如何使用matplotlib创建原点远离中心且半径大于0的时间螺旋图？

Question

我对使用matplotlib作图比较陌生，目前我正在尝试绘制一个时间范围螺旋图，我已经工作了几天，我遇到了一个无法解决的问题

我从excel文件中以1分钟为间隔绘制以下格式的图形，我有长达30天的数据

 
Timestamp       alarms summatory
01/12/2018 00:00        3
01/12/2018 00:01        1
01/12/2018 00:02        2
01/12/2018 00:03        1
01/12/2018 00:04        1
01/12/2018 00:05        3
01/12/2018 00:06        1
01/12/2018 00:07        3
01/12/2018 00:08        1
01/12/2018 00:09        4
01/12/2018 00:10        3

通过社区中提出的其他问题和一些文档，我得到了以下图表，其中2π表示一天，每种颜色表示一分钟内激活的警报数量：

Mygraph

我想展示这些1分钟的事件，但从第一天开始，它们的可视化更容易，如下图所示，图中的中心为空，直线开始从中心向远处延伸：

Graph Expected

我一直在尝试修改linespace函数的参数，以尝试获取最后一幅图像的图形，但没有一幅成功，我不知道还能做什么，我希望有人能帮助我

这是我一直在使用的代码和excel文件

Data

代码

import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.patheffects as mpe
import matplotlib.colors as colors
import pandas as pd
from datetime import datetime, timedelta
# styling 
LINEWIDTH=5
EDGEWIDTH=0
CAPSTYLE="projecting"
ALPHA=1
FIRSTHOUR=0 # 0= 24 hrs, 23= 23 hrs

cdict = {'red':  ((0.0, 0.0, 0.0),
(1/6., 0.0, 0.0),
(1/2., 0.8, 1.0),
(5/6., 1.0, 1.0),
(1.0, 0.4, 1.0)),
'green':  ((0.0, 0.0, 0.4),
(1/6., 1.0, 1.0),
(1/2., 1.0, 0.8),
(5/6., 0.0, 0.0),
(1.0, 0.0, 0.0)),
'blue': ((0.0, 0.0, 0.0),
(1/6., 0.0, 0.0),
(1/2., 0.9, 0.9),
(5/6., 0.0, 0.0),
(1.0, 0.0, 0.0))
}

COLORMAP=colors.LinearSegmentedColormap.from_list("", ["green","yellow","red"])
#Read excel
df = pd.read_excel('alarms_boiler_1min_v2.xlsx')
df['Timestamp'] = df['Timestamp'].replace('/','-').apply(pd.to_datetime)

#set origin  
firts_timestamp =df['Timestamp'].min()
origin = (firts_timestamp + pd.to_timedelta(firts_timestamp.hour - FIRSTHOUR, unit='hours'))
day = pd.date_range("00:00", "23:00", freq="60min").strftime('%H:%M').tolist()
# convert alarms sumatory timestamps to day fractions
df['start'] = (df['Timestamp'] - origin) / np.timedelta64(1, 'D')
df['stop'] = (pd.DatetimeIndex(df['Timestamp']) + timedelta(minutes=1)- origin)/np.timedelta64(1, 
'D')

fig = plt.figure(figsize=(8, 6))
ax = fig.gca(projection="polar")

for idx, event in df.iterrows():
    # sample normalized alarms summatory colormap
    alarms_sum = event['alarms summatory']/4        
    color = plt.cm.get_cmap(COLORMAP)(alarms_sum)
    tstart, tstop = event.loc[['start', 'stop']]
    # timestamps are in day fractions, 2pi is one day
    nsamples = int(10000. * (tstop - tstart))
    t = np.linspace(tstart, (tstop),nsamples)
    theta = 2 * np.pi * (t) 
    arc, = ax.plot(theta, t, lw=LINEWIDTH, color=color, solid_capstyle=CAPSTYLE, alpha=ALPHA)
    arc.set_path_effects([mpe.Stroke(linewidth=LINEWIDTH+EDGEWIDTH, foreground='black'),mpe.Normal()])

#set grid, labels
ax.set_rticks([])
ax.set_theta_zero_location("N")
ax.set_theta_direction(-1)
ax.set_xticks(np.linspace(0, 2*np.pi, 24, endpoint=False))
ax.set_xticklabels(day)
ax.tick_params('x', pad=2)
ax.grid(True)
#show graph
norm = mpl.colors.Normalize(vmin=0, vmax=4)
sm = plt.cm.ScalarMappable(cmap=COLORMAP, norm=norm)
sm.set_array([])
plt.colorbar(sm, ticks=np.linspace(0, 4, 10), fraction=0.04, aspect=60, pad=0.1, label="alarms summatory", ax=ax)
plt.show()

提前非常感谢您，祝您愉快

Answer 1

欢迎来到SO！第一个问题很好

通过设置负y/r限制，可以非常轻松地将极坐标图转换为“圆环”图：

ax.set_rlim(bottom=-10) # or ax.set_ylim(bottom=-10)