100 lines
4.5 KiB
Python
100 lines
4.5 KiB
Python
from AtmosphericDelayAuxData import NcHandle,ReadImage
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from AtmosphericDelayAlg import AtmosphericDelay as Ady
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from tool.algorithm.image import ImageHandle
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from tool.algorithm.algtools.PreProcess import PreProcess as pp
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import gc
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import os
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import sys
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from pathlib import Path
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env_str = os.path.dirname(os.path.abspath(sys.argv[0]))
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os.environ['PROJ_LIB'] = env_str
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def searchfile(source, pattern='*', recursive=False):
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if not isinstance(source, Path):
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source = Path(source)
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if source.is_dir() and source.exists():
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if recursive:
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res = list(source.rglob(pattern))
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else:
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res = list(source.glob(pattern))
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else:
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print(f"{source} is a file path or folder path is not exists!")
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return [] # False
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file_list = [i for i in res if i.is_file()]
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return file_list
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def get_nc_dic(nc_dir):
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imageHandler = ImageHandle.ImageHandler()
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arr_dict = {}
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nc_list = ["AuxiliaryNC_geo_h", "AuxiliaryNC_re_hum", "AuxiliaryNC_temp",
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"MasterNC_geo_h", "MasterNC_re_hum", "MasterNC_temp"]
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arr_name = ["a_geo", "a_re_hum", "a_temp", "m_geo", "m_re_hum", "m_temp"]
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path = nc_dir
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for i in range(0, len(nc_list)):
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in_path = path + nc_list[i] + ".tif"
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array = imageHandler.get_all_band_array(in_path)
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arr_dict.update({arr_name[i]: array})
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return arr_dict["a_geo"], arr_dict["a_re_hum"], arr_dict["a_temp"], arr_dict["m_geo"], arr_dict["m_re_hum"], arr_dict["m_temp"]
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def resampe_image(aux_dry_wet_unresame_path, mas_dry_wet_unresame_path, temp_dir):
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"""
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将延迟图（ztd）恢复为非正方形的样子
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para: aux_dry_wet_unresame_path：待裁剪的辅影像延迟图路径
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para: mas_dry_wet_unresame_path：待裁剪的主影像延迟图路径
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return :裁剪后的辅影像延迟图路径、主影像延迟图路径
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"""
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# 1.3 裁剪主辅影像延迟值
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out_aux_dry_wet_resamed_path = temp_dir + "resamed_aux_dry_wet.tif"
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out_mas_dry_wet_resamed_path = temp_dir + "resamed_mas_dry_wet.tif"
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pp.resampling_by_scale(aux_dry_wet_unresame_path, out_aux_dry_wet_resamed_path,
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temp_dir + "MasterNC_geo_h.tif")
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pp.resampling_by_scale(mas_dry_wet_unresame_path, out_mas_dry_wet_resamed_path,
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temp_dir + "MasterNC_geo_h.tif")
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return out_aux_dry_wet_resamed_path, out_mas_dry_wet_resamed_path
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if __name__ == '__main__':
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mas_nc = r'D:\micro\WorkSpace\AtmosphericDelay\ERA5\ERA5_N42.0_N46.0_E114.0_E118.0_20230615_08.nc'
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aux_nc = r'D:\micro\WorkSpace\AtmosphericDelay\ERA5\ERA5_N42.0_N46.0_E114.0_E118.0_20230910_08.nc'
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dem_file = r'D:\micro\WorkSpace\AtmosphericDelay\ERA5\cut_dem.tif'
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temp_dir = r'D:\micro\WorkSpace\AtmosphericDelay\test_ztd\test'
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out_path = r'D:\micro\WorkSpace\AtmosphericDelay\test_ztd' + '\\'
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# 读取气象数据
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ncHandle = NcHandle()
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imageHandle = ImageHandle.ImageHandler()
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ncHandle.tran_tif(mas_nc, out_path, "MasterNC")
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ncHandle.tran_tif(aux_nc, out_path, "AuxiliaryNC")
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a_geo, a_re_hum, a_temp, m_geo, m_re_hum, m_temp = get_nc_dic(out_path)
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base_file = out_path + "MasterNC_geo_h.tif" # 用来获取裁剪后气象数据的经纬度、分辨率等信息
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#
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# 1.2 计算主、辅影像垂直分层湿干延迟值，并保存干湿延迟图
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m_cdstack_dry_wet = Ady().aps_wrf_sar(base_file, m_temp, m_re_hum, m_geo, dem_file) # 主影像垂直分层干湿延迟值数组
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m_dry_wet_path = out_path + "m_dry_wet.tif" # 主影像干湿延迟图保存路径
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Ady().write_dry_wet_tif(dem_file, base_file, m_cdstack_dry_wet, m_dry_wet_path)
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a_cdstack_dry_wet = Ady().aps_wrf_sar(base_file, a_temp, a_re_hum, a_geo, dem_file) # 辅影像垂直分层干湿延迟值数组
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a_dry_wet_path = out_path + "a_dry_wet.tif" # 辅影像干湿延迟图保存路径
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Ady().write_dry_wet_tif(dem_file, base_file, a_cdstack_dry_wet, a_dry_wet_path)
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#
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# aux_dry_wet_resamed_path, mas_dry_wet_resamed_path = resampe_image(a_dry_wet_path, m_dry_wet_path, temp_dir) # 参照气象数据分辨率进行重采样
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gc.collect() # 回收内存
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# 1.4 输出主辅影像ztd数组
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a_ztd = Ady().calc_ztd(a_dry_wet_path, dem_file) # 辅影像的ztd数组
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m_ztd = Ady().calc_ztd(m_dry_wet_path, dem_file) # 主影像的ztd数组
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out_aux_ztd_path = out_path + "aux_ztd.tif"
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out_mas_ztd_path = out_path + "mas_ztd.tif"
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Ady().write_ztd_tif(dem_file, base_file, a_ztd, out_aux_ztd_path) # ztd数组->ztd影像
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Ady().write_ztd_tif(dem_file, base_file, m_ztd, out_mas_ztd_path) # ztd数组->ztd影像
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