下面是将网格数据插值到站点的代from pathlib import Pathimport pandas as pdimport numpy as npimport netCDF4 as nc
# 读取站点信息stations_info = pd.read_excel(r'D:MLgrid to stationsstations.xlsx', 'Sheet1', index_col=None, na_values=['NA'])# 读取网格数据dataset = nc.Dataset(r"D:MLgrid to stationsERA5.TEST.nc")print(dataset)# 经纬度longitude = dataset.variables['longitude'][:].datalatitude = dataset.variables['latitude'][:].data# 温度t = dataset.variables['t'][:, :, :].data # 获取所有时次的温度数据# 将格点范围内的站点筛选出来lonSta, latSta = stations_info['经度'].to_numpy(), stations_info['纬度'].to_numpy()# 定义获取最临近格点坐标索引的方法def nearest_position(stn_lat, stn_lon, lat2d, lon2d):difflat = stn_lat - lat2ddifflon = stn_lon - lon2drad = np.multiply(difflat, difflat) + np.multiply(difflon, difflon)aa = np.where(rad == np.min(rad))ind = np.squeeze(np.array(aa))return tuple(ind)# 将一维的经纬度数据网格二维化lon2D, lat2D = np.meshgrid(longitude, latitude)# 创建一个 DataFrame 用于存储插值数据t_sta_nearest_df = pd.DataFrame(index=range(len(lonSta)), columns=[f'{hour:02d}h' for hour in range(24)])# 对每个站点进行插值计算for i in range(len(lonSta)):t_nearest = []for t_index in range(t.shape[0]): # 对每个时间点进行操作indexSta = nearest_position(latSta[i], lonSta[i], lat2D, lon2D)jSta, iSta = indexSta[0], indexSta[1]t_nearest.append(t[t_index, jSta, iSta]) # 将当前时间点的结果添加到列表中t_sta_nearest_df.loc[i] = t_nearest# 添加时间标题行t_sta_nearest_df.columns = [f'{hour:02d}h' for hour in range(24)]# 将插值数据添加到站点信息 DataFrame 中stations_info = pd.concat([stations_info, t_sta_nearest_df], axis=1)# 将数据保存为新的xlsx文件stations_info.to_excel('D:/ML/grid to stations/weather_station_data.xlsx', sheet_name='Sheet1', index=False)
from pathlib import Pathimport pandas as pdimport numpy as npimport netCDF4 as ncfrom scipy.interpolate import griddata# 读取站点信息stations_info = pd.read_excel(r'D:MLgrid to stationsstations.xlsx', 'Sheet1', index_col=None, na_values=['NA'])# 读取网格数据dataset = nc.Dataset(r"D:MLgrid to stationsERA5.TEST.nc")# 经纬度longitude = dataset.variables['longitude'][:].datalatitude = dataset.variables['latitude'][:].data# 温度t = dataset.variables['t'][:, :, :].data # 获取所有时次的温度数据# 将格点范围内的站点筛选出来lonSta, latSta = stations_info['经度'].to_numpy(), stations_info['纬度'].to_numpy()# 将一维的经纬度数据网格化lon2D, lat2D = np.meshgrid(longitude, latitude)# 创建一个 DataFrame 用于存储插值数据t_sta_nearest_df = pd.DataFrame(index=range(len(lonSta)), columns=[f'{hour:02d}h' for hour in range(24)])# 对每个站点进行插值计算for i in range(len(lonSta)):t_nearest = []for t_index in range(t.shape[0]): # 对每个时间点进行操作t_values = t[t_index, :, :].flatten() # 获取当前时间点的所有温度值grid_points = np.vstack((lon2D.flatten(), lat2D.flatten())).T # 网格坐标station_point = np.array([[lonSta[i], latSta[i]]]) # 站点坐标t_interp = griddata(grid_points, t_values, station_point, method='cubic') # 双三次插值t_nearest.append(t_interp[0]) # 将插值结果添加到列表中t_sta_nearest_df.loc[i] = t_nearest# 添加时间标题行t_sta_nearest_df.columns = [f'{hour:02d}h' for hour in range(24)]# 将插值数据添加到站点信息 DataFrame 中stations_info = pd.concat([stations_info, t_sta_nearest_df], axis=1)# 将数据保存为新的xlsx文件stations_info.to_excel('D:/ML/grid to stations/weather_station_data_SSC.xlsx', sheet_name='Sheet1', index=False)#printf("hello world!")删掉
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