def main(args):
import argparse
import gc
import pathlib
from sklearn.externals import joblib
import numpy as np
from peakachu import scoreUtils, utils
np.seterr(divide='ignore', invalid='ignore')
pathlib.Path(args.output).mkdir(parents=True, exist_ok=True)
model = joblib.load(args.model)
# more robust to check if a file is .hic
hic_info = utils.read_hic_header(args.path)
if hic_info is None:
hic = False
import cooler
Lib = cooler.Cooler(args.path)
chromosomes = Lib.chromnames[:]
#nam = args.path.split('.cool')[0]
else:
hic = True
chromosomes = utils.get_hic_chromosomes(args.path, args.resolution)
#nam = args.path.split('.hic')[0]
#nam = nam.split('/')[-1]
for key in chromosomes:
if key.startswith('chr'):
cname = key
else:
cname = 'chr'+key
if not hic:
X = scoreUtils.Chromosome(Lib.matrix(balance=args.balance, sparse=True).fetch(key).tocsr(),
model=model,
cname=cname, lower=args.lower,
upper=args.upper, res=args.resolution,
width=args.width)
else:
if args.balance:
X = scoreUtils.Chromosome(utils.csr_contact_matrix('KR', args.path, key, key, 'BP', args.resolution),
model=model,
cname=cname, lower=args.lower,
upper=args.upper, res=args.resolution,
width=args.width)
else:
X = scoreUtils.Chromosome(utils.csr_contact_matrix('NONE', args.path, key, key, 'BP', args.resolution),
model=model,
cname=cname, lower=args.lower,
upper=args.upper, res=args.resolution,
width=args.width)
result, R = X.score()
X.writeBed(args.output, result, R)
def main(args):
import numpy as np
np.seterr(divide='ignore', invalid='ignore')
from peakachu import scoreUtils
pathlib.Path(args.output).mkdir(parents=True, exist_ok=True)
import cooler
Lib = cooler.Cooler(args.path)
nam = args.path.split('.cool')[0]
nam = nam.split('/')[-1]
#resolution = Lib.binsize
resolution = args.resolution
chroms = []
model = joblib.load(args.model)
for key in Lib.chromnames:
if key.startswith('c'):
cname = key
else:
cname = 'chr' + key
X = scoreUtils.Chromosome(Lib.matrix(balance=args.balance,
sparse=True).fetch(key).tocsr(),
model=model,
cname=cname,
lower=args.lower,
upper=args.upper,
res=resolution,
width=args.width)
result, R = X.score()
X.writeBed(args.output, result, R)
def main(args):
import numpy as np
np.seterr(divide='ignore', invalid='ignore')
from peakachu import scoreUtils
pathlib.Path(args.output).mkdir(parents=True, exist_ok=True)
cikada = args.model
cikada = cikada.split('.pk')[0]
cikada = 'ch' + cikada.split('ch')[-1]
model = joblib.load(args.model)
import cooler
Lib = cooler.Cooler(args.path)
if cikada in Lib.chromnames:
ccname = cikada
else:
ccname = cikada.split('hr')[1]
#resolution = Lib.binsize
resolution = args.resolution
X = scoreUtils.Chromosome(Lib.matrix(balance=args.balance,
sparse=True).fetch(ccname).tocsr(),
model=model,
cname=cikada,
lower=args.lower,
upper=args.upper,
res=resolution,
width=args.width)
result, R = X.score()
X.writeBed(args.output, result, R)
def main(args):
import argparse, gc, pathlib, os
import numpy as np
from sklearn.externals import joblib
from peakachu import scoreUtils, utils
np.seterr(divide='ignore', invalid='ignore')
pathlib.Path(args.output).mkdir(parents=True, exist_ok=True)
model = joblib.load(args.model)
hic_info = utils.read_hic_header(
args.path) # more robust to check if a file is .hic
if hic_info is None:
hic = False
else:
hic = True
if not hic:
import cooler
Lib = cooler.Cooler(args.path)
chromosomes = Lib.chromnames[:]
else:
chromosomes = list(hic_info['chromsizes'])
pre = utils.find_chrom_pre(chromosomes)
tmp = os.path.split(args.model)[1] # support full path
ccname = pre + tmp.split('.pk')[0].lstrip(
'chr') # ccname is consistent with chromosome labels in .hic / .cool
cikada = 'chr' + ccname.lstrip('chr') # cikada always has prefix "chr"
if not hic:
X = scoreUtils.Chromosome(Lib.matrix(
balance=args.balance, sparse=True).fetch(ccname).tocsr(),
model=model,
cname=cikada,
lower=args.lower,
upper=args.upper,
res=args.resolution,
width=args.width)
else:
if args.balance:
X = scoreUtils.Chromosome(utils.csr_contact_matrix(
'KR', args.path, ccname, ccname, 'BP', args.resolution),
model=model,
cname=cikada,
lower=args.lower,
upper=args.upper,
res=args.resolution,
width=args.width)
else:
X = scoreUtils.Chromosome(utils.csr_contact_matrix(
'NONE', args.path, ccname, ccname, 'BP', args.resolution),
model=model,
cname=cikada,
lower=args.lower,
upper=args.upper,
res=args.resolution,
width=args.width)
result, R = X.score()
X.writeBed(args.output, result, R)