metawards.Demographics

class metawards.Demographics(demographics: List[metawards._demographic.Demographic] = <factory>, random_seed: Optional[int] = None, interaction_matrix: Optional[List[List[int]]] = None, _names: Dict[str, int] = <factory>, _name: Optional[str] = None, _version: Optional[str] = None, _authors: Optional[str] = None, _contacts: Optional[str] = None, _references: Optional[str] = None, _filename: Optional[str] = None, _repository: Optional[str] = None, _repository_version: Optional[str] = None, _repository_branch: Optional[str] = None)

This class holds metadata about all of the demographics being modelled

__init__(demographics: List[metawards._demographic.Demographic] = <factory>, random_seed: Optional[int] = None, interaction_matrix: Optional[List[List[int]]] = None, _names: Dict[str, int] = <factory>, _name: Optional[str] = None, _version: Optional[str] = None, _authors: Optional[str] = None, _contacts: Optional[str] = None, _references: Optional[str] = None, _filename: Optional[str] = None, _repository: Optional[str] = None, _repository_version: Optional[str] = None, _repository_branch: Optional[str] = None) → None

Methods

__add__(other)
__delattr__(name, /)	Implement delattr(self, name).
__dir__()	Default dir() implementation.
__eq__(other)	Return self==value.
__format__(format_spec, /)	Default object formatter.
__ge__(value, /)	Return self>=value.
__getattribute__(name, /)	Return getattr(self, name).
__getitem__(item)
__gt__(value, /)	Return self>value.
__init__([demographics, random_seed, ...])
__init_subclass__	This method is called when a class is subclassed.
__le__(value, /)	Return self<=value.
__len__()
__lt__(value, /)	Return self<value.
__ne__(value, /)	Return self!=value.
__new__(**kwargs)
__radd__(other)
__reduce__()	Helper for pickle.
__reduce_ex__(protocol, /)	Helper for pickle.
__repr__()	Return repr(self).
__setattr__(name, value, /)	Implement setattr(self, name, value).
__sizeof__()	Size of object in memory, in bytes.
__str__()	Return str(self).
__subclasshook__	Abstract classes can override this to customize issubclass().
add(demographic)	Add a demographic to the set to be modelled
build(params[, population, max_nodes, ...])	Build the set of networks described by these demographics and the passed parameters
copy()	Return a copy of this demographics object that should allow a safe reset between runs.
from_data(data[, json_dir])	Construct and return a Demographics object constructed from a (json-deserialised) data dictionary
from_json(s)	Construct and return Demographics loaded from the passed json file
get_index(item)	Return the index of the passed item
get_name(item)	Return the name of the demographic at 'item'
is_multi_network()	Return whether or not these demographics need to use multiple custom networks (e.g.
load([name, repository, folder, filename])	Load the parameters for the specified set of demographics.
specialise(network[, profiler, nthreads])	Build the set of networks that will model this set of demographics applied to the passed Network.
to_data()	Return a data dictionary for this object that can be serialised to json
to_json([filename, indent, auto_bzip])	Serialise the Demographics to JSON.
uses_named_network()	Return whether or not at least one of these demographics specifies the use of a named network model

Attributes

__annotations__
__dataclass_fields__
__dataclass_params__
__dict__
__doc__
__hash__
__module__
__weakref__	list of weak references to the object (if defined)
_authors
_contacts
_filename
_name
_references
_repository
_repository_branch
_repository_version
_version
interaction_matrix	The interaction matrix between demographics.
random_seed	The random seed to used when using any random number generator to resolve decisions needed when allocating individuals to demographics.
demographics	The list of individual Demographic objects, one for each demographic being modelled
_names	Map from index to names of demographics - enables lookup by name

__eq__(other)

Return self==value.

__hash__ = None

__init__(demographics: List[metawards._demographic.Demographic] = <factory>, random_seed: Optional[int] = None, interaction_matrix: Optional[List[List[int]]] = None, _names: Dict[str, int] = <factory>, _name: Optional[str] = None, _version: Optional[str] = None, _authors: Optional[str] = None, _contacts: Optional[str] = None, _references: Optional[str] = None, _filename: Optional[str] = None, _repository: Optional[str] = None, _repository_version: Optional[str] = None, _repository_branch: Optional[str] = None) → None

__repr__()

Return repr(self).

__str__()

Return str(self).

add(demographic: metawards._demographic.Demographic)

Add a demographic to the set to be modelled

build(params: Parameters, population: Population = None, max_nodes: int = 16384, max_links: int = 4194304, nthreads: int = 1, profiler: Profiler = None) → _Union[Network, Networks]

Build the set of networks described by these demographics and the passed parameters

Parameters

• params (Parameters) – Parameters used to help build the model networks

• max_nodes (int) – Initial guess for the maximum number of nodes(wards)

• max_links (int) – Initial guess for the maximum number of links between wards

• profiler (Profiler) – Profiler used to profile the specialisation

• nthreads (int) – Number of threads over which to parallelise the work

Returns

The set of Networks that represent the model run over the full set of different demographics(or Network if there is just a single demographic)

Return type

Network or Networks

copy()

Return a copy of this demographics object that should allow a safe reset between runs. This deepcopies things that may change, while shallow copying things that won’t

demographics: List[metawards._demographic.Demographic]

The list of individual Demographic objects, one for each demographic being modelled

static from_data(data, json_dir=None) → metawards._demographics.Demographics

Construct and return a Demographics object constructed from a (json-deserialised) data dictionary

static from_json(s: str)

Construct and return Demographics loaded from the passed json file

get_index(item)

Return the index of the passed item

get_name(item)

Return the name of the demographic at ‘item’

interaction_matrix: List[List[int]] = None

The interaction matrix between demographics. This should be a list of lists that shows how demographic ‘i’ affects demographic ‘j’

is_multi_network()

Return whether or not these demographics need to use multiple custom networks (e.g. refer to different network models)

static load(name: Optional[str] = None, repository: Optional[str] = None, folder: str = 'demographics', filename: Optional[str] = None)

Load the parameters for the specified set of demographics. This will look for a file called f”{name}.json” in the directory f”{repository}/{folder}/{name}.json”

By default this will load nothing.

Alternatively you can provide the full path to the json file via the “filename” argument

Parameters

• name (str) – The name of the demographics to load. This is the name that will be searched for in the METAWARDSDATA diseases directory

• repository (str) – The location of the cloned METAWARDSDATA repository

• folder (str) – The name of the folder within the METAWARDSDATA repository that contains the diseases

• filename (str) – The name of the file to load the disease from - this directly loads this file without searching through the METAWARDSDATA repository

Returns

demographics – The constructed and validated demographics

Return type

Demographics

random_seed: int = None

The random seed to used when using any random number generator to resolve decisions needed when allocating individuals to demographics. This is set here so that the Demographics are uniquely determined and reproducible across runs

specialise(network: Network, profiler: Profiler = None, nthreads: int = 1)

Build the set of networks that will model this set of demographics applied to the passed Network.

Parameters

• network (Network) – The overall population model - this contains the base parameters, wards, work and play links that define the model outbreak

• profiler (Profiler) – Profiler used to profile the specialisation

• nthreads (int) – Number of threads over which to parallelise the work

Returns

networks – The set of Networks that represent the model run over the full set of different demographics

Return type

Networks

to_data()

Return a data dictionary for this object that can be serialised to json

to_json(filename: Optional[str] = None, indent: Optional[int] = None, auto_bzip: bool = True) → str

Serialise the Demographics to JSON. This will write to a file if filename is set, otherwise it will return a JSON string.

Parameters

• filename (str) – The name of the file to write the JSON to. The absolute path to the written file will be returned. If filename is None then this will serialise to a JSON string which will be returned.

• indent (int) – The number of spaces of indent to use when writing the json

• auto_bzip (bool) – Whether or not to automatically bzip2 the written json file

Returns

Returns either the absolute path to the written file, or the json-serialised string

Return type

str

uses_named_network()

Return whether or not at least one of these demographics specifies the use of a named network model