Context#

class pharmpy.workflows.Context(name, ref=None)[source]#

Bases: ABC

Context for runs

A database of results, metadata and run files for one tool run

Parameters:

name (str) – Name of the context
ref (str) – A reference (path) to the context

Attributes Summary

`broadcaster`
`context_path`
`dispatcher`
`model_database`	ModelDatabase to store results of models run in context
`name`
`ref`
`seed`

Methods Summary

`abort_workflow`(message)	Ask the dispatcher to abort the currently running workflow directly
`call_workflow`(workflow, unique_name)	Ask the dispatcher to call a subworkflow
`create_rng`(index)	Create a random number generator
`create_subcontext`(name)	Create a new subcontext of this context
`exists`(name[, ref])
`finalize`()	Called after a tool has finished its run in a context can be implemented to do cleanup of the context
`get_model_context_path`(model)
`get_ncores_for_execution`()	Get number of cores for execution (using available cores among allocation)
`get_parent_context`()	Get the parent context of this context
`get_subcontext`(name)	Get one of the subcontexts of this context
`get_top_level_context`()	Get the top level context of this context
`has_completed`()	Check if the tool running in the context has completed
`has_started`()	Check if the tool running in the context has started
`list_all_names`()	Retrieve a list of all model names in the context
`list_all_subcontexts`()	Retrieve a list of the names of all subcontexts in the context
`log_error`(message[, model])	Add an error message to the log
`log_info`(message[, model])	Add an info message to the log
`log_message`(severity, message[, model])	Add a message to the log
`log_warning`(message[, model])	Add a warning message to the log
`retrieve_annotation`(name)	Retrieve an annotation for a model
`retrieve_common_options`()
`retrieve_dispatching_options`()
`retrieve_final_model_entry`()	Retrieve the ModelEntry of the model marked as final
`retrieve_input_model_entry`()	Retrieve the ModelEntry of the model marked as input
`retrieve_key`(name)	Retrive the key corresponding to a model name
`retrieve_log`([level])	Retrieve the entire log all - the entire log current - only the current Context level lower - current and sub levels
`retrieve_metadata`()	Read tool metadata
`retrieve_model_entry`(name)
`retrieve_results`()	Retrieve tool results
`spawn_seed`(rng[, n])	Spawn a new seed using a random number generator
`store_annotation`(name, annotation)	Store an annotation string (description) for a model
`store_final_model_entry`(me)
`store_input_model_entry`(me)
`store_key`(name, key)	Associate a key with a model name
`store_message`(severity, ctxpath, date, message)
`store_metadata`(metadata)	Store tool metadata
`store_model_entry`(me)
`store_results`(res)	Store tool results

Attributes Documentation

broadcaster#

context_path#

dispatcher#

model_database#: ModelDatabase to store results of models run in context

name#

ref#

seed#

Methods Documentation

abort_workflow(message)[source]#: Ask the dispatcher to abort the currently running workflow directly

call_workflow(workflow, unique_name)[source]#: Ask the dispatcher to call a subworkflow

create_rng(index)[source]#

Create a random number generator

Creating the generator will be using the seed common option, the index and the context path to get a unique sequence.

abstractmethod create_subcontext(name)[source]#: Create a new subcontext of this context

abstractmethod static exists(name, ref=None)[source]#

abstractmethod finalize()[source]#: Called after a tool has finished its run in a context can be implemented to do cleanup of the context

get_model_context_path(model)[source]#

get_ncores_for_execution()[source]#: Get number of cores for execution (using available cores among allocation)

abstractmethod get_parent_context()[source]#: Get the parent context of this context

abstractmethod get_subcontext(name)[source]#: Get one of the subcontexts of this context

abstractmethod get_top_level_context()[source]#: Get the top level context of this context

has_completed()[source]#: Check if the tool running in the context has completed

has_started()[source]#: Check if the tool running in the context has started

abstractmethod list_all_names()[source]#: Retrieve a list of all model names in the context

abstractmethod list_all_subcontexts()[source]#: Retrieve a list of the names of all subcontexts in the context

log_error(message, model=None)[source]#: Add an error message to the log

log_info(message, model=None)[source]#

Add an info message to the log

Currently with echo to stdout. In the future this could be changed or be configurable.

log_message(severity, message, model=None)[source]#: Add a message to the log

log_warning(message, model=None)[source]#: Add a warning message to the log

abstractmethod retrieve_annotation(name)[source]#: Retrieve an annotation for a model

abstractmethod retrieve_common_options()[source]#

abstractmethod retrieve_dispatching_options()[source]#

retrieve_final_model_entry()[source]#: Retrieve the ModelEntry of the model marked as final

retrieve_input_model_entry()[source]#: Retrieve the ModelEntry of the model marked as input

abstractmethod retrieve_key(name)[source]#

Retrive the key corresponding to a model name

This key can be used to lookup the model in the model database

abstractmethod retrieve_log(level='all')[source]#: Retrieve the entire log all - the entire log current - only the current Context level lower - current and sub levels

abstractmethod retrieve_metadata()[source]#: Read tool metadata

retrieve_model_entry(name)[source]#

abstractmethod retrieve_results()[source]#

Retrieve tool results

Returns:: Results – Tool results object

spawn_seed(rng, n=128)[source]#

Spawn a new seed using a random number generator

Parameters:

rng (Random number generator) – Random number generator
n (int) – Size of seed to generate in number of bits

Returns:

int – New random seed

abstractmethod store_annotation(name, annotation)[source]#: Store an annotation string (description) for a model

store_final_model_entry(me)[source]#

store_input_model_entry(me)[source]#

abstractmethod store_key(name, key)[source]#: Associate a key with a model name

abstractmethod store_message(severity, ctxpath, date, message)[source]#

abstractmethod store_metadata(metadata)[source]#

Store tool metadata

Parameters:: metadata (dict) – Tool metadata dictionary

store_model_entry(me)[source]#

abstractmethod store_results(res)[source]#

Store tool results

Parameters:: res (Results) – Tool results object