fd.writelines(l)
fd.close()
l_xtics = []
l_sort.sort()
for average,s_col,s in l_sort:
print s
## l_xtics += [s.split('\t')[-1].strip()]
l_xtics += [s_col]
l_xtics.reverse()
for method in ['alpha','heavy','chi1',]:
prefix = 'rmsd_statistics_%s_%s' %(protein,method,)
gnuplot.histogram(
prefix, d[method], l_xtics,
ylabel = '%s RMSD' %(method),
## ymax = 5,
## bool_remove = False,
)
##
## statistics, excluding cross effects
##
for prop,l_col1,l_col2, in [
['author',[5,7,11,],[10,9,12,4,],],
['model',[5,10,],[11,4,],],
['spacegroup',['sameSG',],['diffSG',],],
['A_spacegroup',[4,5,7,8,9,10,11,12,],[3,],],
['B_all_excl_special',range(3,6)+range(7,13),[],],
['C_all',range(2,13),[],],
]:
for method in ['alpha','heavy','chi1',]:
def main(
self,
l_wts,
d_pred,
l_xtics,
):
import os, sys
sys.path.append('/home/people/tc/svn/tc_sandbox/misc/')
import gnuplot, statistics
## parse experimental data
d_exp = self.dic2csv(l_xtics)
## get cwd
dir_main = os.getcwd()
l_r = []
for pdb in l_wts:
print pdb, l_wts.index(pdb)
if not os.path.isdir('%s/%s' % (dir_main, pdb)):
os.mkdir('%s/%s' % (dir_main, pdb))
os.chdir('%s/%s' % (dir_main, pdb))
self.pre_whatif(pdb)
if pdb in [
'2vb1',
'1vdp',
]:
os.system('cp %s_monomer.pdb %s_protonated.pdb' % (pdb, pdb))
## else:
## self.whatif(pdb)
## self.calculate_chemical_shifts(pdb)
## parse computational predictions
d_pred = self.parse_chemical_shifts(pdb, d_pred)
## calculate correlation coefficients
l_exp = []
l_pred = []
for titgrp in d_exp.keys():
res_number = int(titgrp[1:])
res_symbol = titgrp[0]
res_name = self.d_ressymbol2resname[res_symbol]
for nucleus in d_exp[titgrp].keys():
cs_exp = d_exp[titgrp][nucleus]
l_exp += [cs_exp]
index = nucleus.index('N-HN')
cs_pred = d_pred['%s%i' %
(res_name,
res_number)][nucleus[:index]][-1]
l_pred += [cs_pred]
r = statistics.correlation(l_exp, l_pred)
l_r += [r]
## print titgrp,r
## print sum(l_r)/len(l_r), min(l_r), max(l_r)
## change from local dir to main dir
os.chdir(dir_main)
## plots
for titgrp1 in d_exp.keys() + ['E35']:
res_number = int(titgrp1[1:])
res_symbol = titgrp1[0]
res_name = self.d_ressymbol2resname[res_symbol]
titgrp3 = '%s%i' % (res_name, res_number)
prefix = 'delta_cs_%s' % (titgrp3)
ylabel = '{/Symbol D}{/Symbol w}_H'
title = titgrp3
gnuplot.histogram(
d_pred[titgrp3],
prefix,
l_xtics,
ylabel=ylabel,
title=title,
## l_plotdatafiles=['E34.txt'],
)
return
def main(
self,l_wts,d_pred,l_xtics,
):
import os, sys
sys.path.append('/home/people/tc/svn/tc_sandbox/misc/')
import gnuplot, statistics
## parse experimental data
d_exp = self.dic2csv(l_xtics)
## get cwd
dir_main = os.getcwd()
l_r = []
for pdb in l_wts:
print pdb, l_wts.index(pdb)
if not os.path.isdir('%s/%s' %(dir_main,pdb)):
os.mkdir('%s/%s' %(dir_main,pdb))
os.chdir('%s/%s' %(dir_main,pdb))
self.pre_whatif(pdb)
if pdb in ['2vb1','1vdp',]:
os.system('cp %s_monomer.pdb %s_protonated.pdb' %(pdb,pdb))
## else:
## self.whatif(pdb)
## self.calculate_chemical_shifts(pdb)
## parse computational predictions
d_pred = self.parse_chemical_shifts(pdb,d_pred)
## calculate correlation coefficients
l_exp = []
l_pred = []
for titgrp in d_exp.keys():
res_number = int(titgrp[1:])
res_symbol = titgrp[0]
res_name = self.d_ressymbol2resname[res_symbol]
for nucleus in d_exp[titgrp].keys():
cs_exp = d_exp[titgrp][nucleus]
l_exp += [cs_exp]
index = nucleus.index('N-HN')
cs_pred = d_pred['%s%i' %(res_name,res_number)][nucleus[:index]][-1]
l_pred += [cs_pred]
r = statistics.correlation(l_exp,l_pred)
l_r += [r]
## print titgrp,r
## print sum(l_r)/len(l_r), min(l_r), max(l_r)
## change from local dir to main dir
os.chdir(dir_main)
## plots
for titgrp1 in d_exp.keys()+['E35']:
res_number = int(titgrp1[1:])
res_symbol = titgrp1[0]
res_name = self.d_ressymbol2resname[res_symbol]
titgrp3 = '%s%i' %(res_name,res_number)
prefix = 'delta_cs_%s' %(titgrp3)
ylabel = '{/Symbol D}{/Symbol w}_H'
title = titgrp3
gnuplot.histogram(
d_pred[titgrp3],prefix,l_xtics,
ylabel=ylabel,title=title,
## l_plotdatafiles=['E34.txt'],
)
return
