根据事件的发生向Seaborn Facet Grid图添加垂直线 [英] Add vertical lines to Seaborn Facet Grid plots based on the occurrence of an event

问题描述

在60个月内，我有来自50个不同个体的小组数据.我已经使用Seaborn创建了多面网格，以绘制每个人随时间变化的音调，现在想为事件发生时添加垂直线.理想情况下，我想在事件1发生时添加一条蓝线，在事件2发生时添加一条红线.

I have panel data with 50 different individuals over 60 months. I've created facet grids using Seaborn to plot the tone for each individual over time and now want to add vertical lines for when an event happens. Ideally, I want to add say a blue line for when event 1 occurs and a red line when event 2 occurs.

数据样本:

f_a    tone    t     event1    event2
01_01  -1.9    0       0         0
01_01  -1.1    1       1         0
01_01  -2.5    2       0         0
01_01  -3.0    3       0         1
...
01_01   1.3    40      1         0
01_01   0.7    41      0         0
01_01  -0.6    42      0         0
01_01  -2.3    43      0         1

1. "f_a"是ID(分组变量)
2. 音调"是y轴
3. 't'是时间变量
如果事件在时间段t内发生，
4. "event1"和"event2"等于1，否则为零.

1. 'f_a' is the ID (grouping variable)
2. 'tone' is the y-axis
3. 't' is the time variable
4. 'event1' and 'event2' equal 1 if the event occurs during period t and zero otherwise.

这是创建情节的代码:

# Initialize a grid of plots with an Axes for each pair
grid1 = sns.FacetGrid(df,col='f_a',hue='f_a',col_wrap=5,height=1.5)

#Draw a horizontal line to show the starting point
grid1.map(plt.axhline,y=0,ls=":",c=".5")

#Draw a line plot to show the trajectory of tone for each f-a pair over time
grid1.map(plt.plot,"t","tone",marker='o')

这是情节输出的示例:情节

Here's a sample of the plot output: Plots

这是我用来生成数据的代码:

Here's the code I used to generate the data:

# Create list of observation pairs
fs = np.arange(1,11,1)
ans = np.arange(1,6,1)
f_a=[str(f).zfill(2)+'_'+str(a).zfill(2) 
     for f in fs for a in ans]
# Create dataframe with ARMA process by f_a pair with 60 months of observations of tone
# per pair
d={}
for f in f_a:
    arparams = np.array([.5, .25])
    maparams = np.array([.5, .3])
    ar = np.r_[1,-arparams]
    ma = np.r_[1, maparams]
    y = sm.tsa.arma_generate_sample(ar,ma,60)
    d[f]=y
    
df=pd.melt(pd.DataFrame(d)).rename(columns={'variable':'f_a','value':'tone'})
df['t']=df.groupby('f_a').cumcount()

# One occurrence of event 1 and 2 per f_a pair
up = [np.random.choice(np.arange(2,61,1)) for f in f_a]
down = [np.random.choice(np.arange(2,61,1)) for f in f_a]

# Dataframe with event 1 and 2
events=pd.DataFrame(data=[f_a,up,down]).T.rename(columns={0:'f_a',1:'event1_t',2:'event2_t'})

# Merge Datasets
df=df.merge(right=events,how='left',on='f_a')

# Create dummies for event1/event2
df['event1']=(df.t==df.event1_t)*1
df['event2']=(df.t==df.event2_t)*1

# Clean up dataset
df=df.drop(columns=['event1_t','event2_t'])

推荐答案

您可以使用 axvline 遍历您的组并绘制线条.要遍历子图，请使用 grid1.axes :

You can iterate over your groups and plot lines using axvline. To iterate over subplots, use grid1.axes:

for ax, (_, subdata) in zip(grid1.axes, df.groupby('f_a')):
    xs = subdata[subdata['event1'] == 1].t
    for x in xs:
        ax.axvline(x, color='r', ls='--')

    xs = subdata[subdata['event2'] == 1].t
    for x in xs:
        ax.axvline(x, color='b', ls='--')

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