.mOutfile.write( "%s\t%s\t%s\t%s\n" % \
( id, 0, length, self.mPatternFamily % family_id ) )
family2domains[ self.mPatternFamily % family_id ].append( (nid, length) )
nfull_singletons += 1
hist_domains_full_singletons[length] += 1
continue
domains = nid2domains[nid]
domains.sort()
last = 0
for start, end, family in domains:
hist_domains_mst[end-start] += 1
if start - last > self.mMinDomainSize:
family_id += 1
self.mOutfile.write( "%s\t%s\t%s\t%s\n" % \
( id, last, start, self.mPatternFamily % family_id ) )
npartial_singletons += 1
family2domains[ self.mPatternFamily % family_id ].append( (nid, start-last) )
ndomains += 1
hist_domains_partial_singletons[start-last] += 1
self.mOutfile.write( "%s\t%s\t%s\t%s\n" % \
( id, start, end, family ) )
last = end
ndomains += 1
if length - last > self.mMinDomainSize:
family_id += 1
self.mOutfile.write( "%s\t%s\t%s\t%s\n" % \
( id, last, length, self.mPatternFamily % family_id ) )
npartial_singletons += 1
family2domains[ self.mPatternFamily % family_id ].append( (nid, start-last) )
hist_domains_partial_singletons[start-last] += 1
ndomains += 1
self.info( "output: nsequences=%i, ndomains=%i,nfamilies=%i, nfull_singletons=%i, npartial_singletons=%i" % (len(nids), ndomains, len(family2domains), npartial_singletons, nfull_singletons))
self.mOutfileFamilies.write( "family\tnunits\tnsequences\tnresidues\tlength\tlength_median\tlength_stddev\n" )
family_size_sequences, family_size_domains = [], []
for family in sorted(family2domains.keys()):
nids = set()
lengths = []
for nid, length in family2domains[family]:
lengths.append( length )
nids.add(nid)
ndomains = len(lengths)
self.mOutfileFamilies.write( "\t".join( (family,
str(ndomains),
str(len(nids)),
str(sum(lengths)),
"%5.2f" % numpy.mean(lengths),
"%5.2f" % numpy.median(lengths),
"%5.2f" % numpy.std(lengths) ) ) + "\n" )
family_size_sequences.append( len(nids) )
family_size_domains.append( ndomains )
if PLOT:
## output length distributions
lines, legends = [], []
for title, vals in (
("sequences", hist_sequences),
("domains", hist_domains_mst),
("partial singletons", hist_domains_full_singletons),
("full singletons", hist_domains_partial_singletons), ):
vv = numpy.zeros( max_length )
for x in range( 0, max_length, 10 ):
vv[x] = sum( vals[x:x+10] )
x = numpy.flatnonzero( vv > 0 )
s = sum(vals)
if s > 0: vv /= s
lines.append( pylab.plot( x, vv[x] ) )
legends.append( title )
pylab.xlabel( "sequence or domain length / residues" )
pylab.ylabel( "relative frequency" )
pylab.legend( lines, legends )
pylab.savefig( os.path.expanduser( self.mFilenameDomains + "_domainsizes_all.png" ) )
pylab.xlim( 0, 2000 )
pylab.savefig( os.path.expanduser( self.mFilenameDomains + "_domainsizes_small.png" ) )
pylab.xlim( max_length - max_length // 4, max_length + 1 )
pylab.savefig( os.path.expanduser( self.mFilenameDomains + "_domainsize_large.png" ) )
pylab.clf()
## output domain family sizes
lines = []
(yvals, xvals) = numpy.histogram( family_size_sequences, bins=50, new = True)
lines.append( pylab.loglog( xvals[:-1], yvals ) )
(yvals, xvals) = numpy.histogram( family_size_domains, bins=50, new = True)
lines.append( pylab.loglog( xvals[:-1], yvals ) )
pylab.legend( lines, ( "sequeces", "domains") )
pylab.xlabel( "sequences/domains per family" )
pylab.ylabel( "relative frequency" )
pylab.savefig( os.path.expanduser( self.mFilenameDomains + "_familysizes.png" ) )
def applyMethod(self):
"""apply the method.
"""
infile = SegmentedFile.openfile(self.mFilenameClusters, "r")
family2domains = collections.defaultdict(list)
nid2domains = collections.defaultdict(list)
ndomains = 0
for line in infile:
if line[0] == "#": continue
if line.startswith("nid"): continue
nid, start, end, family = line[:-1].split("\t")
nid = int(nid)
nid2domains[nid].append((int(start), int(end), family))
family2domains[family].append((nid, int(end) - int(start)))
ndomains += 1
self.info( "collected: nsequences=%i, ndomains=%i, nfamilies=%i" %\
(len(nid2domains), ndomains, len(family2domains) ) )
family_id = len(family2domains)
self.mOutfile.write("nid\tstart\tend\tfamily\n")
# output domains per nid
seqs = self.mFasta.getContigSizes()
nids = sorted(seqs.keys())
nfull_singletons = 0
npartial_singletons = 0
ndomains = 0
# compute stats at the same time
seq_lengths = seqs.values()
max_length = max(seq_lengths)
# compute summary per family
# and compute full histograms of length distributions
hist_domains_mst = numpy.zeros(max_length + 1, numpy.float)
hist_domains_full_singletons = numpy.zeros(max_length + 1, numpy.float)
hist_domains_partial_singletons = numpy.zeros(max_length + 1,
numpy.float)
hist_sequences = numpy.zeros(max_length + 1, numpy.float)
for x in seq_lengths:
hist_sequences[x] += 1
for nid in nids:
length = self.mFasta.getLength(nid)
id = self.mMapNid2Id[nid]
if nid not in nid2domains:
family_id += 1
self.mOutfile.write( "%s\t%s\t%s\t%s\n" % \
( id, 0, length, self.mPatternFamily % family_id ) )
family2domains[self.mPatternFamily % family_id].append(
(nid, length))
nfull_singletons += 1
hist_domains_full_singletons[length] += 1
continue
domains = nid2domains[nid]
domains.sort()
last = 0
for start, end, family in domains:
hist_domains_mst[end - start] += 1
if start - last > self.mMinDomainSize:
family_id += 1
self.mOutfile.write( "%s\t%s\t%s\t%s\n" % \
( id, last, start, self.mPatternFamily % family_id ) )
npartial_singletons += 1
family2domains[self.mPatternFamily % family_id].append(
(nid, start - last))
ndomains += 1
hist_domains_partial_singletons[start - last] += 1
self.mOutfile.write( "%s\t%s\t%s\t%s\n" % \
( id, start, end, family ) )
last = end
ndomains += 1
if length - last > self.mMinDomainSize:
family_id += 1
self.mOutfile.write( "%s\t%s\t%s\t%s\n" % \
( id, last, length, self.mPatternFamily % family_id ) )
npartial_singletons += 1
family2domains[self.mPatternFamily % family_id].append(
(nid, start - last))
hist_domains_partial_singletons[start - last] += 1
ndomains += 1
self.info(
"output: nsequences=%i, ndomains=%i,nfamilies=%i, nfull_singletons=%i, npartial_singletons=%i"
% (len(nids), ndomains, len(family2domains), npartial_singletons,
nfull_singletons))
self.mOutfileFamilies.write(
"family\tnunits\tnsequences\tnresidues\tlength\tlength_median\tlength_stddev\n"
)
family_size_sequences, family_size_domains = [], []
for family in sorted(family2domains.keys()):
nids = set()
lengths = []
for nid, length in family2domains[family]:
lengths.append(length)
nids.add(nid)
ndomains = len(lengths)
self.mOutfileFamilies.write("\t".join(
(family, str(ndomains), str(len(nids)), str(sum(lengths)),
"%5.2f" % numpy.mean(lengths), "%5.2f" %
numpy.median(lengths), "%5.2f" % numpy.std(lengths))) + "\n")
family_size_sequences.append(len(nids))
family_size_domains.append(ndomains)
if PLOT:
## output length distributions
lines, legends = [], []
for title, vals in (
("sequences", hist_sequences),
("domains", hist_domains_mst),
("partial singletons", hist_domains_full_singletons),
("full singletons", hist_domains_partial_singletons),
):
vv = numpy.zeros(max_length)
for x in range(0, max_length, 10):
vv[x] = sum(vals[x:x + 10])
x = numpy.flatnonzero(vv > 0)
s = sum(vals)
if s > 0: vv /= s
lines.append(pylab.plot(x, vv[x]))
legends.append(title)
pylab.xlabel("sequence or domain length / residues")
pylab.ylabel("relative frequency")
pylab.legend(lines, legends)
pylab.savefig(
os.path.expanduser(self.mFilenameDomains +
"_domainsizes_all.png"))
pylab.xlim(0, 2000)
pylab.savefig(
os.path.expanduser(self.mFilenameDomains +
"_domainsizes_small.png"))
pylab.xlim(max_length - max_length // 4, max_length + 1)
pylab.savefig(
os.path.expanduser(self.mFilenameDomains +
"_domainsize_large.png"))
pylab.clf()
## output domain family sizes
lines = []
(yvals, xvals) = numpy.histogram(family_size_sequences,
bins=50,
new=True)
lines.append(pylab.loglog(xvals[:-1], yvals))
(yvals, xvals) = numpy.histogram(family_size_domains,
bins=50,
new=True)
lines.append(pylab.loglog(xvals[:-1], yvals))
pylab.legend(lines, ("sequeces", "domains"))
pylab.xlabel("sequences/domains per family")
pylab.ylabel("relative frequency")
pylab.savefig(
os.path.expanduser(self.mFilenameDomains + "_familysizes.png"))
def create(self):
outfile = SegmentedFile.openfile( self.mFilename, "w" )
for x in range(10):
outfile.write( "%i\n" % x )
outfile.close()