def __init__(self,filename=None,usecols=(0,1,2,3,3),**kwargs):
self.data = {}
self.__sorted_keys = []
self.itr = 0
if not filename is None:
if isinstance(filename,str):
from aio import loadstream
f = loadstream(filename)
readtable = np.genfromtxt(
f,
dtype=self._bedgraphdtype,
usecols=usecols,
**kwargs)
f.close()
elif isinstance(filename,np.ndarray) or isinstance(filename,list):
readtable = np.core.records.fromarrays(
np.array(filename).transpose()[[usecols]],
dtype = self._bedgraphdtype)
for line in readtable:
chrom = line['chrom']
#if np.isnan(line['value']):
#line['value'] = 0
if not chrom in self.data:
self.data[chrom] = []
self.data[chrom].append(line)
else:
#self.data[chrom] = np.append(self.data[chrom],line)
self.data[chrom].append(line)
for chrom in self.data:
self.data[chrom] = np.core.records.fromrecords(self.data[chrom],
dtype = self._bedgraphdtype)
self.data[chrom].sort(kind='heapsort',order='start')
self._flush()
def __init__(self,genomeName,usechr=['#','X']):
datafile = os.path.join(os.path.dirname(os.path.abspath(__file__)),'genomes/' + genomeName + '.info')
f = loadstream(datafile)
self.info = np.genfromtxt(f,dtype=[('chrom','S30'),('length',int)])
f.close()
choices = np.zeros(len(self.info),dtype=bool)
for chrnum in usechr:
if chrnum == '#':
choices = np.logical_or([re.search('chr[0-9]',c) != None for c in self.info['chrom']],choices)
else:
choices = np.logical_or(self.info['chrom'] == ('chr'+str(chrnum)), choices)
self.info = self.info[choices]
def __init__(self,filename,genome=None,resolution=None,usechr=['#','X']):
self._applyedMethods = {}
if isinstance(filename,int):
self.matrix=np.zeros((filename,filename),dtype = np.float32)
elif isinstance(filename,str):
if not os.path.isfile(filename):
raise IOError,"File %s doesn't exist!\n" % (filename)
if os.path.splitext(filename)[1] == '.hdf5' or os.path.splitext(filename)[1] == '.hmat':
h5f = h5py.File(filename,'r')
self.matrix = h5f['matrix'][:]
self.idx = h5f['idx'][:]
if 'applyedMethods' in h5f.keys():
self._applyedMethods = cPickle.loads(h5f['applyedMethods'].value)
if 'genome' in h5f.keys() and 'resolution' in h5f.keys():
self.genome = cPickle.loads(h5f['genome'].value)
self.resolution = cPickle.loads(h5f['resolution'].value)
h5f.close()
else:
from aio import loadstream
f = loadstream(filename)
s = f.next()
line = re.split('\t+|\s+',s.rstrip())
n = len(line) - 3
idx = []
i = 0
tidx = line[0:3];tidx.append('')
idx.append(tidx)
self.matrix = np.zeros((n,n),dtype = np.float32)
self.matrix[i] = line[3:]
for s in f:
i += 1
line = re.split('\t+|\s+',s.rstrip())
tidx = line[0:3];tidx.append('')
idx.append(tidx)
self.matrix[i] = line[3:]
f.close()
self.idx = np.core.records.fromarrays(np.array(idx).transpose(),dtype=self._idxdtype)
#----------------end filename
if isinstance(genome,str) and isinstance(resolution,int):
if hasattr(self,"genome") and hasattr(self,"resolution"):
raise RuntimeError, "Genome and resolution has already been specified."
genomedb = utils.genome(genome,usechr=usechr)
bininfo = genomedb.bininfo(resolution)
flaglist = ['' for i in range(len(bininfo.chromList))]
self.genome = genome
self.resolution = resolution
self._buildindex(bininfo.chromList,bininfo.startList,bininfo.endList,flaglist)
