result.get_points()具体可以得到什么
时间: 2023-10-06 19:09:20 浏览: 106
`result.get_points()` 是一个方法,用于从 InfluxDB 查询结果中获取数据点的生成器。
当你执行一个 InfluxDB 查询并获得结果后,可以使用 `result.get_points()` 方法来迭代访问查询结果中的每个数据点。每个数据点是一个字典对象,包含了查询结果中的一行数据。
通过遍历这个生成器,你可以逐个访问和处理每个数据点的字段和值。例如,你可以使用 `data['field']` 来获取数据点中的某个字段的值,其中 `data` 是生成器返回的数据点字典。
下面是一个示例代码,演示如何使用 `result.get_points()` 方法来获取查询结果中的数据点:
```python
result = client.query("SELECT * FROM measurement_name LIMIT 10") # 执行查询
points = result.get_points() # 获取数据点生成器
for point in points:
print(point) # 打印数据点字典
print(point['field']) # 获取字段值
```
通过遍历数据点生成器,你可以对每个数据点进行自定义的处理和分析。这样你就可以根据查询结果中的字段和值来执行进一步的操作,如数据可视化、计算统计指标等。
相关问题
Traceback (most recent call last): File "C:\Users\GW00321286\.conda\envs\python_39\lib\site-packages\pandas\core\indexes\base.py", line 3802, in get_loc return self._engine.get_loc(casted_key) File "pandas\_libs\index.pyx", line 138, in pandas._libs.index.IndexEngine.get_loc File "pandas\_libs\index.pyx", line 165, in pandas._libs.index.IndexEngine.get_loc File "pandas\_libs\hashtable_class_helper.pxi", line 5745, in pandas._libs.hashtable.PyObjectHashTable.get_item File "pandas\_libs\hashtable_class_helper.pxi", line 5753, in pandas._libs.hashtable.PyObjectHashTable.get_item KeyError: 0 The above exception was the direct cause of the following exception: Traceback (most recent call last): File "E:\code-study\coda\cross_nostopline.py", line 68, in <module> points = MultiPoint(dense_gdf) File "C:\Users\GW00321286\.conda\envs\python_39\lib\site-packages\shapely\geometry\multipoint.py", line 54, in __new__ p = point.Point(points[i]) File "C:\Users\GW00321286\.conda\envs\python_39\lib\site-packages\geopandas\geodataframe.py", line 1415, in __getitem__ result = super().__getitem__(key) File "C:\Users\GW00321286\.conda\envs\python_39\lib\site-packages\pandas\core\frame.py", line 3807, in __getitem__ indexer = self.columns.get_loc(key) File "C:\Users\GW00321286\.conda\envs\python_39\lib\site-packages\pandas\core\indexes\base.py", line 3804, in get_loc raise KeyError(key) from err KeyError: 0
这是一个Python的错误信息,提示了在第68行代码处出现了一个问题。具体地,它说“KeyError: 0”,这意味着您尝试使用一个键值“0”,但是这个键值在您的数据中不存在。这可能是由于尝试访问不存在的列或行所致。您可以检查代码中与这个键值相关的部分,看看是否有错误或缺少某些内容。建议您在出现错误的代码行前面加上print语句,输出一些变量的值,以便更好地理解错误的原因。
创建多线程加速处理这一段for循环int m = m_vpdEquinoxPoints.size();vector<Edge1D_Result>edges;for (int i = 0; i < m; i++) { edges = Extract1DEdgeCircle.Get1DEdge(RoiMat, m_vpdEquinoxPoints[i], m_dMeasureLength, m_dMeasureHeight, m_vdMeasureAngle[i] , m_dSigma, m_nThresholdCircle, m_nTranslationCircle == 1 ? Translation::Poisitive : Translation::Negative, Selection::Strongest); for (int i = 0; i < edges.size(); i++) { m_vpdEdgePoints.push_back(edges[i].m_pdEdgePoint); m_vdEdgeGradient.push_back(edges[i].m_dGradient); } }并且使变量不冲突
可以使用std::thread来创建多线程,每个线程处理一定数量的循环迭代。为了避免变量冲突,可以使用锁(std::mutex)来保证每个线程只访问自己的变量。下面是一个可能的实现:
```c++
#include <thread>
#include <mutex>
// 用于保护m_vpdEdgePoints和m_vdEdgeGradient的锁
std::mutex g_mutex;
void process_edges(const cv::Mat& RoiMat,
const std::vector<cv::Point2d>& m_vpdEquinoxPoints,
const double m_dMeasureLength,
const double m_dMeasureHeight,
const double m_dSigma,
const int m_nThresholdCircle,
const int m_nTranslationCircle,
const std::vector<double>& m_vdMeasureAngle,
std::vector<cv::Point2d>& m_vpdEdgePoints,
std::vector<double>& m_vdEdgeGradient,
int start_idx, int end_idx)
{
std::vector<Edge1D_Result> edges;
for (int i = start_idx; i < end_idx; i++)
{
edges = Extract1DEdgeCircle.Get1DEdge(RoiMat, m_vpdEquinoxPoints[i], m_dMeasureLength, m_dMeasureHeight, m_vdMeasureAngle[i], m_dSigma, m_nThresholdCircle, m_nTranslationCircle == 1 ? Translation::Poisitive : Translation::Negative, Selection::Strongest);
// 使用锁保护m_vpdEdgePoints和m_vdEdgeGradient
std::lock_guard<std::mutex> lock(g_mutex);
for (int j = 0; j < edges.size(); j++)
{
m_vpdEdgePoints.push_back(edges[j].m_pdEdgePoint);
m_vdEdgeGradient.push_back(edges[j].m_dGradient);
}
}
}
// 在主函数中调用多线程处理
int main()
{
// ...
const int num_threads = 4; // 假设有4个线程
const int m = m_vpdEquinoxPoints.size();
std::vector<std::thread> threads(num_threads);
std::vector<std::vector<cv::Point2d>> edge_points(num_threads);
std::vector<std::vector<double>> edge_gradients(num_threads);
for (int i = 0; i < num_threads; i++)
{
int start_idx = i * m / num_threads;
int end_idx = (i + 1) * m / num_threads;
threads[i] = std::thread(process_edges, std::ref(RoiMat), std::ref(m_vpdEquinoxPoints), m_dMeasureLength, m_dMeasureHeight, m_dSigma, m_nThresholdCircle, m_nTranslationCircle, std::ref(m_vdMeasureAngle), std::ref(edge_points[i]), std::ref(edge_gradients[i]), start_idx, end_idx);
}
for (int i = 0; i < num_threads; i++)
{
threads[i].join();
// 合并结果
m_vpdEdgePoints.insert(m_vpdEdgePoints.end(), edge_points[i].begin(), edge_points[i].end());
m_vdEdgeGradient.insert(m_vdEdgeGradient.end(), edge_gradients[i].begin(), edge_gradients[i].end());
}
// ...
}
```
这个实现中,我们将处理结果存储在每个线程的本地变量edge_points和edge_gradients中,然后在主线程中合并结果。为了避免多个线程同时访问m_vpdEdgePoints和m_vdEdgeGradient,我们使用了一个锁g_mutex来保护这两个变量。同时,我们使用了std::ref函数来将引用传递给子线程,以保证子线程能够修改主线程中的变量。