How to use the metpy.units.units.degC function in MetPy

def test_stationplot_api():
    """Test the StationPlot API."""
    fig = plt.figure(figsize=(9, 9))

    # testing data
    x = np.array([1, 5])
    y = np.array([2, 4])

    # Make the plot
    sp = StationPlot(fig.add_subplot(1, 1, 1), x, y, fontsize=16)
    sp.plot_barb([20, 0], [0, -50])
    sp.plot_text('E', ['KOKC', 'ICT'], color='blue')
    sp.plot_parameter('NW', [10.5, 15] * units.degC, color='red')
    sp.plot_symbol('S', [5, 7], high_clouds, color='green')

    sp.ax.set_xlim(0, 6)
    sp.ax.set_ylim(0, 6)

    return fig

def test_stationplot_clipping():
    """Test the that clipping can be enabled as a default parameter."""
    fig = plt.figure(figsize=(9, 9))

    # testing data
    x = np.array([1, 5])
    y = np.array([2, 4])

    # Make the plot
    sp = StationPlot(fig.add_subplot(1, 1, 1), x, y, fontsize=16, clip_on=True)
    sp.plot_barb([20, 0], [0, -50])
    sp.plot_text('E', ['KOKC', 'ICT'], color='blue')
    sp.plot_parameter('NW', [10.5, 15] * units.degC, color='red')
    sp.plot_symbol('S', [5, 7], high_clouds, color='green')

    sp.ax.set_xlim(1, 5)
    sp.ax.set_ylim(1.75, 4.25)

    return fig

def test_skewt_units():
    """Test that plotting with SkewT works with units properly."""
    fig = plt.figure(figsize=(9, 9))
    skew = SkewT(fig, aspect='auto')

    skew.ax.axvline(np.array([273]) * units.kelvin, color='purple')
    skew.ax.axhline(np.array([50000]) * units.Pa, color='red')
    skew.ax.axvline(np.array([-20]) * units.degC, color='darkred')
    skew.ax.axvline(-10, color='orange')
    return fig

def plot_metpy(data, title="", saveplot=None, showplot=True):

    # Convert data into a suitable format for metpy.
    _altitude = data[:,0] * units('m')
    p = mpcalc.height_to_pressure_std(_altitude)
    T = data[:,3] * units.degC
    Td = data[:,4] * units.degC
    wind_speed = data[:,1] * units('m/s')
    wind_direction = data[:,2] * units.degrees
    u, v = mpcalc.wind_components(wind_speed, wind_direction)


    fig = plt.figure(figsize=(6,8))
    skew = SkewT(fig=fig)
    skew.plot(p, T, 'r')
    skew.plot(p, Td, 'g')

    my_interval = np.arange(300, 1000, 50) * units('mbar')
    ix = mpcalc.resample_nn_1d(p, my_interval)
    skew.plot_barbs(p[ix], u[ix], v[ix])
    skew.ax.set_ylim(1000,300)
    skew.ax.set_xlim(-40, 30)
    skew.plot_dry_adiabats()

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

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

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

# 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.get_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

# Compute the Gradient Wind via an approximation
dydx = mpcalc.first_derivative(Z, delta=dx, axis=1)
d2ydx2 = mpcalc.first_derivative(dydx, delta=dx, axis=1)
R = ((1 + dydx.m**2)**(3. / 2.)) / d2ydx2.m

geo_mag = mpcalc.wind_speed(ugeo, vgeo)
grad_mag = geo_mag.m - (geo_mag.m**2) / (f.magnitude * R)

ugrad, vgrad = mpcalc.wind_components(grad_mag * units('m/s'), wdir)

# Calculate Ageostrophic wind
uageo = ugrad - ugeo
vageo = vgrad - vgeo

# Compute QVectors
uqvect, vqvect = mpcalc.q_vector(ugeo, vgeo, T * units.degC, 500 * units.hPa, dx, dy)

# Calculate divergence of the ageostrophic wind
div = mpcalc.divergence(uageo, vageo, dx, dy, dim_order='yx')

# Calculate Relative Vorticity Advection
relvor = mpcalc.vorticity(ugeo, vgeo, dx, dy, dim_order='yx')
adv = mpcalc.advection(relvor, (ugeo, vgeo), (dx, dy), dim_order='yx')


######################################################################
# Create figure containing Geopotential Heights, Temperature, Divergence
# of the Ageostrophic Wind, Relative Vorticity Advection (shaded),
# geostrphic wind barbs, and Q-vectors.
#

fig = plt.figure(figsize=(10, 10))

This is called from /request/coop/fe.phtml
"""
import datetime
import zipfile
from io import BytesIO, StringIO

import pandas as pd
from pandas.io.sql import read_sql
import psycopg2.extras
from paste.request import parse_formvars
from pyiem.network import Table as NetworkTable
from pyiem.util import get_dbconn
from metpy.units import units

DEGC = units.degC
DEGF = units.degF
EXL = "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"


def f2c(val):
    """Convert F to C."""
    return (val * DEGF).to(DEGC).m


def get_scenario_period(ctx):
    """ Compute the inclusive start and end dates to fetch scenario data for
    Arguments:
        ctx dictionary context this app was called with
    """
    sts = datetime.date(ctx["scenario_year"], ctx["ets"].month, ctx["ets"].day)
    ets = datetime.date(ctx["scenario_year"], 12, 31)

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)

# 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

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)

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

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

# Grid for plots
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

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.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)

###########################################
# 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.get_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')