def wrapper(args):
T_LibCounts = args.totallibcounts
T_mRNACounts = args.totalmRNAcounts
if T_LibCounts <=0 or T_mRNACounts <= 0:
raise SortSeqError('Counts must be greater than zero')
model_df = io.load_model(args.model)
if args.i:
df = pd.io.parsers.read_csv(args.i,delim_whitespace=True)
else:
df = pd.io.parsers.read_csv(sys.stdin,delim_whitespace=True)
#make sure the library is not already sorted
if len(utils.get_column_headers(df)) > 0:
raise SortSeqError('Library already sorted!')
header = df.columns
libcounts,expcounts = main(df,model_df,T_LibCounts,T_mRNACounts,start=args.start,end=args.end)
#add these counts to input dataframe
lc = pd.Series(libcounts,name='ct_0')
ec = pd.Series(expcounts,name='ct_1')
df['ct_0'] = lc
df['ct_1'] = ec
df['ct'] = df[['ct_0','ct_1']].sum(axis=1)
if args.out:
outloc = open(args.out,'w')
else:
outloc = sys.stdout
pd.set_option('max_colwidth',int(1e8))
# Validate dataframe for writting
df = qc.validate_dataset(df,fix=True)
io.write(df,outloc)
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,coarse_graining_level = args.coarse_graining_level)
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)
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,
coarse_graining_level=args.coarse_graining_level,
rsquared=args.rsquared,
return_freg=args.return_freg)
#format output
output_df = pd.DataFrame([MI], columns=['info'])
#if you calculated error add column to your data frame
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
#set output option, this will remove column length restriction
pd.set_option('max_colwidth', int(1e8))
#write to file.
output_df.to_string(outloc,
index=False,
col_space=10,
float_format=utils.format_string)
import mpathic.qc as qc
import mpathic.io as io
import os
import mpathic.profile_ct as profile_ct
import pdb
from mpathic import SortSeqError
import cProfile
import mpathic.profile_info as profile_info
import mpathic.learn_model as learn_model
import mpathic.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'
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 mpathic.qc as qc
import mpathic.io as io
import os
import mpathic.profile_ct as profile_ct
import pdb
from mpathic import SortSeqError
import cProfile
import mpathic.profile_info as profile_info
import mpathic.learn_model as learn_model
import mpathic.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'
import mpathic.qc as qc
import mpathic.io as io
import os
import mpathic.profile_ct as profile_ct
import pdb
from mpathic import SortSeqError
import cProfile
import mpathic.profile_info as profile_info
import mpathic.learn_model as learn_model
import mpathic.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_dms'
import mpathic.qc as qc
import mpathic.io as io
import os
import mpathic.profile_ct as profile_ct
import pdb
from mpathic import SortSeqError
import cProfile
import mpathic.profile_info as profile_info
import mpathic.learn_model as learn_model
import mpathic.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'
import mpathic.qc as qc
import mpathic.io as io
import os
import mpathic.profile_ct as profile_ct
import pdb
from mpathic import SortSeqError
import cProfile
import mpathic.profile_info as profile_info
import mpathic.learn_model as learn_model
import mpathic.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 mpathic.qc as qc
import mpathic.io as io
import os
import mpathic.profile_ct as profile_ct
import pdb
from mpathic import SortSeqError
import cProfile
import mpathic.profile_info as profile_info
import mpathic.learn_model as learn_model
import mpathic.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'
