How to use the metpy.plots.add_metpy_logo function in MetPy

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

hght = df['height'].values * units.hPa
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
skew.plot(p, T, 'r')
skew.plot(p, Td, 'g')
skew.plot_barbs(p, u, v)
skew.ax.set_ylim(1000, 100)

# Add the relevant special lines
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
skew.plot_mixing_lines()

# variable for plotting.

height, temp = log_interpolate_1d(plevs, pres, hgt, temperature, axis=1)

####################################
# **Plotting the Data for 700 hPa.**

# Set up our projection
crs = ccrs.LambertConformal(central_longitude=-100.0, central_latitude=45.0)

# Set the forecast hour
FH = 1

# Create the figure and grid for subplots
fig = plt.figure(figsize=(17, 12))
add_metpy_logo(fig, 470, 320, size='large')

# Plot 700 hPa
ax = plt.subplot(111, projection=crs)
ax.add_feature(cfeature.COASTLINE.with_scale('50m'), linewidth=0.75)
ax.add_feature(cfeature.STATES, linewidth=0.5)

# Plot the heights
cs = ax.contour(lon, lat, height[FH, 0, :, :], transform=ccrs.PlateCarree(),
                colors='k', linewidths=1.0, linestyles='solid')
ax.clabel(cs, fontsize=10, inline=1, inline_spacing=7,
          fmt='%i', rightside_up=True, use_clabeltext=True)

# Contour the temperature
cf = ax.contourf(lon, lat, temp[FH, 0, :, :], range(-20, 20, 1), cmap=plt.cm.RdBu_r,
                 transform=ccrs.PlateCarree())
cb = fig.colorbar(cf, orientation='horizontal', extend='max', aspect=65, shrink=0.5,

date = testdata['DATE']

# ID For Plotting on Meteogram
probe_id = '0102A'

data = {'wind_speed': (np.array(ws) * units('m/s')).to(units('knots')),
        'wind_speed_max': (np.array(wsmax) * units('m/s')).to(units('knots')),
        'wind_direction': np.array(wd) * units('degrees'),
        'dewpoint': dewpoint_rh((np.array(temp) * units('degC')).to(units('K')),
                                np.array(rh) / 100.).to(units('degF')),
        'air_temperature': (np.array(temp) * units('degC')).to(units('degF')),
        'mean_slp': calc_mslp(np.array(temp), np.array(pres), hgt_example) * units('hPa'),
        'relative_humidity': np.array(rh), 'times': np.array(date)}

fig = plt.figure(figsize=(20, 16))
add_metpy_logo(fig, 250, 180)
meteogram = Meteogram(fig, data['times'], probe_id)
meteogram.plot_winds(data['wind_speed'], data['wind_direction'], data['wind_speed_max'])
meteogram.plot_thermo(data['air_temperature'], data['dewpoint'])
meteogram.plot_rh(data['relative_humidity'])
meteogram.plot_pressure(data['mean_slp'])
fig.subplots_adjust(hspace=0.5)
plt.show()

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

# Create CartoPy projection information for the file
globe = ccrs.Globe(ellipse='sphere', semimajor_axis=proj_var.earth_radius,
                   semiminor_axis=proj_var.earth_radius)
proj = ccrs.LambertConformal(central_longitude=proj_var.longitude_of_central_meridian,
                             central_latitude=proj_var.latitude_of_projection_origin,
                             standard_parallels=[proj_var.standard_parallel],
                             globe=globe)

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

# Plot the image
fig = plt.figure(figsize=(10, 12))
add_metpy_logo(fig, 125, 145)
ax = fig.add_subplot(1, 1, 1, projection=proj)
wv_norm, wv_cmap = ctables.registry.get_with_range('WVCIMSS', 100, 260)
wv_cmap.set_under('k')
im = ax.imshow(dat[:], cmap=wv_cmap, norm=wv_norm, zorder=0,
               extent=ds.img_extent, origin='upper')
ax.coastlines(resolution='50m', zorder=2, color='black')

plt.show()

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])
    skew.plot(lcl_pressure, lcl_temperature, 'ko', markerfacecolor='black')

    # Calculate full parcel profile and add to plot as black line
    prof = mpcalc.parcel_profile(p, T[0], Td[0]).to('degC')

# First item in ray is header, which has azimuth angle
az = np.array([ray[0].az_angle for ray in f.sweeps[sweep]])

# 5th item is a dict mapping a var name (byte string) to a tuple
# of (header, data array)
ref_hdr = f.sweeps[sweep][0][4][b'REF'][0]
ref_range = np.arange(ref_hdr.num_gates) * ref_hdr.gate_width + ref_hdr.first_gate
ref = np.array([ray[4][b'REF'][1] for ray in f.sweeps[sweep]])

rho_hdr = f.sweeps[sweep][0][4][b'RHO'][0]
rho_range = (np.arange(rho_hdr.num_gates + 1) - 0.5) * rho_hdr.gate_width + rho_hdr.first_gate
rho = np.array([ray[4][b'RHO'][1] for ray in f.sweeps[sweep]])

###########################################
fig, axes = plt.subplots(1, 2, figsize=(15, 8))
add_metpy_logo(fig, 190, 85, size='large')
for var_data, var_range, ax in zip((ref, rho), (ref_range, rho_range), axes):
    # Turn into an array, then mask
    data = np.ma.array(var_data)
    data[np.isnan(data)] = np.ma.masked

    # Convert az,range to x,y
    xlocs = var_range * np.sin(np.deg2rad(az[:, np.newaxis]))
    ylocs = var_range * np.cos(np.deg2rad(az[:, np.newaxis]))

    # Plot the data
    ax.pcolormesh(xlocs, ylocs, data, cmap='viridis')
    ax.set_aspect('equal', 'datalim')
    ax.set_xlim(-40, 20)
    ax.set_ylim(-30, 30)
    add_timestamp(ax, f.dt, y=0.02, high_contrast=True)

date = testdata['DATE']

# ID For Plotting on Meteogram
probe_id = '0102A'

data = {'wind_speed': (np.array(ws) * units('m/s')).to(units('knots')),
        'wind_speed_max': (np.array(wsmax) * units('m/s')).to(units('knots')),
        'wind_direction': np.array(wd) * units('degrees'),
        'dewpoint': dewpoint_rh((np.array(temp) * units('degC')).to(units('K')),
                                np.array(rh) / 100.).to(units('degF')),
        'air_temperature': (np.array(temp) * units('degC')).to(units('degF')),
        'mean_slp': calc_mslp(np.array(temp), np.array(pres), hgt_example) * units('hPa'),
        'relative_humidity': np.array(rh), 'times': np.array(date)}

fig = plt.figure(figsize=(20, 16))
add_metpy_logo(fig, 250, 180)
meteogram = Meteogram(fig, data['times'], probe_id)
meteogram.plot_winds(data['wind_speed'], data['wind_direction'], data['wind_speed_max'])
meteogram.plot_thermo(data['air_temperature'], data['dewpoint'])
meteogram.plot_rh(data['relative_humidity'])
meteogram.plot_pressure(data['mean_slp'])
fig.subplots_adjust(hspace=0.5)
plt.show()

# 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)

# Add the relevant special lines
skew.plot_dry_adiabats()
skew.plot_moist_adiabats()
skew.plot_mixing_lines()
skew.ax.set_ylim(1000, 100)

# Add the MetPy logo!
fig = plt.gcf()
add_metpy_logo(fig, 115, 100)

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

# Example of defining your own vertical barb spacing
skew = SkewT()

# 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')

# Set spacing interval--Every 50 mb from 1000 to 100 mb
my_interval = np.arange(100, 1000, 50) * units('mbar')

# Get indexes of values closest to defined interval
ix = mpcalc.resample_nn_1d(p, my_interval)

###########################################
# Get temperature information
x_masked, y_masked, t = remove_nan_observations(xp, yp, data['temperature'].values)
tempx, tempy, temp = interpolate_to_grid(x_masked, y_masked, t, interp_type='cressman',
                                         minimum_neighbors=3, search_radius=400000, hres=35000)

temp = np.ma.masked_where(np.isnan(temp), temp)

###########################################
# Set up the map and plot the interpolated grids appropriately.
levels = list(range(-20, 20, 1))
cmap = plt.get_cmap('viridis')
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)

fig = plt.figure(figsize=(20, 10))
add_metpy_logo(fig, 360, 120, size='large')
view = fig.add_subplot(1, 1, 1, projection=to_proj)

view.set_extent([-120, -70, 20, 50])
view.add_feature(cfeature.STATES.with_scale('50m'))
view.add_feature(cfeature.OCEAN)
view.add_feature(cfeature.COASTLINE.with_scale('50m'))
view.add_feature(cfeature.BORDERS, linestyle=':')

cs = view.contour(slpgridx, slpgridy, slp, colors='k', levels=list(range(990, 1034, 4)))
view.clabel(cs, inline=1, fontsize=12, fmt='%i')

mmb = view.pcolormesh(tempx, tempy, temp, cmap=cmap, norm=norm)
fig.colorbar(mmb, shrink=.4, pad=0.02, boundaries=levels)

view.barbs(windgridx, windgridy, uwind, vwind, alpha=.4, length=5)

###########################################
# Get temperature information
x_masked, y_masked, t = remove_nan_observations(xp, yp, data['temperature'].values)
tempx, tempy, temp = interpolate_to_grid(x_masked, y_masked, t, interp_type='cressman',
                                         minimum_neighbors=3, search_radius=400000, hres=35000)

temp = np.ma.masked_where(np.isnan(temp), temp)

###########################################
# Set up the map and plot the interpolated grids appropriately.
levels = list(range(-20, 20, 1))
cmap = plt.get_cmap('viridis')
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)

fig = plt.figure(figsize=(20, 10))
add_metpy_logo(fig, 360, 120, size='large')
view = fig.add_subplot(1, 1, 1, projection=to_proj)

view.set_extent([-120, -70, 20, 50])
view.add_feature(cfeature.STATES.with_scale('50m'))
view.add_feature(cfeature.OCEAN)
view.add_feature(cfeature.COASTLINE.with_scale('50m'))
view.add_feature(cfeature.BORDERS, linestyle=':')

cs = view.contour(slpgridx, slpgridy, slp, colors='k', levels=list(range(990, 1034, 4)))
view.clabel(cs, inline=1, fontsize=12, fmt='%i')

mmb = view.pcolormesh(tempx, tempy, temp, cmap=cmap, norm=norm)
fig.colorbar(mmb, shrink=.4, pad=0.02, boundaries=levels)

view.barbs(windgridx, windgridy, uwind, vwind, alpha=.4, length=5)