printf("File %s Line %d, RETRIEVE: Failed creating thread",__FILE__,__LINE__);其中__FILE__,__LINE__是什么意思

__FILE__和__LINE__是C语言中的预定义宏，用于在编译时获取当前源文件名和行号。

__FILE__表示当前源文件的文件名，是一个字符串常量。例如，如果你的源文件名是example.c，那么__FILE__将被替换为字符串"example.c"。

__LINE__表示当前代码所在的行号，是一个整数常量。例如，如果你的代码在源文件的第10行，那么__LINE__将被替换为整数10。

在你提供的代码中，printf函数使用了__FILE__和__LINE__来输出错误信息时的文件名和行号。这样可以方便地定位错误发生的位置。

范例：<<引用：printf("File %s Line %d, RETRIEVE: Failed creating thread",__FILE__,__LINE__);。引用：printf("Error in file %s at line %d\n", __FILE__, __LINE__);。
以下是一个示例，演示了如何使用__FILE__和__LINE__宏输出错误信息时的文件名和行号：

#include <stdio.h>

int main() {
    printf("Error in file %s at line %d\n", __FILE__, __LINE__);
    return 0;
}

输出结果为：
Error in file main.c at line 5

相关问题

根据前端传过来的前端传递过来的开始时间和结束时间参数 筛选创建时间在开始时间和结束时间区间的数据 修改后端写法def get_bugs_data(): api_url = "https://api.tapd.cn/bugs" api_headers = {"Authorization": "#", "content-type": "application/json"}iterations_url = "https:/"count_url ="https://at" params_count = {"status": "closed","workspace_id": 41571821, } # 发送请求，获取缺陷总数 response = requests.get(count_url, params=params_count, headers=api_headers) if response.status_code == 200: total_bug = response.json()total_count = total_bug['data']['count'] print(total_count) else: print("Error: Failed to retrieve count. Status code: ", response.status_code) start_time = request.get('start_time') end_time = request.get('end_time') start_time =datetime.strptime(start_time, '%Y-%m-%d %H:%M:%S') end_time = datetime.strptime(end_time, '%Y-%m-%d %H:%M:%S') bug_list = [] for i in range(1,total_count//200): params = {"limit": 200, "status": "closed", "workspace_id": 41571821, "page": i } response = requests.get(api_url, params=params, headers=api_headers) if response.status_code == 200: res = response.json() for bug_item in res['data']: if bug_item['Bug']['fixer'] and bug_item['Bug']['fixer'] in white_name:bug_data_dict = {"bug_title": bug_item['Bug']['title'], "bug_created": bug_item['Bug']['created'], "bug_fixer": bug_item['Bug']['fixer'], "bug_resolved": bug_item['Bug']['resolved'], "bug_url": f"https://www.tapd.cn/41571821/bugtrace/bugs/view?bug_id={bug_item['Bug']['id']}" } params_iteration = {"limit": 200, "workspace_id": 41571821, "id": bug_item['Bug']['iteration_id'] }response =requests.get(iterations_url, params=params_iteration, headers=api_headers)if response.status_code == 200: iterations = response.json() iteration_name = iterations['data'][0]['Iteration']['name'] bug_data_dict['iteration_name'] = iteration_name bug_list.append(bug_data_dict) else: print("Error: Failed to retrieve iterations. Status code: ", response.status_code) return {"code": 200, "msg": "成功", "data": bug_list}

可以将代码中的bug数据筛选部分修改如下：

bug_list = []
for i in range(1, total_count//200 + 2):
    params = {"limit": 200, "status": "closed", "workspace_id": 41571821, "page": i}
    response = requests.get(api_url, params=params, headers=api_headers)
    if response.status_code == 200:
        res = response.json()
        for bug_item in res['data']:
            created_time = datetime.strptime(bug_item['Bug']['created'], '%Y-%m-%d %H:%M:%S')
            if start_time <= created_time <= end_time:
                if bug_item['Bug']['fixer'] and bug_item['Bug']['fixer'] in white_name:
                    bug_data_dict = {"bug_title": bug_item['Bug']['title'],
                                     "bug_created": bug_item['Bug']['created'],
                                     "bug_fixer": bug_item['Bug']['fixer'],
                                     "bug_resolved": bug_item['Bug']['resolved'],
                                     "bug_url": f"https://www.tapd.cn/41571821/bugtrace/bugs/view?bug_id={bug_item['Bug']['id']}"}
                    params_iteration = {"limit": 200, "workspace_id": 41571821, "id": bug_item['Bug']['iteration_id']}
                    response = requests.get(iterations_url, params=params_iteration, headers=api_headers)
                    if response.status_code == 200:
                        iterations = response.json()
                        iteration_name = iterations['data'][0]['Iteration']['name']
                        bug_data_dict['iteration_name'] = iteration_name
                        bug_list.append(bug_data_dict)
                    else:
                        print("Error: Failed to retrieve iterations. Status code: ", response.status_code)
    else:
        print("Error: Failed to retrieve bugs. Status code: ", response.status_code)
return {"code": 200, "msg": "成功", "data": bug_list}

在每个缺陷被处理时，将其创建时间转换为datetime对象，然后与开始时间和结束时间进行比较。如果创建时间在时间范围内，则将该缺陷加入到结果列表中。

这段代码为什么会报一下错误，请修改代码 Traceback (most recent call last): File "D:\tokamaka\ExperimentalSet\Python\SVM\DisruptionPredictor\B_train.py", line 159, in <module> fit_svm_model(number_of_batches) File "D:\tokamaka\ExperimentalSet\Python\SVM\DisruptionPredictor\B_train.py", line 126, in fit_svm_model models = Parallel(n_jobs=num_cores, verbose=10)( File "D:\python\lib\site-packages\joblib\parallel.py", line 1098, in __call__ self.retrieve() File "D:\python\lib\site-packages\joblib\parallel.py", line 975, in retrieve self._output.extend(job.get(timeout=self.timeout)) File "D:\python\lib\multiprocessing\pool.py", line 774, in get raise self._value File "D:\python\lib\multiprocessing\pool.py", line 125, in worker result = (True, func(*args, **kwds)) File "D:\python\lib\site-packages\joblib\_parallel_backends.py", line 620, in __call__ return self.func(*args, **kwargs) File "D:\python\lib\site-packages\joblib\parallel.py", line 288, in __call__ return [func(*args, **kwargs) File "D:\python\lib\site-packages\joblib\parallel.py", line 288, in <listcomp> return [func(*args, **kwargs) File "D:\tokamaka\ExperimentalSet\Python\SVM\DisruptionPredictor\B_train.py", line 104, in fit_model

根据您提供的错误信息，问题可能出现在 `fit_model` 函数中的某一处。由于我无法查看您完整的代码，我无法确定确切的问题所在。但是，我可以提供一些常见的错误和建议来帮助您修复问题。

1. 检查变量是否正确初始化：确保在使用变量之前，它们已经正确初始化。如果变量未初始化或为空，可能会导致错误。

2. 检查函数的参数和调用是否匹配：确保函数的参数数量和类型与调用时传递的参数相匹配。如果参数不匹配，可能会导致错误。

3. 检查代码逻辑：仔细检查代码中的逻辑错误或语法错误。确认每一行代码的语法和语义是否正确。

4. 检查依赖项和导入：确认所有需要的依赖项都已正确导入，并且版本兼容。升级或降级某些依赖项可能会导致代码不兼容。

5. 检查并处理异常：在代码中添加适当的异常处理机制，以捕获和处理潜在的异常情况。

请您根据以上提示检查代码，并尝试逐步调试以找到问题所在。如果您需要更具体的帮助，请提供相关代码片段或更多细节，以便我能够更好地帮助您解决问题。