Overlay a line plot around diffrent region in Seaborn or Matplotlib-CodePudding

I would like to plot a line plot and make different overlay based on condition as illustrated below.

May I know how, or if possible, please kindly redirect me to right material on achieving the intended objective.

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
np.random.seed(0)
rng = np.random.default_rng(2)
mlist=[]
for _ in range(4):

    m=np.random.rand(4).tolist()
    n=rng.integers(0, 6, size=(1)).tolist()*4
    df = pd.DataFrame(zip(m,n), columns=['yval','type'])
    mlist.append(df)

df=pd.concat(mlist).reset_index(drop=True).reset_index()
sns.lineplot(data=df, x="index", y="yval")
plt.show()

Suggestion using Matplotlib or Seaborn, or any other package are welcome

CodePudding user response：

The filling of the section was achieved using axvspan. I also used text for annotations.

import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
np.random.seed(0)
rng = np.random.default_rng(2)
mlist=[]
for _ in range(4):
    m=np.random.rand(4).tolist()
    n=rng.integers(0, 6, size=(1)).tolist()*4
    df = pd.DataFrame(zip(m,n), columns=['yval','type'])
    mlist.append(df)

df=pd.concat(mlist).reset_index(drop=True).reset_index()
g = sns.lineplot(data=df, x="index", y="yval")

overlay = {0:'m',1:'gray',5:'r'}

for i in np.arange(0,len(df),4):
    tmp = df.iloc[i:i 4, :]
    v = overlay.get(tmp.type.unique()[0])
    g.axvspan(min(tmp.index), max(tmp.index) 1, color=v, alpha=0.3)
    g.text(((min(tmp.index) max(tmp.index) 1) / 2)-1, 0.1,'type {}'.format(tmp.type.unique()[0]), fontsize=12)

plt.show()

CodePudding user response：

Using Matplotlib add_patch and text

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.patches as patches

import matplotlib.colors as mcolors
# nn=mcolors.CSS4_COLORS
all_colors=list(mcolors.TABLEAU_COLORS.keys())

b=1
np.random.seed(0)
rng = np.random.default_rng(2)
mlist=[]
for _ in range(4):

    m=np.random.rand(4).tolist()
    n=rng.integers(0, 6, size=(1)).tolist()*4
    df = pd.DataFrame(zip(m,n), columns=['yval','type'])
    mlist.append(df)

df=pd.concat(mlist).reset_index(drop=True).reset_index()
# df.to_feather('test.feather')
# df=pd.read_feather('test.feather')
df['C'] = df['type'].diff()
df['C']=df['C'].fillna(10)

nb=df.type[(df['C'] != 0)].to_frame().reset_index()
unique_val=nb['type'].drop_duplicates().sort_values().tolist()
ngroup_type=dict(zip(unique_val,[f'type {idx}' for idx in unique_val]))
nb['ngroup']=nb["type"].map(ngroup_type)
color_group=all_colors[:len(unique_val)]
res = dict(zip(unique_val, color_group))
nb["color"] = nb["type"].map(res)


starting_point=nb["index"].values.tolist()
mcolor=nb["color"].values.tolist()
group_type=nb["ngroup"].values.tolist()
nspace=4
nheight=1
fg=sns.lineplot(data=df, x="index", y="yval")
for ncolor,spoint,gtype in zip(mcolor,starting_point,group_type):
    fg.axes.add_patch(patches.Rectangle((spoint, 0),
            nspace,nheight,edgecolor = 'blue',
                                   facecolor = ncolor,fill=True,alpha=0.1,ls=':') )
    fg.axes.text(spoint 1.5, 0.1, gtype , size=10,
         va="baseline", ha="left", multialignment="left")
plt.show()