x values for interpolant
f(x) values for interpolant
Constraints: grid, values must have the same length >= 2
R -> R: Linear interpolation
import mir.algorithm.iteration; import mir.ndslice; import mir.math.common: approxEqual; static immutable x = [0, 1, 2, 3, 5.00274, 7.00274, 10.0055, 20.0137, 30.0192]; static immutable y = [0.0011, 0.0011, 0.0030, 0.0064, 0.0144, 0.0207, 0.0261, 0.0329, 0.0356,]; static immutable xs = [1, 2, 3, 4.00274, 5.00274, 6.00274, 7.00274, 8.00548, 9.00548, 10.0055, 11.0055, 12.0082, 13.0082, 14.0082, 15.0082, 16.011, 17.011, 18.011, 19.011, 20.0137, 21.0137, 22.0137, 23.0137, 24.0164, 25.0164, 26.0164, 27.0164, 28.0192, 29.0192, 30.0192]; auto interpolant = linear!double(x.rcslice!(immutable double), y.rcslice!(const double)); static immutable data = [0.0011, 0.0030, 0.0064, 0.0104, 0.0144, 0.0176, 0.0207, 0.0225, 0.0243, 0.0261, 0.0268, 0.0274, 0.0281, 0.0288, 0.0295, 0.0302, 0.0309, 0.0316, 0.0322, 0.0329, 0.0332, 0.0335, 0.0337, 0.0340, 0.0342, 0.0345, 0.0348, 0.0350, 0.0353, 0.0356]; assert(xs.sliced.vmap(interpolant).all!((a, b) => approxEqual(a, b, 1e-4, 1e-4))(data)); auto d = interpolant.withDerivative(9.0); auto de = interpolant.opCall!2(9.0); assert(de[0 .. 2] == d); assert(de[2] == 0);
R^2 -> R: Bilinear interpolation
import mir.math.common: approxEqual; import mir.ndslice; alias appreq = (a, b) => approxEqual(a, b, 10e-10, 10e-10); //// set test function //// enum y_x0 = 2; enum y_x1 = -7; enum y_x0x1 = 3; // this function should be approximated very well alias f = (x0, x1) => y_x0 * x0 + y_x1 * x1 + y_x0x1 * x0 * x1 - 11; ///// set interpolant //// static immutable x0 = [-1.0, 2, 8, 15]; static immutable x1 = [-4.0, 2, 5, 10, 13]; auto grid = cartesian(x0, x1) .map!f .rcslice .lightConst; auto interpolant = linear!(double, 2)( x0.rcslice!(immutable double), x1.rcslice!(immutable double), grid ); ///// compute test data //// auto test_grid = cartesian(x0.sliced + 1.23, x1.sliced + 3.23); auto real_data = test_grid.map!f; auto interp_data = test_grid.vmap(interpolant); ///// verify result //// assert(all!appreq(interp_data, real_data)); //// check derivatives //// auto z0 = 1.23; auto z1 = 3.21; auto d = interpolant.withDerivative(z0, z1); assert(appreq(interpolant(z0, z1), f(z0, z1))); assert(appreq(d[0][0], f(z0, z1))); assert(appreq(d[1][0], y_x0 + y_x0x1 * z1)); assert(appreq(d[0][1], y_x1 + y_x0x1 * z0)); assert(appreq(d[1][1], y_x0x1));
R^3 -> R: Trilinear interpolation
import mir.math.common: approxEqual; import mir.ndslice; alias appreq = (a, b) => approxEqual(a, b, 10e-10, 10e-10); ///// set test function //// enum y_x0 = 2; enum y_x1 = -7; enum y_x2 = 5; enum y_x0x1 = 10; enum y_x0x1x2 = 3; // this function should be approximated very well static auto f(double x0, double x1, double x2) { return y_x0 * x0 + y_x1 * x1 + y_x2 * x2 + y_x0x1 * x0 * x1 + y_x0x1x2 * x0 * x1 * x2 - 11; } ///// set interpolant //// static immutable x0 = [-1.0, 2, 8, 15]; static immutable x1 = [-4.0, 2, 5, 10, 13]; static immutable x2 = [3, 3.7, 5]; auto grid = cartesian(x0, x1, x2) .map!f .as!(const double) .rcslice; auto interpolant = linear!(double, 3)( x0.rcslice!(immutable double), x1.rcslice!(immutable double), x2.rcslice!(immutable double), grid); ///// compute test data //// auto test_grid = cartesian(x0.sliced + 1.23, x1.sliced + 3.23, x2.sliced - 3); auto real_data = test_grid.map!f; auto interp_data = test_grid.vmap(interpolant); ///// verify result //// assert(all!appreq(interp_data, real_data)); //// check derivatives //// auto z0 = 1.23; auto z1 = 3.21; auto z2 = 4; auto d = interpolant.withDerivative(z0, z1, z2); assert(appreq(interpolant(z0, z1, z2), f(z0, z1, z2))); assert(appreq(d[0][0][0], f(z0, z1, z2))); assert(appreq(d[1][0][0], y_x0 + y_x0x1 * z1 + y_x0x1x2 * z1 * z2)); assert(appreq(d[0][1][0], y_x1 + y_x0x1 * z0 + y_x0x1x2 * z0 * z2)); assert(appreq(d[1][1][0], y_x0x1 + y_x0x1x2 * z2)); assert(appreq(d[1][1][1], y_x0x1x2));
Constructs multivariate linear interpolant with nodes on rectilinear grid.