def spread_filters(args: dict, run_filters: RunFilters, score_dict: dict):
plt.figure(1)
col_num = len(list(run_filters.keys()))
lims = {'sasa': [0, 2000], 'ddg': [-40, 5], 'packstat': [0.0, 1.0], 'shape': [0.0, 1.0], 'score': [-500, 10],
'rmsd': [0.0, 30.0]}
for i, flt in enumerate(run_filters.filters.values()):
plt.subplot(1, col_num, i+1)
plt.title(flt.filter_type)
if flt.filter_type == 'buried_2':
plt.hist(flt.all_seen)
else:
plt.boxplot(flt.all_seen)
plt.ylim(lims[flt.filter_type])
plt.show()
def all_who_pass_run_filters(args: dict, score_dict: dict, runfilters: RunFilters) -> (dict, dict):
"""
:param args: run arguments
:param score_dict: score dictionary
:param runfilters: run filters
:return: a list of dict of scores
"""
passed, failed = {}, {}
msg = False
for k, v in score_dict.items():
res, msg = runfilters.test_all(v)
if res:
passed[k] = v
else:
failed[k] = v
if msg:
print(msg)
return passed, failed
def find_thresholds_by_rmsd(args):
"""
:param args: run args
:return: minimal/maximal (depending on threshold type) of the different filters that will pass all structures with
RMSD under args['rmsd_threshold'].
"""
score_dict = score2dict(args['score_file'])
filter_thresholds = dict(a_score=-100000., a_sasa=100000., a_shape=1.0, total_score=1000000., a_ddg=-100.,
a_packstat=1.0, a_buried_2=0)
passed_rmsd = []
for name, sc in score_dict.items():
if sc['rmsd'] <= args['rmsd_threshold']:
filter_thresholds['a_score'] = max([filter_thresholds['a_score'], sc['score']])
filter_thresholds['a_sasa'] = min([filter_thresholds['a_sasa'], sc['sasa']])
filter_thresholds['a_shape'] = min([filter_thresholds['a_shape'], sc['shape']])
filter_thresholds['total_score'] = min([filter_thresholds['total_score'], sc['total_score']])
filter_thresholds['a_ddg'] = max([filter_thresholds['a_ddg'], sc['ddg']])
filter_thresholds['a_packstat'] = min([filter_thresholds['a_packstat'], sc['packstat']])
filter_thresholds['a_buried_2'] = max([filter_thresholds['a_buried_2'], sc['buried_2']])
passed_rmsd.append(sc)
print('found %i scores with rmsd <= %f' % (len(passed_rmsd), args['rmsd_threshold']))
# print('the old thresholds were:\n%s' % '\n'.join(['%s %f' % (k, v) for k, v in dimer_data().items()]))
print('the old thresholds were:', generate_run_filters().report())
print('defined these new filters:\n%s' % '\n'.join(['%s %f' % (k, v) for k, v in filter_thresholds.items()]))
args['dimer_data'] = filter_thresholds
run_filters_updated = RunFilters()
run_filters_updated.append_filter(Filter(name='a_ddg', typ='ddg', threshold=filter_thresholds['a_ddg'],
limits=[-10000, 10000], under_over='under', g_name='$\Delta$$\Delta$G'))
run_filters_updated.append_filter(Filter(name='a_score', typ='score', threshold=filter_thresholds['a_score'],
limits=[-10000, 10000], under_over='under', g_name='Score'))
run_filters_updated.append_filter(Filter(name='a_sasa', typ='sasa', threshold=filter_thresholds['a_sasa'],
limits=[0, 100000], under_over='over', g_name='SASA'))
run_filters_updated.append_filter(Filter(name='a_shape', typ='shape', threshold=filter_thresholds['a_shape'],
limits=[0.0, 1.0], under_over='over', g_name='Shape Complementarity'))
run_filters_updated.append_filter(Filter(name='a_packstat', typ='packstat',
threshold=filter_thresholds['a_packstat'], limits=[0.0, 1.0],
under_over='over', g_name='PackStat'))
run_filters_updated.append_filter(Filter(name='a_buried_2', typ='buried_2',
threshold=filter_thresholds['a_buried_2'], limits=[0, 100],
under_over='under', g_name='UnsatisfiedHBonds'))
run_filters_updated.append_filter(Filter(name='a_rms', typ='rmsd', threshold=1000, limits=[0, 1000],
under_over='under', g_name='RMSD'))
passed, failed = all_who_pass_run_filters(args, score_dict, run_filters_updated)
# this_vs_that(args, run_filters_updated, passed, failed, score_dict)
multiple_plots(args, run_filters_updated, passed, failed, score_dict)
args['x'], args['y'] = 'rmsd', 'ddg'
this_vs_that(args, run_filters_updated, passed, failed, score_dict)
plt.show()
def generate_run_filters(args=None, filter_thresholds=None) -> RunFilters:
"""
the original thresholds were: ddg -22.5, score -390, sasa 1460, shape 0.623, packstat 0.641, buried_2 3
:rtype : RunFilters
"""
if args is None:
args = {'ddg': -18.0, 'sasa': 1200, 'shape': 0.6, 'packstat': 0.6}
run_filters = RunFilters()
if filter_thresholds is None:
run_filters.append_filter(Filter(name='a_ddg', typ='ddg', threshold=-abs(args['ddg']), limits=[-10000, 10000],
under_over='under', g_name='$\Delta$$\Delta$G'))
run_filters.append_filter(Filter(name='a_score', typ='score', threshold=-390.0, limits=[-10000, 10000],
under_over='under', g_name='Score'))
run_filters.append_filter(Filter(name='a_sasa', typ='sasa', threshold=args['sasa'], limits=[0, 100000],
under_over='over', g_name='SASA'))
run_filters.append_filter(Filter(name='a_shape', typ='shape', threshold=args['shape'], limits=[0.0, 1.0],
under_over='over', g_name='Shape Complementarity'))
run_filters.append_filter(Filter(name='a_packstat', typ='packstat', threshold=args['packstat'], limits=[0.0, 1.0],
under_over='over', g_name='PackStat'))
run_filters.append_filter(Filter(name='a_buried_2', typ='buried_2', threshold=args['buried_2'], limits=[0, 100],
under_over='under', g_name='UnsatisfiedHBonds'))
run_filters.append_filter(Filter(name='a_rms', typ='rmsd', threshold=1000, limits=[0, 1000],
under_over='under', g_name='RMSD'))
run_filters.append_filter(Filter(name='a_hbonds', typ='hbonds', threshold=-abs(args['hbonds']),
limits=[-1000, 1000], under_over='under', g_name='H. bonds'))
# run_filters.append_filter(Filter(name='coh_packstat', typ='packstat', threshold=))
return run_filters
