w_stateprobs

WARNING: w_stateprobs is being deprecated. Please use w_direct instead.

usage:

w_stateprobs 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 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 “direct.h5”) contains the following dataset:

/avg_state_probs [state]
  (Structured -- see below) Population of each state across entire
  range specified.

/avg_color_probs [state]
  (Structured -- see below) Population of each ensemble across entire
  range specified.

If –evolution-mode is specified, then the following additional datasets are 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.

/color_prob_evolution [window][state]
  (Structured -- see below). Ensemble 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 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: stateprobs.h5).

input/output options:

-k KINETICS, --kinetics KINETICS
                      Populations and transition rates are stored in KINETICS (default: assign.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_stateprobs module

class westpa.cli.tools.w_stateprobs.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_stateprobs.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.

westpa.cli.tools.w_stateprobs.warn(message, category=None, stacklevel=1, source=None)

Issue a warning, or maybe ignore it or raise an exception.

class westpa.cli.tools.w_stateprobs.DStateProbs(parent)

Bases: AverageCommands

subcommand = 'probs'

help_text = 'Calculates color and state probabilities via tracing.'

default_kinetics_file = 'direct.h5'

description = 'Calculate average populations and associated errors in state populations from\nweighted ensemble data. Bin assignments, including macrostate definitions,\nare required. (See "w_assign --help" for more information).\n\n-----------------------------------------------------------------------------\nOutput format\n-----------------------------------------------------------------------------\n\nThe output file (-o/--output, usually "direct.h5") contains the following\ndataset:\n\n  /avg_state_probs [state]\n    (Structured -- see below) Population of each state across entire\n    range specified.\n\n  /avg_color_probs [state]\n    (Structured -- see below) Population of each ensemble across entire\n    range specified.\n\nIf --evolution-mode is specified, then the following additional datasets are\navailable:\n\n  /state_pop_evolution [window][state]\n    (Structured -- see below). State populations 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  /color_prob_evolution [window][state]\n    (Structured -- see below). Ensemble populations 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\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'

calculate_state_populations(pops)

w_stateprobs()

go()

class westpa.cli.tools.w_stateprobs.WStateProbs(parent)

Bases: DStateProbs

subcommand = 'trace'

help_text = 'averages and CIs for path-tracing kinetics analysis'

default_output_file = 'stateprobs.h5'

default_kinetics_file = 'assign.h5'

class westpa.cli.tools.w_stateprobs.WDirect

Bases: WESTMasterCommand, WESTParallelTool

prog = 'w_stateprobs'

subcommands = [<class 'westpa.cli.tools.w_stateprobs.WStateProbs'>]

subparsers_title = 'calculate state-to-state kinetics by tracing trajectories'

description = 'Calculate average populations and associated errors in state populations from\nweighted ensemble data. Bin assignments, including macrostate definitions,\nare required. (See "w_assign --help" for more information).\n\n-----------------------------------------------------------------------------\nOutput format\n-----------------------------------------------------------------------------\n\nThe output file (-o/--output, usually "stateprobs.h5") contains the following\ndataset:\n\n  /avg_state_pops [state]\n    (Structured -- see below) Population of each state across entire\n    range specified.\n\nIf --evolution-mode is specified, then the following additional dataset is\navailable:\n\n  /state_pop_evolution [window][state]\n    (Structured -- see below). State populations 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\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_stateprobs.entry_point()