def get_svd_learn_clusters(accu_path, data=None, sing_threshold=2.0, assign_clstr=0.1, vis=False):
"""First runs the decomposition for maximum number of singular values.
Then reruns on a subset > than some value"""
(N, f) = data.shape
all_components = min(N,f)
U, Sigma, VT = randomized_svd(data, n_components=all_components, n_iter=5, random_state=None)
# print "Sigma:", Sigma
best_components = sum(Sigma > sing_threshold)
U, Sigma, VT = randomized_svd(data, n_components=best_components, n_iter=5, random_state=None)
pred_labels = [np.argmax(doc) if np.max(doc) > assign_clstr else 100 for doc in U]
# print "predicted classes:", pred_labels
utils.screeplot(accu_path, Sigma, all_components, vis)
"""Plot a graph for each right singular vector (VT)"""
max_, min_ = 0, 100
min_=100
for i in VT:
if max(i)>max_: max_ = max(i)
if min(i)<min_: min_ = min(i)
if vis:
with open(accu_path + "/graphlets.p", 'r') as f:
graphlets = pickle.load(f)
for i, vocabulary in enumerate(VT):
title = 'Latent Concept %s' % i
utils.genome(accu_path, vocabulary, [min_, max_], title)
if vis:
for c, v in enumerate(vocabulary):
if v > 0.1:
print "\n",c, graphlets[c]
return U, Sigma, VT
def loadhic(filename,
genome='hg19',
resolution=100000,
usechr=['#', 'X'],
verbose=False):
from . import straw
tgenome = alabutils.genome(genome)
bininfo = tgenome.bininfo(resolution)
m = contactmatrix(len(bininfo.chromList),
genome=genome,
resolution=resolution,
usechr=usechr)
for chr1 in tgenome.info['chrom']:
i = tgenome.getchrnum(chr1)
for chr2 in tgenome.info['chrom']:
j = tgenome.getchrnum(chr2)
if i > j:
continue
if verbose:
print chr1, chr2
result = straw.straw("NONE", filename, chr1[3:], chr2[3:], 'BP',
resolution)
for t in range(len(result[0])):
x = int(result[0][t] / resolution) + bininfo.binStart[i]
y = int(result[1][t] / resolution) + bininfo.binStart[j]
m.matrix[x, y] = result[2][t]
m.matrix[y, x] = result[2][t]
#-
#--
#--
return m
def get_svd_learn_clusters(accu_path,
data=None,
sing_threshold=2.0,
assign_clstr=0.1,
vis=False):
"""First runs the decomposition for maximum number of singular values.
Then reruns on a subset > than some value"""
(N, f) = data.shape
all_components = min(N, f)
U, Sigma, VT = randomized_svd(data,
n_components=all_components,
n_iter=5,
random_state=None)
# print "Sigma:", Sigma
best_components = sum(Sigma > sing_threshold)
U, Sigma, VT = randomized_svd(data,
n_components=best_components,
n_iter=5,
random_state=None)
pred_labels = [
np.argmax(doc) if np.max(doc) > assign_clstr else 100 for doc in U
]
# print "predicted classes:", pred_labels
utils.screeplot(accu_path, Sigma, all_components, vis)
"""Plot a graph for each right singular vector (VT)"""
max_, min_ = 0, 100
min_ = 100
for i in VT:
if max(i) > max_: max_ = max(i)
if min(i) < min_: min_ = min(i)
if vis:
with open(accu_path + "/graphlets.p", 'r') as f:
graphlets = pickle.load(f)
for i, vocabulary in enumerate(VT):
title = 'Latent Concept %s' % i
utils.genome(accu_path, vocabulary, [min_, max_], title)
if vis:
for c, v in enumerate(vocabulary):
if v > 0.1:
print "\n", c, graphlets[c]
return U, Sigma, VT
def dump_lda_output(path, doc_topic, topic_word):
f = open(os.path.join(path, "doc_topic.p"), "w")
pickle.dump(doc_topic, f)
f.close()
f = open(os.path.join(path, "topic_word.p"), "w")
pickle.dump(topic_word, f)
f.close()
"""Plot a graph for each topic word distribution (vocabulary)"""
max_, min_ = 0, 100
min_ = 100
for i in topic_word:
if max(i) > max_: max_ = max(i)
if min(i) < min_: min_ = min(i)
for i, vocabulary in enumerate(topic_word):
title = 'Topic %s' % i
utils.genome(path, vocabulary, [min_, max_], title)
def dump_lda_output(path, doc_topic, topic_word):
f = open(os.path.join(path, "doc_topic.p"), "w")
pickle.dump(doc_topic, f)
f.close()
f = open(os.path.join(path, "topic_word.p"), "w")
pickle.dump(topic_word, f)
f.close()
"""Plot a graph for each topic word distribution (vocabulary)"""
max_, min_ = 0, 100
min_=100
for i in topic_word:
if max(i)>max_: max_ = max(i)
if min(i)<min_: min_ = min(i)
for i, vocabulary in enumerate(topic_word):
title = 'Topic %s' % i
utils.genome(path, vocabulary, [min_, max_], title)
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 alabio 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)
else:
raise RuntimeError, "Undefined input filename type!\n"
#----------------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 = alabutils.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)
def __init__(self,probfile,nucleusRadius=5000.0,contactRange=1,level=None,record=-1):
self.probmat = matrix.contactmatrix(probfile)
self.nbead = len(self.probmat)
#setup log
LEVELS={'debug':logging.DEBUG,'info':logging.INFO,'warning':logging.WARNING,'error':logging.ERROR,'critical':logging.CRITICAL}
loglevel = LEVELS.get(level,logging.NOTSET)
self.logger = logging.getLogger()
self.logger.setLevel(loglevel)
self._log_capture_string = StringIO()
chhandler = logging.StreamHandler(self._log_capture_string)
chhandler.setLevel(loglevel)
self.logger.addHandler(chhandler)
self.logger.setLevel(loglevel)
#setup record
self._record_step = record
if record >= 100:
self.record = []
#CONST
rscale = 1.38 # 20% occupancy
self.nucleusRadius = nucleusRadius # nm
cdensity = 107.45 # bp/nm assuming 197 bp/nucleosomes and 6 nucleosome/11 nm
kscale = (0.75*15**2)**(1.0/3.0) # 3/4*r**2 where r=15nm
self.contactRange = contactRange # surface to surface distance scale of (r1+r2)
# for which 2 beads are considered as contact
#get radius of each bead
self.beadRadius = [rscale * kscale * ((index['end'] - index['start'])/cdensity) ** (1.0/3.0) for index in self.probmat.idx]
#calculate the total volumn of DNA (diploid) and nucleus
dnavol = sum(4. * 3.1415/3. * np.array(self.beadRadius)**3) * 2
nucvol = (4*3.1415/3)*self.nucleusRadius**3
#And chromosome occupancy
dnaocc = dnavol / nucvol
self.logger.debug('occupancy: %.2f with Rnuc %d'%(dnaocc,self.nucleusRadius))
#diploid Rb; 2xtotal haploid beads
self.beadRadius = self.beadRadius + self.beadRadius
# Chromosome territory apply
self.genome = utils.genome(self.probmat.genome)
cscale=1.0
chrvol = nucvol * self.genome.info['length']/sum(self.genome.info['length'])/2
self.chromRadius=cscale*((chrvol/4*3/3.1415)**(1./3.))
#record starting time
self.model = IMP.Model()
self.chain = IMP.container.ListSingletonContainer(self.model)
self.restraints = IMP.RestraintSet(self.model)
#IMP.set_check_level(IMP.USAGE)
IMP.set_check_level(IMP.NONE)
IMP.set_log_level(IMP.SILENT)
#setup nucleus envelope
self.center = IMP.algebra.Vector3D(0,0,0)
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 alabio import loadstream
f = loadstream(filename)
s = f.next()
line = re.split('\t+|\s+', s.rstrip())
n = len(line) - 3
expectn = n
if isinstance(genome, str) and isinstance(resolution, int):
genomedb = alabutils.genome(genome, usechr=usechr)
bininfo = genomedb.bininfo(resolution)
expectn = len(bininfo.chromList)
if expectn != n:
raise RuntimeError, "Dimension don't match, expected %s bins , get %s bins. Please check the input." % (
expectn, n)
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)
else:
raise RuntimeError, "Undefined input filename type!\n"
#----------------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 = alabutils.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)
def __init__(self,
probfile,
nucleusRadius=5000.0,
chromosomeOccupancy=0.2,
contactRange=1,
level=None):
self.probmat = alabmatrix.contactmatrix(probfile)
self.nbead = len(self.probmat)
#setup log
LEVELS = {
'debug': logging.DEBUG,
'info': logging.INFO,
'warning': logging.WARNING,
'error': logging.ERROR,
'critical': logging.CRITICAL
}
loglevel = LEVELS.get(level, logging.NOTSET)
self.logger = logging.getLogger()
self.logger.setLevel(loglevel)
self._log_capture_string = io.StringIO()
chhandler = logging.StreamHandler(self._log_capture_string)
chhandler.setLevel(loglevel)
self.logger.addHandler(chhandler)
self.logger.setLevel(loglevel)
#CONST
#rscale = 1.38 # 20% occupancy
self.occupancy = chromosomeOccupancy #chromosome occupancy in nucleus, defined as diploid_domain_total_volume/nuclear_volume
self.nucleusRadius = nucleusRadius # nm
#cdensity = 107.45 # bp/nm assuming 197 bp/nucleosomes and 6 nucleosome/11 nm
#kscale = (0.75*15**2)**(1.0/3.0) # 3/4*r**2 where r=15nm
self.contactRange = contactRange # surface to surface distance scale of (r1+r2)
# for which 2 beads are considered as contact
self.genome = alabutils.genome(self.probmat.genome)
rho = self.occupancy * self.nucleusRadius**3 / (
2 * sum(self.genome.info['length']))
#get radius of each bead
self.beadRadius = [(rho * (index['end'] - index['start']))**(1.0 / 3.0)
for index in self.probmat.idx]
#self.beadRadius = [rscale * kscale * ((index['end'] - index['start'])/cdensity) ** (1.0/3.0) for index in self.probmat.idx]
#calculate the total volumn of DNA (diploid) and nucleus
dnavol = sum(4. * 3.1415 / 3. * np.array(self.beadRadius)**3) * 2
nucvol = (4 * 3.1415 / 3) * self.nucleusRadius**3
#And chromosome occupancy
dnaocc = dnavol / nucvol
self.logger.debug(u'Occupancy: %.2f with Rnuc %d' %
(dnaocc, self.nucleusRadius))
#diploid Rb; 2xtotal haploid beads
self.beadRadius = self.beadRadius + self.beadRadius
# Chromosome territory apply
#self.genome = alabutils.genome(self.probmat.genome)
cscale = 1.0
chrvol = nucvol * self.genome.info['length'] / sum(
self.genome.info['length']) / 2
self.chromRadius = cscale * ((chrvol / 4 * 3 / 3.1415)**(1. / 3.))
#record starting time
self.model = IMP.Model()
self.chain = IMP.container.ListSingletonContainer(self.model)
self.restraints = IMP.RestraintSet(self.model)
#IMP.set_check_level(IMP.USAGE)
IMP.set_check_level(IMP.NONE)
IMP.set_log_level(IMP.SILENT)
#setup nucleus envelope
self.center = IMP.algebra.Vector3D(0, 0, 0)