How to use the siphon._tools.get_wind_components function in siphon

site_id : str
            The three letter ICAO identifier of the station for which data should be
            downloaded.

        Returns
        -------
            :class:`pandas.DataFrame` containing the data

        """
        raw_data = self._get_data_raw(time, site_id)
        soup = BeautifulSoup(raw_data, 'html.parser')
        tabular_data = StringIO(soup.find_all('pre')[0].contents[0])
        col_names = ['pressure', 'height', 'temperature', 'dewpoint', 'direction', 'speed']
        df = pd.read_fwf(tabular_data, skiprows=5, usecols=[0, 1, 2, 3, 6, 7], names=col_names)
        df['u_wind'], df['v_wind'] = get_wind_components(df['speed'],
                                                         np.deg2rad(df['direction']))

        # Drop any rows with all NaN values for T, Td, winds
        df = df.dropna(subset=('temperature', 'dewpoint', 'direction', 'speed',
                               'u_wind', 'v_wind'), how='all').reset_index(drop=True)

        # Parse metadata
        meta_data = soup.find_all('pre')[1].contents[0]
        lines = meta_data.splitlines()

        # If the station doesn't have a name identified we need to insert a
        # record showing this for parsing to proceed.
        if 'Station number' in lines[1]:
            lines.insert(1, 'Station identifier: ')

        station = lines[1].split(':')[1].strip()

idx = np.argmax(~(np.isnan(data['tmpc']) | np.isnan(data['dwpc'])))

        # Stuff data into a pandas dataframe
        df = pd.DataFrame()
        df['pressure'] = ma.masked_invalid(data['pres'][idx:])
        df['height'] = ma.masked_invalid(data['hght'][idx:])
        df['temperature'] = ma.masked_invalid(data['tmpc'][idx:])
        df['dewpoint'] = ma.masked_invalid(data['dwpc'][idx:])
        df['direction'] = ma.masked_invalid(data['drct'][idx:])
        df['speed'] = ma.masked_invalid(data['sknt'][idx:])
        df['station'] = data['station'][idx:]
        df['time'] = [datetime.strptime(valid, '%Y-%m-%dT%H:%M:%SZ')
                      for valid in data['valid'][idx:]]

        # Calculate the u and v winds
        df['u_wind'], df['v_wind'] = get_wind_components(df['speed'],
                                                         np.deg2rad(df['direction']))

        # Drop any rows with all NaN values for T, Td, winds
        df = df.dropna(subset=('temperature', 'dewpoint', 'direction', 'speed',
                               'u_wind', 'v_wind'), how='all').reset_index(drop=True)

        # Add unit dictionary
        df.units = {'pressure': 'hPa',
                    'height': 'meter',
                    'temperature': 'degC',
                    'dewpoint': 'degC',
                    'direction': 'degrees',
                    'speed': 'knot',
                    'u_wind': 'knot',
                    'v_wind': 'knot',
                    'station': None,

'potential_temperature': 'Kelvin',
                        'potential_temperature_gradient': 'Kelvin / kilometer',
                        'virtual_temperature': 'Kelvin',
                        'virtual_potential_temperature': 'Kelvin',
                        'vapor_pressure': 'Pascal',
                        'saturation_vapor_pressure': 'Pascal',
                        'reported_relative_humidity': 'percent',
                        'calculated_relative_humidity': 'percent',
                        'u_wind': 'meter / second',
                        'u_wind_gradient': '(meter / second) / kilometer)',
                        'v_wind': 'meter / second',
                        'v_wind_gradient': '(meter / second) / kilometer)',
                        'refractive_index': 'unitless'}

        else:
            df['u_wind'], df['v_wind'] = get_wind_components(df['speed'],
                                                             np.deg2rad(df['direction']))
            df['u_wind'] = np.round(df['u_wind'], 1)
            df['v_wind'] = np.round(df['v_wind'], 1)

            df = df.dropna(subset=('temperature', 'direction', 'speed',
                           'dewpoint_depression', 'u_wind', 'v_wind'),
                           how='all').reset_index(drop=True)

            df['dewpoint'] = df['temperature'] - df['dewpoint_depression']

            df.drop('dewpoint_depression', axis=1, inplace=True)

            df.units = {'etime': 'second',
                        'pressure': 'hPa',
                        'height': 'meter',
                        'temperature': 'degC',