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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Cross Section Analysis
The MetPy function metpy.interpolate.cross_section can obtain a cross-sectional slice through
gridded data.
Step1:  Getting the data
This example uses NARR reanalysis data
for 18 UTC 04 April 1987 from NCEI.
We use MetPy's CF parsing to get the data ready for use, and squeeze down the size-one time
dimension.
Step2:  Define start and end points
Step3:  Get the cross section, and convert lat/lon to supplementary coordinates
Step4:  For this example, we will be plotting potential temperature, relative humidity, and
tangential/normal winds. And so, we need to calculate those, and add them to the dataset
Step5:  Now, we can make the plot.
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Python Code:
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
import numpy as np
import xarray as xr
import metpy.calc as mpcalc
from metpy.cbook import get_test_data
from metpy.interpolate import cross_section
Explanation: Cross Section Analysis
The MetPy function metpy.interpolate.cross_section can obtain a cross-sectional slice through
gridded data.
End of explanation
data = xr.open_dataset(get_test_data('narr_example.nc', False))
data = data.metpy.parse_cf().squeeze()
print(data)
Explanation: Getting the data
This example uses NARR reanalysis data
for 18 UTC 04 April 1987 from NCEI.
We use MetPy's CF parsing to get the data ready for use, and squeeze down the size-one time
dimension.
End of explanation
start = (37.0, -105.0)
end = (35.5, -65.0)
Explanation: Define start and end points:
End of explanation
cross = cross_section(data, start, end).set_coords(('lat', 'lon'))
print(cross)
Explanation: Get the cross section, and convert lat/lon to supplementary coordinates:
End of explanation
cross['Potential_temperature'] = mpcalc.potential_temperature(
    cross['isobaric'],
    cross['Temperature']
)
cross['Relative_humidity'] = mpcalc.relative_humidity_from_specific_humidity(
    cross['isobaric'],
    cross['Temperature'],
    cross['Specific_humidity']
)
cross['u_wind'] = cross['u_wind'].metpy.convert_units('knots')
cross['v_wind'] = cross['v_wind'].metpy.convert_units('knots')
cross['t_wind'], cross['n_wind'] = mpcalc.cross_section_components(
    cross['u_wind'],
    cross['v_wind']
)
print(cross)
Explanation: For this example, we will be plotting potential temperature, relative humidity, and
tangential/normal winds. And so, we need to calculate those, and add them to the dataset:
End of explanation
# Define the figure object and primary axes
fig = plt.figure(1, figsize=(16., 9.))
ax = plt.axes()
# Plot RH using contourf
rh_contour = ax.contourf(cross['lon'], cross['isobaric'], cross['Relative_humidity'],
                         levels=np.arange(0, 1.05, .05), cmap='YlGnBu')
rh_colorbar = fig.colorbar(rh_contour)
# Plot potential temperature using contour, with some custom labeling
theta_contour = ax.contour(cross['lon'], cross['isobaric'], cross['Potential_temperature'],
                           levels=np.arange(250, 450, 5), colors='k', linewidths=2)
theta_contour.clabel(theta_contour.levels[1::2], fontsize=8, colors='k', inline=1,
                     inline_spacing=8, fmt='%i', rightside_up=True, use_clabeltext=True)
# Plot winds using the axes interface directly, with some custom indexing to make the barbs
# less crowded
wind_slc_vert = list(range(0, 19, 2)) + list(range(19, 29))
wind_slc_horz = slice(5, 100, 5)
ax.barbs(cross['lon'][wind_slc_horz], cross['isobaric'][wind_slc_vert],
         cross['t_wind'][wind_slc_vert, wind_slc_horz],
         cross['n_wind'][wind_slc_vert, wind_slc_horz], color='k')
# Adjust the y-axis to be logarithmic
ax.set_yscale('symlog')
ax.set_yticklabels(np.arange(1000, 50, -100))
ax.set_ylim(cross['isobaric'].max(), cross['isobaric'].min())
ax.set_yticks(np.arange(1000, 50, -100))
# Define the CRS and inset axes
data_crs = data['Geopotential_height'].metpy.cartopy_crs
ax_inset = fig.add_axes([0.125, 0.665, 0.25, 0.25], projection=data_crs)
# Plot geopotential height at 500 hPa using xarray's contour wrapper
ax_inset.contour(data['x'], data['y'], data['Geopotential_height'].sel(isobaric=500.),
                 levels=np.arange(5100, 6000, 60), cmap='inferno')
# Plot the path of the cross section
endpoints = data_crs.transform_points(ccrs.Geodetic(),
                                      *np.vstack([start, end]).transpose()[::-1])
ax_inset.scatter(endpoints[:, 0], endpoints[:, 1], c='k', zorder=2)
ax_inset.plot(cross['x'], cross['y'], c='k', zorder=2)
# Add geographic features
ax_inset.coastlines()
ax_inset.add_feature(cfeature.STATES.with_scale('50m'), edgecolor='k', alpha=0.2, zorder=0)
# Set the titles and axes labels
ax_inset.set_title('')
ax.set_title(f'NARR Cross-Section \u2013 {start} to {end} \u2013 '
             f'Valid: {cross["time"].dt.strftime("%Y-%m-%d %H:%MZ").item()}\n'
             'Potential Temperature (K), Tangential/Normal Winds (knots), Relative Humidity '
             '(dimensionless)\nInset: Cross-Section Path and 500 hPa Geopotential Height')
ax.set_ylabel('Pressure (hPa)')
ax.set_xlabel('Longitude (degrees east)')
rh_colorbar.set_label('Relative Humidity (dimensionless)')
plt.show()
Explanation: Now, we can make the plot.
End of explanation
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