# very simple script to test interpolation with regrid and surfit
# based on example data from netlib->dierckx->regrid
 
from numpy import *
from scipy.interpolate.fitpack2 import SmoothBivariateSpline, \
                                       RectBivariateSpline
import matplotlib
matplotlib.use('Agg')
import pylab

# x,y coordinates
x = linspace(-1.5,1.5,11)
y = x                        
# data taken from daregr
z = array([
[-0.0325, 0.0784, 0.0432, 0.0092, 0.1523, 0.0802, 0.0925, -0.0098, \
                                                    0.0810, -0.0146, -0.0019],  
[0.1276, 0.0223, 0.0357, 0.1858, 0.2818, 0.1675, 0.2239, 0.1671, \
                                                    0.0843, 0.0151, 0.0427],  
[0.0860, 0.1267, 0.1839, 0.3010, 0.5002, 0.4683, 0.4562, 0.2688, \
                                                    0.1276, 0.1244, 0.0377],  
[0.0802, 0.1803, 0.3055, 0.4403, 0.6116, 0.7178, 0.6797, 0.5218, \
                                                    0.2624, 0.1341, -0.0233],  
[0.1321, 0.2023, 0.4446, 0.7123, 0.7944, 0.9871, 0.8430, 0.6440, \
                                                    0.4682, 0.1319, 0.1075],  
[0.2561, 0.1900, 0.4614, 0.7322, 0.9777, 1.0463, 0.9481, 0.6649, \
                                                    0.4491, 0.2442, 0.1341],  
[0.0981, 0.2009, 0.4616, 0.5514, 0.7692, 0.9831, 0.7972, 0.5937, \
                                                    0.4190, 0.1436, 0.0995], 
[0.0991, 0.1545, 0.3399, 0.4940, 0.6328, 0.7168, 0.6886, 0.3925, \
                                                    0.3015, 0.1758, 0.0928],  
[-0.0197, 0.1479, 0.1225, 0.3254, 0.3847, 0.4767, 0.4324, 0.2827, \
                                                    0.2287, 0.0999, 0.0785],  
[0.0032, 0.0917, 0.0246, 0.1780, 0.2394, 0.1765, 0.1642, 0.2081, \
                                                    0.1049, 0.0493, -0.0502],  
[0.0101, 0.0297, 0.0468, 0.0221, 0.1074, 0.0433, 0.0626, 0.1436, \
                                                    0.1092, -0.0232, 0.0132]])

# plot original data
pylab.subplot(1,3,1)
pylab.imshow(z)
pylab.title('orig')

# check regrid
mrs = RectBivariateSpline(x,y,z)
zr = mrs(x,y)
print sum(abs(zr-z))
pylab.subplot(1,3,2)
pylab.imshow(zr)
pylab.title('regrid')

# check surfit
# x increases in columns, y increases along rows
ym,xm = meshgrid(y,x) # deal with meshgrid badness
mrs = SmoothBivariateSpline(ravel(xm),ravel(ym),ravel(z),kx=3,ky=3,s=0)
zr = mrs(x,y)
print sum(abs(zr-z))
pylab.subplot(1,3,3)
pylab.imshow(zr)
pylab.title('surfit')

pylab.savefig('intplot.png')
pylab.close()