metawards.Parameters

class metawards.Parameters(input_files: metawards._inputfiles.InputFiles = None, disease_params: metawards._disease.Disease = None, additional_seeds: List[str] = None, length_day: float = 0.7, static_play_at_home: float = 0.0, dyn_play_at_home: float = 0.0, dyn_dist_cutoff: float = 10000000.0, play_to_work: float = 0.0, work_to_play: float = 0.0, daily_imports: float = 0.0, ward_seed_index: int = None, initial_inf: int = 5, stage_0: str = 'R', UV: float = 0.0, UV_max: datetime.date = None, scale_uv: float = 1.0, bg_foi: float = 0.0, user_params: Dict[str, float] = <factory>, adjustments: List[metawards._variableset.VariableSet] = None, _name: str = None, _version: str = None, _authors: str = None, _contacts: str = None, _references: str = None, _filename: str = None, _repository: str = None, _repository_version: str = None, _repository_branch: str = None, _repository_dir: str = None)[source]

The full set of Parameters that are used to control the model outbreak over a Network. The combination of a Network and a Parameters defines the model outbreak.

Load the Parameters using the Parameters.load function, and then add extra data using the various “set” and “add” functions, e.g.

Examples

>>> params = Parameters.load("march29")
>>> params.set_disease("ncov")
>>> params.set_input_files("2011Data")
>>> params.add_seeds("ExtraSeedsBrighton.dat")
__init__(input_files: metawards._inputfiles.InputFiles = None, disease_params: metawards._disease.Disease = None, additional_seeds: List[str] = None, length_day: float = 0.7, static_play_at_home: float = 0.0, dyn_play_at_home: float = 0.0, dyn_dist_cutoff: float = 10000000.0, play_to_work: float = 0.0, work_to_play: float = 0.0, daily_imports: float = 0.0, ward_seed_index: int = None, initial_inf: int = 5, stage_0: str = 'R', UV: float = 0.0, UV_max: datetime.date = None, scale_uv: float = 1.0, bg_foi: float = 0.0, user_params: Dict[str, float] = <factory>, adjustments: List[metawards._variableset.VariableSet] = None, _name: str = None, _version: str = None, _authors: str = None, _contacts: str = None, _references: str = None, _filename: str = None, _repository: str = None, _repository_version: str = None, _repository_branch: str = None, _repository_dir: str = None) → None

Initialize self. See help(type(self)) for accurate signature.

Methods

__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__(demographic)

Return the parameters that should be used for the demographic subnetwork called ‘demographic’.

__gt__(value, /)

Return self>value.

__init__([input_files, disease_params, …])

Initialize self.

__init_subclass__

This method is called when a class is subclassed.

__le__(value, /)

Return self<=value.

__lt__(value, /)

Return self<value.

__ne__(value, /)

Return self!=value.

__new__(**kwargs)

Create and return a new object.

__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_seeds(filename)

Add an ‘additional seeds’ file that can be used to seed wards with new infections at different times and locations.

copy([include_subparams])

Return a safe copy of these parameters, which does not include any subnetwork parameters if ‘include_subparams’ is False

default()

Return the default set of parameters

get_repository([repository])

Return the repository location and version information for the passed repository

load([parameters, repository, folder, filename])

This will return a Parameters object containing all of the parameters loaded from the parameters found in file f”{repository}/{folder}/{parameters}.json”

read_variables(filename, line_numbers)

Read in extra variable parameters from the specified line number(s) of the specified file, returning the list of the dictionaries of variables that have been read.

set_disease(disease[, silent])

“Set the disease that will be modelled

set_input_files(input_files)

Set the input files that are used to initialise the simulation

set_variables(variables)

This function sets the adjustable variable values to those specified in ‘variables’ in A COPY OF THIS PARAMETERS OBJECT.

specialised_demographics()

Return the names of demographics that have specialised parameters that are different to those of the overall network

Attributes

UV

Seasonality parameter

UV_max

Date when transmission should be at a maximum

__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_dir

_repository_version

_subparams

The parameters for demographic sub-networks.

_version

additional_seeds

The set of files that contain additional seeds that seed the outbreak during the model run

adjustments

All of the VariableSet adjustments that have been applied to these parameters

bg_foi

The global background force of infection (FOI).

daily_imports

proportion of daily imports if there are additional infections

disease_params

The set of parameters that define the disease

dyn_dist_cutoff

The cutoff distance in km beyond which workers or players cannot move

dyn_play_at_home

initial_inf

The number of initial infections if there are daily imports

input_files

The set of input files that define the model Network

length_day

The fraction of day considered “day” for work, e.g.

play_to_work

scale_uv

The global scale_uv.

stage_0

how to treat the * state (stage 0).

static_play_at_home

ward_seed_index

The index of the seeding ward if there are daily imports

work_to_play

UV: float = 0.0

Seasonality parameter

UV_max: datetime.date = None

Date when transmission should be at a maximum

__getitem__(demographic: str)[source]

Return the parameters that should be used for the demographic subnetwork called ‘demographic’. If these have not been set specifically then the parameters for the overall network are used

__str__()[source]

Return str(self).

add_seeds(filename: str)[source]

Add an ‘additional seeds’ file that can be used to seed wards with new infections at different times and locations. Several additional_seed files can be added

Parameters

filename (str) – Name of the file containing the additional seeds

additional_seeds: List[str] = None

The set of files that contain additional seeds that seed the outbreak during the model run

adjustments: List[metawards._variableset.VariableSet] = None

All of the VariableSet adjustments that have been applied to these parameters

bg_foi: float = 0.0

The global background force of infection (FOI). This is combined with the per-ward level bg_foi to give a single value

copy(include_subparams: bool = False)[source]

Return a safe copy of these parameters, which does not include any subnetwork parameters if ‘include_subparams’ is False

daily_imports: float = 0.0

proportion of daily imports if there are additional infections

static default()[source]

Return the default set of parameters

disease_params: metawards._disease.Disease = None

The set of parameters that define the disease

dyn_dist_cutoff: float = 10000000.0

The cutoff distance in km beyond which workers or players cannot move

static get_repository(repository: str = None)[source]

Return the repository location and version information for the passed repository

Parameters

repository (str) – Location on the filesystem of the repository. If this is None then it will be searched for using first the environment variable METAWARDSDATA, then $HOME/GitHub/MetaWardsData, then ./METAWARDSDATA

Returns

(repository, version) – A tuple of the location on disk of the repository, plus the version information (git ID etc)

Return type

tuple

initial_inf: int = 5

The number of initial infections if there are daily imports

input_files: metawards._inputfiles.InputFiles = None

The set of input files that define the model Network

length_day: float = 0.7

The fraction of day considered “day” for work, e.g. 0.7 * 24 hours

static load(parameters: str = 'march29', repository: str = None, folder: str = 'parameters', filename: str = None)[source]

This will return a Parameters object containing all of the parameters loaded from the parameters found in file f”{repository}/{folder}/{parameters}.json”

By default this will load the march29 parameters from $HOME/GitHub/model_data/2011Data/parameters/march29.json

Alternatively, you can provide the exact path to the filename via the ‘filename’ argument

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

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

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

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

Returns

params – The constructed and validated parameters

Return type

Parameters

static read_variables(filename: str, line_numbers: List[int])[source]

Read in extra variable parameters from the specified line number(s) of the specified file, returning the list of the dictionaries of variables that have been read. You can then apply those variable parameters using the ‘set_variables’ function

Parameters
  • filename (str) – The file from which to read the adjustable variables

  • line_numbers (List[int]) – All of the line numbers from which to read. If this is None then all lines will be read.

Returns

variables – The VariableSets containing all of the adjustable variables

Return type

VariableSets

scale_uv: float = 1.0

The global scale_uv. This is combined with the population and per-ward level scale_uvs to give a single value

set_disease(disease: metawards._disease.Disease, silent: bool = True)[source]

“Set the disease that will be modelled

Parameters
  • disease – The disease to be modelled. If a string is passed

  • the disease will be loaded using that string (then) –

  • silent – Whether or not to suppress printing out the disease

set_input_files(input_files: metawards._inputfiles.InputFiles)[source]

Set the input files that are used to initialise the simulation

Parameters

input_files (InputFiles) – The set of input files that will be used to load the Network. If a string is passed then the InputFiles will be loaded based on that string.

set_variables(variables: metawards._variableset.VariableSet)[source]

This function sets the adjustable variable values to those specified in ‘variables’ in A COPY OF THIS PARAMETERS OBJECT. This returns the copy. It does not change this object

Parameters

variables (VariableSet) – The variables that will be adjusted before the model run. This adjusts the parameters and returns them in a deep copy

Returns

params – A copy of this set of parameters with the variables adjusted

Return type

Parameters

specialised_demographics() → List[str][source]

Return the names of demographics that have specialised parameters that are different to those of the overall network

stage_0: str = 'R'

how to treat the * state (stage 0). This should be a string describing the method. Currently “R”, “E” and “disable” are supported. Not needed if the mapping is specified explicitly in the disease

user_params: Dict[str, float]

User parameters

ward_seed_index: int = None

The index of the seeding ward if there are daily imports