for (a,b,p) in zpd:
dst[ (a,b) ] += p
x = linspace(0,1,nx)
y = linspace(0,1,ny)
print "Interpolating data..."
fit = interpolate.RectBivariateSpline(x,y,dst,kx=1,ky=1,s=0)
xi = linspace(0,1,nx*10)
yi = linspace(0,1,ny*10)
smoothDat = fit(xi,yi)
print "Making the plot..."
data = l10( smoothDat )
data[ where( data==-inf ) ] = 0
# This makes it log scale!
figure()
imshow(data.T,origin='lower',vmin=0,vmax=5,extent=[0,1,0,1])
# Y-axis is actually the "primary" axis...
xlabel(options.xFN[:-4])
ylabel(options.yFN[:-4])
colorbar().set_label('Log10(counts)')
if options.outFN:
if options.outFN[-4:] == '.pdf':
filename = options.outFN
title(options.outFN[:-4])
else:
continue
x = linspace(minX,maxX,nx)
y = linspace(minY,maxY,ny)
print "Interpolating data..."
fit = interpolate.RectBivariateSpline(x,y,dst,kx=1,ky=1,s=0)
print minX, maxX
print minY, maxY
#minX=0 # need to do this so that both origins are the same, since only one value is printed
#minY=0
xi = linspace(minX,maxX,nx*10)
yi = linspace(minY,maxY,ny*10)
smoothDat = fit(xi,yi)
data = - l10( smoothDat.max() ) + l10( smoothDat )
data[ where( data==-inf ) ] = data[ where( data != -inf ) ].min()
savetxt( "%s_vs_%s.dat" % ( options.xFN[:-4].split('/')[-1], options.yFN[:-4].split('/')[-1] ), data.T )
# This makes it log scale!
figure(figsize=(8,6))
h=0.18
axes((h,h,1-2*h,1-2*h))
imshow(data.T,origin='lower',vmin= - options.orders,vmax=0,aspect='auto',cmap='hot_r')
# Y-axis is actually the "primary" axis...
print minX, maxX, minY, maxY
#xticks( xticks()[0][1:-1], [ round(xi[i],2) for i in xticks()[0][1:-1] ] )
#yticks( yticks()[0][1:-1], [ round(yi[i],2) for i in yticks()[0][1:-1] ] )
xLbl = linspace( minX , maxX, (maxX - minX) * 2 + 1 )