关于数据插值

1、对于旋转插值，使用scipy.spatial.transform.Slerp
from scipy.spatial.transform import Rotation as R
from scipy.spatial.transform import Slerp

key_rots = R.random(5, random_state=2342345)
key_times = [0, 1, 2, 3, 4]

构建对象，确定对应的时间和旋转量，Create the interpolator object:
slerp = Slerp(key_times, key_rots)

对于新的时间进行插值，Interpolate the rotations at the given times:
times = [0, 0.5, 0.25, 1, 1.5, 2, 2.75, 3, 3.25, 3.60, 4]
interp_rots = slerp(times)

需要给出插值前的旋转：The keyframe rotations expressed as Euler angles:
key_rots.as_euler('xyz', degrees=True)
array([[ 14.31443779, -27.50095894,  -3.7275787 ],
       [ -1.79924227, -24.69421529, 164.57701743],
       [146.15020772,  43.22849451, -31.34891088],
       [ 46.39959442,  11.62126073, -45.99719267],
       [-88.94647804, -49.64400082, -65.80546984]])
       
得到最后的结果：The interpolated rotations expressed as Euler angles. These agree with the keyframe rotations at both endpoints of the range of keyframe times.
interp_rots.as_euler('xyz', degrees=True)
array([[  14.31443779,  -27.50095894,   -3.7275787 ],
       [   4.74588574,  -32.44683966,   81.25139984],
       [  10.71094749,  -31.56690154,   38.06896408],
       [  -1.79924227,  -24.69421529,  164.57701743],
       [  11.72796022,   51.64207311, -171.7374683 ],
       [ 146.15020772,   43.22849451,  -31.34891088],
       [  68.10921869,   20.67625074,  -48.74886034],
       [  46.39959442,   11.62126073,  -45.99719267],
       [  12.35552615,    4.21525086,  -64.89288124],
       [ -30.08117143,  -19.90769513,  -78.98121326],
       [ -88.94647804,  -49.64400082,  -65.80546984]])

2、对于其他数值，scipy.interpolate.interp1d
class scipy.interpolate.interp1d(x, y, kind=‘linear’, axis=- 1, copy=True, bounds_error=None, fill_value=nan, assume_sorted=False)

axis（int, optional）指的是对哪一维进行插值
Specifies the axis of y along which to interpolate. Interpolation defaults to the last axis of y.

import matplotlib.pyplot as plt
from scipy import interpolate
x = np.arange(0, 10)
y = np.exp(-x/3.0)
f = interpolate.interp1d(x, y)
xnew = np.arange(0, 9, 0.1)
ynew = f(xnew)   # use interpolation function returned by `interp1d`
plt.plot(x, y, 'o', xnew, ynew, '-')
plt.show()

或者可以直接：

import matplotlib.pyplot as plt
from scipy import interpolate
x = np.arange(0, 10)
y = np.exp(-x/3.0)
xnew = np.arange(0, 9, 0.1)
ynew = interpolate.interp1d(x, y, axis=0)（xnew）
plt.plot(x, y, 'o', xnew, ynew, '-')
plt.show()