Produce a 4-panel plot from a gridded data file produced by the windsyn analysis program



In [2]:

    
# Load the needed packages
from glob import glob
import os
import matplotlib.pyplot as plt

import awot
from awot.graph.common import create_basemap
from awot.graph import RadarHorizontalPlot, FlightLevel

%matplotlib inline

Set user specified variables



In [3]:

    
# Set the project name
Project="DYNAMO"

# Choose what file to process
yymmdd, modn = '111124', '0705'

# Set the data directory
fdir = "/Users/guy/data/dynamo/"

# Construct the full path name for windsyn NetCDF file
P3Radf = os.path.join(fdir, yymmdd+"I", modn+"_windsyn.nc")

# Construct the full path name for Flight level NetCDF file
FltLevf = os.path.join(fdir, yymmdd+"I", "20111124I1_DJ_AC.nc")
#str(glob(fDir+"20*"+yymmdd+"*_DJ*.nc")).strip('[]')

# Optional settings
start_time = "2011-11-24 07:05:00"
end_time = "2011-11-24 07:45:00"
corners = [78.5, -0.8, 80.1, 0.8]

dLon = 0.5
dLat = 0.5

figtitle = '24 Nov RCE'

Set up some characteristics for plotting.

Use Cylindrical Equidistant Area map projection.
Set the spacing of the barbs, X-axis time step for labels, and whether to use Landmarks.



In [4]:

    
# 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

# Should landmarks be plotted? [If yes, then modify the section below
Lmarks=False

Read in flight and radar data from the NOAA P-3 aircraft



In [5]:

    
# Get the airborne data
fl1 = awot.io.read_netcdf(fname=FltLevf, platform='p-3', time_var=None)
fl2 = awot.io.read_netcdf(fname=FltLevf, platform='p-3', time_var=None)
fl3 = awot.io.read_netcdf(fname=FltLevf, platform='p-3', time_var=None)
fl4 = awot.io.read_netcdf(fname=FltLevf, platform='p-3', time_var=None)
# Get the tail radar data
r1 = awot.io.read_windsyn_tdr_netcdf(fname=P3Radf, field_mapping=None)
r2 = awot.io.read_windsyn_tdr_netcdf(fname=P3Radf, field_mapping=None)
r3 = awot.io.read_windsyn_tdr_netcdf(fname=P3Radf, field_mapping=None)
r4 = awot.io.read_windsyn_tdr_netcdf(fname=P3Radf)









    



No time variable found, using StarTime to make AWOT time variable
No time variable found, using StarTime to make AWOT time variable
No time variable found, using StarTime to make AWOT time variable
No time variable found, using StarTime to make AWOT time variable
time_diff does not exist in file...
time_diff does not exist in file...
time_diff does not exist in file...
time_diff does not exist in file...

Create the figure and set up plotting instances.

Create basemaps to use for the horizontal plots.

Execute the plotting routines.



In [7]:

    
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(12, 12))#, sharex='col', sharey='row')

bm1 = create_basemap(corners=corners, proj=proj, resolution='l', area_thresh=1., 
                  lat_spacing=dLat, lon_spacing=dLon, ax=ax1)
bm2 = create_basemap(corners=corners, proj=proj, resolution='l', area_thresh=1., 
                  lat_spacing=dLat, lon_spacing=dLon, ax=ax2)
bm3 = create_basemap(corners=corners, proj=proj, resolution='l', area_thresh=1., 
                  lat_spacing=dLat, lon_spacing=dLon, ax=ax3)
bm4 = create_basemap(corners=corners, proj=proj, resolution='l', area_thresh=1., 
                  lat_spacing=dLat, lon_spacing=dLon, ax=ax4)

# Create a RadarGrid
rgp1 = RadarHorizontalPlot(r1, basemap=bm1)
rgp2 = RadarHorizontalPlot(r2, basemap=bm2)
rgp3 = RadarHorizontalPlot(r3, basemap=bm3)
rgp4 = RadarHorizontalPlot(r4, basemap=bm4)

# Create a Flightlevel instance for the track
flp1 = FlightLevel(fl1, basemap=bm1)
flp2 = FlightLevel(fl2, basemap=bm2)
flp3 = FlightLevel(fl3, basemap=bm3)
flp4 = FlightLevel(fl4, basemap=bm4)

rgp1.plot_cappi('reflectivity', 2., vmin=15., vmax=60., title=' ',
              color_bar=True, cb_pad="10%", cb_loc='bottom', cb_tick_int=4,
              ax=ax1)
rgp2.plot_cappi('Wwind', 2., vmin=-5., vmax=5., title=' ',
              mask_procedure='inside', mask_tuple=('Wwind', -1., 1.),
              cmap='RdBu_r',
              color_bar=True, cb_pad="10%", cb_loc='bottom', 
              ax=ax2)
rgp3.plot_cappi('reflectivity', 5., vmin=15., vmax=60., title=' ',
              color_bar=True, cb_pad="10%", cb_loc='bottom', cb_tick_int=4,
              ax=ax3)
rgp4.plot_cappi('Wwind', 5., vmin=-5., vmax=5., title=' ',
              mask_procedure='inside', mask_tuple=('Wwind', -1., 1.),
              cmap='RdBu_r',
              color_bar=True, cb_pad="10%", cb_loc='bottom', 
              ax=ax4)

flp1.plot_trackmap(
              start_time=start_time, end_time=end_time,
#             color_by_altitude=True, track_cmap='spectral',
#              min_altitude=50., max_altitude= 8000.,
              addlegend=False, addtitle=False, ax=ax1)
flp2.plot_trackmap(
              start_time=start_time, end_time=end_time,
#              min_altitude=50., max_altitude= 8000.,
              addlegend=False, addtitle=False, ax=ax2)
flp3.plot_trackmap(
              start_time=start_time, end_time=end_time,
#              min_altitude=50., max_altitude= 8000.,
              addlegend=False, addtitle=False, ax=ax3)
flp4.plot_trackmap(
              start_time=start_time, end_time=end_time,
#              min_altitude=50., max_altitude= 8000.,
              addlegend=False, addtitle=False, ax=ax4)

#flp.draw_scale(location='lower_middle')
#flp.draw_barbs(barbspacing=Wbarb_Spacing)
        
flp1.time_stamps(labelspacing=600,
              start_time=start_time, end_time=end_time, ax=ax1)
flp1.time_stamps(labelspacing=600,
              start_time=start_time, end_time=end_time, ax=ax2)
flp1.time_stamps(labelspacing=600,
              start_time=start_time, end_time=end_time, ax=ax3)
flp1.time_stamps(labelspacing=600,
              start_time=start_time, end_time=end_time, ax=ax4)









    



Closest level: 500.0
Closest level: 500.0
Closest level: 500.0
Closest level: 500.0



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