[Python] Python Matplotlib : How to fix axes size regardness of cbar or extent

I'm using matplotlib library for visualizing atmospheric figure. I want to fix my axes size, but it doesn't work well. For example, everything is same but only cbar orientation(horizontal -> vertical) changes, axes size became diffrent. Moreover, I want to change ax.extent range but same axes size. For example, I did 2 options

1. axis = [126.1,127.4,34.77,35.9]
2. axis = [125,127.7,33.8,36.2] ax.set_extent(axis,ccrs.PlateCarree()) In this case, 2 axis size is different. I want to fix all of my figures' sizes

Here's my entire code I marked bolde for plotting and figure code (https://i.sstatic.net/twlPsRyf.png) (https://i.sstatic.net/JG0FwR2C.png) these two images code are not correctly same, but figsize is same (10,10) two images extent range are different, so axes size is different.

from netCDF4 import Dataset
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import numpy as np
from wrf import (ll_to_xy, to_np, getvar, get_cartopy, extract_times, ALL_TIMES, latlon_coords)
from cartopy.feature import NaturalEarthFeature
from cartopy.io.shapereader import Reader
from cartopy.feature import ShapelyFeature
import cmaps
import pytz
from datetime import datetime
import matplotlib
import time
import os
import pandas as pd
import matplotlib.collections as mcoll
matplotlib.use('Agg')
start_time = time.time()

cases = ['ndown','x10', 'x01']
for case in cases :
print(case)
k = 24 #TIME DURATION

start_date = '2021-12-17_00'
end_date = '2021-12-18_00'
variable = 'RAINC'
variable2 = 'RAINNC'

axis = [126.1,127.4,34.77,35.9]
x_ticks = [126,126.5,127,127.5]
y_ticks = [35,35.5,36]
'''
axis = [125,127.7,33.8,36.2]
x_ticks = [125,125.5,126,126.5,127,127.5]
y_ticks = [34,34.5,35,35.5,36]
'''
axis_on =True
print(f"원하는 시간 : {start_date} ~ {end_date}")

#컬러바 설정
newcmp = cmaps.WhBlGrYeRe
start = 5
end = 25
r = 2.5
levels = np.arange(start,end+r,r)
colorbar = list(np.arange(start,end+r,5))
# 경로 설정
shp_path = "/home/chwan/data/shp/241002/gadm41_KOR_1.shp"
base_path1 = f"/home/chwan/wrfrun/ROUGHNESS/nesting/YSU_{case.split('_')[0]}/run/"
#base_path1 = "/home/chwan/wrfrun/ROUGHNESS/landuse24_znt_df/run/"
#output_dir = f"ACC_{base_path1.split('/')[-3]}_{variable}single"
output_dir = f"ACC_{case}_{variable}single"

os.makedirs(output_dir, exist_ok=True)

##코드 시작##
kst = 'Asia/Seoul'
sd_utc = pd.to_datetime(start_date, format='%Y-%m-%d_%H').tz_localize(kst).tz_convert('UTC')
sd_utc = sd_utc-pd.Timedelta(hours=3)
ed_utc = pd.to_datetime(end_date, format='%Y-%m-%d_%H').tz_localize(kst).tz_convert('UTC')
dates = (pd.date_range(start=sd_utc, end=ed_utc, freq='H'))
files = np.unique([f"wrfout_d01_{d.year:04d}-{d.month:02d}-{d.day:02d}_00:00:00" for d in dates])
print(f"사용된 자료 : {files}")
with Dataset(f"{base_path1}{files[0]}") as ncfile:
first_times = pd.to_datetime(getvar(ncfile, 'times', timeidx=ALL_TIMES).values).tz_localize('UTC')

with Dataset(f"{base_path1}{files[-1]}") as ncfile:
end_times = pd.to_datetime(getvar(ncfile, 'times', timeidx=ALL_TIMES).values).tz_localize('UTC')
start_idx = (first_times == sd_utc).argmax()
end_idx = (end_times == ed_utc).argmax()
print(f"Start index: {start_idx}, End index: {end_idx}")
d = 0
for data in files:
filename1 = f"{base_path1}{data}"
ncfile1 = Dataset(filename1, "r")
T1 = getvar(ncfile1, f"{variable}", timeidx = ALL_TIMES) + getvar(ncfile1, f"{variable2}", timeidx = ALL_TIMES)
times = extract_times(ncfile1, timeidx=ALL_TIMES)
if len(files) !=1 :
if int(data.split("_")[-2].split("-")[-1]) == dates[0].day :
T1 = T1[start_idx:]
times = times[start_idx:]
elif int(data.split("_")[-2].split("-")[-1]) == dates[-1].day :
T1 = T1[:end_idx+1]
times = times[:end_idx+1]
else :
T1 = T1[start_idx:end_idx+1]
times = times[start_idx:end_idx+1]
print(times)
** for i, time in enumerate(times):
print(f"{case} , 처리 중인 시간(UTC) : {time}")
fig = plt.figure(figsize=(10,10))
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8], projection=ccrs.PlateCarree())
shpfile = Reader(shp_path)
shapefile_feature = ShapelyFeature(shpfile.geometries(),ccrs.PlateCarree(), edgecolor = 'black', facecolor = 'none', linewidth=1)
ax.add_feature(shapefile_feature)
if axis_on :
ax.set_extent(axis,ccrs.PlateCarree())
else :
pass
ax.set_xticks(x_ticks)
ax.set_yticks(y_ticks)
ax.tick_params(axis='both', labelsize=12)
#ax.set_xticklabels([str(x) for x in np.arange(axis[0], axis[1] + 1, 1)], fontsize=14)
#ax.set_yticklabels([str(y) for y in np.arange(axis[2], axis[3] + 1, 1)], fontsize=14)
ax.set_xlabel("Longitude", fontsize=18, fontweight = 'bold',labelpad=0)
ax.set_ylabel("Latitude", fontsize=18, fontweight = 'bold',labelpad=15)
lats, lons = latlon_coords(T1)
if i < k and data == files[0] :
print("continue")
continue
elif i < k and data != files[0] :
T_former = getvar(Dataset(f"{base_path1}{files[d-1]}","r",timeidx= ALL_TIMES), f"{variable}")+getvar(Dataset(f"{base_path1}{files[d-1]}","r"), f"{variable2}", timeidx = ALL_TIMES)
T_form = T_former.isel(Time=i-k)
times_start = T1['Time']
times_end = T_form['Time']
hfx_diff = T1 - T_form
else :
hfx_diff = T1 - T1[i-k]
times_start = T1['Time']
times_end = T1[i-k]['Time']
#hfx_diff = T1 - T1[i-k]
contourf = ax.contourf(to_np(lons), to_np(lats), hfx_diff, levels=levels,
transform=ccrs.PlateCarree(), cmap=newcmp, vmin=start, vmax=end, extend='max')
line2 = ax.plot([126.5,126.95],[35.975,35.375], color = 'black', linewidth=4,transform = ccrs.PlateCarree())
#ax.plot(126.89, 35.45961, color='black', marker='o', markersize=8, transform=ccrs.PlateCarree())
target_lon = 126.9 #정읍
target_lat = 35.45961 #정읍세로
#plot1 = ax.plot(126.7503, 35.5813, color = 'pink', marker='o', markersize=8, transform=ccrs.PlateCarree())
#plot1 = ax.plot(126.6396, 35.6714, color = 'pink', marker='o', markersize=8, transform=ccrs.PlateCarree())

#plot1 = ax.plot(126.9,35.45961, 'bo', markersize=8,transform = ccrs.PlateCarree())
#plot1 = ax.plot(126.861017,35.491204, color = 'pink',marker='o', markersize=8,transform = ccrs.PlateCarree())
#ax.plot(126.67, 35.5, color='black', marker='o', markersize=10, transform=ccrs.PlateCarree())
#ax.plot(127.2, 35.45961, color='black', marker='o', markersize=10, transform=ccrs.PlateCarree())

# 시간 처리
# 컬러바 추가
cbar = plt.colorbar(contourf, orientation='horizontal', shrink=0.5, pad=0.075, aspect=15)
cbar.ax.tick_params(labelsize=11)
cbar.set_ticks(colorbar)
cbar.set_ticklabels(colorbar)
cbar.set_ticklabels([f"{int(x)}" for x in colorbar])
cbar.set_label("Accumulated Precipitation(mm)", labelpad=10, rotation=0, ha='center', va='center',fontsize=15)

start_time_str = str(times_start.values) # '2021-12-18T15:00:00.000000000'
end_time_str = str(times_end.values) # '2021-12-18T18:00:00.000000000'
print(start_time_str,end_time_str)
# datetime64에서 Python datetime으로 변환
start_time_obj_utc = pd.Timestamp(start_time_str).to_pydatetime()
end_time_obj_utc = pd.Timestamp(end_time_str).to_pydatetime()

# UTC -> KST 변환
start_time_obj_kst = pytz.utc.localize(start_time_obj_utc).astimezone(pytz.timezone(kst))
end_time_obj_kst = pytz.utc.localize(end_time_obj_utc).astimezone(pytz.timezone(kst))
print(start_time_obj_kst,end_time_obj_kst)
ax.text(0.2, 1.05, f"{end_time_obj_kst.strftime('%Y-%m-%d %H:%M:%S')} ~ {start_time_obj_kst.strftime('%Y-%m-%d %H:%M:%S')}",
transform=ax.transAxes, fontsize=12, verticalalignment='top', horizontalalignment='left')

image_filename = f"{output_dir}/T2diff_{str(time).split('.')[0]}.png"
plt.savefig(image_filename, dpi=300)
plt.close()**
print(f"{data.split('_')[-2].split('-')[-1]}일 생성 완료")
d +=1

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