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)
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 w_kinetics(self):
pi = self.progress.indicator
pi.new_operation('Initializing')
self.data_reader.open('r')
self.open_files()
nstates = self.assignments_file.attrs['nstates']
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
durations_ds = self.output_file.replace_dataset(
'durations',
shape=(iter_count, 0),
maxshape=(iter_count, None),
dtype=ed_list_dtype,
chunks=(1, 15360) if self.do_compression else None,
shuffle=self.do_compression,
compression=9 if self.do_compression else None,
)
durations_count_ds = self.output_file.replace_dataset(
'duration_count',
shape=(iter_count, ),
dtype=np.int_,
shuffle=True,
compression=9)
cond_fluxes_ds = self.output_file.replace_dataset(
'conditional_fluxes',
shape=(iter_count, nstates, nstates),
dtype=weight_dtype,
chunks=(h5io.calc_chunksize(
(iter_count, nstates,
nstates), weight_dtype) if self.do_compression else None),
shuffle=self.do_compression,
compression=9 if self.do_compression else None,
)
total_fluxes_ds = self.output_file.replace_dataset(
'total_fluxes',
shape=(iter_count, nstates),
dtype=weight_dtype,
chunks=(h5io.calc_chunksize(
(iter_count,
nstates), weight_dtype) if self.do_compression else None),
shuffle=self.do_compression,
compression=9 if self.do_compression else None,
)
cond_arrival_counts_ds = self.output_file.replace_dataset(
'conditional_arrivals',
shape=(iter_count, nstates, nstates),
dtype=np.uint,
chunks=(h5io.calc_chunksize(
(iter_count, nstates,
nstates), np.uint) if self.do_compression else None),
shuffle=self.do_compression,
compression=9 if self.do_compression else None,
)
arrival_counts_ds = self.output_file.replace_dataset(
'arrivals',
shape=(iter_count, nstates),
dtype=np.uint,
chunks=(h5io.calc_chunksize(
(iter_count,
nstates), np.uint) if self.do_compression else None),
shuffle=self.do_compression,
compression=9 if self.do_compression else None,
)
# copy state labels for convenience
self.output_file.replace_dataset(
'state_labels', data=self.assignments_file['state_labels'][...])
# Put nice labels on things
for ds in (self.output_file, durations_count_ds, cond_fluxes_ds,
total_fluxes_ds):
h5io.stamp_iter_range(ds, start_iter, stop_iter)
# Calculate instantaneous rate matrices and trace trajectories
last_state = None
pi.new_operation('Tracing trajectories', iter_count)
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']
# parent_ids = seg_index['parent_id']
parent_ids = self.data_reader.parent_id_dsspec.get_iter_data(
n_iter)
# 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)
label_assignments = np.require(
self.assignments_file['trajlabels'][assignment_iiter +
np.s_[:nsegs, :npts]],
dtype=index_dtype)
state_assignments = np.require(
self.assignments_file['statelabels'][assignment_iiter +
np.s_[:nsegs, :npts]],
dtype=index_dtype)
# Prepare to run analysis
cond_fluxes = np.zeros((nstates, nstates), weight_dtype)
total_fluxes = np.zeros((nstates, ), weight_dtype)
cond_counts = np.zeros((nstates, nstates), np.uint)
total_counts = np.zeros((nstates, ), np.uint)
durations = []
# Estimate macrostate fluxes and calculate event durations using trajectory tracing
# state is opaque to the find_macrostate_transitions function
dt = 1.0 if npts == 1 else 1.0 / (npts - 1)
state = _fast_transition_state_copy(iiter, nstates, parent_ids,
last_state)
find_macrostate_transitions(
nstates,
weights,
label_assignments,
state_assignments,
dt,
state,
cond_fluxes,
cond_counts,
total_fluxes,
total_counts,
durations,
)
last_state = state
# Store trace-based kinetics data
cond_fluxes_ds[iiter] = cond_fluxes
total_fluxes_ds[iiter] = total_fluxes
arrival_counts_ds[iiter] = total_counts
cond_arrival_counts_ds[iiter] = cond_counts
durations_count_ds[iiter] = len(durations)
if len(durations) > 0:
durations_ds.resize(
(iter_count, max(len(durations), durations_ds.shape[1])))
durations_ds[iiter, :len(durations)] = durations
# Do a little manual clean-up to prevent memory explosion
del iter_group, weights, parent_ids, bin_assignments, label_assignments, state, cond_fluxes, total_fluxes
pi.progress += 1