Numpy 在Y值较低的情况下,插值单变量样条线和ax.fill_会产生意外结果(填充错误区域)
我有一个函数,它应该获取一些原始数据,将其绘制到画布上,然后填充基线和预定义峰值之间的区域,这对高Y值很有效,但在使用低Y值时会给出相反的结果。我的问题有两个方面:Numpy 在Y值较低的情况下,插值单变量样条线和ax.fill_会产生意外结果(填充错误区域),numpy,matplotlib,scipy,curve-fitting,Numpy,Matplotlib,Scipy,Curve Fitting,我有一个函数,它应该获取一些原始数据,将其绘制到画布上,然后填充基线和预定义峰值之间的区域,这对高Y值很有效,但在使用低Y值时会给出相反的结果。我的问题有两个方面: 为什么会发生这种情况 解决此问题的可靠方法是什么(我尝试将所有Y值乘以1E6,然后执行插值单变量样线拟合,然后将返回拟合再次除以1E6,但必须有更好的方法来解决此问题) 片段: X = [16.08278,16.090878,16.098978,16.107077,16.115177,16.123279,16.13138,16.13
X = [16.08278,16.090878,16.098978,16.107077,16.115177,16.123279,16.13138,16.139482,16.147586,16.155689,16.163793,16.171899,16.180004,16.18811,16.196218,16.204325,16.212433,16.220543,16.228652,16.236762,16.244874,16.252985,16.261097,16.269211,16.277324,16.285439,16.293554,16.30167,16.309786,16.317904,16.326021,16.334139,16.342259,16.350379,16.358499,16.366621,16.374742]
Y = [1.496555,1.766111,2.074339,2.426317,2.825952,3.274024,3.764088,4.288722,4.839724,5.406741,5.978055,6.536869,7.064041,7.540824,7.948076,8.267242,8.48543,8.596198,8.598762,8.492928,8.279867,7.962899,7.55062,7.059239,6.508092,5.91964,5.318298,4.7234,4.148229,3.602356,3.094568,2.635609,2.231337,1.882143,1.58295,1.328678,1.113859]
Y2 = [1496555,1766111,2074339,2426317,2825952,3274024,3764088,4288722,4839724,5406741,5978055,6536869,7064041,7540824,7948076,8267242,8485430,8596198,8598762,8492928,8279867,7962899,7550620,7059239,6508092,5919640,5318298,4723400,4148229,3602356,3094568,2635609,2231337,1882143,1582950,1328678,1113859]
# Toggle low vs high Y-values
#Y = Y2
import matplotlib.pyplot as plt
import numpy as np
from scipy.interpolate import InterpolatedUnivariateSpline
fig = plt.figure(figsize=(8, 6))
ax = fig.add_subplot(111)
plt.plot(X, Y, 'b-')
plt.legend(['Raw Data'], loc='best')
plt.xlabel("Retention Time [m]")
plt.ylabel("Intensity [au]")
newTime = np.linspace(X[0], X[-1], len(X))
f = InterpolatedUnivariateSpline(X, Y)
newIntensity = f(newTime)
ax.fill_between(X, newTime, newIntensity, alpha=0.5)
plt.show(fig)
由此得出以下数字:
这是我所期望的(并且在Y值较高时发生)
这发生在Y值较低的情况下。很抱歉我这么快回答了自己的问题,但我注意到,我在最初实现这一点时犯了一个以前从未出现过的错误,因为我总是有高强度的数据 之间的
ax.fill_需要x
、y1
和y2
,使用高Y值数据,它不是从0而是从x值开始填充区域。由于标度差异,这并不明显,只有在切换到低Y值后才变得明显。只需将(X,newTime,newIntensity,alpha=0.5)之间的ax.fill\u更改为ax.fill\u(X,0,newIntensity,alpha=0.5)
即可得到预期结果