w_eddist
usage:
w_eddist [-h] [-r RCFILE] [--quiet | --verbose | --debug] [--version]
[--max-queue-length MAX_QUEUE_LENGTH] [-b BINEXPR] [-C] [--loose] --istate ISTATE
--fstate FSTATE [--first-iter ITER_START] [--last-iter ITER_STOP] [-k KINETICS]
[-o OUTPUT] [--serial | --parallel | --work-manager WORK_MANAGER]
[--n-workers N_WORKERS] [--zmq-mode MODE] [--zmq-comm-mode COMM_MODE]
[--zmq-write-host-info INFO_FILE] [--zmq-read-host-info INFO_FILE]
[--zmq-upstream-rr-endpoint ENDPOINT] [--zmq-upstream-ann-endpoint ENDPOINT]
[--zmq-downstream-rr-endpoint ENDPOINT] [--zmq-downstream-ann-endpoint ENDPOINT]
[--zmq-master-heartbeat MASTER_HEARTBEAT] [--zmq-worker-heartbeat WORKER_HEARTBEAT]
[--zmq-timeout-factor FACTOR] [--zmq-startup-timeout STARTUP_TIMEOUT]
[--zmq-shutdown-timeout SHUTDOWN_TIMEOUT]
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 ``np`` available for
use [e.g. to specify bins using np.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
optional arguments:
-h, --help show this help message and exit
-b BINEXPR, --bins BINEXPR
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.)
-C, --compress Compress histograms. May make storage of higher-dimensional histograms more
tractable, at the (possible extreme) expense of increased analysis time.
(Default: no compression.)
--loose 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.)
--istate ISTATE Initial state defining transition event
--fstate FSTATE Final state defining transition event
general options:
-r RCFILE, --rcfile RCFILE
use RCFILE as the WEST run-time configuration file (default: west.cfg)
--quiet emit only essential information
--verbose emit extra information
--debug enable extra checks and emit copious information
--version show program's version number and exit
- parallelization options:
- --max-queue-length MAX_QUEUE_LENGTH
Maximum number of tasks that can be queued. Useful to limit RAM use for tasks that have very large requests/response. Default: no limit.
iteration range options:
--first-iter ITER_START
Iteration to begin analysis (default: 1)
--last-iter ITER_STOP
Iteration to end analysis
input/output options:
-k KINETICS, --kinetics KINETICS
Populations and transition rates (including evolution) are stored in KINETICS
(default: kintrace.h5).
-o OUTPUT, --output OUTPUT
Store results in OUTPUT (default: eddist.h5).
- parallelization options:
- --serial
run in serial mode
- --parallel
run in parallel mode (using processes)
- --work-manager WORK_MANAGER
use the given work manager for parallel task distribution. Available work managers are (‘serial’, ‘threads’, ‘processes’, ‘zmq’); default is ‘processes’
- --n-workers N_WORKERS
Use up to N_WORKERS on this host, for work managers which support this option. Use 0 for a dedicated server. (Ignored by work managers which do not support this option.)
options for ZeroMQ (“zmq”) work manager (master or node):
--zmq-mode MODE Operate as a master (server) or a node (workers/client). "server" is a
deprecated synonym for "master" and "client" is a deprecated synonym for
"node".
--zmq-comm-mode COMM_MODE
Use the given communication mode -- TCP or IPC (Unix-domain) -- sockets for
communication within a node. IPC (the default) may be more efficient but is not
available on (exceptionally rare) systems without node-local storage (e.g.
/tmp); on such systems, TCP may be used instead.
--zmq-write-host-info INFO_FILE
Store hostname and port information needed to connect to this instance in
INFO_FILE. This allows the master and nodes assisting in coordinating the
communication of other nodes to choose ports randomly. Downstream nodes read
this file with --zmq-read-host-info and know where how to connect.
--zmq-read-host-info INFO_FILE
Read hostname and port information needed to connect to the master (or other
coordinating node) from INFO_FILE. This allows the master and nodes assisting
in coordinating the communication of other nodes to choose ports randomly,
writing that information with --zmq-write-host-info for this instance to read.
--zmq-upstream-rr-endpoint ENDPOINT
ZeroMQ endpoint to which to send request/response (task and result) traffic
toward the master.
--zmq-upstream-ann-endpoint ENDPOINT
ZeroMQ endpoint on which to receive announcement (heartbeat and shutdown
notification) traffic from the master.
--zmq-downstream-rr-endpoint ENDPOINT
ZeroMQ endpoint on which to listen for request/response (task and result)
traffic from subsidiary workers.
--zmq-downstream-ann-endpoint ENDPOINT
ZeroMQ endpoint on which to send announcement (heartbeat and shutdown
notification) traffic toward workers.
--zmq-master-heartbeat MASTER_HEARTBEAT
Every MASTER_HEARTBEAT seconds, the master announces its presence to workers.
--zmq-worker-heartbeat WORKER_HEARTBEAT
Every WORKER_HEARTBEAT seconds, workers announce their presence to the master.
--zmq-timeout-factor FACTOR
Scaling factor for heartbeat timeouts. If the master doesn't hear from a worker
in WORKER_HEARTBEAT*FACTOR, the worker is assumed to have crashed. If a worker
doesn't hear from the master in MASTER_HEARTBEAT*FACTOR seconds, the master is
assumed to have crashed. Both cases result in shutdown.
--zmq-startup-timeout STARTUP_TIMEOUT
Amount of time (in seconds) to wait for communication between the master and at
least one worker. This may need to be changed on very large, heavily-loaded
computer systems that start all processes simultaneously.
--zmq-shutdown-timeout SHUTDOWN_TIMEOUT
Amount of time (in seconds) to wait for workers to shut down.
westpa.cli.tools.w_eddist module
- class westpa.cli.tools.w_eddist.WESTParallelTool(wm_env=None)
Bases:
WESTTool
Base class for command-line tools parallelized with wwmgr. This automatically adds and processes wwmgr command-line arguments and creates a work manager at self.work_manager.
- make_parser_and_process(prog=None, usage=None, description=None, epilog=None, args=None)
A convenience function to create a parser, call add_all_args(), and then call process_all_args(). The argument namespace is returned.
- add_args(parser)
Add arguments specific to this tool to the given argparse parser.
- process_args(args)
Take argparse-processed arguments associated with this tool and deal with them appropriately (setting instance variables, etc)
- go()
Perform the analysis associated with this tool.
- main()
A convenience function to make a parser, parse and process arguments, then run self.go() in the master process.
- class westpa.cli.tools.w_eddist.ProgressIndicatorComponent
Bases:
WESTToolComponent
- add_args(parser)
Add arguments specific to this component to the given argparse parser.
- process_args(args)
Take argparse-processed arguments associated with this component and deal with them appropriately (setting instance variables, etc)
- westpa.cli.tools.w_eddist.histnd(values, binbounds, weights=1.0, out=None, binbound_check=True, ignore_out_of_range=False)
Generate an N-dimensional PDF (or contribution to a PDF) from the given values.
binbounds
is a list of arrays of boundary values, with one entry for each dimension (values
must have as many columns as there are entries inbinbounds
)weight
, if provided, specifies the weight each value contributes to the histogram; this may be a scalar (for equal weights for all values) or a vector of the same length asvalues
(for unequal weights). Ifbinbound_check
is True, then the boundaries are checked for strict positive monotonicity; set to False to shave a few microseconds if you know your bin boundaries to be monotonically increasing.
- westpa.cli.tools.w_eddist.normhistnd(hist, binbounds)
Normalize the N-dimensional histogram
hist
with corresponding bin boundariesbinbounds
. Modifieshist
in place and returns the normalization factor used.
- class westpa.cli.tools.w_eddist.DurationDataset(dataset, mask, iter_start=1)
Bases:
object
A facade for the ‘dsspec’ dataclass that incorporates the mask into get_iter_data method
- get_iter_data(n_iter)
- westpa.cli.tools.w_eddist.isiterable(x)
- class westpa.cli.tools.w_eddist.WEDDist
Bases:
WESTParallelTool
- prog = 'w_eddist'
- description = 'Calculate time-resolved transition-event duration distribution from kinetics results\n\n\n-----------------------------------------------------------------------------\nSource data\n-----------------------------------------------------------------------------\n\nSource data is collected from the results of \'w_kinetics trace\' (see w_kinetics trace --help for\nmore information on generating this dataset).\n\n\n-----------------------------------------------------------------------------\nHistogram binning\n-----------------------------------------------------------------------------\n\nBy default, histograms are constructed with 100 bins in each dimension. This\ncan be overridden by specifying -b/--bins, which accepts a number of different\nkinds of arguments:\n\n a single integer N\n N uniformly spaced bins will be used in each dimension.\n\n a sequence of integers N1,N2,... (comma-separated)\n N1 uniformly spaced bins will be used for the first dimension, N2 for the\n second, and so on.\n\n a list of lists [[B11, B12, B13, ...], [B21, B22, B23, ...], ...]\n The bin boundaries B11, B12, B13, ... will be used for the first dimension,\n B21, B22, B23, ... for the second dimension, and so on. These bin\n boundaries need not be uniformly spaced. These expressions will be\n evaluated with Python\'s ``eval`` construct, with ``np`` available for\n use [e.g. to specify bins using np.arange()].\n\nThe first two forms (integer, list of integers) will trigger a scan of all\ndata in each dimension in order to determine the minimum and maximum values,\nwhich may be very expensive for large datasets. This can be avoided by\nexplicitly providing bin boundaries using the list-of-lists form.\n\nNote that these bins are *NOT* at all related to the bins used to drive WE\nsampling.\n\n\n-----------------------------------------------------------------------------\nOutput format\n-----------------------------------------------------------------------------\n\nThe output file produced (specified by -o/--output, defaulting to "pdist.h5")\nmay be fed to plothist to generate plots (or appropriately processed text or\nHDF5 files) from this data. In short, the following datasets are created:\n\n ``histograms``\n Normalized histograms. The first axis corresponds to iteration, and\n remaining axes correspond to dimensions of the input dataset.\n\n ``/binbounds_0``\n Vector of bin boundaries for the first (index 0) dimension. Additional\n datasets similarly named (/binbounds_1, /binbounds_2, ...) are created\n for additional dimensions.\n\n ``/midpoints_0``\n Vector of bin midpoints for the first (index 0) dimension. Additional\n datasets similarly named are created for additional dimensions.\n\n ``n_iter``\n Vector of iteration numbers corresponding to the stored histograms (i.e.\n the first axis of the ``histograms`` dataset).\n\n\n-----------------------------------------------------------------------------\nSubsequent processing\n-----------------------------------------------------------------------------\n\nThe output generated by this program (-o/--output, default "pdist.h5") may be\nplotted by the ``plothist`` program. See ``plothist --help`` for more\ninformation.\n\n\n-----------------------------------------------------------------------------\nParallelization\n-----------------------------------------------------------------------------\n\nThis tool supports parallelized binning, including reading of input data.\nParallel processing is the default. For simple cases (reading pre-computed\ninput data, modest numbers of segments), serial processing (--serial) may be\nmore efficient.\n\n\n-----------------------------------------------------------------------------\nCommand-line options\n-----------------------------------------------------------------------------\n\n'
- add_args(parser)
Add arguments specific to this tool to the given argparse parser.
- process_args(args)
Take argparse-processed arguments associated with this tool and deal with them appropriately (setting instance variables, etc)
- go()
Perform the analysis associated with this tool.
- static parse_binspec(binspec)
- construct_bins(bins)
Construct bins according to
bins
, which may be:A scalar integer (for that number of bins in each dimension)
A sequence of integers (specifying number of bins for each dimension)
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 withself.midpoints
to the midpoints of the bins.
- scan_data_shape()
- scan_data_range()
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.
- construct_histogram()
Construct a histogram using bins previously constructed with
construct_bins()
. The time series of histogram values is stored inhistograms
. Each histogram in the time series is normalized.
- westpa.cli.tools.w_eddist.entry_point()