metawards.Workspace

class metawards.Workspace(n_inf_classes: int = 0, nnodes: int = 0, inf_tot: Optional[List[int]] = None, pinf_tot: Optional[List[int]] = None, n_inf_wards: Optional[List[int]] = None, ward_inf_tot: Optional[List[List[int]]] = None, total_inf_ward: Optional[List[int]] = None, total_new_inf_ward: Optional[List[int]] = None, incidence: Optional[List[int]] = None, S_in_wards: Optional[List[int]] = None, E_in_wards: Optional[List[int]] = None, I_in_wards: Optional[List[int]] = None, R_in_wards: Optional[List[int]] = None, X_in_wards: Optional[Dict[str, List[int]]] = None)

This class provides a workspace for the running calculation. This pre-allocates all of the memory into arrays, which can then be used via cython memory views

__init__(n_inf_classes: int = 0, nnodes: int = 0, inf_tot: Optional[List[int]] = None, pinf_tot: Optional[List[int]] = None, n_inf_wards: Optional[List[int]] = None, ward_inf_tot: Optional[List[List[int]]] = None, total_inf_ward: Optional[List[int]] = None, total_new_inf_ward: Optional[List[int]] = None, incidence: Optional[List[int]] = None, S_in_wards: Optional[List[int]] = None, E_in_wards: Optional[List[int]] = None, I_in_wards: Optional[List[int]] = None, R_in_wards: Optional[List[int]] = None, X_in_wards: Optional[Dict[str, List[int]]] = None) → None

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).
__gt__(value, /)	Return self>value.
__init__([n_inf_classes, nnodes, inf_tot, ...])
__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)
__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().
build(network)	Create the workspace needed to run the model for the passed network
zero_all([zero_subspaces])	Reset the values of all of the arrays to zero.

Attributes

E_in_wards	The size of the E population in each ward
I_in_wards	The size of the I population in each ward
R_in_wards	The size of the R population in each ward
S_in_wards	The size of the S population in each ward
X_in_wards	The sizes of the X populations in each ward - this is for named disease stages that don't fit into S, E, I or R
__annotations__
__dataclass_fields__
__dataclass_params__
__dict__
__doc__
__hash__
__module__
__weakref__	list of weak references to the object (if defined)
incidence	The incidence of the infection (sum of infections up to disease_class == I_start)
inf_tot	Size of population in each disease stage for work infections
n_inf_classes	Number of disease classes (stages)
n_inf_wards	Number of wards with at least one individual in this disease stage
nnodes	Number of wards (nodes)
pinf_tot	Size of population in each disease stage for play infections
subspaces	The sub-workspaces used for the subnets of a multi-demographic Networks (list[Workspace])
total_inf_ward	Total number of infections in each ward over the last day This is also equal to the prevalence
total_new_inf_ward	Number of new infections in each ward over the last day
ward_inf_tot	Size of population in each disease stage in each ward

E_in_wards: List[int] = None

The size of the E population in each ward

I_in_wards: List[int] = None

The size of the I population in each ward

R_in_wards: List[int] = None

The size of the R population in each ward

S_in_wards: List[int] = None

The size of the S population in each ward

X_in_wards: Dict[str, List[int]] = None

The sizes of the X populations in each ward - this is for named disease stages that don’t fit into S, E, I or R

__eq__(other)

Return self==value.

__hash__ = None

__init__(n_inf_classes: int = 0, nnodes: int = 0, inf_tot: Optional[List[int]] = None, pinf_tot: Optional[List[int]] = None, n_inf_wards: Optional[List[int]] = None, ward_inf_tot: Optional[List[List[int]]] = None, total_inf_ward: Optional[List[int]] = None, total_new_inf_ward: Optional[List[int]] = None, incidence: Optional[List[int]] = None, S_in_wards: Optional[List[int]] = None, E_in_wards: Optional[List[int]] = None, I_in_wards: Optional[List[int]] = None, R_in_wards: Optional[List[int]] = None, X_in_wards: Optional[Dict[str, List[int]]] = None) → None

__repr__()

Return repr(self).

static build(network: Union[metawards._network.Network, metawards._networks.Networks])

Create the workspace needed to run the model for the passed network

incidence: List[int] = None

The incidence of the infection (sum of infections up to disease_class == I_start)

inf_tot: List[int] = None

Size of population in each disease stage for work infections

n_inf_classes: int = 0

Number of disease classes (stages)

n_inf_wards: List[int] = None

Number of wards with at least one individual in this disease stage

nnodes: int = 0

Number of wards (nodes)

pinf_tot: List[int] = None

Size of population in each disease stage for play infections

subspaces = None

The sub-workspaces used for the subnets of a multi-demographic Networks (list[Workspace])

total_inf_ward: List[int] = None

Total number of infections in each ward over the last day This is also equal to the prevalence

total_new_inf_ward: List[int] = None

Number of new infections in each ward over the last day

ward_inf_tot: List[List[int]] = None

Size of population in each disease stage in each ward

zero_all(zero_subspaces=True)

Reset the values of all of the arrays to zero. By default we zero the subspace networks (change this by setting zero_subspaces to False)