How to use the cartopy.crs.LambertConformal function in Cartopy

data_arr['wind_dir'].values * units.degree)
data['eastward_wind'], data['northward_wind'] = u, v

# Convert the fraction value into a code of 0-8, which can be used to pull out
# the appropriate symbol
data['cloud_coverage'] = (8 * data_arr['cloud_fraction']).fillna(10).values.astype(int)

# Map weather strings to WMO codes, which we can use to convert to symbols
# Only use the first symbol if there are multiple
wx_text = data_arr['weather'].fillna('')
data['present_weather'] = [wx_code_map[s.split()[0] if ' ' in s else s] for s in wx_text]

###########################################
# All the data wrangling is finished, just need to set up plotting and go:
# Set up the map projection and set up a cartopy feature for state borders
proj = ccrs.LambertConformal(central_longitude=-95, central_latitude=35,
                             standard_parallels=[35])
state_boundaries = feat.NaturalEarthFeature(category='cultural',
                                            name='admin_1_states_provinces_lines',
                                            scale='110m', facecolor='none')

###########################################
# The payoff
# ----------

# Change the DPI of the resulting figure. Higher DPI drastically improves the
# look of the text rendering
plt.rcParams['savefig.dpi'] = 255

# Create the figure and an axes set to the projection
fig = plt.figure(figsize=(20, 10))
add_metpy_logo(fig, 1080, 290, size='large')

u_300[mask_300] = np.nan
v_300[mask_300] = np.nan

# 850 hPa
mask_850 = ma.masked_less_equal(wspd_850, 0.66 * np.max(wspd_850)).mask
u_850[mask_850] = np.nan
v_850[mask_850] = np.nan

################################
# **Create the Plot**
#
# With the data now ready, we will create the plot


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


# Coordinates to limit map area
bounds = [-122., -75., 25., 50.]

#########################
# Plot the composite
fig = plt.figure(1, figsize=(17, 12))
ax = fig.add_subplot(1, 1, 1, projection=crs)
ax.set_extent(bounds, crs=ccrs.PlateCarree())
ax.coastlines('50m', edgecolor='black', linewidth=0.75)
ax.add_feature(cfeature.STATES, linewidth=0.25)

# Plot Lifted Index
cs1 = ax.contour(lon, lat, lifted_index, range(-8, -2, 2), transform=ccrs.PlateCarree(),
                 colors='red', linewidths=0.75, linestyles='solid', zorder=7)

def as_cartopy_crs(self):
        # We're either north or south polar. Set a cutoff accordingly.
        if self.secant_latitudes is not None:
            lats = self.secant_latitudes
            max_lat = lats[0]
            if len(lats) == 2:
                max_lat = lats[0] if abs(lats[0]) > abs(lats[1]) else lats[1]
            cutoff = -30 if max_lat > 0 else 30
        else:
            cutoff = None

        globe = self._ellipsoid_to_globe(self.ellipsoid, ccrs.Globe())

        return ccrs.LambertConformal(
            central_longitude=self.central_lon,
            central_latitude=self.central_lat,
            false_easting=self.false_easting,
            false_northing=self.false_northing,
            globe=globe,
            cutoff=cutoff,
            standard_parallels=self.secant_latitudes,
        )

# **Interpolate The Data**
#
# Now that the data is ready, we can interpolate to the new isobaric levels. The data is
# interpolated from the irregular pressure values for each sigma level to the new input
# mandatory isobaric levels. `mpcalc.log_interp` will interpolate over a specified dimension
# with the `axis` argument. In this case, `axis=1` will correspond to interpolation on the
# vertical axis. The interpolated data is output in a list, so we will pull out each
# 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')

# get data for the plot
        data = self._get_data(
            field, sweep, mask_tuple, filter_transitions, gatefilter)
        x, y = self._get_x_y(sweep, edges, filter_transitions)

        # mask the data where outside the limits
        if mask_outside:
            data = np.ma.masked_outside(data, vmin, vmax)

        # initialize instance of GeoAxes if not provided
        if hasattr(ax, 'projection'):
            projection = ax.projection
        else:
            if projection is None:
                # set map projection to LambertConformal if none is specified
                projection = cartopy.crs.LambertConformal(
                    central_longitude=lon_0, central_latitude=lat_0)
                warnings.warn("No projection was defined for the axes."
                              + " Overridding defined axes and using default "
                              + "axes.", UserWarning)
            ax = plt.axes(projection=projection)

        if min_lon:
            ax.set_extent([min_lon, max_lon, min_lat, max_lat],
                          crs=cartopy.crs.PlateCarree())
        elif width:
            ax.set_extent([-width/2., width/2., -height/2., height/2.],
                          crs=self.grid_projection)

        # plot the data
        if norm is not None:  # if norm is set do not override with vmin/vmax
            vmin = vmax = None

avor_500 = mpcalc.absolute_vorticity(uwnd_er, vwnd_er, dx, dy, lats * units.degrees,
                                     dim_order='yx')


######################################################################
# Map Creation
# ------------
#
# This next set of code creates the plot and draws contours on a Lambert
# Conformal map centered on -100 E longitude. The main view is over the
# CONUS with geopotential heights contoured every 60 m and absolute
# vorticity colorshaded (:math:`*10^5`).
#

# Set up the projection that will be used for plotting
mapcrs = ccrs.LambertConformal(central_longitude=-100, central_latitude=35,
                               standard_parallels=(30, 60))

# Set up the projection of the data; if lat/lon then PlateCarree is what you want
datacrs = ccrs.PlateCarree()

# Start the figure and create plot axes with proper projection
fig = plt.figure(1, figsize=(14, 12))
ax = plt.subplot(111, projection=mapcrs)
ax.set_extent([-130, -72, 20, 55], ccrs.PlateCarree())

# Add geopolitical boundaries for map reference
ax.add_feature(cfeature.COASTLINE.with_scale('50m'))
ax.add_feature(cfeature.STATES.with_scale('50m'))

# Absolute Vorticity colors
# Use two different colormaps from matplotlib and combine into one color set

"""

###########################################
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
import numpy as np
import scipy.ndimage as ndimage
import xarray as xr

from metpy.cbook import get_test_data
from metpy.plots import add_metpy_logo

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

crs = ccrs.LambertConformal(central_longitude=-100.0, central_latitude=45.0)

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


# Function used to create the map subplots
def plot_background(ax):
    ax.set_extent([235., 290., 20., 55.])
    ax.add_feature(cfeature.COASTLINE.with_scale('50m'), linewidth=0.5)
    ax.add_feature(cfeature.STATES, linewidth=0.5)
    ax.add_feature(cfeature.BORDERS, linewidth=0.5)
    return ax


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

# Open the example netCDF data

# **Interpolate The Data**
#
# Now that the data is ready, we can interpolate to the new isobaric levels. The data is
# interpolated from the irregular pressure values for each sigma level to the new input
# mandatory isobaric levels. `mpcalc.log_interp` will interpolate over a specified dimension
# with the `axis` argument. In this case, `axis=1` will correspond to interpolation on the
# vertical axis. The interpolated data is output in a list, so we will pull out each
# 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')

def lookup_projection(projection_code):
    """Get a Cartopy projection based on a short abbreviation."""
    import cartopy.crs as ccrs

    projections = {'lcc': ccrs.LambertConformal(central_latitude=40, central_longitude=-100,
                                                standard_parallels=[30, 60]),
                   'ps': ccrs.NorthPolarStereo(central_longitude=-100),
                   'mer': ccrs.Mercator()}
    return projections[projection_code]

)
        mp.axes.append(mp.ax)
    elif mp.sector == "custom":
        mp.ax = make_axes(
            axbounds,
            [kwargs["west"], kwargs["east"], kwargs["south"], kwargs["north"]],
            kwargs.get("projection", ccrs.Mercator()),
            aspect,
        )
        mp.axes.append(mp.ax)

    elif mp.sector == "north_america":
        mp.ax = make_axes(
            axbounds,
            [-145.5, -2.566, 1, 46.352],
            ccrs.LambertConformal(
                central_longitude=-107.0, central_latitude=50.0
            ),
            aspect,
        )
        mp.axes.append(mp.ax)

    elif mp.sector in ["conus", "nws"]:
        mp.ax = make_axes(
            axbounds,
            [
                reference.CONUS_WEST + 14,
                reference.CONUS_EAST - 12,
                reference.CONUS_SOUTH,
                reference.CONUS_NORTH + 1,
            ],
            reference.EPSG[5070],