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()

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_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()