使用线、日期、文本创建时间轴图
如何使用Matplotlib发布日期创建简单的时间轴。
可以使用日期和文本的集合创建时间轴。在本例中，我们将展示如何使用Matplotlib最新版本的日期创建一个简单的时间轴。首先，我们将从GitHub中提取数据。
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.dates as mdates
from datetime import datetime
# A list of Matplotlib releases and their dates
# Taken from https://api.github.com/repos/matplotlib/matplotlib/releases
names = ['v2.2.2', 'v2.2.1', 'v2.2.0', 'v2.1.2', 'v2.1.1', 'v2.1.0', 'v2.0.2',
'v2.0.1', 'v2.0.0', 'v1.5.3', 'v1.5.2', 'v1.5.1', 'v1.5.0', 'v1.4.3',
'v1.4.2', 'v1.4.1', 'v1.4.0']
dates = ['2018-03-17T03:00:07Z', '2018-03-16T22:06:39Z',
'2018-03-06T12:53:32Z', '2018-01-18T04:56:47Z',
'2017-12-10T04:47:38Z', '2017-10-07T22:35:12Z',
'2017-05-10T02:11:15Z', '2017-05-02T01:59:49Z',
'2017-01-17T02:59:36Z', '2016-09-09T03:00:52Z',
'2016-07-03T15:52:01Z', '2016-01-10T22:38:50Z',
'2015-10-29T21:40:23Z', '2015-02-16T04:22:54Z',
'2014-10-26T03:24:13Z', '2014-10-18T18:56:23Z',
'2014-08-26T21:06:04Z']
dates = [datetime.strptime(ii, "%Y-%m-%dT%H:%M:%SZ") for ii in dates]
接下来，我们将遍历每个日期并将其绘制在水平线上。我们将为文本添加一些样式，以便重叠不那么严重。
请注意，Matplotlib将自动绘制日期时间输入。
levels = np.array([-5, 5, -3, 3, -1, 1])
fig, ax = plt.subplots(figsize=(8, 5))
# Create the base line
start = min(dates)
stop = max(dates)
ax.plot((start, stop), (0, 0), 'k', alpha=.5)
# Iterate through releases annotating each one
for ii, (iname, idate) in enumerate(zip(names, dates)):
level = levels[ii % 6]
vert = 'top' if level < 0 else 'bottom'
ax.scatter(idate, 0, s=100, facecolor='w', edgecolor='k', zorder=9999)
# Plot a line up to the text
ax.plot((idate, idate), (0, level), c='r', alpha=.7)
# Give the text a faint background and align it properly
ax.text(idate, level, iname,
horizontalalignment='right', verticalalignment=vert, fontsize=14,
backgroundcolor=(1., 1., 1., .3))
ax.set(title="Matplotlib release dates")
# Set the xticks formatting
# format xaxis with 3 month intervals
ax.get_xaxis().set_major_locator(mdates.MonthLocator(interval=3))
ax.get_xaxis().set_major_formatter(mdates.DateFormatter("%b %Y"))
fig.autofmt_xdate()
# Remove components for a cleaner look
plt.setp((ax.get_yticklabels() + ax.get_yticklines() +
list(ax.spines.values())), visible=False)
plt.show()
