Fix read fy awx file problem.
This commit is contained in:
NMC-DAVE
parent
bded9aa8d8
commit
51879d4744
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
@ -4,4 +4,4 @@ from various data sources.
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"""
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__author__ = "The R & D Center for Weather Forecasting Technology in NMC, CMA"
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__version__ = '0.1.9.1'
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__version__ = '0.1.9.2'
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@ -7,49 +7,45 @@
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Read FY satellite awx format file.
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"""
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from datetime import datetime, timedelta
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import numpy as np
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import xarray as xr
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def read_awx_cloud(fname):
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def resolve_awx_bytearray(btarray, units=''):
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"""
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Read satellite awx format file.
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解析awx文件的字节内容, 返回数据信息
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:param fname: file pathname.
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:return: data list
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:Example:
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>>> headinfo, data = read_awx_cloud("./data/ANI_IR1_R04_20191026_2100_FY2G.AWX")
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Args:
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btarray: byte array.
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"""
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# the first class file head 一级文件头记录采用定长方式, 共40字节
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head1_dtype = [
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('SAT96', 'S12'), # SAT96 filename
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('byteSequence', 'i2'), # 整型数的字节顺序, 0 低字节在前, 高字节在后; !=0 高字节在前, 低字节在后.
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('firstClassHeadLength', 'i2'), # 第一节文件头长度
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('secondClassHeadLength', 'i2'), # 第二节文件头长度
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('padDataLength', 'i2'), # 填充段数据长度
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('recordLength', 'i2'), # 记录长度(字节), 图像产品: 记录长度=图形宽度, 格点场产品: 记录长度=横向格点数x格点数据字长
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('headRecordNumber', 'i2'), # 文件头占用记录数, 一级文件头、二填充段扩展以及的所占用总记录个数
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('dataRecordNumber', 'i2'), # 产品数据占用记录数
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('productCategory', 'i2'), # 产品类别, 1：静止, 2：极轨, 3：格点定量, 4：离散, 5: 图形和分析
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('compressMethod', 'i2'), # 压缩方式, 0: 未压缩; 1 行程编码压缩; 2 LZW方式压缩; 3 特点方式压缩
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('formatString', 'S8'), # 格式说明字符串, 'SAT2004', 'SAT98'为早期版本(不支持)
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('qualityFlag', 'i2')] # 产品数据质量标记, 1 完全可靠; 2 基本可靠; 3 有缺值, 可用; 4 不可用
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head1_info = np.frombuffer(btarray[0:40], dtype=head1_dtype)
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ind = 40
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# read part of binary
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# refer to
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# https://stackoverflow.com/questions/14245094/how-to-read-part-of-binary-file-with-numpy
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# open file
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with open(fname, 'rb') as fh:
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# read file content
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ba = bytearray(fh.read())
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# define head structure
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head_dtype = [
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('SAT96', 'S12'), # SAT96 filename
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('byteSequence', 'i2'), # integer number byte sequence
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('firstClassHeadLength', 'i2'),
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('secondClassHeadLength', 'i2'),
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('padDataLength', 'i2'),
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('recordLength', 'i2'),
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('headRecordNumber', 'i2'),
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('dataRecordNumber', 'i2'),
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('productCategory', 'i2'),
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('compressMethod', 'i2'),
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('formatString', 'S8'),
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('qualityFlag', 'i2'),
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('satelliteName', 'S8'),
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if head1_info['productCategory'] == 1:
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# the second class file head 二级文件头采用不定长方式，内容依据产品的不同而不同.
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head2_dtype = [
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('satelliteName', 'S8'), # 卫星名
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('year', 'i2'), ('month', 'i2'),
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('day', 'i2'), ('hour', 'i2'),
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('minute', 'i2'),
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('channel', 'i2'),
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('flagOfProjection', 'i2'),
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('channel', 'i2'), # 通道号, 1红外, 2水汽, 3红外分裂, 4可见光, 5中红外, 6备用
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('flagOfProjection', 'i2'), # 投影, 0为投影, 1兰勃托, 2麦卡托, 3极射, 4等经纬度, 5等面积
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('widthOfImage', 'i2'),
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('heightOfImage', 'i2'),
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('scanLineNumberOfImageTopLeft', 'i2'),
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@ -71,29 +67,152 @@ def read_awx_cloud(fname):
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('dataLengthOfCalibration', 'i2'),
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('dataLengthOfGeolocation', 'i2'),
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('reserved', 'i2')]
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head2_info = np.frombuffer(btarray[ind:(ind+64)], dtype=head2_dtype)
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ind += 64
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head_info = np.frombuffer(ba[0:104], dtype=head_dtype)
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ind = 104
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# color table
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if head2_info['dataLengthOfColorTable'] != 0:
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table_R = np.frombuffer(btarray[ind:(ind + 256)], dtype='u1')
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ind += 256
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table_G = np.frombuffer(btarray[ind:(ind + 256)], dtype='u1')
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ind += 256
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table_B = np.frombuffer(btarray[ind:(ind + 256)], dtype='u1')
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ind += 256
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# calibration table 定标数据块, 描述的是图像灰度值与探测物理量之间的关系
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# refer to
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# http://bbs.06climate.com/forum.php?mod=viewthread&tid=89296
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# https://github.com/myyd/China_AWX/blob/master/awx/FY4A_AWX.py
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# 2022/3/31日更改, 采用无符号整型'u2'读取, 并且区分了可见光和红外, 水汽的定标表
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calibration_table = None
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if head2_info['dataLengthOfCalibration'] != 0:
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calibration_table = np.frombuffer(btarray[ind:(ind + 2048)], dtype='u2')
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if head2_info['channel'] == 4:
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# 对于可见光通道图像, 定标数据在0-63的范围内查找
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calibration_table = calibration_table * 0.01
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calibration_table = calibration_table[1:64]
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calibration_table = calibration_table.repeat(4)
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else:
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# 对于红外,水汽图像, 在全局范围内查找, 但要隔4进行跳点
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calibration_table = calibration_table * 0.01
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calibration_table = calibration_table[0::4]
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ind += 2048
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# head rest information
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head_rest_len = (head_info['recordLength'][0].astype(np.int) *
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head_info['headRecordNumber'][0] - ind)
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head_rest = np.frombuffer(
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ba[ind:(ind + head_rest_len)],
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dtype='u1', count=head_rest_len)
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ind += head_rest_len
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# geolocation table
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if head2_info['dataLengthOfGeolocation'] != 0:
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geolocation_dtype = [
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('coordinate', 'i2'),
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('source', 'i2'),
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('delta', 'i2'),
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('left_top_lat', 'i2'),
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('left_top_lon', 'i2'),
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('horizontalNumber', 'i2'),
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('verticalNumber', 'i2'),
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('reserved', 'i2')]
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geolocation_info = np.frombuffer(btarray[ind:(ind+16)], dtype=geolocation_dtype)
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ind += 16
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geolocation_length = geolocation_info['horizontal_number'][0] * geolocation_info['vertical_number'][0] * 2
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geolocation_table = np.frombuffer(btarray[ind:(ind+geolocation_length)], dtype='i2')
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ind += geolocation_length
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# retrieve data records
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data_len = (head_info['dataRecordNumber'][0].astype(np.int) *
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head_info['recordLength'][0])
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data = np.frombuffer(
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ba[ind:(ind + data_len)], dtype='u1',
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count=data_len)
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data.shape = (head_info['dataRecordNumber'][0],
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head_info['recordLength'][0])
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# pad field
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pad_field = np.frombuffer(btarray[ind:(ind+head1_info['padDataLength'][0])], dtype='u1')
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ind += head1_info['padDataLength'][0]
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# retrieve data records
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data_len = (head1_info['dataRecordNumber'][0].astype(int) *
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head1_info['recordLength'][0])
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data = np.frombuffer(btarray[ind:(ind + data_len)], dtype='u1', count=data_len)
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if calibration_table is not None:
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data = calibration_table[data]
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data.shape = (head1_info['dataRecordNumber'][0], head1_info['recordLength'][0])
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# 由于数据是按照左上角开始放置, 为此需要对纬度顺序进行反转
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data = np.flip(data, axis=0)
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# construct longitude and latitude coordinates
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# if use the verticalResolution and horizontalResolution, lon and lat will not be correct.
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#lat = (
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# head2_info['latitudeOfNorth'][0]/100. -
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# np.arange(head2_info['heightOfImage'][0])*head2_info['verticalResolution'][0]/100.)
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#lon = (
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# head2_info['longitudeOfWest'][0]/100. +
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# np.arange(head2_info['widthOfImage'][0])*head2_info['horizontalResolution'][0]/100.)
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lat = np.linspace(
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head2_info['latitudeOfSouth'][0]/100., head2_info['latitudeOfNorth'][0]/100.,
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num=head2_info['heightOfImage'][0])
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lon = np.linspace(
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head2_info['longitudeOfWest'][0]/100., head2_info['longitudeOfEast'][0]/100.,
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num=head2_info['widthOfImage'][0])
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# construct time
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time = datetime(
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head2_info['year'][0], head2_info['month'][0],
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head2_info['day'][0], head2_info['hour'][0], head2_info['minute'][0])
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time = np.array([time], dtype='datetime64[ms]')
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# define coordinates
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time_coord = ('time', time)
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lon_coord = ('lon', lon, {
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'long_name':'longitude', 'units':'degrees_east',
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'_CoordinateAxisType':'Lon', 'axis': "X"})
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lat_coord = ('lat', lat, {
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'long_name':'latitude', 'units':'degrees_north',
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'_CoordinateAxisType':'Lat', 'axis': "Y"})
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channel_coord = ('channel', [head2_info['channel'][0]],
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{'long_name':'channel', 'units':''})
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# create xarray
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data = data[np.newaxis, np.newaxis, ...]
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varattrs = {
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'productCategory': head1_info['productCategory'][0], # 产品类型, 1:静止, 2:极轨, 3:格点, 4:离散, 5:图形和分析
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'formatString': head1_info['formatString'][0], # 产品格式名称
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'qualityFlag': head1_info['qualityFlag'][0], # 产品质量标识
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'satelliteName': head2_info['satelliteName'][0], # 卫星名称
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'flagOfProjection': head2_info['flagOfProjection'][0], # 投影方式, 0:未投影, 1:兰勃托, 2:麦卡托, 3:极射, 4:等经纬, 5:等面积
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'units': units}
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data = xr.Dataset({
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'image':(['time', 'channel', 'lat', 'lon'], data, varattrs)},
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coords={ 'time':time_coord, 'channel':channel_coord,
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'lat':lat_coord, 'lon':lon_coord})
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# add attributes
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data.attrs['Conventions'] = "CF-1.6"
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data.attrs['Origin'] = 'MICAPS Cassandra Server'
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# return
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return head_info, data
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return data
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elif head1_info['productCategory'] == 3:
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# TODO，参考https://github.com/wqshen/AwxReader 读取格点定量数据
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print("The productCategory is not supported.")
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return None
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else:
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print("The productCategory is not supported.")
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return None
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def read_awx_cloud(fname, units=''):
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"""
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Read satellite awx format file.
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:param fname: file pathname.
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:return: data list
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:Example:
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>>> headinfo, data = read_awx_cloud("./data/ANI_IR1_R04_20191026_2100_FY2G.AWX")
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"""
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# read part of binary
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# refer to
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# https://stackoverflow.com/questions/14245094/how-to-read-part-of-binary-file-with-numpy
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# open file
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with open(fname, 'rb') as fh:
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# read file content
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ba = bytearray(fh.read())
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return resolve_awx_bytearray(ba, units=units)
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def read_himawari(fname, resolution):
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@ -27,6 +27,7 @@ from tqdm import tqdm
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from nmc_met_io import DataBlock_pb2
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import nmc_met_io.config as CONFIG
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from nmc_met_io.read_radar import StandardData
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from nmc_met_io.read_satellite import resolve_awx_bytearray
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def get_http_result(host, port, url):
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@ -868,177 +869,21 @@ def get_fy_awx(directory, filename=None, suffix="*.AWX", units='', cache=True, c
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print('There is no data ' + filename + ' in ' + directory)
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return None
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# the first class file head 一级文件头记录采用定长方式, 共40字节
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head1_dtype = [
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('SAT96', 'S12'), # SAT96 filename
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('byteSequence', 'i2'), # 整型数的字节顺序, 0 低字节在前, 高字节在后; !=0 高字节在前, 低字节在后.
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('firstClassHeadLength', 'i2'), # 第一节文件头长度
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('secondClassHeadLength', 'i2'), # 第二节文件头长度
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('padDataLength', 'i2'), # 填充段数据长度
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('recordLength', 'i2'), # 记录长度(字节), 图像产品: 记录长度=图形宽度, 格点场产品: 记录长度=横向格点数x格点数据字长
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('headRecordNumber', 'i2'), # 文件头占用记录数, 一级文件头、二填充段扩展以及的所占用总记录个数
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('dataRecordNumber', 'i2'), # 产品数据占用记录数
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('productCategory', 'i2'), # 产品类别, 1：静止, 2：极轨, 3：格点定量, 4：离散, 5: 图形和分析
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('compressMethod', 'i2'), # 压缩方式, 0: 未压缩; 1 行程编码压缩; 2 LZW方式压缩; 3 特点方式压缩
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('formatString', 'S8'), # 格式说明字符串, 'SAT2004', 'SAT98'为早期版本(不支持)
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('qualityFlag', 'i2')] # 产品数据质量标记, 1 完全可靠; 2 基本可靠; 3 有缺值, 可用; 4 不可用
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head1_info = np.frombuffer(byteArray[0:40], dtype=head1_dtype)
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ind = 40
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if head1_info['productCategory'] == 1:
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# the second class file head 二级文件头采用不定长方式，内容依据产品的不同而不同.
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head2_dtype = [
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('satelliteName', 'S8'), # 卫星名
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('year', 'i2'), ('month', 'i2'),
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('day', 'i2'), ('hour', 'i2'),
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('minute', 'i2'),
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('channel', 'i2'), # 通道号, 1红外, 2水汽, 3红外分裂, 4可见光, 5中红外, 6备用
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('flagOfProjection', 'i2'), # 投影, 0为投影, 1兰勃托, 2麦卡托, 3极射, 4等经纬度, 5等面积
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('widthOfImage', 'i2'),
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('heightOfImage', 'i2'),
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('scanLineNumberOfImageTopLeft', 'i2'),
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('pixelNumberOfImageTopLeft', 'i2'),
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('sampleRatio', 'i2'),
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('latitudeOfNorth', 'i2'),
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('latitudeOfSouth', 'i2'),
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('longitudeOfWest', 'i2'),
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('longitudeOfEast', 'i2'),
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('centerLatitudeOfProjection', 'i2'),
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('centerLongitudeOfProjection', 'i2'),
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('standardLatitude1', 'i2'),
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('standardLatitude2', 'i2'),
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('horizontalResolution', 'i2'),
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('verticalResolution', 'i2'),
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('overlapFlagGeoGrid', 'i2'),
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('overlapValueGeoGrid', 'i2'),
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('dataLengthOfColorTable', 'i2'),
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('dataLengthOfCalibration', 'i2'),
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('dataLengthOfGeolocation', 'i2'),
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('reserved', 'i2')]
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head2_info = np.frombuffer(byteArray[ind:(ind+64)], dtype=head2_dtype)
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ind += 64
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# color table
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if head2_info['dataLengthOfColorTable'] != 0:
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table_R = np.frombuffer(byteArray[ind:(ind + 256)], dtype='u1')
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ind += 256
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table_G = np.frombuffer(byteArray[ind:(ind + 256)], dtype='u1')
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ind += 256
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table_B = np.frombuffer(byteArray[ind:(ind + 256)], dtype='u1')
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ind += 256
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# calibration table 定标数据块
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calibration_table = None
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if head2_info['dataLengthOfCalibration'] != 0:
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calibration_table = np.frombuffer(byteArray[ind:(ind + 2048)], dtype='u2')
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calibration_table = calibration_table * 0.01
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#if (np.array_equal(calibration_table[0::4], calibration_table[1::4]) and
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# np.array_equal(calibration_table[0::4], calibration_table[2::4]) and
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# np.array_equal(calibration_table[0::4], calibration_table[3::4])):
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# This is a trick, refer to http://bbs.06climate.com/forum.php?mod=viewthread&tid=89296
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# calibration_table = calibration_table[0::4]
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calibration_table = calibration_table[0::4]
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ind += 2048
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# geolocation table
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if head2_info['dataLengthOfGeolocation'] != 0:
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geolocation_dtype = [
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('coordinate', 'i2'),
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('source', 'i2'),
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('delta', 'i2'),
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('left_top_lat', 'i2'),
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('left_top_lon', 'i2'),
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('horizontalNumber', 'i2'),
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('verticalNumber', 'i2'),
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('reserved', 'i2')]
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geolocation_info = np.frombuffer(byteArray[ind:(ind+16)], dtype=geolocation_dtype)
|
||||
ind += 16
|
||||
geolocation_length = geolocation_info['horizontal_number'][0] * geolocation_info['vertical_number'][0] * 2
|
||||
geolocation_table = np.frombuffer(byteArray[ind:(ind+geolocation_length)], dtype='i2')
|
||||
ind += geolocation_length
|
||||
|
||||
# pad field
|
||||
pad_field = np.frombuffer(byteArray[ind:(ind+head1_info['padDataLength'][0])], dtype='u1')
|
||||
ind += head1_info['padDataLength'][0]
|
||||
|
||||
# retrieve data records
|
||||
data_len = (head1_info['dataRecordNumber'][0].astype(int) *
|
||||
head1_info['recordLength'][0])
|
||||
data = np.frombuffer(byteArray[ind:(ind + data_len)], dtype='u1', count=data_len)
|
||||
if calibration_table is not None:
|
||||
data = calibration_table[data]
|
||||
data.shape = (head1_info['dataRecordNumber'][0], head1_info['recordLength'][0])
|
||||
|
||||
# 由于数据是按照左上角开始放置, 为此需要对纬度顺序进行反转
|
||||
data = np.flip(data, axis=0)
|
||||
|
||||
# construct longitude and latitude coordinates
|
||||
# if use the verticalResolution and horizontalResolution, lon and lat will not be correct.
|
||||
#lat = (
|
||||
# head2_info['latitudeOfNorth'][0]/100. -
|
||||
# np.arange(head2_info['heightOfImage'][0])*head2_info['verticalResolution'][0]/100.)
|
||||
#lon = (
|
||||
# head2_info['longitudeOfWest'][0]/100. +
|
||||
# np.arange(head2_info['widthOfImage'][0])*head2_info['horizontalResolution'][0]/100.)
|
||||
lat = np.linspace(
|
||||
head2_info['latitudeOfSouth'][0]/100., head2_info['latitudeOfNorth'][0]/100.,
|
||||
num=head2_info['heightOfImage'][0])
|
||||
lon = np.linspace(
|
||||
head2_info['longitudeOfWest'][0]/100., head2_info['longitudeOfEast'][0]/100.,
|
||||
num=head2_info['widthOfImage'][0])
|
||||
|
||||
# construct time
|
||||
time = datetime(
|
||||
head2_info['year'][0], head2_info['month'][0],
|
||||
head2_info['day'][0], head2_info['hour'][0], head2_info['minute'][0])
|
||||
time = np.array([time], dtype='datetime64[ms]')
|
||||
|
||||
# define coordinates
|
||||
time_coord = ('time', time)
|
||||
lon_coord = ('lon', lon, {
|
||||
'long_name':'longitude', 'units':'degrees_east',
|
||||
'_CoordinateAxisType':'Lon', 'axis': "X"})
|
||||
lat_coord = ('lat', lat, {
|
||||
'long_name':'latitude', 'units':'degrees_north',
|
||||
'_CoordinateAxisType':'Lat', 'axis': "Y"})
|
||||
channel_coord = ('channel', [head2_info['channel'][0]],
|
||||
{'long_name':'channel', 'units':''})
|
||||
|
||||
# create xarray
|
||||
data = data[np.newaxis, np.newaxis, ...]
|
||||
varattrs = {
|
||||
'productCategory': head1_info['productCategory'][0], # 产品类型, 1:静止, 2:极轨, 3:格点, 4:离散, 5:图形和分析
|
||||
'formatString': head1_info['formatString'][0], # 产品格式名称
|
||||
'qualityFlag': head1_info['qualityFlag'][0], # 产品质量标识
|
||||
'satelliteName': head2_info['satelliteName'][0], # 卫星名称
|
||||
'flagOfProjection': head2_info['flagOfProjection'][0], # 投影方式, 0:未投影, 1:兰勃托, 2:麦卡托, 3:极射, 4:等经纬, 5:等面积
|
||||
'units': units}
|
||||
data = xr.Dataset({
|
||||
'image':(['time', 'channel', 'lat', 'lon'], data, varattrs)},
|
||||
coords={ 'time':time_coord, 'channel':channel_coord,
|
||||
'lat':lat_coord, 'lon':lon_coord})
|
||||
|
||||
# add attributes
|
||||
data.attrs['Conventions'] = "CF-1.6"
|
||||
data.attrs['Origin'] = 'MICAPS Cassandra Server'
|
||||
|
||||
# cache data
|
||||
if cache:
|
||||
with open(cache_file, 'wb') as f:
|
||||
pickle.dump(data, f, protocol=pickle.HIGHEST_PROTOCOL)
|
||||
|
||||
# return
|
||||
return data
|
||||
else:
|
||||
print("The productCategory is not supported.")
|
||||
# 解析字节数据
|
||||
data = resolve_awx_bytearray(byteArray, units=units)
|
||||
if data is None:
|
||||
return None
|
||||
|
||||
# cache data
|
||||
if cache:
|
||||
with open(cache_file, 'wb') as f:
|
||||
pickle.dump(data, f, protocol=pickle.HIGHEST_PROTOCOL)
|
||||
return data
|
||||
else:
|
||||
return None
|
||||
else:
|
||||
return None
|
||||
|
||||
directory = "SATELLITE/FY2G/L1/IR1/EQUAL/"
|
||||
data = get_fy_awx(directory, cache=False)
|
||||
|
||||
def get_fy_awxs(directory, filenames, allExists=True, pbar=False, **kargs):
|
||||
"""
|
||||
|
||||
Loading…
Reference in New Issue
Block a user