Hi - Ive now improved my code and confirmed the use of the right grib file but i cant for the life of me figure out the missing data near the coastline..? Could anyone help?
`import Nio from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt import numpy as np f = Nio.open_file('nww3.t12z.grib(2).grib2') lons = f.variables['lon_0'][:] lats = f.variables['lat_0'][::-1] # flip latitudes so data goes S-->N times = f.variables['forecast_time0'][:] ntime = 5 data = f.variables['HTSGW_P0_L1_GLL0'][ntime,::-1] fig = plt.figure(figsize=(16,16)) m = Basemap(llcrnrlon=-35.,llcrnrlat=42.,urcrnrlon=5.,urcrnrlat=65., projection='lcc',lat_1=10.,lat_2=15.,lon_0=10., resolution ='h',area_thresh=1000.) x, y = m(*np.meshgrid(lons, lats)) m.fillcontinents(color='#477519') m.drawcoastlines(linewidth=0.5, color='k', antialiased=1, ax=None, zorder=None ) m.contourf(x, y, data, np.arange(0,9.9,0.1)) plt.show() ` Resulting plot is here <http://matplotlib.1069221.n5.nabble.com/file/n42790/figure_7.png> -- View this message in context: http://matplotlib.1069221.n5.nabble.com/Plotting-NOAA-grib2-data-in-basemap-tp42698p42790.html Sent from the matplotlib - users mailing list archive at Nabble.com. ------------------------------------------------------------------------------ WatchGuard Dimension instantly turns raw network data into actionable security intelligence. It gives you real-time visual feedback on key security issues and trends. Skip the complicated setup - simply import a virtual appliance and go from zero to informed in seconds. http://pubads.g.doubleclick.net/gampad/clk?id=123612991&iu=/4140/ostg.clktrk _______________________________________________ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users