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)) )