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Merge pull request #37 from openpathsampling/release-0.2.0
Release 0.2.0
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,2 @@ | ||
| python -m pip install sqlalchemy dill pytest | ||
| py.test --pyargs paths_cli |
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| Original file line number | Diff line number | Diff line change |
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| @@ -1,2 +1,2 @@ | ||
| INSTALL_AUTORELEASE="python -m pip install autorelease==0.2.3 nose" | ||
| INSTALL_AUTORELEASE="python -m pip install autorelease==0.2.3 nose sqlalchemy dill" | ||
| PACKAGE_IMPORT_NAME=paths_cli |
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| Original file line number | Diff line number | Diff line change |
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|
@@ -3,3 +3,6 @@ nose | |
| pytest | ||
| pytest-cov | ||
| coveralls | ||
| # following are for SimStore integration | ||
| dill | ||
| sqlalchemy | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,203 @@ | ||
| import click | ||
|
|
||
| import paths_cli.utils | ||
| from paths_cli.parameters import (INPUT_FILE, OUTPUT_FILE, ENGINE, | ||
| MULTI_ENSEMBLE, INIT_SNAP) | ||
|
|
||
| import logging | ||
| logger = logging.getLogger(__name__) | ||
|
|
||
| @click.command( | ||
| "md", | ||
| short_help=("Run MD for fixed time or until a given ensemble is " | ||
| "satisfied"), | ||
| ) | ||
| @INPUT_FILE.clicked(required=True) | ||
| @OUTPUT_FILE.clicked(required=True) | ||
| @ENGINE.clicked(required=False) | ||
| @MULTI_ENSEMBLE.clicked(required=False) | ||
| @click.option('-n', '--nsteps', type=int, | ||
| help="number of MD steps to run") | ||
| @INIT_SNAP.clicked(required=False) | ||
| def md(input_file, output_file, engine, ensemble, nsteps, init_frame): | ||
| """Run MD for for time of steps or until ensembles are satisfied. | ||
| This can either take a --nsteps or --ensemble, but not both. If the | ||
| --ensemble option is specfied more than once, then this will attempt to | ||
| run until all ensembles are satisfied by a subtrajectory. | ||
| This still respects the maximum number of frames as set in the engine, | ||
| and will terminate if the trajectory gets longer than that. | ||
| """ | ||
| storage = INPUT_FILE.get(input_file) | ||
| md_main( | ||
| output_storage=OUTPUT_FILE.get(output_file), | ||
| engine=ENGINE.get(storage, engine), | ||
| ensembles=MULTI_ENSEMBLE.get(storage, ensemble), | ||
| nsteps=nsteps, | ||
| initial_frame=INIT_SNAP.get(storage, init_frame) | ||
| ) | ||
|
|
||
| class ProgressReporter(object): | ||
| """Generic class for a callable that reports progress. | ||
| Base class for ends-with-ensemble and fixed-length tricks. | ||
| Parameters | ||
| ---------- | ||
| timestep : Any | ||
| timestep, optionally with units | ||
| update_freq : int | ||
| how often to report updates | ||
| """ | ||
| def __init__(self, timestep, update_freq): | ||
| self.timestep = timestep | ||
| self.update_freq = update_freq | ||
|
|
||
| def steps_progress_string(self, n_steps): | ||
| """Return string for number of frames run and time elapsed | ||
| Not newline-terminated. | ||
| """ | ||
| report_str = "Ran {n_steps} frames" | ||
| if self.timestep is not None: | ||
| report_str += " [{}]".format(str(n_steps * self.timestep)) | ||
| report_str += '.' | ||
| return report_str.format(n_steps=n_steps) | ||
|
|
||
| def progress_string(self, n_steps): | ||
| """Return the progress string. Subclasses may override. | ||
| """ | ||
| report_str = self.steps_progress_string(n_steps) + "\n" | ||
| return report_str.format(n_steps=n_steps) | ||
|
|
||
| def report_progress(self, n_steps, force=False): | ||
| """Report the progress to the terminal. | ||
| """ | ||
| import openpathsampling as paths | ||
| if (n_steps % self.update_freq == 0) or force: | ||
| string = self.progress_string(n_steps) | ||
| paths.tools.refresh_output(string) | ||
|
|
||
| def __call__(self, trajectory, trusted=False): | ||
| raise NotImplementedError() | ||
|
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|
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| class EnsembleSatisfiedContinueConditions(ProgressReporter): | ||
| """Continuation condition for including subtrajs for each ensemble. | ||
| This object creates a continuation condition (a callable) analogous with | ||
| the ensemble ``can_append`` method. This will tell the trajectory to | ||
| keep running until, for each of the given ensembles, a subtrajectory has | ||
| been found that will satisfy the ensemble. | ||
| Parameters | ||
| ---------- | ||
| ensembles: List[:class:`openpathsampling.Ensemble`] | ||
| the ensembles to satisfy | ||
| timestep : Any | ||
| timestep, optionally with units | ||
| update_freq : int | ||
| how often to report updates | ||
| """ | ||
| def __init__(self, ensembles, timestep=None, update_freq=10): | ||
| super().__init__(timestep, update_freq) | ||
| self.satisfied = {ens: False for ens in ensembles} | ||
|
|
||
| def progress_string(self, n_steps): | ||
| report_str = self.steps_progress_string(n_steps) | ||
| report_str += (" Found ensembles [{found}]. " | ||
| "Looking for [{missing}].\n") | ||
| found = [ens.name for ens, done in self.satisfied.items() if done] | ||
| missing = [ens.name for ens, done in self.satisfied.items() | ||
| if not done] | ||
| found_str = ",".join(found) | ||
| missing_str = ",".join(missing) | ||
| return report_str.format(n_steps=n_steps, | ||
| found=found_str, | ||
| missing=missing_str) | ||
|
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|
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| def _check_previous_frame(self, trajectory, start, unsatisfied): | ||
| if -start > len(trajectory): | ||
| # we've done the whole traj; don't keep going | ||
| return False | ||
| subtraj = trajectory[start:] | ||
| logger.debug(str(subtraj) + "/" + str(trajectory)) | ||
| for ens in unsatisfied: | ||
| if not ens.strict_can_prepend(subtraj, trusted=True): | ||
| # test if we can't prepend because we satsify | ||
| self.satisfied[ens] = ens(subtraj) or ens(subtraj[1:]) | ||
| unsatisfied.remove(ens) | ||
| return bool(unsatisfied) | ||
|
|
||
| def _call_untrusted(self, trajectory): | ||
| self.satisfied = {ens: False for ens in self.satisfied} | ||
| for i in range(1, len(trajectory)): | ||
| keep_going = self(trajectory[:i], trusted=True) | ||
| if not keep_going: | ||
| return False | ||
| return self(trajectory, trusted=True) | ||
|
|
||
| def __call__(self, trajectory, trusted=False): | ||
| if not trusted: | ||
| return self._call_untrusted(trajectory) | ||
|
|
||
| # below here, trusted is True | ||
| self.report_progress(len(trajectory) - 1) | ||
|
|
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| unsatisfied = [ens for ens, done in self.satisfied.items() | ||
| if not done] | ||
| # TODO: update on how many ensembles left, what frame number we are | ||
|
|
||
| start = -1 | ||
| while self._check_previous_frame(trajectory, start, unsatisfied): | ||
| start -= 1 | ||
|
|
||
| return not all(self.satisfied.values()) | ||
|
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|
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||
| class FixedLengthContinueCondition(ProgressReporter): | ||
| """Continuation condition for fixed-length runs. | ||
| Parameters | ||
| ---------- | ||
| length : int | ||
| final length of the trajectory in frames | ||
| timestep : Any | ||
| timestep, optionally with units | ||
| update_freq : int | ||
| how often to report updates | ||
| """ | ||
| def __init__(self, length, timestep=None, update_freq=10): | ||
| super().__init__(timestep, update_freq) | ||
| self.length = length | ||
|
|
||
| def __call__(self, trajectory, trusted=False): | ||
| len_traj = len(trajectory) | ||
| self.report_progress(len_traj - 1) | ||
| return len_traj < self.length | ||
|
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|
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|
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| def md_main(output_storage, engine, ensembles, nsteps, initial_frame): | ||
| import openpathsampling as paths | ||
| if nsteps is not None and ensembles: | ||
| raise RuntimeError("Options --ensemble and --nsteps cannot both be" | ||
| " used at once.") | ||
|
|
||
| if ensembles: | ||
| continue_cond = EnsembleSatisfiedContinueConditions(ensembles) | ||
| else: | ||
| continue_cond = FixedLengthContinueCondition(nsteps) | ||
|
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| trajectory = engine.generate(initial_frame, running=continue_cond) | ||
| continue_cond.report_progress(len(trajectory) - 1, force=True) | ||
| paths_cli.utils.tag_final_result(trajectory, output_storage, | ||
| 'final_conditions') | ||
| return trajectory, None | ||
|
|
||
| CLI = md | ||
| SECTION = "Simulation" | ||
| REQUIRES_OPS = (1, 0) | ||
|
|
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