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September 25, 2018 14:08
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# 2d comparison | |
import numpy as np | |
x = np.linspace(-1,1,100) | |
y = np.linspace(-1,1,100) | |
f = lambda x,y: np.sinc(x**2+y**2) | |
vals = np.array( [[f(xx,yy) for yy in y] for xx in x] ) | |
N = 1000000 # number of points to evaluate | |
eval_points = -1+2*np.random.rand(N*2).reshape((N,2)) | |
# irregular grid (linear) | |
print(f"# Interpolating a {len(x)}×{len(y)} grid at {N} points.") | |
print() | |
print("## Irregular Grid") | |
print("\nscipy.interpolate.RectBivariateSpline") | |
import scipy | |
import scipy.interpolate | |
bspl = scipy.interpolate.RectBivariateSpline(x,y,vals, kx=1,ky=1) | |
out1 = bspl.ev(eval_points[:,0], eval_points[:,1]) | |
%time out1 = bspl.ev(eval_points[:,0], eval_points[:,1]) | |
print("\nHARK.interpolate.BilinearInterp") | |
from HARK import interpolation as eai | |
bli = eai.BilinearInterp(vals, x, y) | |
out2 = bli(eval_points[:,0], eval_points[:,1]) | |
%time out2 = bli(eval_points[:,0], eval_points[:,1]) | |
# regular spacing (linear) | |
print("\ninterpolation.interp") | |
from interpolation import interp | |
out3 = interp(x,y,vals,eval_points) | |
%time out3 = interp(x,y,vals,eval_points) | |
# new API | |
print("\ninterpolation.mlinterp") | |
from interpolation import mlinterp | |
grid_uneven = (x,y) # tuple with two vectorw | |
out4 = mlinterp(grid_even,vals,eval_points) | |
%time out4 = mlinterp(grid_uneven,vals,eval_points) | |
print("\n## Regular Grid") | |
print("\ninterpolation.mlinterp") | |
grid_even = ( | |
(-1.0,1.0,100), | |
(-1.0,1.0,100) | |
) | |
out5 = mlinterp(grid_even,vals,eval_points) | |
%time out5 = mlinterp(grid_even,vals,eval_points) | |
# old API (optimized code generation) | |
print("\ninterpolation.LinearSpline") | |
from interpolation.splines import LinearSpline | |
ls = LinearSpline([-1.0,-1.0],[1.0,1.0], [100,100], vals) | |
out6 = ls(eval_points) | |
%time out6 = ls(eval_points) | |
l = [out1,out2,out3,out4,out5,out6] | |
print( abs(sum(l)-out5*len(l)).max() ) |
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