june.logger.read_logger

class june.logger.read_logger.ReadLogger(output_path: str = 'results', root_output_file: str = 'logger', n_processes: int = 1)

Read hdf5 file saved by the logger, and produce useful data frames

output_path:

path to simulation’s output

root_output_path:

name of file saved by simulation

__init__(output_path: str = 'results', root_output_file: str = 'logger', n_processes: int = 1)

Read hdf5 file saved by the logger, and produce useful data frames

output_path:

path to simulation’s output

root_output_path:

name of file saved by simulation

_load_infected_data_for_rank(rank: int)

Load data on infected people over time and convert to a list of data frames self.infections_per_super_area

_load_infection_location_for_rank(rank: int) → pandas.core.frame.DataFrame

Load data frame with informtion on where did people get infected

data frame with infection locations, and average count of infections per group type

age_summary(age_ranges: List[int]) → pandas.core.frame.DataFrame

Generate a summary per age range, on how many people are recovered, dead, infected, susceptible, hospitalised or in intensive care, per time step.

age_ranges:

list of ages that determine the boundaries of the bins. Example: [0,5,10,100] -> Bins : [0,4] , [5,9], [10,99]

A data frame whose index is the date recorded, and columns are super area, number of recovered, dead, infected…

draw_symptom_trajectories(window_length: int = 50, n_people: int = 4) → pandas.core.frame.DataFrame

Get data frame with symptoms trajectories of n_people random people that are infected in a time window starting at a random time and recording for window_length time steps

window_lengh:

number of time steps to record

n_people:

number of random infected people to follow

data frame summarising people’s trajectories identified by their id

get_config()
get_location_infections_timeseries(start_date=None, end_date=None)

Get a data frame timeseries with the number of infection happening at each type of place, within the given time period

start_date:

first date to count

end_date:

last date to count

get_locations_infections(start_date=None, end_date=None) → pandas.core.frame.DataFrame

Get a data frame with the number of infection happening at each type of place, within the given time period

start_date:

first date to count

end_date:

last date to count

get_meta_info(parameters=None)
get_parameters(parameters=None, max_depth=8)

Get the parameters which are stored in the logger.

parameters:

which parameters to recover. default [“beta”, “alpha_physical”, “infection_seed”, “asyptomatic_ratio”].

max_depth:

maximum nested dictionary depth to stop searching. Default = 8.

Returns

nested dictionary of parameters the simulation was run with.

get_r() → pandas.core.frame.DataFrame

Get R value as a function of time

data frame with R value, date as index

load_hospital_capacity() → pandas.core.frame.DataFrame

Load data on variation of number of patients in hospitals over time

data frame indexed by time stamp

load_hospital_characteristics() → pandas.core.frame.DataFrame

Get data frame with the coordinates of all hospitals in the world, and their number of beds

data frame indexed by the hospital id

load_infected_data()
load_infection_location() → pandas.core.frame.DataFrame
load_population_data()

Load data related to population (age, sex, …)

process_symptoms(symptoms_df: pandas.core.frame.DataFrame, n_people: int) → pandas.core.frame.DataFrame

Given a dataframe with time stamp and a list of symptoms representing the symptoms of every infected person, produce a summary with the number of recovered, dead, infected, susceptible and hospitalised people

symptoms_df:

data frame with a list of symptoms per time step

n_people:

number of total people (including susceptibles)

A data frame whose index is the date recorded, and columns are number of recovered, dead, infected…

region_summary() → pandas.core.frame.DataFrame

Generate a summary for regions, on how many people are recovered, dead, infected, susceptible, hospitalised or in intensive care, per time step. Returns ——- A data frame whose index is the date recorded, and columns are regions, number of recovered, dead, infected…

repack_dict(hdf5_obj, output_dict, base_path, output_name=None, depth=0, max_depth=8)

Pack datesets into a (nested) dictionary.

hdf5_obj

an open hdf5 object.

output_dict

an empty dictionary to store output data in

base_path

the path to start at

run_summary()
super_area_summary() → pandas.core.frame.DataFrame

Generate a summary for super areas, on how many people are recovered, dead, infected, susceptible, hospitalised or in intensive care, per time step.

A data frame whose index is the date recorded, and columns are super area, number of recovered, dead, infected…

super_areas_to_region_mapping(super_areas, super_area_region_path=PosixPath('/home/sadie/JUNE/data/input/geography/area_super_area_region.csv'))
world_summary() → pandas.core.frame.DataFrame

Generate a summary at the world level, on how many people are recovered, dead, infected, susceptible, hospitalised or in intensive care, per time step.

A data frame whose index is the date recorded, and columns are number of recovered, dead, infected…