public_folder / "mobility.csv")
# Create the v1 CSV files which only require simple column mapping
v1_v2_name_map = {"response": "oxford-government-response"}
for v1_name, v2_name in v1_v2_name_map.items():
data = read_file(f"{URL_OUTPUTS_PROD}/{v2_name}.csv")
rename_columns = {
col: snake_to_camel_case(col)
for col in data.columns
}
export_csv(data.rename(columns=rename_columns),
public_folder / f"{v1_name}.csv")
# Create the v1 forecast.csv file
export_csv(
build_forecast(read_file(public_folder / "data_minimal.csv")),
public_folder / "data_forecast.csv",
)
# Convert all v1 CSV files to JSON using record format
for csv_file in pbar([*(public_folder).glob("*.csv")],
desc="V1 JSON conversion"):
data = read_file(csv_file, low_memory=False)
json_path = str(csv_file).replace("csv", "json")
data.to_json(json_path, orient="records")
# Create the v2 folder
v2_folder = public_folder / "v2"
v2_folder.mkdir(exist_ok=True, parents=True)
# Download the v2 tables which can fit under 100MB
def main():
# Create the folder which will be published
public_folder = SRC / ".." / "output" / "public"
public_folder.mkdir(exist_ok=True, parents=True)
# Create the v1 data.csv file
main_table = read_file(f"{URL_OUTPUTS_PROD}/main.csv", low_memory=False)
data = main_table[main_table.aggregation_level < 2]
rename_columns = {
"date": "Date",
"key": "Key",
"country_code": "CountryCode",
"country_name": "CountryName",
"subregion1_code": "RegionCode",
"subregion1_name": "RegionName",
"total_confirmed": "Confirmed",
"total_deceased": "Deaths",
"latitude": "Latitude",
"longitude": "Longitude",
"population": "Population",
}
data = data[rename_columns.keys()].rename(columns=rename_columns)
data = data.dropna(subset=["Confirmed", "Deaths"], how="all")
data = data.sort_values(["Date", "Key"])
export_csv(data, public_folder / "data.csv")
# Create the v1 data_minimal.csv file
export_csv(data[["Date", "Key", "Confirmed", "Deaths"]], public_folder / "data_minimal.csv")
# Create the v1 data_latest.csv file
latest = main_table[main_table.aggregation_level < 2]
latest = latest.sort_values("date").groupby("key").last().reset_index()
latest = latest[rename_columns.keys()].rename(columns=rename_columns)
latest = latest.dropna(subset=["Confirmed", "Deaths"], how="all")
latest = latest.sort_values(["Key", "Date"])
export_csv(latest, public_folder / "data_latest.csv")
# Create the v1 weather.csv file
weather = read_file(f"{URL_OUTPUTS_PROD}/weather.csv")
weather = weather[weather.key.apply(lambda x: len(x.split("_")) < 3)]
weather = weather.rename(columns={"noaa_distance": "distance", "noaa_station": "station"})
rename_columns = {col: snake_to_camel_case(col) for col in weather.columns}
export_csv(weather.rename(columns=rename_columns), public_folder / "weather.csv")
# Create the v1 mobility.csv file
mobility = read_file(f"{URL_OUTPUTS_PROD}/mobility.csv")
mobility = mobility[mobility.key.apply(lambda x: len(x.split("_")) < 3)]
mobility = drop_na_records(mobility, ["date", "key"])
rename_columns = {
col: snake_to_camel_case(col).replace("Mobility", "") for col in mobility.columns
}
export_csv(mobility.rename(columns=rename_columns), public_folder / "mobility.csv")
# Create the v1 CSV files which only require simple column mapping
v1_v2_name_map = {"response": "oxford-government-response"}
for v1_name, v2_name in v1_v2_name_map.items():
data = read_file(f"{URL_OUTPUTS_PROD}/{v2_name}.csv")
rename_columns = {col: snake_to_camel_case(col) for col in data.columns}
export_csv(data.rename(columns=rename_columns), public_folder / f"{v1_name}.csv")
# Create the v1 forecast.csv file
export_csv(
build_forecast(read_file(public_folder / "data_minimal.csv")),
public_folder / "data_forecast.csv",
)
# Convert all v1 CSV files to JSON using record format
for csv_file in pbar([*(public_folder).glob("*.csv")], desc="V1 JSON conversion"):
data = read_file(csv_file, low_memory=False)
json_path = str(csv_file).replace("csv", "json")
data.to_json(json_path, orient="records")
# Create the v2 folder
v2_folder = public_folder / "v2"
v2_folder.mkdir(exist_ok=True, parents=True)
# Download the v2 tables which can fit under 100MB
for table_name in pbar(
(
"by-age",
"by-sex",
"demographics",
"economy",
"epidemiology",
"geography",
"health",
"hospitalizations",
"index",
"mobility",
"oxford-government-response",
"weather",
"worldbank",
"worldpop",
),
desc="V2 download",
):
for ext in ("csv", "json"):
with tempfile.NamedTemporaryFile() as tmp:
tmp_path = Path(tmp.name)
download(f"{URL_OUTPUTS_PROD}/{table_name}.{ext}", tmp)
# Check that the output is less than 100 MB before copying it to the output folder
if tmp_path.stat().st_size < 100 * 1000 * 1000:
shutil.copyfile(tmp_path, v2_folder / f"{table_name}.{ext}")
mobility = read_file(v2_folder / "mobility.csv")
mobility = mobility[mobility.key.apply(lambda x: len(x.split("_")) < 3)]
mobility = mobility.drop(
columns=["mobility_driving", "mobility_transit", "mobility_walking"])
mobility = drop_na_records(mobility, ["date", "key"])
rename_columns = {
col: snake_to_camel_case(col).replace("Mobility", "")
for col in mobility.columns
}
export_csv(mobility.rename(columns=rename_columns), v1_folder / "mobility.csv")
# Create the v1 CSV files which only require simple column mapping
v1_v2_name_map = {"response": "oxford-government-response"}
for v1_name, v2_name in v1_v2_name_map.items():
data = read_file(v2_folder / f"{v2_name}.csv")
rename_columns = {col: snake_to_camel_case(col) for col in data.columns}
export_csv(data.rename(columns=rename_columns),
v1_folder / f"{v1_name}.csv")
# Create the v1 forecast.csv file
print("Computing forecast...")
export_csv(build_forecast(read_file(v1_folder / "data_minimal.csv")),
v1_folder / "data_forecast.csv")
# Convert all v1 CSV files to JSON using record format
print("Converting V1 CSV to JSON...")
for csv_file in (v1_folder).glob("*.csv"):
data = read_file(csv_file, low_memory=False)
json_path = str(csv_file).replace("csv", "json")
data.to_json(json_path, orient="records")
# Create the v1 weather.csv file
weather = read_file(v2_folder / "weather.csv")
weather = weather[weather.key.apply(lambda x: len(x.split("_")) < 3)]
weather = weather.rename(columns={"noaa_distance": "distance", "noaa_station": "station",})
rename_columns = {col: snake_to_camel_case(col) for col in weather.columns}
export_csv(weather.rename(columns=rename_columns), v1_folder / "weather.csv")
# TMP: Get mobility from V1
mobility = read_file("https://open-covid-19.github.io/data/mobility.csv")
export_csv(mobility, v1_folder / "mobility.csv")
# Create the v1 CSV files which only require column mapping
v1_v2_name_map = {"response": "oxford-government-response"}
for v1_name, v2_name in v1_v2_name_map.items():
data = read_file(v2_folder / f"{v2_name}.csv")
rename_columns = {col: snake_to_camel_case(col) for col in data.columns}
export_csv(data.rename(columns=rename_columns), v1_folder / f"{v1_name}.csv")
# Create the v1 forecast.csv file
print("Computing forecast...")
export_csv(
build_forecast(read_file(v1_folder / "data_minimal.csv")), v1_folder / "data_forecast.csv"
)
# Convert all v1 CSV files to JSON using record format
print("Converting V1 CSV to JSON...")
for csv_file in (v1_folder).glob("*.csv"):
data = read_file(csv_file, low_memory=False)
json_path = str(csv_file).replace("csv", "json")
data.to_json(json_path, orient="records")