提取NetCDF4数据使用Python点

Question

我正在研究气候数据。我使用ERE5数据提取了我所需的LAT的数据，并且代码非常有效。现在，我正在使用另一个数据集，但它不起作用。代码的编写方式不受LEAP年的打扰。但是在此数据集中，该代码在一年中的第366天被错误“索引超过尺寸约束”时停止工作。 python上的变量数据显示，temp（温度是我的数据中我使用temp = date.variables ['t2']访问的数据的变量）具有[365,1800,3600]的窗口，如果在LEAP的情况下应为366年。当我在CDO中检查文件时，会有366个时间段。除了Leap年代的运行良好，但在Leap年中，它会失败。
在这方面需要一些帮助。谢谢 还附加了更多的元数据信息，我认为这可能会有所帮助

#importing libraries
from netCDF4 import Dataset
import numpy as np
import glob
import pandas as pd
all_years = []
#Reading Netcdf file
for file in glob.glob('*.nc'):
    print (file)
    data = Dataset(file,'r')
    time = data.variables['time']
    year = time.units[11:15]
    temp = data.variables['t2']
    all_years.append(year)
year_start = min(all_years)
year_end = max(all_years)
#creating a list for the data/time column
date_range = pd.date_range(start = str(year_start) + '-01-01',
                           end = str(year_end) + '-12-31',
                           freq = 'D')
#Creating a data frame having an index column and 't2' column
df = pd.DataFrame(0.0, columns = ['t2'], index = date_range)
locations = pd.read_csv('Locations.csv')
for index, row in locations.iterrows():
#getting data from my csv file   
    locations = row['Name']
    loc_lat = row['Latitude']
    loc_long = row['Longitude']
    all_years.sort()
    for yr in all_years:
        data = Dataset(year+'.nc', 'r')
#storing lat lon data into variables
        lati = data.variables['lat'][:]
        long = data.variables['lon'][:]
#squared diff of lat and lon
        sq_diff_lat = (lati - loc_lat)**2 
        sq_diff_lon = (long - loc_long)**2
#identify the index of minimum value of lat and lon
        min_index_lat = sq_diff_lat.argmin()
        min_index_lon = sq_diff_lon.argmin()
        temp = data.variables['t2']
        start = str(yr) + '-01-01'
        end = str(yr) + '-12-31'
        d_range = pd.date_range(start = start,
                                end = end,
                                freq = 'D')
        for t_index in np.arange(0, len(d_range)):
            df.loc[d_range[t_index]]['t2'] = temp[t_index,             
            min_index_lat, min_index_lon]
        
    df.to_csv(locations + '.csv')