adaptivemd.Project¶

class adaptivemd.Project(name)[source]¶

A simulation project

Notes

You will later create Scheduler objects that explicitly correspond to a specific cue on a specific cluster that is accessible from within this shared FS resource.

Variables:

name (str) – a short descriptive name for the project. This name will be used in the database creation also.
resource (Resource) – a resource to run the project on. The resource specifies the memory storage location. Not necessarily which cluster is used. An example is, if at an institute several clusters (CPU, GPU) share the same shared FS. If clusters use the same FS you can run simulations across clusters without problems and so so this resource is the most top-level limitation.
files (Bundle) – a set of file objects that are available in the project and are believed to be available within the resource as long as the project lives
trajectories (ViewBundle) – all File object that are of Trajectory type and which have a positive created attribute. This means the file was really created and has not been altered yet.
workers (Bundle) – a set of all registered Worker instanced in the project
files – a set of file objects that are available in the project and are believed to be available within the resource as long as the project lives
models (Bundle) – a set of stored models in the DB
tasks (Bundle) – a set of all queued `Task`s in the project
logs (Bundle) – a set of all stored log entries
data (Bundle) – a set of DataDict objects that represent completely stored files in the database of arbitrary size
schedulers (set of Scheduler) – a set of attached schedulers with controlled shutdown and reference
storage (MongoDBStorage) – the mongodb storage wrapper to access the database of the project
_worker_dead_time (int) – the time after which an unresponsive worker is considered dead. Its tasks will be assigned the state set in _set_task_state_from_dead_workers. Default is 60s. Make sure that the heartbeat of a worker is much less that this.
_set_task_state_from_dead_workers (str) – if a worker is dead then its tasks are assigned this state. Default is created which means the task will be restarted by another worker. You can also chose halt or cancelled. See Task for details

See also

Task

__init__(name)[source]¶

Methods

__init__(name)

add_event(event) Attach an event to the project

close() Close the project and all related sessions and DB connections

close_rp() Close the RP session

delete(name) Delete a complete project

events_done() Check if all events are done

find_ml_next_frame([n_pick]) Find initial frames picked by inverse equilibrium distribution

get_scheduler([name])

param name:	name of the scheduler class provided by the Resource used in

initialize(resource) Initialize a project with a specific resource.

list() List all projects in the DB

new_ml_trajectory(engine, length, number) Find trajectories that have initial points picked by inverse eq dist

new_trajectory(frame, length[, engine, number]) Convenience function to create a new Trajectory object

on_nmodel(numbers) Return a condition representing the reach of a certain number of models

on_ntraj(numbers) Return a condition that is true as soon a the project has n trajectories

queue(*tasks) Submit jobs to the worker queue

reconnect() Reconnect the DB

run() Starts observing events in the project

stop() Stop observing events

trigger() Trigger a check of state changes that leads to task execution

wait_until(condition) Block until the given condition evaluates to true

initialize(resource)[source]¶

Initialize a project with a specific resource.

Notes

This should only be called to setup the project and only the very first time.

Parameters:	resource (Resource) – the resource used in this project

reconnect()[source]¶: Reconnect the DB

close_rp()[source]¶

Close the RP session

Before using RP you need to re-open and then you will run in a new session.

classmethod list()[source]¶

List all projects in the DB

Returns:	a list of all project names
Return type:	list of str

classmethod delete(name)[source]¶

Delete a complete project

Notes

Attention!!!! This cannot be undone!!!!

Parameters:	name (str) – the project name to be deleted

get_scheduler(name=None, **kwargs)[source]¶

Parameters:	name (str) – name of the scheduler class provided by the Resource used in this project. If None (default) the cluster/queue `default` is used that needs to be implemented for every resource kwargs (`**kwargs`) – Additional arguments to initialize the cluster scheduler provided by the Resource

Notes

the scheduler is automatically entered/opened so the pilot jobs is submitted to the queueing system and it counts against your simulation time! If you do not want to do so directly. Create the Scheduler by yourself and later call scheduler.enter(project) to start using it. To close the scheduler call scheduler.exit()

Returns:	the scheduler object that can be used to execute tasks on that cluster/queue
Return type:	Scheduler

close()[source]¶: Close the project and all related sessions and DB connections

queue(*tasks)[source]¶

Submit jobs to the worker queue

Parameters:	tasks ((list of) Task or Trajectory) – anything that can be run like a Task or a Trajectory with engine

new_trajectory(frame, length, engine=None, number=1)[source]¶

Convenience function to create a new Trajectory object

It will use incrementing numbers to create trajectory names used in the engine executions. Use this function to always get an unused trajectory name.

Parameters:	frame (File or Frame) – if given a File it is assumed to be a `.pdb` file that contains initial coordinates. If a frame is given one assumes that this Frame is the initial structure / frame zero in this trajectory length (int) – the length of the trajectory engine (Engine or None) – the engine used to generate the trajectory. The engine contains all the specifics about the trajectory internal structure since it is the responsibility of the engine to really create the trajectory. number (int) – the number of trajectory objects to be returned. If `1` it will be a single object. Otherwise a list of Trajectory objects.
Returns:
Return type:	Trajectory or list of Trajectory

on_ntraj(numbers)[source]¶

Return a condition that is true as soon a the project has n trajectories

Parameters:	numbers (int or iterator of int) – either a single int or an iterator that returns several ints
Returns:	the single condition or a generator of conditions matching the ints in the iterator
Return type:	NTrajectories or generator of NTrajectories

on_nmodel(numbers)[source]¶

Return a condition representing the reach of a certain number of models

Parameters:	numbers (int or iterator of int) – the number(s) of the models to be reached
Returns:	a (list of) Condition
Return type:	(generator of) Condition

find_ml_next_frame(n_pick=10)[source]¶

Find initial frames picked by inverse equilibrium distribution

This is the simplest adaptive strategy possible. Start from the states more likely if a state has not been seen so much. Effectively stating that less knowledge of a state implies a higher likelihood to find a new state.

Parameters:	n_pick (int) – number of returned trajectories
Returns:	the list of trajectories with the selected initial points.
Return type:	list of Frame

new_ml_trajectory(engine, length, number)[source]¶

Find trajectories that have initial points picked by inverse eq dist

Parameters:	engine (Engine) – the engine to be used length (int) – length of the trajectories returned number (int) – number of trajectories returned
Returns:	the list of Trajectory objects with initial frames chosen using `find_ml_next_frame()`
Return type:	list of Trajectory