Note
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1-Dimensional Least Squares FitΒΆ
Out:
Computed coefficients close? False
7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | import ndsplines import matplotlib.pyplot as plt import numpy as np from scipy import interpolate x = np.linspace(-3, 3, 50) y = np.exp(-x**2) + 0.1 * np.random.randn(50) t = [-1, 0, 1] k = 3 t = np.r_[(x[0],)*(k+1), t, (x[-1],)*(k+1)] ndspl = ndsplines.make_lsq_spline(x[:, None], y[:, None], [t], np.array([k])) ispl = interpolate.make_lsq_spline(x, y, t, k) xs = np.linspace(-3, 3, 100) plt.figure() plt.plot(x, y, 'o', ms=5) plt.plot(xs, ndspl(xs).squeeze(), label='LSQ ND spline') plt.plot(xs, ispl(xs), '--', label='LSQ scipy.interpolate spline') plt.legend(loc='best') plt.show() print("Computed coefficients close?", np.allclose(ndspl.coefficients, ispl.c)) |
Total running time of the script: ( 0 minutes 0.161 seconds)