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)
from interpolation.splines.misc import mlinspace
import numpy
K = 50
d = 2
N = 10**6
# N = 100
a = numpy.array([0.0] * d)
b = numpy.array([1.0] * d)
orders = numpy.array([K] * d, dtype=int)
V = numpy.random.random(orders)
C = filter_coeffs(a, b, orders, V)
X = numpy.random.random((N, d))
res = numpy.zeros(N)
res2 = res.copy()
if d == 3:
vec_eval_cubic_spline = vec_eval_cubic_spline_3
elif d == 2:
vec_eval_cubic_spline = vec_eval_cubic_spline_2
vec_eval_cubic_spline(a, b, orders, C, X, res)
multilinear_interpolation(a, b, orders, V, X, res)
import time
from interpolation.splines.misc import mlinspace import numpy K = 50 d = 3 N = 10**6 # N = 100 a = numpy.array([0.0,0.0,0.0]) b = numpy.array([1.0,1.0,1.0]) orders = numpy.array([K,K,K],dtype=int) V = numpy.random.random(orders) C = filter_coeffs(a,b,orders,V) X = numpy.random.random((N,3)) res = numpy.zeros(N) res2 = res.copy() vec_eval_cubic_spline_3(a,b,orders,C,X,res) multilinear_interpolation(a,b,orders,V,X,res) import time t1 = time.time()
N = 100 # nb of points to evaluate
a = array([0.0]*d, dtype=float)
b = array([1.0]*d, dtype=float)
orders = array([K]*d, dtype=int)
grid = mlinspace(a,b,orders)
# single valued function to interpolate
f = lambda vec: sqrt(vec.sum(axis=1))
# df
# single valued function to interpolate
vals = f(grid)
from interpolation.splines.filter_cubic import filter_coeffs
cc = filter_coeffs(a,b,orders,vals)
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)
