w_kinavg
WARNING: w_kinavg is being deprecated. Please use w_direct instead.
usage:
w_kinavg trace [-h] [-W WEST_H5FILE] [--first-iter N_ITER] [--last-iter N_ITER] [--step-iter STEP]
[-a ASSIGNMENTS] [-o OUTPUT] [-k KINETICS] [--disable-bootstrap] [--disable-correl]
[--alpha ALPHA] [--autocorrel-alpha ACALPHA] [--nsets NSETS]
[-e {cumulative,blocked,none}] [--window-frac WINDOW_FRAC] [--disable-averages]
Calculate average rates/fluxes and associated errors from weighted ensemble data. Bin assignments (usually “assign.h5”) and kinetics data (usually “direct.h5”) data files must have been previously generated (see “w_assign –help” and “w_direct init –help” for information on generating these files).
The evolution of all datasets may be calculated, with or without confidence intervals.
Output format
The output file (-o/–output, usually “direct.h5”) contains the following dataset:
/avg_rates [state,state]
(Structured -- see below) State-to-state rates based on entire window of
iterations selected.
/avg_total_fluxes [state]
(Structured -- see below) Total fluxes into each state based on entire
window of iterations selected.
/avg_conditional_fluxes [state,state]
(Structured -- see below) State-to-state fluxes based on entire window of
iterations selected.
If –evolution-mode is specified, then the following additional datasets are available:
/rate_evolution [window][state][state]
(Structured -- see below). State-to-state rates 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.
/target_flux_evolution [window,state]
(Structured -- see below). Total flux into a given macro state based on
windows of iterations of varying width, as in /rate_evolution.
/conditional_flux_evolution [window,state,state]
(Structured -- see below). State-to-state fluxes based on windows of
varying width, as in /rate_evolution.
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 observable as evaluated within
this window, in units of inverse tau.
ci_lbound
(Floating-point) Lower bound of the confidence interval of the observable
within this window, in units of inverse tau.
ci_ubound
(Floating-point) Upper bound of the confidence interval of the observable
within this window, in units of inverse tau.
stderr
(Floating-point) The standard error of the mean of the observable
within this window, in units of inverse tau.
corr_len
(Integer) Correlation length of the observable 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
optional arguments:
-h, --help show this help message and exit
WEST input data options:
-W WEST_H5FILE, --west-data WEST_H5FILE
Take WEST data from WEST_H5FILE (default: read from the HDF5 file specified in
west.cfg).
iteration range:
--first-iter N_ITER Begin analysis at iteration N_ITER (default: 1).
--last-iter N_ITER Conclude analysis with N_ITER, inclusive (default: last completed iteration).
--step-iter STEP Analyze/report in blocks of STEP iterations.
input/output options:
-a ASSIGNMENTS, --assignments ASSIGNMENTS
Bin assignments and macrostate definitions are in ASSIGNMENTS (default:
assign.h5).
-o OUTPUT, --output OUTPUT
Store results in OUTPUT (default: kinavg.h5).
input/output options:
-k KINETICS, --kinetics KINETICS
Populations and transition rates are stored in KINETICS (default: kintrace.h5).
confidence interval calculation options:
--disable-bootstrap, -db
Enable the use of Monte Carlo Block Bootstrapping.
--disable-correl, -dc
Disable the correlation analysis.
--alpha ALPHA Calculate a (1-ALPHA) confidence interval' (default: 0.05)
--autocorrel-alpha ACALPHA
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.)
--nsets NSETS Use NSETS samples for bootstrapping (default: chosen based on ALPHA)
calculation options:
-e {cumulative,blocked,none}, --evolution-mode {cumulative,blocked,none}
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.
--window-frac WINDOW_FRAC
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.
misc options:
--disable-averages, -da
Whether or not the averages should be printed to the console (set to FALSE if flag
is used).
westpa.cli.tools.w_kinavg module
- class westpa.cli.tools.w_kinavg.WESTMasterCommand
Bases:
WESTTool
Base class for command-line tools that employ subcommands
- subparsers_title = None
- subcommands = None
- include_help_command = True
- 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.
- class westpa.cli.tools.w_kinavg.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_kinavg.DKinAvg(parent)
Bases:
AverageCommands
- subcommand = 'kinetics'
- help_text = 'Generates rate and flux values from a WESTPA simulation via tracing.'
- default_kinetics_file = 'direct.h5'
- description = 'Calculate average rates/fluxes and associated errors from weighted ensemble\ndata. Bin assignments (usually "assign.h5") and kinetics data (usually\n"direct.h5") data files must have been previously generated (see\n"w_assign --help" and "w_direct init --help" for information on\ngenerating these files).\n\nThe evolution of all datasets may be calculated, with or without confidence\nintervals.\n\n-----------------------------------------------------------------------------\nOutput format\n-----------------------------------------------------------------------------\n\nThe output file (-o/--output, usually "direct.h5") contains the following\ndataset:\n\n /avg_rates [state,state]\n (Structured -- see below) State-to-state rates based on entire window of\n iterations selected.\n\n /avg_total_fluxes [state]\n (Structured -- see below) Total fluxes into each state based on entire\n window of iterations selected.\n\n /avg_conditional_fluxes [state,state]\n (Structured -- see below) State-to-state fluxes based on entire window of\n iterations selected.\n\nIf --evolution-mode is specified, then the following additional datasets are\navailable:\n\n /rate_evolution [window][state][state]\n (Structured -- see below). State-to-state rates based on windows of\n iterations of varying width. If --evolution-mode=cumulative, then\n these windows all begin at the iteration specified with\n --start-iter and grow in length by --step-iter for each successive\n element. If --evolution-mode=blocked, then these windows are all of\n width --step-iter (excluding the last, which may be shorter), the first\n of which begins at iteration --start-iter.\n\n /target_flux_evolution [window,state]\n (Structured -- see below). Total flux into a given macro state based on\n windows of iterations of varying width, as in /rate_evolution.\n\n /conditional_flux_evolution [window,state,state]\n (Structured -- see below). State-to-state fluxes based on windows of\n varying width, as in /rate_evolution.\n\nThe structure of these datasets is as follows:\n\n iter_start\n (Integer) Iteration at which the averaging window begins (inclusive).\n\n iter_stop\n (Integer) Iteration at which the averaging window ends (exclusive).\n\n expected\n (Floating-point) Expected (mean) value of the observable as evaluated within\n this window, in units of inverse tau.\n\n ci_lbound\n (Floating-point) Lower bound of the confidence interval of the observable\n within this window, in units of inverse tau.\n\n ci_ubound\n (Floating-point) Upper bound of the confidence interval of the observable\n within this window, in units of inverse tau.\n\n stderr\n (Floating-point) The standard error of the mean of the observable\n within this window, in units of inverse tau.\n\n corr_len\n (Integer) Correlation length of the observable within this window, in units\n of tau.\n\nEach of these datasets is also stamped with a number of attributes:\n\n mcbs_alpha\n (Floating-point) Alpha value of confidence intervals. (For example,\n *alpha=0.05* corresponds to a 95% confidence interval.)\n\n mcbs_nsets\n (Integer) Number of bootstrap data sets used in generating confidence\n intervals.\n\n mcbs_acalpha\n (Floating-point) Alpha value for determining correlation lengths.\n\n\n-----------------------------------------------------------------------------\nCommand-line options\n-----------------------------------------------------------------------------\n'
- w_kinavg()
- go()
- westpa.cli.tools.w_kinavg.warn()
Issue a warning, or maybe ignore it or raise an exception.
- message
Text of the warning message.
- category
The Warning category subclass. Defaults to UserWarning.
- stacklevel
How far up the call stack to make this warning appear. A value of 2 for example attributes the warning to the caller of the code calling warn().
- source
If supplied, the destroyed object which emitted a ResourceWarning
- skip_file_prefixes
An optional tuple of module filename prefixes indicating frames to skip during stacklevel computations for stack frame attribution.
- class westpa.cli.tools.w_kinavg.WKinAvg(parent)
Bases:
DKinAvg
- subcommand = 'trace'
- help_text = 'averages and CIs for path-tracing kinetics analysis'
- default_kinetics_file = 'kintrace.h5'
- default_output_file = 'kinavg.h5'
- class westpa.cli.tools.w_kinavg.WDirect
Bases:
WESTMasterCommand
,WESTParallelTool
- prog = 'w_kinavg'
- subcommands = [<class 'westpa.cli.tools.w_kinavg.WKinAvg'>]
- subparsers_title = 'direct kinetics analysis schemes'
- description = 'Calculate average rates and associated errors from weighted ensemble data. Bin\nassignments (usually "assignments.h5") and kinetics data (usually\n"kintrace.h5" or "kinmat.h5") data files must have been previously generated\n(see "w_assign --help" and "w_kinetics --help" for information on generating\nthese files).\n\n-----------------------------------------------------------------------------\nOutput format\n-----------------------------------------------------------------------------\n\nThe output file (-o/--output, usually "kinavg.h5") contains the following\ndataset:\n\n /avg_rates [state,state]\n (Structured -- see below) State-to-state rates based on entire window of\n iterations selected.\n\nFor trace mode, the following additional datasets are generated:\n\n /avg_total_fluxes [state]\n (Structured -- see below) Total fluxes into each state based on entire\n window of iterations selected.\n\n /avg_conditional_fluxes [state,state]\n (Structured -- see below) State-to-state fluxes based on entire window of\n iterations selected.\n\nIf --evolution-mode is specified, then the following additional dataset is\navailable:\n\n /rate_evolution [window][state][state]\n (Structured -- see below). State-to-state rates based on windows of\n iterations of varying width. If --evolution-mode=cumulative, then\n these windows all begin at the iteration specified with\n --start-iter and grow in length by --step-iter for each successive\n element. If --evolution-mode=blocked, then these windows are all of\n width --step-iter (excluding the last, which may be shorter), the first\n of which begins at iteration --start-iter.\n\nIf --evolution-mode is specified in trace mode, the following additional\ndatasets are available:\n\n /target_flux_evolution [window,state]\n (Structured -- see below). Total flux into a given macro state based on\n windows of iterations of varying width, as in /rate_evolution.\n\n /conditional_flux_evolution [window,state,state]\n (Structured -- see below). State-to-state fluxes based on windows of\n varying width, as in /rate_evolution.\n\nThe structure of these datasets is as follows:\n\n iter_start\n (Integer) Iteration at which the averaging window begins (inclusive).\n\n iter_stop\n (Integer) Iteration at which the averaging window ends (exclusive).\n\n expected\n (Floating-point) Expected (mean) value of the rate as evaluated within\n this window, in units of inverse tau.\n\n ci_lbound\n (Floating-point) Lower bound of the confidence interval on the rate\n within this window, in units of inverse tau.\n\n ci_ubound\n (Floating-point) Upper bound of the confidence interval on the rate\n within this window, in units of inverse tau.\n\n corr_len\n (Integer) Correlation length of the rate within this window, in units\n of tau.\n\nEach of these datasets is also stamped with a number of attributes:\n\n mcbs_alpha\n (Floating-point) Alpha value of confidence intervals. (For example,\n *alpha=0.05* corresponds to a 95% confidence interval.)\n\n mcbs_nsets\n (Integer) Number of bootstrap data sets used in generating confidence\n intervals.\n\n mcbs_acalpha\n (Floating-point) Alpha value for determining correlation lengths.\n\n\n-----------------------------------------------------------------------------\nCommand-line options\n-----------------------------------------------------------------------------\n'
- westpa.cli.tools.w_kinavg.entry_point()