Revision: 6831
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=6831&view=rev
Author: jswhit
Date: 2009-01-26 19:48:47 +0000 (Mon, 26 Jan 2009)
Log Message:
-----------
update URL, use new date2index function.
Modified Paths:
--------------
trunk/toolkits/basemap/examples/plotsst.py
Modified: trunk/toolkits/basemap/examples/plotsst.py
===================================================================
--- trunk/toolkits/basemap/examples/plotsst.py 2009-01-26 19:48:04 UTC (rev
6830)
+++ trunk/toolkits/basemap/examples/plotsst.py 2009-01-26 19:48:47 UTC (rev
6831)
@@ -1,26 +1,30 @@
-from mpl_toolkits.basemap import Basemap, NetCDFFile
+from mpl_toolkits.basemap import Basemap, NetCDFFile, date2index, num2date
import numpy as np
import matplotlib.pyplot as plt
-import sys
+import sys, datetime
# read in sea-surface temperature and ice data
# can be a local file, a URL for a remote opendap dataset,
-# or (if PyNIO is installed) a GRIB or HDF file.
if len(sys.argv) == 1:
date = '20071215'
else:
date = sys.argv[1]
-if date[0:4] > '2005':
- ncfile =
NetCDFFile('http://nomads.ncdc.noaa.gov/thredds/dodsC/oisst/'+date[0:4]+'/AVHRR/sst4-navy-eot.'+date+'.nc')
-else:
- ncfile =
NetCDFFile('http://nomads.ncdc.noaa.gov/thredds/dodsC/oisst/'+date[0:4]+'/AVHRR/sst4-path-eot.'+date+'.nc')
+# convert datestring to datetime object.
+date = datetime.datetime(int(date[0:4]),int(date[4:6]),int(date[6:8]))
+print date
+# open dataset.
+dataset =
NetCDFFile('http://nomads.ncdc.noaa.gov/thredds/dodsC/oisst/totalAagg')
+# find index of desired time.
+time = dataset.variables['time']
+nt = date2index(date, time)
+print num2date(time[nt],time.units)
# read sst. Will automatically create a masked array using
# missing_value variable attribute.
-sst = ncfile.variables['sst'][:]
+sst = dataset.variables['sst'][nt]
# read ice.
-ice = ncfile.variables['ice'][:]
+ice = dataset.variables['ice'][nt]
# read lats and lons (representing centers of grid boxes).
-lats = ncfile.variables['lat'][:]
-lons = ncfile.variables['lon'][:]
+lats = dataset.variables['lat'][:]
+lons = dataset.variables['lon'][:]
# shift lats, lons so values represent edges of grid boxes
# (as pcolor expects).
delon = lons[1]-lons[0]
@@ -34,7 +38,6 @@
# create Basemap instance for mollweide projection.
# coastlines not used, so resolution set to None to skip
# continent processing (this speeds things up a bit)
-#m = Basemap(projection='ortho',lon_0=-110,lat_0=20,resolution=None)
m = Basemap(projection='moll',lon_0=lons.mean(),lat_0=0,resolution=None)
# compute map projection coordinates of grid.
x, y = m(*np.meshgrid(lons, lats))
@@ -42,7 +45,7 @@
# color background of map projection region.
# missing values over land will show up this color.
m.drawmapboundary(fill_color='0.3')
-# plot ice, then with pcolor
+# plot sst, then ice with pcolor
im1 = m.pcolor(x,y,sst,shading='flat',cmap=plt.cm.jet)
im2 = m.pcolor(x,y,ice,shading='flat',cmap=plt.cm.gist_gray)
# draw parallels and meridians, but don't bother labelling them.
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