def wrapper(args):
#validate some of the input arguments
qc.validate_input_arguments_for_learn_model(
foreground=args.foreground,background=args.background,alpha=args.penalty,
modeltype=args.modeltype,learningmethod=args.learningmethod,
start=args.start,end=args.end,iteration=args.iteration,
burnin=args.burnin,thin=args.thin,pseudocounts=args.pseudocounts,)
inloc = io.validate_file_for_reading(args.i) if args.i else sys.stdin
input_df = io.load_dataset(inloc)
outloc = io.validate_file_for_writing(args.out) if args.out else sys.stdout
#pdb.set_trace()
output_df = main(input_df,lm=args.learningmethod,\
modeltype=args.modeltype,db=args.db_filename,\
LS_means_std=args.LS_means_std,\
iteration=args.iteration,\
burnin=args.burnin,thin=args.thin,start=args.start,end=args.end,\
runnum=args.runnum,initialize=args.initialize,\
foreground=args.foreground,background=args.background,\
alpha=args.penalty,pseudocounts=args.pseudocounts,
verbose=args.verbose)
io.write(output_df,outloc)
def wrapper(args):
""" Commandline wrapper for main()
"""
inloc = io.validate_file_for_reading(args.i) if args.i else sys.stdin
outloc = io.validate_file_for_writing(args.out) if args.out else sys.stdout
input_df = io.load_dataset(inloc)
output_df = main(input_df, bin=args.bin, start=args.start, end=args.end, err=args.err)
io.write(output_df, outloc)
def wrapper(args):
inloc = io.validate_file_for_reading(args.i) if args.i else sys.stdin
dataset_df = io.load_dataset(inloc)
model_df = io.load_model(args.model)
output_df = main(dataset_df=dataset_df, model_df=model_df,\
left=args.left, right=args.right)
outloc = io.validate_file_for_writing(args.out) if args.out else sys.stdout
io.write(output_df,outloc,fast=args.fast)
def wrapper(args):
""" Commandline wrapper for main()
"""
inloc = io.validate_file_for_reading(args.i) if args.i else sys.stdin
outloc = io.validate_file_for_writing(args.out) if args.out else sys.stdout
# Get filelist
filelist_df = io.load_filelist(inloc)
inloc.close()
# Get tagkeys dataframe if provided
if args.tagkeys:
tagloc = io.validate_file_for_reading(args.tagkeys)
tags_df = io.load_tagkey(tagloc)
tagloc.close()
else:
tags_df = None
output_df = main(filelist_df,tags_df=tags_df,seq_type=args.seqtype)
io.write(output_df,outloc,fast=args.fast)
def wrapper(args):
""" Commandline wrapper for main()
"""
inloc = io.validate_file_for_reading(args.i) if args.i else sys.stdin
outloc = io.validate_file_for_writing(args.out) if args.out else sys.stdout
if args.bins_df_name:
bins_df = pd.io.parsers.read_csv(args.bins_df_name,
delim_whitespace=True)
else:
bins_df = None
input_df = io.load_dataset(inloc)
output_df = main(input_df,
bin=args.bin,
start=args.start,
end=args.end,
bins_df=bins_df,
pseudocounts=args.pseudocounts,
return_profile=args.return_profile)
io.write(output_df, outloc)
def wrapper(args):
""" Wrapper for functions io.load_* and io.write
"""
# Determine input and output
inloc = io.validate_file_for_reading(args.i) if args.i else sys.stdin
outloc = io.validate_file_for_writing(args.out) if args.out else sys.stdout
try:
# Get load function corresponding to file type
func = filetype_to_loadfunc_dict[str(args.type)]
# Run load function on input
df = func(inloc)
# Write df to stdout or to outfile
io.write(df,outloc,fast=args.fast)
except SortSeqError:
raise
def wrapper(args):
""" Wrapper for function for scan_model.main()
"""
# Prepare input to main
model_df = io.load_model(args.model)
seqtype, modeltype = qc.get_model_type(model_df)
L = model_df.shape[0]
if modeltype == "NBR":
L += 1
chunksize = args.chunksize
if not chunksize > 0:
raise SortSeqError("chunksize=%d must be positive" % chunksize)
if args.numsites <= 0:
raise SortSeqError("numsites=%d must be positive." % args.numsites)
if args.i and args.seq:
raise SortSeqError("Cannot use flags -i and -s simultaneously.")
# If sequence is provided manually
if args.seq:
pos_offset = 0
contig_str = args.seq
# Add a bit on end if circular
if args.circular:
contig_str += contig_str[: L - 1]
contig_list = [(contig_str, "manual", pos_offset)]
# Otherwise, read sequence from FASTA file
else:
contig_list = []
inloc = io.validate_file_for_reading(args.i) if args.i else sys.stdin
for i, record in enumerate(SeqIO.parse(inloc, "fasta")):
name = record.name if record.name else "contig_%d" % i
# Split contig up into chunk)size bits
full_contig_str = str(record.seq)
# Add a bit on end if circular
if args.circular:
full_contig_str += full_contig_str[: L - 1]
# Define chunks containing chunksize sites
start = 0
end = start + chunksize + L - 1
while end < len(full_contig_str):
contig_str = full_contig_str[start:end]
contig_list.append((contig_str, name, start))
start += chunksize
end = start + chunksize + L - 1
contig_str = full_contig_str[start:]
contig_list.append((contig_str, name, start))
if len(contig_list) == 0:
raise SortSeqError("No input sequences to read.")
# Compute results
outloc = io.validate_file_for_writing(args.out) if args.out else sys.stdout
output_df = main(model_df, contig_list, numsites=args.numsites, verbose=args.verbose)
# Write df to stdout or to outfile
io.write(output_df, outloc, fast=args.fast)