class WESTKineticsBase(WESTSubcommand):
'''
Common argument processing for w_direct/w_reweight subcommands.
Mostly limited to handling input and output from w_assign.
'''
def __init__(self, parent):
super(WESTKineticsBase,self).__init__(parent)
self.data_reader = WESTDataReader()
self.iter_range = IterRangeSelection()
self.progress = ProgressIndicatorComponent()
self.output_filename = None
# This is actually applicable to both.
self.assignment_filename = None
self.output_file = None
self.assignments_file = None
self.evolution_mode = None
self.mcbs_alpha = None
self.mcbs_acalpha = None
self.mcbs_nsets = None
# Now we're adding in things that come from the old w_kinetics
self.do_compression = True
def add_args(self, parser):
self.progress.add_args(parser)
self.data_reader.add_args(parser)
self.iter_range.include_args['iter_step'] = True
self.iter_range.add_args(parser)
iogroup = parser.add_argument_group('input/output options')
iogroup.add_argument('-a', '--assignments', default='assign.h5',
help='''Bin assignments and macrostate definitions are in ASSIGNMENTS
(default: %(default)s).''')
iogroup.add_argument('-o', '--output', dest='output', default=self.default_output_file,
help='''Store results in OUTPUT (default: %(default)s).''')
def process_args(self, args):
self.progress.process_args(args)
self.data_reader.process_args(args)
with self.data_reader:
self.iter_range.process_args(args, default_iter_step=None)
if self.iter_range.iter_step is None:
#use about 10 blocks by default
self.iter_range.iter_step = max(1, (self.iter_range.iter_stop - self.iter_range.iter_start) // 10)
self.output_filename = args.output
self.assignments_filename = args.assignments
class KineticsSubcommands(WESTSubcommand):
'''Base class for common options for both kinetics schemes'''
def __init__(self, parent):
super(KineticsSubcommands,self).__init__(parent)
self.progress = ProgressIndicatorComponent()
self.data_reader = WESTDataReader()
self.iter_range = IterRangeSelection()
self.output_file = None
self.assignments_file = None
self.do_compression = True
def add_args(self, parser):
self.data_reader.add_args(parser)
self.iter_range.add_args(parser)
iogroup = parser.add_argument_group('input/output options')
iogroup.add_argument('-a', '--assignments', default='assign.h5',
help='''Bin assignments and macrostate definitions are in ASSIGNMENTS
(default: %(default)s).''')
# default_kinetics_file will be picked up as a class attribute from the appropriate
# subclass
iogroup.add_argument('-o', '--output', dest='output', default=self.default_kinetics_file,
help='''Store results in OUTPUT (default: %(default)s).''')
iogroup.add_argument('--no-compression', dest='compression', action='store_false',
help='''Do not store kinetics results compressed. This can increase disk
use about 100-fold, but can dramatically speed up subsequent analysis
for "w_kinavg matrix". Default: compress kinetics results.''')
self.progress.add_args(parser)
parser.set_defaults(compression=True)
def process_args(self, args):
self.progress.process_args(args)
self.assignments_file = h5io.WESTPAH5File(args.assignments, 'r')
self.data_reader.process_args(args)
with self.data_reader:
self.iter_range.process_args(args)
self.output_file = h5io.WESTPAH5File(args.output, 'w', creating_program=True)
h5io.stamp_creator_data(self.output_file)
if not self.iter_range.check_data_iter_range_least(self.assignments_file):
raise ValueError('assignments do not span the requested iterations')
self.do_compression = args.compression
class KinAvgSubcommands(WESTSubcommand):
'''Common argument processing for w_kinavg subcommands'''
def __init__(self, parent):
super(KinAvgSubcommands,self).__init__(parent)
self.data_reader = WESTDataReader()
self.iter_range = IterRangeSelection()
self.progress = ProgressIndicatorComponent()
self.output_filename = None
self.kinetics_filename = None
self.assignment_filename = None
self.output_file = None
self.assignments_file = None
self.kinetics_file = None
self.evolution_mode = None
self.mcbs_alpha = None
self.mcbs_acalpha = None
self.mcbs_nsets = None
def stamp_mcbs_info(self, dataset):
dataset.attrs['mcbs_alpha'] = self.mcbs_alpha
dataset.attrs['mcbs_acalpha'] = self.mcbs_acalpha
dataset.attrs['mcbs_nsets'] = self.mcbs_nsets
def add_args(self, parser):
self.progress.add_args(parser)
self.data_reader.add_args(parser)
self.iter_range.include_args['iter_step'] = True
self.iter_range.add_args(parser)
iogroup = parser.add_argument_group('input/output options')
iogroup.add_argument('-a', '--assignments', default='assign.h5',
help='''Bin assignments and macrostate definitions are in ASSIGNMENTS
(default: %(default)s).''')
# self.default_kinetics_file will be picked up as a class attribute from the appropriate subclass
iogroup.add_argument('-k', '--kinetics', default=self.default_kinetics_file,
help='''Populations and transition rates are stored in KINETICS
(default: %(default)s).''')
iogroup.add_argument('-o', '--output', dest='output', default='kinavg.h5',
help='''Store results in OUTPUT (default: %(default)s).''')
cgroup = parser.add_argument_group('confidence interval calculation options')
cgroup.add_argument('--alpha', type=float, default=0.05,
help='''Calculate a (1-ALPHA) confidence interval'
(default: %(default)s)''')
cgroup.add_argument('--autocorrel-alpha', type=float, dest='acalpha', metavar='ACALPHA',
help='''Evaluate autocorrelation 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('--nsets', type=int,
help='''Use NSETS samples for bootstrapping (default: chosen based on ALPHA)''')
cogroup = parser.add_argument_group('calculation options')
cogroup.add_argument('-e', '--evolution-mode', choices=['cumulative', 'blocked', 'none'], default='none',
help='''How to calculate time evolution of rate estimates.
``cumulative`` evaluates rates over windows starting with --start-iter and getting progressively
wider to --stop-iter by steps of --step-iter.
``blocked`` evaluates rates over windows of width --step-iter, the first of which begins at
--start-iter.
``none`` (the default) disables calculation of the time evolution of rate estimates.''')
cogroup.add_argument('--window-frac', type=float, default=1.0,
help='''Fraction of iterations to use in each window when running in ``cumulative`` mode.
The (1 - frac) fraction of iterations will be discarded from the start of each window.''')
def open_files(self):
self.output_file = h5io.WESTPAH5File(self.output_filename, 'w', creating_program=True)
h5io.stamp_creator_data(self.output_file)
self.assignments_file = h5io.WESTPAH5File(self.assignments_filename, 'r')#, driver='core', backing_store=False)
self.kinetics_file = h5io.WESTPAH5File(self.kinetics_filename, 'r')#, driver='core', backing_store=False)
if not self.iter_range.check_data_iter_range_least(self.assignments_file):
raise ValueError('assignments data do not span the requested iterations')
if not self.iter_range.check_data_iter_range_least(self.kinetics_file):
raise ValueError('kinetics data do not span the requested iterations')
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, default_iter_step=None)
if self.iter_range.iter_step is None:
#use about 10 blocks by default
self.iter_range.iter_step = max(1, (self.iter_range.iter_stop - self.iter_range.iter_start) // 10)
self.output_filename = args.output
self.assignments_filename = args.assignments
self.kinetics_filename = args.kinetics
self.mcbs_alpha = args.alpha
self.mcbs_acalpha = args.acalpha if args.acalpha else self.mcbs_alpha
self.mcbs_nsets = args.nsets if args.nsets else mclib.get_bssize(self.mcbs_alpha)
self.evolution_mode = args.evolution_mode
self.evol_window_frac = args.window_frac
if self.evol_window_frac <= 0 or self.evol_window_frac > 1:
raise ValueError('Parameter error -- fractional window defined by --window-frac must be in (0,1]')
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 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 StateProbTool(WESTParallelTool):
prog='w_stateprobs'
description = '''\
Calculate average populations and associated errors in state populations from
weighted ensemble data. Bin assignments, including macrostate definitions,
are required. (See "w_assign --help" for more information).
-----------------------------------------------------------------------------
Output format
-----------------------------------------------------------------------------
The output file (-o/--output, usually "stateprobs.h5") contains the following
dataset:
/avg_state_pops [state]
(Structured -- see below) Population of each state across entire
range specified.
If --evolution-mode is specified, then the following additional dataset is
available:
/state_pop_evolution [window][state]
(Structured -- see below). State populations based on windows of
iterations of varying width. If --evolution-mode=cumulative, then
these windows all begin at the iteration specified with
--start-iter and grow in length by --step-iter for each successive
element. If --evolution-mode=blocked, then these windows are all of
width --step-iter (excluding the last, which may be shorter), the first
of which begins at iteration --start-iter.
The structure of these datasets is as follows:
iter_start
(Integer) Iteration at which the averaging window begins (inclusive).
iter_stop
(Integer) Iteration at which the averaging window ends (exclusive).
expected
(Floating-point) Expected (mean) value of the rate as evaluated within
this window, in units of inverse tau.
ci_lbound
(Floating-point) Lower bound of the confidence interval on the rate
within this window, in units of inverse tau.
ci_ubound
(Floating-point) Upper bound of the confidence interval on the rate
within this window, in units of inverse tau.
corr_len
(Integer) Correlation length of the rate within this window, in units
of tau.
Each of these datasets is also stamped with a number of attributes:
mcbs_alpha
(Floating-point) Alpha value of confidence intervals. (For example,
*alpha=0.05* corresponds to a 95% confidence interval.)
mcbs_nsets
(Integer) Number of bootstrap data sets used in generating confidence
intervals.
mcbs_acalpha
(Floating-point) Alpha value for determining correlation lengths.
-----------------------------------------------------------------------------
Command-line options
-----------------------------------------------------------------------------
'''
def __init__(self):
super(StateProbTool,self).__init__()
self.data_reader = WESTDataReader()
self.iter_range = IterRangeSelection()
self.progress = ProgressIndicatorComponent()
self.output_filename = None
self.kinetics_filename = None
self.output_file = None
self.assignments_file = None
self.evolution_mode = None
self.mcbs_alpha = None
self.mcbs_acalpha = None
self.mcbs_nsets = None
def stamp_mcbs_info(self, dataset):
dataset.attrs['mcbs_alpha'] = self.mcbs_alpha
dataset.attrs['mcbs_acalpha'] = self.mcbs_acalpha
dataset.attrs['mcbs_nsets'] = self.mcbs_nsets
def add_args(self, parser):
self.progress.add_args(parser)
self.data_reader.add_args(parser)
self.iter_range.include_args['iter_step'] = True
self.iter_range.add_args(parser)
iogroup = parser.add_argument_group('input/output options')
iogroup.add_argument('-a', '--assignments', default='assign.h5',
help='''Bin assignments and macrostate definitions are in ASSIGNMENTS
(default: %(default)s).''')
iogroup.add_argument('-o', '--output', dest='output', default='stateprobs.h5',
help='''Store results in OUTPUT (default: %(default)s).''')
cgroup = parser.add_argument_group('confidence interval calculation options')
cgroup.add_argument('--alpha', type=float, default=0.05,
help='''Calculate a (1-ALPHA) confidence interval'
(default: %(default)s)''')
cgroup.add_argument('--autocorrel-alpha', type=float, dest='acalpha', metavar='ACALPHA',
help='''Evaluate autocorrelation 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('--nsets', type=int,
help='''Use NSETS samples for bootstrapping (default: chosen based on ALPHA)''')
cogroup = parser.add_argument_group('calculation options')
cogroup.add_argument('-e', '--evolution-mode', choices=['cumulative', 'blocked', 'none'], default='none',
help='''How to calculate time evolution of rate estimates.
``cumulative`` evaluates rates over windows starting with --start-iter and getting progressively
wider to --stop-iter by steps of --step-iter.
``blocked`` evaluates rates over windows of width --step-iter, the first of which begins at
--start-iter.
``none`` (the default) disables calculation of the time evolution of rate estimates.''')
def open_files(self):
self.output_file = h5io.WESTPAH5File(self.output_filename, 'w', creating_program=True)
h5io.stamp_creator_data(self.output_file)
self.assignments_file = h5io.WESTPAH5File(self.assignments_filename, 'r')#, driver='core', backing_store=False)
if not self.iter_range.check_data_iter_range_least(self.assignments_file):
raise ValueError('assignments data do not span the requested iterations')
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, default_iter_step=None)
if self.iter_range.iter_step is None:
#use about 10 blocks by default
self.iter_range.iter_step = max(1, (self.iter_range.iter_stop - self.iter_range.iter_start) // 10)
self.output_filename = args.output
self.assignments_filename = args.assignments
self.mcbs_alpha = args.alpha
self.mcbs_acalpha = args.acalpha if args.acalpha else self.mcbs_alpha
self.mcbs_nsets = args.nsets if args.nsets else mclib.get_bssize(self.mcbs_alpha)
self.evolution_mode = args.evolution_mode
def calc_state_pops(self):
start_iter, stop_iter = self.iter_range.iter_start, self.iter_range.iter_stop
nstates = self.nstates
state_map = self.state_map
iter_count = stop_iter-start_iter
pi = self.progress.indicator
pi.new_operation('Calculating state populations')
pops = h5io.IterBlockedDataset(self.assignments_file['labeled_populations'])
iter_state_pops = numpy.empty((nstates+1,), weight_dtype)
all_state_pops = numpy.empty((iter_count,nstates+1), weight_dtype)
avg_state_pops = numpy.zeros((nstates+1,), weight_dtype)
pops.cache_data(max_size='available')
try:
for iiter,n_iter in enumerate(xrange(start_iter,stop_iter)):
iter_state_pops.fill(0)
labeled_pops = pops.iter_entry(n_iter)
accumulate_state_populations_from_labeled(labeled_pops, state_map, iter_state_pops, check_state_map=False)
all_state_pops[iiter] = iter_state_pops
avg_state_pops += iter_state_pops
del labeled_pops
pi.progress += 1
finally:
pops.drop_cache()
self.output_file.create_dataset('state_pops', data=all_state_pops, compression=9, shuffle=True)
h5io.stamp_iter_range(self.output_file['state_pops'], start_iter, stop_iter)
self.all_state_pops = all_state_pops
avg_state_pops = numpy.zeros((nstates+1,), ci_dtype)
pi.new_operation('Calculating overall average populations and CIs', nstates)
# futures = []
# for istate in xrange(nstates):
# futures.append(self.work_manager.submit(_eval_block,kwargs=dict(iblock=None,istate=istate,
# start=start_iter,stop=stop_iter,
# state_pops=all_state_pops[:,istate],
# mcbs_alpha=self.mcbs_alpha, mcbs_nsets=self.mcbs_nsets,
# mcbs_acalpha = self.mcbs_acalpha)))
# for future in self.work_manager.as_completed(futures):
def taskgen():
for istate in xrange(nstates):
yield (_eval_block, (), dict(iblock=None,istate=istate,
start=start_iter,stop=stop_iter,
state_pops=all_state_pops[:,istate],
mcbs_alpha=self.mcbs_alpha, mcbs_nsets=self.mcbs_nsets,
mcbs_acalpha = self.mcbs_acalpha))
for future in self.work_manager.submit_as_completed(taskgen(), self.max_queue_len):
(_iblock,istate,ci_res) = future.get_result(discard=True)
avg_state_pops[istate] = ci_res
pi.progress += 1
self.output_file['avg_state_pops'] = avg_state_pops
self.stamp_mcbs_info(self.output_file['avg_state_pops'])
pi.clear()
maxlabellen = max(map(len,self.state_labels))
print('average state populations:')
for istate in xrange(nstates):
print('{:{maxlabellen}s}: mean={:21.15e} CI=({:21.15e}, {:21.15e})'
.format(self.state_labels[istate],
avg_state_pops['expected'][istate],
avg_state_pops['ci_lbound'][istate],
avg_state_pops['ci_ubound'][istate],
maxlabellen=maxlabellen))
def calc_evolution(self):
nstates = self.nstates
start_iter, stop_iter, step_iter = self.iter_range.iter_start, self.iter_range.iter_stop, self.iter_range.iter_step
start_pts = range(start_iter, stop_iter, step_iter)
pop_evol = numpy.zeros((len(start_pts), nstates), dtype=ci_dtype)
pi = self.progress.indicator
pi.new_operation('Calculating population evolution', len(start_pts)*nstates)
# futures = []
# for iblock, start in enumerate(start_pts):
# if self.evolution_mode == 'cumulative':
# block_start = start_iter
# else: # self.evolution_mode == 'blocked'
# block_start = start
# stop = min(start+step_iter, stop_iter)
#
# for istate in xrange(nstates):
# future = self.work_manager.submit(_eval_block,kwargs=dict(iblock=iblock,istate=istate,
# start=block_start,stop=stop,
# state_pops=self.all_state_pops[block_start-start_iter:stop-start_iter,istate],
# mcbs_alpha=self.mcbs_alpha, mcbs_nsets=self.mcbs_nsets,
# mcbs_acalpha = self.mcbs_acalpha))
# futures.append(future)
def taskgen():
for iblock, start in enumerate(start_pts):
if self.evolution_mode == 'cumulative':
block_start = start_iter
else: # self.evolution_mode == 'blocked'
block_start = start
stop = min(start+step_iter, stop_iter)
for istate in xrange(nstates):
yield (_eval_block,(),dict(iblock=iblock,istate=istate,
start=block_start,stop=stop,
state_pops=self.all_state_pops[block_start-start_iter:stop-start_iter,istate],
mcbs_alpha=self.mcbs_alpha, mcbs_nsets=self.mcbs_nsets,
mcbs_acalpha = self.mcbs_acalpha))
#for future in self.work_manager.as_completed(futures):
for future in self.work_manager.submit_as_completed(taskgen(), self.max_queue_len):
(iblock,istate,ci_res) = future.get_result(discard=True)
pop_evol[iblock,istate] = ci_res
pi.progress += 1
self.output_file.create_dataset('state_pop_evolution', data=pop_evol, shuffle=True, compression=9)
pi.clear()
def go(self):
pi = self.progress.indicator
with pi:
pi.new_operation('Initializing')
self.open_files()
nstates = self.nstates = self.assignments_file.attrs['nstates']
state_labels = self.state_labels = self.assignments_file['state_labels'][...]
state_map = self.state_map = self.assignments_file['state_map'][...]
if (state_map > nstates).any():
raise ValueError('invalid state mapping')
# copy metadata to output
self.output_file.attrs['nstates'] = nstates
self.output_file['state_labels'] = state_labels
# calculate overall averages
self.calc_state_pops()
# calculate evolution, if requested
if self.evolution_mode != 'none' and self.iter_range.iter_step:
self.calc_evolution()
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 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 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 WPostAnalysisReweightTool(WESTTool):
prog ='w_postanalysis_reweight'
description = '''\
Calculate average rates from weighted ensemble data using the postanalysis
reweighting scheme. Bin assignments (usually "assignments.h5") and pre-calculated
iteration flux matrices (usually "flux_matrices.h5") data files must have been
previously generated using w_postanalysis_matrix.py (see "w_assign --help" and
"w_kinetics --help" for information on generating these files).
-----------------------------------------------------------------------------
Output format
-----------------------------------------------------------------------------
The output file (-o/--output, usually "kinrw.h5") contains the following
dataset:
/state_prob_evolution [window,state]
The reweighted state populations based on windows
/color_prob_evolution [window,state]
The reweighted populations last assigned to each state based on windows
/bin_prob_evolution [window, bin]
The reweighted populations of each bin based on windows. Bins contain
one color each, so to recover the original un-colored spatial bins,
one must sum over all states.
/conditional_flux_evolution [window,state,state]
(Structured -- see below). State-to-state fluxes based on windows of
varying width
The structure of the final dataset is as follows:
iter_start
(Integer) Iteration at which the averaging window begins (inclusive).
iter_stop
(Integer) Iteration at which the averaging window ends (exclusive).
expected
(Floating-point) Expected (mean) value of the rate as evaluated within
this window, in units of inverse tau.
-----------------------------------------------------------------------------
Command-line options
-----------------------------------------------------------------------------
'''
def __init__(self):
super(WPostAnalysisReweightTool, self).__init__()
self.data_reader = WESTDataReader()
self.iter_range = IterRangeSelection()
self.progress = ProgressIndicatorComponent()
self.output_filename = None
self.kinetics_filename = None
self.assignment_filename = None
self.output_file = None
self.assignments_file = None
self.kinetics_file = None
self.evolution_mode = None
def add_args(self, parser):
self.progress.add_args(parser)
self.data_reader.add_args(parser)
self.iter_range.include_args['iter_step'] = True
self.iter_range.add_args(parser)
iogroup = parser.add_argument_group('input/output options')
iogroup.add_argument('-a', '--assignments', default='assign.h5',
help='''Bin assignments and macrostate definitions are in ASSIGNMENTS
(default: %(default)s).''')
iogroup.add_argument('-k', '--kinetics', default='flux_matrices.h5',
help='''Per-iteration flux matrices calculated by w_postanalysis_matrix
(default: %(default)s).''')
iogroup.add_argument('-o', '--output', dest='output', default='kinrw.h5',
help='''Store results in OUTPUT (default: %(default)s).''')
cogroup = parser.add_argument_group('calculation options')
cogroup.add_argument('-e', '--evolution-mode', choices=['cumulative', 'blocked'], default='cumulative',
help='''How to calculate time evolution of rate estimates.
``cumulative`` evaluates rates over windows starting with --start-iter and getting progressively
wider to --stop-iter by steps of --step-iter.
``blocked`` evaluates rates over windows of width --step-iter, the first of which begins at
--start-iter.''')
cogroup.add_argument('--window-frac', type=float, default=1.0,
help='''Fraction of iterations to use in each window when running in ``cumulative`` mode.
The (1 - frac) fraction of iterations will be discarded from the start of each window.''')
cogroup.add_argument('--obs-threshold', type=int, default=1,
help='''The minimum number of observed transitions between two states i and j necessary to include
fluxes in the reweighting estimate''')
def open_files(self):
self.output_file = h5io.WESTPAH5File(self.output_filename, 'w', creating_program=True)
h5io.stamp_creator_data(self.output_file)
self.assignments_file = h5io.WESTPAH5File(self.assignments_filename, 'r')#, driver='core', backing_store=False)
self.kinetics_file = h5io.WESTPAH5File(self.kinetics_filename, 'r')#, driver='core', backing_store=False)
if not self.iter_range.check_data_iter_range_least(self.assignments_file):
raise ValueError('assignments data do not span the requested iterations')
if not self.iter_range.check_data_iter_range_least(self.kinetics_file):
raise ValueError('kinetics data do not span the requested iterations')
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, default_iter_step=None)
if self.iter_range.iter_step is None:
#use about 10 blocks by default
self.iter_range.iter_step = max(1, (self.iter_range.iter_stop - self.iter_range.iter_start) // 10)
self.output_filename = args.output
self.assignments_filename = args.assignments
self.kinetics_filename = args.kinetics
self.evolution_mode = args.evolution_mode
self.evol_window_frac = args.window_frac
if self.evol_window_frac <= 0 or self.evol_window_frac > 1:
raise ValueError('Parameter error -- fractional window defined by --window-frac must be in (0,1]')
self.obs_threshold = args.obs_threshold
def go(self):
pi = self.progress.indicator
with pi:
pi.new_operation('Initializing')
self.open_files()
nstates = self.assignments_file.attrs['nstates']
nbins = self.assignments_file.attrs['nbins']
state_labels = self.assignments_file['state_labels'][...]
state_map = self.assignments_file['state_map'][...]
nfbins = self.kinetics_file.attrs['nrows']
npts = self.kinetics_file.attrs['npts']
assert nstates == len(state_labels)
assert nfbins == nbins * nstates
start_iter, stop_iter, step_iter = self.iter_range.iter_start, self.iter_range.iter_stop, self.iter_range.iter_step
start_pts = range(start_iter, stop_iter, step_iter)
flux_evol = np.zeros((len(start_pts), nstates, nstates), dtype=ci_dtype)
color_prob_evol = np.zeros((len(start_pts), nstates))
state_prob_evol = np.zeros((len(start_pts), nstates))
bin_prob_evol = np.zeros((len(start_pts), nfbins))
pi.new_operation('Calculating flux evolution', len(start_pts))
if self.evolution_mode == 'cumulative' and self.evol_window_frac == 1.0:
print('Using fast streaming accumulation')
total_fluxes = np.zeros((nfbins, nfbins), weight_dtype)
total_obs = np.zeros((nfbins, nfbins), np.int64)
for iblock, start in enumerate(start_pts):
pi.progress += 1
stop = min(start + step_iter, stop_iter)
params = dict(start=start, stop=stop, nstates=nstates, nbins=nbins,
state_labels=state_labels, state_map=state_map, nfbins=nfbins,
total_fluxes=total_fluxes, total_obs=total_obs,
h5file=self.kinetics_file, obs_threshold=self.obs_threshold)
rw_state_flux, rw_color_probs, rw_state_probs, rw_bin_probs, rw_bin_flux = reweight(**params)
for k in xrange(nstates):
for j in xrange(nstates):
# Normalize such that we report the flux per tau (tau being the weighted ensemble iteration)
# npts always includes a 0th time point
flux_evol[iblock]['expected'][k,j] = rw_state_flux[k,j] * (npts - 1)
flux_evol[iblock]['iter_start'][k,j] = start
flux_evol[iblock]['iter_stop'][k,j] = stop
color_prob_evol[iblock] = rw_color_probs
state_prob_evol[iblock] = rw_state_probs[:-1]
bin_prob_evol[iblock] = rw_bin_probs
else:
for iblock, start in enumerate(start_pts):
pi.progress += 1
stop = min(start + step_iter, stop_iter)
if self.evolution_mode == 'cumulative':
windowsize = max(1, int(self.evol_window_frac * (stop - start_iter)))
block_start = max(start_iter, stop - windowsize)
else: # self.evolution_mode == 'blocked'
block_start = start
params = dict(start=block_start, stop=stop, nstates=nstates, nbins=nbins,
state_labels=state_labels, state_map=state_map, nfbins=nfbins,
total_fluxes=None, total_obs=None,
h5file=self.kinetics_file)
rw_state_flux, rw_color_probs, rw_state_probs, rw_bin_probs, rw_bin_flux = reweight(**params)
for k in xrange(nstates):
for j in xrange(nstates):
# Normalize such that we report the flux per tau (tau being the weighted ensemble iteration)
# npts always includes a 0th time point
flux_evol[iblock]['expected'][k,j] = rw_state_flux[k,j] * (npts - 1)
flux_evol[iblock]['iter_start'][k,j] = start
flux_evol[iblock]['iter_stop'][k,j] = stop
color_prob_evol[iblock] = rw_color_probs
state_prob_evol[iblock] = rw_state_probs[:-1]
bin_prob_evol[iblock] = rw_bin_probs
ds_flux_evol = self.output_file.create_dataset('conditional_flux_evolution', data=flux_evol, shuffle=True, compression=9)
ds_state_prob_evol = self.output_file.create_dataset('state_prob_evolution', data=state_prob_evol, compression=9)
ds_color_prob_evol = self.output_file.create_dataset('color_prob_evolution', data=color_prob_evol, compression=9)
ds_bin_prob_evol = self.output_file.create_dataset('bin_prob_evolution', data=bin_prob_evol, compression=9)
ds_state_labels = self.output_file.create_dataset('state_labels', data=state_labels)
class WPostanalysisPush(WESTTool):
prog ='w_postanalysis_push'
description = '''\
Apply weights calculated using the postanalysis reweighting scheme (see
"w_postanalysis_reweight --help" or "w_multi_reweight --help") to a WESTPA data
file (usually "west.h5") by scaling the probability in each bin. Bin
assignments (usually "assign.h5") and a corresponding postanalysis reweighting
output file (usually "kinrw.h5" or "multi_rw.h5") must be supplied, in addition
to the iteration from which to pull weights.
WARNING: Output from this script may not be compatible with some tools. Due to
the nature of the postanalysis reweighting process, some walkers may be assigned
zero weight. Additionally, total weight may not sum to one for some iterations,
as a bin predicted to have nonzero weight by the postanalysis reweighting scheme
may depopulate during the course a simulation.
--------------------------------------------------------------------------------
Output format
--------------------------------------------------------------------------------
The output file (-o/--output, usually "west_rw.h5") is of the same format as
the original WESTPA data file. New weights are found in place of the original
weights, located in:
/iterations/iter_{N_ITER:08d}/seg_index/
--------------------------------------------------------------------------------
Command-line options
--------------------------------------------------------------------------------
'''
def __init__(self):
super(WPostanalysisPush, self).__init__()
self.data_reader = WESTDataReader()
self.progress = ProgressIndicatorComponent()
self.output_filename = None
self.rw_filename = None
self.assignment_filename = None
self.output_file = None
self.rw_file = None
self.assignments_file = None
self.weights_attributes_initialized = False
self.weights_already_calculated = False
self.time_average_scaling_vector_calculated = False
def add_args(self, parser):
self.progress.add_args(parser)
self.data_reader.add_args(parser)
iogroup = parser.add_argument_group('input/output options')
iogroup.add_argument('-W', '--west', dest='westH5_path',
default='west.h5', metavar='WESTH5',
help='''Apply weights to data from WESTH5, creating
a new file that either links to or duplicates data
in WESTH5 (WESTH5 will not be altered).''')
iogroup.add_argument('-rw', dest='rw_H5_path', metavar='RW_FILE',
default='kinrw.h5',
help='''Pull weights from RW_FILE. This should be
the output file from either w_postanalysis_reweight
or w_multi_reweight.''')
iogroup.add_argument('-a', '--assignments', dest='assignH5_path',
default='assign.h5', metavar='ASSIGNH5',
help='''Rescale weights based on bin assignments
in ASSIGNH5. This file should be consistent with
RW_FILE and WESTH5''')
iogroup.add_argument('-o', '--output', dest='output',
default='west_rw.h5',
help='''Store results in OUTPUT
(default: %(default)s).''')
iogroup.add_argument('-c', '--copy', dest='copy', action='store_true',
help='''If specified, copy all data from WESTH5
to OUTPUT. Otherwise (default), link to data in
WESTH5.''')
cogroup = parser.add_argument_group('calculation options')
cogroup.add_argument('-n', '--n-iter', dest='n_iter', default=None,
type=int,
help='''Pull weights from N_ITER and push to data
from all iterations in WESTH5. By default, use
the final iteration available in RW_FILE.
Alternatively, the weights from each iteration may
be push to the data from the corresponding
iteration in WESTH5 (see "-e/--evolution-mode").
''')
cogroup.add_argument('-e', '--evolution-mode', action='store_true',
help='''If specified, push weights from each
iteration available in RW_FILE to the data from the
corresponding iteration in WESTH5. For iterations
not available in RW_FILE, copy weights directly
from WESTH5 without rescaling. By default, pull
weights from a single iteration, specified using
"-n/--n-iter".''')
cogroup.add_argument('-nc', '--no-color', action='store_true',
dest='no_color',
help='''If specified, do not use colored bins for
rescaling weights. By default, use colored bins.
''')
cogroup.add_argument('--time-average', action='store_true',
dest='time_average',
help='''If specifed, scale weights of walkers the
total weight of all walkers in all iterations in a
given bin sum to the weight given by the
postanalysis reweighting output. Weights in a
given iteration will likely no longer sum to one.
This options is not compatible with evolution mode
(see -e/--evolution-mode).''')
cogroup.add_argument('--iter-range', dest='iter_range', type=str,
default=None,
help='''This option may be used only if
--time-average is specified. ITER_RANGE should be
a tuple of the form (first_iter, last_iter). If
this option is specified, use only information on
walkers between first_iter and last_iter
(inclusive) when calculating scaling coefficients.
However, all iterations in the WESTPA data file
will be rescaled according to these weights.''')
def process_args(self, args):
'''Process the arguments defined in ``add_arguments``, making them
available as attributes of the main tool class.'''
self.progress.process_args(args)
self.data_reader.process_args(args)
# I/O arguments
self.westH5_path = args.westH5_path
self.rwH5_path = args.rw_H5_path
self.assignH5_path = args.assignH5_path
self.output_path = args.output
# Calculation arguments
self.copy = args.copy
self.n_iter = args.n_iter
self.evolution_mode = args.evolution_mode
if args.no_color:
self.i_use_color = False
else:
self.i_use_color = True
if args.time_average:
self.i_time_average = True
else:
self.i_time_average = False
if self.i_time_average and self.evolution_mode:
raise ArgumentError("Error. Time averaging and evolution modes are "
"not compatible! See options "
"-e/--evolution-mode and --time-average for "
"more information.")
self.first_iter = None
self.last_iter = None
if args.iter_range is not None:
if not self.i_time_average:
raise ArgumentError("Error. Specifying an iteration range is "
"only compatible with time-averaging! See "
"--time-average for more information.")
iter_tuple = eval(args.iter_range)
try:
self.first_iter = int(iter_tuple[0])
self.last_iter = int(iter_tuple[1])
except (ValueError, TypeError):
raise ArgumentError("An error occurred while parsing the "
"supplied iteration range. The iteration "
"range should evaluate to a Python tuple "
"of integers. Input: ({:s}). Please see "
"the documentation for the --iter-range "
"option for more information."
.format(args.iter_range) )
print("Using data between iterations {:d} and {:d} for calculation "
"of rescaling vector."
.format(self.first_iter, self.last_iter)
)
def open_files(self):
'''Open the WESTPA data file, the reweighting output file, the
assignments file, and the output file.'''
self.westH5 = h5py.File(self.westH5_path, 'r')
self.rwH5 = h5py.File(self.rwH5_path, 'r')
self.assignments = h5py.File(self.assignH5_path, 'r')
self.output = h5py.File(self.output_path, 'w')
def check_consistency_of_input_files(self):
'''Check that the assignment file and west.h5 file have the same number
of walkers for each iteration, and check that the reweighting output
file and the assignment file use the same number of bins.'''
## First check that the assignment and west.h5 file have the same ##
## number of walkers. ##
# Assume that the assignments file includes data for ALL iterations.
# At the time of this code's writing, it must, but this may change
# later.
last_iter = self.assignments['assignments'].shape[0] # Zero-indexed
# Get the total number of bins. Indices corresponding to walkers not
# present in a given iteration will be assigned this integer.
n_bins = self.assignments['bin_labels'].shape[0]
self.pi.new_operation('Checking input files for consistency',
last_iter)
for iiter in xrange(1, last_iter+1): #iiter is one-indexed
try:
iter_group = self.westH5['iterations/iter_{:08d}'.format(iiter)]
except KeyError:
raise ConsistencyError("Iteration {:d} exists in {:s} but not "
" in {:s}!".format(iiter,
self.assignH5_path,
self.westH5_path) )
# Number of walkers in this iteration, for the westh5 file.
westh5_n_walkers = iter_group['seg_index'].shape[0]
# Number of walkers in this iteration, for the assignh5 file. The
# size of the assignments array is the same for all iterations, and
# equals the maximum observed number of walkers. For iterations
# with fewer than the maximum number of walkers, the rest of the
# indices are filled with the integer ``n_bins``.
# Switch to zero-indexing, and only look at the first time point.
assign_n_walkers = np.count_nonzero(
np.array(self.assignments['assignments'][iiter-1][:,0]) != n_bins
)
if not westh5_n_walkers == assign_n_walkers:
raise ConsistencyError("The number of walkers in the WESTPA "
"data file ({:s}, {:d}) and the number "
"of walkers in the assignments file "
"({:s}, {:d}) for iteration {:d} do not "
"match!".format(self.westH5_path,
westh5_n_walkers,
self.assignH5_path,
assign_n_walkers,
iiter) )
self.pi.progress += 1
## Now check that the reweighting output file and the assignment file ##
## use the same number of bins. ##
rw_n_bins = self.rwH5['bin_prob_evolution'].shape[1]
# rw_n_bins is colored, but n_bins (from the assignments file) is not.
assign_nstates = self.assignments['state_labels'].shape[0]
rw_nstates = self.rwH5['state_labels'].shape[0]
if not assign_nstates == rw_nstates:
raise ConsistencyError("The number of states used in the "
"assignments file ({:d}) does not match the "
"number of states used in the reweighting "
"file ({:d})!".format(assign_nstates,
rw_nstates) )
if not assign_nstates*n_bins == rw_n_bins:
raise ConsistencyError("The number of bins used in the assignments "
"file ({:d}) does not match the number of "
"bins used in the reweighting file ({:d})!."
.format(nbins, rw_n_bins/rw_nstates) )
self.pi.clear()
def initialize_output(self):
'''Copy or link datasets besides the seg_index datasets from the input
WESTPA data file to the output (reweighted) data file. '''
self.pi.new_operation('Initializing output file',
len(self.westH5['iterations'].keys()))
for key in self.westH5.keys():
if key != 'iterations':
if self.copy:
self.westH5.copy(key, self.output)
else:
self.output[key] = h5py.ExternalLink(self.westH5_path,
key)
for name, val in self.westH5.attrs.items():
self.output.attrs.create(name, val)
self.output.create_group('iterations')
for key1 in self.westH5['iterations']:
self.output.create_group('iterations/{:s}'.format(key1))
for key2 in self.westH5['iterations/{:s}'.format(key1)]:
if key2 != 'seg_index':
key = 'iterations/'+key1+'/'+key2
if self.copy:
self.westH5.copy(key, self.output['iterations/'+key1])
else:
self.output[key] = h5py.ExternalLink(self.westH5_path,
key)
for name, val in self.westH5['iterations/{:s}'.format(key1)].attrs.items():
self.output['iterations/{:s}'.format(key1)].attrs.create(name, val)
self.pi.progress += 1
self.pi.clear()
def get_new_weights(self, n_iter):
'''Generate and return a length-nbins numpy array representing a vector
of weights, where weights[i] represents the total weight that should be
in bin i, based on results from the postanalysis reweighting scheme.'''
# Build map between indexing of 'conditional_flux_evolution' or
# 'bin_prob_evolution' (the indexing is the same for both) and
# weighted ensemble iteration indices
if not self.weights_attributes_initialized:
cfe = self.rwH5['conditional_flux_evolution']
self.idx_map = np.empty(cfe.shape[0],
dtype=self.assignments['assignments'].dtype)
for i in xrange(cfe.shape[0]):
# Axes are (timepoint index, beginning state, ending state)
# and final index gets the "iter_stop" data
# stop_iter is exclusive, so subtract one
self.idx_map[i] = cfe[i,0,0][1]-1
self.weights_attributes_initialized = True
if (not self.evolution_mode) and (not self.weights_already_calculated):
idx = np.where(self.idx_map == self.n_iter)
self.new_weights = np.array(self.rwH5['bin_prob_evolution'])[idx]\
.squeeze()
self.weights_already_calculated = True
if self.i_use_color:
return self.new_weights
else:
# Convert colored to non-colored vector. self.nstates should
# have been set by self.go()
colored_new_weights = np.copy(self.new_weights)
self.new_weights = np.zeros(
int(self.new_weights.shape[0]/self.nstates)
)
for i in xrange(self.new_weights.shape[0]):
self.new_weights[i] = np.sum(
colored_new_weights[self.nstates*i:self.nstates*(i+1)]
)
return self.new_weights
elif (not self.evolution_mode) and self.weights_already_calculated:
return self.new_weights
else: # if self.evolution mode:
idx = np.where(self.idx_map == n_iter)
new_weights = np.array(self.rwH5['bin_prob_evolution'][idx])\
.squeeze()
if self.i_use_color:
return new_weights
else:
# Convert colored to non-colored vector. self.nstates should
# have been set by self.go()
colored_new_weights = new_weights
new_weights = np.zeros(new_weights.shape[0]/self.nstates)
for i in xrange(new_weights.shape[0]/self.nstates):
new_weights[i] = np.sum(
colored_new_weights[self.nstates*i:self.nstates*(i+1)]
)
return new_weights
def get_input_assignments(self, iiter):
'''Get the bin assignments for the first timepoint of iteration
``iiter``, for all walkers in the input assignments file. Return a
1-dimension numpy array ``assignments``, where ``assignments[i]`` gives
the bin index assigned to walker i. Bin indices take into account
whether or not the colored scheme is used.'''
# Get the total weight in each bin for the input assignments file
# Look only at the first time point! -----------------------------V
assignments = np.array(self.assignments['assignments'][iiter-1][:,0])
if self.i_use_color:
traj_labels = np.array(self.assignments['trajlabels'][iiter-1][:,0])
assignments = assignments*nstates+traj_labels
return assignments
def calculate_scaling_coefficients(self, iiter):
'''Calculate and return a vector of scaling coefficients for
the weighted ensemble iteration ``iiter``. scaling_coefficients[i]
gives the value by which to scale (multiply) the weight of any walker
in bin i.
This method first calls self.get_new_weights(iiter) to find what the
weights output by the postanalysis reweighting scheme are. Next, it
considers where time averaging is enabled, and whether or not the
colored scheme is used. Finally, it calculates the input assignments
if necessary.'''
if not self.i_time_average:
# Get length-n_bins vector, where new_weights[i] is the total
# weight in bin i according to the postanalysis reweighting
# scheme.
new_weights = self.get_new_weights(iiter)
input_assignments = self.get_input_assignments(iiter)
input_iter_group = self.westH5['iterations/iter_{:08d}'
.format(iiter)]
seg_weights = np.array(input_iter_group['seg_index']['weight'])
# Calculate the weight in each bin. the assignments should already
# have similar information in 'labeled_populations', but these weights
# invlude ALL time points in each iteration. In this tool, we must
# scale the weight of each segment uniformly across any given weighted
# ensemble iteration, as the weight is only specifed once per iteration.
# Somewhat arbitrarily, we scale the weights only according to the
# first timepoint only (we could also choose the another timepoint, or
# average them perhaps). For this reason, we need to re-calculate the
# labelled populations, only looking at the first time point.
# New weights will already be the correct length (ie, adjusted
# for color/no color).
input_weights = np.zeros(new_weights.shape)
# Calculate the weight in each bin, in the input WESTPA data
# file. This is necessary because w_assign does not calculate
# populations with color labels.
for i in xrange(input_weights.shape[0]):
input_weights[i] = np.sum(
seg_weights[np.where(input_assignments == i)]
)
# Suppress division errors
with np.errstate(all='ignore'):
scaling_coefficients = new_weights/input_weights
# Set nonsensical value to zero; is this really necessary?
#scaling_coefficients[~np.isfinite(scaling_coefficients)] = 0
else: # if self.i_time_average
if self.time_average_scaling_vector_calculated:
return self.time_average_scaling_vector
else: # if not self.time_average_scaling_vector_calculated:
# Calculate the scaling vector
# old_weights will contain the total weight observed in each
# bin during the FIRST timepoint of all iterations; this must
# be calculated here rather than using labeled_populations from
# the assignments file, as labeled_populations includes all
# timepoints
new_weights = self.get_new_weights(iiter)
old_weights = np.zeros(new_weights.shape)
if self.first_iter is None:
iter_strs = self.westH5['iterations/'].keys().sort()
self.first_iter = int(iter_strs[0][5:])
self.last_iter_iter = int(iter_strs[-1][5:])
# Iterate over all WE iterations and sum up the weight in each
# bin
for iiter in xrange(self.first_iter, self.last_iter+1):
weights = np.array(
self.westH5['iterations/iter_{:08d}/seg_index'
.format(iiter)]['weight']
)
assignments = self.get_input_assignments(iiter)
for bin_idx in xrange(old_weights.shape[0]):
where = np.where(assignments == bin_idx)
old_weights[bin_idx] += np.sum(weights[where])
with np.errstate(all='ignore'):
self.time_average_scaling_vector = new_weights/old_weights
self.time_average_scaling_vector[
~np.isfinite(self.time_average_scaling_vector)
] = 0.0
self.time_average_scaling_vector_calculated = True
return self.time_average_scaling_vector
def go(self):
'''
Main function. Calls:
- self.open_files()
- self.check_consistency_of_input_files()
- self.initialize_output()
and then iterates through all weighted ensemble iterations, rescaling
weights of segments and saving a new ``seg_index`` dataset in the output
file using the rescaled weights.
'''
pi = self.progress.indicator
with pi as self.pi:
# Open files
self.open_files()
# Check files for consistency
self.check_consistency_of_input_files()
# Initialize the output file.
self.initialize_output()
last_iter = self.assignments['assignments'].shape[0]
self.nstates = len(self.assignments['state_labels'])
# If weights are to be pulled from a single iteration, get the weights
pi.new_operation('Creating new WESTPA data file with scaled '
'weights.', last_iter)
for iiter in xrange(1, last_iter+1): # iiter is one-indexed
scaling_coefficients = self.calculate_scaling_coefficients(iiter)
input_iter_group = self.westH5['iterations/iter_{:08d}'
.format(iiter)]
input_assignments = self.get_input_assignments(iiter)
# Get the HDF5 group for this iteration. It was already created
# while initializing the output file.
output_iter_group = self.output['iterations/iter_{:08d}'
.format(iiter)]
input_seg_index = np.array(input_iter_group['seg_index'])
seg_weights = input_seg_index['weight']
# Build the new seg_index piece by piece. Start with an empty
# list and add data for one segment at a time. Then convert to
# a numpy array.
output_seg_index = []
for iseg in xrange(input_seg_index.shape[0]):
# Only look at the first time point for assignments!
bin_idx = input_assignments[iseg]
coeff = scaling_coefficients[bin_idx]
output_seg_index.append((seg_weights[iseg]*coeff,)
+ tuple(input_seg_index[iseg])[1:])
output_seg_index = np.array(output_seg_index,
dtype=input_seg_index.dtype)
# Save the newly created seg_index (with new weights)
output_iter_group.create_dataset('seg_index',
data=output_seg_index,
dtype=output_seg_index.dtype)
self.pi.progress += 1
class WAssign(WESTParallelTool):
prog='w_assign'
description = '''\
Assign walkers to bins, producing a file (by default named "assign.h5")
which can be used in subsequent analysis.
For consistency in subsequent analysis operations, the entire dataset
must be assigned, even if only a subset of the data will be used. This
ensures that analyses that rely on tracing trajectories always know the
originating bin of each trajectory.
-----------------------------------------------------------------------------
Source data
-----------------------------------------------------------------------------
Source data is provided either by a user-specified function
(--construct-dataset) or a list of "data set specifications" (--dsspecs).
If neither is provided, the progress coordinate dataset ''pcoord'' is used.
To use a custom function to extract or calculate data whose probability
distribution will be calculated, specify the function in standard Python
MODULE.FUNCTION syntax as the argument to --construct-dataset. This function
will be called as function(n_iter,iter_group), where n_iter is the iteration
whose data are being considered and iter_group is the corresponding group
in the main WEST HDF5 file (west.h5). The function must return data which can
be indexed as [segment][timepoint][dimension].
To use a list of data set specifications, specify --dsspecs and then list the
desired datasets one-by-one (space-separated in most shells). These data set
specifications are formatted as NAME[,file=FILENAME,slice=SLICE], which will
use the dataset called NAME in the HDF5 file FILENAME (defaulting to the main
WEST HDF5 file west.h5), and slice it with the Python slice expression SLICE
(as in [0:2] to select the first two elements of the first axis of the
dataset). The ``slice`` option is most useful for selecting one column (or
more) from a multi-column dataset, such as arises when using a progress
coordinate of multiple dimensions.
-----------------------------------------------------------------------------
Specifying macrostates
-----------------------------------------------------------------------------
Optionally, kinetic macrostates may be defined in terms of sets of bins.
Each trajectory will be labeled with the kinetic macrostate it was most
recently in at each timepoint, for use in subsequent kinetic analysis.
This is required for all kinetics analysis (w_kintrace and w_kinmat).
There are three ways to specify macrostates:
1. States corresponding to single bins may be identified on the command
line using the --states option, which takes multiple arguments, one for
each state (separated by spaces in most shells). Each state is specified
as a coordinate tuple, with an optional label prepended, as in
``bound:1.0`` or ``unbound:(2.5,2.5)``. Unlabeled states are named
``stateN``, where N is the (zero-based) position in the list of states
supplied to --states.
2. States corresponding to multiple bins may use a YAML input file specified
with --states-from-file. This file defines a list of states, each with a
name and a list of coordinate tuples; bins containing these coordinates
will be mapped to the containing state. For instance, the following
file::
---
states:
- label: unbound
coords:
- [9.0, 1.0]
- [9.0, 2.0]
- label: bound
coords:
- [0.1, 0.0]
produces two macrostates: the first state is called "unbound" and
consists of bins containing the (2-dimensional) progress coordinate
values (9.0, 1.0) and (9.0, 2.0); the second state is called "bound"
and consists of the single bin containing the point (0.1, 0.0).
3. Arbitrary state definitions may be supplied by a user-defined function,
specified as --states-from-function=MODULE.FUNCTION. This function is
called with the bin mapper as an argument (``function(mapper)``) and must
return a list of dictionaries, one per state. Each dictionary must contain
a vector of coordinate tuples with key "coords"; the bins into which each
of these tuples falls define the state. An optional name for the state
(with key "label") may also be provided.
-----------------------------------------------------------------------------
Output format
-----------------------------------------------------------------------------
The output file (-o/--output, by default "assign.h5") contains the following
attributes datasets:
``nbins`` attribute
*(Integer)* Number of valid bins. Bin assignments range from 0 to
*nbins*-1, inclusive.
``nstates`` attribute
*(Integer)* Number of valid macrostates (may be zero if no such states are
specified). Trajectory ensemble assignments range from 0 to *nstates*-1,
inclusive, when states are defined.
``/assignments`` [iteration][segment][timepoint]
*(Integer)* Per-segment and -timepoint assignments (bin indices).
``/npts`` [iteration]
*(Integer)* Number of timepoints in each iteration.
``/nsegs`` [iteration]
*(Integer)* Number of segments in each iteration.
``/labeled_populations`` [iterations][state][bin]
*(Floating-point)* Per-iteration and -timepoint bin populations, labeled
by most recently visited macrostate. The last state entry (*nstates-1*)
corresponds to trajectories initiated outside of a defined macrostate.
``/bin_labels`` [bin]
*(String)* Text labels of bins.
When macrostate assignments are given, the following additional datasets are
present:
``/trajlabels`` [iteration][segment][timepoint]
*(Integer)* Per-segment and -timepoint trajectory labels, indicating the
macrostate which each trajectory last visited.
``/state_labels`` [state]
*(String)* Labels of states.
``/state_map`` [bin]
*(Integer)* Mapping of bin index to the macrostate containing that bin.
An entry will contain *nbins+1* if that bin does not fall into a
macrostate.
Datasets indexed by state and bin contain one more entry than the number of
valid states or bins. For *N* bins, axes indexed by bin are of size *N+1*, and
entry *N* (0-based indexing) corresponds to a walker outside of the defined bin
space (which will cause most mappers to raise an error). More importantly, for
*M* states (including the case *M=0* where no states are specified), axes
indexed by state are of size *M+1* and entry *M* refers to trajectories
initiated in a region not corresponding to a defined macrostate.
Thus, ``labeled_populations[:,:,:].sum(axis=1)[:,:-1]`` gives overall per-bin
populations, for all defined bins and
``labeled_populations[:,:,:].sum(axis=2)[:,:-1]`` gives overall
per-trajectory-ensemble populations for all defined states.
-----------------------------------------------------------------------------
Parallelization
-----------------------------------------------------------------------------
This tool supports parallelized binning, including reading/calculating input
data.
-----------------------------------------------------------------------------
Command-line options
-----------------------------------------------------------------------------
'''
def __init__(self):
super(WAssign,self).__init__()
# Parallel processing by default (this is not actually necessary, but it is
# informative!)
self.wm_env.default_work_manager = self.wm_env.default_parallel_work_manager
self.data_reader = WESTDataReader()
self.dssynth = WESTDSSynthesizer(default_dsname='pcoord')
self.binning = BinMappingComponent()
self.progress = ProgressIndicatorComponent()
self.output_file = None
self.output_filename = None
self.states = []
def add_args(self, parser):
self.data_reader.add_args(parser)
self.binning.add_args(parser, suppress=['--bins-from-h5file'])
self.dssynth.add_args(parser)
sgroup = parser.add_argument_group('macrostate definitions').add_mutually_exclusive_group()
sgroup.add_argument('--states', nargs='+', metavar='STATEDEF',
help='''Single-bin kinetic macrostate, specified by a coordinate tuple (e.g. '1.0' or '[1.0,1.0]'),
optionally labeled (e.g. 'bound:[1.0,1.0]'). States corresponding to multiple bins
must be specified with --states-from-file.''')
sgroup.add_argument('--states-from-file', metavar='STATEFILE',
help='''Load kinetic macrostates from the YAML file STATEFILE. See description
above for the appropriate structure.''')
sgroup.add_argument('--states-from-function', metavar='STATEFUNC',
help='''Load kinetic macrostates from the function STATEFUNC, specified as
module_name.func_name. This function is called with the bin mapper as an argument,
and must return a list of dictionaries {'label': state_label, 'coords': 2d_array_like}
one for each macrostate; the 'coords' entry must contain enough rows to identify all bins
in the macrostate.''')
agroup = parser.add_argument_group('other options')
agroup.add_argument('-o', '--output', dest='output', default='assign.h5',
help='''Store results in OUTPUT (default: %(default)s).''')
def process_args(self, args):
self.progress.process_args(args)
self.data_reader.process_args(args)
with self.data_reader:
self.dssynth.h5filename = self.data_reader.we_h5filename
self.dssynth.process_args(args)
self.binning.process_args(args)
if args.states:
self.parse_cmdline_states(args.states)
elif args.states_from_file:
self.load_state_file(args.states_from_file)
elif args.states_from_function:
self.load_states_from_function(get_object(args.states_from_function,path=['.']))
if self.states and len(self.states) < 2:
raise ValueError('zero, two, or more macrostates are required')
#self.output_file = WESTPAH5File(args.output, 'w', creating_program=True)
self.output_filename = args.output
log.debug('state list: {!r}'.format(self.states))
def parse_cmdline_states(self, state_strings):
states = []
for istring, state_string in enumerate(state_strings):
try:
(label, coord_str) = state_string.split(':')
except ValueError:
label = 'state{}'.format(istring)
coord_str = state_string
coord = parse_pcoord_value(coord_str)
states.append({'label': label, 'coords': coord})
self.states = states
def load_state_file(self, state_filename):
import yaml
ydict = yaml.load(open(state_filename, 'rt'))
ystates = ydict['states']
states = []
for istate, ystate in enumerate(ystates):
state = {}
state['label'] = ystate.get('label', 'state{}'.format(istate))
# coords can be:
# - a scalar, in which case it is one bin, 1-D
# - a single list, which is rejected as ambiguous
# - a list of lists, which is a list of coordinate tuples
coords = numpy.array(ystate['coords'])
if coords.ndim == 0:
coords.shape = (1,1)
elif coords.ndim == 1:
raise ValueError('list {!r} is ambiguous (list of 1-d coordinates, or single multi-d coordinate?)'
.format(ystate['coords']))
elif coords.ndim > 2:
raise ValueError('coordinates must be 2-D')
state['coords'] = coords
states.append(state)
self.states = states
def load_states_from_function(self, statefunc):
states = statefunc(self.binning.mapper)
for istate, state in enumerate(states):
state.setdefault('label','state{}'.format(istate))
try:
state['coords'] = numpy.array(state['coords'])
except KeyError:
raise ValueError('state function {!r} returned a state {!r} without coordinates'.format(statefunc,state))
self.states = states
log.debug('loaded states: {!r}'.format(self.states))
def assign_iteration(self, n_iter, nstates, nbins, state_map, last_labels):
''' Method to encapsulate the segment slicing (into n_worker slices) and parallel job submission
Submits job(s), waits on completion, splices them back together
Returns: assignments, trajlabels, pops for this iteration'''
futures = []
iter_group = self.data_reader.get_iter_group(n_iter)
nsegs, npts = iter_group['pcoord'].shape[:2]
n_workers = self.work_manager.n_workers or 1
assignments = numpy.empty((nsegs, npts), dtype=index_dtype)
trajlabels = numpy.empty((nsegs, npts), dtype=index_dtype)
pops = numpy.zeros((nstates+1,nbins+1), dtype=weight_dtype)
#Submit jobs to work manager
blocksize = nsegs // n_workers
if nsegs % n_workers > 0:
blocksize += 1
def task_gen():
if __debug__:
checkset = set()
for lb in xrange(0, nsegs, blocksize):
ub = min(nsegs, lb+blocksize)
if __debug__:
checkset.update(set(xrange(lb,ub)))
args = ()
kwargs = dict(n_iter=n_iter,
lb=lb, ub=ub, mapper=self.binning.mapper, nstates=nstates, state_map=state_map,
last_labels=last_labels,
parent_id_dsspec=self.data_reader.parent_id_dsspec,
weight_dsspec=self.data_reader.weight_dsspec,
pcoord_dsspec=self.dssynth.dsspec)
yield (_assign_label_pop, args, kwargs)
#futures.append(self.work_manager.submit(_assign_label_pop,
#kwargs=)
if __debug__:
assert checkset == set(xrange(nsegs)), 'segments missing: {}'.format(set(xrange(nsegs)) - checkset)
#for future in self.work_manager.as_completed(futures):
for future in self.work_manager.submit_as_completed(task_gen(), queue_size=self.max_queue_len):
assign_slice, traj_slice, slice_pops, lb, ub = future.get_result(discard=True)
assignments[lb:ub, :] = assign_slice
trajlabels[lb:ub, :] = traj_slice
pops += slice_pops
del assign_slice, traj_slice, slice_pops
del futures
return (assignments, trajlabels, pops)
def go(self):
assert self.data_reader.parent_id_dsspec._h5file is None
assert self.data_reader.weight_dsspec._h5file is None
if hasattr(self.dssynth.dsspec, '_h5file'):
assert self.dssynth.dsspec._h5file is None
pi = self.progress.indicator
pi.operation = 'Initializing'
with pi, self.data_reader, WESTPAH5File(self.output_filename, 'w', creating_program=True) as self.output_file:
assign = self.binning.mapper.assign
# We always assign the entire simulation, so that no trajectory appears to start
# in a transition region that doesn't get initialized in one.
iter_start = 1
iter_stop = self.data_reader.current_iteration
h5io.stamp_iter_range(self.output_file, iter_start, iter_stop)
nbins = self.binning.mapper.nbins
self.output_file.attrs['nbins'] = nbins
state_map = numpy.empty((self.binning.mapper.nbins+1,), index_dtype)
state_map[:] = 0 # state_id == nstates => unknown state
# Recursive mappers produce a generator rather than a list of labels
# so consume the entire generator into a list
labels = [label for label in self.binning.mapper.labels]
self.output_file.create_dataset('bin_labels', data=labels, compression=9)
if self.states:
nstates = len(self.states)
state_map[:] = nstates # state_id == nstates => unknown state
state_labels = [state['label'] for state in self.states]
for istate, sdict in enumerate(self.states):
assert state_labels[istate] == sdict['label'] #sanity check
state_assignments = assign(sdict['coords'])
for assignment in state_assignments:
state_map[assignment] = istate
self.output_file.create_dataset('state_map', data=state_map, compression=9, shuffle=True)
self.output_file['state_labels'] = state_labels #+ ['(unknown)']
else:
nstates = 0
self.output_file.attrs['nstates'] = nstates
iter_count = iter_stop - iter_start
nsegs = numpy.empty((iter_count,), seg_id_dtype)
npts = numpy.empty((iter_count,), seg_id_dtype)
# scan for largest number of segments and largest number of points
pi.new_operation ('Scanning for segment and point counts', iter_stop-iter_start)
for iiter, n_iter in enumerate(xrange(iter_start,iter_stop)):
iter_group = self.data_reader.get_iter_group(n_iter)
nsegs[iiter], npts[iiter] = iter_group['pcoord'].shape[0:2]
pi.progress += 1
del iter_group
pi.new_operation('Preparing output')
# create datasets
self.output_file.create_dataset('nsegs', data=nsegs, shuffle=True, compression=9)
self.output_file.create_dataset('npts', data=npts, shuffle=True, compression=9)
max_nsegs = nsegs.max()
max_npts = npts.max()
assignments_shape = (iter_count,max_nsegs,max_npts)
assignments_dtype = numpy.min_scalar_type(nbins)
assignments_ds = self.output_file.create_dataset('assignments', dtype=assignments_dtype, shape=assignments_shape,
compression=4, shuffle=True,
chunks=h5io.calc_chunksize(assignments_shape, assignments_dtype),
fillvalue=nbins)
if self.states:
trajlabel_dtype = numpy.min_scalar_type(nstates)
trajlabels_ds = self.output_file.create_dataset('trajlabels', dtype=trajlabel_dtype, shape=assignments_shape,
compression=4, shuffle=True,
chunks=h5io.calc_chunksize(assignments_shape, trajlabel_dtype),
fillvalue=nstates)
pops_shape = (iter_count,nstates+1,nbins+1)
pops_ds = self.output_file.create_dataset('labeled_populations', dtype=weight_dtype, shape=pops_shape,
compression=4, shuffle=True,
chunks=h5io.calc_chunksize(pops_shape, weight_dtype))
h5io.label_axes(pops_ds, ['iteration', 'state', 'bin'])
pi.new_operation('Assigning to bins', iter_stop-iter_start)
last_labels = None # mapping of seg_id to last macrostate inhabited
for iiter, n_iter in enumerate(xrange(iter_start,iter_stop)):
#get iteration info in this block
if iiter == 0:
last_labels = numpy.empty((nsegs[iiter],), index_dtype)
last_labels[:] = nstates #unknown state
#Slices this iteration into n_workers groups of segments, submits them to wm, splices results back together
assignments, trajlabels, pops = self.assign_iteration(n_iter, nstates, nbins, state_map, last_labels)
##Do stuff with this iteration's results
last_labels = trajlabels[:,-1].copy()
assignments_ds[iiter, 0:nsegs[iiter], 0:npts[iiter]] = assignments
pops_ds[iiter] = pops
if self.states:
trajlabels_ds[iiter, 0:nsegs[iiter], 0:npts[iiter]] = trajlabels
pi.progress += 1
del assignments, trajlabels, pops
for dsname in 'assignments', 'npts', 'nsegs', 'labeled_populations':
h5io.stamp_iter_range(self.output_file[dsname], iter_start, iter_stop)
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 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 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 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)
