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 = np.empty((len(fluxdata), ), dtype=target_index_dtype)
avg_fluxdata = np.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, np.mean, self.alpha, self.n_sets,
# autocorrel_alpha=self.autocorrel_alpha, subsample=np.mean)
avg, lb_ci, ub_ci, sterr, correl_len = mclib.mcbs_ci_correl(
{'dataset': fluxes},
estimator=(lambda stride, dataset: np.mean(dataset)),
alpha=self.alpha,
n_sets=self.n_sets,
autocorrel_alpha=self.autocorrel_alpha,
subsample=np.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 w_postanalysis_matrix(self):
pi = self.progress.indicator
pi.new_operation('Initializing')
self.data_reader.open('r')
nbins = self.assignments_file.attrs['nbins']
state_labels = self.assignments_file['state_labels'][...]
state_map = self.assignments_file['state_map'][...]
nstates = len(state_labels)
start_iter, stop_iter = self.iter_range.iter_start, self.iter_range.iter_stop # h5io.get_iter_range(self.assignments_file)
iter_count = stop_iter - start_iter
nfbins = nbins * nstates
flux_shape = (iter_count, nfbins, nfbins)
pop_shape = (iter_count, nfbins)
h5io.stamp_iter_range(self.output_file, start_iter, stop_iter)
bin_populations_ds = self.output_file.create_dataset(
'bin_populations', shape=pop_shape, dtype=weight_dtype)
h5io.stamp_iter_range(bin_populations_ds, start_iter, stop_iter)
h5io.label_axes(bin_populations_ds, ['iteration', 'bin'])
flux_grp = self.output_file.create_group('iterations')
self.output_file.attrs['nrows'] = nfbins
self.output_file.attrs['ncols'] = nfbins
fluxes = np.empty(flux_shape[1:], weight_dtype)
populations = np.empty(pop_shape[1:], weight_dtype)
trans = np.empty(flux_shape[1:], np.int64)
# Check to make sure this isn't a data set with target states
#tstates = self.data_reader.data_manager.get_target_states(0)
#if len(tstates) > 0:
# raise ValueError('Postanalysis reweighting analysis does not support WE simulation run under recycling conditions')
pi.new_operation('Calculating flux matrices', iter_count)
# Calculate instantaneous statistics
for iiter, n_iter in enumerate(range(start_iter, stop_iter)):
# Get data from the main HDF5 file
iter_group = self.data_reader.get_iter_group(n_iter)
seg_index = iter_group['seg_index']
nsegs, npts = iter_group['pcoord'].shape[0:2]
weights = seg_index['weight']
# Get bin and traj. ensemble assignments from the previously-generated assignments file
assignment_iiter = h5io.get_iteration_entry(
self.assignments_file, n_iter)
bin_assignments = np.require(
self.assignments_file['assignments'][assignment_iiter +
np.s_[:nsegs, :npts]],
dtype=index_dtype)
mask_unknown = np.zeros_like(bin_assignments, dtype=np.uint16)
macrostate_iiter = h5io.get_iteration_entry(
self.assignments_file, n_iter)
macrostate_assignments = np.require(
self.assignments_file['trajlabels'][macrostate_iiter +
np.s_[:nsegs, :npts]],
dtype=index_dtype)
# Transform bin_assignments to take macrostate membership into account
bin_assignments = nstates * bin_assignments + macrostate_assignments
mask_indx = np.where(macrostate_assignments == nstates)
mask_unknown[mask_indx] = 1
# Calculate bin-to-bin fluxes, bin populations and number of obs transitions
calc_stats(bin_assignments, weights, fluxes, populations, trans,
mask_unknown, self.sampling_frequency)
# Store bin-based kinetics data
bin_populations_ds[iiter] = populations
# Setup sparse data structures for flux and obs
fluxes_sp = sp.coo_matrix(fluxes)
trans_sp = sp.coo_matrix(trans)
assert fluxes_sp.nnz == trans_sp.nnz
flux_iter_grp = flux_grp.create_group('iter_{:08d}'.format(n_iter))
flux_iter_grp.create_dataset('flux',
data=fluxes_sp.data,
dtype=weight_dtype)
flux_iter_grp.create_dataset('obs',
data=trans_sp.data,
dtype=np.int32)
flux_iter_grp.create_dataset('rows',
data=fluxes_sp.row,
dtype=np.int32)
flux_iter_grp.create_dataset('cols',
data=fluxes_sp.col,
dtype=np.int32)
flux_iter_grp.attrs['nrows'] = nfbins
flux_iter_grp.attrs['ncols'] = nfbins
# Do a little manual clean-up to prevent memory explosion
del iter_group, weights, bin_assignments
del macrostate_assignments
pi.progress += 1
# Check and save the number of intermediate time points; this will be used to normalize the
# flux and kinetics to tau in w_postanalysis_reweight.
if self.assignments_file.attrs[
'subsampled'] == True or self.sampling_frequency == 'iteration':
self.output_file.attrs['npts'] = 2
else:
#self.output_file.attrs['npts'] = npts if self.sampling_frequency == 'timepoint' else 2
self.output_file.attrs['npts'] = npts
def go(self):
assert self.data_reader.parent_id_dsspec._h5file is None
assert self.data_reader.weight_dsspec._h5file is None
if hasattr(self.dssynth.dsspec, '_h5file'):
assert self.dssynth.dsspec._h5file is None
pi = self.progress.indicator
pi.operation = 'Initializing'
with pi, self.data_reader, WESTPAH5File(
self.output_filename, 'w',
creating_program=True) as self.output_file:
assign = self.binning.mapper.assign
# We always assign the entire simulation, so that no trajectory appears to start
# in a transition region that doesn't get initialized in one.
iter_start = 1
iter_stop = self.data_reader.current_iteration
h5io.stamp_iter_range(self.output_file, iter_start, iter_stop)
nbins = self.binning.mapper.nbins
self.output_file.attrs['nbins'] = nbins
state_map = np.empty((self.binning.mapper.nbins + 1, ),
index_dtype)
state_map[:] = 0 # state_id == nstates => unknown state
# Recursive mappers produce a generator rather than a list of labels
# so consume the entire generator into a list
labels = [
np.string_(label) for label in self.binning.mapper.labels
]
self.output_file.create_dataset('bin_labels',
data=labels,
compression=9)
if self.states:
nstates = len(self.states)
state_map[:] = nstates # state_id == nstates => unknown state
state_labels = [
np.string_(state['label']) for state in self.states
]
for istate, sdict in enumerate(self.states):
assert state_labels[istate] == np.string_(
sdict['label']) # sanity check
state_assignments = assign(sdict['coords'])
for assignment in state_assignments:
state_map[assignment] = istate
self.output_file.create_dataset('state_map',
data=state_map,
compression=9,
shuffle=True)
self.output_file[
'state_labels'] = state_labels # + ['(unknown)']
else:
nstates = 0
self.output_file.attrs['nstates'] = nstates
# Stamp if this has been subsampled.
self.output_file.attrs['subsampled'] = self.subsample
iter_count = iter_stop - iter_start
nsegs = np.empty((iter_count, ), seg_id_dtype)
npts = np.empty((iter_count, ), seg_id_dtype)
# scan for largest number of segments and largest number of points
pi.new_operation('Scanning for segment and point counts',
iter_stop - iter_start)
for iiter, n_iter in enumerate(range(iter_start, iter_stop)):
iter_group = self.data_reader.get_iter_group(n_iter)
nsegs[iiter], npts[iiter] = iter_group['pcoord'].shape[0:2]
pi.progress += 1
del iter_group
pi.new_operation('Preparing output')
# create datasets
self.output_file.create_dataset('nsegs',
data=nsegs,
shuffle=True,
compression=9)
self.output_file.create_dataset('npts',
data=npts,
shuffle=True,
compression=9)
max_nsegs = nsegs.max()
max_npts = npts.max()
assignments_shape = (iter_count, max_nsegs, max_npts)
assignments_dtype = np.min_scalar_type(nbins)
assignments_ds = self.output_file.create_dataset(
'assignments',
dtype=assignments_dtype,
shape=assignments_shape,
compression=4,
shuffle=True,
chunks=h5io.calc_chunksize(assignments_shape,
assignments_dtype),
fillvalue=nbins,
)
if self.states:
trajlabel_dtype = np.min_scalar_type(nstates)
trajlabels_ds = self.output_file.create_dataset(
'trajlabels',
dtype=trajlabel_dtype,
shape=assignments_shape,
compression=4,
shuffle=True,
chunks=h5io.calc_chunksize(assignments_shape,
trajlabel_dtype),
fillvalue=nstates,
)
statelabels_ds = self.output_file.create_dataset(
'statelabels',
dtype=trajlabel_dtype,
shape=assignments_shape,
compression=4,
shuffle=True,
chunks=h5io.calc_chunksize(assignments_shape,
trajlabel_dtype),
fillvalue=nstates,
)
pops_shape = (iter_count, nstates + 1, nbins + 1)
pops_ds = self.output_file.create_dataset(
'labeled_populations',
dtype=weight_dtype,
shape=pops_shape,
compression=4,
shuffle=True,
chunks=h5io.calc_chunksize(pops_shape, weight_dtype),
)
h5io.label_axes(
pops_ds,
[np.string_(i) for i in ['iteration', 'state', 'bin']])
pi.new_operation('Assigning to bins', iter_stop - iter_start)
last_labels = None # mapping of seg_id to last macrostate inhabited
for iiter, n_iter in enumerate(range(iter_start, iter_stop)):
# get iteration info in this block
if iiter == 0:
last_labels = np.empty((nsegs[iiter], ), index_dtype)
last_labels[:] = nstates # unknown state
# Slices this iteration into n_workers groups of segments, submits them to wm, splices results back together
assignments, trajlabels, pops, statelabels = self.assign_iteration(
n_iter, nstates, nbins, state_map, last_labels)
# Do stuff with this iteration's results
last_labels = trajlabels[:, -1].copy()
assignments_ds[iiter, 0:nsegs[iiter],
0:npts[iiter]] = assignments
pops_ds[iiter] = pops
if self.states:
trajlabels_ds[iiter, 0:nsegs[iiter],
0:npts[iiter]] = trajlabels
statelabels_ds[iiter, 0:nsegs[iiter],
0:npts[iiter]] = statelabels
pi.progress += 1
del assignments, trajlabels, pops, statelabels
for dsname in 'assignments', 'npts', 'nsegs', 'labeled_populations', 'statelabels':
h5io.stamp_iter_range(self.output_file[dsname], iter_start,
iter_stop)