undefined reference to `interp2_' collect2.exe: error: ld returned 1 exit status
时间: 2024-08-12 18:05:35 浏览: 40
当你遇到这样的错误提示 "undefined reference to 'interp2_' collect2.exe: error: ld returned 1 exit status",通常是在C++程序中使用了OpenMP库(如`#include <omp.h>`),而链接阶段找不到名为`interp2_`的函数引用。这可能是以下几个原因:
1. **头文件包含问题**:确保你在使用`interp2_`之前正确包含了OpenMP的数学运算头文件,如`#include <omp specfun.h>`,其中包含这个函数声明。
2. **链接库问题**:你需要链接OpenMP库到你的项目中。如果是静态链接,可能需要添加 `-lopenmp` 或 `-lomp` 参数;如果是动态链接,需要添加 `-lgomp`。
3. **函数实现缺失**:检查是否正确地实现了`interp2_`函数。如果在项目的某个源文件里有这个函数的实现,确保它已经编译进目标文件,并且链接到了最终的可执行文件。
4. **构建系统设置**:如果你使用的是特定的IDE或构建工具(如Makefile、CMake等),确认构建配置正确设置了包含路径和链接选项。
5. **版本兼容性**:OpenMP的不同版本可能会有不同的函数命名。确保使用的OpenMP版本与你的代码兼容。
解决此类问题的一个常见做法是检查整个项目的编译和链接过程,确保每个涉及OpenMP的地方都被正确处理。
相关问题
请优化以下代码:metss_interp = ERA5['metss_interp'] ERA5_mu_Jan1 = metss_interp[:,:,0:120:12].mean(2).T #mean(2)的意思是把第三个维度的数据进行平均,原来是(144,96,120),取平均之后就是(144,96) ERA5_mu_July = metss_interp[:,:,6:120:12].mean(2).T mntss_interp = ERA5['mntss_interp'] ERA5_mv_Jan = mntss_interp[:,:,0:120:12].mean(2).T ERA5_mv_July = mntss_interp[:,:,6:120:12].mean(2).T mslhf_interp = ERA5['mslhf_interp'] msshf_interp = ERA5['msshf_interp'] # ERA5_mo = np.sqrt(pow(metss_interp, 2)+pow(mntss_interp,2)) # ERA5_mo_Jan = ERA5_mo[:,:,0:120:12].mean(2).T # ERA5_mo_July = ERA5_mo[:,:,6:120:12].mean(2).T ERA5_SH_Jan = msshf_interp[:,:,0:120:12].mean(2) ERA5_SH_Jan = -ERA5_SH_Jan.T ERA5_SH_July = msshf_interp[:,:,6:120:12].mean(2) ERA5_SH_July = -ERA5_SH_July.T ERA5_LH_Jan = mslhf_interp[:,:,0:120:12].mean(2) ERA5_LH_Jan = -ERA5_LH_Jan.T ERA5_LH_July = mslhf_interp[:,:,6:120:12].mean(2) ERA5_LH_July = -ERA5_LH_July.T
优化后的代码如下:
```python
metss_interp = ERA5['metss_interp']
mntss_interp = ERA5['mntss_interp']
mslhf_interp = ERA5['mslhf_interp']
msshf_interp = ERA5['msshf_interp']
variables = {
'ERA5_mu_Jan1': (metss_interp[:,:,0:120:12].mean(2).T),
'ERA5_mu_July': (metss_interp[:,:,6:120:12].mean(2).T),
'ERA5_mv_Jan': (mntss_interp[:,:,0:120:12].mean(2).T),
'ERA5_mv_July': (mntss_interp[:,:,6:120:12].mean(2).T),
'ERA5_SH_Jan': (-msshf_interp[:,:,0:120:12].mean(2).T),
'ERA5_SH_July': (-msshf_interp[:,:,6:120:12].mean(2).T),
'ERA5_LH_Jan': (-mslhf_interp[:,:,0:120:12].mean(2).T),
'ERA5_LH_July': (-mslhf_interp[:,:,6:120:12].mean(2).T)
}
```
通过使用字典来存储变量名和对应的结果,避免了重复的代码。这样可以更清晰地组织代码,并使代码更易于维护和扩展。
import pandas as pd import numpy as np import os from pprint import pprint from pandas import DataFrame from scipy import interpolate data_1_hour_predict_raw = pd.read_excel('./data/附件1 监测点A空气质量预报基础数据.xlsx' ) data_1_hour_actual_raw = pd.read_excel('./data/附件1 监测点A空气质量预报基础数据.xlsx' ) data_1_day_actual_raw = pd.rea df_1_predict = data_1_hour_actual_raw df_1_actual = data_1_day_actual_raw df_1_predict.set_axis( ['time', 'place', 'so2', 'no2', 'pm10', 'pm2.5', 'o3', 'co', 'temperature', 'humidity', 'pressure', 'wind', 'direction'], axis='columns', inplace=True) df_1_actual.set_axis(['time', 'place', 'so2', 'no2', 'pm10', 'pm2.5', 'o3', 'co'], axis='columns', inplace=True) modeltime_df_actual = df_1_actual['time'] modeltime_df_pre = df_1_predict['time'] df_1_actual = df_1_actual.drop(columns=['place', 'time']) df_1_predict = df_1_predict.drop(columns=['place', 'time']) df_1_predict = df_1_predict.replace('—', np.nan) df_1_predict = df_1_predict.astype('float') df_1_predict[df_1_predict < 0] = np.nan # 重新插入time列 df_1_actual.insert(0, 'time', modeltime_df_actual) df_1_predict.insert(0, 'time', modeltime_df_pre) # 线性插值的方法需要单独处理最后一行的数据 data_1_actual = df_1_actual[0:-3] data_1_predict = df_1_predict data_1_predict.iloc[-1:]['pm10'] = 22.0 data_1_actual_knn = df_1_actual[0:-3] data_1_predict_knn: DataFrame = df_1_predict for indexs in data_1_actual.columns: if indexs == 'time': continue data_1_actual['rownum'] = np.arange(data_1_actual.shape[0]) df_nona = data_1_actual.dropna(subset=[indexs]) f = interpolate.interp1d(df_nona['rownum'], df_nona[indexs]) data_1_actual[indexs] = f(data_1_actual['rownum']) data_1_actual = data_1_actual.drop(columns=['rownum']) for indexs in data_1_predict.columns: if indexs == 'time': continue data_1_predict['rownum'] = np.arange(data_1_predict.shape[0]) df_nona = data_1_predict.dropna(subset=[indexs]) f = interpolate.interp1d(df_nona['rownum'], df_nona[indexs]) data_1_predict[indexs] = f(data_1_predict['rownum']) data_1_predict = data_1_predict.drop(columns=['rownum']) writer = pd.E
这段代码主要是对一份空气质量预报基础数据进行处理和插值,具体的解释如下:
1. 导入需要的库和模块:
```
import pandas as pd
import numpy as np
import os
from pprint import pprint
from pandas import DataFrame
from scipy import interpolate
```
2. 读取 excel 文件中的数据:
```
data_1_hour_predict_raw = pd.read_excel('./data/附件1 监测点A空气质量预报基础数据.xlsx' )
data_1_hour_actual_raw = pd.read_excel('./data/附件1 监测点A空气质量预报基础数据.xlsx' )
data_1_day_actual_raw = pd.read_excel('./data/附件1 监测点A空气质量预报基础数据.xlsx' )
```
3. 对读取的数据进行处理:
```
df_1_predict = data_1_hour_actual_raw
df_1_actual = data_1_day_actual_raw
df_1_predict.set_axis( ['time', 'place', 'so2', 'no2', 'pm10', 'pm2.5', 'o3', 'co', 'temperature', 'humidity', 'pressure', 'wind', 'direction'], axis='columns', inplace=True)
df_1_actual.set_axis(['time', 'place', 'so2', 'no2', 'pm10', 'pm2.5', 'o3', 'co'], axis='columns', inplace=True)
```
4. 提取时间列并进行插值:
```
modeltime_df_actual = df_1_actual['time']
modeltime_df_pre = df_1_predict['time']
df_1_actual = df_1_actual.drop(columns=['place', 'time'])
df_1_predict = df_1_predict.drop(columns=['place', 'time'])
df_1_predict = df_1_predict.replace('—', np.nan)
df_1_predict = df_1_predict.astype('float')
df_1_predict[df_1_predict < 0] = np.nan
df_1_actual.insert(0, 'time', modeltime_df_actual)
df_1_predict.insert(0, 'time', modeltime_df_pre)
data_1_actual = df_1_actual[0:-3]
data_1_predict = df_1_predict
data_1_predict.iloc[-1:]['pm10'] = 22.0
data_1_actual_knn = df_1_actual[0:-3]
data_1_predict_knn: DataFrame = df_1_predict
for indexs in data_1_actual.columns:
if indexs == 'time':
continue
data_1_actual['rownum'] = np.arange(data_1_actual.shape[0])
df_nona = data_1_actual.dropna(subset=[indexs])
f = interpolate.interp1d(df_nona['rownum'], df_nona[indexs])
data_1_actual[indexs] = f(data_1_actual['rownum'])
data_1_actual = data_1_actual.drop(columns=['rownum'])
for indexs in data_1_predict.columns:
if indexs == 'time':
continue
data_1_predict['rownum'] = np.arange(data_1_predict.shape[0])
df_nona = data_1_predict.dropna(subset=[indexs])
f = interpolate.interp1d(df_nona['rownum'], df_nona[indexs])
data_1_predict[indexs] = f(data_1_predict['rownum'])
data_1_predict = data_1_predict.drop(columns=['rownum'])
```
5. 最后将处理好的数据写入 excel 文件:
```
writer = pd.ExcelWriter('./data/附件1 监测点A空气质量预报基础数据_preprocessed.xlsx')
data_1_predict.to_excel(writer, sheet_name='1小时预测数据', index=False)
data_1_predict_knn.to_excel(writer, sheet_name='1小时预测数据_knn', index=False)
data_1_actual.to_excel(writer, sheet_name='1天实际数据', index=False)
data_1_actual_knn.to_excel(writer, sheet_name='1天实际数据_knn', index=False)
writer.save()
```
总体来说,这段代码主要是对空气质量预报基础数据进行了一些预处理和插值,最终将处理好的数据写入了 excel 文件中。