june.groups.school.Schools

class june.groups.school.Schools(schools: List[School], school_trees: Optional[Dict[int, sklearn.neighbors._ball_tree.BallTree]] = None, agegroup_to_global_indices: dict = None)

Create a group of Schools, and provide functionality to access closest school

area_names

list of areas for which to build schools

schools:

list of school instances

school_tree:

BallTree built on all schools coordinates

agegroup_to_global_indices:

dictionary to map the

__init__(schools: List[School], school_trees: Optional[Dict[int, sklearn.neighbors._ball_tree.BallTree]] = None, agegroup_to_global_indices: dict = None)

Create a group of Schools, and provide functionality to access closest school

area_names

list of areas for which to build schools

schools:

list of school instances

school_tree:

BallTree built on all schools coordinates

agegroup_to_global_indices:

dictionary to map the

static _create_school_tree(schools_coordinates: numpy.ndarray) → sklearn.neighbors._ball_tree.BallTree

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

school_df:

dataframe with school characteristics data

Tree to query nearby schools

_make_member_ids_dict(members)

Makes a dictionary with the ids of the members.

add(group)
classmethod build_schools_for_areas(areas: june.geography.geography.Areas, school_df: pandas.core.frame.DataFrame, age_range: Tuple[int, int] = 0, 19, employee_per_clients: Dict[str, int] = None)june.groups.school.Schools

area Returns ——-

An infrastructure of schools

clear()
classmethod for_areas(areas: june.geography.geography.Areas, data_file: str = PosixPath('/home/sadie/JUNE/data/input/schools/england_schools.csv'), config_file: str = PosixPath('/home/sadie/JUNE/configs/defaults/groups/schools.yaml'))june.groups.school.Schools
area_names

list of areas for which to create populations

data_path

The path to the data directory

config

classmethod for_box_mode()
classmethod for_geography(geography: june.geography.geography.Geography, data_file: str = PosixPath('/home/sadie/JUNE/data/input/schools/england_schools.csv'), config_file: str = PosixPath('/home/sadie/JUNE/configs/defaults/groups/schools.yaml'))june.groups.school.Schools
geography

an instance of the geography class

classmethod from_file(areas: june.geography.geography.Areas, data_file: str = PosixPath('/home/sadie/JUNE/data/input/schools/england_schools.csv'), config_file: str = PosixPath('/home/sadie/JUNE/configs/defaults/groups/schools.yaml'))june.groups.school.Schools

Initialize Schools from path to data frame, and path to config file

filename:

path to school dataframe

config_filename:

path to school config dictionary

Schools instance

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

Get the k-th closest school to a given coordinate, that accepts pupils aged age

age:

age of the pupil

coordinates:

latitude and longitude

k:

k-th neighbour

ID of the k-th closest school, within school trees for a given age group

get_from_id(id)
get_spec() → str

Returns the speciailization of the super group.

static init_trees(school_df: pandas.core.frame.DataFrame, age_range: Tuple[int, int])june.groups.school.Schools

Create trees to easily find the closest school that accepts a pupil given their age

school_df:

dataframe with school characteristics data

property group_spec
property member_ids