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Add finished buoy plotter demo script #373

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jrleeman merged 1 commit into from
Oct 26, 2018
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Add finished buoy plotter demo script #373

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68 changes: 68 additions & 0 deletions notebooks/Command_Line_Tools/buoy_plotter.py
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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()