我从Keil保存了一个数据集(200个双数据值(，它变成了一个IntelHex格式的.hex文件，我在python中安装了IntelHex并加载了它。现在的问题是我不知道如何解释它，例如，这篇文章告诉您使用dict，但它不适用于包含双重数据的十六进制文件。我的代码：
from intelhex import IntelHex
ih = IntelHex() # create empty object
ih.loadhex('output.hex')
ihdict = ih.todict()
datastr = ""
startAddress = 536871952
while ihdict.get(startAddress) != None:
datastr += str("%0.2X" %ihdict.get(startAddress))
startAddress += 1
文件output.hex:
:020000042000DA
:0802A8003FB7809F5BC03F409F
:1002B000DFB56EEF5AB73F407F717CBF38BE3F401D
:1002C000DFD369EFE9B43F407F717CBF38BE3F4068
:1002D0003F895E9F44AF3F401F706A0F38B53F4073
:1002E0009F20584F10AC3F405F5F72AF2FB93F4027
:1002F000DFB56EEF5AB73F40DF5B7DEFADBE3F40ED
:10030000BFA364DF51B23F40DF62676FB1B33F40CC
:100310001F9E8C0F4FC63F405F0C6B2F86B53F4032
:10032000BF7542DF3AA13F403F4D689F26B43F4032
:100330009F2742CF13A13F40DF2D5BEF96AD3F409B
:100340009F915ACF48AD3F40DF874CEF43A63F40D7
:100350007FD2573FE9AB3F40FD721E7F398F3F4050
:10036000FF892FFFC4973F409D5311CFA9883F407D
:100370001F706A0F38B53F407F78663F3CB33F40FF
:100380001DFD148F7E8A3F401F954F8FCAA73F40A7
:100390005FC04D2FE0A63F401F0D3C8F069E3F40A3
:1003A0007F4A443F25A23F40DFE13DEFF09E3F40C2
:1003B0003F185C1F0CAE3F403F79379FBC9B3F40CE
:1003C000FF2F3EFF179F3F40DFBC586F5EAC3F40A2
:1003D000FD36287F1B943F403F3D419F9EA03F40FC
:1003E000FFFA317FFD983F409FF2354FF99A3F4029
:1003F0007D0511BF82883F40DF703B6FB89D3F4055
:10040000FF1143FF88A13F40DD60146F308A3F40F9
:100410001F49328F24993F407D230CBF11863F40F6
:100420009DBD29CFDE943F40BFED2EDF76973F4044
:10043000DDBA056FDD823F407D58183F2C8C3F4070
:100440007F3333BF99993F40DD9C0A6F4E853F4013
:100450007F3333BF99993F403DBC171FDE8B3F4030
:10046000BDF4185F7A8C3F403D16091F8B843F40D6
:100470003DE1FC9E707E3F40DD0D0DEF86863F40E6
:100480003F1F469F0FA33F403DFFF79EFF7B3F402E
:10049000DD42196FA18C3F40BDC6F65E637B3F40D5
:1004A0009D5AFB4EAD7D3F40FD4BE6FE25733F4020
:1004B0001D12D30E89693F403D8EF51EC77A3F401D
:1004C0003DBC171FDE8B3F409D7FE0CE3F703F401D
:1004D000BD6C055FB6823F40DD4903EFA4813F401C
:1004E000DD14F76E8A7B3F40DDF6FB6EFB7D3F40FF
:1004F000BD20E85E10743F40DD6EE86E37743F400B
:100500001DB1F78ED87B3F407DFCD33EFE693F4056
:100510009D4C274FA6933F407DD7EEBE6B773F4063
:10052000DDAADE6E556F3F401D55B38EAA593F4080
:100530009DF7CCCE7B663F40DDCFC3EEE7613F4009
:10054000BDF2C55EF9623F40BD7AD95EBD6C3F40E9
:100550005D90D82E486C3F40BD7AD95EBD6C3F405F
:100560003D67BD9EB35E3F403D0DCC9E06663F405D
:10057000BD88AD5EC4563F40BDA6A85E53543F4003
:10058000BDD4CA5E6A653F40FD95B0FE4A583F4003
:100590003DA3B39ED1593F405DF89D2EFC4E3F4098
:1005A000FD69E1FEB4703F40FD59BAFE2C5D3F404D
:1005B000BD63C8DE31643F407DB0B63E585B3F400E
:1005C0001DCD9F8EE64F3F405DEAC92EF5643F404A
:1005D000FD4993FEA4493F405D8E852EC7423F40B2
:1005E0007D4D88BE26443F401D3EA20E1F513F4018
:1005F0003D938C9E49463F40FD7E9F7EBF4F3F40CE
:10060000DDB1C8EE58643F40BD7F70DE3F383F40EB
:100610005DBCA72EDE533F409D4197CEA04B3F408F
:100620003D0B799E853C3F403D0B799E853C3F408C
:10063000BD7F70DE3F383F407D6B83BEB5413F409C
:10064000FD32827E19413F409D2A864E15433F4030
:10065000FDEFA1FEF7503F401DC4620E62313F40E6
:100660003D476F9EA3373F401D98930ECC493F40B6
:100670001D53608E29303F403DF4671EFA333F40E2
:100680003D048F1E82473F407D726D3EB9363F402C
:10069000FDF68B7EFB453F40FD5767FEAB333F4089
:1006A0005D7774AE3B3A3F40BD2C695E96343F4067
:1006B0009DF579CEFA3C3F40DD4C476EA6233F4086
:1006C0001D1E540E0F2A3F407DE36FBEF1373F40A1
:1006D000FD7C4C7E3E263F40DD8153EEC0293F40ED
:1006E0009D034ECE01273F409D1375CE893A3F4072
:1006F000DDC4336EE2193F407D444B3EA2253F40AE
:100700005DD84F2EEC273F407D0F3FBE871F3F40F7
:100710007DF143BEF8213F40BDE04B5EF0253F40F8
:100720005D8548AE42243F405DD84F2EEC273F40C8
:100730007D5B5CBE2D2E3F40FD40567E202B3F4012
:10074000BD514EDE28273F40FD2945FE94223F4003
:100750001DD9208E6C103F409DE552CE72293F403E
:100760003D91399EC81C3F407D16293E8B143F4069
:100770007D6930BE34183F409D9935CECC1A3F403C
:100780005DFD34AE7E1A3F409DB730CE5B183F40D2
:100790003DAF349E571A3F405D8C322E46193F4084
:1007A0003D81129E40093F405DC8282E64143F40A1
:1007B0007D34243E1A123F405DC13EAE601F3F4073
:1007C0003D2E0B1E97053F405D6E372EB71B3F40F9
:1007D0003D09269E04133F403DCD2F9EE6173F4026
:1007E000BD6F49DEB7243F403D5C2D1EAE163F4035
:1007F0001DE00A0E70053F403DBD089E5E043F406F
:100800009D890ECE44073F401D86190EC30C3F4004
:10081000BDD70EDE6B073F401DBB258EDD123F406E
:100820001DBB258EDD123F407D06023E03013F4089
:10083000BDD0245E68123F40FDA81B7ED40D3F4012
:100840005D14462E0A233F40FD12347E091A3F40B4
:100850009D180C4E0C063F401D681E0E340F3F4085
:100860001D510D8EA8063F403DD4191EEA0C3F4095
:100870001B94ED0DCAF63E405D40152EA00A3F4088
:100880009D64294EB2143F407DF82D3EFC163F403A
:100890001DD9208E6C103F405DE6232EF3113F40A2
:1008A0007DA526BE52133F40BDB913DEDC093F4093
:1008B0005D2904AE14023F40BBE5E2DD72F13E402B
:1008C0009D180C4E0C063F40BD84075EC2033F409E
:1008D0005D221A2E110D3F40FDC6167E630B3F4070
:0108E0009D7A
:00000001FF
假设数据表示要解码的64位浮点数字的列表，则过程是收集适当数量的八位字节，并将其解码为双字节。
重复使用您提出的结构：
from intelhex import IntelHex
import struct
ih = IntelHex()
ih.loadhex('output.hex')
ihdict = ih.todict()
# Read all the data into a long list of int octets
data = []
startAddress = 536871952
while ihdict.get(startAddress) is not None:
data.append(ihdict.get(startAddress))
startAddress += 1
# slice the list into 8-byte bytearrays
bin_arr = [bytearray(data[n:n+8]) for n in range(0, len(data), 8)]
# unpack each bytearray as a double
# Filter for 8 byte arrays because len(data) is not divisible by 8.
# Is the data properly aligned?
doubles_list = [struct.unpack('d', b) for b in bin_arr if len(b) == 8]
print(doubles_list)
值得一提的是，上面假设了big-endian字节排序。我相信您可以使用<作为格式定义的一部分来假设一个小的endian排序。更多信息请参阅struct.unpack文档。