Revision: 4535
http://matplotlib.svn.sourceforge.net/matplotlib/?rev=4535&view=rev
Author: jswhit
Date: 2007-12-01 05:59:01 -0800 (Sat, 01 Dec 2007)
Log Message:
-----------
update basemap examples for version 0.9.8
Modified Paths:
--------------
trunk/py4science/examples/basemap1.py
trunk/py4science/examples/basemap2.py
trunk/py4science/examples/basemap3.py
trunk/py4science/examples/basemap4.py
trunk/py4science/examples/basemap5.py
trunk/py4science/examples/skel/basemap1_skel.py
trunk/py4science/examples/skel/basemap2_skel.py
trunk/py4science/examples/skel/basemap3_skel.py
trunk/py4science/examples/skel/basemap4_skel.py
trunk/py4science/examples/skel/basemap5_skel.py
trunk/py4science/workbook/basemap.tex
Modified: trunk/py4science/examples/basemap1.py
===================================================================
--- trunk/py4science/examples/basemap1.py 2007-11-30 20:06:59 UTC (rev
4534)
+++ trunk/py4science/examples/basemap1.py 2007-12-01 13:59:01 UTC (rev
4535)
@@ -2,9 +2,7 @@
from matplotlib.toolkits.basemap import Basemap
# create figure.
-# background color will be used for 'wet' areas.
fig = pylab.figure()
-fig.add_axes([0.1,0.1,0.8,0.8],axisbg='aqua')
# create map by specifying lat/lon values at corners.
resolution = 'l'
projection = 'lcc'
@@ -18,8 +16,10 @@
resolution=resolution,projection=projection)
# draw coastlines. Make liness a little thinner than default.
m.drawcoastlines(linewidth=0.5)
-# fill continents.
-m.fillcontinents(color='coral')
+# background fill color will show ocean areas.
+m.drawmapboundary(fill_color='aqua')
+# fill continents, lakes within continents.
+m.fillcontinents(color='coral',lake_color='aqua')
# draw states and countries.
m.drawcountries()
m.drawstates()
Modified: trunk/py4science/examples/basemap2.py
===================================================================
--- trunk/py4science/examples/basemap2.py 2007-11-30 20:06:59 UTC (rev
4534)
+++ trunk/py4science/examples/basemap2.py 2007-12-01 13:59:01 UTC (rev
4535)
@@ -2,9 +2,7 @@
from matplotlib.toolkits.basemap import Basemap
# create figure.
-# background color will be used for 'wet' areas.
fig = pylab.figure()
-fig.add_axes([0.1,0.1,0.8,0.8],axisbg='aqua')
# create map by specifying width and height in km.
resolution = 'l'
projection = 'lcc'
@@ -15,11 +13,9 @@
m = Basemap(lon_0=lon_0,lat_0=lat_0,\
width=width,height=height,\
resolution=resolution,projection=projection)
-# draw coastlines.
m.drawcoastlines(linewidth=0.5)
-# fill continents.
-m.fillcontinents(color='coral')
-# draw states and countries.
+m.drawmapboundary(fill_color='aqua')
+m.fillcontinents(color='coral',lake_color='aqua')
m.drawcountries()
m.drawstates()
pylab.title('map region specified using width and height')
Modified: trunk/py4science/examples/basemap3.py
===================================================================
--- trunk/py4science/examples/basemap3.py 2007-11-30 20:06:59 UTC (rev
4534)
+++ trunk/py4science/examples/basemap3.py 2007-12-01 13:59:01 UTC (rev
4535)
@@ -2,9 +2,7 @@
from matplotlib.toolkits.basemap import Basemap
# create figure.
-# background color will be used for 'wet' areas.
fig = pylab.figure()
-fig.add_axes([0.1,0.1,0.8,0.8],axisbg='aqua')
# create map by specifying width and height in km.
resolution = 'l'
lon_0 = -50
@@ -37,7 +35,8 @@
pylab.text(lon_x-100000,lon_y+100000,'London',fontsize=12,\
color='k',horizontalalignment='right',fontweight='bold')
m.drawcoastlines(linewidth=0.5)
-m.fillcontinents(color='coral')
+m.drawmapboundary(fill_color='aqua')
+m.fillcontinents(color='coral',lake_color='aqua')
m.drawcountries()
m.drawstates()
pylab.title('NY to London Great Circle')
Modified: trunk/py4science/examples/basemap4.py
===================================================================
--- trunk/py4science/examples/basemap4.py 2007-11-30 20:06:59 UTC (rev
4534)
+++ trunk/py4science/examples/basemap4.py 2007-12-01 13:59:01 UTC (rev
4535)
@@ -1,9 +1,7 @@
import pylab, numpy
from matplotlib.toolkits.basemap import Basemap
# create figure.
-# background color will be used for 'wet' areas.
fig = pylab.figure()
-fig.add_axes([0.1,0.1,0.8,0.8],axisbg='aqua')
# create map by specifying width and height in km.
resolution = 'l'
lon_0 = -50
@@ -15,7 +13,8 @@
width=width,height=height,\
resolution=resolution,projection=projection)
m.drawcoastlines(linewidth=0.5)
-m.fillcontinents(color='coral')
+m.drawmapboundary(fill_color='aqua')
+m.fillcontinents(color='coral',lake_color='aqua')
m.drawcountries()
m.drawstates()
# label meridians where they intersect the left, right and bottom
Modified: trunk/py4science/examples/basemap5.py
===================================================================
--- trunk/py4science/examples/basemap5.py 2007-11-30 20:06:59 UTC (rev
4534)
+++ trunk/py4science/examples/basemap5.py 2007-12-01 13:59:01 UTC (rev
4535)
@@ -1,28 +1,22 @@
from matplotlib.toolkits.basemap import Basemap, NetCDFFile
import pylab, numpy
-from numpy import ma
# read in netCDF sea-surface temperature data
+# can be a local file, a URL for a remote opendap dataset,
+# or (if PyNIO is installed) a GRIB or HDF file.
ncfile = NetCDFFile('data/sst.nc')
-sstv = ncfile.variables['analysed_sst']
-sst = ma.masked_values(numpy.squeeze(sstv[:]), sstv._FillValue)
-sst = sstv.scale_factor*sst + sstv.add_offset
+sst = ncfile.variables['analysed_sst'][:]
lats = ncfile.variables['lat'][:]
lons = ncfile.variables['lon'][:]
+
print sst.shape, sst.min(), sst.max()
-# make sure middle of map region is middle of data grid.
-lon_0 = lons.mean()
-lat_0 = lats.mean()
# set colormap
cmap = pylab.cm.gist_ncar
-# set so masked values in an image will be black
-# (i.e. continents will be painted this color)
-cmap.set_bad('k')
# 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='moll',lon_0=lon_0,lat_0=lat_0,resolution=None)
+m = Basemap(projection='moll',lon_0=0,lat_0=0,resolution=None)
# compute map projection coordinates of grid.
x, y = m(*numpy.meshgrid(lons, lats))
# plot with pcolor
@@ -31,8 +25,9 @@
m.drawparallels(numpy.arange(-90.,120.,30.))
m.drawmeridians(numpy.arange(0.,420.,60.))
# draw line around map projection limb.
-m.drawmapboundary()
+# color map region background black (missing values will be this color)
+m.drawmapboundary(fill_color='k')
# draw horizontal colorbar.
pylab.colorbar(orientation='horizontal')
-pylab.savefig('basemap5.pdf') # eps files are too huge when pcolor used.
+pylab.savefig('basemap5.pdf')
pylab.savefig('basemap5.png')
Modified: trunk/py4science/examples/skel/basemap1_skel.py
===================================================================
--- trunk/py4science/examples/skel/basemap1_skel.py 2007-11-30 20:06:59 UTC
(rev 4534)
+++ trunk/py4science/examples/skel/basemap1_skel.py 2007-12-01 13:59:01 UTC
(rev 4535)
@@ -2,9 +2,7 @@
from matplotlib.toolkits.basemap import Basemap
# create figure.
-# background color will be used for 'wet' areas.
fig = pylab.figure()
-fig.add_axes([0.1,0.1,0.8,0.8],axisbg='aqua')
# create map by specifying lat/lon values at corners.
projection = 'lcc' # map projection
resolution = XX # resolution of boundaries ('c','l','i',or 'h')
@@ -18,8 +16,10 @@
resolution=resolution,projection=projection)
# draw coastlines. Make liness a little thinner than default.
m.drawcoastlines(linewidth=0.5)
-# fill continents.
-m.fillcontinents(color='coral')
+# background fill color will show ocean areas.
+m.drawmapboundary(fill_color='aqua')
+# fill continents, lakes within continents.
+m.fillcontinents(color='coral',lake_color='aqua')
# draw states and countries.
m.drawcountries()
m.drawstates()
Modified: trunk/py4science/examples/skel/basemap2_skel.py
===================================================================
--- trunk/py4science/examples/skel/basemap2_skel.py 2007-11-30 20:06:59 UTC
(rev 4534)
+++ trunk/py4science/examples/skel/basemap2_skel.py 2007-12-01 13:59:01 UTC
(rev 4535)
@@ -2,9 +2,7 @@
from matplotlib.toolkits.basemap import Basemap, supported_projections
# create figure.
-# background color will be used for 'wet' areas.
fig = pylab.figure()
-fig.add_axes([0.1,0.1,0.8,0.8],axisbg='aqua')
# create map by specifying width and height in km.
projection = XX # map projection ('lcc','stere','laea','aea' etc)
# 'print supported_projections' gives a list
@@ -16,11 +14,9 @@
m = Basemap(lon_0=lon_0,lat_0=lat_0,\
width=width,height=height,\
resolution=resolution,projection=projection)
-# draw coastlines.
m.drawcoastlines(linewidth=0.5)
-# fill continents.
-m.fillcontinents(color='coral')
-# draw states and countries.
+m.drawmapboundary(fill_color='aqua')
+m.fillcontinents(color='coral',lake_color='aqua')
m.drawcountries()
m.drawstates()
pylab.title('map region specified using width and height')
Modified: trunk/py4science/examples/skel/basemap3_skel.py
===================================================================
--- trunk/py4science/examples/skel/basemap3_skel.py 2007-11-30 20:06:59 UTC
(rev 4534)
+++ trunk/py4science/examples/skel/basemap3_skel.py 2007-12-01 13:59:01 UTC
(rev 4535)
@@ -4,7 +4,6 @@
# create figure.
# background color will be used for 'wet' areas.
fig = pylab.figure()
-fig.add_axes([0.1,0.1,0.8,0.8],axisbg='aqua')
# create map by specifying width and height in km.
resolution = 'l'
lon_0 = -50
@@ -39,7 +38,8 @@
pylab.text(x2-100000,y2+100000,name2,fontsize=12,\
color='k',horizontalalignment='right',fontweight='bold')
m.drawcoastlines(linewidth=0.5)
-m.fillcontinents(color='coral')
+m.drawmapboundary(fill_color='aqua')
+m.fillcontinents(color='coral',lake_color='aqua')
m.drawcountries()
m.drawstates()
pylab.title(name1+' to '+name2+' Great Circle')
Modified: trunk/py4science/examples/skel/basemap4_skel.py
===================================================================
--- trunk/py4science/examples/skel/basemap4_skel.py 2007-11-30 20:06:59 UTC
(rev 4534)
+++ trunk/py4science/examples/skel/basemap4_skel.py 2007-12-01 13:59:01 UTC
(rev 4535)
@@ -1,9 +1,7 @@
import pylab, numpy
from matplotlib.toolkits.basemap import Basemap
# create figure.
-# background color will be used for 'wet' areas.
fig = pylab.figure()
-fig.add_axes([0.1,0.1,0.8,0.8],axisbg='aqua')
# create map by specifying width and height in km.
resolution = 'l'
lon_0 = -50
@@ -15,7 +13,8 @@
width=width,height=height,\
resolution=resolution,projection=projection)
m.drawcoastlines(linewidth=0.5)
-m.fillcontinents(color='coral')
+m.drawmapboundary(fill_color='aqua')
+m.fillcontinents(color='coral',lake_color='aqua')
m.drawcountries()
m.drawstates()
# draw and label parallels.
Modified: trunk/py4science/examples/skel/basemap5_skel.py
===================================================================
--- trunk/py4science/examples/skel/basemap5_skel.py 2007-11-30 20:06:59 UTC
(rev 4534)
+++ trunk/py4science/examples/skel/basemap5_skel.py 2007-12-01 13:59:01 UTC
(rev 4535)
@@ -1,33 +1,24 @@
from matplotlib.toolkits.basemap import Basemap, NetCDFFile, cm
import pylab, numpy
-from numpy import ma
# read in netCDF sea-surface temperature data
+# can be a local file, a URL for a remote opendap dataset,
+# or (if PyNIO is installed) a GRIB or HDF file.
ncfile = NetCDFFile('data/sst.nc')
-sstv = ncfile.variables['analysed_sst']
-sst = ma.masked_values(numpy.squeeze(sstv[:]), sstv._FillValue)
-sst = sstv.scale_factor*sst + sstv.add_offset
+sst = ncfile.variables['analysed_sst'][:]
lats = ncfile.variables['lat'][:]
lons = ncfile.variables['lon'][:]
+
print sst.shape, sst.min(), sst.max()
-# make sure middle of map region is middle of data grid.
-lon_0 = lons.mean()
-lat_0 = lats.mean()
-# set colormap
-#cmap = pylab.cm.gist_ncar
# Basemap comes with extra colormaps from Generic Mapping Tools
# (imported as cm, pylab colormaps in pylab.cm)
cmap = XX
-# set so masked values in an image will be painted specified color
-# (i.e. continents will be painted this color)
-color = XX
-cmap.set_bad(color)
# create Basemap instance for mollweide projection.
projection = XX # try moll, robin, sinu or ortho.
# coastlines not used, so resolution set to None to skip
# continent processing (this speeds things up a bit)
-m = Basemap(projection=projection,lon_0=lon_0,lat_0=lat_0,resolution=None)
+m = Basemap(projection=projection,lon_0=0,lat_0=0,resolution=None)
# compute map projection coordinates of grid.
x, y = m(*numpy.meshgrid(lons, lats))
# plot with pcolor
@@ -36,7 +27,9 @@
m.drawparallels(numpy.arange(-90.,120.,30.))
m.drawmeridians(numpy.arange(0.,420.,60.))
# draw line around map projection limb.
-m.drawmapboundary()
+# color map region background (missing values will be this color)
+color = XX
+m.drawmapboundary(fill_color=color)
# draw horizontal colorbar.
pylab.colorbar(orientation='horizontal')
pylab.show()
Modified: trunk/py4science/workbook/basemap.tex
===================================================================
--- trunk/py4science/workbook/basemap.tex 2007-11-30 20:06:59 UTC (rev
4534)
+++ trunk/py4science/workbook/basemap.tex 2007-12-01 13:59:01 UTC (rev
4535)
@@ -96,10 +96,16 @@
One of the most common uses of Basemap is to visualize earth science
data, such as output from climate models. These data often come on
latitude/longitude grids. One common data format for storing such
-grids is NetCDF. Basemap includes a NetCDF file reader (written in
-pure python by Roberto D'Almeida). There are python packages available
-for reading just about every other scientific data format imaginable,
-including HDF, GRIB, FITS and many others. Following is an example
+grids is NetCDF. Basemap includes a NetCDF file reader (written in
+pure python by Roberto D'Almeida).
+You can also access remote datasets over the web using the OPeNDAP
+protocol - just give the NetCDFFile function a URL instead of a local file name
+and Roberto's pydap module (\texttt{http://pydap.org}) will be used.
+The pydap client is included in Basemap.
+If the PyNIO module (\texttt{http://www.pyngl.ucar.edu/Nio.shtml}) is
installed, the
+NetCDFFile function can also be used to open the formats that
+PyNIO supports, like GRIB and HDF.
+Following is an example
of how to read sea-surface temperature data from a NetCDF file and
plot it on a global mollweide projection.
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