class WBinTool(WESTTool):
prog='w_bins'
description = '''\
Display information and statistics about binning in a WEST simulation, or
modify the binning for the current iteration of a WEST simulation.
-------------------------------------------------------------------------------
'''
def __init__(self):
super(WBinTool,self).__init__()
self.subcommand = None
self.data_reader = WESTDataReader()
self.binning = BinMappingComponent()
self.args = None
self.n_iter = None
# Interface for command-line tools
def add_args(self, parser):
self.data_reader.add_args(parser)
subparsers = parser.add_subparsers(help='available commands')
info_parser = subparsers.add_parser('info', help='Display information about binning.')
info_parser.add_argument('-n', '--n-iter', type=int,
help='''Consider initial points of segment N_ITER (default: current iteration).''')
info_parser.add_argument('--detail', action='store_true',
help='''Display detailed per-bin information in addition to summary
information.''')
self.binning.add_args(info_parser)
info_parser.set_defaults(func=self.cmd_info)
rebin_parser = subparsers.add_parser('rebin',help='Rebuild current iteration with new binning.')
rebin_parser.add_argument('--confirm', action='store_true',
help='''Commit the revised iteration to HDF5; without this option, the effects of the
new binning are only calculated and printed.''')
rebin_parser.add_argument('--detail', action='store_true',
help='''Display detailed per-bin information in addition to summary
information.''')
self.binning.add_args(rebin_parser, suppress=['--bins-from-file'])
self.binning.add_target_count_args(rebin_parser)
rebin_parser.set_defaults(func=self.cmd_rebin)
def process_args(self, args):
self.data_reader.process_args(args)
self.data_reader.open(mode='r+')
self.n_iter = getattr(args,'n_iter',None) or self.data_reader.current_iteration
# we cannot read bin information during rebins
# interesting note: '==' is required here; 'is' fails
if args.func == self.cmd_rebin:
self.binning.target_counts_required = True
else:
self.binning.set_we_h5file_info(self.n_iter, self.data_reader)
self.binning.process_args(args)
self.args = args
self.subcommand = args.func
def go(self):
self.subcommand()
def cmd_info(self):
mapper = self.binning.mapper
# Get target states and their assignments
target_states = self.data_reader.get_target_states(self.n_iter)
n_target_states = len(target_states)
iter_group = self.data_reader.get_iter_group(self.n_iter)
# bin initial pcoords for iteration n_iter
initial_pcoords = iter_group['pcoord'][:,0,:]
assignments = mapper.assign(initial_pcoords)
del initial_pcoords
print('Bin information for iteration {:d}'.format(self.n_iter))
# Get bin counts and weights
weights = iter_group['seg_index']['weight']
write_bin_info(mapper, assignments, weights, n_target_states, detailed=self.args.detail)
def cmd_rebin(self):
mapper = self.binning.mapper
assert mapper is not None
if self.n_iter == 1:
sys.stderr.write('rebin is not supported for the first iteration; reinitialize with w_init instead\n')
sys.exit(1)
n_target_states = len(self.data_reader.get_target_states(self.n_iter))
we_driver = westpa.rc.get_we_driver()
data_manager = self.data_reader.data_manager
segments = data_manager.get_segments(self.n_iter,load_pcoords=True)
last_iter_segments = data_manager.get_segments(self.n_iter-1,load_pcoords=False,load_auxdata=False)
# Bin on this iteration's initial points
# We don't have to worry about recycling because we are binning on
# initial points rather than final points, so recycling has already
# occurred for this iteration.
# We do need initial states, in case we merge a newly-created walker out of existence
#avail_initial_states = {state.state_id: state
# for state in data_manager.get_unused_initial_states(n_iter = self.n_iter)}
avail_initial_states = data_manager.get_unused_initial_states(n_iter = self.n_iter)
used_initial_states = data_manager.get_segment_initial_states(segments)
we_driver.new_iteration(initial_states=avail_initial_states,
bin_mapper=mapper, bin_target_counts=self.binning.bin_target_counts)
we_driver.used_initial_states = {state.state_id: state for state in used_initial_states}
we_driver.assign(segments,initializing=True)
we_driver.rebin_current(parent_segments=last_iter_segments)
weights = numpy.array([segment.weight for segment in we_driver.next_iter_segments])
assignments = numpy.fromiter(we_driver.next_iter_assignments,dtype=int,count=len(weights))
write_bin_info(mapper, assignments, weights, n_target_states, detailed=self.args.detail)
if self.args.confirm:
data_manager.prepare_iteration(self.n_iter, list(we_driver.next_iter_segments))
# manually update endpoint statuses only
endpoint_types = sorted([(segment.seg_id, segment.endpoint_type) for segment in last_iter_segments])
last_iter_group = data_manager.get_iter_group(self.n_iter-1)
last_iter_index = last_iter_group['seg_index'][...]
last_iter_index['endpoint_type'] = [pair[1] for pair in endpoint_types]
last_iter_group['seg_index'][...] = last_iter_index
data_manager.save_iter_binning(self.n_iter, self.binning.mapper_hash, self.binning.mapper_pickle,
we_driver.bin_target_counts)
data_manager.update_initial_states(we_driver.all_initial_states)
data_manager.flush_backing()
class WAssign(WESTParallelTool):
prog = 'w_assign'
description = '''\
Assign walkers to bins, producing a file (by default named "assign.h5")
which can be used in subsequent analysis.
For consistency in subsequent analysis operations, the entire dataset
must be assigned, even if only a subset of the data will be used. This
ensures that analyses that rely on tracing trajectories always know the
originating bin of each trajectory.
-----------------------------------------------------------------------------
Source data
-----------------------------------------------------------------------------
Source data is provided either by a user-specified function
(--construct-dataset) or a list of "data set specifications" (--dsspecs).
If neither is provided, the progress coordinate dataset ''pcoord'' is used.
To use a custom function to extract or calculate data whose probability
distribution will be calculated, specify the function in standard Python
MODULE.FUNCTION syntax as the argument to --construct-dataset. This function
will be called as function(n_iter,iter_group), where n_iter is the iteration
whose data are being considered and iter_group is the corresponding group
in the main WEST HDF5 file (west.h5). The function must return data which can
be indexed as [segment][timepoint][dimension].
To use a list of data set specifications, specify --dsspecs and then list the
desired datasets one-by-one (space-separated in most shells). These data set
specifications are formatted as NAME[,file=FILENAME,slice=SLICE], which will
use the dataset called NAME in the HDF5 file FILENAME (defaulting to the main
WEST HDF5 file west.h5), and slice it with the Python slice expression SLICE
(as in [0:2] to select the first two elements of the first axis of the
dataset). The ``slice`` option is most useful for selecting one column (or
more) from a multi-column dataset, such as arises when using a progress
coordinate of multiple dimensions.
-----------------------------------------------------------------------------
Specifying macrostates
-----------------------------------------------------------------------------
Optionally, kinetic macrostates may be defined in terms of sets of bins.
Each trajectory will be labeled with the kinetic macrostate it was most
recently in at each timepoint, for use in subsequent kinetic analysis.
This is required for all kinetics analysis (w_kintrace and w_kinmat).
There are three ways to specify macrostates:
1. States corresponding to single bins may be identified on the command
line using the --states option, which takes multiple arguments, one for
each state (separated by spaces in most shells). Each state is specified
as a coordinate tuple, with an optional label prepended, as in
``bound:1.0`` or ``unbound:(2.5,2.5)``. Unlabeled states are named
``stateN``, where N is the (zero-based) position in the list of states
supplied to --states.
2. States corresponding to multiple bins may use a YAML input file specified
with --states-from-file. This file defines a list of states, each with a
name and a list of coordinate tuples; bins containing these coordinates
will be mapped to the containing state. For instance, the following
file::
---
states:
- label: unbound
coords:
- [9.0, 1.0]
- [9.0, 2.0]
- label: bound
coords:
- [0.1, 0.0]
produces two macrostates: the first state is called "unbound" and
consists of bins containing the (2-dimensional) progress coordinate
values (9.0, 1.0) and (9.0, 2.0); the second state is called "bound"
and consists of the single bin containing the point (0.1, 0.0).
3. Arbitrary state definitions may be supplied by a user-defined function,
specified as --states-from-function=MODULE.FUNCTION. This function is
called with the bin mapper as an argument (``function(mapper)``) and must
return a list of dictionaries, one per state. Each dictionary must contain
a vector of coordinate tuples with key "coords"; the bins into which each
of these tuples falls define the state. An optional name for the state
(with key "label") may also be provided.
-----------------------------------------------------------------------------
Output format
-----------------------------------------------------------------------------
The output file (-o/--output, by default "assign.h5") contains the following
attributes datasets:
``nbins`` attribute
*(Integer)* Number of valid bins. Bin assignments range from 0 to
*nbins*-1, inclusive.
``nstates`` attribute
*(Integer)* Number of valid macrostates (may be zero if no such states are
specified). Trajectory ensemble assignments range from 0 to *nstates*-1,
inclusive, when states are defined.
``/assignments`` [iteration][segment][timepoint]
*(Integer)* Per-segment and -timepoint assignments (bin indices).
``/npts`` [iteration]
*(Integer)* Number of timepoints in each iteration.
``/nsegs`` [iteration]
*(Integer)* Number of segments in each iteration.
``/labeled_populations`` [iterations][state][bin]
*(Floating-point)* Per-iteration and -timepoint bin populations, labeled
by most recently visited macrostate. The last state entry (*nstates-1*)
corresponds to trajectories initiated outside of a defined macrostate.
``/bin_labels`` [bin]
*(String)* Text labels of bins.
When macrostate assignments are given, the following additional datasets are
present:
``/trajlabels`` [iteration][segment][timepoint]
*(Integer)* Per-segment and -timepoint trajectory labels, indicating the
macrostate which each trajectory last visited.
``/state_labels`` [state]
*(String)* Labels of states.
``/state_map`` [bin]
*(Integer)* Mapping of bin index to the macrostate containing that bin.
An entry will contain *nbins+1* if that bin does not fall into a
macrostate.
Datasets indexed by state and bin contain one more entry than the number of
valid states or bins. For *N* bins, axes indexed by bin are of size *N+1*, and
entry *N* (0-based indexing) corresponds to a walker outside of the defined bin
space (which will cause most mappers to raise an error). More importantly, for
*M* states (including the case *M=0* where no states are specified), axes
indexed by state are of size *M+1* and entry *M* refers to trajectories
initiated in a region not corresponding to a defined macrostate.
Thus, ``labeled_populations[:,:,:].sum(axis=1)[:,:-1]`` gives overall per-bin
populations, for all defined bins and
``labeled_populations[:,:,:].sum(axis=2)[:,:-1]`` gives overall
per-trajectory-ensemble populations for all defined states.
-----------------------------------------------------------------------------
Parallelization
-----------------------------------------------------------------------------
This tool supports parallelized binning, including reading/calculating input
data.
-----------------------------------------------------------------------------
Command-line options
-----------------------------------------------------------------------------
'''
def __init__(self):
super(WAssign, self).__init__()
# Parallel processing by default (this is not actually necessary, but it is
# informative!)
self.wm_env.default_work_manager = self.wm_env.default_parallel_work_manager
self.data_reader = WESTDataReader()
self.dssynth = WESTDSSynthesizer(default_dsname='pcoord')
self.binning = BinMappingComponent()
self.progress = ProgressIndicatorComponent()
self.output_file = None
self.output_filename = None
self.states = []
self.subsample = False
def add_args(self, parser):
self.data_reader.add_args(parser)
self.binning.add_args(parser)
self.dssynth.add_args(parser)
sgroup = parser.add_argument_group(
'macrostate definitions').add_mutually_exclusive_group()
sgroup.add_argument(
'--states',
nargs='+',
metavar='STATEDEF',
help=
'''Single-bin kinetic macrostate, specified by a coordinate tuple (e.g. '1.0' or '[1.0,1.0]'),
optionally labeled (e.g. 'bound:[1.0,1.0]'). States corresponding to multiple bins
must be specified with --states-from-file.''')
sgroup.add_argument(
'--states-from-file',
metavar='STATEFILE',
help=
'''Load kinetic macrostates from the YAML file STATEFILE. See description
above for the appropriate structure.''')
sgroup.add_argument(
'--states-from-function',
metavar='STATEFUNC',
help=
'''Load kinetic macrostates from the function STATEFUNC, specified as
module_name.func_name. This function is called with the bin mapper as an argument,
and must return a list of dictionaries {'label': state_label, 'coords': 2d_array_like}
one for each macrostate; the 'coords' entry must contain enough rows to identify all bins
in the macrostate.''')
agroup = parser.add_argument_group('other options')
agroup.add_argument(
'-o',
'--output',
dest='output',
default='assign.h5',
help='''Store results in OUTPUT (default: %(default)s).''')
agroup.add_argument(
'--subsample',
dest='subsample',
action='store_const',
const=True,
help='''Determines whether or not the data should be subsampled.
This is rather useful for analysing steady state simulations.'''
)
agroup.add_argument(
'--config-from-file',
dest='config_from_file',
action='store_true',
help=
'''Load bins/macrostates from a scheme specified in west.cfg.''')
agroup.add_argument('--scheme-name',
dest='scheme',
help='''Name of scheme specified in west.cfg.''')
def process_args(self, args):
self.progress.process_args(args)
self.data_reader.process_args(args)
# Necessary to open the file to get the current iteration
# if we want to use the mapper in the file
self.data_reader.open(mode='r+')
self.n_iter = self.data_reader.current_iteration
# If we decide to use this option for iteration selection:
# getattr(args,'bins_from_h5file',None) or self.data_reader.current_iteration
with self.data_reader:
self.dssynth.h5filename = self.data_reader.we_h5filename
self.dssynth.process_args(args)
if args.config_from_file == False:
self.binning.set_we_h5file_info(self.n_iter, self.data_reader)
self.binning.process_args(args)
self.output_filename = args.output
if args.config_from_file:
if not args.scheme:
raise ValueError('A scheme must be specified.')
else:
self.load_config_from_west(args.scheme)
elif args.states:
self.parse_cmdline_states(args.states)
elif args.states_from_file:
self.load_state_file(args.states_from_file)
elif args.states_from_function:
self.load_states_from_function(
get_object(args.states_from_function, path=['.']))
if self.states and len(self.states) < 2:
raise ValueError('zero, two, or more macrostates are required')
#self.output_file = WESTPAH5File(args.output, 'w', creating_program=True)
log.debug('state list: {!r}'.format(self.states))
self.subsample = args.subsample if args.subsample is not None else False
def parse_cmdline_states(self, state_strings):
states = []
for istring, state_string in enumerate(state_strings):
try:
(label, coord_str) = state_string.split(':')
except ValueError:
label = 'state{}'.format(istring)
coord_str = state_string
coord = parse_pcoord_value(coord_str)
states.append({'label': label, 'coords': coord})
self.states = states
def load_config_from_west(self, scheme):
try:
config = westpa.rc.config['west']['analysis']
except:
raise ValueError('There is no configuration file specified.')
ystates = config['analysis_schemes'][scheme]['states']
self.states_from_dict(ystates)
try:
self.subsample = config['subsample']
except:
pass
from westpa._rc import bins_from_yaml_dict
self.binning.mapper = bins_from_yaml_dict(
config['analysis_schemes'][scheme]['bins'][0])
import os
path = os.path.join(os.getcwd(), config['directory'], scheme)
try:
os.mkdir(config['directory'])
os.mkdir(path)
except:
pass
self.output_filename = os.path.join(path, 'assign.h5')
def load_state_file(self, state_filename):
import yaml
ydict = yaml.load(open(state_filename, 'rt'))
ystates = ydict['states']
self.states_from_dict(ystates)
def states_from_dict(self, ystates):
states = []
for istate, ystate in enumerate(ystates):
state = {}
state['label'] = ystate.get('label', 'state{}'.format(istate))
# coords can be:
# - a scalar, in which case it is one bin, 1-D
# - a single list, which is rejected as ambiguous
# - a list of lists, which is a list of coordinate tuples
coords = numpy.array(ystate['coords'])
if coords.ndim == 0:
coords.shape = (1, 1)
elif coords.ndim == 1:
raise ValueError(
'list {!r} is ambiguous (list of 1-d coordinates, or single multi-d coordinate?)'
.format(ystate['coords']))
elif coords.ndim > 2:
raise ValueError('coordinates must be 2-D')
state['coords'] = coords
states.append(state)
self.states = states
def load_states_from_function(self, statefunc):
states = statefunc(self.binning.mapper)
for istate, state in enumerate(states):
state.setdefault('label', 'state{}'.format(istate))
try:
state['coords'] = numpy.array(state['coords'])
except KeyError:
raise ValueError(
'state function {!r} returned a state {!r} without coordinates'
.format(statefunc, state))
self.states = states
log.debug('loaded states: {!r}'.format(self.states))
def assign_iteration(self, n_iter, nstates, nbins, state_map, last_labels):
''' Method to encapsulate the segment slicing (into n_worker slices) and parallel job submission
Submits job(s), waits on completion, splices them back together
Returns: assignments, trajlabels, pops for this iteration'''
futures = []
iter_group = self.data_reader.get_iter_group(n_iter)
nsegs, npts = iter_group['pcoord'].shape[:2]
n_workers = self.work_manager.n_workers or 1
assignments = numpy.empty((nsegs, npts), dtype=index_dtype)
trajlabels = numpy.empty((nsegs, npts), dtype=index_dtype)
statelabels = numpy.empty((nsegs, npts), dtype=index_dtype)
pops = numpy.zeros((nstates + 1, nbins + 1), dtype=weight_dtype)
#Submit jobs to work manager
blocksize = nsegs // n_workers
if nsegs % n_workers > 0:
blocksize += 1
def task_gen():
if __debug__:
checkset = set()
for lb in range(0, nsegs, blocksize):
ub = min(nsegs, lb + blocksize)
if __debug__:
checkset.update(set(range(lb, ub)))
args = ()
kwargs = dict(
n_iter=n_iter,
lb=lb,
ub=ub,
mapper=self.binning.mapper,
nstates=nstates,
state_map=state_map,
last_labels=last_labels,
parent_id_dsspec=self.data_reader.parent_id_dsspec,
weight_dsspec=self.data_reader.weight_dsspec,
pcoord_dsspec=self.dssynth.dsspec,
subsample=self.subsample)
yield (_assign_label_pop, args, kwargs)
#futures.append(self.work_manager.submit(_assign_label_pop,
#kwargs=)
if __debug__:
assert checkset == set(
range(nsegs)), 'segments missing: {}'.format(
set(range(nsegs)) - checkset)
#for future in self.work_manager.as_completed(futures):
for future in self.work_manager.submit_as_completed(
task_gen(), queue_size=self.max_queue_len):
assign_slice, traj_slice, slice_pops, lb, ub, state_slice = future.get_result(
discard=True)
assignments[lb:ub, :] = assign_slice
trajlabels[lb:ub, :] = traj_slice
statelabels[lb:ub, :] = state_slice
pops += slice_pops
del assign_slice, traj_slice, slice_pops, state_slice
del futures
return (assignments, trajlabels, pops, statelabels)
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 = numpy.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 = [
numpy.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 = [
numpy.string_(state['label']) for state in self.states
]
for istate, sdict in enumerate(self.states):
assert state_labels[istate] == numpy.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 = numpy.empty((iter_count, ), seg_id_dtype)
npts = numpy.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 = numpy.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 = numpy.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,
[numpy.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 = numpy.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)
class WBinTool(WESTTool):
prog = 'w_bins'
description = '''\
Display information and statistics about binning in a WEST simulation, or
modify the binning for the current iteration of a WEST simulation.
-------------------------------------------------------------------------------
'''
def __init__(self):
super(WBinTool, self).__init__()
self.subcommand = None
self.data_reader = WESTDataReader()
self.binning = BinMappingComponent()
self.args = None
self.n_iter = None
# Interface for command-line tools
def add_args(self, parser):
self.data_reader.add_args(parser)
subparsers = parser.add_subparsers(help='available commands')
info_parser = subparsers.add_parser(
'info', help='Display information about binning.')
info_parser.add_argument(
'-n',
'--n-iter',
type=int,
help=
'''Consider initial points of segment N_ITER (default: current iteration).'''
)
info_parser.add_argument(
'--detail',
action='store_true',
help='''Display detailed per-bin information in addition to summary
information.''')
self.binning.add_args(info_parser)
info_parser.set_defaults(func=self.cmd_info)
rebin_parser = subparsers.add_parser(
'rebin', help='Rebuild current iteration with new binning.')
rebin_parser.add_argument(
'--confirm',
action='store_true',
help=
'''Commit the revised iteration to HDF5; without this option, the effects of the
new binning are only calculated and printed.'''
)
rebin_parser.add_argument(
'--detail',
action='store_true',
help='''Display detailed per-bin information in addition to summary
information.''')
self.binning.add_args(rebin_parser, suppress=['--bins-from-file'])
self.binning.add_target_count_args(rebin_parser)
rebin_parser.set_defaults(func=self.cmd_rebin)
def process_args(self, args):
self.data_reader.process_args(args)
self.data_reader.open(mode='r+')
self.n_iter = getattr(args, 'n_iter',
None) or self.data_reader.current_iteration
# we cannot read bin information during rebins
# interesting note: '==' is required here; 'is' fails
if args.func == self.cmd_rebin:
self.binning.target_counts_required = True
else:
self.binning.set_we_h5file_info(self.n_iter, self.data_reader)
self.binning.process_args(args)
self.args = args
self.subcommand = args.func
def go(self):
self.subcommand()
def cmd_info(self):
mapper = self.binning.mapper
# Get target states and their assignments
target_states = self.data_reader.get_target_states(self.n_iter)
n_target_states = len(target_states)
iter_group = self.data_reader.get_iter_group(self.n_iter)
# bin initial pcoords for iteration n_iter
initial_pcoords = iter_group['pcoord'][:, 0, :]
assignments = mapper.assign(initial_pcoords)
del initial_pcoords
print('Bin information for iteration {:d}'.format(self.n_iter))
# Get bin counts and weights
weights = iter_group['seg_index']['weight']
write_bin_info(mapper,
assignments,
weights,
n_target_states,
detailed=self.args.detail)
def cmd_rebin(self):
mapper = self.binning.mapper
assert mapper is not None
if self.n_iter == 1:
sys.stderr.write(
'rebin is not supported for the first iteration; reinitialize with w_init instead\n'
)
sys.exit(1)
n_target_states = len(self.data_reader.get_target_states(self.n_iter))
we_driver = westpa.rc.get_we_driver()
data_manager = self.data_reader.data_manager
segments = data_manager.get_segments(self.n_iter, load_pcoords=True)
last_iter_segments = data_manager.get_segments(self.n_iter - 1,
load_pcoords=False,
load_auxdata=False)
# Bin on this iteration's initial points
# We don't have to worry about recycling because we are binning on
# initial points rather than final points, so recycling has already
# occurred for this iteration.
# We do need initial states, in case we merge a newly-created walker out of existence
#avail_initial_states = {state.state_id: state
# for state in data_manager.get_unused_initial_states(n_iter = self.n_iter)}
avail_initial_states = data_manager.get_unused_initial_states(
n_iter=self.n_iter)
used_initial_states = data_manager.get_segment_initial_states(segments)
we_driver.new_iteration(
initial_states=avail_initial_states,
bin_mapper=mapper,
bin_target_counts=self.binning.bin_target_counts)
we_driver.used_initial_states = {
state.state_id: state
for state in used_initial_states
}
we_driver.assign(segments, initializing=True)
we_driver.rebin_current(parent_segments=last_iter_segments)
weights = numpy.array(
[segment.weight for segment in we_driver.next_iter_segments])
assignments = numpy.fromiter(we_driver.next_iter_assignments,
dtype=int,
count=len(weights))
write_bin_info(mapper,
assignments,
weights,
n_target_states,
detailed=self.args.detail)
if self.args.confirm:
data_manager.prepare_iteration(self.n_iter,
list(we_driver.next_iter_segments))
# manually update endpoint statuses only
endpoint_types = sorted([(segment.seg_id, segment.endpoint_type)
for segment in last_iter_segments])
last_iter_group = data_manager.get_iter_group(self.n_iter - 1)
last_iter_index = last_iter_group['seg_index'][...]
last_iter_index['endpoint_type'] = [
pair[1] for pair in endpoint_types
]
last_iter_group['seg_index'][...] = last_iter_index
data_manager.save_iter_binning(self.n_iter,
self.binning.mapper_hash,
self.binning.mapper_pickle,
we_driver.bin_target_counts)
data_manager.update_initial_states(we_driver.all_initial_states)
data_manager.flush_backing()
class WAssign(WESTParallelTool):
prog='w_assign'
description = '''\
Assign walkers to bins, producing a file (by default named "assign.h5")
which can be used in subsequent analysis.
For consistency in subsequent analysis operations, the entire dataset
must be assigned, even if only a subset of the data will be used. This
ensures that analyses that rely on tracing trajectories always know the
originating bin of each trajectory.
-----------------------------------------------------------------------------
Source data
-----------------------------------------------------------------------------
Source data is provided either by a user-specified function
(--construct-dataset) or a list of "data set specifications" (--dsspecs).
If neither is provided, the progress coordinate dataset ''pcoord'' is used.
To use a custom function to extract or calculate data whose probability
distribution will be calculated, specify the function in standard Python
MODULE.FUNCTION syntax as the argument to --construct-dataset. This function
will be called as function(n_iter,iter_group), where n_iter is the iteration
whose data are being considered and iter_group is the corresponding group
in the main WEST HDF5 file (west.h5). The function must return data which can
be indexed as [segment][timepoint][dimension].
To use a list of data set specifications, specify --dsspecs and then list the
desired datasets one-by-one (space-separated in most shells). These data set
specifications are formatted as NAME[,file=FILENAME,slice=SLICE], which will
use the dataset called NAME in the HDF5 file FILENAME (defaulting to the main
WEST HDF5 file west.h5), and slice it with the Python slice expression SLICE
(as in [0:2] to select the first two elements of the first axis of the
dataset). The ``slice`` option is most useful for selecting one column (or
more) from a multi-column dataset, such as arises when using a progress
coordinate of multiple dimensions.
-----------------------------------------------------------------------------
Specifying macrostates
-----------------------------------------------------------------------------
Optionally, kinetic macrostates may be defined in terms of sets of bins.
Each trajectory will be labeled with the kinetic macrostate it was most
recently in at each timepoint, for use in subsequent kinetic analysis.
This is required for all kinetics analysis (w_kintrace and w_kinmat).
There are three ways to specify macrostates:
1. States corresponding to single bins may be identified on the command
line using the --states option, which takes multiple arguments, one for
each state (separated by spaces in most shells). Each state is specified
as a coordinate tuple, with an optional label prepended, as in
``bound:1.0`` or ``unbound:(2.5,2.5)``. Unlabeled states are named
``stateN``, where N is the (zero-based) position in the list of states
supplied to --states.
2. States corresponding to multiple bins may use a YAML input file specified
with --states-from-file. This file defines a list of states, each with a
name and a list of coordinate tuples; bins containing these coordinates
will be mapped to the containing state. For instance, the following
file::
---
states:
- label: unbound
coords:
- [9.0, 1.0]
- [9.0, 2.0]
- label: bound
coords:
- [0.1, 0.0]
produces two macrostates: the first state is called "unbound" and
consists of bins containing the (2-dimensional) progress coordinate
values (9.0, 1.0) and (9.0, 2.0); the second state is called "bound"
and consists of the single bin containing the point (0.1, 0.0).
3. Arbitrary state definitions may be supplied by a user-defined function,
specified as --states-from-function=MODULE.FUNCTION. This function is
called with the bin mapper as an argument (``function(mapper)``) and must
return a list of dictionaries, one per state. Each dictionary must contain
a vector of coordinate tuples with key "coords"; the bins into which each
of these tuples falls define the state. An optional name for the state
(with key "label") may also be provided.
-----------------------------------------------------------------------------
Output format
-----------------------------------------------------------------------------
The output file (-o/--output, by default "assign.h5") contains the following
attributes datasets:
``nbins`` attribute
*(Integer)* Number of valid bins. Bin assignments range from 0 to
*nbins*-1, inclusive.
``nstates`` attribute
*(Integer)* Number of valid macrostates (may be zero if no such states are
specified). Trajectory ensemble assignments range from 0 to *nstates*-1,
inclusive, when states are defined.
``/assignments`` [iteration][segment][timepoint]
*(Integer)* Per-segment and -timepoint assignments (bin indices).
``/npts`` [iteration]
*(Integer)* Number of timepoints in each iteration.
``/nsegs`` [iteration]
*(Integer)* Number of segments in each iteration.
``/labeled_populations`` [iterations][state][bin]
*(Floating-point)* Per-iteration and -timepoint bin populations, labeled
by most recently visited macrostate. The last state entry (*nstates-1*)
corresponds to trajectories initiated outside of a defined macrostate.
``/bin_labels`` [bin]
*(String)* Text labels of bins.
When macrostate assignments are given, the following additional datasets are
present:
``/trajlabels`` [iteration][segment][timepoint]
*(Integer)* Per-segment and -timepoint trajectory labels, indicating the
macrostate which each trajectory last visited.
``/state_labels`` [state]
*(String)* Labels of states.
``/state_map`` [bin]
*(Integer)* Mapping of bin index to the macrostate containing that bin.
An entry will contain *nbins+1* if that bin does not fall into a
macrostate.
Datasets indexed by state and bin contain one more entry than the number of
valid states or bins. For *N* bins, axes indexed by bin are of size *N+1*, and
entry *N* (0-based indexing) corresponds to a walker outside of the defined bin
space (which will cause most mappers to raise an error). More importantly, for
*M* states (including the case *M=0* where no states are specified), axes
indexed by state are of size *M+1* and entry *M* refers to trajectories
initiated in a region not corresponding to a defined macrostate.
Thus, ``labeled_populations[:,:,:].sum(axis=1)[:,:-1]`` gives overall per-bin
populations, for all defined bins and
``labeled_populations[:,:,:].sum(axis=2)[:,:-1]`` gives overall
per-trajectory-ensemble populations for all defined states.
-----------------------------------------------------------------------------
Parallelization
-----------------------------------------------------------------------------
This tool supports parallelized binning, including reading/calculating input
data.
-----------------------------------------------------------------------------
Command-line options
-----------------------------------------------------------------------------
'''
def __init__(self):
super(WAssign,self).__init__()
# Parallel processing by default (this is not actually necessary, but it is
# informative!)
self.wm_env.default_work_manager = self.wm_env.default_parallel_work_manager
self.data_reader = WESTDataReader()
self.dssynth = WESTDSSynthesizer(default_dsname='pcoord')
self.binning = BinMappingComponent()
self.progress = ProgressIndicatorComponent()
self.output_file = None
self.output_filename = None
self.states = []
def add_args(self, parser):
self.data_reader.add_args(parser)
self.binning.add_args(parser, suppress=['--bins-from-h5file'])
self.dssynth.add_args(parser)
sgroup = parser.add_argument_group('macrostate definitions').add_mutually_exclusive_group()
sgroup.add_argument('--states', nargs='+', metavar='STATEDEF',
help='''Single-bin kinetic macrostate, specified by a coordinate tuple (e.g. '1.0' or '[1.0,1.0]'),
optionally labeled (e.g. 'bound:[1.0,1.0]'). States corresponding to multiple bins
must be specified with --states-from-file.''')
sgroup.add_argument('--states-from-file', metavar='STATEFILE',
help='''Load kinetic macrostates from the YAML file STATEFILE. See description
above for the appropriate structure.''')
sgroup.add_argument('--states-from-function', metavar='STATEFUNC',
help='''Load kinetic macrostates from the function STATEFUNC, specified as
module_name.func_name. This function is called with the bin mapper as an argument,
and must return a list of dictionaries {'label': state_label, 'coords': 2d_array_like}
one for each macrostate; the 'coords' entry must contain enough rows to identify all bins
in the macrostate.''')
agroup = parser.add_argument_group('other options')
agroup.add_argument('-o', '--output', dest='output', default='assign.h5',
help='''Store results in OUTPUT (default: %(default)s).''')
def process_args(self, args):
self.progress.process_args(args)
self.data_reader.process_args(args)
with self.data_reader:
self.dssynth.h5filename = self.data_reader.we_h5filename
self.dssynth.process_args(args)
self.binning.process_args(args)
if args.states:
self.parse_cmdline_states(args.states)
elif args.states_from_file:
self.load_state_file(args.states_from_file)
elif args.states_from_function:
self.load_states_from_function(get_object(args.states_from_function,path=['.']))
if self.states and len(self.states) < 2:
raise ValueError('zero, two, or more macrostates are required')
#self.output_file = WESTPAH5File(args.output, 'w', creating_program=True)
self.output_filename = args.output
log.debug('state list: {!r}'.format(self.states))
def parse_cmdline_states(self, state_strings):
states = []
for istring, state_string in enumerate(state_strings):
try:
(label, coord_str) = state_string.split(':')
except ValueError:
label = 'state{}'.format(istring)
coord_str = state_string
coord = parse_pcoord_value(coord_str)
states.append({'label': label, 'coords': coord})
self.states = states
def load_state_file(self, state_filename):
import yaml
ydict = yaml.load(open(state_filename, 'rt'))
ystates = ydict['states']
states = []
for istate, ystate in enumerate(ystates):
state = {}
state['label'] = ystate.get('label', 'state{}'.format(istate))
# coords can be:
# - a scalar, in which case it is one bin, 1-D
# - a single list, which is rejected as ambiguous
# - a list of lists, which is a list of coordinate tuples
coords = numpy.array(ystate['coords'])
if coords.ndim == 0:
coords.shape = (1,1)
elif coords.ndim == 1:
raise ValueError('list {!r} is ambiguous (list of 1-d coordinates, or single multi-d coordinate?)'
.format(ystate['coords']))
elif coords.ndim > 2:
raise ValueError('coordinates must be 2-D')
state['coords'] = coords
states.append(state)
self.states = states
def load_states_from_function(self, statefunc):
states = statefunc(self.binning.mapper)
for istate, state in enumerate(states):
state.setdefault('label','state{}'.format(istate))
try:
state['coords'] = numpy.array(state['coords'])
except KeyError:
raise ValueError('state function {!r} returned a state {!r} without coordinates'.format(statefunc,state))
self.states = states
log.debug('loaded states: {!r}'.format(self.states))
def assign_iteration(self, n_iter, nstates, nbins, state_map, last_labels):
''' Method to encapsulate the segment slicing (into n_worker slices) and parallel job submission
Submits job(s), waits on completion, splices them back together
Returns: assignments, trajlabels, pops for this iteration'''
futures = []
iter_group = self.data_reader.get_iter_group(n_iter)
nsegs, npts = iter_group['pcoord'].shape[:2]
n_workers = self.work_manager.n_workers or 1
assignments = numpy.empty((nsegs, npts), dtype=index_dtype)
trajlabels = numpy.empty((nsegs, npts), dtype=index_dtype)
pops = numpy.zeros((nstates+1,nbins+1), dtype=weight_dtype)
#Submit jobs to work manager
blocksize = nsegs // n_workers
if nsegs % n_workers > 0:
blocksize += 1
def task_gen():
if __debug__:
checkset = set()
for lb in xrange(0, nsegs, blocksize):
ub = min(nsegs, lb+blocksize)
if __debug__:
checkset.update(set(xrange(lb,ub)))
args = ()
kwargs = dict(n_iter=n_iter,
lb=lb, ub=ub, mapper=self.binning.mapper, nstates=nstates, state_map=state_map,
last_labels=last_labels,
parent_id_dsspec=self.data_reader.parent_id_dsspec,
weight_dsspec=self.data_reader.weight_dsspec,
pcoord_dsspec=self.dssynth.dsspec)
yield (_assign_label_pop, args, kwargs)
#futures.append(self.work_manager.submit(_assign_label_pop,
#kwargs=)
if __debug__:
assert checkset == set(xrange(nsegs)), 'segments missing: {}'.format(set(xrange(nsegs)) - checkset)
#for future in self.work_manager.as_completed(futures):
for future in self.work_manager.submit_as_completed(task_gen(), queue_size=self.max_queue_len):
assign_slice, traj_slice, slice_pops, lb, ub = future.get_result(discard=True)
assignments[lb:ub, :] = assign_slice
trajlabels[lb:ub, :] = traj_slice
pops += slice_pops
del assign_slice, traj_slice, slice_pops
del futures
return (assignments, trajlabels, pops)
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 = numpy.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 = [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 = [state['label'] for state in self.states]
for istate, sdict in enumerate(self.states):
assert state_labels[istate] == 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
iter_count = iter_stop - iter_start
nsegs = numpy.empty((iter_count,), seg_id_dtype)
npts = numpy.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(xrange(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 = numpy.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 = numpy.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)
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, ['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(xrange(iter_start,iter_stop)):
#get iteration info in this block
if iiter == 0:
last_labels = numpy.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 = 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
pi.progress += 1
del assignments, trajlabels, pops
for dsname in 'assignments', 'npts', 'nsegs', 'labeled_populations':
h5io.stamp_iter_range(self.output_file[dsname], iter_start, iter_stop)