python 3 compatibility fixes (mostly for examples)

Changelog

@@ -1,5 +1,7 @@
-version 1.0.3 (not yet released)
---------------------------------
+version 1.0.3 (git tag v1.0.3)
+------------------------------
+* fix some more python 3 compatibility issues (all examples now
+  work with python 3.2).
 * added alpha keyword to fillcontinents (to set transparency).
 * update geos from 3.3.1 to 3.3.3.
 * upgrade proj4 source to version 4.8.0, pyproj to version 1.9.2.

examples/allskymap.py

@@ -1,3 +1,4 @@
+from __future__ import unicode_literals
 """
 AllSkyMap is a subclass of Basemap, specialized for handling common plotting
 tasks for celestial data.
@@ -35,9 +36,9 @@ def angle_symbol(angle, round_to=1.0):
     """
     value = np.round(angle / round_to) * round_to
     if pl.rcParams['text.usetex'] and not pl.rcParams['text.latex.unicode']:
-        return r"$%0.0f^\circ$" % value
+        return r'$%0.0f^\circ$' % value
     else:
-        return u"%0.0f\u00b0" % value
+        return '%0.0f\N{DEGREE SIGN}' % value
 
 
 class AllSkyMap(Basemap):
@@ -224,7 +225,7 @@ def geodesic(self, lon1, lat1, lon2, lat2, del_s=.01, clip=True, **kwargs):
         # If there are multiple segments and no color args, reconcile the
         # colors, which mpl will have autoset to different values.
         # *** Does this screw up mpl's color set sequence for later lines?
-        if not kwargs.has_key('c') or kwargs.has_key('color'):
+        if 'c' not in kwargs or 'color' in kwargs:
             if len(lines) > 1:
                 c1 = lines[0].get_color()
                 for line in lines[1:]:

examples/allskymap_cr_example.py

@@ -10,10 +10,13 @@
 Created 2011-02-07 by Tom Loredo
 """
 
-from cStringIO import StringIO
+try:
+   from cStringIO import StringIO
+except:
+   from io import StringIO
 import numpy as np
 from numpy import cos, sin, arccos, deg2rad, rad2deg
-import csv, re
+import csv, re, sys
 
 import matplotlib.pyplot as plt
 from allskymap import AllSkyMap
@@ -110,7 +113,7 @@ def gcangle(self, src):
     # Make an integer ID from Year+Day (presumes none on same day!).
     src = Source(id, row[0], row[1], row[7], row[8], E=float(row[4]))
     CRs[src.id] = src
-print 'Parsed data for', len(CRs), 'UHE CRs...'
+sys.stdout.write('Parsed data for %s UHE CRs...\n'%len(CRs))
 
 # Partly fictitious candidate source locations.
 # src.id src.l_deg	src.b_deg	src.xProj	src.yProj
@@ -141,7 +144,7 @@ def gcangle(self, src):
 for row in CandTable:
     src = Source(row[0], 0, 0, row[1], row[2])
     cands[src.id] = src
-print 'Parsed data for', len(cands), 'candidate sources...'
+sys.stdout.write('Parsed data for %s candidate sources...\n' % len(cands))
 
 # Calculate the separation matrix; track the closest candidate to each CR.
 sepn = {}

examples/animate.py

@@ -12,25 +12,20 @@
 # times for March 1993 'storm of the century'
 date1 = datetime.datetime(1993,3,10,0)
 date2 = datetime.datetime(1993,3,17,0)
-print date1, date2
 
 # set OpenDAP server URL.
 URL="http://nomad2.ncep.noaa.gov:9090/dods/reanalyses/reanalysis-2/6hr/pgb/pgb"
-print URL
 try:
     data = NetCDFFile(URL)
 except:
-    raise IOError, 'opendap server not providing the requested data'
+    raise IOError('opendap server not providing the requested data')
 
 # read lats,lons,times.
-print data.variables.keys()
 latitudes = data.variables['lat'][:]
 longitudes = data.variables['lon'][:].tolist()
 times = data.variables['time']
 ntime1 = date2index(date1,times,calendar='standard')
 ntime2 = date2index(date2,times,calendar='standard')
-print 'ntime1,ntime2:',ntime1,ntime2
-print num2date(times[ntime1],times.units,calendar='standard'), num2date(times[ntime2],times.units,calendar='standard')
 # get sea level pressure and 10-m wind data.
 slpdata = data.variables['presmsl']
 udata = data.variables['ugrdprs']
@@ -50,12 +45,6 @@
 longitudes.append(360.); longitudes = np.array(longitudes)
 # make 2-d grid of lons, lats
 lons, lats = np.meshgrid(longitudes,latitudes)
-print 'min/max slp,u,v'
-print slp.min(), slp.max()
-print uin.min(), uin.max()
-print vin.min(), vin.max()
-print 'dates'
-print dates
 # make orthographic basemap.
 m = Basemap(resolution='c',projection='ortho',lat_0=60.,lon_0=-60.)
 uin = udata[ntime1:ntime2+1,0,:,:] 

examples/contour_demo.py

@@ -1,6 +1,7 @@
 from mpl_toolkits.basemap import Basemap, shiftgrid
 import numpy as np
 import matplotlib.pyplot as plt
+import sys
 
 # examples of filled contour plots on map projections.
 
@@ -34,7 +35,7 @@
 meridians = np.arange(-360.,360.,30.)
 m.drawmeridians(meridians)
 plt.title('Sinusoidal Filled Contour Demo')
-print 'plotting with sinusoidal basemap ...'
+sys.stdout.write('plotting with sinusoidal basemap ...\n')
 
 # create new figure
 fig=plt.figure()
@@ -56,7 +57,7 @@
 meridians = np.arange(-360.,360.,30.)
 m.drawmeridians(meridians)
 plt.title('Mollweide Filled Contour Demo')
-print 'plotting with mollweide basemap ...'
+sys.stdout.write('plotting with mollweide basemap ...\n')
 
 # create new figure
 fig=plt.figure()
@@ -78,7 +79,7 @@
 meridians = np.arange(-360.,360.,60.)
 m.drawmeridians(meridians,labels=[0,0,0,1])
 plt.title('Robinson Filled Contour Demo')
-print 'plotting with robinson basemap ...'
+sys.stdout.write('plotting with robinson basemap ...\n')
 
 # create new figure
 fig=plt.figure()
@@ -100,7 +101,7 @@
 meridians = np.arange(10.,360.,20.)
 m.drawmeridians(meridians,labels=[1,1,1,1])
 plt.title('Azimuthal Equidistant Filled Contour Demo',y=1.075)
-print 'plotting with azimuthal equidistant basemap ...'
+sys.stdout.write('plotting with azimuthal equidistant basemap ...\n')
 
 # create new figure
 fig=plt.figure()
@@ -123,5 +124,5 @@
 meridians = np.arange(0.,360.,20.)
 m.drawmeridians(meridians)
 plt.title('Orthographic Filled Contour Demo')
-print 'plotting with orthographic basemap ..'
+sys.stdout.write('plotting with orthographic basemap ..\n')
 plt.show()

examples/fcstmaps.py

@@ -1,3 +1,5 @@
+from __future__ import print_function
+from __future__ import unicode_literals
 # this example reads today's numerical weather forecasts 
 # from the NOAA OpenDAP servers and makes a multi-panel plot.
 import numpy as np
@@ -29,12 +31,12 @@
         msg = """
 opendap server not providing the requested data.
 Try another date by providing YYYYMMDD on command line."""
-        raise IOError, msg
+        raise IOError(msg)
 
 
 # read lats,lons,times.
 
-print data.variables.keys()
+print(data.variables.keys())
 latitudes = data.variables['lat']
 longitudes = data.variables['lon']
 fcsttimes = data.variables['time']
@@ -49,8 +51,8 @@
 for fdate in fdates:
     fdiff = fdate-fdates[0]
     fcsthrs.append(fdiff.days*24. + fdiff.seconds/3600.)
-print fcsthrs
-print verifdates
+print(fcsthrs)
+print(verifdates)
 lats = latitudes[:]
 nlats = len(lats)
 lons1 = longitudes[:]
@@ -85,7 +87,7 @@
     plt.title('%d-h forecast valid '%fcsthr+verifdates[nt],fontsize=9)
 # figure title
 plt.figtext(0.5,0.95,
-            u"2-m temp (\N{DEGREE SIGN}C) forecasts from %s"%verifdates[0],
+            "2-m temp (\N{DEGREE SIGN}C) forecasts from %s"%verifdates[0],
             horizontalalignment='center',fontsize=14)
 # a single colorbar.
 cax = plt.axes([0.1, 0.05, 0.8, 0.025])

examples/fcstmaps_axesgrid.py

@@ -1,3 +1,4 @@
+from __future__ import print_function
 # this example reads today's numerical weather forecasts 
 # from the NOAA OpenDAP servers and makes a multi-panel plot.
 # This version demonstrates the use of the AxesGrid toolkit.
@@ -31,12 +32,12 @@
         msg = """
 opendap server not providing the requested data.
 Try another date by providing YYYYMMDD on command line."""
-        raise IOError, msg
+        raise IOError(msg)
 
 
 # read lats,lons,times.
 
-print data.variables.keys()
+print(data.variables.keys())
 latitudes = data.variables['lat']
 longitudes = data.variables['lon']
 fcsttimes = data.variables['time']
@@ -51,8 +52,8 @@
 for fdate in fdates:
     fdiff = fdate-fdates[0]
     fcsthrs.append(fdiff.days*24. + fdiff.seconds/3600.)
-print fcsthrs
-print verifdates
+print(fcsthrs)
+print(verifdates)
 lats = latitudes[:]
 nlats = len(lats)
 lons1 = longitudes[:]
@@ -99,7 +100,7 @@
     ax.set_title('%d-h forecast valid '%fcsthr+verifdates[nt],fontsize=9)
 # figure title
 plt.figtext(0.5,0.95,
-            u"2-m temp (\N{DEGREE SIGN}C) forecasts from %s"%verifdates[0],
+            "2-m temp (\N{DEGREE SIGN}C) forecasts from %s"%verifdates[0],
             horizontalalignment='center',fontsize=14)
 # a single colorbar.
 cbar = fig.colorbar(cs, cax=grid.cbar_axes[0], orientation='horizontal')

examples/fillstates.py

@@ -1,3 +1,4 @@
+from __future__ import print_function
 import numpy as np
 import matplotlib.pyplot as plt
 from mpl_toolkits.basemap import Basemap as Basemap
@@ -64,13 +65,13 @@
 'Montana':     2.39,
 'Wyoming':      1.96,
 'Alaska':     0.42}
-print shp_info
+print(shp_info)
 # choose a color for each state based on population density.
 colors={}
 statenames=[]
 cmap = plt.cm.hot # use 'hot' colormap
 vmin = 0; vmax = 450 # set range.
-print m.states_info[0].keys()
+print(m.states_info[0].keys())
 for shapedict in m.states_info:
     statename = shapedict['NAME']
     # skip DC and Puerto Rico.

examples/garp.py

@@ -1,6 +1,13 @@
 from mpl_toolkits.basemap import Basemap
 import numpy as np
 import matplotlib.pyplot as plt
+import sys
+
+def get_input(prompt):
+    if sys.hexversion > 0x03000000:
+        return input(prompt)
+    else:
+        return raw_input(prompt)
 
 # the shortest route from the center of the map
 # to any other point is a straight line in the azimuthal
@@ -12,9 +19,9 @@
 # The specified point shows up as a red dot in the center of the map.
 
 # user enters the lon/lat of the point, and it's name
-lon_0 = float(raw_input('input reference lon (degrees):'))
-lat_0 = float(raw_input('input reference lat (degrees):'))
-location = raw_input('name of location:')
+lon_0 = float(get_input('input reference lon (degrees):'))
+lat_0 = float(get_input('input reference lat (degrees):'))
+location = get_input('name of location:')
 
 # no width/height or lat/lon corners specified, so whole world
 # is plotted in a circle.

examples/geos_demo.py

@@ -1,9 +1,16 @@
 from mpl_toolkits.basemap import Basemap
 import numpy as np
 import matplotlib.pyplot as plt
+import sys
+
+def get_input(prompt):
+    if sys.hexversion > 0x03000000:
+        return input(prompt)
+    else:
+        return raw_input(prompt)
 
 # create Basemap instance for Geostationary (satellite view) projection.
-lon_0 = float(raw_input('enter reference longitude (lon_0):'))
+lon_0 = float(get_input('enter reference longitude (lon_0):'))
 
 # map with land/sea mask plotted
 fig=plt.figure()

examples/hires.py

@@ -1,7 +1,8 @@
+from __future__ import print_function
 from mpl_toolkits.basemap import Basemap
 import numpy as np
 import matplotlib.pyplot as plt
-import cPickle, time
+import pickle, time
 
 # create figure with aqua background (will be oceans)
 fig = plt.figure()
@@ -15,16 +16,16 @@
 # make sure countries and rivers are loaded
 m.drawcountries()
 m.drawrivers()
-print time.clock()-t1,' secs to create original Basemap instance'
+print(time.clock()-t1,' secs to create original Basemap instance')
 
-# cPickle the class instance.
-cPickle.dump(m,open('map.pickle','wb'),-1)
+# pickle the class instance.
+pickle.dump(m,open('map.pickle','wb'),-1)
 
 # clear the figure
 plt.clf()
-# read cPickle back in and plot it again (should be much faster).
+# read pickle back in and plot it again (should be much faster).
 t1 = time.clock()
-m2 = cPickle.load(open('map.pickle','rb'))
+m2 = pickle.load(open('map.pickle','rb'))
 # draw coastlines and fill continents.
 m.drawcoastlines()
 # fill continents and lakes
@@ -36,7 +37,7 @@
 m.drawmapboundary(fill_color='aqua')
 # draw major rivers.
 m.drawrivers(color='b')
-print time.clock()-t1,' secs to plot using using a pickled Basemap instance'
+print(time.clock()-t1,' secs to plot using using a pickled Basemap instance')
 # draw parallels
 circles = np.arange(48,65,2).tolist()
 m.drawparallels(circles,labels=[1,1,0,0])

examples/hurrtracks.py

@@ -1,3 +1,4 @@
+from __future__ import 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.
@@ -14,7 +15,7 @@
 fig=plt.figure()
 # read shapefile.
 shp_info = m.readshapefile('huralll020','hurrtracks',drawbounds=False)
-print shp_info
+print(shp_info)
 # find names of storms that reached Cat 4.
 names = []
 for shapedict in m.hurrtracks_info:
@@ -23,8 +24,8 @@
     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)
+print(names)
+print(len(names))
 # plot tracks of those storms.
 for shapedict,shape in zip(m.hurrtracks_info,m.hurrtracks):
     name = shapedict['NAME']

examples/nsper_demo.py

@@ -1,11 +1,18 @@
 from mpl_toolkits.basemap import Basemap
 import numpy as np
 import matplotlib.pyplot as plt
+import sys
+
+def get_input(prompt):
+    if sys.hexversion > 0x03000000:
+        return input(prompt)
+    else:
+        return raw_input(prompt)
 
 # create Basemap instance for Near-Sided Perspective (satellite view) projection.
-lon_0 = float(raw_input('enter reference longitude (lon_0):'))
-lat_0 = float(raw_input('enter reference latitude (lat_0):'))
-h = float(raw_input('enter altitude of camera in km (h):'))
+lon_0 = float(get_input('enter reference longitude (lon_0):'))
+lat_0 = float(get_input('enter reference latitude (lat_0):'))
+h = float(get_input('enter altitude of camera in km (h):'))
 h=h*1000.
 
 # map with continents drawn and filled.