Representation of confidence interval

err_inter = result.conf_int()
# The center of the confidence interval 
err_mean = (err_inter[1]+err_inter[0])/2
#  The length of the confidence interval 
err_long = (err_inter[1]-err_inter[0])/2

# Confidence interval mapping 
import matplotlib.pyplot as plt
from scipy import stats
import numpy as np
plt.style.use('seaborn-whitegrid')
fig, ax = plt.subplots(figsize=(50, 20),dpi=60)
ax.spines['bottom'].set_linewidth('2.0')# Set the border line width to 2.0
ax.spines['bottom'].set_color('black')
ax.spines['top'].set_linewidth('2.0')# Set the border line width to 2.0
ax.spines['top'].set_color('black')
ax.spines['right'].set_linewidth('2.0')# Set the border line width to 2.0
ax.spines['right'].set_color('black')
ax.spines['left'].set_linewidth('2.0')# Set the border line width to 2.0
ax.spines['left'].set_color('black')
plt.xticks(fontsize = 50)
plt.yticks(fontsize = 50)
for i in range(1,len(err_inter)):
    ax.errorbar(i-10,err_mean[i] , yerr=err_long[i],
                fmt='.', markersize=10, capsize=15, color='black')
xticks = [-9,-8, -7, -6, -5, -4,-3,-2,-1,0,1,2,3,4,5,6,7,8,9,10,11]
xticklabes =['-9','-8','-7','-6','-5','-4','-3','-2','-1','0','1','2','3','4','5','6','7','8','9','10','11']
plt.xticks(xticks,xticklabes,size=50)

yticks = [-2,-1,0,1,2,3,4,5]
yticklabes =['-2','-1','0','1','2','3','4','5']

ax.plot([-9.5,11.5],[0,0], color='black',linestyle = '--',linewidth=2, markersize=12)

plt.xticks(xticks,xticklabes,size=50)
ax.set_ylim(-2.5, 5.5)
ax.set_xlim(-9.5, 11.5)
#ax.fill_betweenx([1, 0], facecolor='lightgrey', alpha=0.3)
#ax.legend(loc='best', fontsize=11, framealpha=1, frameon=True)
#ax.set_xticklabels()
#ax.set_ticklabels()
#ax.set_yticklabels(labels=labels)

ax.set_xlabel('time', fontsize=50)
ax.set_ylabel('coefficient', fontsize=50)
#ax.yaxis.set_major_formatter(plt.NullFormatter())
#fig.tight_layout();
#plt.show()

#plt.savefig(r'C:\Users\sc35131\Desktop\wangyao\Cell_consistence_alogrithm\picture9\\'+'1234',bbox_inches = 'tight',pad_inches = 0,dpi =350)

plt.savefig(' Parallel trend test .png')