def go(self):
self.data_reader.open('r')
#Create a new 'trajectories' group if this is the first trace
try:
trajs_group = h5io.create_hdf5_group(self.output_file,
'trajectories',
replace=False,
creating_program=self.prog)
except ValueError:
trajs_group = self.output_file['trajectories']
for n_iter, seg_id in self.endpoints:
trajname = self.output_pattern % (n_iter, seg_id)
trajgroup = trajs_group.create_group(trajname)
trace = Trace.from_data_manager(n_iter, seg_id,
self.data_reader.data_manager)
with open(trajname + '_trace.txt', 'wt') as trace_output:
self.emit_trace_text(trace, trace_output)
self.emit_trace_h5(trace, trajgroup)
aux_h5files = {}
for dsinfo in self.datasets:
dsname = dsinfo['dsname']
filename = dsinfo.get('file')
if filename:
try:
aux_h5file = aux_h5files[filename]
except KeyError:
aux_h5file = aux_h5files[filename] = h5py.File(
filename, 'r')
else:
aux_h5file = None
slice_ = dsinfo.get('slice')
alias = dsinfo.get('alias', dsname)
index = dsinfo.get('index')
data, weights = trace.trace_timepoint_dataset(
dsname, auxfile=aux_h5file, slice_=slice_, index_ds=index)
# Save data to HDF5
try:
del trajgroup[alias]
except KeyError:
pass
trajgroup[alias] = data
# All weight vectors will be the same length, so only store in HDF5 once
if not ('weights' in trajgroup
and trajgroup['weights'].shape == weights.shape):
try:
del trajgroup['weights']
except KeyError:
pass
trajgroup['weights'] = weights
def go(self):
self.data_reader.open('r')
#Create a new 'trajectories' group if this is the first trace
try:
trajs_group = h5io.create_hdf5_group(self.output_file, 'trajectories', replace=False, creating_program=self.prog)
except ValueError:
trajs_group = self.output_file['trajectories']
for n_iter, seg_id in self.endpoints:
trajname = self.output_pattern % (n_iter,seg_id)
trajgroup = trajs_group.create_group(trajname)
trace = Trace.from_data_manager(n_iter,seg_id, self.data_reader.data_manager)
with open(trajname + '_trace.txt', 'wt') as trace_output:
self.emit_trace_text(trace, trace_output)
self.emit_trace_h5(trace, trajgroup)
aux_h5files = {}
for dsinfo in self.datasets:
dsname = dsinfo['dsname']
filename = dsinfo.get('file')
if filename:
try:
aux_h5file = aux_h5files[filename]
except KeyError:
aux_h5file = aux_h5files[filename] = h5py.File(filename, 'r')
else:
aux_h5file = None
slice_ = dsinfo.get('slice')
alias = dsinfo.get('alias', dsname)
index = dsinfo.get('index')
data, weights = trace.trace_timepoint_dataset(dsname, auxfile=aux_h5file, slice_=slice_,index_ds=index)
# Save data to HDF5
try:
del trajgroup[alias]
except KeyError:
pass
trajgroup[alias] = data
# All weight vectors will be the same length, so only store in HDF5 once
if not ('weights' in trajgroup and trajgroup['weights'].shape == weights.shape):
try:
del trajgroup['weights']
except KeyError:
pass
trajgroup['weights'] = weights
def calc_store_flux_data(self):
westpa.rc.pstatus(
'Calculating mean flux and confidence intervals for iterations [{},{})'
.format(self.iter_range.iter_start, self.iter_range.iter_stop))
fluxdata = extract_fluxes(self.iter_range.iter_start,
self.iter_range.iter_stop, self.data_reader)
# Create a group to store data in
output_group = h5io.create_hdf5_group(self.output_h5file,
'target_flux',
replace=False,
creating_program=self.prog)
self.output_group = output_group
output_group.attrs['version_code'] = self.output_format_version
self.iter_range.record_data_iter_range(output_group)
n_targets = len(fluxdata)
index = numpy.empty((len(fluxdata), ), dtype=target_index_dtype)
avg_fluxdata = numpy.empty((n_targets, ), dtype=ci_dtype)
for itarget, (target_label,
target_fluxdata) in enumerate(fluxdata.items()):
# Create group and index entry
index[itarget]['target_label'] = str(target_label)
target_group = output_group.create_group(
'target_{}'.format(itarget))
self.target_groups[target_label] = target_group
# Store per-iteration values
target_group['n_iter'] = target_fluxdata['n_iter']
target_group['count'] = target_fluxdata['count']
target_group['flux'] = target_fluxdata['flux']
h5io.label_axes(target_group['flux'], ['n_iter'], units=['tau^-1'])
# Calculate flux autocorrelation
fluxes = target_fluxdata['flux']
mean_flux = fluxes.mean()
fmm = fluxes - mean_flux
acorr = fftconvolve(fmm, fmm[::-1])
acorr = acorr[len(acorr) // 2:]
acorr /= acorr[0]
acorr_ds = target_group.create_dataset('flux_autocorrel',
data=acorr)
h5io.label_axes(acorr_ds, ['lag'], ['tau'])
# Calculate overall averages and CIs
#avg, lb_ci, ub_ci, correl_len = mclib.mcbs_ci_correl(fluxes, numpy.mean, self.alpha, self.n_sets,
# autocorrel_alpha=self.autocorrel_alpha, subsample=numpy.mean)
avg, lb_ci, ub_ci, sterr, correl_len = mclib.mcbs_ci_correl(
{'dataset': fluxes},
estimator=(lambda stride, dataset: numpy.mean(dataset)),
alpha=self.alpha,
n_sets=self.n_sets,
autocorrel_alpha=self.autocorrel_alpha,
subsample=numpy.mean,
do_correl=self.do_correl,
mcbs_enable=self.mcbs_enable)
avg_fluxdata[itarget] = (self.iter_range.iter_start,
self.iter_range.iter_stop, avg, lb_ci,
ub_ci, sterr, correl_len)
westpa.rc.pstatus('target {!r}:'.format(target_label))
westpa.rc.pstatus(
' correlation length = {} tau'.format(correl_len))
westpa.rc.pstatus(
' mean flux and CI = {:e} ({:e},{:e}) tau^(-1)'.format(
avg, lb_ci, ub_ci))
index[itarget]['mean_flux'] = avg
index[itarget]['mean_flux_ci_lb'] = lb_ci
index[itarget]['mean_flux_ci_ub'] = ub_ci
index[itarget]['mean_flux_correl_len'] = correl_len
# Write index and summary
index_ds = output_group.create_dataset('index', data=index)
index_ds.attrs['mcbs_alpha'] = self.alpha
index_ds.attrs['mcbs_autocorrel_alpha'] = self.autocorrel_alpha
index_ds.attrs['mcbs_n_sets'] = self.n_sets
self.fluxdata = fluxdata
self.output_h5file['avg_flux'] = avg_fluxdata
def calc_store_flux_data(self):
westpa.rc.pstatus('Calculating mean flux and confidence intervals for iterations [{},{})'
.format(self.iter_range.iter_start, self.iter_range.iter_stop))
fluxdata = extract_fluxes(self.iter_range.iter_start, self.iter_range.iter_stop, self.data_reader)
# Create a group to store data in
output_group = h5io.create_hdf5_group(self.output_h5file, 'target_flux', replace=False, creating_program=self.prog)
self.output_group = output_group
output_group.attrs['version_code'] = self.output_format_version
self.iter_range.record_data_iter_range(output_group)
n_targets = len(fluxdata)
index = numpy.empty((len(fluxdata),), dtype=target_index_dtype)
avg_fluxdata = numpy.empty((n_targets,), dtype=ci_dtype)
for itarget, (target_label, target_fluxdata) in enumerate(fluxdata.iteritems()):
# Create group and index entry
index[itarget]['target_label'] = str(target_label)
target_group = output_group.create_group('target_{}'.format(itarget))
self.target_groups[target_label] = target_group
# Store per-iteration values
target_group['n_iter'] = target_fluxdata['n_iter']
target_group['count'] = target_fluxdata['count']
target_group['flux'] = target_fluxdata['flux']
h5io.label_axes(target_group['flux'], ['n_iter'], units=['tau^-1'])
# Calculate flux autocorrelation
fluxes = target_fluxdata['flux']
mean_flux = fluxes.mean()
fmm = fluxes - mean_flux
acorr = fftconvolve(fmm,fmm[::-1])
acorr = acorr[len(acorr)//2:]
acorr /= acorr[0]
acorr_ds = target_group.create_dataset('flux_autocorrel', data=acorr)
h5io.label_axes(acorr_ds, ['lag'], ['tau'])
# Calculate overall averages and CIs
#avg, lb_ci, ub_ci, correl_len = mclib.mcbs_ci_correl(fluxes, numpy.mean, self.alpha, self.n_sets,
# autocorrel_alpha=self.autocorrel_alpha, subsample=numpy.mean)
avg, lb_ci, ub_ci, sterr, correl_len = mclib.mcbs_ci_correl({'dataset': fluxes}, estimator=(lambda stride, dataset: numpy.mean(dataset)), alpha=self.alpha, n_sets=self.n_sets,
autocorrel_alpha=self.autocorrel_alpha, subsample=numpy.mean, do_correl=self.do_correl, mcbs_enable=self.mcbs_enable )
avg_fluxdata[itarget] = (self.iter_range.iter_start, self.iter_range.iter_stop, avg, lb_ci, ub_ci, sterr, correl_len)
westpa.rc.pstatus('target {!r}:'.format(target_label))
westpa.rc.pstatus(' correlation length = {} tau'.format(correl_len))
westpa.rc.pstatus(' mean flux and CI = {:e} ({:e},{:e}) tau^(-1)'.format(avg,lb_ci,ub_ci))
index[itarget]['mean_flux'] = avg
index[itarget]['mean_flux_ci_lb'] = lb_ci
index[itarget]['mean_flux_ci_ub'] = ub_ci
index[itarget]['mean_flux_correl_len'] = correl_len
# Write index and summary
index_ds = output_group.create_dataset('index', data=index)
index_ds.attrs['mcbs_alpha'] = self.alpha
index_ds.attrs['mcbs_autocorrel_alpha'] = self.autocorrel_alpha
index_ds.attrs['mcbs_n_sets'] = self.n_sets
self.fluxdata = fluxdata
self.output_h5file['avg_flux'] = avg_fluxdata