def plot_topo_file(topoplotdata):
"""
Read in a topo or bathy file and produce a pcolor map.
"""
import os
import pylab
from pyclaw.data import Data
fname = topoplotdata.fname
topotype = topoplotdata.topotype
if topoplotdata.climits:
# deprecated option
cmin = topoplotdata.climits[0]
cmax = topoplotdata.climits[1]
else:
cmin = topoplotdata.cmin
cmax = topoplotdata.cmax
figno = topoplotdata.figno
addcolorbar = topoplotdata.addcolorbar
addcontour = topoplotdata.addcontour
contour_levels = topoplotdata.contour_levels
xlimits = topoplotdata.xlimits
ylimits = topoplotdata.ylimits
coarsen = topoplotdata.coarsen
imshow = topoplotdata.imshow
gridedges_show = topoplotdata.gridedges_show
neg_cmap = topoplotdata.neg_cmap
pos_cmap = topoplotdata.pos_cmap
print_fname = topoplotdata.print_fname
if neg_cmap is None:
neg_cmap = colormaps.make_colormap({
cmin: [0.3, 0.2, 0.1],
0: [0.95, 0.9, 0.7]
})
if pos_cmap is None:
pos_cmap = colormaps.make_colormap({
0: [.5, .7, 0],
cmax: [.2, .5, .2]
})
if abs(topotype) == 1:
X, Y, topo = topotools.topofile2griddata(fname, topotype)
topo = pylab.flipud(topo)
Y = pylab.flipud(Y)
x = X[0, :]
y = Y[:, 0]
xllcorner = x[0]
yllcorner = y[0]
cellsize = x[1] - x[0]
elif abs(topotype) == 3:
file = open(fname, 'r')
lines = file.readlines()
ncols = int(lines[0].split()[0])
nrows = int(lines[1].split()[0])
xllcorner = float(lines[2].split()[0])
yllcorner = float(lines[3].split()[0])
cellsize = float(lines[4].split()[0])
NODATA_value = int(lines[5].split()[0])
print "Loading file ", fname
print " nrows = %i, ncols = %i" % (nrows, ncols)
topo = pylab.loadtxt(fname, skiprows=6, dtype=float)
print " Done loading"
if 0:
topo = []
for i in range(nrows):
topo.append(pylab.array(lines[6 + i], ))
print '+++ topo = ', topo
topo = pylab.array(topo)
topo = pylab.flipud(topo)
x = pylab.linspace(xllcorner, xllcorner + ncols * cellsize, ncols)
y = pylab.linspace(yllcorner, yllcorner + nrows * cellsize, nrows)
print "Shape of x, y, topo: ", x.shape, y.shape, topo.shape
else:
raise Exception("*** Only topotypes 1 and 3 supported so far")
if coarsen > 1:
topo = topo[slice(0, nrows, coarsen), slice(0, ncols, coarsen)]
x = x[slice(0, ncols, coarsen)]
y = y[slice(0, nrows, coarsen)]
print "Shapes after coarsening: ", x.shape, y.shape, topo.shape
if topotype < 0:
topo = -topo
if figno:
pylab.figure(figno)
if topoplotdata.imshow:
color_norm = Normalize(cmin, cmax, clip=True)
xylimits = (x[0], x[-1], y[0], y[-1])
#pylab.imshow(pylab.flipud(topo.T), extent=xylimits, \
pylab.imshow(pylab.flipud(topo), extent=xylimits, \
cmap=cmap, interpolation='nearest', \
norm=color_norm)
else:
neg_topo = ma.masked_where(topo > 0., topo)
all_masked = (ma.count(neg_topo) == 0)
if not all_masked:
pylab.pcolormesh(x, y, neg_topo, cmap=neg_cmap)
pylab.clim([cmin, 0])
if addcolorbar:
pylab.colorbar()
pos_topo = ma.masked_where(topo < 0., topo)
all_masked = (ma.count(pos_topo) == 0)
if not all_masked:
pylab.pcolormesh(x, y, pos_topo, cmap=pos_cmap)
pylab.clim([0, cmax])
if addcolorbar:
pylab.colorbar()
pylab.axis('scaled')
if addcontour:
pylab.contour(x, y, topo, levels=contour_levels, colors='k')
if gridedges_show:
pylab.plot([x[0], x[-1]], [y[0], y[0]], 'k')
pylab.plot([x[0], x[-1]], [y[-1], y[-1]], 'k')
pylab.plot([x[0], x[0]], [y[0], y[-1]], 'k')
pylab.plot([x[-1], x[-1]], [y[0], y[-1]], 'k')
if print_fname:
fname2 = os.path.splitext(fname)[0]
pylab.text(xllcorner + cellsize,
yllcorner + cellsize,
fname2,
color='m')
topodata = Data()
topodata.x = x
topodata.y = y
topodata.topo = topo
return topodata
def plot_topo_file(topoplotdata):
"""
Read in a topo or bathy file and produce a pcolor map.
"""
import os
import pylab
from pyclaw.data import Data
fname = topoplotdata.fname
topotype = topoplotdata.topotype
if topoplotdata.climits:
# deprecated option
cmin = topoplotdata.climits[0]
cmax = topoplotdata.climits[1]
else:
cmin = topoplotdata.cmin
cmax = topoplotdata.cmax
figno = topoplotdata.figno
addcolorbar = topoplotdata.addcolorbar
addcontour = topoplotdata.addcontour
contour_levels = topoplotdata.contour_levels
xlimits = topoplotdata.xlimits
ylimits = topoplotdata.ylimits
coarsen = topoplotdata.coarsen
imshow = topoplotdata.imshow
gridedges_show = topoplotdata.gridedges_show
neg_cmap = topoplotdata.neg_cmap
pos_cmap = topoplotdata.pos_cmap
print_fname = topoplotdata.print_fname
if neg_cmap is None:
neg_cmap = colormaps.make_colormap({cmin:[0.3,0.2,0.1],
0:[0.95,0.9,0.7]})
if pos_cmap is None:
pos_cmap = colormaps.make_colormap({ 0:[.5,.7,0],
cmax:[.2,.5,.2]})
if abs(topotype) == 1:
X,Y,topo = topotools.topofile2griddata(fname, topotype)
topo = pylab.flipud(topo)
Y = pylab.flipud(Y)
x = X[0,:]
y = Y[:,0]
xllcorner = x[0]
yllcorner = y[0]
cellsize = x[1]-x[0]
elif abs(topotype) == 3:
file = open(fname, 'r')
lines = file.readlines()
ncols = int(lines[0].split()[0])
nrows = int(lines[1].split()[0])
xllcorner = float(lines[2].split()[0])
yllcorner = float(lines[3].split()[0])
cellsize = float(lines[4].split()[0])
NODATA_value = int(lines[5].split()[0])
print "Loading file ",fname
print " nrows = %i, ncols = %i" % (nrows,ncols)
topo = pylab.loadtxt(fname,skiprows=6,dtype=float)
print " Done loading"
if 0:
topo = []
for i in range(nrows):
topo.append(pylab.array(lines[6+i],))
print '+++ topo = ',topo
topo = pylab.array(topo)
topo = pylab.flipud(topo)
x = pylab.linspace(xllcorner, xllcorner+ncols*cellsize, ncols)
y = pylab.linspace(yllcorner, yllcorner+nrows*cellsize, nrows)
print "Shape of x, y, topo: ", x.shape, y.shape, topo.shape
else:
raise Exception("*** Only topotypes 1 and 3 supported so far")
if coarsen > 1:
topo = topo[slice(0,nrows,coarsen), slice(0,ncols,coarsen)]
x = x[slice(0,ncols,coarsen)]
y = y[slice(0,nrows,coarsen)]
print "Shapes after coarsening: ", x.shape, y.shape, topo.shape
if topotype < 0:
topo = -topo
if figno:
pylab.figure(figno)
if topoplotdata.imshow:
color_norm = Normalize(cmin,cmax,clip=True)
xylimits = (x[0],x[-1],y[0],y[-1])
#pylab.imshow(pylab.flipud(topo.T), extent=xylimits, \
pylab.imshow(pylab.flipud(topo), extent=xylimits, \
cmap=cmap, interpolation='nearest', \
norm=color_norm)
else:
neg_topo = ma.masked_where(topo>0., topo)
all_masked = (ma.count(neg_topo) == 0)
if not all_masked:
pylab.pcolormesh(x,y,neg_topo,cmap=neg_cmap)
pylab.clim([cmin,0])
if addcolorbar:
pylab.colorbar()
pos_topo = ma.masked_where(topo<0., topo)
all_masked = (ma.count(pos_topo) == 0)
if not all_masked:
pylab.pcolormesh(x,y,pos_topo,cmap=pos_cmap)
pylab.clim([0,cmax])
if addcolorbar:
pylab.colorbar()
pylab.axis('scaled')
if addcontour:
pylab.contour(x,y,topo,levels=contour_levels,colors='k')
if gridedges_show:
pylab.plot([x[0],x[-1]],[y[0],y[0]],'k')
pylab.plot([x[0],x[-1]],[y[-1],y[-1]],'k')
pylab.plot([x[0],x[0]],[y[0],y[-1]],'k')
pylab.plot([x[-1],x[-1]],[y[0],y[-1]],'k')
if print_fname:
fname2 = os.path.splitext(fname)[0]
pylab.text(xllcorner+cellsize, yllcorner+cellsize, fname2, color='m')
topodata = Data()
topodata.x = x
topodata.y = y
topodata.topo = topo
return topodata
def plot_profiles(profiles,topo_type=1,factor=4,verbose=True):
import tempfile
import shutil
import matplotlib.pyplot as plt
# Assume an amr2ez.data file is present
data = Data('./amr2ez.data')
dx = abs(data.xupper-data.xlower) / (data.mx)
dy = abs(data.yupper-data.ylower) / (data.my)
d = min(dx,dy)
mx = int((data.xupper-data.xlower) / d) + 8
my = int((data.yupper-data.ylower) / d) + 8
xlower = data.xlower-d*4.0
ylower = data.ylower-d*4.0
xupper = data.xupper+d*4.0
yupper = data.yupper+d*4.0
bathy_path = tempfile.mkdtemp()
grid_size = int(np.ceil(np.sqrt(len(profiles.keys()))))
index = 1
plt.figure(1)
for (name,profile) in profiles.iteritems():
topo_file = os.path.join(bathy_path,name)
# Write out temporary file
bathy_func = util.create_topo_func(profile,verbose=verbose)
N = len(profile)
factor = 4
if topo_type == 1:
tt.topo1writer(topo_file,bathy_func,xlower,xupper,ylower,yupper,
factor*mx,factor*my)
elif topo_type == 2:
tt.topo2writer(topo_file,bathy_func,xlower,xupper,ylower,yupper,
factor*mx,factor*my)
else:
raise Exception("Invalid topo_type %s." % topo_type)
# Read it back in
(X,Y,Z) = tt.topofile2griddata(topo_file,topotype=topo_type)
slice_index = int(X.shape[0] / 2.0)
x = X[slice_index,:]
b = Z[slice_index,:]
limits = [np.min(x),np.max(x),np.min(b)*1.05,np.max(b)*1.05]
wet_index = np.nonzero(b < 0.0)
plt.subplot(grid_size,grid_size,index)
plt.hold(True)
plt.plot(x,b,'k',linewidth=2)
plt.plot(x[wet_index],np.zeros(x[wet_index].shape),'b')
plt.axis(limits)
plt.title(name)
plt.xlabel('km')
plt.ylabel('m')
locs,labels = plt.xticks()
labels = locs/1e3
plt.xticks(locs,labels)
plt.hold(False)
index = index + 1
plt.show()
shutil.rmtree(bathy_path)