MetaWards.utils API Detail

Functions

accepts_stage(func)	Return whether the passed function accepts the “stage” argument, meaning that it can do different things for different day stages
add_lookup(network[, nthreads])	Add in metadata about the network that can be used to look up wards by name of location or region etc.
add_wards_network_distance(network, nthreads)	Reads the location data in network.parameters.input_files.position and adds those locations to all of the nodes in the passed network.
aggregate_networks(network, profiler, nthreads)	Aggregate all of the Susceptibles data from the demographic sub-networks into an overall total set of data that is stored in the overall network
assert_sane_network(network, profiler)	This function runs through and checks that the passed network is sane.
build_play_matrix(network, max_nodes, …)	Build the play matrix for the passed network
build_wards_network(params, profiler, …)	Creates a network of wards using the information provided in the file specified in parameters.input_files.work.
call_function_on_network(network, …[, …])	Call either ‘func’ or ‘parallel’ (depending on the number of threads, nthreads) on the passed Network, or on all demographic subnetworks
check_for_updates([dry_run])	Check if a newer version of MetaWards is available.
clear_all_infections(infections, …)	Clears all infections associated with a model run
Console()	This is a singleton class that provides access to printing and logging functions to the console.
create_int_array(size, default)	Create a new array.array of the specified size.
create_double_array(size, default)	Create a new array.array of the specified size.
create_string_array(size, unicode default)	Create an array of python strings of size ‘size’, optionally initialised with ‘default’
create_thread_generators(rng, nthreads)	Return a set of random number generators, one for each thread - these are seeded using the next ‘nthreads’ random numbers drawn from the passed generator
delete_ran_binomial(rng)	Delete the passed random number generator.
fill_in_gaps(network, max_nodes)	Fills in gaps in the network
get_available_num_threads()	Return the maximum number of threads that are recommended for this computer (the OMP_NUM_THREADS value)
get_functions(stage, network, population, …)	Return the functions that must be called for the specified stage of the day;
get_initialise_functions(**kwargs)	Convenience function that returns all of the functions that should be called during the initialisation step of the model (e.g.
get_finalise_functions(trajectory, **kwargs)	Convenience function that returns all of the functions that should be called during the finalisation step of the model (e.g.
get_model_loop_functions(**kwargs)	Convenience function that returns all of the functions that should be called during the model loop (i.e.
get_min_max_distances(network, nthreads, …)	Return the minimum and maximum distances recorded in the network
get_number_of_processes(parallel_scheme[, …])	This function works out how many processes have been set by the paralellisation system called ‘parallel_scheme’
initialise_infections(network)	Initialise the data structure used to store the infections
initialise_play_infections(network)	Initialise the space used to store the play infections
move_population_from_work_to_play(network, …)	This function is not used or implemented, but is implied by the naming scheme…
move_population_from_play_to_work(network, …)	And Vice Versa From Work to Play The relevant parameters are network.params.play_to_work and network.params.work_to_play
prepare_worker(params, demographics, options)	Prepare a worker to receive work to run a model using the passed parameters.
ran_binomial(rng, double p, n)	Return a random number drawn from the binomial distribution [p,n] (see gsl_ran_binomial for documentation)
ran_int(rng[, lower, upper])	Draw a random integer from [0,upper] inclusive
ran_uniform(rng)	Return a random double drawn from a uniform distribution between zero and one
read_done_file(filename)	This function reads the ‘done_file’ from ‘filename’ returning the list of seeded nodes
recalculate_work_denominator_day(network, …)	Recalculate the denominator_d for the wards (nodes) in the network for the normal links
recalculate_play_denominator_day(network, …)	Recalculate the denominator_d for the wards (nodes) in the network for the play links
rescale_play_matrix(network, nthreads, profiler)	Static Play At Home rescaling.
resize_array(a, size, default)	Resize the passed array to size ‘size’, adding ‘default’ if this will grow the array
reset_everything(network, profiler, nthreads)	Reset everything in the passed network so that it can be used for a new model run
reset_play_matrix(network, nthreads)	Resets the play entries in the passed Network.
reset_play_susceptibles(network, nthreads)	Resets the ward entries in the passed Network.
reset_work_matrix(network[, nthreads])	Resets the work entries in the passed Network.
run_model(network, infections, rngs, output_dir)	Actually run the model.
run_models(network, variables, population, …)	Run all of the models on the passed Network that are described by the passed VariableSets
run_worker(arguments)	Ask the worker to run a model using the passed variables and options.
safe_eval_number(s)	Convert ‘s’ to a number.
scale_link_susceptibles(links, ratio)	Scale the number of susceptibles in the passed Links by the passed scale ratio.
scale_node_susceptibles(nodes, ratio, …)	Scale the number of susceptibles in the passed Nodes by the passed scale ratios.
seed_ran_binomial(seed)	Seed and return the random binomial generator.
string_to_ints([string, strings])	Convert the passed string (or strings) containing integers (or ranges of integers) into a single sorted list of integers where no value is repeated
update_metawards([dry_run, user])	Update MetaWards to the latest version.
zero_workspace(workspace)

Classes

Profiler(name[, parent])	This is a simple profiling class that supports manual instrumenting of the code.
NullProfiler(name[, parent])	This is a null profiler that does nothing