从scipy.interpolate.bisplrep到bisplev：无效的输入

如何解决从scipy.interpolate.bisplrep到bisplev：无效的输入

我正在尝试使用scipy.interpolate.bisplrep进行一些2D插值，并运行一些我无法理解的错误，希望能有所帮助。我花了很多时间来解决这个问题，但是没有希望，也没有在我们的社区中找到答案，所以我想将其发布在这里。

这是我的代码。

x_data = numpy.array(
    [-8.300e+00,-8.056e+00,-7.813e+00,-7.569e+00,-7.326e+00,-7.082e+00,-6.838e+00,-6.595e+00,-6.351e+00,-6.108e+00,-5.864e+00,-5.620e+00,-5.377e+00,-5.133e+00,-4.890e+00,-4.646e+00,-4.402e+00,-4.159e+00,-3.915e+00,-3.672e+00,-3.428e+00,-3.185e+00,-2.941e+00,-2.697e+00,-2.454e+00,-2.210e+00,-1.967e+00,-1.723e+00,-1.479e+00,-4.758e-01,-1.617e-01,1.518e-01,4.652e-01,7.786e-01,1.092e+00,1.405e+00,1.719e+00,2.032e+00,2.346e+00,2.659e+00,2.973e+00,3.286e+00,3.599e+00,3.913e+00,4.226e+00,4.540e+00,4.853e+00,5.166e+00,5.480e+00,5.793e+00,6.107e+00,6.420e+00,6.734e+00,7.047e+00,7.360e+00,7.674e+00,7.987e+00,8.300e+00,1.480e+00,1.726e+00,1.969e+00,2.213e+00,2.456e+00,2.700e+00,2.944e+00,3.187e+00,3.431e+00,3.674e+00,3.918e+00,4.161e+00,4.405e+00,4.648e+00,4.892e+00,5.135e+00,5.379e+00,5.623e+00,5.866e+00,6.110e+00,6.353e+00,6.597e+00,6.840e+00,7.084e+00,7.327e+00,7.571e+00,7.815e+00,8.058e+00,-8.300e+00,-7.986e+00,-7.673e+00,-7.360e+00,-7.046e+00,-6.733e+00,-6.419e+00,-6.106e+00,-5.792e+00,-5.479e+00,-5.165e+00,-4.852e+00,-4.539e+00,-4.225e+00,-3.912e+00,-3.598e+00,-3.285e+00,-2.971e+00,-2.934e+00,-2.658e+00,-2.345e+00,-2.031e+00,-1.718e+00,-1.404e+00,-1.091e+00,-7.773e-01,-4.638e-01,-1.504e-01,1.630e-01,4.765e-01,-7.917e+00,-7.533e+00,-7.150e+00,-6.767e+00,-6.383e+00,-6.000e+00,-5.617e+00,-5.234e+00,-4.850e+00,-4.467e+00,-4.084e+00,-3.700e+00,-3.317e+00,-2.550e+00,-2.167e+00,-1.784e+00,-1.401e+00,-1.017e+00,-9.779e-01,-6.340e-01,-2.507e-01,1.326e-01,5.159e-01,8.991e-01,1.282e+00,1.666e+00,2.049e+00,2.432e+00,-7.825e+00,-7.406e+00,-6.986e+00,-6.567e+00,-6.148e+00,-5.729e+00,-5.309e+00,-4.471e+00,-4.052e+00,-3.633e+00,-3.213e+00,-2.794e+00,-2.375e+00,-1.956e+00,-1.537e+00,-1.117e+00,-6.982e-01,-2.790e-01,1.402e-01,5.594e-01,9.779e-01,9.786e-01,1.398e+00,1.817e+00,2.236e+00,2.655e+00,3.075e+00,3.494e+00,-6.847e+00,-6.428e+00,-6.008e+00,-5.589e+00,-5.170e+00,-4.751e+00,-4.332e+00,-3.493e+00,-3.074e+00,-2.655e+00,-2.236e+00,-1.816e+00,-1.397e+00,-5.587e-01,-1.395e-01,2.797e-01,6.989e-01,1.118e+00,1.537e+00,1.957e+00,2.376e+00,2.795e+00,2.934e+00,3.214e+00,3.633e+00,4.053e+00,4.472e+00,4.891e+00,-5.869e+00,-5.450e+00,-5.030e+00,-4.611e+00,-4.192e+00,-3.773e+00,-3.354e+00,-2.515e+00,-2.096e+00,-1.677e+00,-1.258e+00,-8.384e-01,-4.192e-01,4.329e-13,4.192e-01,8.384e-01,1.258e+00,1.677e+00,2.096e+00,2.515e+00,3.354e+00,3.773e+00,4.192e+00,4.611e+00,4.890e+00,5.030e+00,5.450e+00,5.869e+00,-4.891e+00,-4.472e+00,-3.214e+00,-2.795e+00,-1.118e+00,-6.987e-01,-2.795e-01,1.397e-01,5.589e-01,9.781e-01,1.397e+00,3.074e+00,3.493e+00,4.332e+00,4.751e+00,5.170e+00,5.589e+00,6.009e+00,6.428e+00,6.846e+00,6.847e+00,-2.235e+00,5.310e+00,5.729e+00,6.149e+00,6.568e+00,6.987e+00,7.406e+00,7.825e+00,-2.431e+00,-2.048e+00,-1.664e+00,-1.281e+00,-8.979e-01,-5.146e-01,-1.313e-01,2.519e-01,6.352e-01,1.018e+00,1.402e+00,1.785e+00,2.168e+00,2.551e+00,2.935e+00,3.318e+00,3.701e+00,4.085e+00,4.468e+00,4.851e+00,5.234e+00,5.618e+00,6.001e+00,6.384e+00,6.767e+00,7.151e+00,7.534e+00,7.917e+00,8.300e+00])

y_data = numpy.array(
    [1.479e+00,1.723e+00,1.967e+00,2.210e+00,2.454e+00,2.697e+00,2.941e+00,3.184e+00,3.428e+00,3.672e+00,3.915e+00,4.159e+00,4.402e+00,4.646e+00,5.133e+00,5.377e+00,5.620e+00,5.864e+00,6.108e+00,6.351e+00,6.595e+00,6.838e+00,7.082e+00,7.326e+00,7.569e+00,7.813e+00,8.056e+00,-5.793e+00,-5.166e+00,-3.599e+00,-2.972e+00,-2.032e+00,-1.405e+00,-7.779e-01,-4.645e-01,-1.511e-01,1.623e-01,4.758e-01,-6.839e+00,-6.352e+00,-5.621e+00,-5.134e+00,-4.647e+00,-4.403e+00,-4.160e+00,-3.916e+00,-3.429e+00,-2.942e+00,-2.698e+00,-2.455e+00,-2.211e+00,-1.968e+00,-1.724e+00,-1.480e+00,-4.765e-01,-1.631e-01,1.504e-01,4.638e-01,7.773e-01,1.091e+00,1.404e+00,1.718e+00,2.031e+00,2.344e+00,2.658e+00,2.971e+00,3.285e+00,3.598e+00,3.912e+00,4.225e+00,4.539e+00,4.852e+00,5.165e+00,5.479e+00,5.792e+00,6.106e+00,6.419e+00,6.733e+00,7.046e+00,7.673e+00,7.986e+00,-2.432e+00,-2.049e+00,-1.666e+00,-1.282e+00,-8.992e-01,-5.159e-01,-1.326e-01,2.507e-01,6.340e-01,1.017e+00,1.401e+00,1.784e+00,2.167e+00,2.550e+00,3.317e+00,3.700e+00,4.084e+00,4.467e+00,4.850e+00,5.617e+00,6.000e+00,6.383e+00,7.150e+00,7.533e+00,-3.913e+00,-3.494e+00,-3.075e+00,-1.817e+00,-1.398e+00,-9.786e-01,-5.594e-01,-1.402e-01,2.790e-01,6.982e-01,1.117e+00,1.956e+00,2.375e+00,2.794e+00,3.213e+00,4.052e+00,4.471e+00,5.309e+00,6.148e+00,6.567e+00,6.986e+00,-4.053e+00,-2.376e+00,-1.957e+00,-6.989e-01,-2.797e-01,1.395e-01,5.587e-01,1.816e+00,6.008e+00,-6.009e+00,-9.781e-01,-5.589e-01,-1.397e-01,2.795e-01,6.987e-01,-6.987e+00,-6.568e+00,-6.149e+00,-5.310e+00,2.235e+00,-6.384e+00,-4.851e+00,-3.701e+00,-2.551e+00,-2.168e+00,-1.018e+00,-6.347e-01,-2.514e-01,1.319e-01,5.151e-01,8.984e-01,1.665e+00,2.048e+00,2.431e+00])

z_data = numpy.array(
    [-2.64457936e-01,-2.20249620e+00,-2.79582950e+00,-3.64372124e+00,-3.12562410e+00,-3.33754401e+00,-3.64584256e+00,-2.87014642e+00,-2.71529004e+00,-2.58801082e+00,-2.12839141e+00,-1.49199531e+00,-1.23036580e+00,-5.23259018e-01,3.25269119e-01,5.23259018e-01,1.30107648e+00,1.50613744e+00,2.13546248e+00,2.53851334e+00,2.71529004e+00,2.86236825e+00,2.90055202e+00,3.30643131e+00,3.11438111e+00,1.00338452e+00,2.75983777e+00,2.22660854e+00,2.70114790e-01,6.67508801e-01,4.01424520e+00,4.30486608e+00,6.15324321e+00,6.66448141e+00,7.86939137e+00,8.98449876e+00,4.75882864e+00,7.31148412e+00,9.68029183e+00,7.33269732e+00,5.88312842e+00,4.26385389e+00,2.70114790e+00,-1.20208153e+00,-4.29920923e+00,-5.91141269e+00,-7.34683946e+00,-9.69443397e+00,-7.24784451e+00,-4.38406204e+00,-8.98520587e+00,-7.87787665e+00,-6.67225959e+00,-6.15819296e+00,-4.29072395e+00,-4.04323657e+00,-6.44174278e-01,3.46482323e-01,2.19613224e+00,2.79229397e+00,3.73776645e+00,3.12548268e+00,3.34320086e+00,3.41815418e+00,2.86590378e+00,2.58093975e+00,2.12132034e+00,1.49906638e+00,1.22329473e+00,-3.32340187e-01,-1.30107648e+00,-1.49906638e+00,-2.55265548e+00,-2.70821897e+00,-2.86307536e+00,-2.88287435e+00,-3.31067395e+00,-3.07075262e+00,-1.04298250e+00,-2.74871498e+00,-2.22399225e+00,-3.52139177e-01,-5.96091017e-01,-4.04747922e+00,-4.28365288e+00,-6.16950667e+00,-6.66801695e+00,-7.89696853e+00,-8.97389216e+00,-4.39820418e+00,-7.20541810e+00,-9.70150504e+00,-5.90434162e+00,2.70821897e+00,4.28506709e+00,5.90434162e+00,7.19834703e+00,7.33976839e+00,9.76514465e+00,7.22663130e+00,4.39113311e+00,8.97884191e+00,7.89131168e+00,6.65741034e+00,6.16243560e+00,4.29850212e+00,6.23668181e-01,-7.11349422e-01,-4.10475486e+00,-3.50512831e+00,-6.57750728e+00,-8.01540892e+00,-1.08371185e+01,-1.37313066e+01,-1.59431366e+01,-1.89928881e+01,-1.91555227e+01,-2.01242590e+01,-1.79958676e+01,-1.81019336e+01,-8.48528137e+00,-7.77817459e-01,8.55599205e+00,1.71826948e+01,1.78190909e+01,2.07960104e+01,1.96646396e+01,1.95727157e+01,1.83847763e+01,1.59784919e+01,1.32639090e+01,1.08321688e+01,8.18900363e+00,6.51103924e+00,3.80190103e+00,2.99622356e+00,5.14066630e-01,4.27799603e-01,-1.17818132e+00,-3.12894751e+00,-4.59223428e+00,-5.88878527e+00,-1.09106576e+01,-1.39731371e+01,-2.03950809e+01,-2.82489159e+01,-3.43583185e+01,-3.91666446e+01,-4.08707720e+01,-4.85287384e+01,-2.02939646e+01,-7.07106781e-02,2.00111219e+01,4.95540432e+01,4.03050865e+01,3.81201266e+01,3.41037601e+01,2.85246876e+01,2.04806408e+01,2.04721555e+01,1.33876527e+01,1.17181736e+01,5.86120811e+00,4.47825574e+00,3.26966176e+00,1.07918637e+00,-2.31082496e-01,4.37699098e-02,-7.15662773e-01,-1.62634560e+00,-2.64952911e+00,-4.30840162e+00,-5.60169992e+00,-8.31274732e+00,-1.32370389e+01,-3.04550891e+01,-4.08919852e+01,-3.17066681e+01,1.76776695e+00,-4.66690476e+00,-9.60251009e+00,-1.25157900e+01,-1.60513239e+01,4.73761543e+00,-1.06066017e+00,3.16430285e+01,4.06798531e+01,2.92317943e+01,1.30249069e+01,9.09127189e+00,5.85413704e+00,5.50765472e+00,4.33456457e+00,2.59508189e+00,1.66099383e+00,5.48856284e-01,2.17788889e-01,-1.90918831e-03,-7.07106781e-04,-1.41421356e-03,-1.41421356e-02,-7.07106781e-03,2.12132034e-02,-2.12132034e-02,-5.65685425e-02,-1.41421356e-01,0.00000000e+00,7.07106781e-02,3.57796031e+00,3.84666089e+00,1.41421356e-01,3.53553391e-02,1.41421356e-02,4.94974747e-03,1.20208153e-02,-2.82842712e-03,4.24264069e-03,-4.87196572e-02,7.21814602e-01,1.62351717e+00,2.65023622e+00,4.29355238e+00,5.62998419e+00,8.39265039e+00,1.31875415e+01,3.01298199e+01,4.10192644e+01,3.16359574e+01,4.80832611e+00,-1.81726443e+01,-1.09601551e+01,-4.73761543e+00,1.62634560e+00,-4.05242896e+01,-2.94368553e+01,-1.30531912e+01,-9.08702925e+00,-5.85837968e+00,-4.36355595e+00,-2.58730371e+00,-1.67018622e+00,-5.18464834e-01,-2.48901587e-01,-2.60215295e-01,-4.06586399e-01,1.17252446e+00,3.08722821e+00,4.55624254e+00,5.94676803e+00,1.08753023e+01,1.40650610e+01,2.04014449e+01,2.81074946e+01,3.45209531e+01,3.89262283e+01,4.07293506e+01,4.85358095e+01,2.00818326e+01,-2.03646753e+01,-4.92712005e+01,-4.03050865e+01,-3.80352738e+01,-3.39764808e+01,-2.85904485e+01,-2.04891261e+01,-1.31840059e+01,-1.18475741e+01,-5.81383195e+00,-4.50666022e+00,-3.33330137e+00,-1.07869139e+00,2.36244376e-01,7.26198664e-01,3.92727106e+00,3.58785981e+00,6.53437376e+00,8.03407654e+00,1.08201480e+01,1.37175887e+01,1.59374797e+01,1.89716749e+01,1.91343095e+01,2.00959747e+01,1.79605122e+01,1.81019336e+01,8.41457070e+00,7.77817459e-01,-8.55599205e+00,-1.71826948e+01,-1.77695934e+01,-2.08172236e+01,-1.96717107e+01,-1.83847763e+01,-1.59890985e+01,-1.32455242e+01,-1.08321688e+01,-8.17627571e+00,-6.57821439e+00,-3.72963472e+00,-3.03879139e+00,-5.09116882e-01])

from scipy import interpolate
tck_bisplrep = \
    interpolate.bisplrep(x_data,y_data,z_data)

x_interp = numpy.array(
    [-6.22255339,-6.15184271,-6.08113203,-6.01042136,-5.93971068,-5.869,-5.79828932,-5.72757864,-5.65686797,-5.58615729,-5.51544661])
y_interp = numpy.array(
    [-5.51544661,-6.22255339])

# Interpolate over a 2-D grid
z_interp = interpolate.bisplev(x_interp,y_interp,tck_bisplrep)
# ValueError: Invalid input data

我收到以下错误。似乎 bisplrep 给 bisplev 提供了无效的输入，这让我感到困惑。

C:\Anaconda3\lib\site-packages\scipy\interpolate\_fitpack_impl.py:976: RuntimeWarning: The required storage space exceeds the available storage space.
Probable causes: nxest or nyest too small or s is too small. (fp>s)
    kx,ky=3,3 nx,ny=15,15 m=326 fp=3130.819397 s=300.465709
  warnings.warn(RuntimeWarning(_iermess2[ierm][0] + _mess))
Traceback (most recent call last):
  File "C:/Users/hnguy/Projects/Structural/Abaqus Fnd/examples.py",line 2700,in <module>
    z_interp = interpolate.bisplev(x_interp,tck_bisplrep)
  File "C:\Anaconda3\lib\site-packages\scipy\interpolate\_fitpack_impl.py",line 1049,in bisplev
    raise ValueError("Invalid input data")
**ValueError: Invalid input data**

Process finished with exit code 1

当我查看bisplrep的输出时，我发现有（x，y）个重复项，但z值不同。

tck_bisplrep = [

array([**-8.3,-8.3,-8.3**,-3.0475504,-1.39024131,-0.86206474,-0.01373625,1.15636504,2.54668465,4.21997856,8.3,8.3       ]),array([**-8.3,-4.33905797,-2.52847444,-1.16813594,-0.02787165,0.80906345,1.35409442,2.8739393,array([**-8.91927285e+01,3.16604738e+01,-1.74429417e+01,1.14476963e+01,**
       -2.93298456e+01,1.38263987e+01,-4.77862376e+01,1.63322407e+01,-6.92036241e+01,-6.49161520e+01,-1.07832270e+03,3.98276354e+00,-5.55208499e+00,6.84718801e+00,-4.66787782e+00,3.04428289e+01,9.96005415e+00,4.54722521e+01,-1.79355865e-02,2.92389135e+01,5.76915815e+01,-1.45677217e+02,-7.65184475e+00,-1.56352371e+00,3.31247358e+00,-2.44231871e+01,-5.94713454e+01,-9.76014827e+01,-6.99916615e+01,-4.42062893e+01,-4.64078924e+00,-3.79648283e+01,9.02577370e+01,3.03481595e+00,1.85031352e+01,-9.33512546e+00,4.29622544e+01,-4.64161939e+01,4.94196871e+01,-4.89883284e+01,-6.44943348e-01,4.10934238e+01,7.66148247e+00,-2.44014638e+01,-1.49184277e+01,1.12601402e+01,1.68450381e+01,5.57861735e+01,-2.82432256e+00,1.96180815e+01,-2.34168843e+01,3.00779644e+01,9.14447495e+01,-6.43488343e+01,5.75289366e+01,2.74111585e+01,-3.26094009e+01,6.85663664e+01,5.99203026e+00,-4.44011262e+01,1.46622468e+01,-4.61728937e+01,-3.65962008e+01,8.22367995e+01,3.21484012e+00,-1.96897748e+01,-4.51226968e+00,1.94280353e+01,-2.53684349e+01,1.07083644e+02,-3.63650036e+01,3.20612615e+01,4.26510527e+01,2.29436044e+01,8.39504957e+01,-4.98539089e+01,3.85397246e+01,7.37902237e+00,5.11191513e+01,-4.01948907e+01,1.54574098e+01,-8.54578231e+01,-9.88877812e+00,-5.92703012e+01,-3.62990648e+01,1.85520294e+01,1.20099379e+01,-1.60371937e+01,-7.20333489e+02,2.37856096e+02,2.42756817e+01,-2.04851530e+01,1.90073766e+01,-6.89319418e+01,-1.46276485e+01,-1.94501286e+00,7.38250566e+00,-1.93795040e+01,2.47228259e+01,-8.70617387e+03,-4.86722436e+02,-2.36795475e+01,5.26724536e+00,-2.47890217e+01,3.77612888e+01,-1.36438331e+01,-1.12883833e+01,-9.58140411e+00,2.25551921e+01,-3.66102778e+01,-1.11058779e+05,2.53823008e+03,-2.83122139e+01,4.70348637e+00,5.32203520e-02,-2.66881236e+01,1.26301627e+01,-3.85931385e+00,1.05863258e+01,-9.81978664e+00,-7.00061445e+00]
       ),3,3]

您知道发生了什么吗？任何帮助将非常感激。谢谢！