def _body(self, args):
results = self.calculate(subset=(args.lower_bound, args.upper_bound))
keys = ('raw', 'mbar', 'umbrella')
# plot titration curves and fitted models
self.figures['titration'], ax = plt.subplots()
for key in keys:
line = results[key].occupancies.plot(ax, fmt='o', show_error=False)
results[key].model.plot(ax, color=line.get_color(), label=key,
xlabel='B Value', ylabel='Occupancy')
ax.legend(loc='best')
# plot insertion_pmfs
self.figures['insertion_pmf'], ax = plt.subplots()
for key in keys:
results[key].insertion_pmf.plot(ax, label=key, xlabel='Occupancy')
ax.legend(loc='best')
# print out binding free energies in table format
for key in keys:
table = fe.Table(
key.upper(), fmts=['%d', '%.3f'],
headers=['Number of Waters', 'Binding Free Energy'])
for i, dA in enumerate(results[key].insertion_pmf):
table.add_row([i, dA])
self.tables.append(table)
return results
def state_data_table(results, directories, signs, states, estimator):
"""Returns a Table object containing the free energy differences for the
individual states i.e. bound, free and gas.
Parameters
----------
results: dict such that results[root][state][estimator] = Result object
The free energy results to tabulate
directories: list of strings
List of root directories containing calculation data
signs: list of '+' or '-'
The signs to use when calculating the free energy cycle
states: list of strings
The thermodynamic states simulated
estimator: Estimator Class
The estimator to tabulate results of
"""
table = fe.Table(estimator.__name__,
fmts=["%s:", "%.3f", "%.3f", "%.3f"],
headers=['', 'dG gas', 'dG free', 'dG bound'])
closures = {state: fe.Quantity(0., 0.) for state in states}
for root, sign in zip(directories, signs):
root_dGs = (root, )
for state in states:
dG = results[root][state][estimator]
root_dGs += (dG, )
if sign == '+':
closures[state] += dG
else:
closures[state] -= dG
table.add_row(root_dGs)
table.add_row(['Cycle Closure'] + [closures[state] for state in states])
return table
def solv_bind_table(dG_solvs, dG_binds, directories, signs, estimator):
"""Returns a Table object containing free energies of solvation and
binding.
Parameters
----------
dG_solvs: dictionary[root][estimator] = Result object
Calculated solvation free energy results
dG_binds: dictionary[root][estimator] = Result object
Calculated binding free energy results
directories: list of strings
List of root directories containing calculation data
signs: list of '+' or '-'
The signs to use when calculating the free energy cycle
estimator: Estimator Class
The estimator to tabulate results of
"""
table = fe.Table('',
fmts=["%s:", "%.3f", "%.3f"],
headers=['', 'ddG Solvation', 'ddG Binding'])
closure_solv = fe.Quantity(0., 0.)
closure_bind = fe.Quantity(0., 0.)
for root, sign in zip(directories, signs):
dG_solv = dG_solvs[root][estimator]
dG_bind = dG_binds[root][estimator]
table.add_row([root, dG_solv, dG_bind])
if sign == '+':
closure_solv += dG_solv
closure_bind += dG_bind
else:
closure_solv -= dG_solv
closure_bind -= dG_bind
table.add_row(["Cycle Closure", closure_solv, closure_bind])
return table
def _body(self, args):
"""Calculation business logic.
Parameters
----------
args: argparse.Namespace object
Namespace from argumentparser
"""
subset = (args.lower_bound, args.upper_bound)
results = self.calculate(subset=subset)
decomp = results[TI_decomposed]
if (args.bound or args.gas) is not None:
calc2 = DecomposedCalculation(args.bound or args.gas)
results2 = calc2.calculate(subset=subset)
decomp2 = results2[TI_decomposed]
# iterate over the unique keys from both calc and calc2
for term in set(chain(decomp, decomp2)):
try:
decomp[term] -= decomp2[term]
except KeyError:
try:
decomp[term] = -decomp2[term]
except KeyError:
# term is not in decomp2, no furthor action needed
pass
if args.bound is not None:
decomp[term] = -decomp[term]
# update standard TI result as well
results[fe.TI] -= results2[fe.TI]
# swap the sign if this is a binding calculation
if args.bound is not None:
results[fe.TI] = -results[fe.TI]
if args.dualtopology:
_consolidate_terms(decomp)
self.figures['decomposed'] = plot_terms(decomp)
if args.pmf:
self.figures['decomposed_pmfs'] = plot_pmfs(decomp)
table = fe.Table('', fmts=["%s:", "%.3f"])
table.add_row(["FDTI", results[fe.TI].dG])
table.add_blank_row()
for term in sorted(decomp):
if args.full and decomp[term].dG.value == 0.0:
continue
else:
table.add_row([term, decomp[term].dG])
table.add_row(['sum of terms', np.sum(list(decomp.values())).dG])
self.tables.append(table)
return results
def make_result_tables(directories, results):
"""Print calculated free energies. If multiple repeats are present
the mean and standard error are also printed.
Parameters
----------
directories : list of list (or other sequences) of strings
the directory name associated with each result
results : dict of Estimator class-Result object pairs
"""
tables = []
for estimator in sorted(results, key=lambda x: x.__name__):
table = fe.Table(estimator.__name__, ['%s', "%.2f"])
for i, result in enumerate(results[estimator].data):
dGs = []
for j, pmf in enumerate(result):
dGs.append(pmf.dG)
table.add_row([directories[i][j], pmf.dG.value])
table.add_row(['Mean', fe.Quantity.fromData(dGs)])
table.add_blank_row()
table.add_row(['Total Mean', results[estimator].dG])
tables.append(table)
return tables