Matplotlib中的多项式函数显示多行

发布于 2025-01-31 06:53:06 字数 805 浏览 1 评论 0原文

该问题的解决方案似乎是根据X轴对点进行排序。在我的情况下，我很确定我的数据已经分类了，因为我将X数组放入图中：

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
x = np.array([0,0,0,0,0,0,26,0,0,0,0,0,0,0,0,0,214,67,225,250,0,0,0,94,0,0,1366,137])
y = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,5,0,0,0,0,0,0,4,0])
fig3, ax3 = plt.subplots()
ax3.scatter(x, y)
ax3.plot(x, 0 + 0.004*x + 1.63e-06*(x**2), label='squared')
ax3.legend()
plt.show()

我想只是绘制二次行：

原文

I am experiencing the same issue that is asked in this question: Too many lines and curves on the polynomial graph

The solution for that issue seems to be sorting the points based on the x axis. In my case im pretty sure my data is already sorted as I am placing my x array into the plot like so:

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
x = np.array([0,0,0,0,0,0,26,0,0,0,0,0,0,0,0,0,214,67,225,250,0,0,0,94,0,0,1366,137])
y = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,5,0,0,0,0,0,0,4,0])
fig3, ax3 = plt.subplots()
ax3.scatter(x, y)
ax3.plot(x, 0 + 0.004*x + 1.63e-06*(x**2), label='squared')
ax3.legend()
plt.show()

I would like to plot just the quadratic line:

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游魂 2025-02-07 06:53:06

您的示例确实确实在X轴上具有未分类的数据，而解决方案就像您链接的问题一样：

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
x = np.array([0,0,0,0,0,0,26,0,0,0,0,0,0,0,0,0,214,67,225,250,0,0,0,94,0,0,1366,137])
y = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,5,0,0,0,0,0,0,4,0])
order = np.argsort(x)
fig3, ax3 = plt.subplots()
ax3.scatter(x[order], y[order])
ax3.plot(x[order], 0 + 0.004*x[order] + 1.63e-06*(x[order]**2), label='squared')
ax3.legend()
plt.show()

Your example does indeed have unsorted data in the x axis, and the solution is just as in the question you linked:

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
x = np.array([0,0,0,0,0,0,26,0,0,0,0,0,0,0,0,0,214,67,225,250,0,0,0,94,0,0,1366,137])
y = np.array([0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,5,0,0,0,0,0,0,4,0])
order = np.argsort(x)
fig3, ax3 = plt.subplots()
ax3.scatter(x[order], y[order])
ax3.plot(x[order], 0 + 0.004*x[order] + 1.63e-06*(x[order]**2), label='squared')
ax3.legend()
plt.show()

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