似乎是一个有趣的任务来练习python。在
下面是一个使用一些伪造数据的绘图：import plotly.plotly as py
import plotly.graph_objs as go
import random
import datetime
# setup the date series
# we need day of week (dow) and if it is a weekday (wday) too
sdate = datetime.datetime.strptime("2016-01-01", '%Y-%m-%d').date()
edate = datetime.datetime.strptime("2016-02-28", '%Y-%m-%d').date()
ndays = (edate - sdate).days + 1
dates = [sdate + datetime.timedelta(days=x) for x in range(ndays)]
dow = [(x + 5) % 7 for x in range(ndays)]
wday = [1 if dow[x]<=4 else 0 for x in range(ndays)]
# now some fake power consumption
# weekdays will have 150 power consumption on average
# weekend will have 100 power consumption on average
# and we add about 20 in random noise to both
pwval = [90 + wday[x] * 50 + random.randrange(0, 20) for x in range(ndays)]
# limits - higher limits during the week (150) compared to the weekend (100)
pwlim = [150 if dow[x] <= 4 else 100 for x in range(ndays)]
# now the colors
clrred = 'rgb(222,0,0)'
clrgrn = 'rgb(0,222,0)'
clrs = [clrred if pwval[x] >= pwlim[x] else clrgrn for x in range(ndays)]
# first trace (layer) is our power consumption bar
trace0 = go.Bar(
x=dates,
y=pwval,
name='Power Consumption',
marker=dict(color=clrs)
)
# second trace is our line showing the power limit
trace1 = go.Scatter(
x=dates,
y=pwlim,
name='Power Limit',
line=dict(
color=('rgb(0,0,222)'),
width=2,
dash='dot')
)
data = [trace0, trace1]
layout = go.Layout(title='Power')
fig = go.Figure(data=data, layout=layout)
py.iplot(fig, filename='power-limits-1')
它看起来是这样的：
