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 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 WEDDist(WESTParallelTool):
prog='w_eddist'
description = '''\
Calculate time-resolved transition-event duration distribution from kinetics results
-----------------------------------------------------------------------------
Source data
-----------------------------------------------------------------------------
Source data is collected from the results of 'w_kinetics trace' (see w_kinetics trace --help for
more information on generating this dataset).
-----------------------------------------------------------------------------
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(WEDDist,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.default_kinetics_file = 'kintrace.h5'
self.kinetics_filename = None
self.kinetics_file = None #Kinavg file
self.istate = None
self.fstate = None
##Duration and weight dsspecs
self.duration_dsspec = None
self.wt_dsspec = None
self.binspec = None
self.output_filename = None
self.output_file = None
# 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):
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('-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.)''')
parser.add_argument('--istate', type=int, required=True, dest='istate',
help='''Initial state defining transition event''')
parser.add_argument('--fstate', type=int, required=True, dest='fstate',
help='''Final state defining transition event''')
itergroup = parser.add_argument_group('iteration range options')
itergroup.add_argument('--first-iter', default=1, dest='iter_start', type=int,
help='''Iteration to begin analysis (default: 1)''')
itergroup.add_argument('--last-iter', dest='iter_stop', type=int,
help='''Iteration to end analysis''')
iogroup = parser.add_argument_group('input/output options')
# 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 (including evolution) are stored in KINETICS
(default: %(default)s).''')
iogroup.add_argument('-o', '--output', dest='output', default='eddist.h5',
help='''Store results in OUTPUT (default: %(default)s).''')
self.progress.add_args(parser)
def process_args(self, args):
self.progress.process_args(args)
self.kinetics_filename = args.kinetics
self.istate = args.istate
self.fstate = args.fstate
self.kinetics_file = h5io.WESTPAH5File(self.kinetics_filename, 'r')
self.iter_start = args.iter_start
if args.iter_stop is None:
self.iter_stop = self.kinetics_file.attrs['iter_stop']
else:
self.iter_stop = args.iter_stop + 1
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):
pi = self.progress.indicator
pi.operation = 'Initializing'
with pi:
self.duration = self.kinetics_file['durations'][self.iter_start-1:self.iter_stop-1]
##Only select transition events from specified istate to fstate
mask = (self.duration['istate'] == self.istate) & (self.duration['fstate'] == self.fstate)
self.duration_dsspec = DurationDataset(self.kinetics_file['durations']['duration'], mask, self.iter_start)
self.wt_dsspec = DurationDataset(self.kinetics_file['durations']['weight'], mask, self.iter_start)
self.output_file = h5py.File(self.output_filename, 'w')
h5io.stamp_creator_data(self.output_file)
# 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 = numpy.arange(self.iter_start, self.iter_stop, 1, dtype=(numpy.min_scalar_type(self.iter_stop)))
self.output_file['n_iter'] = iter_range
self.output_file['histograms'].attrs['iter_start'] = self.iter_start
self.output_file['histograms'].attrs['iter_stop'] = self.iter_stop
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.duration_dsspec
#self.ntimepoints = dset.shape[1]
#self.ndim = dset.shape[2]
self.ndim = 1
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.duration_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()
#print(self.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
lb -= 0.01
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.duration_dsspec, self.wt_dsspec, 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 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 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 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 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 CommonPloterrs(WESTSubcommand):
def __init__(self, parent):
super(CommonPloterrs,self).__init__(parent)
self.progress = ProgressIndicatorComponent()
self.xscale = None
self.yscale = None
self.xrange = None
self.yrange = None
self.xlabel = None
self.ylabel = None
self.title = None
self.plot_options_group = None
def add_args(self, parser):
self.progress.add_args(parser)
pogroup = self.plot_options_group = parser.add_argument_group('plot options')
pogroup.add_argument('--xscale', choices=['linear', 'log', 'symlog'], default='linear',
help='''Use "linear", "log", or "symlog" scaling for the x axis.
(Default: %(default)s).''')
pogroup.add_argument('--yscale', choices=['linear', 'log', 'symlog'], default='linear',
help='''Use "linear", "log", or "symlog" scaling for the y axis.
(Default: %(default)s).''')
pogroup.add_argument('--xrange',
help='''Restrict X range to XRANGE, which must be formatted as "xmin,xmax".
(Default: determined by input data.)''')
pogroup.add_argument('--yrange',
help='''Restrict Y range to YRANGE, which must be formatted as "ymin,ymax".
(Default: determined by input data.)''')
pogroup.add_argument('--xlabel',
help='''Use XLABEL for the x-axis label. (Default: varies.)''')
pogroup.add_argument('--ylabel',
help='''Use YLABEL for the y-axis label. (Default: varies.)''')
pogroup.add_argument('--title',
help='''Use TITLE for the plot title. (Default: varies.)''')
pogroup.add_argument('--terminal', '-t', dest='plotting', action='store_true',
help='''Plot output in terminal.''')
def process_args(self, args):
self.progress.process_args(args)
if args.xrange:
self.xrange = self.parse_range(args.xrange)
if args.yrange:
self.yrange = self.parse_range(args.yrange)
self.xscale = args.xscale
self.yscale = args.yscale
self.xlabel = args.xlabel or 'Iteration'
self.ylabel = args.ylabel
self.title = args.title
if args.plotting or os.environ.get('DISPLAY') is None:
self.interface = 'text'
else:
import matplotlib
from matplotlib import pyplot
self.interface = 'matplotlib'
def parse_range(self, rangespec):
try:
(lbt,ubt) = rangespec.split(',')
return float(lbt), float(ubt)
except (ValueError,TypeError) as e:
raise ValueError('invalid range specification {!r}: {!s}'.format(rangespec, e))
def do_plot(self, data, output_filename, title=None, x_range=None, y_range=None, x_label=None, y_label=None):
if not output_filename:
return
title = title or self.title
x_range = x_range or self.xrange
y_range = y_range or self.yrange
x_label = x_label or self.xlabel
y_label = y_label or self.ylabel
iters = data['iter_stop'] - 1
pyplot.figure()
pyplot.plot(iters, data['expected'], color='black')
pyplot.plot(iters, data['ci_lbound'], color='gray')
pyplot.plot(iters, data['ci_ubound'], color='gray')
pyplot.gca().set_xscale(self.xscale)
pyplot.gca().set_yscale(self.yscale)
if title:
pyplot.title(title)
if x_range is not None:
pyplot.xlim(x_range)
if y_range is not None:
pyplot.ylim(y_range)
if x_label:
pyplot.xlabel(x_label)
if y_label:
pyplot.ylabel(y_label)
pyplot.savefig(output_filename)
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 WMultiWest(WESTMultiTool):
prog = 'w_multi_west'
description = '''\
Tool designed to combine multiple WESTPA simulations while accounting for
reweighting. Test code thus far.
-----------------------------------------------------------------------------
Command-line options
-----------------------------------------------------------------------------
'''
def __init__(self):
super(WESTTool, self).__init__()
self.progress = ProgressIndicatorComponent()
# We no longer care about a lot of this.
self.ntrials = 0
self.nstates = 0
self.kin_trial = {}
self.west = {}
self.niters = 0
def add_args(self, parser):
self.progress.add_args(parser)
iogroup = parser.add_argument_group('input/output options')
iogroup.add_argument(
'--output-file',
default='multi.h5',
help='''The name of the output file to store results in.''')
iogroup.add_argument(
'-w',
'--west',
'--WEST_H5FILE',
default='west.h5',
help='''The name of the main .h5 file inside each simulation
directory''')
iogroup.add_argument(
'-a',
'--aux',
action='append',
help='''Names of additional auxiliary datasets to be combined''')
iogroup.add_argument(
'-aa',
'--auxall',
action='store_true',
help='''Combine all auxiliary datasets. Default: False''')
iogroup.add_argument('-nr',
'--no-reweight',
action='store_true',
help='''Do not reweight. Default: False''')
def open_files(self):
self.output_file = h5io.WESTPAH5File(self.output_file,
'w',
creating_program=True)
h5io.stamp_creator_data(self.output_file)
opened_files = self.generate_file_list([self.west])
self.westH5 = opened_files[self.west]
# Just some temp things while I clean everything up...
# west_files = self.westH5
# Determine max iteration ...
# We can't really use the old method anymore, as we need to calculate rates in the bootstrap.
# Ergo, we're going to load things like w_kinavg, but that's all.
# We'll just load them up and store them internally, for the moment.
def process_args(self, args):
self.progress.process_args(args)
self.output_file = args.output_file
self.output_file_name = args.output_file
self.west = args.west
self.sims = args.sims
self.aux = args.aux
self.auxall = args.auxall
self.reweight = args.no_reweight
def total_number_of_walkers(self):
self.total_walkers = [0] * self.niters
for key, west in self.westH5.items():
# Sometimes, we're smaller or larger by one. Hm.
try:
self.total_walkers[:] += west['summary'][:-1]['n_particles']
except (ValueError):
self.total_walkers[:] += west['summary'][:-1][
'n_particles'][:len(self.total_walkers)]
class Segment():
def __init__(self, weight=0, iteration=0, simid=0, recycled_in=0):
self.weight = weight
self.iteration = iteration
self.simid = simid
self.recycled_in = recycled_in
def go(self):
pi = self.progress.indicator
with pi:
pi.new_operation('Initializing')
self.open_files()
self.total_number_of_walkers()
if self.auxall is True:
self.aux = list(
self.westH5[1]['iterations/iter_00000001/auxdata'].keys())
# Create a giant WEST.h5 file, separating the individual walkers, and renormalizing the weights.
# It should then be compatible with existing toolsets.
# Isn't really going to start with auxdata, but we'll add it in.
#self.niters = 500
# Initialize data manager...
# Just bullshit for the current system.
#self.niters = self.westH5[1].attrs['west_current_iteration'] - 1
#print(self.niters, len(self.westH5))
#self.data_manager = data_manager.WESTDataManager()
westh5 = []
self.source_sinks = []
self.n_sims = {}
for ifile, (key, west) in enumerate(self.westH5.items()):
d = {
'west': west,
'wm': None,
'rt': None,
'remove_next_cycle': [],
'seg_index': None
}
# We're getting the bin mapper, then setting the recycling target...
binhash = west['iterations/iter_{0:08d}'.format(
2)].attrs['binhash']
bin_mapper = get_bin_mapper(west, bytes(binhash, 'utf-8'))
try:
d['rt'] = bin_mapper.assign(
west['tstates']['0']['pcoord'][...])[0]
self.source_sinks.append(
bin_mapper.assign(
west['tstates']['0']['pcoord'][...])[0])
except KeyError:
d['rt'] = None
self.source_sinks.append(None)
pass
# We're going to make a set of source and sink states that we can iterate through, eventually.
# Keep a count of how many simulations for this particular recycling target we have...
try:
self.n_sims[d['rt']] += 1
except KeyError:
self.n_sims[d['rt']] = 1
westh5.append(d)
if ifile == 0:
self.niters = west.attrs['west_current_iteration'] - 1
else:
self.niters = min(west.attrs['west_current_iteration'] - 1,
self.niters)
start_point = []
self.source_sinks = list(set(self.source_sinks))
# We'll need a global list of walkers to add to and take care of during the next round of simulations, as well as the current one.
# We'll organize it by source and sink states.
self.past_iter = {}
self.futr_iter = {}
self.past_rm = {}
self.futr_rm = {}
for i in self.source_sinks:
self.past_iter[i] = []
self.futr_iter[i] = []
self.past_rm[i] = []
self.futr_rm[i] = []
print(pi.new_operation('Recreating...', self.niters))
#tracker = SummaryTracker()
#self.output_file.close()
for iter in range(self.niters):
# We have the following datasets in each iteration:
# ibstates, which aren't important.
# pcoord
# seg_index
# wtgraph
# wtgraph is going to be a little more complex to handle, but not too bad.
iter += 1
ifile = 0
#self.output_file = h5io.WESTPAH5File(self.output_file_name, 'w', creating_program=True)
# Determine how many simulations to append or remove per west file.
#self.segments = {}
#for key,value in self.n_sims.items():
# self.segments[key] = int(np.floor(len(self.past_iter[key]) / value))
run_once = 0
#total_current_sims = 0
#for i in self.source_sinks:
# total_current_sims += len(self.past_iter[i])
# total_current_sims += len(self.past_rm[i])
for ifile, west in enumerate(westh5):
westdict = west['west']
seg_index = westdict['iterations/iter_{0:08d}'.format(
iter)]['seg_index'][...]
pcoord = westdict['iterations/iter_{0:08d}'.format(
iter)]['pcoord'][...]
wtgraph = westdict['iterations/iter_{0:08d}'.format(
iter)]['wtgraph'][...]
if self.aux:
auxdata = {}
for i in self.aux:
auxdata[str(i)] = westdict[
'iterations/iter_{0:08d}'.format(
iter)]['auxdata'][str(i)][...]
if iter == 1 and ifile == 0:
new_dtype = np.dtype(seg_index.dtype.descr +
[('group', '<i8')])
new_seg_index = np.zeros(seg_index.shape, dtype=new_dtype)
for dt, val in seg_index.dtype.fields.items():
new_seg_index[dt] = seg_index[dt]
new_seg_index['group'] = ifile
del seg_index
seg_index = new_seg_index[...]
del new_seg_index
if ifile == 0:
mseg = seg_index
mpco = pcoord
mwtg = wtgraph
if self.aux:
maux = {}
for i in self.aux:
maux[str(i)] = auxdata[str(i)]
if iter == 1:
summary = westdict['summary'][...]
start_point.append(0)
if ifile != 0:
#print(mseg.shape, seg_index.shape, ifile)
#print(mpco.shape, pcoord.shape, ifile)
#print(mwtg.shape, wtgraph.shape, ifile)
if iter != 1:
addition = prev_start_point[ifile]
else:
addition = mseg.shape[0]
seg_index['parent_id'][np.where(
seg_index['parent_id'] >= 0)] += addition
seg_index['parent_id'][np.where(
seg_index['parent_id'] < 0)] -= addition
seg_index['wtg_offset'] += mwtg.shape[0]
start_point.append(mseg.shape[0])
wtgraph += mwtg.shape[0]
mseg = np.concatenate((mseg, seg_index))
mpco = np.concatenate((mpco, pcoord))
mwtg = np.concatenate((mwtg, wtgraph))
if self.aux:
for i in self.aux:
maux[str(i)] = np.concatenate(
(maux[str(i)], auxdata[str(i)]))
ifile += 1
del seg_index, pcoord, wtgraph, westdict
if self.aux:
del auxdata
gc.collect()
# Make a real copy to use in the next iteration.
#self.past_iter = self.futr_iter.copy()
#self.past_rm[i] = self.futr_rm.copy()
prev_start_point = start_point
start_point = []
# This is... maybe wrong, actually? Or at least, it's not ALL that is required for normalizing things.
# We need to weight everything by 1/N, then just normalize if that normalization was wrong. Keep the relative weights sane.
# ... or actually, no, that's fine, nevermind, what's wrong with me? But we'll leave it in for now.
if not self.reweight:
mseg['weight'] /= mseg['weight'].sum()
summary['n_particles'][iter - 1] = mseg.shape[0]
summary['norm'][iter - 1] = mseg['weight'].sum()
summary['min_seg_prob'][iter - 1] = min(mseg['weight'])
summary['max_seg_prob'][iter - 1] = max(mseg['weight'])
curr_iter = self.output_file.create_group(
'iterations/iter_{0:08d}'.format(iter))
curr_iter.attrs['n_iter'] = iter
ds_rate_evol = curr_iter.create_dataset('wtgraph',
data=mwtg,
shuffle=True,
compression=9)
ds_rate_evol = curr_iter.create_dataset('seg_index',
data=mseg,
shuffle=True,
compression=9)
ds_rate_evol = curr_iter.create_dataset('pcoord',
data=mpco,
shuffle=True,
compression=9)
if self.aux:
aux_iter = self.output_file.create_group(
'iterations/iter_{0:08d}/auxdata'.format(iter))
for i in self.aux:
ds_rate_evol = aux_iter.create_dataset(
str(i),
data=maux[str(i)],
shuffle=True,
compression=9)
# We need to be careful about memory, here. We are blowing uppppp.
# We're STILL blowing up. Criiiiiipes.
#self.segments = {}
del mseg, mpco, mwtg, ds_rate_evol, curr_iter #, self.segments
if self.aux:
del maux, aux_iter
gc.collect()
self.output_file.flush()
#self.output_file.close()
#print("How big is our summary?")
#print(sys.getsizeof(summary))
#objgraph.show_most_common_types(limit=50)
#objgraph.show_growth(limit=10)
#objgraph.show_most_common_types(objects=objgraph.get_leaking_objects())
pi.progress += 1
pi.new_operation('Writing to file...')
ds_rate_evol = self.output_file.create_dataset('summary',
data=summary,
shuffle=True,
compression=9)
self.output_file.attrs['west_current_iteration'] = self.niters
self.output_file.attrs['west_file_format_version'] = 7
self.output_file.attrs['west_iter_prec'] = 8
self.output_file.attrs['westpa_fileformat_version'] = 7
self.output_file.attrs['westpa_iter_prec'] = 8
class WAssign(WESTParallelTool):
prog = 'w_assign'
description = '''\
Assign walkers to bins, producing a file (by default named "assign.h5")
which can be used in subsequent analysis.
For consistency in subsequent analysis operations, the entire dataset
must be assigned, even if only a subset of the data will be used. This
ensures that analyses that rely on tracing trajectories always know the
originating bin of each trajectory.
-----------------------------------------------------------------------------
Source data
-----------------------------------------------------------------------------
Source data is provided either by a user-specified function
(--construct-dataset) or a list of "data set specifications" (--dsspecs).
If neither is provided, the progress coordinate dataset ''pcoord'' is used.
To use a custom function to extract or calculate data whose probability
distribution will be calculated, specify the function in standard Python
MODULE.FUNCTION syntax as the argument to --construct-dataset. This function
will be called as function(n_iter,iter_group), where n_iter is the iteration
whose data are being considered and iter_group is the corresponding group
in the main WEST HDF5 file (west.h5). The function must return data which can
be indexed as [segment][timepoint][dimension].
To use a list of data set specifications, specify --dsspecs and then list the
desired datasets one-by-one (space-separated in most shells). These data set
specifications are formatted as NAME[,file=FILENAME,slice=SLICE], which will
use the dataset called NAME in the HDF5 file FILENAME (defaulting to the main
WEST HDF5 file west.h5), and slice it with the Python slice expression SLICE
(as in [0:2] to select the first two elements of the first axis of the
dataset). The ``slice`` option is most useful for selecting one column (or
more) from a multi-column dataset, such as arises when using a progress
coordinate of multiple dimensions.
-----------------------------------------------------------------------------
Specifying macrostates
-----------------------------------------------------------------------------
Optionally, kinetic macrostates may be defined in terms of sets of bins.
Each trajectory will be labeled with the kinetic macrostate it was most
recently in at each timepoint, for use in subsequent kinetic analysis.
This is required for all kinetics analysis (w_kintrace and w_kinmat).
There are three ways to specify macrostates:
1. States corresponding to single bins may be identified on the command
line using the --states option, which takes multiple arguments, one for
each state (separated by spaces in most shells). Each state is specified
as a coordinate tuple, with an optional label prepended, as in
``bound:1.0`` or ``unbound:(2.5,2.5)``. Unlabeled states are named
``stateN``, where N is the (zero-based) position in the list of states
supplied to --states.
2. States corresponding to multiple bins may use a YAML input file specified
with --states-from-file. This file defines a list of states, each with a
name and a list of coordinate tuples; bins containing these coordinates
will be mapped to the containing state. For instance, the following
file::
---
states:
- label: unbound
coords:
- [9.0, 1.0]
- [9.0, 2.0]
- label: bound
coords:
- [0.1, 0.0]
produces two macrostates: the first state is called "unbound" and
consists of bins containing the (2-dimensional) progress coordinate
values (9.0, 1.0) and (9.0, 2.0); the second state is called "bound"
and consists of the single bin containing the point (0.1, 0.0).
3. Arbitrary state definitions may be supplied by a user-defined function,
specified as --states-from-function=MODULE.FUNCTION. This function is
called with the bin mapper as an argument (``function(mapper)``) and must
return a list of dictionaries, one per state. Each dictionary must contain
a vector of coordinate tuples with key "coords"; the bins into which each
of these tuples falls define the state. An optional name for the state
(with key "label") may also be provided.
-----------------------------------------------------------------------------
Output format
-----------------------------------------------------------------------------
The output file (-o/--output, by default "assign.h5") contains the following
attributes datasets:
``nbins`` attribute
*(Integer)* Number of valid bins. Bin assignments range from 0 to
*nbins*-1, inclusive.
``nstates`` attribute
*(Integer)* Number of valid macrostates (may be zero if no such states are
specified). Trajectory ensemble assignments range from 0 to *nstates*-1,
inclusive, when states are defined.
``/assignments`` [iteration][segment][timepoint]
*(Integer)* Per-segment and -timepoint assignments (bin indices).
``/npts`` [iteration]
*(Integer)* Number of timepoints in each iteration.
``/nsegs`` [iteration]
*(Integer)* Number of segments in each iteration.
``/labeled_populations`` [iterations][state][bin]
*(Floating-point)* Per-iteration and -timepoint bin populations, labeled
by most recently visited macrostate. The last state entry (*nstates-1*)
corresponds to trajectories initiated outside of a defined macrostate.
``/bin_labels`` [bin]
*(String)* Text labels of bins.
When macrostate assignments are given, the following additional datasets are
present:
``/trajlabels`` [iteration][segment][timepoint]
*(Integer)* Per-segment and -timepoint trajectory labels, indicating the
macrostate which each trajectory last visited.
``/state_labels`` [state]
*(String)* Labels of states.
``/state_map`` [bin]
*(Integer)* Mapping of bin index to the macrostate containing that bin.
An entry will contain *nbins+1* if that bin does not fall into a
macrostate.
Datasets indexed by state and bin contain one more entry than the number of
valid states or bins. For *N* bins, axes indexed by bin are of size *N+1*, and
entry *N* (0-based indexing) corresponds to a walker outside of the defined bin
space (which will cause most mappers to raise an error). More importantly, for
*M* states (including the case *M=0* where no states are specified), axes
indexed by state are of size *M+1* and entry *M* refers to trajectories
initiated in a region not corresponding to a defined macrostate.
Thus, ``labeled_populations[:,:,:].sum(axis=1)[:,:-1]`` gives overall per-bin
populations, for all defined bins and
``labeled_populations[:,:,:].sum(axis=2)[:,:-1]`` gives overall
per-trajectory-ensemble populations for all defined states.
-----------------------------------------------------------------------------
Parallelization
-----------------------------------------------------------------------------
This tool supports parallelized binning, including reading/calculating input
data.
-----------------------------------------------------------------------------
Command-line options
-----------------------------------------------------------------------------
'''
def __init__(self):
super(WAssign, self).__init__()
# Parallel processing by default (this is not actually necessary, but it is
# informative!)
self.wm_env.default_work_manager = self.wm_env.default_parallel_work_manager
self.data_reader = WESTDataReader()
self.dssynth = WESTDSSynthesizer(default_dsname='pcoord')
self.binning = BinMappingComponent()
self.progress = ProgressIndicatorComponent()
self.output_file = None
self.output_filename = None
self.states = []
self.subsample = False
def add_args(self, parser):
self.data_reader.add_args(parser)
self.binning.add_args(parser)
self.dssynth.add_args(parser)
sgroup = parser.add_argument_group(
'macrostate definitions').add_mutually_exclusive_group()
sgroup.add_argument(
'--states',
nargs='+',
metavar='STATEDEF',
help=
'''Single-bin kinetic macrostate, specified by a coordinate tuple (e.g. '1.0' or '[1.0,1.0]'),
optionally labeled (e.g. 'bound:[1.0,1.0]'). States corresponding to multiple bins
must be specified with --states-from-file.''')
sgroup.add_argument(
'--states-from-file',
metavar='STATEFILE',
help=
'''Load kinetic macrostates from the YAML file STATEFILE. See description
above for the appropriate structure.''')
sgroup.add_argument(
'--states-from-function',
metavar='STATEFUNC',
help=
'''Load kinetic macrostates from the function STATEFUNC, specified as
module_name.func_name. This function is called with the bin mapper as an argument,
and must return a list of dictionaries {'label': state_label, 'coords': 2d_array_like}
one for each macrostate; the 'coords' entry must contain enough rows to identify all bins
in the macrostate.''')
agroup = parser.add_argument_group('other options')
agroup.add_argument(
'-o',
'--output',
dest='output',
default='assign.h5',
help='''Store results in OUTPUT (default: %(default)s).''')
agroup.add_argument(
'--subsample',
dest='subsample',
action='store_const',
const=True,
help='''Determines whether or not the data should be subsampled.
This is rather useful for analysing steady state simulations.'''
)
agroup.add_argument(
'--config-from-file',
dest='config_from_file',
action='store_true',
help=
'''Load bins/macrostates from a scheme specified in west.cfg.''')
agroup.add_argument('--scheme-name',
dest='scheme',
help='''Name of scheme specified in west.cfg.''')
def process_args(self, args):
self.progress.process_args(args)
self.data_reader.process_args(args)
# Necessary to open the file to get the current iteration
# if we want to use the mapper in the file
self.data_reader.open(mode='r+')
self.n_iter = self.data_reader.current_iteration
# If we decide to use this option for iteration selection:
# getattr(args,'bins_from_h5file',None) or self.data_reader.current_iteration
with self.data_reader:
self.dssynth.h5filename = self.data_reader.we_h5filename
self.dssynth.process_args(args)
if args.config_from_file == False:
self.binning.set_we_h5file_info(self.n_iter, self.data_reader)
self.binning.process_args(args)
self.output_filename = args.output
if args.config_from_file:
if not args.scheme:
raise ValueError('A scheme must be specified.')
else:
self.load_config_from_west(args.scheme)
elif args.states:
self.parse_cmdline_states(args.states)
elif args.states_from_file:
self.load_state_file(args.states_from_file)
elif args.states_from_function:
self.load_states_from_function(
get_object(args.states_from_function, path=['.']))
if self.states and len(self.states) < 2:
raise ValueError('zero, two, or more macrostates are required')
#self.output_file = WESTPAH5File(args.output, 'w', creating_program=True)
log.debug('state list: {!r}'.format(self.states))
self.subsample = args.subsample if args.subsample is not None else False
def parse_cmdline_states(self, state_strings):
states = []
for istring, state_string in enumerate(state_strings):
try:
(label, coord_str) = state_string.split(':')
except ValueError:
label = 'state{}'.format(istring)
coord_str = state_string
coord = parse_pcoord_value(coord_str)
states.append({'label': label, 'coords': coord})
self.states = states
def load_config_from_west(self, scheme):
try:
config = westpa.rc.config['west']['analysis']
except:
raise ValueError('There is no configuration file specified.')
ystates = config['analysis_schemes'][scheme]['states']
self.states_from_dict(ystates)
try:
self.subsample = config['subsample']
except:
pass
from westpa._rc import bins_from_yaml_dict
self.binning.mapper = bins_from_yaml_dict(
config['analysis_schemes'][scheme]['bins'][0])
import os
path = os.path.join(os.getcwd(), config['directory'], scheme)
try:
os.mkdir(config['directory'])
os.mkdir(path)
except:
pass
self.output_filename = os.path.join(path, 'assign.h5')
def load_state_file(self, state_filename):
import yaml
ydict = yaml.load(open(state_filename, 'rt'))
ystates = ydict['states']
self.states_from_dict(ystates)
def states_from_dict(self, ystates):
states = []
for istate, ystate in enumerate(ystates):
state = {}
state['label'] = ystate.get('label', 'state{}'.format(istate))
# coords can be:
# - a scalar, in which case it is one bin, 1-D
# - a single list, which is rejected as ambiguous
# - a list of lists, which is a list of coordinate tuples
coords = numpy.array(ystate['coords'])
if coords.ndim == 0:
coords.shape = (1, 1)
elif coords.ndim == 1:
raise ValueError(
'list {!r} is ambiguous (list of 1-d coordinates, or single multi-d coordinate?)'
.format(ystate['coords']))
elif coords.ndim > 2:
raise ValueError('coordinates must be 2-D')
state['coords'] = coords
states.append(state)
self.states = states
def load_states_from_function(self, statefunc):
states = statefunc(self.binning.mapper)
for istate, state in enumerate(states):
state.setdefault('label', 'state{}'.format(istate))
try:
state['coords'] = numpy.array(state['coords'])
except KeyError:
raise ValueError(
'state function {!r} returned a state {!r} without coordinates'
.format(statefunc, state))
self.states = states
log.debug('loaded states: {!r}'.format(self.states))
def assign_iteration(self, n_iter, nstates, nbins, state_map, last_labels):
''' Method to encapsulate the segment slicing (into n_worker slices) and parallel job submission
Submits job(s), waits on completion, splices them back together
Returns: assignments, trajlabels, pops for this iteration'''
futures = []
iter_group = self.data_reader.get_iter_group(n_iter)
nsegs, npts = iter_group['pcoord'].shape[:2]
n_workers = self.work_manager.n_workers or 1
assignments = numpy.empty((nsegs, npts), dtype=index_dtype)
trajlabels = numpy.empty((nsegs, npts), dtype=index_dtype)
statelabels = numpy.empty((nsegs, npts), dtype=index_dtype)
pops = numpy.zeros((nstates + 1, nbins + 1), dtype=weight_dtype)
#Submit jobs to work manager
blocksize = nsegs // n_workers
if nsegs % n_workers > 0:
blocksize += 1
def task_gen():
if __debug__:
checkset = set()
for lb in 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,
subsample=self.subsample)
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, state_slice = future.get_result(
discard=True)
assignments[lb:ub, :] = assign_slice
trajlabels[lb:ub, :] = traj_slice
statelabels[lb:ub, :] = state_slice
pops += slice_pops
del assign_slice, traj_slice, slice_pops, state_slice
del futures
return (assignments, trajlabels, pops, statelabels)
def go(self):
assert self.data_reader.parent_id_dsspec._h5file is None
assert self.data_reader.weight_dsspec._h5file is None
if hasattr(self.dssynth.dsspec, '_h5file'):
assert self.dssynth.dsspec._h5file is None
pi = self.progress.indicator
pi.operation = 'Initializing'
with pi, self.data_reader, WESTPAH5File(
self.output_filename, 'w',
creating_program=True) as self.output_file:
assign = self.binning.mapper.assign
# We always assign the entire simulation, so that no trajectory appears to start
# in a transition region that doesn't get initialized in one.
iter_start = 1
iter_stop = self.data_reader.current_iteration
h5io.stamp_iter_range(self.output_file, iter_start, iter_stop)
nbins = self.binning.mapper.nbins
self.output_file.attrs['nbins'] = nbins
state_map = numpy.empty((self.binning.mapper.nbins + 1, ),
index_dtype)
state_map[:] = 0 # state_id == nstates => unknown state
# Recursive mappers produce a generator rather than a list of labels
# so consume the entire generator into a list
labels = [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
# Stamp if this has been subsampled.
self.output_file.attrs['subsampled'] = self.subsample
iter_count = iter_stop - iter_start
nsegs = numpy.empty((iter_count, ), seg_id_dtype)
npts = numpy.empty((iter_count, ), seg_id_dtype)
# scan for largest number of segments and largest number of points
pi.new_operation('Scanning for segment and point counts',
iter_stop - iter_start)
for iiter, n_iter in enumerate(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)
statelabels_ds = self.output_file.create_dataset(
'statelabels',
dtype=trajlabel_dtype,
shape=assignments_shape,
compression=4,
shuffle=True,
chunks=h5io.calc_chunksize(assignments_shape,
trajlabel_dtype),
fillvalue=nstates)
pops_shape = (iter_count, nstates + 1, nbins + 1)
pops_ds = self.output_file.create_dataset(
'labeled_populations',
dtype=weight_dtype,
shape=pops_shape,
compression=4,
shuffle=True,
chunks=h5io.calc_chunksize(pops_shape, weight_dtype))
h5io.label_axes(pops_ds, ['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, statelabels = self.assign_iteration(
n_iter, nstates, nbins, state_map, last_labels)
##Do stuff with this iteration's results
last_labels = trajlabels[:, -1].copy()
assignments_ds[iiter, 0:nsegs[iiter],
0:npts[iiter]] = assignments
pops_ds[iiter] = pops
if self.states:
trajlabels_ds[iiter, 0:nsegs[iiter],
0:npts[iiter]] = trajlabels
statelabels_ds[iiter, 0:nsegs[iiter],
0:npts[iiter]] = statelabels
pi.progress += 1
del assignments, trajlabels, pops, statelabels
for dsname in 'assignments', 'npts', 'nsegs', 'labeled_populations', 'statelabels':
h5io.stamp_iter_range(self.output_file[dsname], iter_start,
iter_stop)
class WSelectTool(WESTParallelTool):
prog = 'w_select'
description = '''\
Select dynamics segments matching various criteria. This requires a
user-provided prediate function. By default, only matching segments are
stored. If the -a/--include-ancestors option is given, then matching segments
and their ancestors will be stored.
-----------------------------------------------------------------------------
Predicate function
-----------------------------------------------------------------------------
Segments are selected based on a predicate function, which must be callable
as ``predicate(n_iter, iter_group)`` and return a collection of segment IDs
matching the predicate in that iteration.
The predicate may be inverted by specifying the -v/--invert command-line
argument.
-----------------------------------------------------------------------------
Output format
-----------------------------------------------------------------------------
The output file (-o/--output, by default "select.h5") contains the following
datasets:
``/n_iter`` [iteration]
*(Integer)* Iteration numbers for each entry in other datasets.
``/n_segs`` [iteration]
*(Integer)* Number of segment IDs matching the predicate (or inverted
predicate, if -v/--invert is specified) in the given iteration.
``/seg_ids`` [iteration][segment]
*(Integer)* Matching segments in each iteration. For an iteration
``n_iter``, only the first ``n_iter`` entries are valid. For example,
the full list of matching seg_ids in the first stored iteration is
``seg_ids[0][:n_segs[0]]``.
``/weights`` [iteration][segment]
*(Floating-point)* Weights for each matching segment in ``/seg_ids``.
-----------------------------------------------------------------------------
Command-line arguments
-----------------------------------------------------------------------------
'''
def __init__(self):
super(WSelectTool, self).__init__()
self.data_reader = WESTDataReader()
self.iter_range = IterRangeSelection()
self.progress = ProgressIndicatorComponent()
self.output_file = None
self.output_filename = None
self.predicate = None
self.invert = False
self.include_ancestors = False
def add_args(self, parser):
self.data_reader.add_args(parser)
self.iter_range.add_args(parser)
sgroup = parser.add_argument_group('selection options')
sgroup.add_argument(
'-p',
'--predicate-function',
metavar='MODULE.FUNCTION',
help=
'''Use the given predicate function to match segments. This function
should take an iteration number and the HDF5 group corresponding to that
iteration and return a sequence of seg_ids matching the predicate, as in
``match_predicate(n_iter, iter_group)``.''')
sgroup.add_argument('-v',
'--invert',
dest='invert',
action='store_true',
help='''Invert the match predicate.''')
sgroup.add_argument(
'-a',
'--include-ancestors',
action='store_true',
help='''Include ancestors of matched segments in output.''')
ogroup = parser.add_argument_group('output options')
ogroup.add_argument(
'-o',
'--output',
default='select.h5',
help='''Write output to OUTPUT (default: %(default)s).''')
self.progress.add_args(parser)
def process_args(self, args):
self.progress.process_args(args)
self.data_reader.process_args(args)
with self.data_reader:
self.iter_range.process_args(args)
predicate = get_object(args.predicate_function, path=['.'])
if not callable(predicate):
raise TypeError(
'predicate object {!r} is not callable'.format(predicate))
self.predicate = predicate
self.invert = bool(args.invert)
self.include_ancestors = bool(args.include_ancestors)
self.output_filename = args.output
def go(self):
self.data_reader.open('r')
output_file = h5io.WESTPAH5File(self.output_filename, mode='w')
pi = self.progress.indicator
iter_start, iter_stop = self.iter_range.iter_start, self.iter_range.iter_stop
iter_count = iter_stop - iter_start
output_file.create_dataset('n_iter',
dtype=n_iter_dtype,
data=list(range(iter_start, iter_stop)))
current_seg_count = 0
seg_count_ds = output_file.create_dataset('n_segs',
dtype=numpy.uint,
shape=(iter_count, ))
matching_segs_ds = output_file.create_dataset(
'seg_ids',
shape=(iter_count, 0),
maxshape=(iter_count, None),
dtype=seg_id_dtype,
chunks=h5io.calc_chunksize((iter_count, 1000000), seg_id_dtype),
shuffle=True,
compression=9)
weights_ds = output_file.create_dataset('weights',
shape=(iter_count, 0),
maxshape=(iter_count, None),
dtype=weight_dtype,
chunks=h5io.calc_chunksize(
(iter_count, 1000000),
weight_dtype),
shuffle=True,
compression=9)
with pi:
pi.new_operation('Finding matching segments', extent=iter_count)
# futures = set()
# for n_iter in xrange(iter_start,iter_stop):
# futures.add(self.work_manager.submit(_find_matching_segments,
# args=(self.data_reader.we_h5filename,n_iter,self.predicate,self.invert)))
# for future in self.work_manager.as_completed(futures):
for future in self.work_manager.submit_as_completed(
((_find_matching_segments,
(self.data_reader.we_h5filename, n_iter, self.predicate,
self.invert), {})
for n_iter in range(iter_start, iter_stop)),
self.max_queue_len):
n_iter, matching_ids = future.get_result()
n_matches = len(matching_ids)
if n_matches:
if n_matches > current_seg_count:
current_seg_count = len(matching_ids)
matching_segs_ds.resize((iter_count, n_matches))
weights_ds.resize((iter_count, n_matches))
current_seg_count = n_matches
seg_count_ds[n_iter - iter_start] = n_matches
matching_segs_ds[n_iter -
iter_start, :n_matches] = matching_ids
weights_ds[n_iter - iter_start, :
n_matches] = self.data_reader.get_iter_group(
n_iter)['seg_index']['weight'][sorted(
matching_ids)]
del matching_ids
pi.progress += 1
if self.include_ancestors:
pi.new_operation('Tracing ancestors of matching segments',
extent=iter_count)
from_previous = set()
current_seg_count = matching_segs_ds.shape[1]
for n_iter in range(iter_stop - 1, iter_start - 1, -1):
iiter = n_iter - iter_start
n_matches = seg_count_ds[iiter]
matching_ids = set(from_previous)
if n_matches:
matching_ids.update(
matching_segs_ds[iiter, :seg_count_ds[iiter]])
from_previous.clear()
n_matches = len(matching_ids)
if n_matches > current_seg_count:
matching_segs_ds.resize((iter_count, n_matches))
weights_ds.resize((iter_count, n_matches))
current_seg_count = n_matches
if n_matches > 0:
seg_count_ds[iiter] = n_matches
matching_ids = sorted(matching_ids)
matching_segs_ds[iiter, :n_matches] = matching_ids
weights_ds[
iiter, :
n_matches] = self.data_reader.get_iter_group(
n_iter)['seg_index']['weight'][sorted(
matching_ids)]
parent_ids = self.data_reader.get_iter_group(n_iter)[
'seg_index']['parent_id'][sorted(matching_ids)]
from_previous.update(
parent_id for parent_id in parent_ids
if parent_id >= 0) # filter initial states
del parent_ids
del matching_ids
pi.progress += 1
class 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 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 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 WCrawl(WESTParallelTool):
prog = 'w_crawl'
description = '''\
Crawl a weighted ensemble dataset, executing a function for each iteration.
This can be used for postprocessing of trajectories, cleanup of datasets,
or anything else that can be expressed as "do X for iteration N, then do
something with the result". Tasks are parallelized by iteration, and
no guarantees are made about evaluation order.
-----------------------------------------------------------------------------
Command-line options
-----------------------------------------------------------------------------
'''
def __init__(self):
super(WCrawl, self).__init__()
# These are used throughout
self.progress = ProgressIndicatorComponent()
self.data_reader = WESTDataReader()
self.iter_range = IterRangeSelection(self.data_reader)
self.crawler = None
self.task_callable = None
def add_args(self, parser):
self.data_reader.add_args(parser)
self.iter_range.add_args(parser)
tgroup = parser.add_argument_group('task options')
tgroup.add_argument(
'-c',
'--crawler-instance',
help=
'''Use CRAWLER_INSTANCE (specified as module.instance) as an instance of
WESTPACrawler to coordinate the calculation. Required only if initialization,
finalization, or task result processing is required.'''
)
tgroup.add_argument(
'task_callable',
help=
'''Run TASK_CALLABLE (specified as module.function) on each iteration.
Required.''')
self.progress.add_args(parser)
def process_args(self, args):
self.progress.process_args(args)
self.data_reader.process_args(args)
with self.data_reader:
self.iter_range.process_args(args)
self.task_callable = get_object(args.task_callable, path=['.'])
if args.crawler_instance is not None:
self.crawler = get_object(args.crawler_instance, path=['.'])
else:
self.crawler = WESTPACrawler()
def go(self):
iter_start = self.iter_range.iter_start
iter_stop = self.iter_range.iter_stop
iter_count = iter_stop - iter_start
self.data_reader.open('r')
pi = self.progress.indicator
with pi:
pi.operation = 'Initializing'
self.crawler.initialize(iter_start, iter_stop)
try:
pi.new_operation('Dispatching tasks & processing results',
iter_count)
task_gen = ((_remote_task, (n_iter, self.task_callable), {})
for n_iter in range(iter_start, iter_stop))
for future in self.work_manager.submit_as_completed(
task_gen, self.max_queue_len):
n_iter, result = future.get_result(discard=True)
if self.crawler is not None:
self.crawler.process_iter_result(n_iter, result)
pi.progress += 1
finally:
pi.new_operation('Finalizing')
self.crawler.finalize()
class WEDDist(WESTParallelTool):
prog='w_eddist'
description = '''\
Calculate time-resolved transition-event duration distribution from kinetics results
-----------------------------------------------------------------------------
Source data
-----------------------------------------------------------------------------
Source data is collected from the results of 'w_kinetics trace' (see w_kinetics trace --help for
more information on generating this dataset).
-----------------------------------------------------------------------------
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(WEDDist,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.default_kinetics_file = 'kintrace.h5'
self.kinetics_filename = None
self.kinetics_file = None #Kinavg file
self.istate = None
self.fstate = None
##Duration and weight dsspecs
self.duration_dsspec = None
self.wt_dsspec = None
self.binspec = None
self.output_filename = None
self.output_file = None
# 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):
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('-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.)''')
parser.add_argument('--istate', type=int, required=True, dest='istate',
help='''Initial state defining transition event''')
parser.add_argument('--fstate', type=int, required=True, dest='fstate',
help='''Final state defining transition event''')
itergroup = parser.add_argument_group('iteration range options')
itergroup.add_argument('--first-iter', default=1, dest='iter_start', type=int,
help='''Iteration to begin analysis (default: 1)''')
itergroup.add_argument('--last-iter', dest='iter_stop', type=int,
help='''Iteration to end analysis''')
iogroup = parser.add_argument_group('input/output options')
# 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 (including evolution) are stored in KINETICS
(default: %(default)s).''')
iogroup.add_argument('-o', '--output', dest='output', default='eddist.h5',
help='''Store results in OUTPUT (default: %(default)s).''')
self.progress.add_args(parser)
def process_args(self, args):
self.progress.process_args(args)
self.kinetics_filename = args.kinetics
self.istate = args.istate
self.fstate = args.fstate
self.kinetics_file = h5io.WESTPAH5File(self.kinetics_filename, 'r')
self.iter_start = args.iter_start
if args.iter_stop is None:
self.iter_stop = self.kinetics_file.attrs['iter_stop']
else:
self.iter_stop = args.iter_stop + 1
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):
pi = self.progress.indicator
pi.operation = 'Initializing'
with pi:
self.duration = self.kinetics_file['durations'][self.iter_start-1:self.iter_stop-1]
##Only select transition events from specified istate to fstate
mask = (self.duration['istate'] == self.istate) & (self.duration['fstate'] == self.fstate)
self.duration_dsspec = DurationDataset(self.kinetics_file['durations']['duration'], mask, self.iter_start)
self.wt_dsspec = DurationDataset(self.kinetics_file['durations']['weight'], mask, self.iter_start)
self.output_file = h5py.File(self.output_filename, 'w')
h5io.stamp_creator_data(self.output_file)
# 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 = numpy.arange(self.iter_start, self.iter_stop, 1, dtype=(numpy.min_scalar_type(self.iter_stop)))
self.output_file['n_iter'] = iter_range
self.output_file['histograms'].attrs['iter_start'] = self.iter_start
self.output_file['histograms'].attrs['iter_stop'] = self.iter_stop
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.duration_dsspec
#self.ntimepoints = dset.shape[1]
#self.ndim = dset.shape[2]
self.ndim = 1
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.duration_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()
#print(self.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
#lb -= 0.01
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.duration_dsspec, self.wt_dsspec, 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 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 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 WIPI(WESTParallelTool):
'''
Welcome to w_ipa (WESTPA Interactive Python Analysis)!
From here, you can run traces, look at weights, progress coordinates, etc.
This is considered a 'stateful' tool; that is, the data you are pulling is always pulled
from the current analysis scheme and iteration.
By default, the first analysis scheme in west.cfg is used, and you are set at iteration 1.
ALL PROPERTIES ARE ACCESSED VIA w or west
To see the current iteration, try:
w.iteration
OR
west.iteration
to set it, simply plug in a new value.
w.iteration = 100
To change/list the current analysis schemes:
w.list_schemes
w.scheme = OUTPUT FROM w.list_schemes
To see the states and bins defined in the current analysis scheme:
w.states
w.bin_labels
All information about the current iteration is available in an object called 'current':
w.current
walkers, summary, states, seg_id, weights, parents, kinavg, pcoord, bins, populations, and auxdata, if it exists.
In addition, the function w.trace(seg_id) will run a trace over a seg_id in the current iteration and return a dictionary
containing all pertinent information about that seg_id's history. It's best to store this, as the trace can be expensive.
Run help on any function or property for more information!
Happy analyzing!
'''
def __init__(self):
super(WIPI, self).__init__()
self.data_reader = WESTDataReader()
self.wm_env.default_work_manager = self.wm_env.default_parallel_work_manager
self.progress = ProgressIndicatorComponent()
self._iter = 1
self.config_required = True
self.version = "1.0B"
# Set to matplotlib if you want that. But why would you?
# Well, whatever, we'll just set it to that for now.
self.interface = 'matplotlib'
self._scheme = None
global iteration
def add_args(self, parser):
self.progress.add_args(parser)
self.data_reader.add_args(parser)
rgroup = parser.add_argument_group('runtime options')
rgroup.add_argument(
'--analysis-only',
'-ao',
dest='analysis_mode',
action='store_true',
help=
'''Use this flag to run the analysis and return to the terminal.'''
)
rgroup.add_argument(
'--reanalyze',
'-ra',
dest='reanalyze',
action='store_true',
help='''Use this flag to delete the existing files and reanalyze.'''
)
rgroup.add_argument('--ignore-hash',
'-ih',
dest='ignore_hash',
action='store_true',
help='''Ignore hash and don't regenerate files.''')
rgroup.add_argument(
'--debug',
'-d',
dest='debug_mode',
action='store_true',
help='''Debug output largely intended for development.''')
rgroup.add_argument('--terminal',
'-t',
dest='plotting',
action='store_true',
help='''Plot output in terminal.''')
# There is almost certainly a better way to handle this, but we'll sort that later.
import argparse
rgroup.add_argument('--f',
'-f',
dest='extra',
default='blah',
help=argparse.SUPPRESS)
parser.set_defaults(compression=True)
def process_args(self, args):
self.progress.process_args(args)
self.data_reader.process_args(args)
with self.data_reader:
self.niters = self.data_reader.current_iteration - 1
self.__config = westpa.rc.config
self.__settings = self.__config['west']['analysis']
for ischeme, scheme in enumerate(self.__settings['analysis_schemes']):
if (self.__settings['analysis_schemes'][scheme]['enabled'] == True
or self.__settings['analysis_schemes'][scheme]['enabled']
== None):
self.scheme = scheme
self.data_args = args
self.analysis_mode = args.analysis_mode
self.reanalyze = args.reanalyze
self.ignore_hash = args.ignore_hash
self.debug_mode = args.debug_mode
if args.plotting:
self.interface = 'text'
def hash_args(self, args, extra=None, path=None):
'''Create unique hash stamp to determine if arguments/file is different from before.'''
'''Combine with iteration to know whether or not file needs updating.'''
# Why are we not loading this functionality into the individual tools?
# While it may certainly be useful to store arguments (and we may well do that),
# it's rather complex and nasty to deal with pickling and hashing arguments through
# the various namespaces.
# In addition, it's unlikely that the functionality is desired at the individual tool level,
# since we'll always just rewrite a file when we call the function.
#return hashlib.md5(pickle.dumps([args, extra])).hexdigest()
# We don't care about the path, so we'll remove it.
# Probably a better way to do this, but who cares.
cargs = list(args)
for iarg, arg in enumerate(cargs):
if path in arg:
cargs[iarg] = arg.replace(path, '').replace('/', '')
if arg == '--disable-averages':
cargs.remove('--disable-averages')
to_hash = cargs + [extra]
#print(args)
#print(to_hash)
#print(str(to_hash).encode('base64'))
if self.debug_mode:
for iarg, arg in enumerate(to_hash):
if type(arg) != list:
print('arg {num:02d} -- {arg:<20}'.format(num=iarg,
arg=arg))
else:
for il, l in enumerate(arg):
print('arg {num:02d} -- {arg:<20}'.format(num=il +
iarg,
arg=l))
#print('args: {}'.format(to_hash))
# This SHOULD produce the same output, maybe? That would be nice, anyway.
# But we'll need to test it more.
return hashlib.md5(str(to_hash).encode('base64')).hexdigest()
def stamp_hash(self, h5file_name, new_hash):
'''Loads a file, stamps it, and returns the opened file in read only'''
h5file = h5io.WESTPAH5File(h5file_name, 'r+')
h5file.attrs['arg_hash'] = new_hash
h5file.close()
h5file = h5io.WESTPAH5File(h5file_name, 'r')
return h5file
def analysis_structure(self):
'''
Run automatically on startup. Parses through the configuration file, and loads up all the data files from the different
analysis schematics. If they don't exist, it creates them automatically by hooking in to existing analysis routines
and going from there.
It does this by calling in the make_parser_and_process function for w_{assign,reweight,direct} using a custom built list
of args. The user can specify everything in the configuration file that would have been specified on the command line.
For instance, were one to call w_direct as follows:
w_direct --evolution cumulative --step-iter 1 --disable-correl
the west.cfg would look as follows:
west:
analysis:
w_direct:
evolution: cumulative
step_iter: 1
extra: ['disable-correl']
Alternatively, if one wishes to use the same options for both w_direct and w_reweight, the key 'w_direct' can be replaced
with 'kinetics'.
'''
# Make sure everything exists.
try:
os.mkdir(self.__settings['directory'])
except:
pass
# Now, check to see whether they exist, and then load them.
self.__analysis_schemes__ = {}
# We really need to implement some sort of default behavior if an analysis scheme isn't set.
# Right now, we just crash. That isn't really graceful.
for scheme in self.__settings['analysis_schemes']:
if self.__settings['analysis_schemes'][scheme]['enabled']:
if self.work_manager.running == False:
self.work_manager.startup()
path = os.path.join(os.getcwd(), self.__settings['directory'],
scheme)
#if 'postanalysis' in self.__settings['analysis_schemes'][scheme] and 'postanalysis' in self.__settings['postanalysis']:
# Should clean this up. But it uses the default global setting if a by-scheme one isn't set.
if 'postanalysis' in self.__settings:
if 'postanalysis' in self.__settings['analysis_schemes'][
scheme]:
pass
else:
self.__settings['analysis_schemes'][scheme][
'postanalysis'] = self.__settings['postanalysis']
try:
os.mkdir(path)
except:
pass
self.__analysis_schemes__[scheme] = {}
try:
if self.__settings['analysis_schemes'][scheme][
'postanalysis'] == True or self.__settings[
'postanalysis'] == True:
analysis_files = ['assign', 'direct', 'reweight']
else:
analysis_files = ['assign', 'direct']
except:
analysis_files = ['assign', 'direct']
self.__settings['analysis_schemes'][scheme][
'postanalysis'] = False
reanalyze_kinetics = False
assign_hash = None
for name in analysis_files:
arg_hash = None
if self.reanalyze == True:
reanalyze_kinetics = True
try:
os.remove(os.path.join(path, '{}.h5'.format(name)))
except:
pass
else:
try:
# Try to load the hash. If we fail to load the hash or the file, we need to reload.
#if self.reanalyze == True:
# raise ValueError('Reanalyze set to true.')
self.__analysis_schemes__[scheme][
name] = h5io.WESTPAH5File(
os.path.join(path, '{}.h5'.format(name)),
'r')
arg_hash = self.__analysis_schemes__[scheme][
name].attrs['arg_hash']
if name == 'assign':
assign_hash = arg_hash
except:
pass
# We shouldn't rely on this.
# self.reanalyze = True
if True:
if name == 'assign':
assign = w_assign.WAssign()
w_assign_config = {
'output': os.path.join(path,
'{}.h5'.format(name))
}
try:
w_assign_config.update(
self.__settings['w_assign'])
except:
pass
try:
w_assign_config.update(
self.__settings['analysis_schemes'][scheme]
['w_assign'])
except:
pass
args = []
for key, value in w_assign_config.iteritems():
if key != 'extra':
args.append(
str('--') + str(key).replace('_', '-'))
args.append(str(value))
# This is for stuff like disabling correlation analysis, etc.
if 'extra' in w_assign_config.keys():
# We're sorting to ensure that the order doesn't matter.
for value in sorted(w_assign_config['extra']):
args.append(
str('--') +
str(value).replace('_', '-'))
# We're just calling the built in function.
# This is a lot cleaner than what we had in before, and far more workable.
args.append('--config-from-file')
args.append('--scheme-name')
args.append('{}'.format(scheme))
# Why are we calling this if we're not sure we're remaking the file?
# We need to load up the bin mapper and states and see if they're the same.
assign.make_parser_and_process(args=args)
import pickle
#new_hash = self.hash_args(args=args, path=path, extra=[self.niters, pickle.dumps(assign.binning.mapper), assign.states])
# We need to encode it properly to ensure that some OS specific thing doesn't kill us. Same goes for the args, ultimately.
# Mostly, we just need to ensure that we're consistent.
new_hash = self.hash_args(
args=args,
path=path,
extra=[
int(self.niters),
pickle.dumps(assign.binning.mapper).encode(
'base64'),
str(assign.states).encode('base64')
])
# Let's check the hash. If the hash is the same, we don't need to reload.
if self.debug_mode == True:
print('{:<10}: old hash, new hash -- {}, {}'.
format(name, arg_hash, new_hash))
if self.ignore_hash == False and (
arg_hash != new_hash
or self.reanalyze == True):
# If the hashes are different, or we need to reanalyze, delete the file.
try:
os.remove(
os.path.join(path,
'{}.h5'.format(name)))
except:
pass
print('Reanalyzing file {}.h5 for scheme {}.'.
format(name, scheme))
#reanalyze_kinetics = True
# We want to use the work manager we have here. Otherwise, just let the tool sort out what it needs, honestly.
assign.work_manager = self.work_manager
assign.go()
assign.data_reader.close()
# Stamp w/ hash, then reload as read only.
self.__analysis_schemes__[scheme][
name] = self.stamp_hash(
os.path.join(path,
'{}.h5'.format(name)),
new_hash)
del (assign)
# Update the assignment hash.
assign_hash = new_hash
# Since these are all contained within one tool, now, we want it to just... load everything.
if name == 'direct' or name == 'reweight':
assignment_file = self.__analysis_schemes__[
scheme]['assign']
if name == 'direct':
analysis = w_direct.WDirect()
if name == 'reweight':
analysis = w_reweight.WReweight()
analysis_config = {
'assignments':
os.path.join(path, '{}.h5'.format('assign')),
'output':
os.path.join(path, '{}.h5'.format(name)),
'kinetics':
os.path.join(path, '{}.h5'.format(name))
}
# Pull from general analysis options, then general SPECIFIC options for each analysis,
# then general options for that analysis scheme, then specific options for the analysis type in the scheme.
try:
analysis_config.update(
self.__settings['kinetics'])
except:
pass
try:
analysis_config.update(
self.__settings['w_{}'.format(name)])
except:
pass
try:
analysis_config.update(
self.__settings['analysis_schemes'][scheme]
['kinetics'])
except:
pass
try:
analysis_config.update(
self.__settings['analysis_schemes'][scheme]
['w_{}'.format(name)])
except:
pass
# We're pulling in a default set of arguments, then updating them with arguments from the west.cfg file, if appropriate, after setting the appropriate command
# Then, we call the magic function 'make_parser_and_process' with the arguments we've pulled in.
# The tool has no real idea it's being called outside of its actual function, and we're good to go.
args = ['all']
for key, value in analysis_config.iteritems():
if key != 'extra':
args.append(
str('--') + str(key).replace('_', '-'))
args.append(str(value))
# This is for stuff like disabling correlation analysis, etc.
if 'extra' in analysis_config.keys():
for value in sorted(analysis_config['extra']):
args.append(
str('--') +
str(value).replace('_', '-'))
# We want to not display the averages, so...
args.append('--disable-averages')
new_hash = self.hash_args(
args=args,
path=path,
extra=[int(self.niters), assign_hash])
#if arg_hash != new_hash or self.reanalyze == True or reanalyze_kinetics == True:
if self.debug_mode == True:
print('{:<10}: old hash, new hash -- {}, {}'.
format(name, arg_hash, new_hash))
if self.ignore_hash == False and (
arg_hash != new_hash
or reanalyze_kinetics == True):
try:
os.remove(
os.path.join(path,
'{}.h5'.format(name)))
except:
pass
print('Reanalyzing file {}.h5 for scheme {}.'.
format(name, scheme))
analysis.make_parser_and_process(args=args)
# We want to hook into the existing work manager.
analysis.work_manager = self.work_manager
analysis.go()
# Open!
self.__analysis_schemes__[scheme][
name] = self.stamp_hash(
os.path.join(path,
'{}.h5'.format(name)),
new_hash)
del (analysis)
# Make sure this doesn't get too far out, here. We need to keep it alive as long as we're actually analyzing things.
# self.work_manager.shutdown()
print("")
print("Complete!")
@property
def assign(self):
return self.__analysis_schemes__[str(self.scheme)]['assign']
@property
def direct(self):
"""
The output from w_kinavg.py from the current scheme.
"""
return self.__analysis_schemes__[str(self.scheme)]['direct']
@property
def state_labels(self):
print("State labels and definitions!")
for istate, state in enumerate(self.assign['state_labels']):
print('{}: {}'.format(istate, state))
print('{}: {}'.format(istate + 1, 'Unknown'))
@property
def bin_labels(self):
print("Bin definitions! ")
for istate, state in enumerate(self.assign['bin_labels']):
print('{}: {}'.format(istate, state))
@property
def west(self):
return self.data_reader.data_manager.we_h5file
@property
def reweight(self):
if self.__settings['analysis_schemes'][str(
self.scheme)]['postanalysis'] == True:
return self.__analysis_schemes__[str(self.scheme)]['reweight']
else:
value = "This sort of analysis has not been enabled."
current = {
'bin_prob_evolution': value,
'color_prob_evolution': value,
'conditional_flux_evolution': value,
'rate_evolution': value,
'state_labels': value,
'state_prob_evolution': value
}
current.update({'bin_populations': value, 'iterations': value})
return current
@property
def scheme(self):
'''
Returns and sets what scheme is currently in use.
To see what schemes are available, run:
w.list_schemes
'''
# Let's do this a few different ways.
# We want to return things about the DIFFERENT schemes, if possible.
if self._scheme == None:
self._scheme = WIPIScheme(scheme=self.__analysis_schemes__,
name=self._schemename,
parent=self,
settings=self.__settings)
# This just ensures that when we call it, it's clean.
self._scheme.name = None
return self._scheme
@scheme.setter
def scheme(self, scheme):
self._future = None
self._current = None
self._past = None
if scheme in self.__settings['analysis_schemes']:
pass
else:
for ischeme, schemename in enumerate(
self.__settings['analysis_schemes']):
if ischeme == scheme:
scheme = schemename
if self.__settings['analysis_schemes'][scheme][
'enabled'] == True or self.__settings['analysis_schemes'][
scheme]['enabled'] == None:
self._schemename = scheme
else:
print("Scheme cannot be changed to scheme: {}; it is not enabled!".
format(scheme))
@property
def list_schemes(self):
'''
Lists what schemes are configured in west.cfg file.
Schemes should be structured as follows, in west.cfg:
west:
system:
analysis:
directory: analysis
analysis_schemes:
scheme.1:
enabled: True
states:
- label: unbound
coords: [[7.0]]
- label: bound
coords: [[2.7]]
bins:
- type: RectilinearBinMapper
boundaries: [[0.0, 2.80, 7, 10000]]
'''
#print("The following schemes are available:")
#print("")
#for ischeme, scheme in enumerate(self.__settings['analysis_schemes']):
# print('{}. Scheme: {}'.format(ischeme, scheme))
#print("")
#print("Set via name, or via the index listed.")
#print("")
#print("Current scheme: {}".format(self.scheme))
self._scheme.list_schemes
@property
def iteration(self):
'''
Returns/sets the current iteration.
'''
#print("The current iteration is {}".format(self._iter))
return self._iter
@iteration.setter
def iteration(self, value):
print("Setting iteration to iter {}.".format(value))
if value <= 0:
print("Iteration must begin at 1.")
value = 1
if value > self.niters:
print("Cannot go beyond {} iterations!".format(self.niters))
print("Setting to {}".format(self.niters))
value = self.niters
# We want to trigger a rebuild on our current/past/future bits.
# The scheme should automatically reset to the proper iteration, but
# future needs to be manually triggered.
self._iter = value
self._future = None
return self._iter
@property
def current(self):
'''
The current iteration. See help for __get_data_for_iteration__
'''
return self.scheme[self.scheme.scheme].current
@property
def past(self):
'''
The previous iteration. See help for __get_data_for_iteration__
'''
return self.scheme[self.scheme.scheme].past
def trace(self, seg_id):
'''
Runs a trace on a seg_id within the current iteration, all the way back to the beginning,
returning a dictionary containing all interesting information:
seg_id, pcoord, states, bins, weights, iteration, auxdata (optional)
sorted in chronological order.
Call with a seg_id.
'''
if seg_id >= self.current.walkers:
print("Walker seg_id # {} is beyond the max count of {} walkers.".
format(seg_id, self.current.walkers))
return 1
pi = self.progress.indicator
with pi:
pi.new_operation(
'Tracing scheme:iter:seg_id {}:{}:{}'.format(
self.scheme, self.iteration, seg_id), self.iteration)
current = {
'seg_id': [],
'pcoord': [],
'states': [],
'weights': [],
'iteration': [],
'bins': []
}
keys = []
try:
current['auxdata'] = {}
for key in self.current['auxdata'].keys():
current['auxdata'][key] = []
key = []
except:
pass
for iter in reversed(range(1, self.iteration + 1)):
iter_group = self.data_reader.get_iter_group(iter)
particles = self.data_reader.data_manager.get_iter_summary(
int(iter))['n_particles']
current['pcoord'].append(iter_group['pcoord'][seg_id, :, :])
current['states'].append(self.assign['trajlabels'][iter - 1,
seg_id, :])
current['bins'].append(self.assign['assignments'][iter - 1,
seg_id, :])
current['seg_id'].append(seg_id)
current['weights'].append(
iter_group['seg_index']['weight'][seg_id])
current['iteration'].append(iter)
try:
for key in keys:
current['auxdata'][key].append(
iter_group['auxdata'][key][seg_id])
except:
pass
seg_id = iter_group['seg_index']['parent_id'][seg_id]
if seg_id < 0:
# Necessary for steady state simulations. This means they started in that iteration.
break
pi.progress += 1
current['seg_id'] = list(reversed(current['seg_id']))
current['iteration'] = list(reversed(current['iteration']))
current['states'] = np.concatenate(
np.array(list(reversed(current['states']))))
current['bins'] = np.concatenate(
np.array(list(reversed(current['bins']))))
current['weights'] = np.array(list(reversed(current['weights'])))
current['pcoord'] = np.concatenate(
np.array(list(reversed(current['pcoord']))))
try:
for key in keys():
current['auxdata'][key] = np.concatenate(
np.array(list(reversed(current['auxdata'][key]))))
except:
pass
current['state_labels'] = self.assign['state_labels']
for i in ['pcoord', 'states', 'bins', 'weights']:
current[i] = WIPIDataset(raw=current[i], key=i)
if i == 'weights':
current[i].plotter = Plotter(np.log10(current[i].raw),
str('log10 of ' + str(i)),
iteration=current[i].raw.shape[0],
interface=self.interface)
else:
current[i].plotter = Plotter(current[i].raw,
i,
iteration=current[i].raw.shape[0],
interface=self.interface)
current[i].plot = current[i].plotter.plot
return WIPIDataset(raw=current, key=seg_id)
@property
def future(self, value=None):
'''
Similar to current/past, but keyed differently and returns different datasets.
See help for Future.
'''
if self._future == None:
self._future = self.Future(raw=self.__get_children__(), key=None)
self._future.iteration = self.iteration + 1
return self._future
class Future(WIPIDataset):
# This isn't a real fancy one.
def __getitem__(self, value):
if type(value) is str:
print(self.__dict__.keys())
try:
return self.__dict__['raw'][value]
except:
print('{} is not a valid data structure.'.format(value))
elif type(value) is int or type(value) is np.int64:
# Otherwise, we assume they're trying to index for a seg_id.
#if value < self.parent.walkers:
current = {}
seg_items = [
'weights', 'pcoord', 'auxdata', 'parents', 'seg_id',
'states'
]
current['pcoord'] = self.__dict__['raw']['pcoord'][value]
current['states'] = self.__dict__['raw']['states'][value]
current['bins'] = self.__dict__['raw']['bins'][value]
current['parents'] = self.__dict__['raw']['parents'][value]
current['seg_id'] = self.__dict__['raw']['seg_id'][value]
current['weights'] = self.__dict__['raw']['weights'][value]
try:
current['auxdata'] = {}
for key in self.__dict__['raw']['auxdata'].keys():
current['auxdata'][key] = self.__dict__['raw'][
'auxdata'][key][value]
except:
pass
current = WIPIDataset(
current,
'Segment {} in Iter {}'.format(value, self.iteration))
return current
def __get_children__(self):
'''
Returns all information about the children of a given walker in the current iteration.
Used to generate and create the future object, if necessary.
'''
if self.iteration == self.niters:
print(
"Currently at iteration {}, which is the max. There are no children!"
.format(self.iteration))
return 0
iter_data = __get_data_for_iteration__(value=self.iteration + 1,
parent=self)
future = {
'weights': [],
'pcoord': [],
'parents': [],
'summary': iter_data['summary'],
'seg_id': [],
'walkers': iter_data['walkers'],
'states': [],
'bins': []
}
for seg_id in range(0, self.current.walkers):
children = np.where(iter_data['parents'] == seg_id)[0]
if len(children) == 0:
error = "No children for seg_id {}.".format(seg_id)
future['weights'].append(error)
future['pcoord'].append(error)
future['parents'].append(error)
future['seg_id'].append(error)
future['states'].append(error)
future['bins'].append(error)
else:
# Now, we're gonna put them in the thing.
value = self.iteration + 1
future['weights'].append(iter_data['weights'][children])
future['pcoord'].append(
iter_data['pcoord'][...][children, :, :])
try:
aux_data = iter_data['auxdata'][...][children, :, :]
try:
future['aux_data'].append(aux_data)
except:
future['aux_data'] = aux_data
except:
pass
future['parents'].append(iter_data['parents'][children])
future['seg_id'].append(iter_data['seg_id'][children])
future['states'].append(self.assign['trajlabels'][value - 1,
children, :])
future['bins'].append(self.assign['assignments'][value - 1,
children, :])
return future
def go(self):
'''
Function automatically called by main() when launched via the command line interface.
Generally, call main, not this function.
'''
print("")
print("Welcome to w_ipa (WESTPA Interactive Python Analysis) v. {}!".
format(w.version))
print(
"Run w.introduction for a more thorough introduction, or w.help to see a list of options."
)
print("Running analysis & loading files.")
self.data_reader.open()
self.analysis_structure()
# Seems to be consistent with other tools, such as w_assign. For setting the iterations.
self.data_reader.open()
self.niters = self.data_reader.current_iteration - 1
self.iteration = self.niters
try:
print('Your current scheme, system and iteration are : {}, {}, {}'.
format(w.scheme, os.getcwd(), w.iteration))
except:
pass
@property
def introduction(self):
'''
Just spits out an introduction, in case someone doesn't call help.
'''
help_string = '''
Call as a dictionary item or a .attribute:
w.past, w.current, w.future:
{current}
Raw schemes can be accessed as follows:
w.scheme.{scheme_keys}
and contain mostly the same datasets associated with w.
The following give raw access to the h5 files associated with the current scheme
w.west
w.assign
w.direct
w.reweight
OTHER:
{w}
'''.format(current=self.__format_keys__(self.current.__dir__(),
split=' ',
offset=12),
scheme_keys=self.__format_keys__(self._scheme.raw.keys()),
w=self.__format_keys__(self.__dir__(),
offset=8,
max_length=0,
split='',
prepend='w.'))
print(help_string)
# Just a little function to be used with the introduction.
def __format_keys__(self,
keys,
split='/',
offset=0,
max_length=80,
prepend=''):
rtn = ''
run_length = 0
for key in keys:
rtn += prepend + str(key) + split
run_length += len(str(key))
if run_length >= max_length:
run_length = offset
rtn += '\n' + ' ' * offset
if rtn[-1] == split:
return rtn[:-1]
else:
return rtn
@property
def help(self):
''' Just a minor function to call help on itself. Only in here to really help someone get help.'''
help(self)
def _repr_pretty_(self, p, cycle):
self.introduction
return " "
def __dir__(self):
return_list = ['past', 'current', 'future']
# For the moment, don't expose direct, reweight, or assign, as these are scheme dependent files.
# They do exist, and always link to the current scheme, however.
return_list += [
'iteration', 'niters', 'scheme', 'list_schemes', 'bin_labels',
'state_labels', 'west', 'trace'
]
return sorted(set(return_list))
class 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 CommonPloterrs(WESTSubcommand):
def __init__(self, parent):
super(CommonPloterrs, self).__init__(parent)
self.progress = ProgressIndicatorComponent()
self.xscale = None
self.yscale = None
self.xrange = None
self.yrange = None
self.xlabel = None
self.ylabel = None
self.title = None
self.plot_options_group = None
def add_args(self, parser):
self.progress.add_args(parser)
pogroup = self.plot_options_group = parser.add_argument_group(
'plot options')
pogroup.add_argument(
'--xscale',
choices=['linear', 'log', 'symlog'],
default='linear',
help='''Use "linear", "log", or "symlog" scaling for the x axis.
(Default: %(default)s).''')
pogroup.add_argument(
'--yscale',
choices=['linear', 'log', 'symlog'],
default='linear',
help='''Use "linear", "log", or "symlog" scaling for the y axis.
(Default: %(default)s).''')
pogroup.add_argument(
'--xrange',
help=
'''Restrict X range to XRANGE, which must be formatted as "xmin,xmax".
(Default: determined by input data.)''')
pogroup.add_argument(
'--yrange',
help=
'''Restrict Y range to YRANGE, which must be formatted as "ymin,ymax".
(Default: determined by input data.)''')
pogroup.add_argument(
'--xlabel',
help='''Use XLABEL for the x-axis label. (Default: varies.)''')
pogroup.add_argument(
'--ylabel',
help='''Use YLABEL for the y-axis label. (Default: varies.)''')
pogroup.add_argument(
'--title',
help='''Use TITLE for the plot title. (Default: varies.)''')
pogroup.add_argument('--terminal',
'-t',
dest='plotting',
action='store_true',
help='''Plot output in terminal.''')
def process_args(self, args):
self.progress.process_args(args)
if args.xrange:
self.xrange = self.parse_range(args.xrange)
if args.yrange:
self.yrange = self.parse_range(args.yrange)
self.xscale = args.xscale
self.yscale = args.yscale
self.xlabel = args.xlabel or 'Iteration'
self.ylabel = args.ylabel
self.title = args.title
if args.plotting or os.environ.get('DISPLAY') is None:
self.interface = 'text'
else:
import matplotlib
from matplotlib import pyplot
self.interface = 'matplotlib'
def parse_range(self, rangespec):
try:
(lbt, ubt) = rangespec.split(',')
return float(lbt), float(ubt)
except (ValueError, TypeError) as e:
raise ValueError('invalid range specification {!r}: {!s}'.format(
rangespec, e))
def do_plot(self,
data,
output_filename,
title=None,
x_range=None,
y_range=None,
x_label=None,
y_label=None):
if not output_filename:
return
title = title or self.title
x_range = x_range or self.xrange
y_range = y_range or self.yrange
x_label = x_label or self.xlabel
y_label = y_label or self.ylabel
iters = data['iter_stop'] - 1
pyplot.figure()
pyplot.plot(iters, data['expected'], color='black')
pyplot.plot(iters, data['ci_lbound'], color='gray')
pyplot.plot(iters, data['ci_ubound'], color='gray')
pyplot.gca().set_xscale(self.xscale)
pyplot.gca().set_yscale(self.yscale)
if title:
pyplot.title(title)
if x_range is not None:
pyplot.xlim(x_range)
if y_range is not None:
pyplot.ylim(y_range)
if x_label:
pyplot.xlabel(x_label)
if y_label:
pyplot.ylabel(y_label)
pyplot.savefig(output_filename)
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 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)