def test_eval_splines():
from interpolation.splines.eval_splines import eval_linear, eval_cubic
from interpolation.multilinear.mlinterp import mlinterp
import numpy as np
N = 1000
K = 100
d = 2
grid = ((0.0, 1.0, K), ) * d
grid_nu = ((0.0, 1.0, K), np.linspace(0, 1, K))
n_x = 6
V = np.random.random((K, K))
VV = np.random.random((K, K, n_x))
C = np.random.random((K + 2, K + 2))
CC = np.random.random((K + 2, K + 2, n_x))
points = np.random.random((N, 2))
out = np.random.random((N))
Cout = np.random.random((N, n_x))
eval_linear(grid, V, points, out)
# eval_cubic(grid, C, points[0,:], out[0,0])
eval_linear(grid, VV, points, Cout)
eval_linear(grid, VV, points[0, :], Cout[0, :])
mlinterp(grid, V, points)
eval_linear(grid, V, points)
eval_linear(grid, V, points)
eval_linear(grid, V, points, out)
print("OK 2")
eval_linear(grid, V, points[0, :])
res_0 = eval_linear(grid, VV, points)
res_1 = eval_linear(grid, VV, points[0, :])
#nonuniform grid:
res_0_bis = eval_linear(grid_nu, VV, points)
res_1_bis = eval_linear(grid_nu, VV, points[0, :])
assert abs(res_0 - res_0_bis).max() < 1e-10
assert abs(res_1 - res_1_bis).max() < 1e-10
print("OK 3")
eval_cubic(grid, C, points, out)
# eval_cubic(grid, C, points[0,:], out[0,0])
eval_cubic(grid, CC, points, Cout)
eval_cubic(grid, CC, points[0, :], Cout[0, :])
print("OK 4")
eval_cubic(grid, C, points)
eval_cubic(grid, C, points[0, :])
eval_cubic(grid, CC, points)
eval_cubic(grid, CC, points[0, :])
print("OK 5")
grid_u = ((0.0, 1.0, K), ) * d
from interpolation.cartesian import mlinspace
s = mlinspace(a, b, n)
f = lambda x: (x**2).sum(axis=1)
x = f(s)
v = x.reshape(n)
from interpolation.splines.eval_splines import eval_linear
pp = np.linspace(-0.5, 1.5, 100)[:, None]
from interpolation.splines.option_types import options
xx_ = eval_linear(grid_u, v, pp)
xx_cst = eval_linear(grid_u, v, pp, options.CONSTANT)
xx_lin = eval_linear(grid_u, v, pp, options.LINEAR)
xx_nea = eval_linear(grid_u, v, pp, options.NEAREST)
yy_ = np.array([eval_linear(grid_u, v, p_) for p_ in pp])
yy_cst = np.array([eval_linear(grid_u, v, p_, options.CONSTANT) for p_ in pp])
yy_lin = np.array([eval_linear(grid_u, v, p_, options.LINEAR) for p_ in pp])
yy_nea = np.array([eval_linear(grid_u, v, p_, options.NEAREST) for p_ in pp])
zz_ = xx_.copy() * 0
zz_cst = xx_cst.copy() * 0
zz_lin = xx_lin.copy() * 0
zz_nea = xx_nea.copy() * 0
print("Inplace")
def test_multilinear_extrap():
import numpy as np
N=100000
K = 10
d = 1
a = np.array([0.0]*d)
b = np.array([1.0]*d)
n = np.array([K]*d)
grid_u = ((0.0,1.0,K),)*d
from interpolation.cartesian import mlinspace
s = mlinspace(a,b,n)
f = lambda x: (x**2).sum(axis=1)
x = f(s)
v = x.reshape(n)
from interpolation.splines.eval_splines import eval_linear
pp = np.linspace(-0.5, 1.5, 100)[:,None]
from interpolation.splines.option_types import options
xx_ = eval_linear(grid_u, v, pp)
xx_cst = eval_linear(grid_u, v, pp, options.CONSTANT)
xx_lin = eval_linear(grid_u, v, pp, options.LINEAR)
xx_nea = eval_linear(grid_u, v, pp, options.NEAREST)
yy_ = np.array([eval_linear(grid_u, v, p_) for p_ in pp])
yy_cst = np.array([eval_linear(grid_u, v, p_, options.CONSTANT) for p_ in pp])
yy_lin = np.array([eval_linear(grid_u, v, p_, options.LINEAR) for p_ in pp])
yy_nea = np.array([eval_linear(grid_u, v, p_, options.NEAREST) for p_ in pp])
zz_ = xx_.copy()*0
zz_cst = xx_cst.copy()*0
zz_lin = xx_lin.copy()*0
zz_nea = xx_nea.copy()*0
print("Inplace")
eval_linear(grid_u, v, pp, zz_)
eval_linear(grid_u, v, pp, zz_cst, options.CONSTANT)
eval_linear(grid_u, v, pp, zz_lin, options.LINEAR)
eval_linear(grid_u, v, pp, zz_nea, options.NEAREST)
assert ((abs(xx_-yy_)+(abs(xx_-zz_))).max() <1e-10)
assert ((abs(xx_cst-yy_cst)+(abs(xx_cst-zz_cst))).max()<1e-10)
assert ((abs(xx_lin-yy_lin)+(abs(xx_lin-zz_lin))).max()<1e-10)
assert ((abs(xx_nea-yy_nea)+(abs(xx_nea-zz_nea))).max()<1e-10)