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 WCrawl(WESTParallelTool):
prog='w_crawl'
description = '''\
Crawl a weighted ensemble dataset, executing a function for each iteration.
This can be used for postprocessing of trajectories, cleanup of datasets,
or anything else that can be expressed as "do X for iteration N, then do
something with the result". Tasks are parallelized by iteration, and
no guarantees are made about evaluation order.
-----------------------------------------------------------------------------
Command-line options
-----------------------------------------------------------------------------
'''
def __init__(self):
super(WCrawl,self).__init__()
# These are used throughout
self.progress = ProgressIndicatorComponent()
self.data_reader = WESTDataReader()
self.iter_range = IterRangeSelection(self.data_reader)
self.crawler = None
self.task_callable = None
def add_args(self, parser):
self.data_reader.add_args(parser)
self.iter_range.add_args(parser)
tgroup = parser.add_argument_group('task options')
tgroup.add_argument('-c', '--crawler-instance',
help='''Use CRAWLER_INSTANCE (specified as module.instance) as an instance of
WESTPACrawler to coordinate the calculation. Required only if initialization,
finalization, or task result processing is required.''')
tgroup.add_argument('task_callable',
help='''Run TASK_CALLABLE (specified as module.function) on each iteration.
Required.''')
self.progress.add_args(parser)
def process_args(self, args):
self.progress.process_args(args)
self.data_reader.process_args(args)
with self.data_reader:
self.iter_range.process_args(args)
self.task_callable = get_object(args.task_callable, path=['.'])
if args.crawler_instance is not None:
self.crawler = get_object(args.crawler_instance, path=['.'])
else:
self.crawler = WESTPACrawler()
def go(self):
iter_start = self.iter_range.iter_start
iter_stop = self.iter_range.iter_stop
iter_count = iter_stop - iter_start
self.data_reader.open('r')
pi = self.progress.indicator
with pi:
pi.operation = 'Initializing'
self.crawler.initialize(iter_start, iter_stop)
try:
pi.new_operation('Dispatching tasks & processing results', iter_count)
task_gen = ((_remote_task, (n_iter, self.task_callable), {}) for n_iter in range(iter_start,iter_stop))
for future in self.work_manager.submit_as_completed(task_gen, self.max_queue_len):
n_iter, result = future.get_result(discard=True)
if self.crawler is not None:
self.crawler.process_iter_result(n_iter,result)
pi.progress += 1
finally:
pi.new_operation('Finalizing')
self.crawler.finalize()
class WFluxanlTool(WESTTool):
prog = 'w_fluxanl'
description = '''\
Extract fluxes into pre-defined target states from WEST data,
average, and construct confidence intervals. Monte Carlo bootstrapping
is used to account for the correlated and possibly non-Gaussian statistical
error in flux measurements.
All non-graphical output (including that to the terminal and HDF5) assumes that
the propagation/resampling period ``tau`` is equal to unity; to obtain results
in familiar units, divide all fluxes and multiply all correlation lengths by
the true value of ``tau``.
'''
output_format_version = 2
def __init__(self):
super(WFluxanlTool, self).__init__()
self.data_reader = WESTDataReader()
self.iter_range = IterRangeSelection()
self.output_h5file = None
self.output_group = None
self.target_groups = {}
self.fluxdata = {}
self.alpha = None
self.autocorrel_alpha = None
self.n_sets = None
self.do_evol = False
self.evol_step = 1
def add_args(self, parser):
self.data_reader.add_args(parser)
self.iter_range.add_args(parser)
ogroup = parser.add_argument_group('output options')
ogroup.add_argument(
'-o',
'--output',
default='fluxanl.h5',
help=
'Store intermediate data and analysis results to OUTPUT (default: %(default)s).'
)
cgroup = parser.add_argument_group('calculation options')
cgroup.add_argument(
'--disable-bootstrap',
'-db',
dest='bootstrap',
action='store_const',
const=False,
help='''Enable the use of Monte Carlo Block Bootstrapping.''')
cgroup.add_argument('--disable-correl',
'-dc',
dest='correl',
action='store_const',
const=False,
help='''Disable the correlation analysis.''')
cgroup.add_argument(
'-a',
'--alpha',
type=float,
default=0.05,
help=
'''Calculate a (1-ALPHA) confidence interval on the average flux'
(default: %(default)s)''')
cgroup.add_argument(
'--autocorrel-alpha',
type=float,
dest='acalpha',
metavar='ACALPHA',
help='''Evaluate autocorrelation of flux to (1-ACALPHA) significance.
Note that too small an ACALPHA will result in failure to detect autocorrelation
in a noisy flux signal. (Default: same as ALPHA.)'''
)
cgroup.add_argument(
'-N',
'--nsets',
type=int,
help=
'''Use NSETS samples for bootstrapping (default: chosen based on ALPHA)'''
)
cgroup.add_argument(
'--evol',
action='store_true',
dest='do_evol',
help=
'''Calculate time evolution of flux confidence intervals (expensive).'''
)
cgroup.add_argument(
'--evol-step',
type=int,
default=1,
metavar='ESTEP',
help=
'''Calculate time evolution of flux confidence intervals every ESTEP
iterations (default: %(default)s)''')
def process_args(self, args):
self.data_reader.process_args(args)
self.data_reader.open()
self.iter_range.data_manager = self.data_reader
self.iter_range.process_args(args)
self.output_h5file = h5py.File(args.output, 'w')
self.alpha = args.alpha
# Disable the bootstrap or the correlation analysis.
self.mcbs_enable = args.bootstrap if args.bootstrap is not None else True
self.do_correl = args.correl if args.correl is not None else True
self.autocorrel_alpha = args.acalpha or self.alpha
self.n_sets = args.nsets or mclib.get_bssize(self.alpha)
self.do_evol = args.do_evol
self.evol_step = args.evol_step or 1
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_evol_flux(self):
westpa.rc.pstatus(
'Calculating cumulative evolution of flux confidence intervals every {} iteration(s)'
.format(self.evol_step))
for itarget, (target_label,
target_fluxdata) in enumerate(self.fluxdata.items()):
fluxes = target_fluxdata['flux']
target_group = self.target_groups[target_label]
iter_start = target_group['n_iter'][0]
iter_stop = target_group['n_iter'][-1]
iter_count = iter_stop - iter_start
n_blocks = iter_count // self.evol_step
if iter_count % self.evol_step > 0: n_blocks += 1
cis = numpy.empty((n_blocks, ), dtype=ci_dtype)
for iblock in range(n_blocks):
block_iter_stop = min(
iter_start + (iblock + 1) * self.evol_step, iter_stop)
istop = min((iblock + 1) * self.evol_step,
len(target_fluxdata['flux']))
fluxes = target_fluxdata['flux'][:istop]
#avg, ci_lb, ci_ub, correl_len = mclib.mcbs_ci_correl(fluxes, numpy.mean, self.alpha, self.n_sets,
# autocorrel_alpha = self.autocorrel_alpha,
# subsample=numpy.mean)
avg, ci_lb, ci_ub, 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)
cis[iblock]['iter_start'] = iter_start
cis[iblock]['iter_stop'] = block_iter_stop
cis[iblock]['expected'], cis[iblock]['ci_lbound'], cis[iblock][
'ci_ubound'] = avg, ci_lb, ci_ub
cis[iblock]['corr_len'] = correl_len
cis[iblock]['sterr'] = sterr
del fluxes
cis_ds = target_group.create_dataset('flux_evolution', data=cis)
cis_ds.attrs['iter_step'] = self.evol_step
cis_ds.attrs['mcbs_alpha'] = self.alpha
cis_ds.attrs['mcbs_autocorrel_alpha'] = self.autocorrel_alpha
cis_ds.attrs['mcbs_n_sets'] = self.n_sets
def go(self):
self.calc_store_flux_data()
if self.do_evol:
self.calc_evol_flux()
class WSelectTool(WESTParallelTool):
prog='w_select'
description = '''\
Select dynamics segments matching various criteria. This requires a
user-provided prediate function. By default, only matching segments are
stored. If the -a/--include-ancestors option is given, then matching segments
and their ancestors will be stored.
-----------------------------------------------------------------------------
Predicate function
-----------------------------------------------------------------------------
Segments are selected based on a predicate function, which must be callable
as ``predicate(n_iter, iter_group)`` and return a collection of segment IDs
matching the predicate in that iteration.
The predicate may be inverted by specifying the -v/--invert command-line
argument.
-----------------------------------------------------------------------------
Output format
-----------------------------------------------------------------------------
The output file (-o/--output, by default "select.h5") contains the following
datasets:
``/n_iter`` [iteration]
*(Integer)* Iteration numbers for each entry in other datasets.
``/n_segs`` [iteration]
*(Integer)* Number of segment IDs matching the predicate (or inverted
predicate, if -v/--invert is specified) in the given iteration.
``/seg_ids`` [iteration][segment]
*(Integer)* Matching segments in each iteration. For an iteration
``n_iter``, only the first ``n_iter`` entries are valid. For example,
the full list of matching seg_ids in the first stored iteration is
``seg_ids[0][:n_segs[0]]``.
``/weights`` [iteration][segment]
*(Floating-point)* Weights for each matching segment in ``/seg_ids``.
-----------------------------------------------------------------------------
Command-line arguments
-----------------------------------------------------------------------------
'''
def __init__(self):
super(WSelectTool,self).__init__()
self.data_reader = WESTDataReader()
self.iter_range = IterRangeSelection()
self.progress = ProgressIndicatorComponent()
self.output_file = None
self.output_filename = None
self.predicate = None
self.invert = False
self.include_ancestors = False
def add_args(self, parser):
self.data_reader.add_args(parser)
self.iter_range.add_args(parser)
sgroup = parser.add_argument_group('selection options')
sgroup.add_argument('-p', '--predicate-function', metavar='MODULE.FUNCTION',
help='''Use the given predicate function to match segments. This function
should take an iteration number and the HDF5 group corresponding to that
iteration and return a sequence of seg_ids matching the predicate, as in
``match_predicate(n_iter, iter_group)``.''')
sgroup.add_argument('-v', '--invert', dest='invert', action='store_true',
help='''Invert the match predicate.''')
sgroup.add_argument('-a', '--include-ancestors', action ='store_true',
help='''Include ancestors of matched segments in output.''')
ogroup = parser.add_argument_group('output options')
ogroup.add_argument('-o', '--output', default='select.h5',
help='''Write output to OUTPUT (default: %(default)s).''')
self.progress.add_args(parser)
def process_args(self, args):
self.progress.process_args(args)
self.data_reader.process_args(args)
with self.data_reader:
self.iter_range.process_args(args)
predicate = get_object(args.predicate_function,path=['.'])
if not callable(predicate):
raise TypeError('predicate object {!r} is not callable'.format(predicate))
self.predicate = predicate
self.invert = bool(args.invert)
self.include_ancestors = bool(args.include_ancestors)
self.output_filename = args.output
def go(self):
self.data_reader.open('r')
output_file = h5io.WESTPAH5File(self.output_filename, mode='w')
pi = self.progress.indicator
iter_start, iter_stop = self.iter_range.iter_start, self.iter_range.iter_stop
iter_count = iter_stop - iter_start
output_file.create_dataset('n_iter', dtype=n_iter_dtype, data=range(iter_start,iter_stop))
current_seg_count = 0
seg_count_ds = output_file.create_dataset('n_segs', dtype=numpy.uint, shape=(iter_count,))
matching_segs_ds = output_file.create_dataset('seg_ids', shape=(iter_count,0), maxshape=(iter_count,None),
dtype=seg_id_dtype,
chunks=h5io.calc_chunksize((iter_count,1000000), seg_id_dtype),
shuffle=True, compression=9)
weights_ds = output_file.create_dataset('weights', shape=(iter_count,0), maxshape=(iter_count,None),
dtype=weight_dtype,
chunks=h5io.calc_chunksize((iter_count,1000000), weight_dtype),
shuffle=True,compression=9)
with pi:
pi.new_operation('Finding matching segments', extent=iter_count)
# futures = set()
# for n_iter in xrange(iter_start,iter_stop):
# futures.add(self.work_manager.submit(_find_matching_segments,
# args=(self.data_reader.we_h5filename,n_iter,self.predicate,self.invert)))
# for future in self.work_manager.as_completed(futures):
for future in self.work_manager.submit_as_completed(((_find_matching_segments,
(self.data_reader.we_h5filename,n_iter,self.predicate,self.invert),
{}) for n_iter in xrange(iter_start,iter_stop)),
self.max_queue_len):
n_iter, matching_ids = future.get_result()
n_matches = len(matching_ids)
if n_matches:
if n_matches > current_seg_count:
current_seg_count = len(matching_ids)
matching_segs_ds.resize((iter_count,n_matches))
weights_ds.resize((iter_count,n_matches))
current_seg_count = n_matches
seg_count_ds[n_iter-iter_start] = n_matches
matching_segs_ds[n_iter-iter_start,:n_matches] = matching_ids
weights_ds[n_iter-iter_start,:n_matches] = self.data_reader.get_iter_group(n_iter)['seg_index']['weight'][sorted(matching_ids)]
del matching_ids
pi.progress += 1
if self.include_ancestors:
pi.new_operation('Tracing ancestors of matching segments', extent=iter_count)
from_previous = set()
current_seg_count = matching_segs_ds.shape[1]
for n_iter in xrange(iter_stop-1, iter_start-1, -1):
iiter = n_iter - iter_start
n_matches = seg_count_ds[iiter]
matching_ids = set(from_previous)
if n_matches:
matching_ids.update(matching_segs_ds[iiter, :seg_count_ds[iiter]])
from_previous.clear()
n_matches = len(matching_ids)
if n_matches > current_seg_count:
matching_segs_ds.resize((iter_count,n_matches))
weights_ds.resize((iter_count,n_matches))
current_seg_count = n_matches
if n_matches > 0:
seg_count_ds[iiter] = n_matches
matching_ids = sorted(matching_ids)
matching_segs_ds[iiter,:n_matches] = matching_ids
weights_ds[iiter,:n_matches] = self.data_reader.get_iter_group(n_iter)['seg_index']['weight'][sorted(matching_ids)]
parent_ids = self.data_reader.get_iter_group(n_iter)['seg_index']['parent_id'][sorted(matching_ids)]
from_previous.update(parent_id for parent_id in parent_ids if parent_id >= 0) # filter initial states
del parent_ids
del matching_ids
pi.progress += 1
class WSelectTool(WESTParallelTool):
prog = 'w_select'
description = '''\
Select dynamics segments matching various criteria. This requires a
user-provided prediate function. By default, only matching segments are
stored. If the -a/--include-ancestors option is given, then matching segments
and their ancestors will be stored.
-----------------------------------------------------------------------------
Predicate function
-----------------------------------------------------------------------------
Segments are selected based on a predicate function, which must be callable
as ``predicate(n_iter, iter_group)`` and return a collection of segment IDs
matching the predicate in that iteration.
The predicate may be inverted by specifying the -v/--invert command-line
argument.
-----------------------------------------------------------------------------
Output format
-----------------------------------------------------------------------------
The output file (-o/--output, by default "select.h5") contains the following
datasets:
``/n_iter`` [iteration]
*(Integer)* Iteration numbers for each entry in other datasets.
``/n_segs`` [iteration]
*(Integer)* Number of segment IDs matching the predicate (or inverted
predicate, if -v/--invert is specified) in the given iteration.
``/seg_ids`` [iteration][segment]
*(Integer)* Matching segments in each iteration. For an iteration
``n_iter``, only the first ``n_iter`` entries are valid. For example,
the full list of matching seg_ids in the first stored iteration is
``seg_ids[0][:n_segs[0]]``.
``/weights`` [iteration][segment]
*(Floating-point)* Weights for each matching segment in ``/seg_ids``.
-----------------------------------------------------------------------------
Command-line arguments
-----------------------------------------------------------------------------
'''
def __init__(self):
super(WSelectTool, self).__init__()
self.data_reader = WESTDataReader()
self.iter_range = IterRangeSelection()
self.progress = ProgressIndicatorComponent()
self.output_file = None
self.output_filename = None
self.predicate = None
self.invert = False
self.include_ancestors = False
def add_args(self, parser):
self.data_reader.add_args(parser)
self.iter_range.add_args(parser)
sgroup = parser.add_argument_group('selection options')
sgroup.add_argument(
'-p',
'--predicate-function',
metavar='MODULE.FUNCTION',
help=
'''Use the given predicate function to match segments. This function
should take an iteration number and the HDF5 group corresponding to that
iteration and return a sequence of seg_ids matching the predicate, as in
``match_predicate(n_iter, iter_group)``.''')
sgroup.add_argument('-v',
'--invert',
dest='invert',
action='store_true',
help='''Invert the match predicate.''')
sgroup.add_argument(
'-a',
'--include-ancestors',
action='store_true',
help='''Include ancestors of matched segments in output.''')
ogroup = parser.add_argument_group('output options')
ogroup.add_argument(
'-o',
'--output',
default='select.h5',
help='''Write output to OUTPUT (default: %(default)s).''')
self.progress.add_args(parser)
def process_args(self, args):
self.progress.process_args(args)
self.data_reader.process_args(args)
with self.data_reader:
self.iter_range.process_args(args)
predicate = get_object(args.predicate_function, path=['.'])
if not callable(predicate):
raise TypeError(
'predicate object {!r} is not callable'.format(predicate))
self.predicate = predicate
self.invert = bool(args.invert)
self.include_ancestors = bool(args.include_ancestors)
self.output_filename = args.output
def go(self):
self.data_reader.open('r')
output_file = h5io.WESTPAH5File(self.output_filename, mode='w')
pi = self.progress.indicator
iter_start, iter_stop = self.iter_range.iter_start, self.iter_range.iter_stop
iter_count = iter_stop - iter_start
output_file.create_dataset('n_iter',
dtype=n_iter_dtype,
data=list(range(iter_start, iter_stop)))
current_seg_count = 0
seg_count_ds = output_file.create_dataset('n_segs',
dtype=numpy.uint,
shape=(iter_count, ))
matching_segs_ds = output_file.create_dataset(
'seg_ids',
shape=(iter_count, 0),
maxshape=(iter_count, None),
dtype=seg_id_dtype,
chunks=h5io.calc_chunksize((iter_count, 1000000), seg_id_dtype),
shuffle=True,
compression=9)
weights_ds = output_file.create_dataset('weights',
shape=(iter_count, 0),
maxshape=(iter_count, None),
dtype=weight_dtype,
chunks=h5io.calc_chunksize(
(iter_count, 1000000),
weight_dtype),
shuffle=True,
compression=9)
with pi:
pi.new_operation('Finding matching segments', extent=iter_count)
# futures = set()
# for n_iter in xrange(iter_start,iter_stop):
# futures.add(self.work_manager.submit(_find_matching_segments,
# args=(self.data_reader.we_h5filename,n_iter,self.predicate,self.invert)))
# for future in self.work_manager.as_completed(futures):
for future in self.work_manager.submit_as_completed(
((_find_matching_segments,
(self.data_reader.we_h5filename, n_iter, self.predicate,
self.invert), {})
for n_iter in range(iter_start, iter_stop)),
self.max_queue_len):
n_iter, matching_ids = future.get_result()
n_matches = len(matching_ids)
if n_matches:
if n_matches > current_seg_count:
current_seg_count = len(matching_ids)
matching_segs_ds.resize((iter_count, n_matches))
weights_ds.resize((iter_count, n_matches))
current_seg_count = n_matches
seg_count_ds[n_iter - iter_start] = n_matches
matching_segs_ds[n_iter -
iter_start, :n_matches] = matching_ids
weights_ds[n_iter - iter_start, :
n_matches] = self.data_reader.get_iter_group(
n_iter)['seg_index']['weight'][sorted(
matching_ids)]
del matching_ids
pi.progress += 1
if self.include_ancestors:
pi.new_operation('Tracing ancestors of matching segments',
extent=iter_count)
from_previous = set()
current_seg_count = matching_segs_ds.shape[1]
for n_iter in range(iter_stop - 1, iter_start - 1, -1):
iiter = n_iter - iter_start
n_matches = seg_count_ds[iiter]
matching_ids = set(from_previous)
if n_matches:
matching_ids.update(
matching_segs_ds[iiter, :seg_count_ds[iiter]])
from_previous.clear()
n_matches = len(matching_ids)
if n_matches > current_seg_count:
matching_segs_ds.resize((iter_count, n_matches))
weights_ds.resize((iter_count, n_matches))
current_seg_count = n_matches
if n_matches > 0:
seg_count_ds[iiter] = n_matches
matching_ids = sorted(matching_ids)
matching_segs_ds[iiter, :n_matches] = matching_ids
weights_ds[
iiter, :
n_matches] = self.data_reader.get_iter_group(
n_iter)['seg_index']['weight'][sorted(
matching_ids)]
parent_ids = self.data_reader.get_iter_group(n_iter)[
'seg_index']['parent_id'][sorted(matching_ids)]
from_previous.update(
parent_id for parent_id in parent_ids
if parent_id >= 0) # filter initial states
del parent_ids
del matching_ids
pi.progress += 1
class WDumpSegs(WESTTool):
prog='w_dumpsegs'
description = '''\
Dump segment data as text. This is very inefficient, so this tool should be used
as a last resort (use hdfview/h5ls to look at data, and access HDF5 directly for
significant analysis tasks).
'''
def __init__(self):
super(WDumpSegs,self).__init__()
self.data_reader = WESTDataReader()
self.n_iter = None
self.output_file = None
self.print_pcoords = False
def add_args(self, parser):
self.data_reader.add_args(parser)
parser.add_argument('-p', '--print-pcoords', dest='print_pcoords', action='store_true',
help='print initial and final progress coordinates for each segment')
parser.add_argument('-i', '--iteration', dest='n_iter', type=int,
help='Use data from iteration N_ITER (default: last complete iteration)')
parser.add_argument('-o', '--output', dest='output_file',
help='Store output in OUTPUT_FILE (default: write to standard output).')
def process_args(self, args):
self.data_reader.process_args(args)
self.data_reader.open()
self.n_iter = args.n_iter or self.data_reader.current_iteration-1
self.output_file = open(args.output_file, 'wt') if args.output_file else sys.stdout
self.print_pcoords = args.print_pcoords
def go(self):
segments = self.data_reader.get_segments(self.n_iter)
max_seg_id_len = len(str(max(segment.seg_id for segment in segments)))
max_status_name_len = max(map(len,Segment.status_names.itervalues()))
max_endpoint_type_len = max(map(len,Segment.endpoint_type_names.itervalues()))
max_n_parents_len = len(str(max(len(segment.wtg_parent_ids) for segment in segments)))
report_line = ( '{segment.n_iter:d} {segment.seg_id:{max_seg_id_len}d} {segment.weight:20.14g}'
+' {status_name:{max_status_name_len}s} ({segment.status})'
+' {segment.walltime:<12.6g} {segment.cputime:<12.6g}'
+' {endpoint_type_name:{max_endpoint_type_len}s} ({segment.endpoint_type})'
+' {n_parents:{max_n_parents_len}d} {segment.parent_id:{max_seg_id_len}d} {parents_str}'
+'\n')
pcoord_lines = (' pcoord[0] = {init_pcoord}\n pcoord[-1] = {final_pcoord}'
+'\n')
for (_seg_id, segment) in enumerate(segments):
parents_str = '['+', '.join(map(str,sorted(segment.wtg_parent_ids)))+']'
init_pcoord_str = '[' + ', '.join('{pcval:<12.6g}'.format(pcval=float(pce)) for pce in segment.pcoord[0]) + ']'
final_pcoord_str = '[' + ', '.join('{pcval:<12.6g}'.format(pcval=float(pce)) for pce in segment.pcoord[-1]) + ']'
self.output_file.write(report_line.format(segment=segment,
status_name = segment.status_names[segment.status],
endpoint_type_name = segment.endpoint_type_names[segment.endpoint_type],
parents_str = parents_str,
n_parents = len(segment.wtg_parent_ids),
max_seg_id_len=max_seg_id_len,
max_status_name_len=max_status_name_len,
max_endpoint_type_len=max_endpoint_type_len,
max_n_parents_len=max_n_parents_len))
if self.print_pcoords:
self.output_file.write(pcoord_lines.format(init_pcoord = init_pcoord_str,final_pcoord = final_pcoord_str))
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 WIPI(WESTParallelTool):
'''
Welcome to w_ipa (WESTPA Interactive Python Analysis)!
From here, you can run traces, look at weights, progress coordinates, etc.
This is considered a 'stateful' tool; that is, the data you are pulling is always pulled
from the current analysis scheme and iteration.
By default, the first analysis scheme in west.cfg is used, and you are set at iteration 1.
ALL PROPERTIES ARE ACCESSED VIA w or west
To see the current iteration, try:
w.iteration
OR
west.iteration
to set it, simply plug in a new value.
w.iteration = 100
To change/list the current analysis schemes:
w.list_schemes
w.scheme = OUTPUT FROM w.list_schemes
To see the states and bins defined in the current analysis scheme:
w.states
w.bin_labels
All information about the current iteration is available in an object called 'current':
w.current
walkers, summary, states, seg_id, weights, parents, kinavg, pcoord, bins, populations, and auxdata, if it exists.
In addition, the function w.trace(seg_id) will run a trace over a seg_id in the current iteration and return a dictionary
containing all pertinent information about that seg_id's history. It's best to store this, as the trace can be expensive.
Run help on any function or property for more information!
Happy analyzing!
'''
def __init__(self):
super(WIPI,self).__init__()
self.data_reader = WESTDataReader()
self.wm_env.default_work_manager = self.wm_env.default_parallel_work_manager
self.progress = ProgressIndicatorComponent()
self._iter = 1
self.config_required = True
self.version = "1.0B"
# Set to matplotlib if you want that. But why would you?
# Well, whatever, we'll just set it to that for now.
self.interface = 'matplotlib'
self._scheme = None
global iteration
def add_args(self, parser):
self.progress.add_args(parser)
self.data_reader.add_args(parser)
rgroup = parser.add_argument_group('runtime options')
rgroup.add_argument('--analysis-only', '-ao', dest='analysis_mode', action='store_true',
help='''Use this flag to run the analysis and return to the terminal.''')
rgroup.add_argument('--reanalyze', '-ra', dest='reanalyze', action='store_true',
help='''Use this flag to delete the existing files and reanalyze.''')
rgroup.add_argument('--ignore-hash', '-ih', dest='ignore_hash', action='store_true',
help='''Ignore hash and don't regenerate files.''')
rgroup.add_argument('--debug', '-d', dest='debug_mode', action='store_true',
help='''Debug output largely intended for development.''')
rgroup.add_argument('--terminal', '-t', dest='plotting', action='store_true',
help='''Plot output in terminal.''')
# There is almost certainly a better way to handle this, but we'll sort that later.
import argparse
rgroup.add_argument('--f', '-f', dest='extra', default='blah',
help=argparse.SUPPRESS)
parser.set_defaults(compression=True)
def process_args(self, args):
self.progress.process_args(args)
self.data_reader.process_args(args)
with self.data_reader:
self.niters = self.data_reader.current_iteration - 1
self.__config = westpa.rc.config
self.__settings = self.__config['west']['analysis']
for ischeme, scheme in enumerate(self.__settings['analysis_schemes']):
if (self.__settings['analysis_schemes'][scheme]['enabled'] == True or self.__settings['analysis_schemes'][scheme]['enabled'] == None):
self.scheme = scheme
self.data_args = args
self.analysis_mode = args.analysis_mode
self.reanalyze = args.reanalyze
self.ignore_hash = args.ignore_hash
self.debug_mode = args.debug_mode
if args.plotting:
self.interface = 'text'
def hash_args(self, args, extra=None, path=None):
'''Create unique hash stamp to determine if arguments/file is different from before.'''
'''Combine with iteration to know whether or not file needs updating.'''
# Why are we not loading this functionality into the individual tools?
# While it may certainly be useful to store arguments (and we may well do that),
# it's rather complex and nasty to deal with pickling and hashing arguments through
# the various namespaces.
# In addition, it's unlikely that the functionality is desired at the individual tool level,
# since we'll always just rewrite a file when we call the function.
#return hashlib.md5(pickle.dumps([args, extra])).hexdigest()
# We don't care about the path, so we'll remove it.
# Probably a better way to do this, but who cares.
cargs = list(args)
for iarg, arg in enumerate(cargs):
if path in arg:
cargs[iarg] = arg.replace(path,'').replace('/', '')
if arg == '--disable-averages':
cargs.remove('--disable-averages')
to_hash = cargs + [extra]
#print(args)
#print(to_hash)
#print(str(to_hash).encode('base64'))
if self.debug_mode:
for iarg, arg in enumerate(to_hash):
if not isinstance(arg, list):
print('arg {num:02d} -- {arg:<20}'.format(num=iarg, arg=arg))
else:
for il, l in enumerate(arg):
print('arg {num:02d} -- {arg:<20}'.format(num=il+iarg, arg=l))
#print('args: {}'.format(to_hash))
# This SHOULD produce the same output, maybe? That would be nice, anyway.
# But we'll need to test it more.
return hashlib.md5(base64.b64encode(str(to_hash).encode())).hexdigest()
def stamp_hash(self, h5file_name, new_hash):
'''Loads a file, stamps it, and returns the opened file in read only'''
h5file = h5io.WESTPAH5File(h5file_name, 'r+')
h5file.attrs['arg_hash'] = new_hash
h5file.close()
h5file = h5io.WESTPAH5File(h5file_name, 'r')
return h5file
def analysis_structure(self):
'''
Run automatically on startup. Parses through the configuration file, and loads up all the data files from the different
analysis schematics. If they don't exist, it creates them automatically by hooking in to existing analysis routines
and going from there.
It does this by calling in the make_parser_and_process function for w_{assign,reweight,direct} using a custom built list
of args. The user can specify everything in the configuration file that would have been specified on the command line.
For instance, were one to call w_direct as follows:
w_direct --evolution cumulative --step-iter 1 --disable-correl
the west.cfg would look as follows:
west:
analysis:
w_direct:
evolution: cumulative
step_iter: 1
extra: ['disable-correl']
Alternatively, if one wishes to use the same options for both w_direct and w_reweight, the key 'w_direct' can be replaced
with 'kinetics'.
'''
# Make sure everything exists.
try:
os.mkdir(self.__settings['directory'])
except:
pass
# Now, check to see whether they exist, and then load them.
self.__analysis_schemes__ = {}
# We really need to implement some sort of default behavior if an analysis scheme isn't set.
# Right now, we just crash. That isn't really graceful.
for scheme in self.__settings['analysis_schemes']:
if self.__settings['analysis_schemes'][scheme]['enabled']:
if self.work_manager.running == False:
self.work_manager.startup()
path = os.path.join(os.getcwd(), self.__settings['directory'], scheme)
#if 'postanalysis' in self.__settings['analysis_schemes'][scheme] and 'postanalysis' in self.__settings['postanalysis']:
# Should clean this up. But it uses the default global setting if a by-scheme one isn't set.
if 'postanalysis' in self.__settings:
if 'postanalysis' in self.__settings['analysis_schemes'][scheme]:
pass
else:
self.__settings['analysis_schemes'][scheme]['postanalysis'] = self.__settings['postanalysis']
try:
os.mkdir(path)
except:
pass
self.__analysis_schemes__[scheme] = {}
try:
if self.__settings['analysis_schemes'][scheme]['postanalysis'] == True or self.__settings['postanalysis'] == True:
analysis_files = ['assign', 'direct', 'reweight']
else:
analysis_files = ['assign', 'direct']
except:
analysis_files = ['assign', 'direct']
self.__settings['analysis_schemes'][scheme]['postanalysis'] = False
reanalyze_kinetics = False
assign_hash = None
for name in analysis_files:
arg_hash = None
if self.reanalyze == True:
reanalyze_kinetics = True
try:
os.remove(os.path.join(path, '{}.h5'.format(name)))
except:
pass
else:
try:
# Try to load the hash. If we fail to load the hash or the file, we need to reload.
#if self.reanalyze == True:
# raise ValueError('Reanalyze set to true.')
self.__analysis_schemes__[scheme][name] = h5io.WESTPAH5File(os.path.join(path, '{}.h5'.format(name)), 'r')
arg_hash = self.__analysis_schemes__[scheme][name].attrs['arg_hash']
if name == 'assign':
assign_hash = arg_hash
except:
pass
# We shouldn't rely on this.
# self.reanalyze = True
if True:
if name == 'assign':
assign = w_assign.WAssign()
w_assign_config = { 'output': os.path.join(path, '{}.h5'.format(name))}
try:
w_assign_config.update(self.__settings['w_assign'])
except:
pass
try:
w_assign_config.update(self.__settings['analysis_schemes'][scheme]['w_assign'])
except:
pass
args = []
for key,value in w_assign_config.items():
if key != 'extra':
args.append(str('--') + str(key).replace('_', '-'))
args.append(str(value))
# This is for stuff like disabling correlation analysis, etc.
if 'extra' in list(w_assign_config.keys()):
# We're sorting to ensure that the order doesn't matter.
for value in sorted(w_assign_config['extra']):
args.append(str('--') + str(value).replace('_', '-'))
# We're just calling the built in function.
# This is a lot cleaner than what we had in before, and far more workable.
args.append('--config-from-file')
args.append('--scheme-name')
args.append('{}'.format(scheme))
# Why are we calling this if we're not sure we're remaking the file?
# We need to load up the bin mapper and states and see if they're the same.
assign.make_parser_and_process(args=args)
import pickle
#new_hash = self.hash_args(args=args, path=path, extra=[self.niters, pickle.dumps(assign.binning.mapper), assign.states])
# We need to encode it properly to ensure that some OS specific thing doesn't kill us. Same goes for the args, ultimately.
# Mostly, we just need to ensure that we're consistent.
new_hash = self.hash_args(args=args, path=path,
extra=[int(self.niters),
codecs.encode(pickle.dumps(assign.binning.mapper), "base64"),
base64.b64encode(str(assign.states).encode())])
# Let's check the hash. If the hash is the same, we don't need to reload.
if self.debug_mode == True:
print('{:<10}: old hash, new hash -- {}, {}'.format(name, arg_hash, new_hash))
if self.ignore_hash == False and (arg_hash != new_hash or self.reanalyze == True):
# If the hashes are different, or we need to reanalyze, delete the file.
try:
os.remove(os.path.join(path, '{}.h5'.format(name)))
except:
pass
print('Reanalyzing file {}.h5 for scheme {}.'.format(name, scheme))
#reanalyze_kinetics = True
# We want to use the work manager we have here. Otherwise, just let the tool sort out what it needs, honestly.
assign.work_manager = self.work_manager
assign.go()
assign.data_reader.close()
# Stamp w/ hash, then reload as read only.
self.__analysis_schemes__[scheme][name] = self.stamp_hash(os.path.join(path, '{}.h5'.format(name)), new_hash)
del(assign)
# Update the assignment hash.
assign_hash = new_hash
# Since these are all contained within one tool, now, we want it to just... load everything.
if name == 'direct' or name == 'reweight':
assignment_file = self.__analysis_schemes__[scheme]['assign']
if name == 'direct':
analysis = w_direct.WDirect()
if name == 'reweight':
analysis = w_reweight.WReweight()
analysis_config = { 'assignments': os.path.join(path, '{}.h5'.format('assign')), 'output': os.path.join(path, '{}.h5'.format(name)), 'kinetics': os.path.join(path, '{}.h5'.format(name))}
# Pull from general analysis options, then general SPECIFIC options for each analysis,
# then general options for that analysis scheme, then specific options for the analysis type in the scheme.
try:
analysis_config.update(self.__settings['kinetics'])
except:
pass
try:
analysis_config.update(self.__settings['w_{}'.format(name)])
except:
pass
try:
analysis_config.update(self.__settings['analysis_schemes'][scheme]['kinetics'])
except:
pass
try:
analysis_config.update(self.__settings['analysis_schemes'][scheme]['w_{}'.format(name)])
except:
pass
# We're pulling in a default set of arguments, then updating them with arguments from the west.cfg file, if appropriate, after setting the appropriate command
# Then, we call the magic function 'make_parser_and_process' with the arguments we've pulled in.
# The tool has no real idea it's being called outside of its actual function, and we're good to go.
args = ['all']
for key,value in analysis_config.items():
if key != 'extra':
args.append(str('--') + str(key).replace('_', '-'))
args.append(str(value))
# This is for stuff like disabling correlation analysis, etc.
if 'extra' in list(analysis_config.keys()):
for value in sorted(analysis_config['extra']):
args.append(str('--') + str(value).replace('_', '-'))
# We want to not display the averages, so...
args.append('--disable-averages')
new_hash = self.hash_args(args=args, path=path, extra=[int(self.niters), assign_hash])
#if arg_hash != new_hash or self.reanalyze == True or reanalyze_kinetics == True:
if self.debug_mode == True:
print('{:<10}: old hash, new hash -- {}, {}'.format(name, arg_hash, new_hash))
if self.ignore_hash == False and (arg_hash != new_hash or reanalyze_kinetics == True):
try:
os.remove(os.path.join(path, '{}.h5'.format(name)))
except:
pass
print('Reanalyzing file {}.h5 for scheme {}.'.format(name, scheme))
analysis.make_parser_and_process(args=args)
# We want to hook into the existing work manager.
analysis.work_manager = self.work_manager
analysis.go()
# Open!
self.__analysis_schemes__[scheme][name] = self.stamp_hash(os.path.join(path, '{}.h5'.format(name)), new_hash)
del(analysis)
# Make sure this doesn't get too far out, here. We need to keep it alive as long as we're actually analyzing things.
# self.work_manager.shutdown()
print("")
print("Complete!")
@property
def assign(self):
return self.__analysis_schemes__[str(self.scheme)]['assign']
@property
def direct(self):
"""
The output from w_kinavg.py from the current scheme.
"""
return self.__analysis_schemes__[str(self.scheme)]['direct']
@property
def state_labels(self):
print("State labels and definitions!")
for istate, state in enumerate(self.assign['state_labels']):
print('{}: {}'.format(istate, state))
print('{}: {}'.format(istate+1, 'Unknown'))
@property
def bin_labels(self):
print("Bin definitions! ")
for istate, state in enumerate(self.assign['bin_labels']):
print('{}: {}'.format(istate, state))
@property
def west(self):
return self.data_reader.data_manager.we_h5file
@property
def reweight(self):
if self.__settings['analysis_schemes'][str(self.scheme)]['postanalysis'] == True:
return self.__analysis_schemes__[str(self.scheme)]['reweight']
else:
value = "This sort of analysis has not been enabled."
current = { 'bin_prob_evolution': value, 'color_prob_evolution': value, 'conditional_flux_evolution': value, 'rate_evolution': value, 'state_labels': value, 'state_prob_evolution': value }
current.update({ 'bin_populations': value, 'iterations': value })
return current
@property
def scheme(self):
'''
Returns and sets what scheme is currently in use.
To see what schemes are available, run:
w.list_schemes
'''
# Let's do this a few different ways.
# We want to return things about the DIFFERENT schemes, if possible.
if self._scheme == None:
self._scheme = WIPIScheme(scheme=self.__analysis_schemes__, name=self._schemename, parent=self, settings=self.__settings)
# This just ensures that when we call it, it's clean.
self._scheme.name = None
return self._scheme
@scheme.setter
def scheme(self, scheme):
self._future = None
self._current = None
self._past = None
if scheme in self.__settings['analysis_schemes']:
pass
else:
for ischeme, schemename in enumerate(self.__settings['analysis_schemes']):
if ischeme == scheme:
scheme = schemename
if self.__settings['analysis_schemes'][scheme]['enabled'] == True or self.__settings['analysis_schemes'][scheme]['enabled'] == None:
self._schemename = scheme
else:
print("Scheme cannot be changed to scheme: {}; it is not enabled!".format(scheme))
@property
def list_schemes(self):
'''
Lists what schemes are configured in west.cfg file.
Schemes should be structured as follows, in west.cfg:
west:
system:
analysis:
directory: analysis
analysis_schemes:
scheme.1:
enabled: True
states:
- label: unbound
coords: [[7.0]]
- label: bound
coords: [[2.7]]
bins:
- type: RectilinearBinMapper
boundaries: [[0.0, 2.80, 7, 10000]]
'''
#print("The following schemes are available:")
#print("")
#for ischeme, scheme in enumerate(self.__settings['analysis_schemes']):
# print('{}. Scheme: {}'.format(ischeme, scheme))
#print("")
#print("Set via name, or via the index listed.")
#print("")
#print("Current scheme: {}".format(self.scheme))
self._scheme.list_schemes
@property
def iteration(self):
'''
Returns/sets the current iteration.
'''
#print("The current iteration is {}".format(self._iter))
return self._iter
@iteration.setter
def iteration(self, value):
print("Setting iteration to iter {}.".format(value))
if value <= 0:
print("Iteration must begin at 1.")
value = 1
if value > self.niters:
print("Cannot go beyond {} iterations!".format(self.niters))
print("Setting to {}".format(self.niters))
value = self.niters
# We want to trigger a rebuild on our current/past/future bits.
# The scheme should automatically reset to the proper iteration, but
# future needs to be manually triggered.
self._iter = value
self._future = None
return self._iter
@property
def current(self):
'''
The current iteration. See help for __get_data_for_iteration__
'''
return self.scheme[self.scheme.scheme].current
@property
def past(self):
'''
The previous iteration. See help for __get_data_for_iteration__
'''
return self.scheme[self.scheme.scheme].past
def trace(self, seg_id):
'''
Runs a trace on a seg_id within the current iteration, all the way back to the beginning,
returning a dictionary containing all interesting information:
seg_id, pcoord, states, bins, weights, iteration, auxdata (optional)
sorted in chronological order.
Call with a seg_id.
'''
if seg_id >= self.current.walkers:
print("Walker seg_id # {} is beyond the max count of {} walkers.".format(seg_id, self.current.walkers))
return 1
pi = self.progress.indicator
with pi:
pi.new_operation('Tracing scheme:iter:seg_id {}:{}:{}'.format(self.scheme, self.iteration, seg_id), self.iteration)
current = { 'seg_id': [], 'pcoord': [], 'states': [], 'weights': [], 'iteration': [], 'bins': [] }
keys = []
try:
current['auxdata'] = {}
for key in list(self.current['auxdata'].keys()):
current['auxdata'][key] = []
key = []
except:
pass
for iter in reversed(list(range(1, self.iteration+1))):
iter_group = self.data_reader.get_iter_group(iter)
particles = self.data_reader.data_manager.get_iter_summary(int(iter))['n_particles']
current['pcoord'].append(iter_group['pcoord'][seg_id, :, :])
current['states'].append(self.assign['trajlabels'][iter-1, seg_id,:])
current['bins'].append(self.assign['assignments'][iter-1, seg_id,:])
current['seg_id'].append(seg_id)
current['weights'].append(iter_group['seg_index']['weight'][seg_id])
current['iteration'].append(iter)
try:
for key in keys:
current['auxdata'][key].append(iter_group['auxdata'][key][seg_id])
except:
pass
seg_id = iter_group['seg_index']['parent_id'][seg_id]
if seg_id < 0:
# Necessary for steady state simulations. This means they started in that iteration.
break
pi.progress += 1
current['seg_id'] = list(reversed(current['seg_id']))
current['iteration'] = list(reversed(current['iteration']))
current['states'] = np.concatenate(np.array(list(reversed(current['states']))))
current['bins'] = np.concatenate(np.array(list(reversed(current['bins']))))
current['weights'] = np.array(list(reversed(current['weights'])))
current['pcoord'] = np.concatenate(np.array(list(reversed(current['pcoord']))))
try:
for key in keys():
current['auxdata'][key] = np.concatenate(np.array(list(reversed(current['auxdata'][key]))))
except:
pass
current['state_labels'] = self.assign['state_labels']
for i in ['pcoord', 'states', 'bins', 'weights']:
current[i] = WIPIDataset(raw=current[i], key=i)
if i == 'weights':
current[i].plotter = Plotter(np.log10(current[i].raw), str('log10 of ' + str(i)), iteration=current[i].raw.shape[0], interface=self.interface)
else:
current[i].plotter = Plotter(current[i].raw, i, iteration=current[i].raw.shape[0], interface=self.interface)
current[i].plot = current[i].plotter.plot
return WIPIDataset(raw=current, key=seg_id)
@property
def future(self, value=None):
'''
Similar to current/past, but keyed differently and returns different datasets.
See help for Future.
'''
if self._future == None:
self._future = self.Future(raw=self.__get_children__(), key=None)
self._future.iteration = self.iteration+1
return self._future
class Future(WIPIDataset):
# This isn't a real fancy one.
def __getitem__(self, value):
if isinstance(value, str):
print(list(self.__dict__.keys()))
try:
return self.__dict__['raw'][value]
except:
print('{} is not a valid data structure.'.format(value))
elif isinstance(value, int) or isinstance(value, np.int64):
# Otherwise, we assume they're trying to index for a seg_id.
#if value < self.parent.walkers:
current = {}
seg_items = ['weights', 'pcoord', 'auxdata', 'parents', 'seg_id', 'states']
current['pcoord'] = self.__dict__['raw']['pcoord'][value]
current['states'] = self.__dict__['raw']['states'][value]
current['bins'] = self.__dict__['raw']['bins'][value]
current['parents'] = self.__dict__['raw']['parents'][value]
current['seg_id'] = self.__dict__['raw']['seg_id'][value]
current['weights'] = self.__dict__['raw']['weights'][value]
try:
current['auxdata'] = {}
for key in list(self.__dict__['raw']['auxdata'].keys()):
current['auxdata'][key] = self.__dict__['raw']['auxdata'][key][value]
except:
pass
current = WIPIDataset(current, 'Segment {} in Iter {}'.format(value, self.iteration))
return current
def __get_children__(self):
'''
Returns all information about the children of a given walker in the current iteration.
Used to generate and create the future object, if necessary.
'''
if self.iteration == self.niters:
print("Currently at iteration {}, which is the max. There are no children!".format(self.iteration))
return 0
iter_data = __get_data_for_iteration__(value=self.iteration+1, parent=self)
future = { 'weights': [], 'pcoord': [], 'parents': [], 'summary': iter_data['summary'], 'seg_id': [], 'walkers': iter_data['walkers'], 'states': [], 'bins': [] }
for seg_id in range(0, self.current.walkers):
children = np.where(iter_data['parents'] == seg_id)[0]
if len(children) == 0:
error = "No children for seg_id {}.".format(seg_id)
future['weights'].append(error)
future['pcoord'].append(error)
future['parents'].append(error)
future['seg_id'].append(error)
future['states'].append(error)
future['bins'].append(error)
else:
# Now, we're gonna put them in the thing.
value = self.iteration+1
future['weights'].append(iter_data['weights'][children])
future['pcoord'].append(iter_data['pcoord'][...][children, :, :])
try:
aux_data = iter_data['auxdata'][...][children, :, :]
try:
future['aux_data'].append(aux_data)
except:
future['aux_data'] = aux_data
except:
pass
future['parents'].append(iter_data['parents'][children])
future['seg_id'].append(iter_data['seg_id'][children])
future['states'].append(self.assign['trajlabels'][value-1, children, :])
future['bins'].append(self.assign['assignments'][value-1, children, :])
return future
def go(self):
'''
Function automatically called by main() when launched via the command line interface.
Generally, call main, not this function.
'''
print("")
print("Welcome to w_ipa (WESTPA Interactive Python Analysis) v. {}!".format(w.version))
print("Run w.introduction for a more thorough introduction, or w.help to see a list of options.")
print("Running analysis & loading files.")
self.data_reader.open()
self.analysis_structure()
# Seems to be consistent with other tools, such as w_assign. For setting the iterations.
self.data_reader.open()
self.niters = self.data_reader.current_iteration - 1
self.iteration = self.niters
try:
print('Your current scheme, system and iteration are : {}, {}, {}'.format(w.scheme, os.getcwd(), w.iteration))
except:
pass
@property
def introduction(self):
'''
Just spits out an introduction, in case someone doesn't call help.
'''
help_string = '''
Call as a dictionary item or a .attribute:
w.past, w.current, w.future:
{current}
Raw schemes can be accessed as follows:
w.scheme.{scheme_keys}
and contain mostly the same datasets associated with w.
The following give raw access to the h5 files associated with the current scheme
w.west
w.assign
w.direct
w.reweight
OTHER:
{w}
'''.format(current=self.__format_keys__(self.current.__dir__(), split=' ', offset=12), scheme_keys=self.__format_keys__(list(self._scheme.raw.keys())),
w=self.__format_keys__(self.__dir__(), offset=8, max_length=0, split='', prepend='w.'))
print(help_string)
# Just a little function to be used with the introduction.
def __format_keys__(self, keys, split='/', offset=0, max_length=80, prepend=''):
rtn = ''
run_length = 0
for key in keys:
rtn += prepend + str(key) + split
run_length += len(str(key))
if run_length >= max_length:
run_length = offset
rtn += '\n' + ' '*offset
if rtn[-1] == split:
return rtn[:-1]
else:
return rtn
@property
def help(self):
''' Just a minor function to call help on itself. Only in here to really help someone get help.'''
help(self)
def _repr_pretty_(self, p, cycle):
self.introduction
return " "
def __dir__(self):
return_list = ['past', 'current', 'future']
# For the moment, don't expose direct, reweight, or assign, as these are scheme dependent files.
# They do exist, and always link to the current scheme, however.
return_list += ['iteration', 'niters', 'scheme', 'list_schemes', 'bin_labels', 'state_labels', 'west', 'trace']
return sorted(set(return_list))
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 WNTopTool(WESTTool):
prog = 'w_ntop'
description = '''\
Select walkers from bins . An assignment file mapping walkers to
bins at each timepoint is required (see``w_assign --help`` for further
information on generating this file). By default, high-weight walkers are
selected (hence the name ``w_ntop``: select the N top-weighted walkers from
each bin); however, minimum weight walkers and randomly-selected walkers
may be selected instead.
-----------------------------------------------------------------------------
Output format
-----------------------------------------------------------------------------
The output file (-o/--output, by default "ntop.h5") contains the following
datasets:
``/n_iter`` [iteration]
*(Integer)* Iteration numbers for each entry in other datasets.
``/n_segs`` [iteration][bin]
*(Integer)* Number of segments in each bin/state in the given iteration.
This will generally be the same as the number requested with
``--n/--count`` but may be smaller if the requested number of walkers
does not exist.
``/seg_ids`` [iteration][bin][segment]
*(Integer)* Matching segments in each iteration for each bin.
For an iteration ``n_iter``, only the first ``n_iter`` entries are
valid. For example, the full list of matching seg_ids in bin 0 in the
first stored iteration is ``seg_ids[0][0][:n_segs[0]]``.
``/weights`` [iteration][bin][segment]
*(Floating-point)* Weights for each matching segment in ``/seg_ids``.
-----------------------------------------------------------------------------
Command-line arguments
-----------------------------------------------------------------------------
'''
def __init__(self):
super(WNTopTool, self).__init__()
self.data_reader = WESTDataReader()
self.iter_range = IterRangeSelection()
self.progress = ProgressIndicatorComponent()
self.output_file = None
self.assignments_filename = None
self.output_filename = None
self.what = None
self.timepoint = None
self.count = None
def add_args(self, parser):
self.data_reader.add_args(parser)
self.iter_range.add_args(parser)
igroup = parser.add_argument_group('input options')
igroup.add_argument(
'-a',
'--assignments',
default='assign.h5',
help=
'''Use assignments from the given ASSIGNMENTS file (default: %(default)s).'''
)
sgroup = parser.add_argument_group('selection options')
sgroup.add_argument(
'-n',
'--count',
type=int,
default=1,
help=
'''Select COUNT walkers from each iteration for each bin (default: %(default)s).'''
)
sgroup.add_argument(
'-t',
'--timepoint',
type=int,
default=-1,
help=
'''Base selection on the given TIMEPOINT within each iteration. Default (-1)
corresponds to the last timepoint.''')
cgroup = parser.add_mutually_exclusive_group()
cgroup.add_argument(
'--highweight',
dest='select_what',
action='store_const',
const='highweight',
help='''Select COUNT highest-weight walkers from each bin.''')
cgroup.add_argument(
'--lowweight',
dest='select_what',
action='store_const',
const='lowweight',
help='''Select COUNT lowest-weight walkers from each bin.''')
cgroup.add_argument(
'--random',
dest='select_what',
action='store_const',
const='random',
help='''Select COUNT walkers randomly from each bin.''')
parser.set_defaults(select_what='highweight')
ogroup = parser.add_argument_group('output options')
ogroup.add_argument(
'-o',
'--output',
default='ntop.h5',
help='''Write output to OUTPUT (default: %(default)s).''')
self.progress.add_args(parser)
def process_args(self, args):
self.progress.process_args(args)
self.data_reader.process_args(args)
with self.data_reader:
self.iter_range.process_args(args)
self.what = args.select_what
self.output_filename = args.output
self.assignments_filename = args.assignments
self.count = args.count
self.timepoint = args.timepoint
def go(self):
self.data_reader.open('r')
assignments_file = h5py.File(self.assignments_filename, mode='r')
output_file = h5io.WESTPAH5File(self.output_filename, mode='w')
pi = self.progress.indicator
count = self.count
timepoint = self.timepoint
nbins = assignments_file.attrs['nbins'] + 1
assignments_ds = assignments_file['assignments']
iter_start, iter_stop = self.iter_range.iter_start, self.iter_range.iter_stop
iter_count = iter_stop - iter_start
h5io.check_iter_range_least(assignments_ds, iter_start, iter_stop)
nsegs = assignments_file['nsegs'][h5io.get_iteration_slice(
assignments_file['nsegs'], iter_start, iter_stop)]
output_file.create_dataset('n_iter',
dtype=n_iter_dtype,
data=list(range(iter_start, iter_stop)))
seg_count_ds = output_file.create_dataset('nsegs',
dtype=numpy.uint,
shape=(iter_count, nbins))
matching_segs_ds = output_file.create_dataset(
'seg_ids',
shape=(iter_count, nbins, count),
dtype=seg_id_dtype,
chunks=h5io.calc_chunksize((iter_count, nbins, count),
seg_id_dtype),
shuffle=True,
compression=9)
weights_ds = output_file.create_dataset('weights',
shape=(iter_count, nbins,
count),
dtype=weight_dtype,
chunks=h5io.calc_chunksize(
(iter_count, nbins, count),
weight_dtype),
shuffle=True,
compression=9)
what = self.what
with pi:
pi.new_operation('Finding matching segments', extent=iter_count)
for iiter, n_iter in enumerate(range(iter_start, iter_stop)):
assignments = numpy.require(assignments_ds[
h5io.get_iteration_entry(assignments_ds, n_iter) +
numpy.index_exp[:, timepoint]],
dtype=westpa.binning.index_dtype)
all_weights = self.data_reader.get_iter_group(
n_iter)['seg_index']['weight']
# the following Cython function just executes this loop:
#for iseg in xrange(nsegs[iiter]):
# segs_by_bin[iseg,assignments[iseg]] = True
segs_by_bin = assignments_list_to_table(
nsegs[iiter], nbins, assignments)
for ibin in range(nbins):
segs = numpy.nonzero(segs_by_bin[:, ibin])[0]
seg_count_ds[iiter, ibin] = min(len(segs), count)
if len(segs):
weights = all_weights.take(segs)
if what == 'lowweight':
indices = numpy.argsort(weights)[:count]
elif what == 'highweight':
indices = numpy.argsort(weights)[::-1][:count]
else:
assert what == 'random'
indices = numpy.random.permutation(len(weights))
matching_segs_ds[iiter,
ibin, :len(segs)] = segs.take(indices)
weights_ds[iiter,
ibin, :len(segs)] = weights.take(indices)
del segs, weights
del assignments, segs_by_bin, all_weights
pi.progress += 1
class WTraceTool(WESTTool):
prog='w_trace'
description = '''\
Trace individual WEST trajectories and emit (or calculate) quantities along the
trajectory.
Trajectories are specified as N_ITER:SEG_ID pairs. Each segment is traced back
to its initial point, and then various quantities (notably n_iter and seg_id)
are printed in order from initial point up until the given segment in the given
iteration.
Output is stored in several files, all named according to the pattern given by
the -o/--output-pattern parameter. The default output pattern is "traj_%d_%d",
where the printf-style format codes are replaced by the iteration number and
segment ID of the terminal segment of the trajectory being traced.
Individual datasets can be selected for writing using the -d/--dataset option
(which may be specified more than once). The simplest form is ``-d dsname``,
which causes data from dataset ``dsname`` along the trace to be stored to
HDF5. The dataset is assumed to be stored on a per-iteration basis, with
the first dimension corresponding to seg_id and the second dimension
corresponding to time within the segment. Further options are specified
as comma-separated key=value pairs after the data set name, as in
-d dsname,alias=newname,index=idsname,file=otherfile.h5,slice=[100,...]
The following options for datasets are supported:
alias=newname
When writing this data to HDF5 or text files, use ``newname``
instead of ``dsname`` to identify the dataset. This is mostly of
use in conjunction with the ``slice`` option in order, e.g., to
retrieve two different slices of a dataset and store then with
different names for future use.
index=idsname
The dataset is not stored on a per-iteration basis for all
segments, but instead is stored as a single dataset whose
first dimension indexes n_iter/seg_id pairs. The index to
these n_iter/seg_id pairs is ``idsname``.
file=otherfile.h5
Instead of reading data from the main WEST HDF5 file (usually
``west.h5``), read data from ``otherfile.h5``.
slice=[100,...]
Retrieve only the given slice from the dataset. This can be
used to pick a subset of interest to minimize I/O.
-------------------------------------------------------------------------------
'''
pcoord_formats = {'u8': '%20d',
'i8': '%20d',
'u4': '%10d',
'i4': '%11d',
'u2': '%5d',
'i2': '%6d',
'f4': '%14.7g',
'f8': '%023.15g'}
def __init__(self):
super(WTraceTool,self).__init__()
self.data_reader = WESTDataReader()
#self.h5storage = HDF5Storage()
self.output_file = None
self.output_pattern = None
self.endpoints = None
self.datasets = []
# Interface for command-line tools
def add_args(self, parser):
self.data_reader.add_args(parser)
#self.h5storage.add_args(parser)
parser.add_argument('-d', '--dataset', dest='datasets',
#this breaks argparse (see http://bugs.python.org/issue11874)
#metavar='DSNAME[,alias=ALIAS][,index=INDEX][,file=FILE][,slice=SLICE]',
metavar='DSNAME',
action='append',
help='''Include the dataset named DSNAME in trace output. An extended form like
DSNAME[,alias=ALIAS][,index=INDEX][,file=FILE][,slice=SLICE] will
obtain the dataset from the given FILE instead of the main WEST HDF5 file,
slice it by SLICE, call it ALIAS in output, and/or access per-segment data by a n_iter,seg_id
INDEX instead of a seg_id indexed dataset in the group for n_iter.''')
parser.add_argument('endpoints', metavar='N_ITER:SEG_ID', nargs='+',
help='''Trace trajectory ending (or at least alive at) N_ITER:SEG_ID.''')
#tgroup = parser.add_argument_group('trace options')
ogroup = parser.add_argument_group('output options')
ogroup.add_argument('--output-pattern', default='traj_%d_%d',
help='''Write per-trajectory data to output files/HDF5 groups whose names begin with OUTPUT_PATTERN,
which must contain two printf-style format flags which will be replaced with the iteration number
and segment ID of the terminal segment of the trajectory being traced.
(Default: %(default)s.)''')
ogroup.add_argument('-o', '--output', default='trajs.h5',
help='Store intermediate data and analysis results to OUTPUT (default: %(default)s).')
def process_args(self, args):
self.data_reader.process_args(args)
#self.h5storage.process_args(args)
self.endpoints = [map(long,endpoint.split(':')) for endpoint in args.endpoints]
self.output_pattern = args.output_pattern
for dsstr in args.datasets or []:
self.datasets.append(self.parse_dataset_string(dsstr))
#self.h5storage.open_analysis_h5file()
self.output_file = h5py.File(args.output)
def parse_dataset_string(self, dsstr):
dsinfo = {}
r = re.compile(r',(?=[^\]]*(?:\[|$))')
fields = r.split(dsstr)
dsinfo['dsname'] = fields[0]
for field in (field.strip() for field in fields[1:]):
k,v = field.split('=')
k = k.lower()
if k in ('alias', 'file', 'index'):
dsinfo[k] = v
elif k == 'slice':
try:
dsinfo['slice'] = eval('numpy.index_exp' + v)
except SyntaxError:
raise SyntaxError('invalid index expression {!r}'.format(v))
else:
raise ValueError('invalid dataset option {!r}'.format(k))
return dsinfo
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 emit_trace_h5(self, trace, output_group):
for dsname in ('basis_state', 'initial_state', 'segments'):
try:
del output_group[dsname]
except KeyError:
pass
if trace.basis_state:
output_group['basis_state'] = trace.basis_state.as_numpy_record()
output_group['initial_state'] = trace.initial_state.as_numpy_record()
output_group['segments'] = trace.summary
def emit_trace_text(self, trace, output_file):
'''Dump summary information about each segment in the given trace to the given output_file,
which must be opened for writing in text mode. Output columns are separated by at least
one space.'''
if not trace:
return
pcoord_ndim = trace[0]['final_pcoord'].shape[0]
lastseg = trace[-1]
len_n_iter = max(6, len(str(lastseg['n_iter'])))
len_seg_id = max(6, max(len(str(seg_id)) for seg_id in trace['seg_id']))
seg_pattern = ' '.join(['{n_iter:{len_n_iter}d}',
'{seg_id:{len_seg_id}d}',
'{weight:22.17e}',
'{walltime:10.6g}',
'{cputime:10.6g}',
'{pcoord_str:s}'
]) + '\n'
output_file.write('''\
# Trace of trajectory ending in n_iter:seg_id {n_iter:d}:{seg_id:d} (endpoint type {endpoint_type_text:s})
# column 0: iteration (0 => initial state)
# column 1: seg_id (or initial state ID)
# column 2: weight
# column 3: wallclock time (s)
# column 4: CPU time (s)
'''.format(n_iter = long(lastseg['n_iter']),
seg_id = long(lastseg['seg_id']),
endpoint_type_text = Segment.endpoint_type_names[trace.endpoint_type]))
if pcoord_ndim == 1:
output_file.write('''\
# column 5: final progress coordinate value
''')
else:
fpcbegin = 5
fpcend = fpcbegin + pcoord_ndim - 1
output_file.write('''\
# columns {fpcbegin:d} -- {fpcend:d}: final progress coordinate value
'''.format(fpcbegin=fpcbegin,fpcend=fpcend))
pcoord_formats = self.pcoord_formats
# Output row for initial state
initial_state = trace.initial_state
pcoord_str = ' '.join(pcoord_formats.get(pcfield.dtype.str[1:], '%s') % pcfield
for pcfield in initial_state.pcoord)
output_file.write(seg_pattern.format(n_iter=0, seg_id=initial_state.state_id,
weight=0.0, walltime=0, cputime=0, pcoord_str=pcoord_str,
len_n_iter=len_n_iter,len_seg_id=len_seg_id))
# Output rows for segments
for segment in trace:
pcoord_str = ' '.join(pcoord_formats.get(pcfield.dtype.str[1:], '%s') % pcfield
for pcfield in segment['final_pcoord'])
output_file.write(seg_pattern.format(n_iter = long(segment['n_iter']),
seg_id = long(segment['seg_id']),
weight = float(segment['weight']),
walltime = float(segment['walltime']),
cputime = float(segment['cputime']),
pcoord_str=pcoord_str,
len_n_iter=len_n_iter,
len_seg_id=len_seg_id))
class WCrawl(WESTParallelTool):
prog='w_crawl'
description = '''\
Crawl a weighted ensemble dataset, executing a function for each iteration.
This can be used for postprocessing of trajectories, cleanup of datasets,
or anything else that can be expressed as "do X for iteration N, then do
something with the result". Tasks are parallelized by iteration, and
no guarantees are made about evaluation order.
-----------------------------------------------------------------------------
Command-line options
-----------------------------------------------------------------------------
'''
def __init__(self):
super(WCrawl,self).__init__()
# These are used throughout
self.progress = ProgressIndicatorComponent()
self.data_reader = WESTDataReader()
self.iter_range = IterRangeSelection(self.data_reader)
self.crawler = None
self.task_callable = None
def add_args(self, parser):
self.data_reader.add_args(parser)
self.iter_range.add_args(parser)
tgroup = parser.add_argument_group('task options')
tgroup.add_argument('-c', '--crawler-instance',
help='''Use CRAWLER_INSTANCE (specified as module.instance) as an instance of
WESTPACrawler to coordinate the calculation. Required only if initialization,
finalization, or task result processing is required.''')
tgroup.add_argument('task_callable',
help='''Run TASK_CALLABLE (specified as module.function) on each iteration.
Required.''')
self.progress.add_args(parser)
def process_args(self, args):
self.progress.process_args(args)
self.data_reader.process_args(args)
with self.data_reader:
self.iter_range.process_args(args)
self.task_callable = get_object(args.task_callable, path=['.'])
if args.crawler_instance is not None:
self.crawler = get_object(args.crawler_instance, path=['.'])
else:
self.crawler = WESTPACrawler()
def go(self):
iter_start = self.iter_range.iter_start
iter_stop = self.iter_range.iter_stop
iter_count = iter_stop - iter_start
self.data_reader.open('r')
pi = self.progress.indicator
with pi:
pi.operation = 'Initializing'
self.crawler.initialize(iter_start, iter_stop)
try:
pi.new_operation('Dispatching tasks & processing results', iter_count)
task_gen = ((_remote_task, (n_iter, self.task_callable), {}) for n_iter in xrange(iter_start,iter_stop))
for future in self.work_manager.submit_as_completed(task_gen, self.max_queue_len):
n_iter, result = future.get_result(discard=True)
if self.crawler is not None:
self.crawler.process_iter_result(n_iter,result)
pi.progress += 1
finally:
pi.new_operation('Finalizing')
self.crawler.finalize()
class WDumpSegs(WESTTool):
prog = 'w_dumpsegs'
description = '''\
Dump segment data as text. This is very inefficient, so this tool should be used
as a last resort (use hdfview/h5ls to look at data, and access HDF5 directly for
significant analysis tasks).
'''
def __init__(self):
super(WDumpSegs, self).__init__()
self.data_reader = WESTDataReader()
self.n_iter = None
self.output_file = None
self.print_pcoords = False
def add_args(self, parser):
self.data_reader.add_args(parser)
parser.add_argument(
'-p',
'--print-pcoords',
dest='print_pcoords',
action='store_true',
help='print initial and final progress coordinates for each segment'
)
parser.add_argument(
'-i',
'--iteration',
dest='n_iter',
type=int,
help=
'Use data from iteration N_ITER (default: last complete iteration)'
)
parser.add_argument(
'-o',
'--output',
dest='output_file',
help=
'Store output in OUTPUT_FILE (default: write to standard output).')
def process_args(self, args):
self.data_reader.process_args(args)
self.data_reader.open()
self.n_iter = args.n_iter or self.data_reader.current_iteration - 1
self.output_file = open(args.output_file,
'wt') if args.output_file else sys.stdout
self.print_pcoords = args.print_pcoords
def go(self):
segments = self.data_reader.get_segments(self.n_iter)
max_seg_id_len = len(str(max(segment.seg_id for segment in segments)))
max_status_name_len = max(
list(map(len, iter(Segment.status_names.values()))))
max_endpoint_type_len = max(
list(map(len, iter(Segment.endpoint_type_names.values()))))
max_n_parents_len = len(
str(max(len(segment.wtg_parent_ids) for segment in segments)))
report_line = (
'{segment.n_iter:d} {segment.seg_id:{max_seg_id_len}d} {segment.weight:20.14g}'
+ ' {status_name:{max_status_name_len}s} ({segment.status})' +
' {segment.walltime:<12.6g} {segment.cputime:<12.6g}' +
' {endpoint_type_name:{max_endpoint_type_len}s} ({segment.endpoint_type})'
+
' {n_parents:{max_n_parents_len}d} {segment.parent_id:{max_seg_id_len}d} {parents_str}'
+ '\n')
pcoord_lines = (
' pcoord[0] = {init_pcoord}\n pcoord[-1] = {final_pcoord}' +
'\n')
for (_seg_id, segment) in enumerate(segments):
parents_str = '[' + ', '.join(
map(str, sorted(segment.wtg_parent_ids))) + ']'
init_pcoord_str = '[' + ', '.join(
'{pcval:<12.6g}'.format(pcval=float(pce))
for pce in segment.pcoord[0]) + ']'
final_pcoord_str = '[' + ', '.join(
'{pcval:<12.6g}'.format(pcval=float(pce))
for pce in segment.pcoord[-1]) + ']'
self.output_file.write(
report_line.format(
segment=segment,
status_name=segment.status_names[segment.status],
endpoint_type_name=segment.endpoint_type_names[
segment.endpoint_type],
parents_str=parents_str,
n_parents=len(segment.wtg_parent_ids),
max_seg_id_len=max_seg_id_len,
max_status_name_len=max_status_name_len,
max_endpoint_type_len=max_endpoint_type_len,
max_n_parents_len=max_n_parents_len))
if self.print_pcoords:
self.output_file.write(
pcoord_lines.format(init_pcoord=init_pcoord_str,
final_pcoord=final_pcoord_str))
class WFluxanlTool(WESTTool):
prog='w_fluxanl'
description = '''\
Extract fluxes into pre-defined target states from WEST data,
average, and construct confidence intervals. Monte Carlo bootstrapping
is used to account for the correlated and possibly non-Gaussian statistical
error in flux measurements.
All non-graphical output (including that to the terminal and HDF5) assumes that
the propagation/resampling period ``tau`` is equal to unity; to obtain results
in familiar units, divide all fluxes and multiply all correlation lengths by
the true value of ``tau``.
'''
output_format_version = 2
def __init__(self):
super(WFluxanlTool,self).__init__()
self.data_reader = WESTDataReader()
self.iter_range = IterRangeSelection()
self.output_h5file = None
self.output_group = None
self.target_groups = {}
self.fluxdata = {}
self.alpha = None
self.autocorrel_alpha = None
self.n_sets = None
self.do_evol = False
self.evol_step = 1
def add_args(self, parser):
self.data_reader.add_args(parser)
self.iter_range.add_args(parser)
ogroup = parser.add_argument_group('output options')
ogroup.add_argument('-o', '--output', default='fluxanl.h5',
help='Store intermediate data and analysis results to OUTPUT (default: %(default)s).')
cgroup = parser.add_argument_group('calculation options')
cgroup.add_argument('--disable-bootstrap', '-db', dest='bootstrap', action='store_const', const=False,
help='''Enable the use of Monte Carlo Block Bootstrapping.''')
cgroup.add_argument('--disable-correl', '-dc', dest='correl', action='store_const', const=False,
help='''Disable the correlation analysis.''')
cgroup.add_argument('-a', '--alpha', type=float, default=0.05,
help='''Calculate a (1-ALPHA) confidence interval on the average flux'
(default: %(default)s)''')
cgroup.add_argument('--autocorrel-alpha', type=float, dest='acalpha', metavar='ACALPHA',
help='''Evaluate autocorrelation of flux to (1-ACALPHA) significance.
Note that too small an ACALPHA will result in failure to detect autocorrelation
in a noisy flux signal. (Default: same as ALPHA.)''')
cgroup.add_argument('-N', '--nsets', type=int,
help='''Use NSETS samples for bootstrapping (default: chosen based on ALPHA)''')
cgroup.add_argument('--evol', action='store_true', dest='do_evol',
help='''Calculate time evolution of flux confidence intervals (expensive).''')
cgroup.add_argument('--evol-step', type=int, default=1, metavar='ESTEP',
help='''Calculate time evolution of flux confidence intervals every ESTEP
iterations (default: %(default)s)''')
def process_args(self, args):
self.data_reader.process_args(args)
self.data_reader.open()
self.iter_range.data_manager = self.data_reader
self.iter_range.process_args(args)
self.output_h5file = h5py.File(args.output, 'w')
self.alpha = args.alpha
# Disable the bootstrap or the correlation analysis.
self.mcbs_enable = args.bootstrap if args.bootstrap is not None else True
self.do_correl = args.correl if args.correl is not None else True
self.autocorrel_alpha = args.acalpha or self.alpha
self.n_sets = args.nsets or mclib.get_bssize(self.alpha)
self.do_evol = args.do_evol
self.evol_step = args.evol_step or 1
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
def calc_evol_flux(self):
westpa.rc.pstatus('Calculating cumulative evolution of flux confidence intervals every {} iteration(s)'
.format(self.evol_step))
for itarget, (target_label, target_fluxdata) in enumerate(self.fluxdata.iteritems()):
fluxes = target_fluxdata['flux']
target_group = self.target_groups[target_label]
iter_start = target_group['n_iter'][0]
iter_stop = target_group['n_iter'][-1]
iter_count = iter_stop - iter_start
n_blocks = iter_count // self.evol_step
if iter_count % self.evol_step > 0: n_blocks += 1
cis = numpy.empty((n_blocks,), dtype=ci_dtype)
for iblock in xrange(n_blocks):
block_iter_stop = min(iter_start + (iblock+1)*self.evol_step, iter_stop)
istop = min((iblock+1)*self.evol_step, len(target_fluxdata['flux']))
fluxes = target_fluxdata['flux'][:istop]
#avg, ci_lb, ci_ub, correl_len = mclib.mcbs_ci_correl(fluxes, numpy.mean, self.alpha, self.n_sets,
# autocorrel_alpha = self.autocorrel_alpha,
# subsample=numpy.mean)
avg, ci_lb, ci_ub, 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 )
cis[iblock]['iter_start'] = iter_start
cis[iblock]['iter_stop'] = block_iter_stop
cis[iblock]['expected'], cis[iblock]['ci_lbound'], cis[iblock]['ci_ubound'] = avg, ci_lb, ci_ub
cis[iblock]['corr_len'] = correl_len
cis[iblock]['sterr'] = sterr
del fluxes
cis_ds = target_group.create_dataset('flux_evolution', data=cis)
cis_ds.attrs['iter_step'] = self.evol_step
cis_ds.attrs['mcbs_alpha'] = self.alpha
cis_ds.attrs['mcbs_autocorrel_alpha'] = self.autocorrel_alpha
cis_ds.attrs['mcbs_n_sets'] = self.n_sets
def go(self):
self.calc_store_flux_data()
if self.do_evol:
self.calc_evol_flux()
class WNTopTool(WESTTool):
prog='w_ntop'
description = '''\
Select walkers from bins . An assignment file mapping walkers to
bins at each timepoint is required (see``w_assign --help`` for further
information on generating this file). By default, high-weight walkers are
selected (hence the name ``w_ntop``: select the N top-weighted walkers from
each bin); however, minimum weight walkers and randomly-selected walkers
may be selected instead.
-----------------------------------------------------------------------------
Output format
-----------------------------------------------------------------------------
The output file (-o/--output, by default "ntop.h5") contains the following
datasets:
``/n_iter`` [iteration]
*(Integer)* Iteration numbers for each entry in other datasets.
``/n_segs`` [iteration][bin]
*(Integer)* Number of segments in each bin/state in the given iteration.
This will generally be the same as the number requested with
``--n/--count`` but may be smaller if the requested number of walkers
does not exist.
``/seg_ids`` [iteration][bin][segment]
*(Integer)* Matching segments in each iteration for each bin.
For an iteration ``n_iter``, only the first ``n_iter`` entries are
valid. For example, the full list of matching seg_ids in bin 0 in the
first stored iteration is ``seg_ids[0][0][:n_segs[0]]``.
``/weights`` [iteration][bin][segment]
*(Floating-point)* Weights for each matching segment in ``/seg_ids``.
-----------------------------------------------------------------------------
Command-line arguments
-----------------------------------------------------------------------------
'''
def __init__(self):
super(WNTopTool,self).__init__()
self.data_reader = WESTDataReader()
self.iter_range = IterRangeSelection()
self.progress = ProgressIndicatorComponent()
self.output_file = None
self.assignments_filename = None
self.output_filename = None
self.what = None
self.timepoint = None
self.count = None
def add_args(self, parser):
self.data_reader.add_args(parser)
self.iter_range.add_args(parser)
igroup = parser.add_argument_group('input options')
igroup.add_argument('-a', '--assignments', default='assign.h5',
help='''Use assignments from the given ASSIGNMENTS file (default: %(default)s).''')
sgroup = parser.add_argument_group('selection options')
sgroup.add_argument('-n', '--count', type=int, default=1,
help='''Select COUNT walkers from each iteration for each bin (default: %(default)s).''')
sgroup.add_argument('-t', '--timepoint', type=int, default=-1,
help='''Base selection on the given TIMEPOINT within each iteration. Default (-1)
corresponds to the last timepoint.''')
cgroup = parser.add_mutually_exclusive_group()
cgroup.add_argument('--highweight', dest='select_what', action='store_const', const='highweight',
help='''Select COUNT highest-weight walkers from each bin.''')
cgroup.add_argument('--lowweight', dest='select_what', action='store_const', const='lowweight',
help='''Select COUNT lowest-weight walkers from each bin.''')
cgroup.add_argument('--random', dest='select_what', action='store_const', const='random',
help='''Select COUNT walkers randomly from each bin.''')
parser.set_defaults(select_what='highweight')
ogroup = parser.add_argument_group('output options')
ogroup.add_argument('-o', '--output', default='ntop.h5',
help='''Write output to OUTPUT (default: %(default)s).''')
self.progress.add_args(parser)
def process_args(self, args):
self.progress.process_args(args)
self.data_reader.process_args(args)
with self.data_reader:
self.iter_range.process_args(args)
self.what = args.select_what
self.output_filename = args.output
self.assignments_filename = args.assignments
self.count = args.count
self.timepoint = args.timepoint
def go(self):
self.data_reader.open('r')
assignments_file = h5py.File(self.assignments_filename, mode='r')
output_file = h5io.WESTPAH5File(self.output_filename, mode='w')
pi = self.progress.indicator
count = self.count
timepoint = self.timepoint
nbins = assignments_file.attrs['nbins']+1
assignments_ds = assignments_file['assignments']
iter_start, iter_stop = self.iter_range.iter_start, self.iter_range.iter_stop
iter_count = iter_stop - iter_start
h5io.check_iter_range_least(assignments_ds, iter_start, iter_stop)
nsegs = assignments_file['nsegs'][h5io.get_iteration_slice(assignments_file['nsegs'], iter_start,iter_stop)]
output_file.create_dataset('n_iter', dtype=n_iter_dtype, data=range(iter_start,iter_stop))
seg_count_ds = output_file.create_dataset('nsegs', dtype=numpy.uint, shape=(iter_count,nbins))
matching_segs_ds = output_file.create_dataset('seg_ids', shape=(iter_count,nbins,count),
dtype=seg_id_dtype,
chunks=h5io.calc_chunksize((iter_count,nbins,count), seg_id_dtype),
shuffle=True, compression=9)
weights_ds = output_file.create_dataset('weights', shape=(iter_count,nbins,count),
dtype=weight_dtype,
chunks=h5io.calc_chunksize((iter_count,nbins,count), weight_dtype),
shuffle=True,compression=9)
what = self.what
with pi:
pi.new_operation('Finding matching segments', extent=iter_count)
for iiter, n_iter in enumerate(xrange(iter_start, iter_stop)):
assignments = numpy.require(assignments_ds[h5io.get_iteration_entry(assignments_ds, n_iter)
+ numpy.index_exp[:,timepoint]], dtype=westpa.binning.index_dtype)
all_weights = self.data_reader.get_iter_group(n_iter)['seg_index']['weight']
# the following Cython function just executes this loop:
#for iseg in xrange(nsegs[iiter]):
# segs_by_bin[iseg,assignments[iseg]] = True
segs_by_bin = assignments_list_to_table(nsegs[iiter],nbins,assignments)
for ibin in xrange(nbins):
segs = numpy.nonzero(segs_by_bin[:,ibin])[0]
seg_count_ds[iiter,ibin] = min(len(segs),count)
if len(segs):
weights = all_weights.take(segs)
if what == 'lowweight':
indices = numpy.argsort(weights)[:count]
elif what == 'highweight':
indices = numpy.argsort(weights)[::-1][:count]
else:
assert what == 'random'
indices = numpy.random.permutation(len(weights))
matching_segs_ds[iiter,ibin,:len(segs)] = segs.take(indices)
weights_ds[iiter,ibin,:len(segs)] = weights.take(indices)
del segs, weights
del assignments, segs_by_bin, all_weights
pi.progress += 1
class WPDist(WESTParallelTool):
prog='w_pdist'
description = '''\
Calculate time-resolved, multi-dimensional probability distributions of WE
datasets.
-----------------------------------------------------------------------------
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.
-----------------------------------------------------------------------------
Histogram binning
-----------------------------------------------------------------------------
By default, histograms are constructed with 100 bins in each dimension. This
can be overridden by specifying -b/--bins, which accepts a number of different
kinds of arguments:
a single integer N
N uniformly spaced bins will be used in each dimension.
a sequence of integers N1,N2,... (comma-separated)
N1 uniformly spaced bins will be used for the first dimension, N2 for the
second, and so on.
a list of lists [[B11, B12, B13, ...], [B21, B22, B23, ...], ...]
The bin boundaries B11, B12, B13, ... will be used for the first dimension,
B21, B22, B23, ... for the second dimension, and so on. These bin
boundaries need not be uniformly spaced. These expressions will be
evaluated with Python's ``eval`` construct, with ``numpy`` available for
use [e.g. to specify bins using numpy.arange()].
The first two forms (integer, list of integers) will trigger a scan of all
data in each dimension in order to determine the minimum and maximum values,
which may be very expensive for large datasets. This can be avoided by
explicitly providing bin boundaries using the list-of-lists form.
Note that these bins are *NOT* at all related to the bins used to drive WE
sampling.
-----------------------------------------------------------------------------
Output format
-----------------------------------------------------------------------------
The output file produced (specified by -o/--output, defaulting to "pdist.h5")
may be fed to plothist to generate plots (or appropriately processed text or
HDF5 files) from this data. In short, the following datasets are created:
``histograms``
Normalized histograms. The first axis corresponds to iteration, and
remaining axes correspond to dimensions of the input dataset.
``/binbounds_0``
Vector of bin boundaries for the first (index 0) dimension. Additional
datasets similarly named (/binbounds_1, /binbounds_2, ...) are created
for additional dimensions.
``/midpoints_0``
Vector of bin midpoints for the first (index 0) dimension. Additional
datasets similarly named are created for additional dimensions.
``n_iter``
Vector of iteration numbers corresponding to the stored histograms (i.e.
the first axis of the ``histograms`` dataset).
-----------------------------------------------------------------------------
Subsequent processing
-----------------------------------------------------------------------------
The output generated by this program (-o/--output, default "pdist.h5") may be
plotted by the ``plothist`` program. See ``plothist --help`` for more
information.
-----------------------------------------------------------------------------
Parallelization
-----------------------------------------------------------------------------
This tool supports parallelized binning, including reading of input data.
Parallel processing is the default. For simple cases (reading pre-computed
input data, modest numbers of segments), serial processing (--serial) may be
more efficient.
-----------------------------------------------------------------------------
Command-line options
-----------------------------------------------------------------------------
'''
def __init__(self):
super(WPDist,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
# These are used throughout
self.progress = ProgressIndicatorComponent()
self.data_reader = WESTDataReader()
self.input_dssynth = WESTDSSynthesizer(default_dsname='pcoord')
self.iter_range = IterRangeSelection(self.data_reader)
self.iter_range.include_args['iter_step'] = False
self.binspec = None
self.output_filename = None
self.output_file = None
self.dsspec = None
self.wt_dsspec = None # dsspec for weights
# These are used during histogram generation only
self.iter_start = None
self.iter_stop = None
self.ndim = None
self.ntimepoints = None
self.dset_dtype = None
self.binbounds = None # bin boundaries for each dimension
self.midpoints = None # bin midpoints for each dimension
self.data_range = None # data range for each dimension, as the pairs (min,max)
self.ignore_out_of_range = False
self.compress_output = False
def add_args(self, parser):
self.data_reader.add_args(parser)
self.iter_range.add_args(parser)
parser.add_argument('-b', '--bins', dest='bins', metavar='BINEXPR', default='100',
help='''Use BINEXPR for bins. This may be an integer, which will be used for each
dimension of the progress coordinate; a list of integers (formatted as [n1,n2,...])
which will use n1 bins for the first dimension, n2 for the second dimension, and so on;
or a list of lists of boundaries (formatted as [[a1, a2, ...], [b1, b2, ...], ... ]), which
will use [a1, a2, ...] as bin boundaries for the first dimension, [b1, b2, ...] as bin boundaries
for the second dimension, and so on. (Default: 100 bins in each dimension.)''')
parser.add_argument('-o', '--output', dest='output', default='pdist.h5',
help='''Store results in OUTPUT (default: %(default)s).''')
parser.add_argument('-C', '--compress', action='store_true',
help='''Compress histograms. May make storage of higher-dimensional histograms
more tractable, at the (possible extreme) expense of increased analysis time.
(Default: no compression.)''')
parser.add_argument('--loose', dest='ignore_out_of_range', action='store_true',
help='''Ignore values that do not fall within bins. (Risky, as this can make buggy bin
boundaries appear as reasonable data. Only use if you are
sure of your bin boundary specification.)''')
igroup = parser.add_argument_group('input dataset options').add_mutually_exclusive_group(required=False)
igroup.add_argument('--construct-dataset',
help='''Use the given function (as in module.function) to extract source data.
This function will be called once per iteration as function(n_iter, iter_group)
to construct data for one iteration. Data returned must be indexable as
[seg_id][timepoint][dimension]''')
igroup.add_argument('--dsspecs', nargs='+', metavar='DSSPEC',
help='''Construct probability distribution from one or more DSSPECs.''')
self.progress.add_args(parser)
def process_args(self, args):
self.progress.process_args(args)
self.data_reader.process_args(args)
self.input_dssynth.h5filename = self.data_reader.we_h5filename
self.input_dssynth.process_args(args)
self.dsspec = self.input_dssynth.dsspec
# Carrying an open HDF5 file across a fork() seems to corrupt the entire HDF5 library
# Open the WEST HDF5 file just long enough to process our iteration range, then close
# and reopen in go() [which executes after the fork]
with self.data_reader:
self.iter_range.process_args(args)
self.wt_dsspec = SingleIterDSSpec(self.data_reader.we_h5filename, 'seg_index', slice=numpy.index_exp['weight'])
self.binspec = args.bins
self.output_filename = args.output
self.ignore_out_of_range = bool(args.ignore_out_of_range)
self.compress_output = args.compress or False
def go(self):
self.data_reader.open('r')
pi = self.progress.indicator
pi.operation = 'Initializing'
with pi:
self.output_file = h5py.File(self.output_filename, 'w')
h5io.stamp_creator_data(self.output_file)
self.iter_start = self.iter_range.iter_start
self.iter_stop = self.iter_range.iter_stop
# Construct bin boundaries
self.construct_bins(self.parse_binspec(self.binspec))
for idim, (binbounds, midpoints) in enumerate(izip(self.binbounds, self.midpoints)):
self.output_file['binbounds_{}'.format(idim)] = binbounds
self.output_file['midpoints_{}'.format(idim)] = midpoints
# construct histogram
self.construct_histogram()
# Record iteration range
iter_range = self.iter_range.iter_range()
self.output_file['n_iter'] = iter_range
self.iter_range.record_data_iter_range(self.output_file['histograms'])
self.output_file.close()
@staticmethod
def parse_binspec(binspec):
namespace = {'numpy': numpy,
'inf': float('inf')}
try:
binspec_compiled = eval(binspec,namespace)
except Exception as e:
raise ValueError('invalid bin specification: {!r}'.format(e))
else:
if log.isEnabledFor(logging.DEBUG):
log.debug('bin specs: {!r}'.format(binspec_compiled))
return binspec_compiled
def construct_bins(self, bins):
'''
Construct bins according to ``bins``, which may be:
1) A scalar integer (for that number of bins in each dimension)
2) A sequence of integers (specifying number of bins for each dimension)
3) A sequence of sequences of bin boundaries (specifying boundaries for each dimension)
Sets ``self.binbounds`` to a list of arrays of bin boundaries appropriate for passing to
fasthist.histnd, along with ``self.midpoints`` to the midpoints of the bins.
'''
if not isiterable(bins):
self._construct_bins_from_scalar(bins)
elif not isiterable(bins[0]):
self._construct_bins_from_int_seq(bins)
else:
self._construct_bins_from_bound_seqs(bins)
if log.isEnabledFor(logging.DEBUG):
log.debug('binbounds: {!r}'.format(self.binbounds))
def scan_data_shape(self):
if self.ndim is None:
dset = self.dsspec.get_iter_data(self.iter_start)
self.ntimepoints = dset.shape[1]
self.ndim = dset.shape[2]
self.dset_dtype = dset.dtype
def scan_data_range(self):
'''Scan input data for range in each dimension. The number of dimensions is determined
from the shape of the progress coordinate as of self.iter_start.'''
self.progress.indicator.new_operation('Scanning for data range', self.iter_stop-self.iter_start)
self.scan_data_shape()
dset_dtype = self.dset_dtype
ndim = self.ndim
dsspec = self.dsspec
try:
minval = numpy.finfo(dset_dtype).min
maxval = numpy.finfo(dset_dtype).max
except ValueError:
minval = numpy.iinfo(dset_dtype).min
maxval = numpy.iinfo(dset_dtype).max
data_range = self.data_range = [(maxval,minval) for _i in xrange(self.ndim)]
#futures = []
#for n_iter in xrange(self.iter_start, self.iter_stop):
#_remote_min_max(ndim, dset_dtype, n_iter, dsspec)
# futures.append(self.work_manager.submit(_remote_min_max, args=(ndim, dset_dtype, n_iter, dsspec)))
#for future in self.work_manager.as_completed(futures):
for future in self.work_manager.submit_as_completed(((_remote_min_max, (ndim, dset_dtype, n_iter, dsspec), {})
for n_iter in xrange(self.iter_start, self.iter_stop)),
self.max_queue_len):
bounds = future.get_result(discard=True)
for idim in xrange(ndim):
current_min, current_max = data_range[idim]
current_min = min(current_min, bounds[idim][0])
current_max = max(current_max, bounds[idim][1])
data_range[idim] = (current_min, current_max)
self.progress.indicator.progress += 1
def _construct_bins_from_scalar(self, bins):
if self.data_range is None:
self.scan_data_range()
self.binbounds = []
self.midpoints = []
for idim in xrange(self.ndim):
lb, ub = self.data_range[idim]
# Advance just beyond the upper bound of the range, so that we catch
# the maximum in the histogram
ub *= 1.01
boundset = numpy.linspace(lb,ub,bins+1)
midpoints = (boundset[:-1] + boundset[1:]) / 2.0
self.binbounds.append(boundset)
self.midpoints.append(midpoints)
def _construct_bins_from_int_seq(self, bins):
if self.data_range is None:
self.scan_data_range()
self.binbounds = []
self.midpoints = []
for idim in xrange(self.ndim):
lb, ub = self.data_range[idim]
# Advance just beyond the upper bound of the range, so that we catch
# the maximum in the histogram
ub *= 1.01
boundset = numpy.linspace(lb,ub,bins[idim]+1)
midpoints = (boundset[:-1] + boundset[1:]) / 2.0
self.binbounds.append(boundset)
self.midpoints.append(midpoints)
def _construct_bins_from_bound_seqs(self, bins):
self.binbounds = []
self.midpoints = []
for boundset in bins:
boundset = numpy.asarray(boundset)
if (numpy.diff(boundset) <= 0).any():
raise ValueError('boundary set {!r} is not strictly monotonically increasing'.format(boundset))
self.binbounds.append(boundset)
self.midpoints.append((boundset[:-1]+boundset[1:])/2.0)
def construct_histogram(self):
'''Construct a histogram using bins previously constructed with ``construct_bins()``.
The time series of histogram values is stored in ``histograms``.
Each histogram in the time series is normalized.'''
self.scan_data_shape()
iter_count = self.iter_stop - self.iter_start
histograms_ds = self.output_file.create_dataset('histograms', dtype=numpy.float64,
shape=((iter_count,) + tuple(len(bounds)-1 for bounds in self.binbounds)),
compression=9 if self.compress_output else None)
binbounds = [numpy.require(boundset, self.dset_dtype, 'C') for boundset in self.binbounds]
self.progress.indicator.new_operation('Constructing histograms',self.iter_stop-self.iter_start)
task_gen = ((_remote_bin_iter, (iiter, n_iter, self.dsspec, self.wt_dsspec, 1 if iiter > 0 else 0, binbounds,
self.ignore_out_of_range), {})
for (iiter,n_iter) in enumerate(xrange(self.iter_start, self.iter_stop)))
#futures = set()
#for iiter, n_iter in enumerate(xrange(self.iter_start, self.iter_stop)):
# initpoint = 1 if iiter > 0 else 0
# futures.add(self.work_manager.submit(_remote_bin_iter,
# args=(iiter, n_iter, self.dsspec, self.wt_dsspec, initpoint, binbounds)))
#for future in self.work_manager.as_completed(futures):
#future = self.work_manager.wait_any(futures)
#for future in self.work_manager.submit_as_completed(task_gen, self.queue_size):
log.debug('max queue length: {!r}'.format(self.max_queue_len))
for future in self.work_manager.submit_as_completed(task_gen, self.max_queue_len):
iiter, n_iter, iter_hist = future.get_result(discard=True)
self.progress.indicator.progress += 1
# store histogram
histograms_ds[iiter] = iter_hist
del iter_hist, future
class WPDist(WESTParallelTool):
prog = 'w_pdist'
description = '''\
Calculate time-resolved, multi-dimensional probability distributions of WE
datasets.
-----------------------------------------------------------------------------
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.
-----------------------------------------------------------------------------
Histogram binning
-----------------------------------------------------------------------------
By default, histograms are constructed with 100 bins in each dimension. This
can be overridden by specifying -b/--bins, which accepts a number of different
kinds of arguments:
a single integer N
N uniformly spaced bins will be used in each dimension.
a sequence of integers N1,N2,... (comma-separated)
N1 uniformly spaced bins will be used for the first dimension, N2 for the
second, and so on.
a list of lists [[B11, B12, B13, ...], [B21, B22, B23, ...], ...]
The bin boundaries B11, B12, B13, ... will be used for the first dimension,
B21, B22, B23, ... for the second dimension, and so on. These bin
boundaries need not be uniformly spaced. These expressions will be
evaluated with Python's ``eval`` construct, with ``numpy`` available for
use [e.g. to specify bins using numpy.arange()].
The first two forms (integer, list of integers) will trigger a scan of all
data in each dimension in order to determine the minimum and maximum values,
which may be very expensive for large datasets. This can be avoided by
explicitly providing bin boundaries using the list-of-lists form.
Note that these bins are *NOT* at all related to the bins used to drive WE
sampling.
-----------------------------------------------------------------------------
Output format
-----------------------------------------------------------------------------
The output file produced (specified by -o/--output, defaulting to "pdist.h5")
may be fed to plothist to generate plots (or appropriately processed text or
HDF5 files) from this data. In short, the following datasets are created:
``histograms``
Normalized histograms. The first axis corresponds to iteration, and
remaining axes correspond to dimensions of the input dataset.
``/binbounds_0``
Vector of bin boundaries for the first (index 0) dimension. Additional
datasets similarly named (/binbounds_1, /binbounds_2, ...) are created
for additional dimensions.
``/midpoints_0``
Vector of bin midpoints for the first (index 0) dimension. Additional
datasets similarly named are created for additional dimensions.
``n_iter``
Vector of iteration numbers corresponding to the stored histograms (i.e.
the first axis of the ``histograms`` dataset).
-----------------------------------------------------------------------------
Subsequent processing
-----------------------------------------------------------------------------
The output generated by this program (-o/--output, default "pdist.h5") may be
plotted by the ``plothist`` program. See ``plothist --help`` for more
information.
-----------------------------------------------------------------------------
Parallelization
-----------------------------------------------------------------------------
This tool supports parallelized binning, including reading of input data.
Parallel processing is the default. For simple cases (reading pre-computed
input data, modest numbers of segments), serial processing (--serial) may be
more efficient.
-----------------------------------------------------------------------------
Command-line options
-----------------------------------------------------------------------------
'''
def __init__(self):
super(WPDist, 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
# These are used throughout
self.progress = ProgressIndicatorComponent()
self.data_reader = WESTDataReader()
self.input_dssynth = WESTDSSynthesizer(default_dsname='pcoord')
self.iter_range = IterRangeSelection(self.data_reader)
self.iter_range.include_args['iter_step'] = False
self.binspec = None
self.output_filename = None
self.output_file = None
self.dsspec = None
self.wt_dsspec = None # dsspec for weights
# These are used during histogram generation only
self.iter_start = None
self.iter_stop = None
self.ndim = None
self.ntimepoints = None
self.dset_dtype = None
self.binbounds = None # bin boundaries for each dimension
self.midpoints = None # bin midpoints for each dimension
self.data_range = None # data range for each dimension, as the pairs (min,max)
self.ignore_out_of_range = False
self.compress_output = False
def add_args(self, parser):
self.data_reader.add_args(parser)
self.iter_range.add_args(parser)
parser.add_argument(
'-b',
'--bins',
dest='bins',
metavar='BINEXPR',
default='100',
help=
'''Use BINEXPR for bins. This may be an integer, which will be used for each
dimension of the progress coordinate; a list of integers (formatted as [n1,n2,...])
which will use n1 bins for the first dimension, n2 for the second dimension, and so on;
or a list of lists of boundaries (formatted as [[a1, a2, ...], [b1, b2, ...], ... ]), which
will use [a1, a2, ...] as bin boundaries for the first dimension, [b1, b2, ...] as bin boundaries
for the second dimension, and so on. (Default: 100 bins in each dimension.)'''
)
parser.add_argument(
'-o',
'--output',
dest='output',
default='pdist.h5',
help='''Store results in OUTPUT (default: %(default)s).''')
parser.add_argument(
'-C',
'--compress',
action='store_true',
help=
'''Compress histograms. May make storage of higher-dimensional histograms
more tractable, at the (possible extreme) expense of increased analysis time.
(Default: no compression.)''')
parser.add_argument(
'--loose',
dest='ignore_out_of_range',
action='store_true',
help=
'''Ignore values that do not fall within bins. (Risky, as this can make buggy bin
boundaries appear as reasonable data. Only use if you are
sure of your bin boundary specification.)''')
igroup = parser.add_argument_group(
'input dataset options').add_mutually_exclusive_group(
required=False)
igroup.add_argument(
'--construct-dataset',
help=
'''Use the given function (as in module.function) to extract source data.
This function will be called once per iteration as function(n_iter, iter_group)
to construct data for one iteration. Data returned must be indexable as
[seg_id][timepoint][dimension]''')
igroup.add_argument(
'--dsspecs',
nargs='+',
metavar='DSSPEC',
help=
'''Construct probability distribution from one or more DSSPECs.''')
self.progress.add_args(parser)
def process_args(self, args):
self.progress.process_args(args)
self.data_reader.process_args(args)
self.input_dssynth.h5filename = self.data_reader.we_h5filename
self.input_dssynth.process_args(args)
self.dsspec = self.input_dssynth.dsspec
# Carrying an open HDF5 file across a fork() seems to corrupt the entire HDF5 library
# Open the WEST HDF5 file just long enough to process our iteration range, then close
# and reopen in go() [which executes after the fork]
with self.data_reader:
self.iter_range.process_args(args)
self.wt_dsspec = SingleIterDSSpec(self.data_reader.we_h5filename,
'seg_index',
slice=numpy.index_exp['weight'])
self.binspec = args.bins
self.output_filename = args.output
self.ignore_out_of_range = bool(args.ignore_out_of_range)
self.compress_output = args.compress or False
def go(self):
self.data_reader.open('r')
pi = self.progress.indicator
pi.operation = 'Initializing'
with pi:
self.output_file = h5py.File(self.output_filename, 'w')
h5io.stamp_creator_data(self.output_file)
self.iter_start = self.iter_range.iter_start
self.iter_stop = self.iter_range.iter_stop
# Construct bin boundaries
self.construct_bins(self.parse_binspec(self.binspec))
for idim, (binbounds, midpoints) in enumerate(
zip(self.binbounds, self.midpoints)):
self.output_file['binbounds_{}'.format(idim)] = binbounds
self.output_file['midpoints_{}'.format(idim)] = midpoints
# construct histogram
self.construct_histogram()
# Record iteration range
iter_range = self.iter_range.iter_range()
self.output_file['n_iter'] = iter_range
self.iter_range.record_data_iter_range(
self.output_file['histograms'])
self.output_file.close()
@staticmethod
def parse_binspec(binspec):
namespace = {'numpy': numpy, 'inf': float('inf')}
try:
binspec_compiled = eval(binspec, namespace)
except Exception as e:
raise ValueError('invalid bin specification: {!r}'.format(e))
else:
if log.isEnabledFor(logging.DEBUG):
log.debug('bin specs: {!r}'.format(binspec_compiled))
return binspec_compiled
def construct_bins(self, bins):
'''
Construct bins according to ``bins``, which may be:
1) A scalar integer (for that number of bins in each dimension)
2) A sequence of integers (specifying number of bins for each dimension)
3) A sequence of sequences of bin boundaries (specifying boundaries for each dimension)
Sets ``self.binbounds`` to a list of arrays of bin boundaries appropriate for passing to
fasthist.histnd, along with ``self.midpoints`` to the midpoints of the bins.
'''
if not isiterable(bins):
self._construct_bins_from_scalar(bins)
elif not isiterable(bins[0]):
self._construct_bins_from_int_seq(bins)
else:
self._construct_bins_from_bound_seqs(bins)
if log.isEnabledFor(logging.DEBUG):
log.debug('binbounds: {!r}'.format(self.binbounds))
def scan_data_shape(self):
if self.ndim is None:
dset = self.dsspec.get_iter_data(self.iter_start)
self.ntimepoints = dset.shape[1]
self.ndim = dset.shape[2]
self.dset_dtype = dset.dtype
def scan_data_range(self):
'''Scan input data for range in each dimension. The number of dimensions is determined
from the shape of the progress coordinate as of self.iter_start.'''
self.progress.indicator.new_operation('Scanning for data range',
self.iter_stop - self.iter_start)
self.scan_data_shape()
dset_dtype = self.dset_dtype
ndim = self.ndim
dsspec = self.dsspec
try:
minval = numpy.finfo(dset_dtype).min
maxval = numpy.finfo(dset_dtype).max
except ValueError:
minval = numpy.iinfo(dset_dtype).min
maxval = numpy.iinfo(dset_dtype).max
data_range = self.data_range = [(maxval, minval)
for _i in range(self.ndim)]
#futures = []
#for n_iter in xrange(self.iter_start, self.iter_stop):
#_remote_min_max(ndim, dset_dtype, n_iter, dsspec)
# futures.append(self.work_manager.submit(_remote_min_max, args=(ndim, dset_dtype, n_iter, dsspec)))
#for future in self.work_manager.as_completed(futures):
for future in self.work_manager.submit_as_completed(
((_remote_min_max, (ndim, dset_dtype, n_iter, dsspec), {})
for n_iter in range(self.iter_start, self.iter_stop)),
self.max_queue_len):
bounds = future.get_result(discard=True)
for idim in range(ndim):
current_min, current_max = data_range[idim]
current_min = min(current_min, bounds[idim][0])
current_max = max(current_max, bounds[idim][1])
data_range[idim] = (current_min, current_max)
self.progress.indicator.progress += 1
def _construct_bins_from_scalar(self, bins):
if self.data_range is None:
self.scan_data_range()
self.binbounds = []
self.midpoints = []
for idim in range(self.ndim):
lb, ub = self.data_range[idim]
# Advance just beyond the upper bound of the range, so that we catch
# the maximum in the histogram
ub *= 1.01
boundset = numpy.linspace(lb, ub, bins + 1)
midpoints = (boundset[:-1] + boundset[1:]) / 2.0
self.binbounds.append(boundset)
self.midpoints.append(midpoints)
def _construct_bins_from_int_seq(self, bins):
if self.data_range is None:
self.scan_data_range()
self.binbounds = []
self.midpoints = []
for idim in range(self.ndim):
lb, ub = self.data_range[idim]
# Advance just beyond the upper bound of the range, so that we catch
# the maximum in the histogram
ub *= 1.01
boundset = numpy.linspace(lb, ub, bins[idim] + 1)
midpoints = (boundset[:-1] + boundset[1:]) / 2.0
self.binbounds.append(boundset)
self.midpoints.append(midpoints)
def _construct_bins_from_bound_seqs(self, bins):
self.binbounds = []
self.midpoints = []
for boundset in bins:
boundset = numpy.asarray(boundset)
if (numpy.diff(boundset) <= 0).any():
raise ValueError(
'boundary set {!r} is not strictly monotonically increasing'
.format(boundset))
self.binbounds.append(boundset)
self.midpoints.append((boundset[:-1] + boundset[1:]) / 2.0)
def construct_histogram(self):
'''Construct a histogram using bins previously constructed with ``construct_bins()``.
The time series of histogram values is stored in ``histograms``.
Each histogram in the time series is normalized.'''
self.scan_data_shape()
iter_count = self.iter_stop - self.iter_start
histograms_ds = self.output_file.create_dataset(
'histograms',
dtype=numpy.float64,
shape=((iter_count, ) +
tuple(len(bounds) - 1 for bounds in self.binbounds)),
compression=9 if self.compress_output else None)
binbounds = [
numpy.require(boundset, self.dset_dtype, 'C')
for boundset in self.binbounds
]
self.progress.indicator.new_operation('Constructing histograms',
self.iter_stop - self.iter_start)
task_gen = (
(_remote_bin_iter,
(iiter, n_iter, self.dsspec, self.wt_dsspec,
1 if iiter > 0 else 0, binbounds, self.ignore_out_of_range), {})
for (iiter,
n_iter) in enumerate(range(self.iter_start, self.iter_stop)))
#futures = set()
#for iiter, n_iter in enumerate(xrange(self.iter_start, self.iter_stop)):
# initpoint = 1 if iiter > 0 else 0
# futures.add(self.work_manager.submit(_remote_bin_iter,
# args=(iiter, n_iter, self.dsspec, self.wt_dsspec, initpoint, binbounds)))
#for future in self.work_manager.as_completed(futures):
#future = self.work_manager.wait_any(futures)
#for future in self.work_manager.submit_as_completed(task_gen, self.queue_size):
log.debug('max queue length: {!r}'.format(self.max_queue_len))
for future in self.work_manager.submit_as_completed(
task_gen, self.max_queue_len):
iiter, n_iter, iter_hist = future.get_result(discard=True)
self.progress.indicator.progress += 1
# store histogram
histograms_ds[iiter] = iter_hist
del iter_hist, future
class WNetworker(WESTTool):
prog = "w_networker"
description = """\
Makes a network file from a transition matrix that can be visualized
by most graph programs.
-----------------------------------------------------------------------------
Output format
-----------------------------------------------------------------------------
The output file (-o/--output, by default "network.gml") contains the network
as described by the transition matrix found in the transition matrix file
-----------------------------------------------------------------------------
Command-line arguments
-----------------------------------------------------------------------------
"""
def __init__(self):
super(WNetworker, self).__init__()
self.data_reader = WESTDataReader()
self.iter_range = IterRangeSelection()
self.progress = ProgressIndicatorComponent()
self.output_filename = None
self.tm_filename = None
self.postprocess_function = None
def add_args(self, parser):
self.data_reader.add_args(parser)
self.iter_range.add_args(parser)
igroup = parser.add_argument_group("input options")
# TODO: Get WESTPA h5 file and add some stuff from it into nodes
igroup.add_argument(
"-tm",
"--transition-matrix",
default="tm.h5",
help="""Use transition matrix from the"""
"""resulting h5 file of w_reweigh (default: %(default)s).""",
)
ogroup = parser.add_argument_group("output options")
ogroup.add_argument(
"-o",
"--output",
default="network.gml",
help="""Write output to OUTPUT (default: %(default)s).""",
)
ppgroup = parser.add_argument_group("postprocess options")
ppgroup.add_argument(
"--postprocess-function",
help=
"""Names a function (as in module.function) that will be called just prior
to saving the graph. The function will be called as ``postprocess(G, tm, prob)``
where ``G`` is the fully built networkx graph, ``tm`` is the transition matrix
used to build the graph and ``prob`` is the probability distribution used""",
)
self.progress.add_args(parser)
def process_args(self, args):
self.progress.process_args(args)
self.data_reader.process_args(args)
with self.data_reader:
self.iter_range.process_args(args)
# Set the attributes according to arguments
self.output_filename = args.output
self.tm_filename = args.transition_matrix
if args.postprocess_function:
self.postprocess_function = get_object(args.postprocess_function,
path=["."])
def _load_from_h5(self, fname, istart, istop):
tmh5 = h5py.File(fname, "r")
# We will need the number of rows and columns to convert from
# sparse matrix format
nrows = tmh5.attrs["nrows"]
ncols = tmh5.attrs["ncols"]
# gotta average over iterations
tm = None
for it in range(istart, istop):
it_str = "iter_{:08}".format(it)
col = tmh5["iterations"][it_str]["cols"]
row = tmh5["iterations"][it_str]["rows"]
flux = tmh5["iterations"][it_str]["flux"]
ctm = coo_matrix((flux, (row, col)),
shape=(nrows, ncols)).toarray()
if tm is None:
tm = ctm
else:
tm += ctm
# We need to convert the "non-markovian" matrix to
# a markovian matrix here
# TODO: support more than 2 states
# Not as straight forward as it seems since there is the
# "unknown" state to deal with and it requires a funky
# fix to go from non-markovian to markovian matrix
nstates = 2
mnrows = int(nrows / nstates)
mncols = int(ncols / nstates)
mtm = numpy.zeros((mnrows, mncols), dtype=flux.dtype)
for i in range(mnrows):
for j in range(mncols):
mtm[i, j] = tm[i * 2:(i + 1) * 2, j * 2:(j + 1) * 2].sum()
mtm = mtm / len(tmh5["iterations"])
# Let's also get probabilities
bin_probs = tmh5["bin_populations"]
avg_bin_probs = numpy.average(bin_probs[istart:istop],
axis=0) / nstates
prob = avg_bin_probs.reshape(mnrows, nstates).sum(axis=1)
return mtm, prob
def read_tmfile(self, fname, istart, istop):
if fname.endswith(".h5"):
tm, prob = self._load_from_h5(fname, istart, istop)
else:
# TODO: error out
pass
return tm, prob
def save_graph(self, outname, graph):
# determine save function
if outname.endswith(".gml"):
func = nx.write_gml
else:
# TODO: error out
pass
func(graph, outname)
def go(self):
self.data_reader.open("r")
# Get the iterations we want to average the tm if needed
iter_start, iter_stop = self.iter_range.iter_start, self.iter_range.iter_stop
# Read transition matrix and probabilities
tm, prob = self.read_tmfile(self.tm_filename, iter_start, iter_stop)
# Start the progress indicator and work on the graph
pi = self.progress.indicator
with pi:
node_sizes = prob
edge_sizes = tm
pi.new_operation("Building graph, adding nodes",
extent=len(node_sizes))
G = nx.DiGraph()
for i in range(tm.shape[0]):
if node_sizes[i] > 0:
G.add_node(i, weight=float(node_sizes[i]))
pi.progress += 1
pi.new_operation("Adding edges", extent=len(edge_sizes.flatten()))
for i in range(tm.shape[0]):
for j in range(tm.shape[1]):
if edge_sizes[i][j] > 0:
G.add_edge(i, j, weight=float(edge_sizes[i][j]))
pi.progress += 1
if self.postprocess_function:
self.postprocess_function(G, tm, prob)
self.save_graph(self.output_filename, G)
