Python Matplotlib-polyfit趋势线似乎重叠，看起来模糊/不清楚

我试图为我掌握的一些数据绘制一些趋势线。问题是，若我使用虚线或虚线样式，它看起来很模糊，而且似乎自身重叠

不知道为什么，但线条看起来也没有进行抗锯齿处理——它们看起来参差不齐。尝试在Excel中生成完全相同的图形会得到清晰的线条

使用其他线条样式没有帮助；也不会通过添加破折号=（1,5）或类似符号来增加间距。即使增加打印大小或更改线宽，它仍然重叠

代码如下：

从matplotlib导入pyplot作为plt
将matplotlib.ticker作为mtick导入
进口numpy
从scipy导入统计信息
氡=[49.6,61.7,58.7,64.1,59.4,64.6,65.4,65.3,65.5,66.0,50.5,64.8,71.9,，
71.9,60.4,54.4,50.9,58.1,52.6,55.6,56.6,41.6,43.0,33.0,41.5,53.5,52.2,45.1,
46.8,63.0,73.8,61.3,44.4,39.3,38.2,45.4,39.9,36.3,41.0,38.5,35.4,40.2,11.3,
34.7,24.2,24.5,32.1,26.3,23.6,32.1,27.5,39.0,24.9,22.0,18.2,23.0,21.1,15.4,
13.9,10.2,26.4,18.2,16.6]
tout=[-2.57，-3.31，-0.63，-0.39，-1.64，-7.62，-1.90，-0.35，-4.88，-1.27，
-0.23,-6.99,-2.87,-12.27,-11.90,-9.42,-4.10,-3.15,0.81,3.87,-11.41,-9.47,
0.25,-6.81,-13.70,-16.41,-14.14,-9.70,-10.32,-21.83,-26.55,-16.88,-6.85,
4.03,-7.89,-6.53,-3.96,-6.09,-3.15,-0.51,-2.62,8.13,2.08,0.58,1.99,-6.64,
-12.13,-4.95,-2.99,-4.81,-0.88,0.28,-1.44,1.92,3.73,0.21,1.11,6.83,13.31,
7.04,1.46,1.78]
#数据的开始和结束索引
a=[0,21,42]
b=[20,41,62]
n=0#只是一个计数器
#设置字体系列
hfont={'family'：'Arial'}
plt.rcParams.update（{'font.family'：'Arial'，'font.size'：12}）
#设置轴小刻度线
plt.axes（）.yaxis.set_minor_locator（mtick.MultipleLocator（4））
plt.axes（）.xaxis.set_minor_定位器（mtick.MultipleLocator（2））
plt.axes（）.yaxis.set_major_格式化程序（mtick.FormatStrFormatter（'.0f%%'））
#配置轴标签
plt.xlabel（“室外温度”**hfont）
plt.ylabel（“散热器开启%”，**hfont）
#设置线条和标记类型及颜色
标记=['o'，'s'，'x']
marker_facecolor=['None'，'k'，'None']
名称=[“手动控制”、“强制计划”、“基于占用率”]
行=['：'，'-'，'-']
行距=[[1,5]，[5,5]，[0,0]]
透明度=[1,0.75,1]
对于i，j（a，b）：
#得到x和y
x=numpy.array（tout[i:j]）
y=numpy.array（radon[i:j]）
#设置轴范围
plt.ylim（01100）
plt.xlim（-30,20）
#绘图数据
plt.绘图（x，y，标记[n]，标记边缘宽度=0.75
，markeredgecolor='k'，markerfacecolor=marker_facecolor[n]，
alpha=透明度[n]，label=名称[n]）
#进行回归
z=numpy.polyfit（x，y，1）
p=numpy.poly1d（z）
#绘制趋势线
plt.plot（x，p（x），'k%s'%lines[n]，线宽=0.85）
#增量计数器
n+=1
plt.图例（位置='右上'）
支腿=plt.legend（）
leg.get_frame（）.set_edgecolor（'k'））
plt.savefig（'tout_vs_radon.png'，dpi=300）
plt.show（）

以下是上述代码的结果： （抱歉，没有足够的声誉发布图像）

---

对于虚线或虚线样式，可以看到趋势线在中心重叠。我在Windows 10上使用matplotlib v3.0.3和Python v3.6.5。

关键是以排序方式绘制数组。否则，直线将前后跳跃并重叠

order = np.argsort(x)
plt.plot(x[order],p(x[order]), ..)

完整代码：

from matplotlib import pyplot as plt
import matplotlib.ticker as mtick
import numpy as np

radon = [49.6,61.7,58.7,64.1,59.4,64.6,65.4,65.3,65.5,66.0,50.5,64.8,71.9,
71.9,60.4,54.4,50.9,58.1,52.6,55.6,56.6,41.6,43.0,33.0,41.5,53.5,52.2,45.1,
46.8,63.0,73.8,61.3,44.4,39.3,38.2,45.4,39.9,36.3,41.0,38.5,35.4,40.2,11.3,
34.7,24.2,24.5,32.1,26.3,23.6,32.1,27.5,39.0,24.9,22.0,18.2,23.0,21.1,15.4,
13.9,10.2,26.4,18.2,16.6]

tout = [-2.57,-3.31,-0.63,-0.60,0.39,-1.64,-7.62,-1.90,-0.35,-4.88,-1.27,
-0.23,-6.99,-2.87,-12.27,-11.90,-9.42,-4.10,-3.15,0.81,3.87,-11.41,-9.47,
0.25,-6.81,-13.70,-16.41,-14.14,-9.70,-10.32,-21.83,-26.55,-16.88,-6.85,
4.03,-7.89,-6.53,-3.96,-6.09,-3.15,-0.51,-2.62,8.13,2.08,0.58,1.99,-6.64,
-12.13,-4.95,-2.99,-4.81,-0.88,0.28,-1.44,1.92,3.73,0.21,1.11,6.83,13.31,
7.04,1.46,1.78]

# start and end index for data
a = [0,21,42]
b = [20,41,62]

n = 0   # just a counter

# set font family
hfont = {'family':'Arial'}
plt.rcParams.update({'font.family': 'Arial', 'font.size':12})

# set axis minor tick marks
plt.gca().yaxis.set_minor_locator(mtick.MultipleLocator(4))
plt.gca().xaxis.set_minor_locator(mtick.MultipleLocator(2))
plt.gca().yaxis.set_major_formatter(mtick.FormatStrFormatter('%.0f%%'))

# config axis labels
plt.xlabel("Outdoor Temperature", **hfont)
plt.ylabel("Radiator on %", **hfont)

# set line and marker types and colors
marker = ['o', 's', 'x']
marker_facecolor = ['None', 'k', 'None']
names = ['Manual Control', 'Enforced Schedule', 'Occupancy-based']
lines = [':','--','-']
line_spacing = [[1, 5], [5, 5], [0, 0]]
transparency = [1, 0.75, 1]

for i,j in zip(a,b):
    # get x and y
    x = np.array(tout[i:j])
    y = np.array(radon[i:j])

    # set axis ranges
    plt.ylim(0, 100)
    plt.xlim(-30, 20)

    # plot data
    plt.plot(x, y, marker[n],markeredgewidth=0.75,
             markeredgecolor='k',markerfacecolor=marker_facecolor[n],
             alpha=transparency[n],label=names[n])

    # perform regressions
    z = np.polyfit(x, y, 1)
    p = np.poly1d(z)

    # plot trendline
    order = np.argsort(x)
    plt.plot(x[order],p(x[order]),'k%s' % lines[n], linewidth=0.85)

    # increment counter
    n+=1

plt.show()

---

关键是以排序的方式绘制数组。否则，直线将前后跳跃并重叠

order = np.argsort(x)
plt.plot(x[order],p(x[order]), ..)

完整代码：

from matplotlib import pyplot as plt
import matplotlib.ticker as mtick
import numpy as np

radon = [49.6,61.7,58.7,64.1,59.4,64.6,65.4,65.3,65.5,66.0,50.5,64.8,71.9,
71.9,60.4,54.4,50.9,58.1,52.6,55.6,56.6,41.6,43.0,33.0,41.5,53.5,52.2,45.1,
46.8,63.0,73.8,61.3,44.4,39.3,38.2,45.4,39.9,36.3,41.0,38.5,35.4,40.2,11.3,
34.7,24.2,24.5,32.1,26.3,23.6,32.1,27.5,39.0,24.9,22.0,18.2,23.0,21.1,15.4,
13.9,10.2,26.4,18.2,16.6]

tout = [-2.57,-3.31,-0.63,-0.60,0.39,-1.64,-7.62,-1.90,-0.35,-4.88,-1.27,
-0.23,-6.99,-2.87,-12.27,-11.90,-9.42,-4.10,-3.15,0.81,3.87,-11.41,-9.47,
0.25,-6.81,-13.70,-16.41,-14.14,-9.70,-10.32,-21.83,-26.55,-16.88,-6.85,
4.03,-7.89,-6.53,-3.96,-6.09,-3.15,-0.51,-2.62,8.13,2.08,0.58,1.99,-6.64,
-12.13,-4.95,-2.99,-4.81,-0.88,0.28,-1.44,1.92,3.73,0.21,1.11,6.83,13.31,
7.04,1.46,1.78]

# start and end index for data
a = [0,21,42]
b = [20,41,62]

n = 0   # just a counter

# set font family
hfont = {'family':'Arial'}
plt.rcParams.update({'font.family': 'Arial', 'font.size':12})

# set axis minor tick marks
plt.gca().yaxis.set_minor_locator(mtick.MultipleLocator(4))
plt.gca().xaxis.set_minor_locator(mtick.MultipleLocator(2))
plt.gca().yaxis.set_major_formatter(mtick.FormatStrFormatter('%.0f%%'))

# config axis labels
plt.xlabel("Outdoor Temperature", **hfont)
plt.ylabel("Radiator on %", **hfont)

# set line and marker types and colors
marker = ['o', 's', 'x']
marker_facecolor = ['None', 'k', 'None']
names = ['Manual Control', 'Enforced Schedule', 'Occupancy-based']
lines = [':','--','-']
line_spacing = [[1, 5], [5, 5], [0, 0]]
transparency = [1, 0.75, 1]

for i,j in zip(a,b):
    # get x and y
    x = np.array(tout[i:j])
    y = np.array(radon[i:j])

    # set axis ranges
    plt.ylim(0, 100)
    plt.xlim(-30, 20)

    # plot data
    plt.plot(x, y, marker[n],markeredgewidth=0.75,
             markeredgecolor='k',markerfacecolor=marker_facecolor[n],
             alpha=transparency[n],label=names[n])

    # perform regressions
    z = np.polyfit(x, y, 1)
    p = np.poly1d(z)

    # plot trendline
    order = np.argsort(x)
    plt.plot(x[order],p(x[order]),'k%s' % lines[n], linewidth=0.85)

    # increment counter
    n+=1

plt.show()