python - 如何根据绘图的 y 值调整太阳图高度

Question

我想根据 KDE 图的最大出现 y 值选择子图的高度（以便在最后一个子图中仍显示整个 KDE 图）。那么如何调整绘制四分位数的公式（ax[i-1].axvline(quatrilesx[k],.....])？为什么这些线现在不再可见？

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib.ticker import NullFormatter
import numpy as np
import random

def plotverweildauernwithuG(df):    

def rand_float_range(start, end):
    return random.random() * (end - start) + start



def find_nearest(array, value):
    array = np.asarray(array)
    idx = (np.abs(array - value)).argmin()
    return idx


df=df[df['method']=='data']


df['years']=df['years'].astype('float64')



sns.set()
#sharex=True: x-Achse für alle Subplots geteilt
#fig,ax= plt.subplots(): tuple containing figure and axes object
fig, ax = plt.subplots(nrows=df['cs'].max(), sharex=True)
nrows=len(ax)
ax[df['cs'].max()-1].set_xlabel('Verweildauer in Jahren')



s=[(df["cs"] == t).sum() for t in range(1, df['cs'].max()+1)]
s.insert(0, 0)
print(s)   


h=0
for i in range(1, df['cs'].max()+1):
    #get quartiles
    quatrilesx = [df[df['cs']==i]["years"].astype(float).describe()['25%'],df[df['cs']==i]["years"].astype(float).describe()['50%'], df[df['cs']==i]["years"].astype(float).describe()['75%']]
    verweildauer_i = df[df['cs'] == i]['years']

    cs_bandwith_i = df[df['cs'] == i]['cs_bandwith']
    color_i=df[df['cs'] == i]['uGW']

    verweildauer_i_nugw = df[(df['cs'] == i) & (df['uGW']==False)]['years']
    cs_bandwith_i_nugw = df[(df['cs'] == i) & (df['uGW']==False)]['cs_bandwith']

    verweildauer_i_ugw = df[(df['cs'] == i) & (df['uGW']==True)]['years']
    cs_bandwith_i_ugw = df[(df['cs'] == i) & (df['uGW']==True)]['cs_bandwith']


    #width=(10*nrows)/3
    #height=(7*nrows)/3
    #fig.set_size_inches(width,height)
    #plt.subplots_adjust(hspace=0)


    ax[i-1].scatter(verweildauer_i_nugw, cs_bandwith_i_nugw-i-0.5, c="blue", label = 'Verweildauer')
    ax[i-1].scatter(verweildauer_i_ugw, cs_bandwith_i_ugw-i-0.5, c="red", label = 'Unterer Grenzwert Verweildauer')  
    sns.kdeplot(verweildauer_i, ax=ax[i-1], shade=True, cumulative=False)
    #Get data points of Kde PLot
    data_x, data_y = ax[i-1].lines[0].get_data()

    if data_y.max()>h:
        h=data_y.max()

    width=(10*nrows)/3
    height=h
    fig.set_size_inches(width,height)
    plt.subplots_adjust(hspace=0)

    print(height)


    #Plot quartiles line
    for k in range(len(quatrilesx)):
        ax[i-1].axvline(quatrilesx[k], ymin=.3255, ymax=.3255+(height/width)*abs(data_y[find_nearest(data_x, quatrilesx[k])]), linestyle='dotted', color='black', linewidth=.6, alpha=.8)



    ax[i-1].set_ylim(-0.5,1)
    ax[i-1].set_ylabel('ZK ' + str(i))
    ax[i-1].set_yticks([])
    #draw the "y=0" line
    ax[i-1].axhline(0, linestyle='--', color='blue', linewidth=.5, alpha=.35) # horizontal lines


    handles, labels = ax[i-1].get_legend_handles_labels()
    # create the legend again skipping this first entry
    leg = ax[i-1].legend(handles[1:], labels[1:])

    ax2=ax[i-1]
    ax2 = ax[i-1].twinx()
    ax2.grid(False)
    ax2.set_ylabel("n = {}".format(s[i]), rotation=0, labelpad=25)
    ax2.set_yticks([])

标签： pythonmatplotlibplotseaborn