我已经计算了散点图的最佳拟合曲线，我想将结果绘制为平滑曲线，类似于SAS的样条曲线。
经过一些谷歌搜索，它发现我应该先在我的数据上使用插值.interp1d，然后再绘制线条。但是，当我尝试根据文档中的教程执行此操作时，我收到错误。提前感谢任何帮助或资源！
from scipy import interpolate
j = np.arange(0, 29, 1) # new x values
k = (model(xdata, g_fit, a_fit, b_fit)) # y values
l = interpolate.interp1d(j, k)
plt.scatter(xdata, ydata, c='g', marker='x')
plt.plot(xdata, model(xdata, g_fit, a_fit, b_fit), color='red')
plt.plot(j, l(k))
plt.axis([-1, 31, 0.5, 1.2]) # xmin, xmax, ymin, ymax
plt.show()
print p
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-56-0db707080a49> in <module>()
2 j = np.arange(0, 29, 1)
3 k = (model(xdata, g_fit, a_fit, b_fit))
----> 4 l = interpolate.interp1d(j, k)
5
6 plt.scatter(xdata, ydata, c='g', marker='x')
C:EnthoughtCanopy32Userlibsitepackagesscipyinterpolate
interpolate.py in __init__
(self, x, y, kind, axis, copy, bounds_error, fill_value)
331 copy=True, bounds_error=True, fill_value=np.nan):
332 """ Initialize a 1D linear interpolation class."""
--> 333 _Interpolator1D.__init__(self, x, y, axis=axis)
334
335 self.copy = copy
C:EnthoughtCanopy32Userlibsite-packagesscipyinterpolate
polyint.py in __init__(self, xi, yi, axis)
33 self.dtype = None
34 if yi is not None:
---> 35 self._set_yi(yi, xi=xi, axis=axis)
36
37 def __call__(self, x):
C:EnthoughtCanopy32Userlibsite-packagesscipyinterpolate
polyint.py in _set_yi(self, yi, xi, axis)
92 shape = (1,)
93 if xi is not None and shape[axis] != len(xi):
---> 94 raise ValueError("x and y arrays must be equal in length along "
95 "interpolation axis.")
96
ValueError: x and y arrays must be equal in length along interpolation axis.
你不先检查数组 i 和 j 的形状吗？也许您的函数模型会还原一个不同大小的数组？
我真的应该发表评论，但我确实有足够的积分来发表评论......