def main()

in covid19_spread/data/usa/symptom_survey/fetch.py [0:0]

• 43 lines of code
• 4 McCabe index (conditional complexity)

def main(geo_value, source, signal):
    # grab start and end date from metadata
    df = covidcast.metadata().drop(columns=["time_type", "min_lag", "max_lag"])
    df.min_time = pd.to_datetime(df.min_time)
    df.max_time = pd.to_datetime(df.max_time)
    df = df.query(
        f"data_source == '{source}' and signal == '{signal}' and geo_type == '{geo_value}'"
    )
    assert len(df) == 1
    base_date = df.iloc[0].min_time - timedelta(1)
    end_date = df.iloc[0].max_time
    dfs = []
    current_date = base_date
    while current_date < end_date:
        current_date = current_date + timedelta(1)
        date_str = current_date.strftime("%Y%m%d")
        os.makedirs(os.path.join(SCRIPT_DIR, geo_value, source), exist_ok=True)
        fout = f"{SCRIPT_DIR}/{geo_value}/{source}/{signal}-{date_str}.csv"

        # d/l only if we don't have the file already
        if os.path.exists(fout):
            dfs.append(pd.read_csv(fout))
            continue

        for _ in range(3):
            res = Epidata.covidcast(source, signal, "day", geo_value, [date_str], "*")
            print(date_str, res["result"], res["message"])
            if res["result"] == 1:
                break

        if res["result"] != 1:
            # response may be non-zero if there aren't enough respondants
            # See: https://github.com/cmu-delphi/delphi-epidata/issues/613#event-4962274038
            print(f"Skipping {source}/{signal} for {date_str}")
            continue

        df = pd.DataFrame(res["epidata"])
        df.rename(
            columns={
                "geo_value": geo_value,
                "time_value": "date",
                "value": signal,
                "direction": f"{signal}_direction",
                "stderr": f"{signal}_stderr",
                "sample_size": f"{signal}_sample_size",
            },
            inplace=True,
        )
        df.to_csv(fout, index=False)
        dfs.append(df)
    pd.concat(dfs).to_csv(f"{SCRIPT_DIR}/{geo_value}/{source}/{signal}.csv")