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

add_args(parser): Add arguments specific to this tool to the given argparse parser.

go(): Perform the analysis associated with this tool.

include_help_command = True

process_args(args): Take argparse-processed arguments associated with this tool and deal with them appropriately (setting instance variables, etc)

subcommands = None

subparsers_title = None

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.

add_args(parser): Add arguments specific to this tool to the given argparse parser.

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.

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.

process_args(args): Take argparse-processed arguments associated with this tool and deal with them appropriately (setting instance variables, etc)

class westpa.cli.tools.w_kinavg.DKinAvg(parent)

Bases: AverageCommands

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'

go()

help_text = 'Generates rate and flux values from a WESTPA simulation via tracing.'

subcommand = 'kinetics'

w_kinavg()

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()