def hydrate_datastore(_):
root = Path("/tmp")
data = root / "data"
figs = root / "figs"
data.mkdir(exist_ok=True)
figs.mkdir(exist_ok=True)
# define data versions for api files
paths = {
"v3": [data_path(i) for i in (1, 2)],
"v4": [data_path(i) for i in (3, 4, 5, 6, 7, 8, 9, 10)]
}
for target in paths['v3'] + paths['v4']:
download_data(data, target)
df = load_all_data(v3_paths=[data / filepath for filepath in paths['v3']],
v4_paths=[data / filepath for filepath in paths['v4']])
data_recency = str(df["date_announced"].max()).split()[0]
run_date = str(pd.Timestamp.now()).split()[0]
print(f"data_recency: {data_recency}")
print(f"run_date : {run_date}")
df["hash"] = df.apply(lambda x: hash(tuple(x)), axis=1)
df["report_date"] = run_date
df.to_csv(data / f"hashed_records_{run_date}.csv")
print(df.tail())
import pandas as pd
from adaptive.etl.commons import download_data
from adaptive.etl.covid19india import data_path, get_time_series, load_all_data
from adaptive.utils import setup
data, _ = setup()
paths = {
"v3": [data_path(i) for i in (1, 2)],
"v4": [data_path(i) for i in range(3, 18)]
}
for target in paths['v3'] + paths['v4']:
download_data(data, target)
df = load_all_data(v3_paths=[data / filepath for filepath in paths['v3']],
v4_paths=[data / filepath for filepath in paths['v4']])
schema = {"Deceased": "dD", "Recovered": "dR", "Hospitalized": "dT"}
def assemble_time_series(df):
ts = get_time_series(df)
deltas = ts[schema.keys()]\
.rename(columns = schema)
deltas = deltas.reindex(pd.date_range(deltas.index.min(),
deltas.index.max()),
fill_value=0)
merged = deltas.merge(deltas.cumsum(axis=0).rename(columns=lambda _: _[1]),
left_index=True,
right_index=True).astype(int)
merged.index.name = "date"
sero = pd.read_stata("data/kadata.labdate.dta")\
.drop(columns = ["_merge"])\
sero["S"] = sero["elisa_pos15"]
sero["t0"] = sero["date_med"]
sero["td"] = sero["t0"] + pd.Timedelta(days=30)
sero["hr"] = sero.hom_region.map(hom_regions_numeric)
# pull down COVID 19 India data
paths = {
"v3": [data_path(i) for i in (1, 2)],
"v4": [data_path(i) for i in range(3, 19)]
}
# for target in paths['v3'] + paths['v4']:
# download_data(data, target)
df = load_all_data(v3_paths = [data/filepath for filepath in paths['v3']], v4_paths = [data/filepath for filepath in paths['v4']])\
.query("detected_state == 'Karnataka'")
# get all deaths in KA on Aug 29 by district
get_time_series(df, "detected_district")\
.query("status_change_date <= 'Aug 29, 2020'", engine = "python")\
.Deceased.sum(level = 0)\
.drop("Other State")\
.astype(int)\
.to_csv(data/"ka_cumulative_deaths_aug29.csv")
# aggregate time series by hom_region
df["detected_region"] = df.detected_district.map(hom_regions_rev)
ka_ts = get_time_series(df.dropna(subset=["detected_region"]),
"detected_region").rename(columns={
"Deceased": "dD",
"Hospitalized": "dT",