How to use the metpy.calc.wind_components function in MetPy

def test_wind_comps_scalar():
    """Test wind components calculation with scalars."""
    u, v = wind_components(8 * units('m/s'), 150 * units.deg)
    assert_almost_equal(u, -4 * units('m/s'), 3)
    assert_almost_equal(v, 6.9282 * units('m/s'), 3)

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

##########################################################################

# We will pull the data out of the example dataset into individual variables and
# assign units.

p = df['pressure'].values * units.hPa
T = df['temperature'].values * units.degC
Td = df['dewpoint'].values * units.degC
wind_speed = df['speed'].values * units.knots
wind_dir = df['direction'].values * units.degrees
u, v = mpcalc.wind_components(wind_speed, wind_dir)

##########################################################################
# Thermodynamic Calculations
# --------------------------
#
# Often times we will want to calculate some thermodynamic parameters of a
# sounding. The MetPy calc module has many such calculations already implemented!
#
# * **Lifting Condensation Level (LCL)** - The level at which an air parcel's
#   relative humidity becomes 100% when lifted along a dry adiabatic path.
# * **Parcel Path** - Path followed by a hypothetical parcel of air, beginning
#   at the surface temperature/pressure and rising dry adiabatically until
#   reaching the LCL, then rising moist adiabatially.

# Calculate the LCL
lcl_pressure, lcl_temperature = mpcalc.lcl(p[0], T[0], Td[0])

skiprows=5, usecols=[0, 1, 2, 3, 6, 7], names=col_names)

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

###########################################
# We will pull the data out of the example dataset into individual variables and
# assign units.

p = df['pressure'].values * units.hPa
T = df['temperature'].values * units.degC
Td = df['dewpoint'].values * units.degC
wind_speed = df['speed'].values * units.knots
wind_dir = df['direction'].values * units.degrees
u, v = mpcalc.wind_components(wind_speed, wind_dir)

###########################################

# Create a new figure. The dimensions here give a good aspect ratio
fig = plt.figure(figsize=(9, 9))
add_metpy_logo(fig, 630, 80, size='large')

# Grid for plots
gs = gridspec.GridSpec(3, 3)
skew = SkewT(fig, rotation=45, subplot=gs[:, :2])

# Plot the data using normal plotting functions, in this case using
# log scaling in Y, as dictated by the typical meteorological plot
skew.plot(p, T, 'r')
skew.plot(p, Td, 'g')
skew.plot_barbs(p, u, v)

def plot_skewt(df):
    # We will pull the data out of the example dataset into individual variables
    # and assign units.
    p = df['pressure'].values * units.hPa
    T = df['temperature'].values * units.degC
    Td = df['dewpoint'].values * units.degC
    wind_speed = df['speed'].values * units.knots
    wind_dir = df['direction'].values * units.degrees
    u, v = mpcalc.wind_components(wind_speed, wind_dir)

    # Create a new figure. The dimensions here give a good aspect ratio.
    fig = plt.figure(figsize=(9, 9))
    add_metpy_logo(fig, 115, 100)
    skew = SkewT(fig, rotation=45)

    # Plot the data using normal plotting functions, in this case using
    # log scaling in Y, as dictated by the typical meteorological plot
    skew.plot(p, T, 'r')
    skew.plot(p, Td, 'g')
    skew.plot_barbs(p, u, v)
    skew.ax.set_ylim(1000, 100)
    skew.ax.set_xlim(-40, 60)

    # Calculate LCL height and plot as black dot
    lcl_pressure, lcl_temperature = mpcalc.lcl(p[0], T[0], Td[0])

from metpy.plots import Hodograph, SkewT
from metpy.units import units

#########################################################################
# Getting Data
# ------------
#
# Upper air data can be obtained using the siphon package, but for this tutorial we will use
# some of MetPy's sample data. This event is the Veterans Day tornado outbreak in 2002.

col_names = ['pressure', 'height', 'temperature', 'dewpoint', 'direction', 'speed']

df = pd.read_fwf(get_test_data('nov11_sounding.txt', as_file_obj=False),
                 skiprows=5, usecols=[0, 1, 2, 3, 6, 7], names=col_names)

df['u_wind'], df['v_wind'] = mpcalc.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)

##########################################################################

# We will pull the data out of the example dataset into individual variables and
# assign units.

p = df['pressure'].values * units.hPa
T = df['temperature'].values * units.degC
Td = df['dewpoint'].values * units.degC
wind_speed = df['speed'].values * units.knots
wind_dir = df['direction'].values * units.degrees

cursor = dbconn.cursor()
    nt = NetworkTable("RAOB")
    df = read_sql(
        f"""
        select f.fid, f.station, pressure, dwpc, tmpc, drct, smps, height,
        levelcode from
        raob_profile_{year} p JOIN raob_flights f
        on (p.fid = f.fid) WHERE not computed and height is not null
        and pressure is not null
        ORDER by pressure DESC
    """,
        dbconn,
    )
    if df.empty or pd.isnull(df["smps"].max()):
        return
    u, v = wind_components(
        df["smps"].values * units("m/s"),
        df["drct"].values * units("degrees_north"),
    )
    df["u"] = u.to(units("m/s")).m
    df["v"] = v.to(units("m/s")).m
    count = 0
    progress = tqdm(df.groupby("fid"), disable=not sys.stdout.isatty())
    for fid, gdf in progress:
        progress.set_description("%s %s" % (year, fid))
        try:
            do_profile(cursor, fid, gdf, nt)
        except (RuntimeError, ValueError, IndexError) as exp:
            LOG.debug(
                "Profile %s fid: %s failed calculation %s",
                gdf.iloc[0]["station"],
                fid,

plot_units = ctx["units"]

    df = read_sql(
        """
        SELECT extract(doy from valid) as doy, sknt, drct from alldata
        where station = %s and sknt >= 0 and drct >= 0 and report_type = 2
    """,
        asos,
        params=(station,),
        index_col=None,
    )
    if df.empty:
        raise NoDataFound("No data Found.")

    # Compute components in MPS
    u, v = wind_components(
        (df["sknt"].values * units("knot")).to(units("meter / second")),
        df["drct"].values * units("degree"),
    )
    df["u"] = u.m
    df["v"] = v.m
    gdf = df.groupby(by="doy").mean()
    u = (
        (gdf["u"].values * units("meter / second"))
        .to(XREF_UNITS[plot_units])
        .m
    )
    v = (
        (gdf["v"].values * units("meter / second"))
        .to(XREF_UNITS[plot_units])
        .m
    )

import metpy.calc as mpcalc
from metpy.cbook import get_test_data
from metpy.plots import add_metpy_logo, Hodograph, SkewT
from metpy.units import units

###########################################
# Upper air data can be obtained using the siphon package, but for this example we will use
# some of MetPy's sample data.

col_names = ['pressure', 'height', 'temperature', 'dewpoint', 'direction', 'speed']

df = pd.read_fwf(get_test_data('may4_sounding.txt', as_file_obj=False),
                 skiprows=5, usecols=[0, 1, 2, 3, 6, 7], names=col_names)

df['u_wind'], df['v_wind'] = mpcalc.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)

###########################################
# We will pull the data out of the example dataset into individual variables and
# assign units.

p = df['pressure'].values * units.hPa
T = df['temperature'].values * units.degC
Td = df['dewpoint'].values * units.degC
wind_speed = df['speed'].values * units.knots
wind_dir = df['direction'].values * units.degrees
u, v = mpcalc.wind_components(wind_speed, wind_dir)

and date(valid at time zone tzname) = %s
    ORDER by valid ASC
    """,
        asos,
        params=(
            ts - datetime.timedelta(days=2),
            ts + datetime.timedelta(days=2),
            ts.strftime("%Y-%m-%d"),
        ),
        index_col=None,
    )
    if df.empty:
        LOG.info("no ASOS database entries for %s", ts)
        return
    # derive some parameters
    df["u"], df["v"] = mcalc.wind_components(
        df["sknt"].values * munits.knots, df["drct"].values * munits.deg
    )
    df["localvalid_lag"] = df.groupby("iemid")["localvalid"].shift(1)
    df["timedelta"] = df["localvalid"] - df["localvalid_lag"]
    ndf = df[pd.isna(df["timedelta"])]
    df.loc[ndf.index.values, "timedelta"] = pd.to_timedelta(
        ndf["localvalid"].dt.hour * 3600.0
        + ndf["localvalid"].dt.minute * 60.0,
        unit="s",
    )
    df["timedelta"] = df["timedelta"] / np.timedelta64(1, "s")

    updates = 0
    for iemid, gdf in df.groupby("iemid"):
        if len(gdf.index) < 6:
            continue