ValueError：形状不匹配：形状的值数组无法广播到形状的索引结果

问题描述

我正在尝试运行一个 Python 脚本，该脚本将计算统计数学，并且在计算中的某处收到一个错误，该错误引发了以下关于不匹配形状（二维数组？）的错误。我的输入文件是一个 netCDF 文件，变量是模拟的天气雷达反射率。

错误信息：

Traceback (most recent call last):
  File "calc_gen.py",line 75,in <module>
    temp[ens_mean_index] = calc
ValueError: shape mismatch: value array of shape (635047,) Could not be broadcast to indexing result of shape (1905141,)

all_ref1km_lAmbert.nc： https://drive.google.com/file/d/1KhlnhB-0dhmt5JTS3E8YJ8QXm0bVyyfQ/view?usp=sharing

Python 脚本 (calc_gen.py)：

import cartopy
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import sys
import time
from array import array
import matplotlib.colors as mcolors
import matplotlib.pyplot as plt
#from mpl_toolkits.basemap import Basemap
import pygrib
import struct
from scipy.io import netcdf
import numpy as np
from netCDF4 import Dataset
from scipy.io.netcdf import netcdf_file
from numpy import arange,dtype

my_example_nc_file = 'all_ref1km_lAmbert.nc'
nc = Dataset(my_example_nc_file,mode='r')

lons = nc.variables['longitude'][:]
lats = nc.variables['latitude'][:]
all_member_data = nc.variables['REFD_1000maboveground'][:]
time = nc.variables['time'][:]

dx = 3.

nx = 1799
ny = 1059

count = 3

all_member_data = np.array(all_member_data)

ens_mean = np.mean(all_member_data,axis=0)

md_shape = ens_mean.shape
    
ens_dist = np.sort(all_member_data.flatten())[::-1]
    
calc = ens_dist[::count]

ens_mean_index = np.argsort(ens_mean.flatten())[::-1]
    
temp = np.empty_like(calc)
    
temp[ens_mean_index] = calc

calc = np.array(temp,dtype="<f")

calc_final = calc.reshape( md_shape )

解决方法

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