def read_json_data() -> dict:
    """
    reads downloaded cached json file contents
    renames some country names according to ref database
    calls prepare_time_series
    adds _Per_Million fields
    NO LONGER exports as json file
    returns as a dict
    """
    d_json_downloaded = helper.read_json_file(file_cache)

    # rename some countries
    d_countries_to_rename = {}
    d_countries_to_rename['US'] = 'United States'
    d_countries_to_rename['Korea, South'] = 'South Korea'
    d_countries_to_rename['Taiwan*'] = 'Taiwan'
    d_countries_to_rename['Burma'] = 'Myanmar'
    d_countries_to_rename['Cote d\'Ivoire'] = 'Ivory Coast'
    d_countries_to_rename['West Bank and Gaza'] = 'Palestinian Territory'
    d_countries_to_rename['Timor-Leste'] = 'Timor Leste'
    d_countries_to_rename['Holy See'] = 'Vatican'
    for country_name_old, country_name_new in d_countries_to_rename.items():
        d_json_downloaded[country_name_new] = d_json_downloaded[
            country_name_old]
        del d_json_downloaded[country_name_old]

    d_countries = {}
    # re-format date using my date_format(y,m,d) function
    for country in d_json_downloaded.keys():
        country_data = d_json_downloaded[country]
        l_time_series = []

        pop = read_population(country)
        if pop != None:
            pop_in_million = pop / 1000000
        else:
            pop_in_million = None

        for entry in country_data:
            d = {}
            # entry in country_data:
            s = entry['date']
            l = s.split("-")
            d['Date'] = helper.date_format(int(l[0]), int(l[1]), int(l[2]))
            d['Cases'] = int(entry['confirmed'])
            d['Deaths'] = int(entry['deaths'])
            l_time_series.append(d)

        l_time_series = helper.prepare_time_series(l_time_series)

        for i in range(len(l_time_series)):
            d = l_time_series[i]

            # _Per_Million
            d = helper.add_per_million(d, pop_in_million)

        d_countries[country] = l_time_series

    return d_countries
def read_csv_to_dict() -> dict:
    """
    read and convert the source csv file, containing: federalstate,infections,deaths,date,newinfections,newdeaths
    re-calc _New via helper.prepare_time_series
    add _Per_Million via helper.add_per_million_via_lookup
    """

    global d_ref_states
    # Preparations
    d_states_data = {'BW': [], 'BY': [], 'BE': [], 'BB': [], 'HB': [], 'HH': [], 'HE': [], 'MV': [
    ], 'NI': [], 'NW': [], 'RP': [], 'SL': [], 'SN': [], 'ST': [], 'SH': [], 'TH': []}
    # add German sum
    d_states_data['DE-total'] = []
    d_german_sums = {}  # date -> 'infections', 'deaths', 'new infections', 'new deaths'

    # data body
    with open(download_file, mode='r', encoding='utf-8') as f:
        csv_reader = csv.DictReader(f, delimiter=",")
        for row in csv_reader:
            d = {}
            s = row['date']
            l = s.split("-")
            d['Date'] = helper.date_format(
                int(l[0]), int(l[1]), int(l[2]))
            d['Cases'] = int(row["infections"])
            d['Deaths'] = int(row["deaths"])

            if row["federalstate"] == 'Baden-Württemberg':
                d_states_data['BW'].append(d)
            elif row["federalstate"] == 'Bavaria':
                d_states_data['BY'].append(d)
            elif row["federalstate"] == 'Berlin':
                d_states_data['BE'].append(d)
            elif row["federalstate"] == 'Brandenburg':
                d_states_data['BB'].append(d)
            elif row["federalstate"] == 'Bremen':
                d_states_data['HB'].append(d)
            elif row["federalstate"] == 'Hamburg':
                d_states_data['HH'].append(d)
            elif row["federalstate"] == 'Hesse':
                d_states_data['HE'].append(d)
            elif row["federalstate"] == 'Lower Saxony':
                d_states_data['NI'].append(d)
            elif row["federalstate"] == 'North Rhine-Westphalia':
                d_states_data['NW'].append(d)
            elif row["federalstate"] == 'Mecklenburg-Western Pomerania':
                d_states_data['MV'].append(d)
            elif row["federalstate"] == 'Rhineland-Palatinate':
                d_states_data['RP'].append(d)
            elif row["federalstate"] == 'Saarland':
                d_states_data['SL'].append(d)
            elif row["federalstate"] == 'Saxony':
                d_states_data['SN'].append(d)
            elif row["federalstate"] == 'Saxony-Anhalt':
                d_states_data['ST'].append(d)
            elif row["federalstate"] == 'Schleswig-Holstein':
                d_states_data['SH'].append(d)
            elif row["federalstate"] == 'Thuringia':
                d_states_data['TH'].append(d)
            else:
                assert 1 == 2, f"ERROR: unknown state: {row['federalstate']}"

            # add to German sum
            if d['Date'] not in d_german_sums:
                d2 = {}
                d2['Cases'] = d['Cases']
                d2['Deaths'] = d['Deaths']
            else:
                d2 = d_german_sums[d['Date']]
                d2['Cases'] += d['Cases']
                d2['Deaths'] += d['Deaths']
            d_german_sums[d['Date']] = d2
            del d2

    # German sum -> same dict
    for datum in d_german_sums.keys():
        d = d_german_sums[datum]
        d['Date'] = datum  # add date field
        d_states_data['DE-total'].append(d)
    del d_german_sums, d

    # check if DE-total of today and yesterday are equal, if so: remove last date
    if d_states_data['DE-total'][-1]['Cases'] == d_states_data['DE-total'][-2]['Cases']:
        print("WARNING: DE cases sum is unchanged")
        for code in d_states_data:
            d_states_data[code].pop()
    print(f"DE-States Last Date: {d_states_data['DE-total'][-1]['Date']}")

    for code in d_states_data.keys():
        l_time_series = d_states_data[code]

        # add days past, _New, _Last_Week, etc
        l_time_series = helper.prepare_time_series(l_time_series)

        for i in range(len(l_time_series)):
            d = l_time_series[i]
            # add per Million rows
            d = helper.add_per_million_via_lookup(d, d_ref_states, code)

        # # fit cases data
        # dataCases = []
        # dataDeaths = []
        # for i in range(1, len(l_time_series)):
        #     # x= day , y = cases
        #     dataCases.append(
        #         (
        #             l_time_series[i]['Days_Past'],
        #             l_time_series[i]['Cases']
        #         )
        #     )
        #     dataDeaths.append(
        #         (
        #             l_time_series[i]['Days_Past'],
        #             l_time_series[i]['Deaths']
        #         )
        #     )

        # fit_series_res = helper.series_of_fits(
        #     dataCases, fit_range=7, max_days_past=60)
        # for i in range(0, len(l_time_series)):
        #     this_Doubling_Time = ""
        #     this_days_past = l_time_series[i]['Days_Past']
        #     if this_days_past in fit_series_res:
        #         this_Doubling_Time = fit_series_res[this_days_past]
        #     l_time_series[i]['Cases_Doubling_Time'] = this_Doubling_Time

        # fit_series_res = helper.series_of_fits(
        #     dataDeaths, fit_range=7, max_days_past=60)
        # for i in range(0, len(l_time_series)):
        #     this_Doubling_Time = ""
        #     this_days_past = l_time_series[i]['Days_Past']
        #     if this_days_past in fit_series_res:
        #         this_Doubling_Time = fit_series_res[this_days_past]
        #     l_time_series[i]['Deaths_Doubling_Time'] = this_Doubling_Time

        d_states_data[code] = l_time_series

    return d_states_data