如何根据数据集以直线和不同的线型绘制数据

时间:2020-11-12 07:42:10

标签: python matplotlib gnuplot

我有一个唯一的数据集(行和列的数量视情况而定。)

0.0       0       0       0       0       0       0
0.5       0       0       0       0       0       0
2.0 156.626 156.626 138.354 138.354 138.354 138.354
2.5 156.626 156.626 138.354 138.354 138.354 138.354
4.0 287.268 287.268 289.808 289.808 271.829 276.304
4.5 287.268 287.268 289.808 289.808 271.829 276.304
6.0 418.931 426.263 418.933 426.259 273.572 273.559
6.5 418.931 426.263 418.933 426.259 273.572 273.559
8.0 417.211 417.21  417.207 417.211 417.207 417.212
8.5 417.211 417.21  417.207 417.211 417.207 417.212

如您所见,它具有数据集的唯一组合(先更改然后再更改,再更改然后再更改。)我想在实线中绘制常量数据集,而不用任何破折号,而将非直线将为不同的破折号。 我需要一个脚本(无论是gnuplot还是matplotlib)都可以按照附图绘制数据。enter image description here在此图中,我仅显示了三行,作为示例。

我已经在gnuplot脚本下创建了可以给我所需的图表(enter image description here,但它没有给我水平线,没有任何破折号的实线。

CASE = "New.dat"
Xi=-2 ; Xf=22; Xs=1

AYf=500 ; AYs=100
reset
set terminal postscript eps enhanced size 20cm,20cm  color solid lw 3 "Times-Bold" 40
set output "data.eps" 
set multiplot \
    layout 1,1 rowsfirst \
    title "{/:Bold=40 }" \
    margins screen 0.15,0.85,0.11,0.950 \
    spacing screen 0.00,0.03

set key spacing 1.2
set    mxtics 2 
set    mytics 2
unset key
unset arrow
set arrow from Xi ,0.00 to Xf,000 nohead lw 3.5  lc rgb "blue" lt 0
set xrange [Xi:Xf]
set yrange [-50:AYf]
set key at graph 0.63, 0.95 font "Times-bold, 30"
set xtics Xi,Xs,Xf format ""
set ytics 0,AYs,AYf format "%g" font "Times-bold, 40"

plot CASE u 1:2 title "Path-1"  w l  lc 1 lw 3 dashtype 2 ,  CASE u 1:3 title "Path-2"  w l lc 2 lw 3 dashtype 3 , CASE u 1:4 title "Path-3" w l lc 4 lw 3 dashtype 4 , CASE u 1:5 title "Path-4" w l lc 6 lw 3 dashtype 5, CASE u 1:6 title "Path-5" w l  lc 7 lw 3 dashtype 6 , CASE u 1:7 title "Path-6" w l lc 9 lw 3 dashtype 9

这是来自theozh的脚本enter image description here

2 个答案:

答案 0 :(得分:1)

在gnuplot中,我会这样做。 绘制两次数据

  1. with lines和不同的破折号
  2. 和水平线with vectors,但前提是y值不变。

区分虚线有点困难,因为其中一些虚线是相互重叠的。您需要对此进行一些优化。

代码:

### plot intermittent horizontal lines 
reset session

$Data <<EOD
0.0       0       0       0       0       0       0
0.5       0       0       0       0       0       0
2.0 156.626 156.626 138.354 138.354 138.354 138.354
2.5 156.626 156.626 138.354 138.354 138.354 138.354
4.0 287.268 287.268 289.808 289.808 271.829 276.304
4.5 287.268 287.268 289.808 289.808 271.829 276.304
6.0 418.931 426.263 418.933 426.259 273.572 273.559
6.5 418.931 426.263 418.933 426.259 273.572 273.559
8.0 417.211 417.21  417.207 417.211 417.207 417.212
8.5 417.211 417.21  417.207 417.211 417.207 417.212
EOD

set key top left

set datafile missing NaN    # apparently necessary for gnuplot 5.2.2 

plot for [i=2:7] $Data u 1:i w l lw 2 lc i-1 dt i title sprintf("Path %d",i-1), \
     for [i=2:7] y1=x1=NaN $Data u (x0=x1,x1=column(1),x0):(y0=y1,y1=column(i)):(x1-x0):(y0==y1?0:NaN) w vectors lw 4 lc i-1 nohead notitle
### end of code

结果:

enter image description here

答案 1 :(得分:0)

计算与数据的平稳段和递增段相对应的索引。然后按段绘制。这是第一条路径的图。要绘制所有路径,可以使用另一个for循环。也许有一个更简单的解决方案,但这应该可行。

# path data
x = np.array([0. , 0.5, 2. , 2.5, 4. , 4.5, 6. , 6.5, 8. , 8.5])
y = np.array([0., 0., 156.626, 156.626, 287.268, 287.268, 418.931, 418.931, 417.211, 417.211])

# indices of plateau and increasing
idx_plat = np.where(np.diff(y) == 0)[0]
idx_incr = np.where(np.diff(y) != 0)[0]

# color for first path
color = 'C0'

# text setup
texts = ['A', 'B', 'C', 'D', 'E']
off = 10 # y offset for text

# plot plateau and increasing segments in a loop
for i in range(len(idx_plat) - 1):
    x_sub = x[idx_plat[i]:idx_plat[i+1]]
    y_sub = y[idx_plat[i]:idx_plat[i+1]]
    
    plt.plot(x_sub, y_sub, linestyle = '--', color = color)
    
    # annotate text
    plt.text(x_sub.mean(), y_sub[0] + off, texts[i])
    
for j in range(len(idx_incr) - 1):
    x_sub = x[idx_incr[j]:idx_incr[j+1]]
    y_sub = y[idx_incr[j]:idx_incr[j+1]]
    
    plt.plot(x_sub, y_sub, linestyle = '-', color = color)

# plot last segment twice with label to create legend
plt.plot(x_sub, y_sub, linestyle = '-', color = color, label = label)
plt.legend(loc = 'best')