def test_cubic_spline():
from interpolation.splines.filter_cubic import filter_coeffs
from interpolation.splines.eval_cubic import eval_cubic_spline, vec_eval_cubic_spline
cc = filter_coeffs(a,b,orders,vals)
assert(tuple(cc.shape)==tuple([o+2 for o in orders]))
ii = eval_cubic_spline(a, b, orders, cc, point)
iii = vec_eval_cubic_spline(a, b, orders, cc, points)
assert(isinstance(ii,float))
assert(iii.ndim==1)
def test_cubic_spline():
from interpolation.splines.filter_cubic import filter_coeffs
from interpolation.splines.eval_cubic import eval_cubic_spline, vec_eval_cubic_spline
cc = filter_coeffs(a, b, orders, vals)
assert (tuple(cc.shape) == tuple([o + 2 for o in orders]))
ii = eval_cubic_spline(a, b, orders, cc, point)
iii = vec_eval_cubic_spline(a, b, orders, cc, points)
assert (isinstance(ii, float))
assert (iii.ndim == 1)
def test_cuda():
mvals = concatenate([vals[:,None],vals[:,None]],axis=1)
# many points
points = row_stack([[0.5, 0.5]]*N)
from numba import cuda
from interpolation.splines.eval_cubic_cuda import vec_eval_cubic_spline_2, Ad, dAd
jitted = cuda.jit(vec_eval_cubic_spline_2)
out = zeros(N)
jitted[(N,1)](a,b,orders,cc,points,out,Ad,dAd)
# compare
from interpolation.splines.eval_cubic import vec_eval_cubic_spline
test = vec_eval_cubic_spline(a,b,orders,cc,points)
assert( (abs(test - out).max() < 1e-8 ) )
# now with multi splines and gradient evaluation
ccc = cc[:,:,None].repeat(3, axis=2) # three times the same spline
# same thing with gradient evaluation
from interpolation.splines.eval_cubic_cuda import vec_eval_cubic_splines_G_2
jitted_G = cuda.jit(vec_eval_cubic_splines_G_2)
out = zeros((N,3))
dout = zeros((N,2,3)) # (n,i,j) i-th derivative of the j-th spline at the n-th point
jitted_G[(N,1)](a,b,orders,ccc,points,out,dout, Ad,dAd)
# compare
from interpolation.splines.eval_cubic import vec_eval_cubic_splines_G
test, dtest = vec_eval_cubic_splines_G(a,b,orders,ccc,points)
assert( (abs(test - out).max() < 1e-8 ) )
assert( (abs(dtest - dout).max() < 1e-8 ) )
