如何在matplotlib / seaborn中突出显示图的线段？

编程问答 2022-06-06

问题描述

我有多个时间序列和多个标签。每当有标签可用时，我都希望以红色突出显示时间序列。

现有情节

我有一个折线图，可以在其中突出显示绘图的某些元素，例如：

enter image description here

for cohort_id in sorted(df.cohort_id.unique()):
    print(cohort_id)
    figsize = (25,9)
    fig,ax = plt.subplots(figsize=figsize)
    ax = sns.lineplot(x='hour',y='metrik_0',data=df[df.cohort_id == cohort_id],ax=ax)
    ax.xaxis.set_major_locator(aut_locator)
    ax.xaxis.set_major_formatter(aut_formatter)
    
    plt.title(f'cohort_id: {cohort_id}',fontsize=45)
    plt.xlabel('')
    plt.ylabel('metrik_0',fontsize=35)
    
    for index,row in marker_labels.iterrows():
        start = row.start
        end = row.end
        marker_type = row.marker_type
        if marker_type == 'b':
            ax.axvspan(start,end,color='gray',alpha=0.2)
        else:
            ax.axvspan(start,color='orange',alpha=0.5)
        
    plt.show()

此图可以转换为突出显示某些周期性的循环图，例如：

enter image description here

for cohort_id in sorted(df.cohort_id.unique()):
    print(cohort_id)
    
    figsize = (25,ax = plt.subplots(figsize=figsize)
    a1 = sns.lineplot(x=df['hour'].dt.hour,hue='device_id',units='dt',style='dt',estimator=None,data=df[(df.cohort_id == cohort_id)],ax=ax)
    handles,labels = a1.get_legend_handles_labels()
    a1.legend(handles=handles[1:],labels=labels[1:],loc='center',bBox_to_anchor=(0.5,-0.25),ncol=6,fontsize=20)

    plt.title(f'cohort_id: {cohort_id}',fontsize=35)
    plt.xlabel('hour of the day',fontsize=35)
    plt.ylabel('metrik_0',fontsize=35)
    plt.show()

但是现在标签不再显示。

问题

如何将标签重新添加到循环图中？任何方法都可以。但是到目前为止，我认为最好用红色突出显示匹配的时间间隔

数据生成

要生成一些示例数据：

%pylab inline

import pandas as pd
import numpy as np
import seaborn as sns; sns.set()
import matplotlib.dates as mdates

aut_locator = mdates.AutoDateLocator(minticks=3,maxticks=7)
aut_formatter = mdates.ConciseDateFormatter(aut_locator)

import random
random_seed = 47
np.random.seed(random_seed)

random.seed(random_seed)

def generate_df_for_device(n_observations,n_metrics,device_id,geo_id,topology_id,cohort_id):
        df = pd.DataFrame(np.random.randn(n_observations,n_metrics),index=pd.date_range('2020',freq='H',periods=n_observations))
        df.columns = [f'metrik_{c}' for c in df.columns]
        df['geospatial_id'] = geo_id
        df['topology_id'] = topology_id
        df['cohort_id'] = cohort_id
        df['device_id'] = device_id
        return df
    
def generate_multi_device(n_observations,n_devices,cohort_levels,topo_levels):
    results = []
    for i in range(1,n_devices +1):
        #print(i)
        r = random.randrange(1,n_devices)
        cohort = random.randrange(1,cohort_levels)
        topo = random.randrange(1,topo_levels)
        df_single_dvice = generate_df_for_device(n_observations,i,r,topo,cohort)
        results.append(df_single_dvice)
        #print(r)
    return pd.concat(results)

# hourly data,1 week of data
n_observations = 7 * 24
n_metrics = 3
n_devices = 20
cohort_levels = 3
topo_levels = 5

df = generate_multi_device(n_observations,topo_levels)
df = df.sort_index()
df = df.reset_index().rename(columns={'index':'hour'})
df['dt'] = df.hour.dt.date

and labels:

marker_labels = pd.DataFrame({'cohort_id':[1,1,1],'marker_type':['a','b','a'],'start':['2020-01-2','2020-01-04 05','2020-01-06'],'end':[np.nan,'2020-01-05 16',np.nan]})
marker_labels['start'] = pd.to_datetime(marker_labels['start'])
marker_labels['end'] = pd.to_datetime(marker_labels['end'])
marker_labels.loc[marker_labels['end'].isnull(),'end'] =  marker_labels.start + pd.timedelta(days=1) - pd.timedelta(seconds=1)
marker_labels

详细的Jupyter笔记本以及示例数据和当前绘图代码可在以下位置找到：https://github.com/geoHeil/plotting_tricks

修改

假设我们对时间段的标签执行LEFT连接：

merged_res = (df.reset_index()
         .merge(marker_labels,on='cohort_id',how='left')
         .query('start <= hour <= end')
         .set_index('index')
         .reindex(df.index)
      )

merged_res = merged_res.combine_first(df)
merged_res.marker_type = merged_res.marker_type.fillna('no_labels_reported')

绘图代码为：

for cohort_id in sorted(merged_res.cohort_id.unique()):
    print(cohort_id)
    
    figsize = (25,ax = plt.subplots(figsize=figsize)
    a1 = sns.lineplot(x=merged_res['hour'].dt.hour,hue='marker_type',data=merged_res[(merged_res.cohort_id == cohort_id)],fontsize=35)
    plt.show()

导致：

enter image description here

但是：

  • 这还是很混乱
  • 在可视化中汇总/平均设备的各个时间序列

解决方法

到目前为止最好的事情似乎是hvplot：

merged_res['hour_time'] = merged_res['hour'].dt.hour
merged_res.device_id = merged_res.device_id.astype(str)

for cohort_id in sorted(merged_res.cohort_id.unique()):
    print(cohort_id)
    current_plot = merged_res[merged_res.cohort_id == cohort_id].set_index(['hour_time'])[['metrik_0','marker_type','device_id','dt']].hvplot(by=['marker_type'],hover_cols=['dt','device_id'],width=width,height=height).opts(active_tools=['box_zoom'])
    display(current_plot)

导致： enter image description here

由于我仍然不完全满意-我将其保持打开状态（未回答），以查看是否有人提出了更好的解决方案。

尤其是，我不喜欢这样显示线条-可能点会更好。即由于某些事情从无标签变为具有标签，因此时间序列不是连续绘制的（=更改颜色），而实际上是跳跃的（=创建了一条新的不同线。所以使用点也只能是一种解决方法（但可能比具有跳线。

highlightmatplotlibpythonseaborntime-series