june.groups.hospital.Hospitals

class june.groups.hospital.Hospitals(hospitals: List[Hospital], neighbour_hospitals: int = 5, box_mode: bool = False, ball_tree=True)

Create a group of hospitals, and provide functionality to locate patients to a nearby hospital. It will check in order the first `neighbour_hospitals`, when one has space available the patient is allocated to it. If none of the closest ones has beds available it will pick one of them at random and that hospital will overflow

hospitals:

list of hospitals to aggrupate

neighbour_hospitals:

number of closest hospitals to look for

box_mode:

whether to run in single box mode, or full simulation

__init__(hospitals: List[Hospital], neighbour_hospitals: int = 5, box_mode: bool = False, ball_tree=True)

Create a group of hospitals, and provide functionality to locate patients to a nearby hospital. It will check in order the first `neighbour_hospitals`, when one has space available the patient is allocated to it. If none of the closest ones has beds available it will pick one of them at random and that hospital will overflow

hospitals:

list of hospitals to aggrupate

neighbour_hospitals:

number of closest hospitals to look for

box_mode:

whether to run in single box mode, or full simulation

_make_member_ids_dict(members)

Makes a dictionary with the ids of the members.

add(group)
clear()
classmethod create_hospital_from_df_row(area, row)
classmethod for_box_mode()
classmethod for_geography(geography, filename: str = PosixPath('/home/sadie/JUNE/data/input/hospitals/trusts.csv'), config_filename: str = PosixPath('/home/sadie/JUNE/configs/defaults/groups/hospitals.yaml'))
classmethod from_file(filename: str = PosixPath('/home/sadie/JUNE/data/input/hospitals/trusts.csv'), config_filename: str = PosixPath('/home/sadie/JUNE/configs/defaults/groups/hospitals.yaml'))june.groups.hospital.Hospitals

Initialize Hospitals from path to data frame, and path to config file.

filename:

path to hospital dataframe

config_filename:

path to hospital config dictionary

Hospitals instance

get_closest_hospitals(coordinates: Tuple[float, float], k: int) → Tuple[float, float]

Get the k-th closest hospital to a given coordinate

coordinates:

latitude and longitude

k:

k-th neighbour

ID of the k-th closest hospital

get_closest_hospitals_idx(coordinates: Tuple[float, float], k: int) → Tuple[float, float]

Get the k-th closest hospital to a given coordinate

coordinates:

latitude and longitude

k:

k-th neighbour

ID of the k-th closest hospital

get_from_id(id)
get_spec() → str

Returns the speciailization of the super group.

init_hospitals(hospital_df: pandas.core.frame.DataFrame) → List[june.groups.hospital.Hospital]

Create Hospital objects with the right characteristics, as given by dataframe.

hospital_df:

dataframe with hospital characteristics data

init_trees(hospital_coordinates: numpy.array) → sklearn.neighbors._ball_tree.BallTree

Reads hospital location and sizes, it initializes a KD tree on a sphere, to query the closest hospital to a given location.

hospital_df:

dataframe with hospital characteristics data

Tree to query nearby schools

property group_spec
property member_ids