99 lines
4.6 KiB
Python
99 lines
4.6 KiB
Python
|
from AtmosphericDelayAuxData import NcHandle,ReadImage
|
|||
|
|
from AtmosphericDelayAlg import AtmosphericDelay as Ady
|
|||
|
|
from tool.algorithm.image import ImageHandle
|
|||
|
|
from tool.algorithm.algtools.PreProcess import PreProcess as pp
|
|||
|
|
import gc
|
|||
|
|
import os
|
|||
|
|
from pathlib import Path
|
|||
|
|
|
|||
|
|
env_str = os.path.split(os.path.realpath(__file__))[0]
|
|||
|
|
os.environ['PROJ_LIB'] = env_str
|
|||
|
|
|
|||
|
|
|
|||
|
|
def searchfile(source, pattern='*', recursive=False):
|
|||
|
|
|
|||
|
|
if not isinstance(source, Path):
|
|||
|
|
source = Path(source)
|
|||
|
|
if source.is_dir() and source.exists():
|
|||
|
|
if recursive:
|
|||
|
|
res = list(source.rglob(pattern))
|
|||
|
|
else:
|
|||
|
|
res = list(source.glob(pattern))
|
|||
|
|
else:
|
|||
|
|
print(f"{source} is a file path or folder path is not exists!")
|
|||
|
|
return [] # False
|
|||
|
|
file_list = [i for i in res if i.is_file()]
|
|||
|
|
return file_list
|
|||
|
|
|
|||
|
|
|
|||
|
|
def get_nc_dic(nc_dir):
|
|||
|
|
imageHandler = ImageHandle.ImageHandler()
|
|||
|
|
arr_dict = {}
|
|||
|
|
nc_list = ["AuxiliaryNC_geo_h", "AuxiliaryNC_re_hum", "AuxiliaryNC_temp",
|
|||
|
|
"MasterNC_geo_h", "MasterNC_re_hum", "MasterNC_temp"]
|
|||
|
|
arr_name = ["a_geo", "a_re_hum", "a_temp", "m_geo", "m_re_hum", "m_temp"]
|
|||
|
|
path = nc_dir
|
|||
|
|
for i in range(0, len(nc_list)):
|
|||
|
|
in_path = path + nc_list[i] + ".tif"
|
|||
|
|
array = imageHandler.get_all_band_array(in_path)
|
|||
|
|
arr_dict.update({arr_name[i]: array})
|
|||
|
|
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"]
|
|||
|
|
|
|||
|
|
|
|||
|
|
def resampe_image(aux_dry_wet_unresame_path, mas_dry_wet_unresame_path, temp_dir):
|
|||
|
|
"""
|
|||
|
|
将延迟图（ztd）恢复为非正方形的样子
|
|||
|
|
para: aux_dry_wet_unresame_path：待裁剪的辅影像延迟图路径
|
|||
|
|
para: mas_dry_wet_unresame_path：待裁剪的主影像延迟图路径
|
|||
|
|
return :裁剪后的辅影像延迟图路径、主影像延迟图路径
|
|||
|
|
"""
|
|||
|
|
# 1.3 裁剪主辅影像延迟值
|
|||
|
|
out_aux_dry_wet_resamed_path = temp_dir + "resamed_aux_dry_wet.tif"
|
|||
|
|
out_mas_dry_wet_resamed_path = temp_dir + "resamed_mas_dry_wet.tif"
|
|||
|
|
|
|||
|
|
pp.resampling_by_scale(aux_dry_wet_unresame_path, out_aux_dry_wet_resamed_path,
|
|||
|
|
temp_dir + "MasterNC_geo_h.tif")
|
|||
|
|
pp.resampling_by_scale(mas_dry_wet_unresame_path, out_mas_dry_wet_resamed_path,
|
|||
|
|
temp_dir + "MasterNC_geo_h.tif")
|
|||
|
|
|
|||
|
|
return out_aux_dry_wet_resamed_path, out_mas_dry_wet_resamed_path
|
|||
|
|
|
|||
|
|
|
|||
|
|
if __name__ == '__main__':
|
|||
|
|
mas_nc = r'F:\干涉大气延迟校正\大气延迟检验\大气延迟检验\ERA5_N25_N35_E115_E125_20220910_10.nc'
|
|||
|
|
aux_nc = r'F:\干涉大气延迟校正\大气延迟检验\大气延迟检验\ERA5_N25_N35_E115_E125_20220922_10.nc'
|
|||
|
|
dem_file = r''
|
|||
|
|
temp_dir = 'F://干涉大气延迟校正//大气延迟检验//大气延迟检验//nc_file//test//'
|
|||
|
|
out_path = 'F://干涉大气延迟校正//大气延迟检验//大气延迟检验//nc_file//'
|
|||
|
|
|
|||
|
|
# 读取气象数据
|
|||
|
|
ncHandle = NcHandle()
|
|||
|
|
imageHandle = ImageHandle.ImageHandler()
|
|||
|
|
ncHandle.tran_tif(mas_nc, out_path, "MasterNC")
|
|||
|
|
ncHandle.tran_tif(aux_nc, out_path, "AuxiliaryNC")
|
|||
|
|
|
|||
|
|
a_geo, a_re_hum, a_temp, m_geo, m_re_hum, m_temp = get_nc_dic(out_path)
|
|||
|
|
|
|||
|
|
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
|
|||
|
|
#
|
|||
|
|
# # 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
|
|||
|
|
# 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
|
|||
|
|
# m_dry_wet_path = out_path + "m_dry_wet.tif" # 主影像干湿延迟图保存路径
|
|||
|
|
# Ady().write_dry_wet_tif(dem_file, base_file, m_cdstack_dry_wet, m_dry_wet_path)
|
|||
|
|
#
|
|||
|
|
# 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
|
|||
|
|
# a_dry_wet_path = out_path + "a_dry_wet.tif" # 辅影像干湿延迟图保存路径
|
|||
|
|
# Ady().write_dry_wet_tif(dem_file, base_file, a_cdstack_dry_wet, a_dry_wet_path)
|
|||
|
|
# #
|
|||
|
|
#
|
|||
|
|
# 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
|
|||
|
|
#
|
|||
|
|
# gc.collect() # 回收内存
|
|||
|
|
# # 1.4 输出主辅影像ztd数组
|
|||
|
|
# a_ztd = Ady().calc_ztd(a_dry_wet_path, dem_file) # 辅影像的ztd数组
|
|||
|
|
# m_ztd = Ady().calc_ztd(m_dry_wet_path, dem_file) # 主影像的ztd数组
|
|||
|
|
# out_aux_ztd_path = out_path + "aux_ztd.tif"
|
|||
|
|
# out_mas_ztd_path = out_path + "mas_ztd.tif"
|
|||
|
|
# Ady().write_ztd_tif(dem_file, base_file, a_ztd, out_aux_ztd_path) # ztd数组->ztd影像
|
|||
|
|
# Ady().write_ztd_tif(dem_file, base_file, m_ztd, out_mas_ztd_path) # ztd数组->ztd影像
|