@@ -0,0 +1,1749 @@
+import scipy.constants as sconsts%0Aimport matplotlib.pyplot as plt%0Afrom matplotlib.mlab import griddata%0Afrom mpl_toolkits.basemap import Basemap%0Afrom metpy import read_mesonet_data, dewpoint, get_wind_components%0Afrom metpy.constants import C2F%0Afrom metpy.cbook import append_fields%0Afrom metpy.vis import station_plot%0Afrom metpy.tools.oban import gaussian_filter%0A%0A# stereogrpaphic projection%0Adata = read_mesonet_data('data/200811210030.mdf',%0A    fields=('STID', 'TIME', 'TAIR', 'RELH', 'WSPD', 'WDIR'))%0A%0A#Calculate dewpoint in F from relative humidity and temperature%0Adewpt = C2F(dewpoint(data%5B'TAIR'%5D, data%5B'RELH'%5D/100.))%0Adata = append_fields(data, ('dewpoint',), (dewpt,))%0A%0A#Convert temperature and dewpoint to Farenheit%0Adata%5B'TAIR'%5D = C2F(data%5B'TAIR'%5D)%0A%0A#Convert wind speeds to MPH%0Adata%5B'WSPD'%5D *= sconsts.hour / sconsts.mile%0Au,v = get_wind_components(data%5B'WSPD'%5D, data%5B'WDIR'%5D)%0Adata = append_fields(data, ('u', 'v'), (u, v))%0A%0Afig = plt.figure(figsize=(20,12))%0Aax = fig.add_subplot(1,1,1)%0Am = Basemap(lon_0=-99, lat_0=35, lat_ts=35, resolution='i',%0A    projection='stere', urcrnrlat=37., urcrnrlon=-94.25, llcrnrlat=33.7,%0A    llcrnrlon=-103., ax=ax)%0Am.bluemarble()%0A%0A#Objectively analyze dewpoint%0Alon_grid, lat_grid, x_grid, y_grid = m.makegrid(125, 50, returnxy=True)%0Ax,y = m(data%5B'longitude'%5D, data%5B'latitude'%5D)%0Adew_grid = griddata(x, y, data%5B'dewpoint'%5D, x_grid, y_grid)%0Adew_grid = gaussian_filter(x_grid.T, y_grid.T, dew_grid.T, 10000, 10000)%0Aplt.pcolor(x_grid.T, y_grid.T, dew_grid, zorder=0, cmap=plt.get_cmap('Greens'),%0A    antialiased=False)%0A%0Astation_plot(data, ax=ax, proj=m,%0A    styles=dict(dewpoint=dict(color='lightgreen')), zorder=10)%0Am.drawstates(ax=ax, zorder=1)%0A%0Aplt.title(data%5B'datetime'%5D%5B0%5D.strftime('%25H%25MZ %25d %25b %25Y'))%0Aplt.show()%0A

Add an example showing how to use some of the oban functions.