contour and basemap

Community matplotlib-users
1

In the mpl basemap ‘test.py’ script, I want to add some contours to the mercator projection map (test #3). Just below line 83 (ie, below the ‘im = imshow…’ command I added the line:

m.contour(topodat,[-1000, -2000])

This returns:

/home/evan/downloads/basemap-0.9.4/examples/test.py
82 # plot image over map.
83 im = m.imshow(topodat,cm.jet)
—> 84 m.contour(topodat,[-1000, -2000])
85 m.drawcoastlines
()
86 m.drawcountries()

TypeError: contour() takes at least 4 arguments (3 given)
WARNING: Failure executing file: <test.py>

I understand the error to mean that I haven’t supplied x and y values; however at this point, if I close all the open figures and enter the line ‘contour(topodat,[-1000, -2000])’ at the ipython command, it gives me the plot I want.

I’ve then tried to use meshgrid to make x and y values (see code below); this is error free but the contours don’t appear. Again, using contour in the command line I get the plot I want. Is it not possible to plot on top of imshow?

Any help appreciated.

Thanks, Evan

from matplotlib.toolkits.basemap import Basemap, shiftgrid
from scipy import interpolate as Pinterp
from pylab import *
import matplotlib.colors
as colors

def doSpline(yVec, newLatRange):
latRange = range(len(yVec))
newLatRange = linspace(latRange[0], latRange[-1], newLatRange)
tck = Pinterp.splrep(latRange, yVec)
yVec = Pinterp.splev(newLatRange, tck)
return yVec

topodatin = load(‘etopo20data.gz’)
lonsin = load(‘etopo20lons.gz’)
latsin = load(‘etopo20lats.gz’)

shift data so lons go from -180 to 180 instead of 20 to 380.

topoin,lons = shiftgrid(180.,topodatin,lonsin,start=False)
lats = latsin

transform to nx x ny regularly spaced native projection grid

nx = len(lons)
ny = int(80.*len(lats)/90.)
lats_ny = doSpline(lats, ny)

lons_mesh, lats_mesh = meshgrid(lons, lats_ny)

setup mercator map projection (-80 to +80).

m = Basemap(llcrnrlon=-180.,llcrnrlat=-80,urcrnrlon=180.,urcrnrlat=80.,
resolution=‘c’,area_thresh=10000.,projection=‘merc’,\

        lon_0=0.5*(lons[0]+lons[-1]),lat_ts=20.)

topodat = m.transform_scalar(topoin,lons,lats,nx,ny)

setup figure with same aspect ratio as map.

fig=figure()
fig.add_axes([0.1,0.1,0.75,0.75])

plot image over map.

im = m.imshow(topodat,cm.jet)

PLOT CONTOURS

m.contour(lons_mesh, lats_mesh, topodat,[-1000, -2000, -3000])
show()

2

Evan Mason wrote:

In the mpl basemap 'test.py' script, I want to add some contours to the mercator projection map (test #3). Just below line 83 (ie, below the 'im = imshow..' command I added the line:

m.contour(topodat,[-1000, -2000])

This returns:

/home/evan/downloads/basemap-0.9.4/examples/test.py
     82 # plot image over map.
     83 im = m.imshow(topodat,cm.jet)
---> 84 m.contour(topodat,[-1000, -2000])
     85 m.drawcoastlines ()
     86 m.drawcountries()

TypeError: contour() takes at least 4 arguments (3 given)
WARNING: Failure executing file: <test.py>

I understand the error to mean that I haven't supplied x and y values; however at this point, if I close all the open figures and enter the line 'contour(topodat,[-1000, -2000])' at the ipython command, it gives me the plot I want.

I've then tried to use meshgrid to make x and y values (see code below); this is error free but the contours don't appear. Again, using contour in the command line I get the plot I want. Is it not possible to plot on top of imshow?

Any help appreciated.

Thanks, Evan

from matplotlib.toolkits.basemap import Basemap, shiftgrid
from scipy import interpolate as Pinterp
from pylab import *
import matplotlib.colors as colors

def doSpline(yVec, newLatRange):
    latRange = range(len(yVec))
    newLatRange = linspace(latRange[0], latRange[-1], newLatRange)
    tck = Pinterp.splrep(latRange, yVec)
    yVec = Pinterp.splev(newLatRange, tck)
    return yVec

topodatin = load('etopo20data.gz')
lonsin = load('etopo20lons.gz')
latsin = load('etopo20lats.gz')
# shift data so lons go from -180 to 180 instead of 20 to 380.
topoin,lons = shiftgrid(180.,topodatin,lonsin,start=False)
lats = latsin
# transform to nx x ny regularly spaced native projection grid
nx = len(lons)
ny = int(80.*len(lats)/90.)
lats_ny = doSpline(lats, ny)

lons_mesh, lats_mesh = meshgrid(lons, lats_ny)

# setup mercator map projection (-80 to +80).
m = Basemap(llcrnrlon=-180.,llcrnrlat=-80,urcrnrlon=180.,urcrnrlat=80.,\
            resolution='c',area_thresh=10000.,projection='merc',\
            lon_0=0.5*(lons[0]+lons[-1]),lat_ts=20.)

topodat = m.transform_scalar(topoin,lons,lats,nx,ny)

# setup figure with same aspect ratio as map.
fig=figure()
fig.add_axes([0.1,0.1,0.75,0.75])
# plot image over map.
im = m.imshow(topodat,cm.jet)
# PLOT CONTOURS
m.contour(lons_mesh, lats_mesh, topodat,[-1000, -2000, -3000])
show()

Evan: This works for me:

from matplotlib.toolkits.basemap import Basemap, shiftgrid
from pylab import *

topodatin = load('etopo20data.gz')
lonsin = load('etopo20lons.gz')
latsin = load('etopo20lats.gz')
# shift data so lons go from -180 to 180 instead of 20 to 380.
topoin,lons = shiftgrid(180.,topodatin,lonsin,start=False)
lats = latsin

# setup mercator map projection (-80 to +80).
m = Basemap(llcrnrlon=-180.,llcrnrlat=-80,urcrnrlon=180.,urcrnrlat=80.,\
            resolution='c',area_thresh=10000.,projection='merc',\
            lon_0=0.5*(lons[0]+lons[-1]),lat_ts=20.)

# transform to nx x ny regularly spaced native projection grid
nx = len(lons); ny = len(lats)
topodat = m.transform_scalar(topoin,lons,lats,nx,ny)

# setup figure with same aspect ratio as map.
# plot image over map.
im = m.imshow(topodat,cm.jet)
# PLOT CONTOURS
longrid,latgrid = meshgrid(lons,lats)
x, y = m(longrid,latgrid)
# reverse sign of data, so contours won't be dashed.
cs = m.contour(x, y, -topoin, [1000, 2000], colors='r')
m.drawcoastlines()
show()

Unlike the pylab contour command, the basemap contour method requires you to supply the X and Y values.

-Jeff

···

--
Jeffrey S. Whitaker Phone : (303)497-6313
NOAA/OAR/CDC R/PSD1 FAX : (303)497-6449
325 Broadway Boulder, CO, USA 80305-3328

3

Jeff Whitaker wrote:

# reverse sign of data, so contours won't be dashed.
cs = m.contour(x, y, -topoin, [1000, 2000], colors='r')

You can also use

rcParams['contour.negative_linestyle'] = 'solid'

to tell contour not to make monochrome negative contours dashed.

Eric