我正在尝试对数据集进行数值导数。我的第一次尝试是使用梯度函数，numpy但在这种情况下，导数图看起来不够“平滑”。因此，我尝试使用scipy.signal库中的 savgol 过滤器来计算它，但现在我得到了错误的比例：

import matplotlib.pyplot as plt
import pandas as pd
from numpy import *
import scipy.signal
data = pd.read_csv('data.dat',header=0,sep=',',decimal=".")
data['a_Gradient'] = gradient(data.v,data.t)
data['a_Savgol'] = scipy.signal.savgol_filter(data.v, window_length=11, polyorder=2, deriv=1)
plot(data.t,data.a_Gradient,'rx--')
plot(data.t,data.a_Savgol)
plt.legend()

哪里data.dat像

t,v
0.424,27.0807
0.52,27.0796
5.728,27.0268
5.823,27.0261
11.032,26.9747
11.127,26.9727
16.218,26.813
16.336,26.8094
20.367,26.6891
21.309,26.6534
25.459,26.4396
26.399,26.3876
30.764,26.1607
31.49,26.1217
35.856,25.854
36.58,25.8028
40.736,25.4843
41.458,25.4308
45.829,25.1174
46.549,25.0587
50.922,24.6318
51.426,24.5805
56.014,24.1311
56.304,24.1023
60.895,23.6316
60.969,23.6241
65.635,23.1459
65.988,23.1052
70.3,22.5615
70.869,22.489
74.965,21.9771
75.538,21.905
79.842,21.3396
80.419,21.2592
84.295,20.7021
85.3,20.5608
88.96,20.0647
90.181,19.9027
93.625,19.4272
95.063,19.2091
98.078,18.7366
99.732,18.4878
102.318,18.0991
104.401,17.7648
106.559,17.4086
109.282,16.965
110.799,16.718
113.952,16.2013
115.04,16.0275
118.409,15.4924
119.28,15.3369
120.764,14.9651
122.866,14.6239
125.217,14.2745
127.323,13.9592
129.669,13.584
131.78,13.2139
133.909,12.8403
136.45,12.4257
138.15,12.1498
141.542,11.5654
141.543,11.5652
145.995,10.8749
146.213,10.8413
150.66,10.1843
151.094,10.1279
155.325,9.653
155.975,9.5859
160.203,9.1748
160.856,9.117
165.293,8.7496
165.948,8.6962
170.171,8.3776
171.041,8.3242
175.261,8.1118
176.133,8.0723
180.352,7.8991
181.225,7.8686
185.044,7.7552
185.655,7.7394
186.529,7.7177
190.348,7.6393
190.746,7.6328
191.833,7.6169
195.652,7.5812
196.05,7.5794
197.136,7.5764
200.956,7.5786
201.353,7.579
206.259,7.5788
206.657,7.5787
211.563,7.5783
211.96,7.5783
216.867,7.578
217.264,7.5779

那么，我做错了什么以及如何解决？有没有更好的方法来获得这些数据的平滑导数？