data.loaders

Load data

Functions

Name	Description
get_age_distribution	Load age distribution for a given country or countries.
get_age_distribution_over_time	Load age distribution for a given country or countries over time.
get_birth_rates	Load crude birth rates for a given country
get_country_aliases	Define aliases for countries with odd names in the data
get_death_rates	Load death rates for a given country or countries.
get_total_pop	Load total population for a given country or countries.
load_calib_data	Normalize calibration target data into a single wide DataFrame.
map_entries	Find a match between the JSON file and the provided location(s).

data.loaders.get_age_distribution(
    location=None,
    year=None,
    total_pop_file=None,
    age_datafile=None,
)

Load age distribution for a given country or countries.

Name	Type	Description	Default
location	str	name of the country to load the age distribution for	`None`
year	int	year to load the age distribution for	`None`
total_pop_file	str	optional filepath to save total population size for every year	`None`

Name	Type	Description
age_data	array	Numpy array of age distributions, or dict if multiple locations

data.loaders.get_age_distribution_over_time(location=None, popage_datafile=None)

Load age distribution for a given country or countries over time.

Name	Type	Description	Default
location	str	name of the country to load the age distribution for	`None`

Name	Type	Description
age_data	`dataframe`	Pandas dataframe with age distribution over time

data.loaders.get_birth_rates(location=None, birth_datafile=None)

Load crude birth rates for a given country

Name	Type	Description	Default
location	str or list	name of the country to load the birth rates for	`None`
birth_datafile	str / `Path`	read from this CSV instead of the bundled data (schema: Time, CBR)	`None`

Name	Type	Description
birth_rates	`arr`	years and crude birth rates

data.loaders.get_country_aliases()

Define aliases for countries with odd names in the data

data.loaders.get_death_rates(
    location=None,
    by_sex=True,
    overall=False,
    death_datafile=None,
)

Load death rates for a given country or countries.

Name	Type	Description	Default
location	str or list	name of the country or countries to load the age distribution for	`None`
by_sex	bool	whether to rates by sex	`True`
overall	bool	whether to load total rate	`False`
death_datafile	str / `Path`	read from this CSV instead of the bundled data (schema: Time, AgeGrpStart, Sex, mx)	`None`

Name	Type	Description
death_rates	dict	death rates by age and sex

data.loaders.get_total_pop(location=None, pop_datafile=None)

Load total population for a given country or countries.

Name	Type	Description	Default
location	str or list	name of the country to load the total population for	`None`

Name	Type	Description
pop_data	`dataframe`	Dataframe of year and pop_size columns

data.loaders.load_calib_data(data)

Normalize calibration target data into a single wide DataFrame.

pd.DataFrame (index=‘t’, dot-scoped columns): assumed standardized; returned unchanged (with the t index enforced).
dict[str, pd.DataFrame]: legacy form; each value is a wide DataFrame keyed by a target name ('all_hpv.cancers'). Multi-column values are flattened with the key as scope prefix.
list / tuple of paths or long-format DataFrames: each item is a long CSV / DataFrame with year, name, [age, sex, genotype,] value columns; dispatched by column presence and merged outer-join on year.

Returns: a single pd.DataFrame with float year index (name=‘t’) and dot-scoped columns.

data.loaders.map_entries(json, location, df=None)

Find a match between the JSON file and the provided location(s).

Name	Type	Description	Default
json	list or dict	the data being loaded	required
location	list or str	the list of locations to pull from	required