Fix some examples with fixes from docs

Author: molinav

examples/hurrtracks.py

@@ -1,22 +1,20 @@
-from __future__ import (absolute_import, division, print_function)
-
 """
 draw Atlantic Hurricane Tracks for storms that reached Cat 4 or 5.
 part of the track for which storm is cat 4 or 5 is shown red.
 ESRI shapefile data from http://nationalatlas.gov/mld/huralll.html
 """
+import os
 import numpy as np
 import matplotlib.pyplot as plt
-from mpl_toolkits.basemap import Basemap as Basemap
-# Lambert Conformal Conic maplt.
+from mpl_toolkits.basemap import Basemap
+# Lambert Conformal Conic map.
 m = Basemap(llcrnrlon=-100.,llcrnrlat=0.,urcrnrlon=-20.,urcrnrlat=57.,
             projection='lcc',lat_1=20.,lat_2=40.,lon_0=-60.,
             resolution ='l',area_thresh=1000.)
 # create figure.
 fig=plt.figure()
 # read shapefile.
 shp_info = m.readshapefile('huralll020','hurrtracks',drawbounds=False)
-print(shp_info)
 # find names of storms that reached Cat 4.
 names = []
 for shapedict in m.hurrtracks_info:
@@ -25,8 +23,6 @@
     if cat in ['H4','H5'] and name not in names:
         # only use named storms.
         if name != 'NOT NAMED':  names.append(name)
-print(names)
-print(len(names))
 # plot tracks of those storms.
 for shapedict,shape in zip(m.hurrtracks_info,m.hurrtracks):
     name = shapedict['NAME']

examples/plothighsandlows.py

@@ -27,7 +27,7 @@ def main():
     """Main function."""
 
     # Plot 00 UTC today.
-    url = "http://nomads.ncep.noaa.gov/dods/gfs_0p25/gfs%Y%m%d/gfs_0p25_00z"
+    url = "http://nomads.ncep.noaa.gov/dods/gfs_0p50/gfs%Y%m%d/gfs_0p50_00z"
     date = dt.datetime.now()
 
     # Open OPeNDAP dataset.

examples/plotsst.py

@@ -1,24 +1,33 @@
-from __future__ import (absolute_import, division, print_function)
-
 from mpl_toolkits.basemap import Basemap
 from netCDF4 import Dataset, date2index
 import numpy as np
 import matplotlib.pyplot as plt
 from datetime import datetime
+try:
+    from urllib.request import urlretrieve
+except ImportError:
+    from urllib import urlretrieve
 date = datetime(2007,12,15,0) # date to plot.
 # open dataset.
-dataset = \
-Dataset('http://www.ncdc.noaa.gov/thredds/dodsC/OISST-V2-AVHRR_agg')
+sstpath, sstheader = urlretrieve("https://downloads.psl.noaa.gov/Datasets/noaa.oisst.v2.highres/sst.day.mean.{0}.nc".format(date.year))
+dataset = Dataset(sstpath)
 timevar = dataset.variables['time']
 timeindex = date2index(date,timevar) # find time index for desired date.
 # read sst.  Will automatically create a masked array using
 # missing_value variable attribute. 'squeeze out' singleton dimensions.
 sst = dataset.variables['sst'][timeindex,:].squeeze()
 # read ice.
-ice = dataset.variables['ice'][timeindex,:].squeeze()
+dataset.close()
+icepath, iceheader = urlretrieve("https://downloads.psl.noaa.gov/Datasets/noaa.oisst.v2.highres/icec.day.mean.{0}.nc".format(date.year))
+dataset = Dataset(icepath)
+ice = dataset.variables['icec'][timeindex,:].squeeze()
 # read lats and lons (representing centers of grid boxes).
 lats = dataset.variables['lat'][:]
 lons = dataset.variables['lon'][:]
+dataset.close()
+latstep, lonstep = np.diff(lats[:2]), np.diff(lons[:2])
+lats = np.append(lats - 0.5 * latstep, lats[-1] + 0.5 * latstep)
+lons = np.append(lons - 0.5 * lonstep, lons[-1] + 0.5 * lonstep)
 lons, lats = np.meshgrid(lons,lats)
 # create figure, axes instances.
 fig = plt.figure()