def vaccinated_to_df(vaccinated: List[Vaccinated]) -> pd.DataFrame:
df = pd.DataFrame(vaccinated)
# Move field to top level.
df["data_date"] = df["source"].apply(lambda s: s["data_date"])
df["real_date"] = df["source"].apply(lambda s: s["real_date"])
df["dose"] = df["slice"].apply(lambda s: s["dose"].csv_str())
df["group"] = df["slice"].apply(lambda s: Group(**s["group"]).csv_str())
df["location"] = df["slice"].apply(
lambda s: Location(**s["location"]).csv_str())
df = df.drop("source", axis=1)
df = df.drop("slice", axis=1)
df["vaccinated"] = df["vaccinated"].astype(int)
return df
def __parse_df_weekly(source: Source,
df: pd.DataFrame) -> Iterable[Vaccinated]:
def is_start(cell) -> bool:
return type(cell) == str and (cell.lower() == "region of residence"
or cell.lower()
== "nhs region of residence")
def is_end(cell) -> bool:
return type(cell) == str and cell.lower() == "data quality notes:"
def is_nan(cell) -> bool:
return type(cell) == float and math.isnan(cell)
a = df.to_numpy()
# Trim.
(start_y, ), (start_x, ) = np.where(np.vectorize(is_start)(a))
(end_y, ), (_, ) = np.where(np.vectorize(is_end)(a))
a = a[start_y:end_y, start_x:]
# Remove NaNs.
is_nans = np.vectorize(is_nan)(a)
a = a[:, ~np.all(is_nans, axis=0)]
a = a[~np.all(is_nans, axis=1), :]
# Fill in dose row.
filled_in_doses = []
current_dose = None
for population in a[0, 1:]:
if not is_nan(population):
current_dose = population
filled_in_doses.append(current_dose)
a[0, 1:] = filled_in_doses
vaccinated_by_slice: DefaultDict[Slice, int] = defaultdict(int)
for y in range(2, a.shape[0]):
for x in range(1, a.shape[1]):
dose = a[0, x]
group = a[1, x]
location = a[y, 0]
vaccinated = a[y, x]
ignore = [
# Ignore population estimates.
"population estimates",
# Ignore precalculated %
"% who have had at least 1 dose",
"% who have had both doses",
# Ignore dose summaries.
"total 1st doses",
"total 2nd doses",
]
if any(map(lambda d: d in dose.lower(), ignore)):
continue
if type(group) == str and "percent of all" in group.lower():
# Ignore percentage reports.
continue
is_dose_and_group_all = "cumulative total doses to date" in dose.lower(
)
if dose in ["1st dose", "1st dose5"]:
dose = Dose.DOSE_1
elif dose in ["2nd dose", "2nd dose5"]:
dose = Dose.DOSE_2
elif is_dose_and_group_all:
dose = Dose.ALL
else:
raise AssertionError(
f"Unexpected dose {dose} in source {source}")
if is_dose_and_group_all:
group = ALL_AGES
else:
group = Group.from_csv_str(group)
if re.match(r"^Total\d?$", location):
location = ALL_LOCATIONS
else:
location = Location(location)
vaccinated_by_slice[Slice(dose, group, location)] += vaccinated
for slice_, vaccinated in vaccinated_by_slice.items():
yield Vaccinated(source, vaccinated, slice_)
def __get_population_by_group() -> Dict[Group, int]:
disjoint_ages = {
Group(0, 15): 10_816_679,
Group(16, 29): 10_910_865,
Group(30, 34): 4_709_736,
Group(35, 39): 4_483_905,
Group(40, 44): 3_414_297,
Group(45, 49): 3_715_812,
Group(50, 54): 3_907_461,
Group(55, 59): 3_670_651,
Group(60, 64): 3_111_835,
Group(65, 69): 2_796_740,
Group(70, 74): 2_779_326,
Group(75, 79): 1_940_686,
Group(80, None): 2_836_964,
}
cumulative_ages = dict()
cumulative_sum = 0
for group in sorted(disjoint_ages.keys(), key=lambda g: g.age_lower):
cumulative_sum += disjoint_ages[group]
if group.age_lower == 0:
continue
cumulative_ages[Group(0, group.age_upper)] = cumulative_sum
return {k.csv_str(): v for k, v in {**disjoint_ages, **cumulative_ages}.items()}