在 matplotlib/gnuplot 中绘制标记区间

Question

我有一个如下所示的数据样本:

a 10:15:22 10:15:30 OK
b 10:15:23 10:15:28 OK
c 10:16:00 10:17:10 FAILED
b 10:16:30 10:16:50 OK

我想要的是按以下方式绘制上述数据:

captions ^
  |
c |         *------*
b |   *---*    *--*
a | *--*
  |___________________
                     time >

线条的颜色取决于 OK/FAILED数据点的状态。标签 ( a/b/c/... ) 可以重复也可以不重复。

正如我从 的文档中收集到的那样gnuplot 和 matplotlib ，这种类型的图在后者中应该更容易做，因为它不是标准图并且需要一些预处理。

问题是:

是否有标准方法可以在任何工具中进行这样的绘图？

如果没有，我应该如何绘制这些数据(指向相关工具/文档/函数/示例的指针，这些工具/文档/函数/示例执行的操作有点类似于此处描述的内容)？

Answer 1

更新:现在包括处理数据样本并使用 mpl 日期功能。

import matplotlib.pyplot as plt
from matplotlib.dates import DateFormatter, MinuteLocator, SecondLocator
import numpy as np
from StringIO import StringIO
import datetime as dt

### The example data
a=StringIO("""a 10:15:22 10:15:30 OK
b 10:15:23 10:15:28 OK
c 10:16:00 10:17:10 FAILED
b 10:16:30 10:16:50 OK
""")

#Converts str into a datetime object.
conv = lambda s: dt.datetime.strptime(s, '%H:%M:%S')

#Use numpy to read the data in. 
data = np.genfromtxt(a, converters={1: conv, 2: conv},
                     names=['caption', 'start', 'stop', 'state'], dtype=None)
cap, start, stop = data['caption'], data['start'], data['stop']

#Check the status, because we paint all lines with the same color 
#together
is_ok = (data['state'] == 'OK')
not_ok = np.logical_not(is_ok)

#Get unique captions and there indices and the inverse mapping
captions, unique_idx, caption_inv = np.unique(cap, 1, 1)

#Build y values from the number of unique captions.
y = (caption_inv + 1) / float(len(captions) + 1)

#Plot function
def timelines(y, xstart, xstop, color='b'):
    """Plot timelines at y from xstart to xstop with given color."""   
    plt.hlines(y, xstart, xstop, color, lw=4)
    plt.vlines(xstart, y+0.03, y-0.03, color, lw=2)
    plt.vlines(xstop, y+0.03, y-0.03, color, lw=2)

#Plot ok tl black    
timelines(y[is_ok], start[is_ok], stop[is_ok], 'k')
#Plot fail tl red
timelines(y[not_ok], start[not_ok], stop[not_ok], 'r')

#Setup the plot
ax = plt.gca()
ax.xaxis_date()
myFmt = DateFormatter('%H:%M:%S')
ax.xaxis.set_major_formatter(myFmt)
ax.xaxis.set_major_locator(SecondLocator(interval=20)) # used to be SecondLocator(0, interval=20)

#To adjust the xlimits a timedelta is needed.
delta = (stop.max() - start.min())/10

plt.yticks(y[unique_idx], captions)
plt.ylim(0,1)
plt.xlim(start.min()-delta, stop.max()+delta)
plt.xlabel('Time')
plt.show()