Python解析SWAN气象雷达数据--（解析、生成ASCII、Image、netCDF）

解析

from datetime import *

import time

import calendar

import json

import numpy as np

from struct import *

import binascii

import netCDF4

file = open(r"D:/radarDataTest/Z_QPF_20140831203600.F030.bin", "rb")

data = file.read();

print(len(data))

file.close()

#

file = open(r"D:/radarDataTest/Z_QPF_20140831203600.F030.bin", "rb")

length = 0

zonName,dataName,flag,version, = unpack("12s38s8s8s", file.read(12+38+8+8))

zonName = zonName.decode("gbk").rstrip('\x00')

dataName = dataName.decode("gbk").rstrip('\x00')

flag = flag.decode("gbk").rstrip('\x00')

version = version.decode("gbk").rstrip('\x00')

length = length + 12+38+8+8

#

print(zonName)

print("数据说明: " + dataName)

print("文件标志: " + flag)

print("数据版本号: " + version)

#

year,month,day,hour,minute,interval, = unpack("HHHHHH", file.read(2+2+2+2+2+2))

print("时间: "+str(year)+"-"+str(month)+"-"+str(day)+" "+str(hour)+":"+str(minute))

print("时段长: "+str(interval))

length = length + 2+2+2+2+2+2

#

XNumGrids,YNumGrids,ZNumGrids, = unpack("HHH", file.read(2+2+2))

print("X: " + str(XNumGrids)+"  Y: "+str(YNumGrids)+"  Z:"+str(ZNumGrids))

length = length + 2+2+2

#

RadarCount, = unpack("i", file.read(4))

print("拼图雷达数: " + str(RadarCount))

length = length + 4

#

StartLon,StartLat,CenterLon,CenterLat,XReso,YReso, = unpack("ffffff", file.read(4+4+4+4+4+4))

print("开始经度: "+str(StartLon)+"  开始纬度:"+str(StartLat)+"  中心经度:"+str(CenterLon)+"  中心纬度:"+str(CenterLat))

print("经度方向分辨率:"+str(XReso)+"  纬度方向分辨率:"+str(YReso))

length = length + 4+4+4+4+4+4

ZhighGrids=[]

for i in range(0, 40):

    ZhighGrid, = unpack("f", file.read(4))

    ZhighGrids.append(ZhighGrid)

print("垂直方向的高度:"+str(ZhighGrids))

length = length + 40*4

#

RadarStationNames=[]

for i in range(0, 20):

    RadarStationName, = unpack("16s", file.read(16))

    RadarStationName = RadarStationName.decode("gbk")

    RadarStationNames.append(RadarStationName.rstrip('\x00'))

print("相关站点名称:"+str(RadarStationNames))

length = length + 20*16

#

RadarLongitudes=[]

for i in range(0, 20):

    RadarLongitude, = unpack("f", file.read(4))

    RadarLongitudes.append(RadarLongitude)

print("相关站点所在经度:"+str(RadarLongitudes))

length = length + 20*4

#

RadarLatitudes=[]

for i in range(0, 20):

    RadarLatitude, = unpack("f", file.read(4))

    RadarLatitudes.append(RadarLatitude)

print("相关站点所在纬度:"+str(RadarLatitudes))

length = length + 20*4

#

RadarAltitudes=[]

for i in range(0, 20):

    RadarAltitude, = unpack("f", file.read(4))

    RadarAltitudes.append(RadarAltitude)

print("相关站点所在海拔高度:"+str(RadarAltitudes))

length = length + 20*4

#

MosaicFlags=[]

for i in range(0, 20):

    MosaicFlag, = unpack("B", file.read(1))

    MosaicFlags.append(MosaicFlag)

print("该相关站点数据是否包含在本次拼图中:"+str(MosaicFlags))

length = length + 20*1

#

m_iDataType, = unpack("h", file.read(2))

print("数据类型定义:"+str(m_iDataType))

if m_iDataType==0:

    print("unsigned char")

elif m_iDataType==1:

    print("char")

elif m_iDataType==2:

    print("unsigned short")

elif m_iDataType==3:

    print("short")

elif m_iDataType==4:

    print("unsigned short")

length = length + 2

#

m_iLevelDimension, = unpack("h", file.read(2))

print("每一层的向量数:"+str(m_iLevelDimension))

length = length + 2

#

Reserveds=[]

Reserveds, = unpack("168s", file.read(168))

Reserveds = Reserveds.decode("gbk").rstrip('\x00')

print("该相关站点数据是否包含在本次拼图中: "+Reserveds)

length = length + 168

#打印数据

valueZYX = []

for i in range(0, ZNumGrids):

    valueYX = []

    for j in range(0, YNumGrids):

        valueX = []

        for k in range(0, XNumGrids):

            value, = unpack("h", file.read(2))

            #value, = unpack("b", file.read(1))

            '''

            if value > 0:

                print(value)

            '''

            valueX.append(value)

        valueYX.append(valueX)

    valueZYX.append(valueYX)

#

#print("数据:"+str(valueZYX))

length = length + ZNumGrids*YNumGrids*XNumGrids*2

print(length)

#

print("----------------------------数据----------------------------")

file.close()

生成ASCII

import time

from struct import *

start = time.clock()

file = open(r"D:/radarDataTest/Z_QPF_20140831203600.F030.bin", "rb")

#

zonName,dataName,flag,version, = unpack("12s38s8s8s", file.read(12+38+8+8))

zonName = zonName.decode("gbk").rstrip('\x00')

dataName = dataName.decode("gbk").rstrip('\x00')

flag = flag.decode("gbk").rstrip('\x00')

version = version.decode("gbk").rstrip('\x00')

#

print(zonName)

print("数据说明: " + dataName)

print("文件标志: " + flag)

print("数据版本号: " + version)

#

length = 0

length = length + 2+2+2+2+2+2  # 时间说明

file.read(length)

XNumGrids,YNumGrids,ZNumGrids, = unpack("HHH", file.read(2+2+2))

print("X: " + str(XNumGrids)+"  Y: "+str(YNumGrids)+"  Z:"+str(ZNumGrids))

length = 0

length = length + 4  # 拼图雷达数

file.read(length)

#

StartLon,StartLat,CenterLon,CenterLat,XReso,YReso, = unpack("ffffff", file.read(4+4+4+4+4+4))

print("开始经度: "+str(StartLon)+"  开始纬度:"+str(StartLat)+"  中心经度:"+str(CenterLon)+"  中心纬度:"+str(CenterLat))

print("经度方向分辨率:"+str(XReso)+"  纬度方向分辨率:"+str(YReso))

ZhighGrids=[]

for i in range(0, 40):

    ZhighGrid, = unpack("f", file.read(4))

    ZhighGrids.append(ZhighGrid)

print("  垂直方向的高度:"+str(ZhighGrids))

#

length = 0

length = length + 20*16  # 相关站点名称

length = length + 20*4   # 相关站点所在经度

length = length + 20*4   # 相关站点所在纬度

length = length + 20*4   # 相关站点所在海拔高度

length = length + 20*1   # 该相关站点数据是否包含在本次拼图中

length = length + 2      # 数据类型定义

length = length + 2      # 每一层的向量数

length = length + 168    # 保留信息

file.read(length)

textZYX = []

for i in range(0, ZNumGrids):

    textYX = []

    for j in range(0, YNumGrids):

        textX = []

        for k in range(0, XNumGrids):

            value, = unpack("h", file.read(2))

            textX.append(str(value))

        textYX.append(' '.join(textX))

    textZYX.append('\n'.join(textYX))

file.close()

#

#-------------------------------------------------------------------------------

file_object = open('ASCIIData.txt', 'w')

file_object.write("NCOLS         " + str(XNumGrids) + "\n")

file_object.write("NROWS         " + str(YNumGrids) + "\n")

file_object.write("XLLCENTER     " + str(StartLon) + "\n")

file_object.write("YLLCENTER     " + str(StartLat - YReso * (YNumGrids - 1)) + "\n")  # round(YReso, 3) *

file_object.write("CELLSIZE      " + str(XReso) + "\n")

file_object.write("NODATA_VALUE  " + str(-9999) + "\n")

#

#

file_object.writelines(textZYX[0])

file_object.close()

end = time.clock()

print("read: %f s" % dateSpanTransfer)

dateSpanTransfer = end - start

#-------------------------------------------------------------------------------

生成Image（.img）

import time

from struct import *

from osgeo import gdal, osr

from osgeo.gdalconst import *

import numpy

start = time.clock()

file = open(r"D:/radarDataTest/Z_QPF_20140831203600.F030.bin", "rb")

#

zonName,dataName,flag,version, = unpack("12s38s8s8s", file.read(12+38+8+8))

zonName = zonName.decode("gbk").rstrip('\x00')

dataName = dataName.decode("gbk").rstrip('\x00')

flag = flag.decode("gbk").rstrip('\x00')

version = version.decode("gbk").rstrip('\x00')

#

print(zonName)

print("数据说明: " + dataName)

print("文件标志: " + flag)

print("数据版本号: " + version)

#

length = 0

length = length + 2+2+2+2+2+2  # 时间说明

file.read(length)

XNumGrids,YNumGrids,ZNumGrids, = unpack("HHH", file.read(2+2+2))

print("X: " + str(XNumGrids)+"  Y: "+str(YNumGrids)+"  Z:"+str(ZNumGrids))

length = 0

length = length + 4  # 拼图雷达数

file.read(length)

#

StartLon,StartLat,CenterLon,CenterLat,XReso,YReso, = unpack("ffffff", file.read(4+4+4+4+4+4))

print("开始经度: "+str(StartLon)+"  开始纬度:"+str(StartLat)+"  中心经度:"+str(CenterLon)+"  中心纬度:"+str(CenterLat))

print("经度方向分辨率:"+str(XReso)+"  纬度方向分辨率:"+str(YReso))

ZhighGrids=[]

for i in range(0, 40):

    ZhighGrid, = unpack("f", file.read(4))

    ZhighGrids.append(ZhighGrid)

print("  垂直方向的高度:"+str(ZhighGrids))

#

length = 0

length = length + 20*16  # 相关站点名称

length = length + 20*4   # 相关站点所在经度

length = length + 20*4   # 相关站点所在纬度

length = length + 20*4   # 相关站点所在海拔高度

length = length + 20*1   # 该相关站点数据是否包含在本次拼图中

length = length + 2      # 数据类型定义

length = length + 2      # 每一层的向量数

length = length + 168    # 保留信息

file.read(length)

valueZYX = []

for i in range(0, ZNumGrids):

    valueYX = []

    for j in range(0, YNumGrids):

        valueX = []

        for k in range(0, XNumGrids):

            value, = unpack("h", file.read(2))

            valueX.append(value)

        valueYX.append(valueX)

    valueZYX.append(valueYX)

file.close()

#

#

#-------------------------------------------------------------------------------

end = time.clock()

dateSpanTransfer = end - start

print("read: %f s" % dateSpanTransfer)

#

#

driver = gdal.GetDriverByName('HFA')

driver.Register()

dataSetImg = driver.Create( "D:/radarDataTest/edarsImage.img", XNumGrids, YNumGrids, 1, gdal.GDT_Float32 )

#

dataSetImg.SetGeoTransform( [ StartLon, XReso, 0, StartLat, 0, -YReso ] )

#

srs = osr.SpatialReference()

srs.SetWellKnownGeogCS( 'WGS84' )

dataSetImg.SetProjection( srs.ExportToWkt() )

#

value2D = numpy.matrix( valueYX, dtype=numpy.float32 )

dataSetImg.GetRasterBand(1).WriteArray( value2D )

#

dataSetImg = None

#datasource.Destroy()

#-------------------------------------------------------------------------------

生成netCDF

from datetime import *

import time

import calendar

import json

import numpy as np

from struct import *

import binascii

import numpy

from numpy.random import uniform

from netCDF4 import Dataset

start = time.clock()

file = open(r"D:/radarDataTest/Z_QPF_20140831203600.F030.bin", "rb")

#

zonName,dataName,flag,version, = unpack("12s38s8s8s", file.read(12+38+8+8))

zonName = zonName.decode("gbk").rstrip('\x00')

dataName = dataName.decode("gbk").rstrip('\x00')

flag = flag.decode("gbk").rstrip('\x00')

version = version.decode("gbk").rstrip('\x00')

#

print(zonName)

print("数据说明: " + dataName)

print("文件标志: " + flag)

print("数据版本号: " + version)

#

length = 0

length = length + 2+2+2+2+2+2  # 时间说明

file.read(length)

XNumGrids,YNumGrids,ZNumGrids, = unpack("HHH", file.read(2+2+2))

print("X: " + str(XNumGrids)+"  Y: "+str(YNumGrids)+"  Z:"+str(ZNumGrids))

length = 0

length = length + 4  # 拼图雷达数

file.read(length)

#

StartLon,StartLat,CenterLon,CenterLat,XReso,YReso, = unpack("ffffff", file.read(4+4+4+4+4+4))

print("开始经度: "+str(StartLon)+"  开始纬度:"+str(StartLat)+"  中心经度:"+str(CenterLon)+"  中心纬度:"+str(CenterLat))

print("  经度方向分辨率:"+str(XReso)+"  纬度方向分辨率:"+str(YReso))

ZhighGrids=[]

for i in range(0, 40):

    ZhighGrid, = unpack("f", file.read(4))

    ZhighGrids.append(ZhighGrid)

print("  垂直方向的高度:"+str(ZhighGrids))

#

length = 0

length = length + 20*16  # 相关站点名称

length = length + 20*4   # 相关站点所在经度

length = length + 20*4   # 相关站点所在纬度

length = length + 20*4   # 相关站点所在海拔高度

length = length + 20*1   # 该相关站点数据是否包含在本次拼图中

length = length + 2      # 数据类型定义

length = length + 2      # 每一层的向量数

length = length + 168    # 保留信息

file.read(length)

valueZYX = []

for i in range(0, ZNumGrids):

    valueYX = []

    for j in range(0, YNumGrids):

        valueX = []

        for k in range(0, XNumGrids):

            #value, = unpack("h", file.read(2))

            #textX.append(str(value/10.0))

            value, = unpack("b", file.read(1))

            textX.append(str(value*2+66.0))

        valueYX.append(valueX)

    valueZYX.append(valueYX)

file.close()

#

valueXYZ = []

for k in range(0, XNumGrids):

    for j in range(0, YNumGrids):

        for i in range(0, ZNumGrids):

            valueXYZ.append(valueZYX[i][j][k])

#

file = open(r"D:/radarDataTest/Z_QPF_20140831203600.F030.bin", "rb")

rootgrp = Dataset("test.nc", "w", format="NETCDF4")

#rootgrp = Dataset("test.nc", "a")

#fcstgrp = rootgrp.createGroup("forecasts")

lon = rootgrp.createDimension("lon", XNumGrids)

lat = rootgrp.createDimension("lat", YNumGrids)

alt = rootgrp.createDimension("alt", ZNumGrids)

lon = rootgrp.createVariable("lon", "f8", ("lon",))

lat = rootgrp.createVariable("lat", "f8", ("lat",))

alt = rootgrp.createVariable("alt", "f8", ("alt",))

#val = rootgrp.createVariable("val","f4",("zz","yy","xx",))

val = rootgrp.createVariable("val","f4",("lon","lat","alt",))

#

rootgrp.description = dataName

rootgrp.history = "创建时间： " +  time.strftime('%Y-%m-%d %X', time.localtime())

rootgrp.Source_Software = "SmartMap"

#

lon.units = "degrees_east"

lon.long_name = "longitude coordinate"

lon.standard_name = "longitude"

#

lat.units = "degrees_north"

lat.long_name = "latitude coordinate"

lat.standard_name = "latitude"

#

alt.units = "m"

alt.long_name = "altitude"

alt.standard_name = "heigh"

#

val.long_name = "value"

val.esri_pe_string = 'GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]]'

val.coordinates = "lon lat alt"

val.units = "Degree"

val.missing_value = -9999

#interval = 0.009999999776482582

interval = 0.01

#x =  numpy.arange(-90,91,2.5)

x = []

for i in range(0, XNumGrids):

    x.append(StartLon + i * round(XReso, 3))

#x =  numpy.array(x)

lon[:] = x

#

#y =  numpy.arange(-180,180,2.5)

y = []

for i in range(0, YNumGrids):

    y.append(StartLat - i * round(YReso, 3))

#y =  numpy.array(y)

lat[:] = y

#

z = []

for i in range(0, ZNumGrids):

    z.append(ZhighGrids[i])

#z =  numpy.array(z)

alt[:] = z

#

#kk = uniform(size=(2,3,4,5))

#print(kk)

#val[::]=valueZYX

val[::] = valueXYZ

#

rootgrp.close()

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Python解析SWAN气象雷达数据--（解析、生成ASCII、Image、netCDF）

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