def METHOD_NAME():
    '''
    DESCRIPTION:
    Calls the National Grid online publication of incoming flows to the NTS and produces
    two numpy tables, one with zonal intakes and one with terminal intakes.
    Units are mcm/day.
    '''
    #--- opening intakes webpage ---#
    os.system('cls' if os.name == 'nt' else 'clear')
    while True:
        driver = webdriver.Firefox()
        try:
            driver.get("https://mip-prd-web.azurewebsites.net/InstantaneousView")
        except:
            print('ONLINE DATABASE UNAVAILABLE, CHECK NETWORK CONNECTION')
            break 
        wait = WebDriverWait(driver,10)
        html = driver.page_source
        #--- converting all the information to a table ---#
        #--- as all data presented in a large html table ---#
        soup = bs.BeautifulSoup(html,'lxml')
        driver.quit()
        table = []
        for tr in soup.find_all('tr')[1:]:
            tds = tr.find_all('td')
            row = []
            for i in tds:
                row.append(i.text)
            table.append(row)
        table = pd.DataFrame(table)
        #--- ontaining only the required values ---#
        table = table.to_numpy()[4:,1:]
        zone_names = table[1:29,0]
        latest_zone_value = table[1:29,6]
        latest_zone_value[6] = latest_zone_value[6][1:]
        latest_zone_value = latest_zone_value.astype(np.float)
        zone_supply = np.concatenate(([zone_names],[latest_zone_value]),axis=0).T
        zone_supply_pd = pd.DataFrame(zone_supply)
        terminal_names = table[47:59,0]
        latest_terminal_value = table[47:59,6].astype(np.float)
        terminal_supply = np.concatenate(([terminal_names],[latest_terminal_value]),axis=0).T
        terminal_supply_pd = pd.DataFrame(terminal_supply)
        overall_df = pd.concat((zone_supply_pd,terminal_supply_pd),axis=1,ignore_index=True)
        header = ['Zone Supply','Instananeous Flow (mcm/day)','Terminal Supply','Instananeous Flow (mcm/day)']
        print(tabulate(overall_df,headers=header,showindex="never"))
        overall_df.to_excel('Intakedata.xlsx')
        for i in reversed(range(120)):
            print('TIME FOR NEXT UPDATE: ',i,' SECONDS',end='\r')
            time.sleep(1)
        os.system('cls' if os.name == 'nt' else 'clear')
    return 