How to use the jwql.instrument_monitors.nirspec_monitors.data_trending.utils.sql_interface.add_data function in jwql

#look for all values that fit to the given conditions
        for element in m:
            temp.append(float(element['value']))

        #return None if no applicable data was found
        if len(temp) > 2:
            length = len(temp)
            mean = statistics.mean(temp)
            deviation = statistics.stdev(temp)

            dataset = (float(m.meta['start']), float(m.meta['end']), length, mean, deviation)
            sql.add_data(conn, identifier, dataset)
        elif len(temp) == 2:
            dataset = (float(element['time']), float(element['time']), 1, temp[0], 0)
            sql.add_data(conn, identifier, dataset)
        else:
            print('No data for {}'.format(identifier))
            print(temp)

        del temp

for data in values:
            sql.add_wheel_data(conn, 'INRSI_C_GWA_X_POSITION_{}'.format(key), data)

    for key, values in GWY.items():
        for data in values:
            sql.add_wheel_data(conn, 'INRSI_C_GWA_Y_POSITION_{}'.format(key), data)

    #put all data to a database that uses a condition
    for key, value in return_data.items():
        m = m_raw_data.mnemonic(key)
        length = len(value)
        if length > 2:
            mean = statistics.mean(value)
            deviation = statistics.stdev(value)
            dataset = (float(m.meta['start']), float(m.meta['end']), length, mean, deviation)
            sql.add_data(conn, key, dataset)


    #add rest of the data to database -> no conditions applied
    for identifier in mn.mnemSet_day:

        m = m_raw_data.mnemonic(identifier)

        temp = []

        #look for all values that fit to the given conditions
        for element in m:
            temp.append(float(element['value']))

        #return None if no applicable data was found
        if len(temp) > 2:
            length = len(temp)

deviation = statistics.stdev(temp)

            dataset = (float(m.meta['start']), float(m.meta['end']), length, mean, deviation)
            sql.add_data(conn, identifier, dataset)
        else:
            print('No data for {}'.format(identifier))
            print(temp)

        del temp
    #add lamp data to database -> distiction over lamps
    for key, values in lamp_data.items():
        for data in values:
            dataset_volt = (data[0], data[1], data[5], data[6], data[7])
            dataset_curr = (data[0], data[1], data[2], data[3], data[4])
            sql.add_data(conn, 'LAMP_{}_VOLT'.format(key), dataset_volt)
            sql.add_data(conn, 'LAMP_{}_CURR'.format(key), dataset_curr)

'''

    #import mnemonic data and append dict to variable below
    m_raw_data = apt.mnemonics(path)

    #process raw data with once a day routine
    returndata = once_a_day_routine(m_raw_data)

    #put all data in a database that uses a condition
    for key, value in returndata.items():
        m = m_raw_data.mnemonic(key)
        length = len(value)
        mean = statistics.mean(value)
        deviation = statistics.stdev(value)
        dataset = (float(m.meta['start']), float(m.meta['end']), length, mean, deviation)
        sql.add_data(conn, key, dataset)

    #add rest of the data to database
    for identifier in mn.mnemSet_15min:

        m = m_raw_data.mnemonic(identifier)

        temp = []

        #look for all values that fit to the given conditions
        for element in m:
            temp.append(float(element['value']))

        #return None if no applicable data was found
        if len(temp) > 2:
            length = len(temp)
            mean = statistics.mean(temp)