for i in range(0,23): tem_ave[i]=np.mean(tem[i:147:24]) IndexError: list assignment index out of range
时间: 2024-04-04 13:31:04 浏览: 15
这个错误提示说明你在对一个列表进行赋值时,使用了一个超出列表索引范围的下标。具体来说,你可能在对`tem_ave`列表的第`i`个元素进行赋值时,使用了一个大于等于`len(tem_ave)`的下标。
要解决这个问题,可以检查一下`tem_ave`列表的长度是否足够,是否已经在循环之前初始化过。还可以检查一下循环中使用的下标是否计算正确。可能需要在循环中加入一些调试打印语句,观察变量的取值情况,以便更好地定位问题所在。
相关问题
import xarray as xr import matplotlib.pyplot as plt from netCDF4 import Dataset import cartopy.crs as ccrs#投影方式 import cartopy.feature as cfeature import cartopy.io.shapereader as shpreader import numpy as np import pandas as pd from matplotlib.font_manager import fontManager da=pd.read_csv(r'E:\python11\STATION_58237.txt',sep='\s+') print(da) tem=da['TEM'] #print(tem.shape) rhu=da['RHU'] tem_ave=[] for i in range(0,24): tem_data=tem[i:147:24] print(tem_data) tem_ave[i]=np.mean(tem_data) print(tem_ave) plt.plot(tem_ave)帮我改一下
import xarray as xr
import matplotlib.pyplot as plt
from netCDF4 import Dataset
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import cartopy.io.shapereader as shpreader
import numpy as np
import pandas as pd
from matplotlib.font_manager import fontManager
da = pd.read_csv(r'E:\python11\STATION_58237.txt', sep='\s+')
print(da)
tem = da['TEM']
rhu = da['RHU']
tem_ave = []
for i in range(0, 24):
tem_data = tem[i:147:24]
print(tem_data)
tem_ave.append(np.mean(tem_data)) # 将结果添加到列表中
print(tem_ave)
plt.plot(tem_ave)
plt.show() # 显示图形
def crop_pointcloud(data_crop, x_o, y_o, x_i, y_i, R_o, R_i, z_critical): K_o = R_o ** 2 / range_z K_i = R_i ** 2 / range_z for z in range(range_z): r_o = np.sqrt(z * K_o) data_layer = data_crop[:, :, z] d_o = np.sqrt(x_o ** 2 + y_o ** 2) d_i = np.sqrt(x_i ** 2 + y_i ** 2) if z < z_critical: r_i = 0 else: r_i = np.sqrt(z * K_i) data_crop[:, :, z] = np.where((d_o > r_o) | (d_i <= r_i), 0, data_layer) return data_crop 转C++ Eigen
以下是将 Python 代码转换为 C++ Eigen 的代码:
```cpp
#include <Eigen/Dense>
#include <cmath>
using Eigen::Array;
using Eigen::Array3f;
Array3f crop_pointcloud(Array3f data_crop, float x_o, float y_o, float x_i, float y_i,
float R_o, float R_i, int z_critical)
{
int range_z = data_crop.dimension(2);
float K_o = std::pow(R_o, 2) / range_z;
float K_i = std::pow(R_i, 2) / range_z;
for (int z = 0; z < range_z; ++z) {
float r_o = std::sqrt(z * K_o);
Array<float, Eigen::Dynamic, Eigen::Dynamic> data_layer = data_crop.slice(z);
float d_o = std::sqrt(std::pow(x_o, 2) + std::pow(y_o, 2));
float d_i = std::sqrt(std::pow(x_i, 2) + std::pow(y_i, 2));
float r_i = (z < z_critical) ? 0 : std::sqrt(z * K_i);
data_crop.slice(z) = (d_o > r_o || d_i <= r_i).select(0, data_layer);
}
return data_crop;
}
int main()
{
int range_x, range_y, range_z; // 假设已经读入了点云数据
Array3f data_crop(range_x, range_y, range_z);
int dx = 550;
int dy = 530;
float x_o, y_o, x_i, y_i;
x_o = x_i = range_x / 2.0;
y_o = y_i = range_y / 2.0;
int z_critical = 50;
float R_o = 550.0;
float R_i = 200.0;
data_crop = crop_pointcloud(data_crop, x_o, y_o, x_i, y_i, R_o, R_i, z_critical);
// 剩余部分请自行完成
return 0;
}
```
需要注意的是,在 C++ Eigen 中使用的是 `Array` 类,因此需要对数组的访问进行适当修改。此外,C++ Eigen 中支持类似于 `numpy` 中的条件选择函数,因此可以直接使用 `select` 函数来实现 `np.where` 的功能。
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