Octave buffering console output - octave

I'm new to Octave.
I'm working with a huge matrix with thousands of rows and just 2 columns.
When I print it the terminal seems to be cutting part of it, but I want the whole thing to be shown.
Any way to do that or maybe to redirect the output in a file or something?
I had the same in problem in Eclipse but it was much easier to solve the same issue back then.

to redirect a Matrix to a file use save
A=rand(5,5)
A =
0.552789 0.570720 0.896715 0.982821 0.622758
0.704791 0.514302 0.380680 0.344768 0.459588
0.230478 0.744468 0.668264 0.659316 0.414710
0.819015 0.572315 0.404128 0.074092 0.813156
0.099461 0.031186 0.499781 0.758091 0.243281
save("-ascii","file-ascii.txt","A")
save("file-text.txt","A")
pure text
$ cat file-ascii.txt
5.52789005e-01 5.70719716e-01 8.96714571e-01 9.82821412e-01 6.22757836e-01
7.04791031e-01 5.14301972e-01 3.80679512e-01 3.44768074e-01 4.59587827e-01
2.30478499e-01 7.44467506e-01 6.68264066e-01 6.59315817e-01 4.14709954e-01
8.19014881e-01 5.72314594e-01 4.04128230e-01 7.40916214e-02 8.13155903e-01
9.94607828e-02 3.11857103e-02 4.99780576e-01 7.58091470e-01 2.43280663e-01
octave default style
$ cat file-text.txt
# Created by Octave 5.2.0, Wed May 27 21:43:22 2020 CEST <Marco#LAPTOP-82F08ILC>
# name: A
# type: matrix
# rows: 5
# columns: 5
0.55278900467056769 0.57071971558810353 0.89671457115004172 0.98282141203898321 0.622757835845834
0.70479103140202992 0.51430197227101204 0.3806795123567624 0.34476807355856237 0.45958782700965189
0.2304784985452572 0.74446750644064241 0.66826406647042169 0.65931581744685042 0.41470995365627888
0.81901488130574696 0.57231459443213173 0.40412823039438278 0.074091621362711677 0.81315590327055964
0.099460782832668723 0.031185710267227097 0.49978057628118927 0.75809147040922908 0.24328066285306477

Related

JQ 1.5: Timestamp / date transformation produce huge file

I using jq 1.5 under a Windows 10 powershell enviroment to transform json files and import them to a MS SQL database. The original json file is around 1,1mb. I stored the file here: Json origin file. I use following jq command to transform the data:
[.legs[] | {Legid: .legId, Farecode: .fareBasisCode, Travelduration: .travelDuration, Traveldistance: .totalTravelDistance, Distanceunit: .totalTravelDistanceUnits, Refundable: .isRefundable , Nonstop: .isNonStop, Departure_Airport: .segments[].departureAirportName, Departure_Code: .segments[].departureAirportCode, Arrival_Airport: .segments[].arrivalAirportName, Arrival_Code: .segments[].arrivalAirportCode, Departure_Time: .segments[].departureTimeEpochSeconds, Arrival_Time: .segments[].arrivalTimeEpochSeconds, Airline: .segments[].airlineName, Airline_Code: .segments[].airlineCode, Flight_Number: .segments[].flightNumber, Equipment: .segments[].equipmentDescription}]
That command produce following file transformed file. Now i had to transform the UNIX Timestamps to Dates. So i modified the command:
[.legs[] | {Legid: .legId, Farecode: .fareBasisCode, Travelduration: .travelDuration, Traveldistance: .totalTravelDistance, Distanceunit: .totalTravelDistanceUnits, Refundable: .isRefundable , Nonstop: .isNonStop, Departure_Airport: .segments[].departureAirportName, Departure_Code: .segments[].departureAirportCode, Arrival_Airport: .segments[].arrivalAirportName, Arrival_Code: .segments[].arrivalAirportCode, Departure_Time: .segments[].departureTimeEpochSeconds, Arrival_Time: .segments[].arrivalTimeEpochSeconds, Airline: .segments[].airlineName, Airline_Code: .segments[].airlineCode, Flight_Number: .segments[].flightNumber, Equipment: .segments[].equipmentDescription}] | .[].Departure_Time |= todate | .[].Arrival_Time |= todate
The transformed file without date Transformation have around 3 mb. After the date Transformation the file have around 40 mb. I think i have a logical error in my command, but cant find it. Tips?
Regards
Timo
Your use of iteration (.segments[]) causes multiplicative behavior: in your case, since there are four cases in which .segments|length is 2, you get a 2^10 expansion locally, four times.
In situations such as this, it would make sense to use a small but well-chosen subset of the data (or maybe more easily, an artificial dataset) to check the code.
Perhaps what you intended is something more like:
[ .legs[] | range(0; .segments|length) as $i | .... ]

Migrated from TCL8.4 to 8.5 and Im facing issues with the file sourcing

My application runs well on RHEL 5 (TCL 8.4). But in RHEL 7 64bit TCL8.5, the database files are not sourced in correctly. The application is by default pointing to the last record file in the db. Hence, im assuming it might be issue with the way 8.5 handles the file sourcing. So i created a file X and wrote the below code. (Please ignore dbname and /path, it works fine)
File X
#!/bin/sh
# \
exec tclsh "$0" "$#"
puts [package require Itcl]
namespace import ::itcl::*
puts $itcl::patchLevel
puts $itcl::library
set databases /dbname
set system ($databases,dbpath) /path
source File.class.tcl
source FareFile.class.tcl
puts [Fare.File formtitle]
source Record1File.class.tcl
puts [Fare.File formtitle]
I source FareFile in and print the form title(o/p: Fare Viewer) using the formtitle method which is declared in the File.class.tcl. And then I source Record1File and print the FareFile formtitle (the first one), its printing the form title of Record1File. The formtitle method is returning the value of the lastest sourced file. This does not happen in 8.4
File.class.tcl:
class File {
variable fileinfo
variable recordarray
variable allads_flag 0
variable updates_is_lastkey 0
method formtitle {} {
variable fileinfo
return $fileinfo(formtitle)
}
}
FareFile.class.tcl
FareFile ::Fare.File
::Fare.File parse_fields {
tabtitle "Fares"
formtitle "Fare Viewer"
}
Record1File.class.tcl
Record1File ::Record1.File
::Record1.File parse_fields {
tabtitle "Record 1"
formtitle "Record 1 Viewer"
output in 8.4 / RHEL 5: (Expected output in 8.5)
3.4
3.4.0
/path
Fare Viewer
Fare Viewer
output in 8.5 / RHEL 7:
3.4
3.4.3
/path
Fare Viewer
Record 1 Viewer
If you see the output on both platforms, its different. Please help
(This is only a tentative answer, given all the blanks of the question, but I need the formatting capabilities:)
Try the following pls., by rewriting the body script of method formtitle as follows:
class File {
method formtitle {} {
set v variable
$v fileinfo
return $fileinfo(formtitle)
}
}
... and report back by posting a comment.

How to compare two different matrices in two different file.mat in octave?

I need to compare two matrix in two different .mat files, I mean that i have two different files: file1.mat and file2.mat, In each file I have 3 matrix:
File1.mat =(M11, M12, M13)
FileƩ.mat =(M21, M22, M23)
I need to compare M11 and M21:
function [Matrice_Result]= difference ()
R1=importdata('file1.mat')
R2=importdata('file2.mat')
Matrice_Result= R1== R2
endfunction
The error that i found is:
error: binary operator '==' not implemented for 'scalar struct' by 'scalar struct' operations
error: called from differences at line 6 column 9
I would be very grateful if you could help me.
The easiest / most appropriate way to load data from a .mat file onto the workspace is via the load command. It allows you to import only a single variable (whose name you know) into the workspace.
You can do this by simply running the load command, without assigning to a variable:
>> load ('file1.mat', 'M11');
>> load ('file2.mat', 'M21');
>> whos
Variables in the current scope:
Attr Name Size Bytes Class
==== ==== ==== ===== =====
M11 1x3 24 double
M21 1x3 24 double
Total is 6 elements using 48 bytes
>> isequal (M11, M21)
ans = 1
However, if you do collect into a variable, this variable becomes a struct, whose fieldnames correspond to the names of the variables you imported, e.g.
>> S1 = load ('file1.mat', 'M11');
>> S2 = load ('file2.mat', 'M21');
>> isequal (S1.M11, S2.M21)
ans = 1

Data left out when reading json online to R

I try to read online json data to R through the below codes in R:
library('jsonlite')
address<-'https://data.cityofchicago.org/resource/qnmj-8ku6.json'
sample<-fromJSON(address)
The codes did run and have results in right format of a table. But only produced 1000 observations while the original city portal database has more than 200,000 observations. I am not sure what to be fixed to download the whole dataset. Please help.
You're using the wrong link to get the data. You can see the correct link by going to 'Export'
library(jsonlite)
address <- "https://data.cityofchicago.org/api/views/qnmj-8ku6/rows.json?accessType=DOWNLOAD"
sample <- fromJSON(address)
length(sample)
# [1]
length(sample[[2]])
# [1] 274228
Although, you may want to get it as a .csv to make it easier to work with straight away?
address <- "https://data.cityofchicago.org/api/views/qnmj-8ku6/rows.csv?accessType=DOWNLOAD"
sample_csv <- read.csv(address)
nrow(sample_csv)
# [1] 274228
str(sample_csv)
# 'data.frame': 274228 obs. of 22 variables:
# $ ID : int 10512552 10517063 10517120 10518590 10518648
# $ Case.Number : Factor w/ 274219 levels "HA107183","HA156050",..
# $ Date : Factor w/ 112977 levels "01/01/2014 01:00:00 AM",..
# $ Block : Factor w/ 27499 levels "0000X E 100TH PL",..
# $ IUCR : Factor w/ 331 levels "0110","0141",..
# $ Primary.Type : Factor w/ 33 levels "ARSON","ASSAULT",..
# $ Description : Factor w/ 310 levels "$500 AND UNDER",..
# ... etc

Unknown CRC Calculation

I'm trying to reverse engineer the communication protocol from an old serial device. I've figured out most of it, but am stuck on the CRC algorithm used. I have host software that I can generate request messages, so I've included a dump of relatively short messages sent by the host software.
This appears to be entirely ASCII based, and is marginally similar to a modbus-type protocol in that it requests register data using an addressing scheme. Numbers are represented using ascii characters corresponding to a readable hex value.
| | 001| 08| 001| E948| |
|Header|Device|Func|Reg |Checksum|trailer|
| Char | Addr |code|Addr| ??? | Char |
| 0x0C | hex |hex |hex | | 0x0D |
Here are a bunch of request messages (host->device) for individual registers. Some registers are not valid, so this is not entirely contiguous, but for the most part, these requests only differ by one character/"digit". I'm 99.9% sure the last 4 characters are the checksum, but I cant figure out how they are calculated. I've tried the usual algorithms, and didn't have much luck with the CRC reveng program (although I was probably doing something wrong). Any thoughts would be greatly appreciated.
00108001E948
00108002DBD3
00108003CA5A
00108004BEE5
00108005AF6C
001080087489
0010800FEE70
00108010E119
00108011F090
00108012C20B
00108013D382
00108014A73D
00108015B6B4
0010801795A6
001080186D51
001080197CD8
0010801A8317
0010801BB18C
0010801CA005
0010801DD4BA
0010801EC533
00108022E863
00108023F9EA
00108026AE47
00108027BFCE
001080284739
0010802956B0
0010802AA97F
0010802B9BE4
0010802C8A6D
0010802DFED2
0010802EEF5B
00108032F1BB
00108033E032
00108034948D
0010803FC418
001080409FA1
001080418E28
00108042BCB3
00108043AD3A
00108044D985
00108045C80C
00108047EB1E
0010804813E9
001080490260
0010804AFDAF
0010804BCF34
0010804CDEBD
0010804DAA02
0010804EBB8B
0010804F8910
001080508679
00108053B4E2
00108054C05D
00108055D1D4
00108056E34F
00108057F2C6
001080580A31
001080591BB8
0010805AE477
0010805BD6EC
0010805CC765
0010805DB3DA
0010805F90C8
00108060AC11
00108061BD98
001080628F03
00108066C927
00108067D8AE
001080682059
0010806931D0
0010806ACE1F
0010806BFC84
0010806CED0D
0010806D99B2
0010806E883B
0010806FBAA0
00108070B5C9
001080783981
001080792808
0010807AD7C7
0010807BE55C
0010807CF4D5
0010807D806A
001080803601
001080812788
001080821513
001080847025
0010808561AC
001080865337
0010808742BE
00108088BA49
00108089ABC0
0010808A540F
0010808B6694
0010808C771D
0010808D03A2
0010808E122B
0010808F20B0
001080902FD9
001080913E50
001080920CCB
001080931D42
0010809469FD
001080964AEF
001080975B66
00108098A391
00108099B218
0010809A4DD7
0010809B7F4C
0010809C6EC5
0010809D1A7A
0010809F3968
001080A011DD
001080A10054
001080A232CF
001080A32346
001080A457F9
001080A54670
001080A674EB
001080AA73D3
001080AB4148
001080AC50C1
001080AD247E
001080B218A7
001080B3092E
001080B47D91
001080B56C18
001080B65E83
001080B74F0A
001080B8B7FD
001080B9A674
001080BA59BB
001080BB6B20
001080BC7AA9
001080BD0E16
001080BE1F9F
001080BF2D04
001080C0226D
001080C133E4
001080C2017F
001080C310F6
001080C46449
001080C575C0
001080C6475B
001080C756D2
001080C8AE25
001080CC6371
001080CD17CE
001080CE0647
001080CF34DC
001080D24C77
001080D35DFE
001080D42941
001080D538C8
001080D60A53
001080D71BDA
001080D8E32D
001080D9F2A4
001080DA0D6B
001080DB3FF0
001080DC2E79
001080DD5AC6
001080DE4B4F
001080DF79D4
001080E16734
001080E255AF
001080E34426
001080E6138B
001080E70202
001080E8FAF5
001080E9EB7C
001080EA14B3
001080EB2628
001080EC37A1
001080ED431E
001080EE5297
001080EF600C
001080F05CD5
001080F14D5C
001080F27FC7
001080F36E4E
001080F9C114
001080FA3EDB
001080FB0C40
001080FC1DC9
001080FD6976
001080FE78FF
00108104E439
00108105F5B0
00108106C72B
00108107D6A2
0010810E8637
0010810FB4AC
00108110BBC5
00108111AA4C
00108118378D
001081192604
0010811AD9CB
0010811BEB50
0010811CFAD9
0010811D8E66
0010811E9FEF
0010811FAD74
0010812091AD
001081218024
00108122B2BF
00108123A336
00108124D789
00108125C600
00108127E512
001081281DE5
001081290C6C
0010812AF3A3
0010812BC138
00108135DFD8
00108136ED43
00108137FCCA
00108138043D
0010813915B4
0010813AEA7B
0010813BD8E0
0010813CC969
0010813DBDD6
0010813EAC5F
0010813F9EC4
00108140C57D
00108141D4F4
00108142E66F
00108143F7E6
0010814592D0
00108146A04B
0010814AA773
0010814B95E8
0010814C8461
0010814DF0DE
0010814EE157
0010814FD3CC
00108150DCA5
00108151CD2C
00108152FFB7
00108153EE3E
001081549A81
001081558B08
00108156B993
00108157A81A
001081594164
0010815ABEAB
00108172CC07
00108173DD8E
00108174A931
00108175B8B8
001081779BAA
00108178635D
0010818609EB
001081871862
00108188E095
00108189F11C
0010818A0ED3
0010818B3C48
0010818C2DC1
0010818D597E
0010818E48F7
0010818F7A6C
001081907505
00108191648C
001081925617
00108193479E
001081943321
0010819522A8
001081961033
0010819701BA
00108198F94D
00108199E8C4
0010819A170B
0010819B2590
0010819C3419
0010819D40A6
0010819E512F
0010819F63B4
001081A04B01
001081A15A88
001081A26813
001081A3799A
001081A40D25
001081A51CAC
001081A62E37
001081A73FBE
001081A8C749
001081A9D6C0
001081AA290F
001081AB1B94
001081AD7EA2
001081AE6F2B
001081AF5DB0
001081B06169
001081B170E0
001081B715D6
001081B9FCA8
001081BA0367
001081BB31FC
001081BC2075
001081BD54CA
001081BE4543
001081BF77D8
001081C078B1
001081C16938
001081C25BA3
001081C34A2A
001081C43E95
001081C52F1C
001081C61D87
001081C70C0E
001081C8F4F9
001081C9E570
001081CA1ABF
001081CC39AD
001081CD4D12
001081D9A878
001081E02C61
001081E13DE8
001081E20F73
001081E31EFA
001081E46A45
001081E57BCC
001081E64957
001081E758DE
001081E8A029
001081EA4E6F
001081EB7CF4
001081EF3AD0
001081F00609
001081F11780
001081F4402D
001081F551A4
001081FC4715
001081FD33AA
001082037FE2
001082040B5D
001082051AD4
00108206284F
0010820739C6
00108208C131
00108209D0B8
0010820A2F77
0010820E6953
0010820F5BC8
0010821054A1
001082114528
0010821277B3
00108217201E
00108218D8E9
00108219C960
0010821C15BD
0010821D6102
0010821E708B
0010821F4210
001082207EC9
001082261BFF
0010822B2E5C
0010822D4B6A
0010822E5AE3
001082306711
001082317698
001082324403
00108233558A
001082342135
0010823530BC
001082360227
0010823713AE
00108238EB59
00108239FAD0
0010823A051F
0010823B3784
00108258BF89
00108259AE00
0010825A51CF
0010825B6354
0010825C72DD
0010825D0662
0010825E17EB
0010825F2570
0010826019A9
001082610820
001082623ABB
001082632B32
001082645F8D
0010826B493C
0010826C58B5
0010826D2C0A
0010826E3D83
0010826F0F18
001082700071
0010827111F8
001082722363
0010827332EA
001082744655
001082766547
0010827774CE
001082788C39
001082799DB0
0010827A627F
0010827B50E4
0010827C416D
0010827D35D2
0010827E245B
0010827F16C0
0010828083B9
001082819230
00108282A0AB
00108284C59D
00108285D414
00108286E68F
00108287F706
001082880FF1
001082891E78
00108294DC45
00108295CDCC
0010829907A0
0010829AF86F
0010829BCAF4
0010829CDB7D
0010829DAFC2
-----More data request strings-----
Here are some more data strings that I haven't fully decoded Again, the first 3 characters are the slave address (similar to a modbus device address scheme), the next 2 characters are the function code. "10" is a data buffer request and I have not decoded this. Interestingly, there are non-numeric characters in this particular request, which is probably a big clue as to the underlying checksum calculation.
00110PPF3000000500351
00210PPF300000050DB2F
00310PPF3000000509305
00410PPF30000005063C2
00510PPF3000000502BE8
00610PPF300000050F396
00710PPF300000050BBBC
00810PPF3000000501A09
00910PPF3000000505223
00A10PPF30000005077DE
00B10PPF300000050AFA0
00C10PPF300000050E78A
00D10PPF300000050174D
00E10PPF3000000505F67
00F10PPF3000000508719
01010PPF300000050C56B
01110PPF3000000508D41
01210PPF300000050553F
01310PPF3000000501D15
10010PPF3000000505B74
00110PF3F30000005017B2
00210PF3F3000000508D93
00310PF3F3000000500383
00410PF3F300000050B1C0
00510PF3F3000000503FD0
00610PF3F300000050A5F1
00710PF3F3000000502BE1
00810PF3F300000050C966
00910PF3F3000000504776
00A10PF3F3000000506B39
00B10PF3F300000050F118
00C10PF3F3000000507F08
00D10PF3F300000050CD4B
00E10PF3F300000050435B
00F10PF3F300000050D97A
01010PF3F30000005089AD
01110PF3F30000005007BD
01210PF3F3000000509D9C
01310PF3F300000050138C
10010PF3F3000000506145
00110SPF30000005084BF
00210SPF3000000505CC1
00310SPF30000005014EB
00410SPF300000050E42C
00510SPF300000050AC06
00610SPF3000000507478
00710SPF3000000503C52
00810SPF3000000509DE7
00910SPF300000050D5CD
00A10SPF300000050F030
00B10SPF300000050284E
00C10SPF3000000506064
00D10SPF30000005090A3
00E10SPF300000050D889
00F10SPF30000005000F7
01010SPF3000000504285
01110SPF3000000500AAF
01210SPF300000050D2D1
01310SPF3000000509AFB
10010SPF300000050DC9A
Function code "09" is a contiguous parameter group, which as best I can tell is followed by the register address (3 numeric characters, followed by the register count, also 3 numeric characters)
0010900013F5CB6 <==== Error, should be 0010900103F5CB6
0020900103F8AB1
0030900103FC74C
0040900103F2EAE
0050900103F6353
0060900103FB554
0070900103FF8A9
0080900103F6E81
0090900103F237C
00A0900103FB278
00B0900103F647F
00C0900103F2982
00D0900103FC060
00E0900103F8D9D
00F0900103F5B9A
0100900103F3D6C
0110900103F7091
0120900103FA696
0130900103FEB6B
1000900103F44DA
0010904003E5F86
0020904003E8981
0030904003EC47C
0040904003E2D9E
0050904003E6063
0060904003EB664
0070904003EFB99
0080904003E6DB1
0090904003E204C
00A0904003EB148
00B0904003E674F
00C0904003E2AB2
00D0904003EC350
00E0904003E8EAD
00F0904003E58AA
0100904003E3E5C
0110904003E73A1
0120904003EA5A6
0130904003EE85B
1000904003E47EA
Here you go, in C:
#include <stddef.h>
unsigned crc16old(unsigned crc, unsigned char *buf, size_t len)
{
int k;
if (buf == NULL)
return 0xffff;
while (len--) {
crc ^= *buf++;
for (k = 0; k < 8; k++)
crc = crc & 1 ? (crc >> 1) ^ 0x8408 : crc >> 1;
}
return crc;
}
You call crc16old() with buf equal to NULL to get the initial CRC. Then you update the CRC using the routine with a series of buffers and lengths.
I don't have a slam dunk for you, but here are some observations:
First off, the 4-byte messages all map to unique 2-byte suffixes (the possible CRCs) in your example, although none of the messages are repeated in the snippet provided, so that doesn't prove a great deal. It would be worth looking to see whether the same messages get the same 2-byte suffix on every run.
Initially, trying out searching with reveng with the 4 data-points the documentation said were needed looked promising:
>reveng -w 16 -s 00108001E948 00108002DBD3 00108003CA5A 00108004BEE5
width=16 poly=0x1189 init=0x18b1 refin=false refout=false xorout=0x0000 check=0xa5c2 name=(none)
Then testing this with the next messages in your list:
>reveng -w 16 -c -p 1189 -i 18b1 00108005
af6c
>reveng -w 16 -c -p 1189 -i 18b1 00108008
7489
which was correct, but unfortunately stopped being correct once we hit 0010800FEE70.
It occurs to me though that the init parameter here may be variable, possibly based on other data being transmitted, so I wrote the following Python script to see how messages got grouped based on the init, if we assume the poly remains the same:
import subprocess
with open("unknownprotocol.txt") as f:
inits = dict()
for line in f.readlines():
line = line.rstrip('\n')
if len(line) < 12:
continue
data = line[0:8]
cmd = "reveng -w 16 -p 1189 -s " + line[0:12]
process = subprocess.Popen(cmd.split(), stdout=subprocess.PIPE)
result = process.communicate()[0]
loc = result.find("init=0x")
init = result[loc+7:loc+11].upper()
if inits.has_key(init):
inits[init].append(line)
else:
inits[init] = [line]
morethanfour = 0
for key in sorted(inits):
print "%s: %s" % (key, inits[key])
if(len(inits[key]) > 4):
morethanfour += 1
print morethanfour, "inits with more than 4 data-points"
With that, we get the following output:
0026: ['001080EC37A1']
0567: ['0010811CFAD9']
06E1: ['001081BB31FC', '001081BD54CA', '001081BF77D8']
0AF6: ['001080AA73D3']
0F7A: ['0010819C3419']
0FB7: ['0010815ABEAB']
1030: ['0010826C58B5']
10E9: ['00108294DC45', '00108295CDCC', '0010829907A0']
11F3: ['001080DB3FF0', '001080DD5AC6', '001080DF79D4']
1275: ['0010807CF4D5']
12AC: ['001080803601', '001080812788', '001080821513', '001080847025', '0010808561AC', '001080865337', '0010808742BE', '00108088BA49', '00108089ABC0']
149B: ['001080FA3EDB', '001080FE78FF']
17D0: ['0010809B7F4C', '0010809D1A7A', '0010809F3968']
188C: ['001081EB7CF4', '001081EF3AD0']
18B1: ['00108001E948', '00108002DBD3', '00108003CA5A', '00108004BEE5', '00108005AF6C', '001080087489']
1AE0: ['0010822E5AE3']
1AF4: ['0010821054A1', '001082114528', '0010821277B3', '00108217201E', '00108218D8E9', '00108219C960']
1DCD: ['0010801BB18C', '0010801DD4BA']
1E4B: ['001080BC7AA9']
1F88: ['0010820F5BC8']
231B: ['0010814C8461']
23D6: ['0010818A0ED3', '0010818E48F7']
25E1: ['001081F00609', '001081F11780', '001081F4402D', '001081F551A4']
29CB: ['0010810E8637']
29DF: ['00108135DFD8', '00108136ED43', '00108137FCCA', '00108138043D', '0010813915B4']
2CA3: ['0010812BC138']
2E4B: ['001081CD4D12']
3409: ['0010802C8A6D']
36E1: ['001080CC6371']
39F4: ['0010825B6354', '0010825D0662', '0010825F2570']
3B8C: ['001081A04B01', '001081A15A88', '001081A26813', '001081A3799A', '001081A40D25', '001081A51CAC', '001081A62E37', '001081A73FBE', '001081A8C749', '001081A9D6C0']
3BB1: ['0010804BCF34', '0010804DAA02', '0010804F8910']
3C9C: ['0010827A627F', '0010827E245B']
3ECD: ['001080508679', '00108053B4E2', '00108054C05D', '00108055D1D4', '00108056E34F', '00108057F2C6', '001080580A31', '001080591BB8']
3ED9: ['0010806ACE1F', '0010806E883B']
4382: ['0010813CC969']
456C: ['001081BA0367', '001081BE4543']
4946: ['00108140C57D', '00108141D4F4', '00108142E66F', '00108143F7E6', '0010814592D0', '00108146A04B']
497B: ['001080AB4148', '001080AD247E']
4FBC: ['001081FC4715']
527E: ['001080DA0D6B', '001080DE4B4F']
545D: ['0010809A4DD7']
5490: ['0010805CC765']
5716: ['001080FB0C40', '001080FD6976']
596D: ['0010822B2E5C', '0010822D4B6A']
5B01: ['001081EA4E6F']
5B28: ['0010803FC418']
5C05: ['0010820A2F77', '0010820E6953']
5C11: ['001082306711', '001082317698', '001082324403', '00108233558A', '001082342135', '0010823530BC', '001082360227', '0010823713AE', '00108238EB59', '00108239FAD0']
5CBC: ['001080C0226D', '001080C133E4', '001080C2017F', '001080C310F6', '001080C46449', '001080C575C0', '001080C6475B', '001080C756D2', '001080C8AE25']
5E40: ['0010801A8317', '0010801EC533']
5E54: ['00108022E863', '00108023F9EA', '00108026AE47', '00108027BFCE', '001080284739', '0010802956B0']
605B: ['0010818B3C48', '0010818D597E', '0010818F7A6C']
6304: ['001081D9A878']
6527: ['001081907505', '00108191648C', '001081925617', '00108193479E', '001081943321', '0010819522A8', '001081961033', '0010819701BA', '00108198F94D', '00108199E8C4']
6A46: ['0010810FB4AC']
6A7B: ['001080E16734', '001080E255AF', '001080E34426', '001080E6138B', '001080E70202', '001080E8FAF5', '001080E9EB7C']
6DC6: ['001081CA1ABF']
6F2E: ['0010812AF3A3']
6F3A: ['00108110BBC5', '00108111AA4C', '00108118378D', '001081192604']
70A9: ['0010821C15BD']
7416: ['001080B218A7', '001080B3092E', '001080B47D91', '001080B56C18', '001080B65E83', '001080B74F0A', '001080B8B7FD', '001080B9A674']
7828: ['00108070B5C9', '001080783981', '001080792808']
783C: ['0010804AFDAF', '0010804EBB8B']
78F1: ['0010808C771D']
7A6D: ['0010826019A9', '001082610820', '001082623ABB', '001082632B32', '001082645F8D']
7A79: ['0010825A51CF', '0010825E17EB']
7AB4: ['0010829CDB7D']
7D54: ['0010806BFC84', '0010806D99B2', '0010806FBAA0']
7F11: ['0010827B50E4', '0010827D35D2', '0010827F16C0']
8081: ['001081C078B1', '001081C16938', '001081C25BA3', '001081C34A2A', '001081C43E95', '001081C52F1C', '001081C61D87', '001081C70C0E', '001081C8F4F9', '001081C9E570']
8269: ['0010812091AD', '001081218024', '00108122B2BF', '00108123A336', '00108124D789', '00108125C600', '00108127E512', '001081281DE5', '001081290C6C']
827D: ['0010811AD9CB', '0010811E9FEF']
8715: ['0010813BD8E0', '0010813DBDD6', '0010813F9EC4']
873C: ['001080EA14B3', '001080EE5297']
8860: ['0010819A170B', '0010819E512F']
8B2B: ['001081FD33AA']
8DEC: ['001080AC50C1']
9007: ['0010805BD6EC', '0010805DB3DA', '0010805F90C8']
9381: ['001080FC1DC9']
9546: ['001081AB1B94', '001081AD7EA2', '001081AF5DB0']
956F: ['0010807AD7C7']
957B: ['001080409FA1', '001080418E28', '00108042BCB3', '00108043AD3A', '00108044D985', '00108045C80C', '00108047EB1E', '0010804813E9', '001080490260']
972A: ['0010826E3D83']
973E: ['00108258BF89', '00108259AE00']
9951: ['001080BA59BB', '001080BE1F9F']
A401: ['0010814AA773', '0010814EE157']
A415: ['00108172CC07', '00108173DD8E', '00108174A931', '00108175B8B8', '001081779BAA', '00108178635D']
A4CC: ['0010818C2DC1']
A82B: ['001081B06169', '001081B170E0', '001081B715D6', '001081B9FCA8']
B142: ['001082037FE2', '001082040B5D', '001082051AD4', '00108206284F', '0010820739C6', '00108208C131', '00108209D0B8']
B156: ['0010823A051F']
B1FB: ['001080CE0647']
B307: ['00108010E119', '00108011F090', '00108012C20B', '00108013D382', '00108014A73D', '00108015B6B4', '0010801795A6', '001080186D51', '001080197CD8']
B313: ['0010802AA97F', '0010802EEF5B']
B43E: ['0010821D6102', '0010821F4210']
B646: ['001081E02C61', '001081E13DE8', '001081E20F73', '001081E31EFA', '001081E46A45', '001081E57BCC', '001081E64957', '001081E758DE', '001081E8A029']
B67B: ['0010800FEE70']
B91A: ['001080902FD9', '001080913E50', '001080920CCB', '001080931D42', '0010809469FD', '001080964AEF', '001080975B66', '00108098A391', '00108099B218']
B9C3: ['0010806CED0D']
BB5F: ['0010828083B9', '001082819230', '00108282A0AB', '00108284C59D', '00108285D414', '00108286E68F', '00108287F706', '001082880FF1', '001082891E78']
BB86: ['0010827C416D']
BC66: ['0010808B6694', '0010808D03A2', '0010808F20B0']
BE23: ['0010829BCAF4', '0010829DAFC2']
BF39: ['001080D24C77', '001080D35DFE', '001080D42941', '001080D538C8', '001080D60A53', '001080D71BDA', '001080D8E32D', '001080D9F2A4']
C1F0: ['0010811BEB50', '0010811D8E66', '0010811FAD74']
C276: ['001081BC2075']
C48C: ['00108104E439', '00108105F5B0', '00108106C72B', '00108107D6A2']
C498: ['0010813AEA7B', '0010813EAC5F']
C4B1: ['001080EB2628', '001080ED431E', '001080EF600C']
CBED: ['0010819B2590', '0010819D40A6', '0010819F63B4']
CE91: ['0010818609EB', '001081871862', '00108188E095', '00108189F11C']
D1DB: ['001082700071', '0010827111F8', '001082722363', '0010827332EA', '001082744655', '001082766547', '0010827774CE', '001082788C39', '001082799DB0']
D347: ['0010809C6EC5']
D38A: ['0010805AE477']
D39E: ['00108060AC11', '00108061BD98', '001080628F03', '00108066C927', '00108067D8AE', '001080682059', '0010806931D0']
D4A7: ['0010826B493C', '0010826D2C0A', '0010826F0F18']
D564: ['001080DC2E79']
D6CB: ['001081AA290F', '001081AE6F2B']
D6E2: ['0010807BE55C', '0010807D806A']
D95A: ['0010801CA005']
DADC: ['001080BB6B20', '001080BD0E16', '001080BF2D04']
E2F0: ['00108150DCA5', '00108151CD2C', '00108152FFB7', '00108153EE3E', '001081549A81', '001081558B08', '00108156B993', '00108157A81A', '001081594164']
E78C: ['0010814B95E8', '0010814DF0DE', '0010814FD3CC']
E7B1: ['001080A011DD', '001080A10054', '001080A232CF', '001080A32346', '001080A457F9', '001080A54670', '001080A674EB']
EADC: ['001081CC39AD']
F09E: ['0010802B9BE4', '0010802DFED2']
F276: ['001080CD17CE', '001080CF34DC']
F2DB: ['0010823B3784']
F5E2: ['00108032F1BB', '00108033E032', '00108034948D']
F7A7: ['001082207EC9', '001082261BFF']
F7B3: ['0010821E708B']
F9DC: ['001080F05CD5', '001080F14D5C', '001080F27FC7', '001080F36E4E', '001080F9C114']
FD63: ['0010825C72DD']
FDAE: ['0010829AF86F']
FF26: ['0010804CDEBD']
FFEB: ['0010808A540F', '0010808E122B']
29 inits with more than 4 data-points
29 cases where there are more than 4 data-points with the same init suggests that there might be something in this.
I'd suggest having a look at the data again and seeing whether it's possible that the CRC is XOR-ed with other data, e.g. data sent by the other side, or with some value that is sent at the start of a conversation, etc. as that would explain init being different in different cases.
Well, this one still has me perplexed. Fortunately I can generate lots of request messages, so I figured I'd do just that and see if I could get some checksum duplicates (the hope being some pattern in the message would pop out at me). I let it run for a few hours generating about 10000 unique messages. Of that, there were 484 duplicate checksums that were each generated by 2-3 different messages.
Recapping the general message structure as I understand it:
[005=device address, ascii encoded hex number][08=function code][2BD=register address, ascii encoded hex number][AB02=checksum]
function code 09 has the register address followed by the register count (also 3 characters)
Here is an interesting set of (4) messages. The 2 different checksums differ by one. I've been staring at these for a while and cant see the correlation. (These messages are ASCII strings, although these particular ones only contain 0-F characters)
005082BDAB02
00D09092002AB02
00B0827AAB03
00D08168AB03
Here are the hex equivalents of the above
303035303832424441423032
303044303930393230303241423032
303042303832374141423033
303044303831363841423033
reveng came up with "no models found" on these 4 messages, but I'm no expert on using that tool.
Heres a bunch more distinct messages that collide to the same checksum
0110930C0020014
005081FF0014
006081A5007D
011091CC002007D
007081870098
011090D60020098
00B0934A002009C
00D09058002009C
00A08284009C
01109004002010D
008080AD010D
009082170146
011090F50010146
005080A00171
00F092820010171
005082370302
00B092A40020302
00C0932E0020415
00A080F90415
00B093340020452
00A082A60452
011090CC0020456
007081A50456
00D0915800204B7
00B0924A00204B7
011091040020526
009080AD0526
004090FE001053C
00D0801D053C
0110907A00205C3
0080808F05C3
004092940020600
007081E30600
00D08124060F
00F0934C002060F
009083450630
00B090AC0020630
00B0919E0020652
00C0935E0010652
01009002002076A
00D0805B076A
00D0934800207A5
00B0905A00207A5
00B082A6082F
00B09034002082F
00D0811A084D
00B08208084D
00C0828408CA
00B0914A00208CA
00D0925800208CA
0050818708CE
011092D600208CE
007080A00927
00F090820010927
011090C40020932
009080F70932
010090BE0020975
009082A80975
00B091920020B36
007082B90B36
00B090A40020B54
007082370B54
007080E60B71
00C0918C0020B71
00D090480020BD8
00B0935A0020BD8
00B082840CE1
00B0904A0020CE1
00D093580020CE1
011091C40020D19
008080F70D19
008082A80D5E
010091BE0020D5E
0080833F0E0F
00B0902C0020E0F
00F0915A0010EF0
00A090880020EF0
006082B90F1D
00B090920020F1D
006080E60F5A
00C0908C0020F5A
006082370F7F
00B091A40020F7F
00D080F91092
010091AE0021092
0080819C116A
00C09060002116A
0110919E00212BC
009080C112BC
0100935E00112BC
0070835A13E9
0100913E00213E9
00C092C200213ED
00A0805B13ED
00B082941539
00F092BA0021539
00D092D8002167D
00B091EA002167D
0100916300116F9
009080EC16F9
0080832F17D7
00F092DC00217D7
00F091BA0021944
00A082941944
00D09168002197B
00B0927A002197B
0060825D1AB3
00F092D40021AB3
00F090740021ADA
005082271ADA
0040921E0021ADE
00C081241ADE
00F092250011AF8
007080E71AF8
00810E01F3F3000000501B88
009081EF1B88
00C090C20021BBB
00C0805B1BBB
0100933E0021BBF
0050835A1BBF
006081971D6B
004092D00021D6B
00C091C20021F90
00B0805B1F90
00F0927C0021FE8
009083551FE8
00A081272013
0040901E0012013
00F091BC0022032
007080D52032
010092F20022157
007081CC2157
00D091B80022198
00B092CA0022198
00B082382225
00D0812A2225
01009242002222C
01109082001222C
00B0811B222C
0110925C002226B
00D08074226B
008080C2240C
0100925E002240C
006080D52419
00F090BC0022419
005080CF2470
00F0921C0022470
00C092EE0022511
00C082D32511
00D08126251D
00B09225001251D
010093420022607
00C0811B2607
00608262266A
00C0927E002266A
00D092C800226A1
00B091BA00226A1
00F0911C002280D
006080CF280D
005081CC2901
010090F20022901
00F090B4002297D
008081C7297D
004090EE0022A6B
0080826F2A6B
010090420022A7A
011092820012A7A
00B092BA0022ADC
00D091C80022ADC
00C092720022B0E
008083102B0E
007080CF2C26
00F0901C0022C26
00D0804A2C29
00B083582C29
009080DA2C33
010092FE0022C33
00C090EE0022D47
00A082D32D47
00C0922D0012E1E
006080A22E1E
010091420022E51
00A0811B2E51
00D092080022E9E
00B0911A0022E9E
010090940012F0F
011092540022F0F
00B0933A0023040
00D090280023040
008081B730A5
00B0924400230A5
00C0804A30F8
00F0909200230F8
00B0829A3113
00D081883113
00E090880023115
00B0915A0013115
00C081B931FF
010092EE00231FF
00A082873207
00C0918A0023207
0040929A0023212
002091880023212
008081F132F3
00F0918E00232F3
00D092380023352
00B0912A0023352
00D09128002346B
00B0923A002346B
00B09344002348E
009081B7348E
00F0919200234D3
00B0804A34D3
00C0929A002353E
00E09188002353E
00B0834835F1
00D0805A35F1
00C0924200236C2
00B0908200136C2
00F0908E00236D8
009081F136D8
00B090FC00236EC
00A0807436EC
00D093380023779
00B0902A0023779
0090827F3798
011092EC0023798
00C0919A0023943
00E092880023943
00A081B939A9
010090EE00239A9
00A0813739CB
0110931C00139CB
00C082873A51
00E090980023A51
006080EB3AF9
00610F01PF3000000503AF9
011092E40023AFC
0070830D3AFC
003093280023B11
00910F01F3F3000000503B11
00B092540023BE1
00C090940013BE1
00D0813A3BFD
00B082283BFD
005081E03CCD
004090940013CCD
002092880023E6F
0040919A0023E6F
00B082873E7A
00E091980023E7A
00C090420023E94
00B092820013E94
011092CC0013E9B
005081A63E9B
00C080743EBA
00B092FC0023EBA
008082053FA7
0110926C0023FA7
00510F01F3F3000000503FF1
004092FE0023FF1
010090DA0024001
007083364001
00A080354005
00C093460024005
002090480024109
0040935A0024109
00C0904A002411C
00E09358002411C
004091FA0024160
00A0814C4160
0040914A002421B
00209258002421B
00B082FA421F
00F09320002421F
002090F80024272
007081A14272
00F091A00024276
00A082A24276
00C092AC002439B
00508086439B
0080808B43E7
0040933C00243E7
00C09017001440C
00B082D5440C
00E092580024537
00C0914A0024537
00D0816A4545
00B082784545
00C0935A0024625
00E090480024625
005081FB4630
002093580024630
0040904A0024630
00C082FA4634
00F092200024634
00A0828046B8
00F091FE00246B8
00E090A80014719
0060820F4719
0040923C00247CC
0090808B47CC
0100907A002483E
0060834E483E
005083364857
010092DA0024857
00E09158002494A
00C0924A002494A
00F09330002495B
00608233495B
00B091AE0024981
00910SF3F3000000504981
005081A14A24
002092F80024A24
006081FB4A4D
0040934A0024A4D
002090580024A4D
008081AE4A58
00E093480024A58
00C0905A0024A58
004090A60014A68
0060830C4A68
00B082804AC5
00F092FE0024AC5
00C0919E0024BAF
00B0935E0014BAF
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