def __call__(self, track, slice=None):
fn = os.path.join(
DATADIR,
"replicated_intervals/%(track)s.peakshape.gz.matrix_%(slice)s.gz" %
locals())
if not os.path.exists(fn):
return
x = IOTools.openFile(fn)
matrix, rownames, colnames = IOTools.readMatrix(x)
nrows = len(rownames)
if nrows == 0:
return
if nrows > self.scale:
take = numpy.array(numpy.floor(
numpy.arange(0, nrows,
float(nrows + 1) / self.scale)),
dtype=int)
rownames = [rownames[x] for x in take]
matrix = matrix[take]
return odict(
(('matrix', matrix), ('rows', rownames), ('columns', colnames)))
def __call__(self, track, slice=None):
fn = "ortholog_pairs_with_feature.matrix2"
if not os.path.exists(fn):
return
x = IOTools.openFile(fn)
matrix, rownames, colnames = IOTools.readMatrix(x)
return odict((("matrix", matrix), ("rows", rownames), ("columns", colnames)))
def __call__(self, track, slice=None):
fn = "ortholog_pairs_with_feature.matrix2"
if not os.path.exists(fn):
return
x = IOTools.openFile(fn)
matrix, rownames, colnames = IOTools.readMatrix(x)
return odict(
(('matrix', matrix), ('rows', rownames), ('columns', colnames)))
def __call__(self, track, slice = None):
fn = os.path.join( DATADIR, "%(track)s.peakshape.tsv.gz.matrix_%(slice)s.gz" % locals() )
if not os.path.exists( fn ):
return
matrix, rownames, colnames = IOTools.readMatrix( IOTools.openFile( fn ))
nrows = len(rownames)
if nrows == 0: return
if nrows > 1000:
take = numpy.array( numpy.floor( numpy.arange( 0, nrows, nrows / 1000 ) ), dtype = int )
rownames = [ rownames[x] for x in take ]
matrix = matrix[ take ]
return odict( (('matrix', matrix),
('rows', rownames),
('columns', colnames)) )
def __call__(self, track, slice = None):
pattern = self.pattern
fn = os.path.join( DATADIR, "liver_vs_testes/%(track)s%(pattern)s.matrix_%(slice)s.gz" % locals() )
if not os.path.exists( fn ):
return
x = IOTools.openFile( fn )
matrix, rownames, colnames = IOTools.readMatrix( x )
nrows = len(rownames)
if nrows == 0: return
if nrows > self.scale:
take = numpy.array( numpy.floor( numpy.arange( 0, nrows, float(nrows + 1) / self.scale ) ), dtype = int )
rownames = [ rownames[x] for x in take ]
matrix = matrix[ take ]
return odict( (('matrix', matrix),
('rows', rownames),
('columns', colnames)) )