Merge pull request #373 from jrleeman/add_buoy_script

Add finished buoy plotter demo script

notebooks/Command_Line_Tools/buoy_plotter.py

@@ -0,0 +1,68 @@
+import argparse
+import cartopy.crs as ccrs
+import cartopy.feature as cfeature
+from datetime import datetime
+import matplotlib.cm as cm
+import matplotlib.pyplot as plt
+import metpy.calc as mpcalc
+from metpy.plots import add_timestamp, add_metpy_logo
+import numpy as np
+from siphon.simplewebservice.ndbc import NDBC
+from metpy.units import units
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Make a plot of buoy data.')
+    parser.add_argument('--cmap', default='Oranges', help='mpl color map')
+    parser.add_argument('--var', default='water_temperature', help='variable to plot')
+    parser.add_argument('--savefig', action='store_true', help='save a figure instead of displaying')
+    parser.add_argument('--imgformat', default='png', help='saved image foramt')
+    parser.add_argument('--min', default=None, type=int, help='Minimum color bar bound.')
+    parser.add_argument('--max', default=None, type=int, help='Maximum color bar bound.')
+    parser.add_argument('--msize', default=5, type=int, help='Marker size')
+    args = parser.parse_args()
+
+    print('Downloading data...')
+    df = NDBC.latest_observations()
+    print('Complete. {} stations'.format(len(df)))
+    print(df.columns)
+    # Drop any rows with NaN for the data we want
+    df.dropna(subset=[args.var], inplace=True)
+    print('{} stations with variable {}\nPlotting...'.format(len(df), args.var))
+    # Make an LCC map projection
+    proj = ccrs.LambertConformal()
+
+    # Plot the map
+    fig = plt.figure(figsize=(12, 7))
+    ax = plt.axes(projection=proj)
+    ax.add_feature(cfeature.COASTLINE.with_scale('50m'))
+    ax.add_feature(cfeature.OCEAN.with_scale('50m'))
+    ax.add_feature(cfeature.LAND.with_scale('50m'))
+    ax.add_feature(cfeature.BORDERS.with_scale('50m'), linestyle=':')
+    ax.add_feature(cfeature.STATES.with_scale('50m'), linestyle=':')
+    ax.add_feature(cfeature.LAKES.with_scale('50m'), alpha=0.5)
+    ax.add_feature(cfeature.RIVERS.with_scale('50m'), alpha=0.5)
+
+    add_timestamp(ax)
+    add_metpy_logo(fig, x=300, y=350)
+
+    scatter = ax.scatter(df.longitude, df.latitude,
+                         c=df[args.var], transform=ccrs.PlateCarree(),
+                         cmap=plt.get_cmap(args.cmap), vmin=args.min, vmax=args.max,
+                         s=args.msize) # cm.Oranges or Use plt.get_cmap(str)
+
+    plt.colorbar(scatter, orientation='horizontal',
+                 label=args.var.replace('_', ' ').title(),
+                 shrink=0.6, pad=0.05)
+
+    #u, v = mpcalc.wind_components(df.wind_direction.values * units('m/s'), df.wind_direction.values * units.degrees)
+    #x = df.longitude.values
+    #y = df.latitude.values
+    #ax.quiver(x, y, u.m, v.m, transform=ccrs.PlateCarree(), units='dots')
+
+    # Save or show figurexs
+    if args.savefig:
+        plt.savefig('buoys_{dt:%Y%m%d_%H%MZ}.{ext}'.format(dt=datetime.utcnow(),
+                                                           ext=args.imgformat))
+    else:
+        plt.show()