如何在图形上添加回归线和回归线方程

Question

我有下面的输入文件和在图上添加回归线的代码/脚本，但是代码给出了此错误：ValueError: x and y must have same first dimension。我不知道那个错误。

如何在图形上添加回归线和回归线方程式？

输入文件：

-5.06   -4.27
-6.69   -7.28
-3.80   -3.51
-3.88   -2.79
-0.90   -0.81
 2.10    2.59
-1.08    0.28
-5.00   -3.39
 2.67    2.92
 2.48    2.85
-5.10   -3.49
 2.88    3.09
 2.30    2.67
-3.47   -2.20
-0.90   -0.79

脚本：

#!/usr/bin/python
import numpy as np
import pylab as plot
import matplotlib.pyplot as plt
import numpy, scipy, pylab, random
from matplotlib.ticker import MultipleLocator
import matplotlib as mpl
from matplotlib.ticker import MaxNLocator
from scipy import stats

with open("input.txt", "r") as f:
    x=[]
    y=[]

    for line in f:
        if not line.strip() or line.startswith('@') or line.startswith('#'): continue
        row = line.split()
        x.append(float(row[0]))
        y.append(float(row[1]))

fig = plt.figure(figsize=(2.2,2.2), dpi=300)
ax = plt.subplot(111)

plt.xlim(4, -8)
plt.ylim(4, -8)

ax.xaxis.set_major_locator(MaxNLocator(6))
ax.yaxis.set_major_locator(MaxNLocator(6))

ax.xaxis.set_minor_locator(MultipleLocator(1))
ax.yaxis.set_minor_locator(MultipleLocator(1))


#regression part
slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)
line = slope*x+intercept
plt.plot(x, line, 'r', label='fitted line')
#end

plt.scatter(x,y,color=['black','black','black','black','black','black','black','black','black','black','black','black','black','black','black'], s=3.5)


plt.savefig("output.png", dpi=300)

Answer 1

您不能将列表与浮点数相乘。因此，您可以从输入列表中创建一个numpy数组x，

line = slope*np.array(x)+intercept

更理想的情况是，您可以使用np.genfromtxt，

x,y = np.genfromtxt("input.txt", unpack=True) 

完整的例子：

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator
from matplotlib.ticker import MaxNLocator
from scipy import stats

x,y = np.genfromtxt("input.txt", unpack=True) 

fig = plt.figure(figsize=(2.2,2.2), dpi=300)
ax = plt.subplot(111)

plt.xlim(4, -8)
plt.ylim(4, -8)

ax.xaxis.set_major_locator(MaxNLocator(6))
ax.yaxis.set_major_locator(MaxNLocator(6))

ax.xaxis.set_minor_locator(MultipleLocator(1))
ax.yaxis.set_minor_locator(MultipleLocator(1))


#regression part
slope, intercept, r_value, p_value, std_err = stats.linregress(x,y)

line = slope*x+intercept
plt.plot(x, line, 'r', label='y={:.2f}x+{:.2f}'.format(slope,intercept))
#end

plt.scatter(x,y, color="k", s=3.5)
plt.legend(fontsize=9)

plt.show()