def calc_expectation(self, values, conds):
decor_enes = self._decor_outs.get_concatenated_datatype('enes')
decor_ops = self._decor_outs.get_concatenated_datatype('ops')
decor_staples = self._decor_outs.get_concatenated_datatype('staples')
rpots = utility.calc_reduced_potentials(decor_enes, decor_ops,
decor_staples, conds)
ave, vari = self._mbar.computeExpectations(values, rpots)
ave = ave[0].astype(float)
std = np.sqrt(vari)[0].astype(float)
return ave, std
def _construct_decorrelation_mask(self, sim_collection, rep, skip):
enes = sim_collection.reps_energies[rep]
ops = sim_collection.reps_order_params[rep]
steps = enes.steps
rpots = utility.calc_reduced_potentials(enes, ops,
sim_collection.conditions)
start_i, g, Neff = timeseries.detectEquilibration(rpots, nskip=skip)
template = '{:<8} {:<8} {:<3} {:<4.1f} {:<.1f}'
print(template.format(sim_collection.conditions.fileformat, steps,
start_i, g, Neff))
indices = (timeseries.subsampleCorrelatedData(rpots[start_i:], g=skip*g))
return [i + start_i for i in indices]
def _calc_decorrelated_rpots_for_all_conditions(self):
conditions_rpots = []
decor_enes = self._decor_outs.get_concatenated_datatype('enes')
decor_ops = self._decor_outs.get_concatenated_datatype('ops')
for conditions in self._decor_outs.all_conditions:
# What if I just want to do one rep?
rpots = utility.calc_reduced_potentials(decor_enes, decor_ops,
conditions)
conditions_rpots.append(rpots)
# I should split this up if I want to separate mbar from calcs that use it
self._conditions_to_decor_rpots[conditions.fileformat] = rpots
return np.array(conditions_rpots)
def _calc_decorrelated_rpots_for_all_conditions(self):
conditions_rpots = []
decor_enes = self._decor_outs.get_concatenated_datatype('enes')
decor_ops = self._decor_outs.get_concatenated_datatype('ops')
for conditions in self._decor_outs.all_conditions:
# What if I just want to do one rep?
rpots = utility.calc_reduced_potentials(decor_enes, decor_ops,
conditions)
conditions_rpots.append(rpots)
# I should split this up if I want to separate mbar from calcs that use it
self._conditions_to_decor_rpots[conditions.fileformat] = rpots
return np.array(conditions_rpots)
def calc_lfes(mbarw, conds, bins, bin_index_series, decor_enes, decor_ops,
decor_staples):
rpots = utility.calc_reduced_potentials(decor_enes, decor_ops,
decor_staples, conds)
return mbarw._mbar.computePMF(rpots, bin_index_series, len(bins))
def main():
args = parse_args()
system_file = files.JSONStructInpFile(args.system_filename)
inp_filebase = create_input_filepathbase(args)
fileformatter = construct_fileformatter()
all_conditions = construct_conditions(args, fileformatter, inp_filebase,
system_file)
staple_lengths = system_file._staple_lengths
sim_collections = create_simplesim_collections(args, inp_filebase,
all_conditions)
decor_outs = decorrelate.SimpleDecorrelatedOutputs(sim_collections,
all_conditions)
decor_outs.read_decors_from_files()
mbarw = mbar_wrapper.MBARWrapper(decor_outs)
mbarw.perform_mbar()
out_filebase = create_output_filepathbase(args)
conds = conditions.SimConditions(
{
'temp': args.temp,
'staple_m': args.staple_m,
'bias': biases.NoBias()
}, fileformatter, staple_lengths)
all_tags = decor_outs.all_conditions.condition_tags
aves = []
stds = []
for tag in decor_outs.all_series_tags:
all_tags.append(tag)
series = decor_outs.get_concatenated_series(tag)
ave, std = mbarw.calc_expectation(series, conds)
aves.append(ave)
stds.append(std)
# Hack calculate LFEs
values = decor_outs.get_concatenated_series(tag)
decor_enes = decor_outs.get_concatenated_datatype('enes')
decor_ops = decor_outs.get_concatenated_datatype('ops')
bins = list(set(values))
bins.sort()
value_to_bin = {value: i for i, value in enumerate(bins)}
bin_index_series = [value_to_bin[i] for i in values]
bin_index_series = np.array(bin_index_series)
rpots = utility.calc_reduced_potentials(decor_enes, decor_ops, conds)
lfes, lfe_stds = mbarw._mbar.computePMF(rpots, bin_index_series,
len(bins))
# Hack write LFEs to file
header = np.array(['ops', args.temp])
lfes_filebase = '{}_{}-lfes-melting'.format(out_filebase, tag)
lfes_file = files.TagOutFile('{}.aves'.format(lfes_filebase))
lfes = np.concatenate([[bins], [lfes]]).T
lfes_file.write(header, lfes)
stds_file = files.TagOutFile('{}.stds'.format(lfes_filebase))
lfe_stds = np.concatenate([[bins], [lfe_stds]]).T
stds_file.write(header, lfe_stds)
# Hack to write expectations to file
aves_file = files.TagOutFile('{}.aves'.format(out_filebase))
cond_char_values = conds.condition_to_characteristic_value
cond_values = [v for k, v in sorted(cond_char_values.items())]
aves_file.write(all_tags, [np.concatenate([cond_values, np.array(aves)])])
stds_file = files.TagOutFile('{}.stds'.format(out_filebase))
stds_file.write(all_tags, [np.concatenate([cond_values, np.array(stds)])])