#!/usr/bin/env python

import sys

import glob

from scipy import stats as ss

filelist=glob.glob('*/*.pu')

#intron_PC_mouse_invivo_18 11_4177548_4177632_3_11;0.5950;0.5950;0.5311;0.5059;0.5059;0.4897;0.0999;0.0396;0.1253;0.0129;0.0066;0.0069;0.0064;0.0040;0.0038;0.0020;0.0019;0.0179;0.0185;0.0024;0.0002;0.0002;0.0004;0.0004;0.0071;0.0042;0.0009;0.0052;0.0604;0.0150;0.0123;0.0179;0.0152;0.0003;0.0000;0.0001;0.0001;0.0001;0.0001;0.0034;0.2731;0.2687;0.4665;0.6558;0.6453;0.6558;0.6349;0.6666;0.7347;0.7347;0.3966;0.3839;0.2821;0.2438;0.1820;0.1820;0.1820;0.1791;0.0027;0.0017;0.0016;0.0018;0.0085;0.0086;0.0117;0.0097;0.0114;0.0002;0.0003;0.0021;0.0021;0.0000;0.0002;0.0002;0.0028;0.0028;0.0028;0.0030;0.0045;

pudict={}

for file in filelist:

with open(file) as infile:

for line in infile:

tmpline=line.strip('[\n;]').split(';')

seqid=tmpline[0].split()[0]

#seqnum=seqid.split('_')[-1]

#sourcefile='_'.join(seqid.split('_')[:-1])

flankleft,flankright=[int(x) for x in tmpline[0].split()[1].split('_')[-2:]]

#we only want the 65 hexamers covering out 70bp of interest, so adjust for flanking regions

#first hexamer will start at flankleft

hexcount=0

for idx,val in enumerate(tmpline[1:]):

if idx >= flankleft:

try:

pudict[seqid][hexcount].append(float(val))

except KeyError:

pudict[seqid]={}

for i in range(0,65):

pudict[seqid][i]=[]

pudict[seqid][hexcount].append(float(val))

except ValueError:

#catch any remaining '-' out there

sys.stderr.write('theres a dash here: %s\n' % tmpline)

hexcount+=1

if hexcount >= 65:

break

metadict={}

with open('meanpu.byseq.out','w') as outfile:

for id,posdict in pudict.items():

sourcefile='_'.join(id.split('_')[:-1])

for pos,puvals in posdict.items():

try:

meanpu=sum(puvals)/float(len(puvals))

sempu=ss.sem(puvals)

except ZeroDivisionError:

sys.stderr.write('%s,%s,%s\n' % (id,pos,puvals))

continue

try:

metadict[sourcefile][pos].append(meanpu)

except KeyError:

metadict[sourcefile]={}

for i in range(0,65):

metadict[sourcefile][i]=[]

metadict[sourcefile][pos].append(meanpu)

outfile.write('%s\t%s\t%s\t%s\n' % (id,pos,meanpu,sempu))

with open('meanpu.bypos.out','w') as outfile:

for sourcefile,poslist in metadict.items():

for pos,vals in poslist.items():

meanpos=sum(vals)/float(len(vals))

sempos=ss.sem(vals)

outfile.write('%s\t%s\t%s\t%s\n' % (sourcefile,pos,meanpos,sempos))