我编写了以下代码来执行样条插值:
import numpy as np
import scipy as sp
x1 = [1., 0.88, 0.67, 0.50, 0.35, 0.27, 0.18, 0.11, 0.08, 0.04, 0.04, 0.02]
y1 = [0., 13.99, 27.99, 41.98, 55.98, 69.97, 83.97, 97.97, 111.96, 125.96, 139.95, 153.95]
x = np.array(x1)
y = np.array(y1)
new_length = 25
new_x = np.linspace(x.min(), x.max(), new_length)
new_y = sp.interpolate.interp1d(x, y, kind='cubic')(new_x)
但我得到了:
ValueError: A value in x_new is below the interpolation range.
在 interpolate.py
任何帮助,将不胜感激.
我正在尝试使用 python gaussian_kde 平滑以下数据，但是它无法正常工作，看起来 kde 正在对整个数据集的分布进行重新采样，而不是对每个点使用带宽并给出权重来进行平滑
from scipy.stats import gaussian_kde
import matplotlib.pyplot as plt
import numpy as np
y=[ 191.78 , 191.59, 191.59, 191.41, 191.47, 191.33, 191.25 \
,191.33 , 191.48 , 191.48, 191.51, 191.43, 191.42, 191.54 \
,191.5975, 191.555, 191.52 , 191.25 , 191.15 , 191.01 ]
x = np.linspace(1 ,20,len(y))
kde= gaussian_kde(y)
kde.set_bandwidth(bw_method=kde.factor / 3)
fig, ax = plt.subplots(figsize=(10, 10))
ax.legend(loc='center left', bbox_to_anchor=(1.05, 0.5), frameon=False)
ax.scatter(x, y, color='black', label='data')
ax.plot(x,y,color='red')
ax.plot(x,kde(x))
这是数据图表
您可以注意到图表并没有平滑线条
