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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:
 <h2>Create a cross-section from AWOT radar instance</h2>
<p> This example uses a gridded NetCDF windsyn file and produces a 2-panel plot
of horizontal CAPPI of reflectivity at 2 km. The flight track is overlaid along 
with wind vectors derived from the radar.
Step1:  <b>Supply input data and set some plotting parameters.</b>
Step2:  <b>Set up some characteristics for plotting.</b>
<ul>
<li>Use Cylindrical Equidistant Area map projection.</li>
<li>Set the spacing of the barbs and X-axis time step for labels.</li>
<li>Set the start and end times for subsetting.</li>
<li>Add landmarks.</li>
</ul>
Step3:  <b>Read in the flight and radar data</b>
Step4:  <b>Make a cross-section following the flight track displayed in the top panel and use the vertical wind velocity field.</b>
Step5:  <b>Now let's make a vertical cross-section along lon/lat pairs of reflectivity</b>
Step6:  <b>Here's an alternative method to produce the same plot above. And notice the second plot has discrete levels by setting the <i>discrete_cmap_levels</i> keyword.</b>
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Python Code:
# Load the needed packages
from glob import glob
import matplotlib.pyplot as plt
import numpy as np
import awot
from awot.graph.common import create_basemap
from awot.graph import RadarHorizontalPlot, RadarVerticalPlot, FlightLevel
%matplotlib inline
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
Explanation: <h2>Create a cross-section from AWOT radar instance</h2>
<p> This example uses a gridded NetCDF windsyn file and produces a 2-panel plot
of horizontal CAPPI of reflectivity at 2 km. The flight track is overlaid along 
with wind vectors derived from the radar.
End of explanation
# Set the project name
Project="DYNAMO"
# Choose what file to process
yymmdd, modn = '111124', '0351'
# Set the data directory
fDir = "/Users/guy/data/dynamo/"+yymmdd+"I/"
# Construct the full path name for windsyn NetCDF file
P3Radf = str(glob(fDir+"/*"+modn+"*windsyn*.nc")).strip('[]')
# Construct the full path name for Flight level NetCDF file
FltLevf = str(glob(fDir+"20*"+yymmdd+"*_DJ*.nc")).strip('[]')
corners = [77.8, -2.0, 79.6, -0.2]
figtitle = '24 Nov RCE'
Explanation: <b>Supply input data and set some plotting parameters.</b>
End of explanation
# Set up some characteristics for plotting
# Set map projection to use
proj = 'cea'
Wbarb_Spacing = 300 # Spacing of wind barbs along flight path (sec)
# Choose the X-axis time step (in seconds) where major labels will be
XlabStride = 60
# Optional settings
start_time = "2011-11-24 03:51:00"
end_time = "2011-11-24 04:57:00"
# Map spacing
dLon = 0.5
dLat = 0.5
# Should landmarks be plotted? [If yes, then modify the section below
Lmarks=True
if Lmarks:
    # Create a list of Landmark data
    LocMark = []
    
    # Add locations as [ StringName, Longitude, Latitude ,XlabelOffset, YlabelOffset]
    LocMark.append(['Diego Garcia', 72.4160, -7.3117, 0.1, -0.6])
    LocMark.append(['R/V Revelle', 80.5010, 0.12167, -0.4, -0.6])
    LocMark.append(['Gan', 73.1017, -0.6308, -0.9, 0.0])
    LocMark.append(['R/V Marai', 80.50, -7.98, -0.1, -0.6])
    # Build a few variables for plotting the labels
    # Build arrays for plotting
    Labels = []
    LabLons = []
    LabLats = []
    XOffset = []
    YOffset = []
    for L1, L2, L3, L4, L5 in LocMark:
        Labels.append(L1)
        LabLons.append(L2)
        LabLats.append(L3)
        XOffset.append(L4)
        YOffset.append(L5)
        # Add PPI plot at 2 km level
cappi_ht = 2000.
Explanation: <b>Set up some characteristics for plotting.</b>
<ul>
<li>Use Cylindrical Equidistant Area map projection.</li>
<li>Set the spacing of the barbs and X-axis time step for labels.</li>
<li>Set the start and end times for subsetting.</li>
<li>Add landmarks.</li>
</ul>
End of explanation
fl1 = awot.io.read_netcdf(fname=FltLevf[1:-1], platform='p-3')
r1 = awot.io.read_windsyn_tdr_netcdf(fname=P3Radf[1:-1], field_mapping=None)
Explanation: <b>Read in the flight and radar data</b>
End of explanation
fig, (axPPI, axXS) = plt.subplots(2, 1, figsize=(8, 8))
# Set the map for plotting
bm1 = create_basemap(corners=corners, proj=proj, resolution='l', area_thresh=1., 
                     lat_spacing=dLat, lon_spacing=dLon, ax=axPPI)
# Create a Flightlevel instance for the track
flp1 = FlightLevel(fl1, basemap=bm1)
flp1.plot_trackmap(start_time=start_time, end_time=end_time,
                   min_altitude=50., max_altitude= 8000.,
                   addlegend=False, addtitle=False, ax=axPPI)
# Create a RadarGrid
rgp1 = RadarHorizontalPlot(r1, basemap=bm1)
rgp1.plot_cappi('reflectivity', cappi_ht, vmin=15., vmax=60., title=' ',
#rgp1.plot_cappi('Uwind', 2., vmin=-20., vmax=20., title=' ',
#              cmap='RdBu_r',
              color_bar=True, cb_pad="10%", cb_loc='right', cb_tick_int=4,
              ax=axPPI)
rgp1.overlay_wind_vector(height_level=cappi_ht, vscale=200, vtrim=6, qcolor='0.50', 
                         refUposX=.75, refUposY=.97, plot_km=True)
flp1.plot_radar_cross_section(r1, 'Wwind', plot_km=True,
              start_time=start_time, end_time=end_time,
              vmin=-3., vmax=3., title=' ',
              cmap='RdBu_r',
              color_bar=True, cb_orient='vertical', cb_tick_int=4,
              x_axis_array='time',
              ax=axXS)
Explanation: <b>Make a cross-section following the flight track displayed in the top panel and use the vertical wind velocity field.</b>
End of explanation
fig, (axPPI2, axXS2) = plt.subplots(2, 1, figsize=(7, 7))
# Set the map for plotting
bm2 = create_basemap(corners=corners, proj=proj, resolution='l', area_thresh=1., 
                     lat_spacing=dLat, lon_spacing=dLon, ax=axPPI2)
# Create a Flightlevel instance for the track
flp2 = FlightLevel(fl1, basemap=bm2)
flp2.plot_trackmap(start_time=start_time, end_time=end_time,
                   min_altitude=50., max_altitude= 8000.,
                   addlegend=False, addtitle=False, ax=axPPI2)
# Create a RadarGrid
rgph = RadarHorizontalPlot(r1, basemap=bm2)
# Add PPI plot at 2 km
rgph.plot_cappi('reflectivity', cappi_ht, vmin=15., vmax=60., title=' ',
              color_bar=True, cb_pad="10%", cb_loc='right', cb_tick_int=4,
              ax=axPPI2)
rgph.overlay_wind_vector(height_level=2., vscale=200, vtrim=6, qcolor='0.50')
# Add Cross-sectional line to horizontal plot
rgph.plot_line_geo([78.3, 79.0], [-1.1, -1.5], lw=4, alpha=.8, line_style='w-',
                   label0=True, label_offset=(0.05,-0.05))
rgph.plot_cross_section('reflectivity', (78.3, -1.1), (79.0, -1.5),
              vmin=15., vmax=60., title=' ',
              color_bar=True, cb_orient='vertical', cb_tick_int=4,
              plot_km=True, ax=axXS2)
# Alternatively the commented out code below will also display the plot
#rgpv = RadarVerticalPlot(fl1, instrument='tdr_grid')
# Add the cross-section along those coordinates
#rgpv.plot_cross_section('dBZ', (78.3, -1.1), (79.0, -1.5),
#              vmin=15., vmax=60., title=' ',
#              color_bar=False, cb_orient='vertical', cb_tick_int=4,
#              ax=axXS)
Explanation: <b>Now let's make a vertical cross-section along lon/lat pairs of reflectivity</b>
End of explanation
fig, (axPPI3, axXS3) = plt.subplots(2, 1, figsize=(7, 7))
# Set the map for plotting
bm3 = create_basemap(corners=corners, proj=proj, resolution='l', area_thresh=1., 
                     lat_spacing=dLat, lon_spacing=dLon, ax=axPPI3)
# Create a Flightlevel instance for the track
flp2 = FlightLevel(fl1, basemap=bm3)
flp2.plot_trackmap(start_time=start_time, end_time=end_time,
                   min_altitude=50., max_altitude= 8000.,
                   addlegend=False, addtitle=False, ax=axPPI3)
# Create a RadarGrid
rgph = RadarHorizontalPlot(r1, basemap=bm3)
# Add PPI plot at 2 km level
rgph.plot_cappi('reflectivity', cappi_ht, vmin=15., vmax=60., title=' ',
              color_bar=True, cb_pad="10%", cb_loc='right', cb_tick_int=4,
              ax=axPPI3)
rgpv = RadarVerticalPlot(r1, basemap=bm3)
# Add the cross-section along those coordinates
rgpv.plot_cross_section('reflectivity', (78.3, -1.1), (79.0, -1.5),
              vmin=15., vmax=60., title=' ',
              color_bar=True, cb_orient='vertical', cb_tick_int=4,
              discrete_cmap_levels=[10., 15., 20., 25., 30., 35., 40., 45., 50., 55., 60.], ax=axXS3)
Explanation: <b>Here's an alternative method to produce the same plot above. And notice the second plot has discrete levels by setting the <i>discrete_cmap_levels</i> keyword.</b>
End of explanation
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