def wrapper(args):
try:
npar = args.noiseparam.strip('[').strip(']').split(',')
except:
npar = []
nbins = args.nbins
# Run funciton
if args.i:
df = pd.io.parsers.read_csv(
args.i,delim_whitespace=True,
dtype={'seqs':str,'batch':int})
else:
df = pd.io.parsers.read_csv(
sys.stdin,delim_whitespace=True,
dtype={'seqs':str,'batch':int})
if len(utils.get_column_headers(df)) > 0:
raise SortSeqError('Library already sorted!')
model_df = io.load_model(args.model)
output_df = main(
df,model_df,args.noisemodel,npar,
nbins,start=args.start,end=args.end)
if args.out:
outloc = open(args.out,'w')
else:
outloc = sys.stdout
pd.set_option('max_colwidth',int(1e8))
# Validate dataframe for writting
output_df = qc.validate_dataset(output_df,fix=True)
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):
data_df = io.load_dataset(args.dataset)
# Take input from standard input or through the -i flag.
if args.model:
model_df = io.load_model(args.model)
else:
model_df = io.load_model(sys.stdin)
MI,Std = main(
data_df,model_df,start=args.start,
end=args.end,err=args.err)
output_df = pd.DataFrame([MI],columns=['info'])
if args.err:
output_df = pd.concat([output_df,pd.Series(Std,name='info_err')],axis=1)
if args.out:
outloc = open(args.out,'w')
else:
outloc = sys.stdout
pd.set_option('max_colwidth',int(1e8))
output_df.to_string(
outloc, index=False,col_space=10,float_format=utils.format_string)
import sortseq_tools.qc as qc
import sortseq_tools.io as io
import os
import sortseq_tools.profile_ct as profile_ct
import pdb
from sortseq_tools import SortSeqError
import cProfile
import sortseq_tools.profile_info as profile_info
import sortseq_tools.learn_model as learn_model
import sortseq_tools.predictiveinfo as predictiveinfo
import pstats
#load in data sets for the test, we will just use the sort-seq crp-wt set
df = io.load_dataset('input/mpra.txt')
model_df = io.load_model('input/mpra_model')
#Profile profile_info
#stats_fn = 'Profile_profile_info'
#stats_fn_hr = 'Profile_profile_info_hr'
#Profile.run('''profile_info.main(df,method='nsb')''',stats_fn)
#Reformat and print to human readable profile
#p = pstats.Stats(stats_fn,stream=open(stats_fn_hr,'w'))
#p.strip_dirs()
#p.sort_stats('cumtime')
#p.print_stats()
df_copy = df.copy()
#profile learn_model lm=LS
stats_fn = 'profile/Profile_learn_model_LS_mpra'
import sortseq_tools.qc as qc
import sortseq_tools.io as io
import os
import sortseq_tools.profile_ct as profile_ct
import pdb
from sortseq_tools import SortSeqError
import cProfile
import sortseq_tools.profile_info as profile_info
import sortseq_tools.learn_model as learn_model
import sortseq_tools.predictiveinfo as predictiveinfo
import pstats
#load in data sets for the test, we will just use the sort-seq crp-wt set
df = io.load_dataset('input/dms_1_formatted')
model_df = io.load_model('input/dms_1_model')
#Profile profile_info
#stats_fn = 'Profile_profile_info'
#stats_fn_hr = 'Profile_profile_info_hr'
#Profile.run('''profile_info.main(df,method='nsb')''',stats_fn)
#Reformat and print to human readable profile
#p = pstats.Stats(stats_fn,stream=open(stats_fn_hr,'w'))
#p.strip_dirs()
#p.sort_stats('cumtime')
#p.print_stats()
df_copy = df.copy()
#profile learn_model lm=LS
stats_fn = 'profile/Profile_learn_model_LS_mpra'
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)
import sortseq_tools.qc as qc
import sortseq_tools.io as io
import os
import sortseq_tools.profile_ct as profile_ct
import pdb
from sortseq_tools import SortSeqError
import cProfile
import sortseq_tools.profile_info as profile_info
import sortseq_tools.learn_model as learn_model
import sortseq_tools.predictiveinfo as predictiveinfo
import pstats
#load in data sets for the test, we will just use the sort-seq crp-wt set
df = io.load_dataset('input/rnap-wt-format.txt')
model_df = io.load_model('input/rnap_model')
#Profile profile_info
#stats_fn = 'Profile_profile_info'
#stats_fn_hr = 'Profile_profile_info_hr'
#Profile.run('''profile_info.main(df,method='nsb')''',stats_fn)
#Reformat and print to human readable profile
#p = pstats.Stats(stats_fn,stream=open(stats_fn_hr,'w'))
#p.strip_dirs()
#p.sort_stats('cumtime')
#p.print_stats()
df_copy = df.copy()
#profile learn_model lm=LS
stats_fn = 'profile/Profile_learn_model_LS'
