Bisplrep警告：不能添加更多的结

import numpy as np from scipy.interpolate import bisplrep,bisplev x=np.array([ 4. , 0. , 0.0100021, 0.0199966, 0.0299988, 0.0400009, 0.0500031, 0.0599976, 0.0699997, 0.0800018, 0.0899963, 0.0999985, 0.110001 , 0.120003 , 0.129997 , 0.139999 , 0.150002 , 0.160004 , 0.169998 , 0.18 , 0.190002 , 0.199997 , 0.209999 , 0.220001 , 0.230003 , 0.239998 , 0.25 , 0.260002 , 0.269997 , 0.279999 , 0.290001 , 0.300003 , 0.309998 , 0.32 , 0.330002 , 0.339996 , 0.349998 , 0.360001 , 0.370003 , 0.379997 , 0.389999 , 0.400002 , 0.410004 , 0.419998 , 0.43 , 0.440002 , 0.449997 , 0.459999 , 0.470001 , 0.480003 , 0.489998 , 0.5 , 0.510002 , 0.519997 , 0.529999 , 0.540001 , 0.550003 , 0.559998 , 0.57 , 0.580002 , 0.589996 , 0.599998 , 0.610001 , 0.620003 , 0.629997 , 0.639999 , 0.650002 , 0.660004 , 0.669998 , 0.68 , 0.690002 , 0.699997 , 0.709999 , 0.720001 , 0.730003 , 0.739998 , 0.75 , 0.760002 , 0.769997 , 0.779999 , 0.790001 , 0.800003 , 0.809998 , 0.82 , 0.830002 , 0.839996 , 0.849998 , 0.860001 , 0.870003 , 0.879997 , 0.889999 , 0.900002 , 0.910004 , 0.919998 , 0.93 , 0.940002 , 0.949997 , 0.959999 , 0.970001 , 0.980003 , 0.989998 , 1. , 1.01 , 1.02 , 1.03 , 1.04 , 1.05 , 1.06 , 1.07 , 1.08 , 1.09 , 1.1 , 1.11 , 1.12 , 1.13 , 1.14 , 1.15 , 1.16 , 1.17 , 1.18 , 1.19 , 1.2 , 1.21 , 1.22 , 1.23 , 1.24 , 1.25 , 1.26 , 1.27 , 1.28 , 1.29 , 1.3 , 1.31 , 1.32 , 1.33 , 1.34 , 1.35 , 1.36 , 1.37 , 1.38 , 1.39 , 1.4 , 1.41 , 1.42 , 1.43 , 1.44 , 1.45 , 1.46 , 1.47 , 1.48 , 1.49 , 1.5 , 1.51 , 1.52 , 1.53 , 1.54 , 1.55 , 1.56 , 1.57 , 1.58 , 1.59 , 1.6 , 1.61 , 1.62 , 1.63 , 1.64 , 1.65 , 1.66 , 1.67 , 1.68 , 1.69 , 1.7 , 1.71 , 1.72 , 1.73 , 1.74 , 1.75 , 1.76 , 1.77 , 1.78 , 1.79 , 1.8 , 1.81 , 1.82 , 1.83 , 1.84 , 1.85 , 1.86 , 1.87 , 1.88 , 1.89 , 1.9 , 1.91 , 1.92 , 1.93 , 1.94 , 1.95 , 1.96 , 1.97 , 1.98 , 1.99 , 2. , 2.01 , 2.02 , 2.03 , 2.04 , 2.05 , 2.06 , 2.07 , 2.08 , 2.09 , 2.1 , 2.11 , 2.12 , 2.13 , 2.14 , 2.15 , 2.16 , 2.17 , 2.18 , 2.19 , 2.2 , 2.21 , 2.22 , 2.23 , 2.24 , 2.25 , 2.26 , 2.27 , 2.28 , 2.29 , 2.3 , 2.31 , 2.32 , 2.33 , 2.34 , 2.35 , 2.36 , 2.37 , 2.38 , 2.39 , 2.4 , 2.41 , 2.42 , 2.43 , 2.44 , 2.45 , 2.46 , 2.47 , 2.48 , 2.49 , 2.5 , 2.51 , 2.52 , 2.53 , 2.54 , 2.55 , 2.56 , 2.57 , 2.58 , 2.59 , 2.6 , 2.61 , 2.62 , 2.63 , 2.64 , 2.65 , 2.66 , 2.67 , 2.68 , 2.69 , 2.7 , 2.71 , 2.72 , 2.73 , 2.74 , 2.75 , 2.76 , 2.77 , 2.78 , 2.79 , 2.8 , 2.81 , 2.82 , 2.83 , 2.84 , 2.85 , 2.86 , 2.87 , 2.88 , 2.89 , 2.9 , 2.91 , 2.92 , 2.93 , 2.94 , 2.95 , 2.96 , 2.97 , 2.98 , 2.99 , 3. , 3.01 , 3.02 , 3.03 , 3.04 , 3.05 , 3.06 , 3.07 , 3.08 , 3.09 , 3.1 , 3.11 , 3.12 , 3.13 , 3.14 , 3.15 , 3.16 , 3.17 , 3.18 , 3.19 , 3.2 , 3.21 , 3.22 , 3.23 , 3.24 , 3.25 , 3.26 , 3.27 , 3.28 , 3.29 , 3.3 , 3.31 , 3.32 , 3.33 , 3.34 , 3.35 , 3.36 , 3.37 , 3.38 , 3.39 , 3.4 , 3.41 , 3.42 , 3.43 , 3.44 , 3.45 , 3.46 , 3.47 , 3.48 , 3.49 , 3.5 , 3.51 , 3.52 , 3.53 , 3.54 , 3.55 , 3.56 , 3.57 , 3.58 , 3.59 , 3.6 , 3.61 , 3.62 , 3.63 , 3.64 , 3.65 , 3.66 , 3.67 , 3.68 , 3.69 , 3.7 , 3.71 , 3.72 , 3.73 , 3.74 , 3.75 , 3.76 , 3.77 , 3.78 , 3.79 , 3.8 , 3.81 , 3.82 , 3.83 , 3.84 , 3.85 , 3.86 , 3.87 , 3.88 , 3.89 , 3.9 , 3.91 , 3.92 , 3.93 , 3.94 , 3.95 , 3.96 , 3.97 , 3.98 , 3.99 , 4. ]) y=np.array([ 14900., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 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16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710., 16710.]) z=np.array([ -1.49380000e+01, 1.33526000e+03, 8.44982000e+02, 6.28443000e+02, 5.24071000e+02, 4.55450000e+02, 4.06332000e+02, 3.69142000e+02, 3.39825000e+02, 3.16075000e+02, 2.96395000e+02, 2.79756000e+02, 2.65453000e+02, 2.52976000e+02, 2.41942000e+02, 2.32055000e+02, 2.23097000e+02, 2.14903000e+02, 2.07344000e+02, 2.00311000e+02, 1.93727000e+02, 1.87533000e+02, 1.81672000e+02, 1.76108000e+02, 1.70808000e+02, 1.65750000e+02, 1.60908000e+02, 1.56265000e+02, 1.51810000e+02, 1.47527000e+02, 1.43405000e+02, 1.39438000e+02, 1.35617000e+02, 1.31933000e+02, 1.28380000e+02, 1.24953000e+02, 1.21645000e+02, 1.18450000e+02, 1.15366000e+02, 1.12387000e+02, 1.09508000e+02, 1.06724000e+02, 1.04033000e+02, 1.01432000e+02, 9.89140000e+01, 9.64774000e+01, 9.41197000e+01, 9.18358000e+01, 8.96233000e+01, 8.74798000e+01, 8.54031000e+01, 8.33888000e+01, 8.14352000e+01, 7.95405000e+01, 7.77009000e+01, 7.59147000e+01, 7.41802000e+01, 7.24957000e+01, 7.08580000e+01, 6.92658000e+01, 6.77180000e+01, 6.62116000e+01, 6.47452000e+01, 6.33180000e+01, 6.19287000e+01, 6.05748000e+01, 5.92551000e+01, 5.79688000e+01, 5.67152000e+01, 5.54919000e+01, 5.42982000e+01, 5.31336000e+01, 5.19961000e+01, 5.08851000e+01, 4.97999000e+01, 4.87398000e+01, 4.77033000e+01, 4.66898000e+01, 4.56990000e+01, 4.47294000e+01, 4.37805000e+01, 4.28519000e+01, 4.19432000e+01, 4.10529000e+01, 4.01810000e+01, 3.93271000e+01, 3.84900000e+01, 3.76693000e+01, 3.68649000e+01, 3.60765000e+01, 3.53029000e+01, 3.45439000e+01, 3.37994000e+01, 3.30690000e+01, 3.23518000e+01, 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-1.58225000e+01, -1.58238000e+01, -1.58243000e+01, -1.58236000e+01, -1.58218000e+01, -1.58185000e+01, -1.58135000e+01, -1.58060000e+01, -1.57946000e+01, -1.57734000e+01, -1.57406000e+01, -1.57455000e+01]) bisplrep(x,y,z,w=None, kx=3,ky=3,s=0,task=0)

1条回答

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1楼 · 发布于 2024-10-01 00:29:11

可能使用

from scipy.interpolate import LinearNDInterpolator
xy = np.asarray([(i,j) for i, j in zip(x,y)]) # Attain a list of all pairs of the elements of x and y.
InterpolatedObject = LinearNDInterpolator(xy, z) # This yields an interpolated object of the data-points.

对你很有帮助。这给出了一个分段线性插值，它正好满足数据点。在

然后，你可以通过

^{pr2}$

这将计算细网格网格点处的插值对象并返回相应的数组。在

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