""" @Project ：microproduct @File ：AtmosphericAuxData.PY @Function ：辅助文件读写 @Author ：LMM @Date ：2021/09/3 14:39 @Version ：1.0.0 """ import os import shutil import cftime import numpy as np import netCDF4 as Nc import math from tool.algorithm.image.ImageHandle import ImageHandler from osgeo import gdal, osr import gc import logging logger = logging.getLogger("mylog") class NcHandle: """ 气象数据的检查 气象数据的读取、降维、转tif """ def __int__(self): pass @staticmethod def check_nc(nc_infile): """ 检查气象数据 nc_infile： 气象数据路径 """ nc_data_obj = Nc.Dataset(nc_infile) keys = nc_data_obj.variables.keys() # 查看气象数据文件包含的变量列表 inform = nc_data_obj.variables variables = ['longitude', 'latitude', 'level', 'time', 'z', 'r', 't'] if len(keys) < len(variables): # 检查缺少了哪种气象数据 logger.error("下载气象数据有误，缺少参数") return False else: for var in variables: if var not in keys: logger.error("缺少气象数据：", inform[var].long_name) return False logger.info("variables check true !") all_vars_units = [] # 检查气象数据单位是否正确 for key in keys: all_vars_units.append(nc_data_obj.variables[key].units) if key == "level": if nc_data_obj.variables[key].units != "millibars": logger.error("units errors", inform[key].long_name) if key == "t": if nc_data_obj.variables[key].units != "K": logger.error("units errors", inform[key].long_name) if key == "r": if nc_data_obj.variables[key].units != "%": logger.error("units errors", inform[key].long_name) if key == "z": if nc_data_obj.variables[key].units != "m**2 s**-2": logger.error("units errors", inform[key].long_name) logger.info("The units of variables check true !") all_vars_shape = [] # 检查气象数据维数是否正确 for key in keys: all_vars_shape.append(inform[key].shape) if inform["level"].shape[0] != 37: # 检查level是否为37压力面 logger.error("shape errors", inform[key].long_name) if inform["time"].shape[0] != 1: # 检查time是否只有一个时间点 logger.error("shape errors", inform[key].long_name) if (inform["t"].shape[0]) != 1 or (inform["t"].shape[1]) != 37: logger.error("shape errors", inform[key].long_name) if inform["r"].shape[0] != 1 or inform["r"].shape[1] != 37: logger.error("shape errors", inform[key].long_name) if inform["z"].shape[0] != 1 or inform["z"].shape[1] != 37: logger.error("shape errors", inform[key].long_name) logger.info("The shape of variables check true !") @staticmethod def read_nc(nc_file): """ function: 从气象数据中读取数组 """ nc_data_obj = Nc.Dataset(nc_file) inform = nc_data_obj.variables lon_array = inform['longitude'][:] # lon数组 lat_array = inform['latitude'][:] # lat数组 n_lat = len(lat_array) # 经线列数量 n_lon = len(lon_array) # 纬线行数量 lon_min, lat_max, lon_max, lat_min = [lon_array.min(), lat_array.max(), lon_array.max(), lat_array.min()] lon_res = (lon_max - lon_min) / (float(n_lon) - 1) # 计算经度方向分辨率 lat_res = (lat_max - lat_min) / (float(n_lat) - 1) # 计算纬度方向分辨率 pre_level = np.array(inform["level"]) # pressure level in m 压力面 temp_array = np.array(inform["t"]) # temp--k (1,37,25,25) rel_hum_array = np.array(inform["r"]) # relative_humidity--% (1,37,25,25) geo_h_array = np.array(inform["z"]) # Geopotential--m**2 s**-2 (1,37,25,25) return lon_array, lat_array, pre_level, temp_array, rel_hum_array, geo_h_array, n_lat, n_lon, lon_res, lat_res def redu_dim(self, nc_file): """ function : 降低数组维度 output: lon_array: (25)-->(1,25,37) lat_array: (25)-->(1,25,37) pre_level: (37)-->(25,25,37) temp_array: (1,37,25,25)-->(25,25,37) rel_hum_array: (1,37,25,25)-->(25,25,37) geo_h_array: (1,37,25,25)-->(25,25,37) """ lon_array, lat_array, pre_level, temp_array, rel_hum_array, geo_h_array, n_lat,\ n_lon, lon_res, lat_res = self.read_nc(nc_file) lat_01 = np.zeros((1, n_lat, 37), dtype=float) lon_01 = np.zeros((1, n_lon, 37), dtype=float) level_01 = np.zeros((n_lat, n_lon, 37), dtype=float) temp_01 = np.zeros((n_lat, n_lon, 37), dtype=float) rel_hum_01 = np.zeros((n_lat, n_lon, 37), dtype=float) geo_h_01 = np.zeros((n_lat, n_lon, 37), dtype=float) for i in range(0, 37): lon_01[0, :, i] = lon_array lat_01[0, :, i] = lat_array # pre_level = pre_level/100 level_01[:, :, i] = level_01[:, :, i] + pre_level[i] temp_01[0:n_lat, 0:n_lon, i] = temp_array[0, i, :, :] rel_hum_01[0:n_lat, 0:n_lon, i] = rel_hum_array[0, i, :, :] geo_h_01[:, :, i] = geo_h_array[0, i, :, :] del lon_array, lat_array, pre_level, temp_array, rel_hum_array, geo_h_array lon_array, lat_array, pre_level, temp_array, rel_hum_array, geo_h_array =\ lon_01, lat_01, level_01, temp_01, rel_hum_01, geo_h_01 return lon_array, lat_array, pre_level, temp_array, rel_hum_array, geo_h_array, lon_res, lat_res def tran_tif(self, nc_file, out_path, str_name): """ function: 气象数据矩阵转成tiff数据 param: nc_infile 气象数据文件 param: out_path 输出的文件夹路径 param: str 气象数据名字 """ lon_array, lat_array, pre_level, temp_array, rel_hum_array, geo_h_array,\ lon_res, lat_res = self.redu_dim(nc_file) # im_bands, n_lat, n_lon = temp_array.shape dst_srs = osr.SpatialReference() dst_srs.ImportFromEPSG(4326) # 定义一个WGS84坐标系 im_proj = dst_srs.ExportToWkt() im_geo = [lon_array[0, 0, 0], lon_res, 0, lat_array[0, 0, 0], 0, -lat_res] # first_name = (nc_file.split("\\", -1)[-1]).split(".nc")[0] # out_level_path = out_path+str_name+"_"+"level.tif" # ImageHandler.write_era_into_img(out_level_path, im_proj, im_geo, pre_level) out_temp_path = out_path+str_name+"_"+"temp.tif" ImageHandler.write_era_into_img(out_temp_path, im_proj, im_geo, temp_array) out_hum_path = out_path+str_name+"_"+"re_hum.tif" ImageHandler.write_era_into_img(out_hum_path, im_proj, im_geo, rel_hum_array) out_geo_h_path = out_path+str_name+"_"+"geo_h.tif" ImageHandler.write_era_into_img(out_geo_h_path, im_proj, im_geo, geo_h_array) def copy_tif(self, nc_tif_file, out_path, key_name): """ tif格式的气象数据：1.识别文件夹中的气象参数；2.复制气象参数至临时文件夹 param: nc_tif_file:存放气象数据的文件夹路径 param: out_path：气象数据的复制路径 param: key_name：MasterNC/AuxiliaryNC """ filename = os.listdir(nc_tif_file) for name in filename: if "geo" in name: geo_path = os.path.join(nc_tif_file, name) out_path1 = os.path.join(out_path, key_name+"_geo_h.tif") shutil.copy(geo_path, out_path1) elif "hum" in name: hum_path = os.path.join(nc_tif_file, name) out_path2 = os.path.join(out_path, key_name+"_re_hum.tif") shutil.copy(hum_path, out_path2) elif "temp" in name: temp_path = os.path.join(nc_tif_file, name) out_path3 = os.path.join(out_path, key_name+"_temp.tif") shutil.copy(temp_path, out_path3) class ReadImage: """ 读取dem影像, 并输出dem矩阵 读取主辅影像，并计算干涉相位图 """ def __int__(self): pass @staticmethod def read_dem(filename): """ function: 从input目录下读取dem,返回DEM对应的数值数组 param: fileName-- dem文件路径 """ dem = ImageHandler().get_band_array(filename, 1) # dem = np.array(Image.open(filename)) max_dem = math.ceil(np.max(dem) / 100) * 100 + 50 # 四舍五入向上取值 return dem, max_dem @staticmethod def read_lon_lat(filename): """ 读取影像的经度，纬度 """ lon = [] lat = [] im_scope = ImageHandler().get_scope(filename) width, height = ImageHandler().get_img_width(filename), ImageHandler().get_img_height(filename) # 获取数据的宽度、高度 point_upleft, point_downleft = im_scope[1], im_scope[3] # 获取左上数据的[经度、纬度]，右下数据的[经度、纬度] geotran = ImageHandler().get_geotransform(filename) pix_width, pix_hight = geotran[1], geotran[5] # 像元的宽度，像元的高度 # 获取像元的宽度和像元的高度 for i in range(0, width): longitude = point_upleft[0] + i * pix_width lon.append(longitude) for j in range(0, height): latitude = point_upleft[1] + j * pix_hight lat.append(latitude) lon = np.asarray(lon) lat = np.asarray(lat) lon_min, lat_max, lon_max, lat_min = [lon.min(), lat.max(), lon.max(), lat.min()] n_lat = len(lat) # 经线列数量 n_lon = len(lon) # 纬线行数量 nncols = n_lat-1 nnrows = n_lon-1 lat_37 = np.zeros((1, n_lat, 37), dtype=float) lon_37 = np.zeros((1, n_lon, 37), dtype=float) for i in range(0, 37): d = lon lon_37[0, :, i] = d e = lat lat_37[0, :, i] = e return lon, lat, lon_min, lat_max, lon_max, lat_min, n_lat, n_lon,\ nncols, nnrows, pix_width, pix_hight, lat_37, lon_37 @staticmethod def read_phase(file1, file2, out_path): """ function： 计算干涉相位 """ f01_dataset = gdal.Open(file1) # 获取主影像波段 f01_width = f01_dataset.RasterXSize f01_height = f01_dataset.RasterYSize f01_bands = f01_dataset.RasterCount img_pro = f01_dataset.GetProjection() img_tra = f01_dataset.GetGeoTransform() f01_bands1 = f01_dataset.GetRasterBand(1) f01_bands2 = f01_dataset.GetRasterBand(2) m_r_1 = f01_bands1.ReadAsArray(0, 0, f01_width, f01_height) m_i_1 = f01_bands2.ReadAsArray(0, 0, f01_width, f01_height) f02_dataset = gdal.Open(file2) # 获取辅影像波段 f02_width = f02_dataset.RasterXSize f02_height = f02_dataset.RasterYSize f02_bands = f02_dataset.RasterCount f02_bands1 = f02_dataset.GetRasterBand(1) f02_bands2 = f02_dataset.GetRasterBand(2) a_r_1 = f02_bands1.ReadAsArray(0, 0, f02_width, f02_height) a_i_1 = f02_bands2.ReadAsArray(0, 0, f02_width, f02_height) real_int_ms = m_r_1 * a_r_1 + m_i_1 * a_i_1 imag_int_ms = m_i_1 * a_r_1 - m_r_1 * a_i_1 a = real_int_ms b = imag_int_ms fan1 = np.where(a <= 0) a = np.where(a == 0, -9999, a) phase1 = np.arctan(b / a) phase1[fan1] = 0 del fan1 gc.collect() fan2 = np.where(a == 0, 1, 0) fan3 = np.where(b > 0, 1, 0) phase2 = fan2 * fan3 * math.pi / 2 del fan2, fan3 gc.collect() fan4 = np.where(a == 0, 1, 0) fan5 = np.where(b < 0, 1, 0) phase3 = fan4 * fan5 * (-math.pi / 2) del fan4, fan5 gc.collect() fan6 = np.where(a < 0, 1, 0) fan7 = np.where(b >= 0, 1, 0) a = np.where(a == 0, -9999, a) phase4 = fan6 * fan7 * (np.arctan(b / a) + math.pi) del fan6, fan7 gc.collect() fan8 = np.where(a < 0, 1, 0) fan9 = np.where(b < 0, 1, 0) a = np.where(a == 0, -9999, a) phase5 = fan8 * fan9 * (np.arctan(b / a) - math.pi) del fan8, fan9 gc.collect() phase = phase1 + phase2 + phase3 + phase4 + phase5 logger.info("Calculate phase complete !") ImageHandler.write_era_into_img(out_path, img_pro, img_tra, phase)