type=int,
default=4,
metavar='<int>',
help='x-fold cross-validation [%(default)s]')
parser.add_argument('--seed',
required=False,
type=int,
metavar='<int>',
help='random seed')
arg = parser.parse_args()
if arg.seed: random.seed(arg.seed)
assert (arg.order >= 1)
# read sequences and reformat
seqs1 = [(1, seq) for name, seq in seqio.read_fasta(arg.file1)]
seqs0 = [(0, seq) for name, seq in seqio.read_fasta(arg.file0)]
seqs = seqs1 + seqs0
random.shuffle(seqs) # just in case for real data
# cross-validation splitting
accs = []
for train, test in seqio.cross_validation(seqs, arg.xvalid):
# make pwms from seqs
trues = [seq for label, seq in train if label == 1]
fakes = [seq for label, seq in train if label == 0]
twam = make_wam(trues, arg.order)
fwam = make_wam(fakes, arg.order)
# score vs. test set
parser.add_argument('--real',
required=False,
action='store_true',
help='If the data is real "possibly remove it later"')
arg = parser.parse_args()
#probability should be <= 1.0
assert (arg.mins1 <= 1.0 and arg.mins1 >= 0.0)
assert (arg.mins1 <= 1.0 and arg.mins1 >= 0.0)
if arg.seed:
random.seed(arg.seed)
#read sequences and create a dataframe out of them
seqs1 = [(1, seq[arg.start:arg.stop])
for name, seq in seqio.read_fasta(arg.file1)]
seqs0 = [(0, seq[arg.start:arg.stop])
for name, seq in seqio.read_fasta(arg.file0)]
seqs = seqs1 + seqs0
random.shuffle(seqs)
accs = {}
#splitting data into training and testing
for train, test in seqio.cross_validation(seqs, arg.xvalid):
#extracting trues and fakes out of the train data
trues_train = [seq for label, seq in train if label == 1]
fakes_train = [seq for label, seq in train if label == 0]
#apriori on converted train sets and getting a set of rules
trues_rules = clust_lib.appr(trues_train, arg.start, arg.stop,
arg.mins1)
help='unconstrained')
arg = parser.parse_args()
# options not used: homopolymer, circular, bedfile
# reverse, complement, invert
# sensitivity - for the peak caller
# residuals, dump
# top
# localaverageenergy
# create working directory if necessary
if not os.path.exists(arg.out): os.system(f'mkdir {arg.out}')
# create fasta file with proper definition line
names = []
with open(f'{arg.out}/fasta', 'w') as fp:
for name, seq in seqio.read_fasta(arg.fasta):
tok = name.split(' ')
names.append(tok[0])
defline = f">{tok[0]} range=z:1:{len(seq)} 5'pad=0 3'pad=0"
defline += ' strand=+ repeatMasking=none'
fp.write(defline)
fp.write('\n')
fp.write(seq)
fp.write('\n')
# run rlooper
cmd = (f'{exe} {arg.out}/fasta {arg.out}.output')
cmd += f' --N {arg.n}'
cmd += f' --sigma {arg.s}'
cmd += f' --a arg.a'
cmd += f' --minlength 2'