Parsing Vector Data

May 14, 2024 · 10 minutes to read

Introduction

This guide should help you decoding the burst messages received after enabling the “Burst data into a single message” feature. This is a new feature in the Treon Aito Release 7.3 for Gateway that is disabled by default. This feature can be enable from here: Data Parsing

Decoding

Base 64 encoded bytes for value described by VectorType-field.

The data is base 64 encoded bytes which are serialized from integer type defined by VectorFormat-field.

To decode this field it is required to:

  1. Decode the base 64 into bytes.
  2. Deserialize the bytes into array of numbers (type and size defined by `VectorFormat`-field).
  3. Multiply all of the numbers in the array with `Multiplier`-field to get correct unit.

Python instructions

Step 1:

>>> import struct  
>>> import base64  
>>> raw = "Data"  
>>> a = base64.standard_b64decode(raw)  

Step 2:

>>> b = struct.unpack("<1600H", a)  

This command will depend on Vector Format. The options are:
- u8 = B
- u16 = H
- i8 = b
- i16 = h

The number (1600) will be Size for u8 and i8 formats and Size/2 for u16 and i16 formats

Step 3:

>>> c = []  
>>> for x in b:  
>>>   c.append(x/100000)    

Here the multiplier will change the formula as well.

Sample data:

{"Axis":"y","Data":"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","GatewayId":"902f1079","MeasDetails":{"CalcId":2,"FFT":{"BinSize":724,"FftSize":4096,"FftWindow":1},"Filter":{"Decimation":9,"HighCutoff":1333.5,"LowCutoff":10.0,"Type":3},"G-range":4,"Id":64251,"MeasurementTimeInterval":37444.0,"Trigger":8},"Multiplier":1e-05,"SensorNodeId":"f39296a3","Size":3200,"SourceAddress":"10720914","Timestamp":1713175532,"Type":"vector","VectorFormat":"u16","VectorType":"a-fft","Version":"1.0.0"}

Important key values:

"Multiplier":1e-05
"Data"= "JAsNAA8AD...."
"Size":3200
"VectorFormat":"u16"

Example

>>> import struct  
>>> import base64  
>>> raw = "JAsNAA8ADgAmACYAJAA5AGUAoADmAMEAAgIVBasEaQKMAzAERwIWAhEBiwC7CfEQxQe9AOMAXwKeAssAfAAKAD4CHwZyBb8CGQKiAXUBEQT0BuIJ4gkFDqwJeAnpCfUE1AaPA6QCzQGPAMwBXwFIAVsAYQH/ADwAtwIOBCoCEQJYA90CHQywB4ATwBL6AEIF9QyyD2YJ8QMUDfcNZQjmAbQFsA55F58GpBLaFl8N+AF1B1AQbBW3O2w/aixaOxETHAdkA/MRRBQWDuEJ1Q5gAmwIVQkUAzgFsQUcEyki1xeiBr0CtgsDDf0aHD/tTuwzJx9kJvoxPCN+F2kaIB4jEWUgNRhDEEcHLyfwJZgIGAoqDwwl6jRHFRoMNAjFDOsVQA2BDdYI0B6qN3g6DhkJTDZvhBHmhqiCrCXkKFgutXbgyF1FpSFSGtwDSSmjLCQRrCo3JS8w5joGC9IRxg62FoJZG1kwFZ8NwgRHE0IaNg6fHQwiyh/yDswWmQ6HCTgDZAvZHokepQW+GQ8DLg1hCvoIxR62KH0uJDv0IgoMjBpnAtA51kfQHAANDB0nC2wjyj+5NFweVAQYQXxKyRf9G6ELFiQCGtkXhlAMe6059CUMDKRThE4/AvIrEhdfIVJNKTaJR95B9CmcjB21cXJVItIlv0J0bN4wR1l6tRa2eV3iM7Ep4RCGIjwHgiOMPMcsoSxeIf8WXDTCIgQ/knHgPn4IjRMPGiAmqAloHyY3PkAtIYMH5BOmKBEWeA6EJ+IXLQrpDngG6xKVE7gO9Q4QHOkW4A0hCo0NPAmhAjwFqhChEN8KhAUKDjwOzwGlCIYP/gObCrIGIwoMF1YLjANCGhIgZhxSFekUPyeFERoP3AiaCM0iEyM+DV8PgA4MFqINBh3eFLQGtgLfBRQMNREBBOMNFA38AoAHCALtB+QLGwoDA7sJ+iRqJ2EIyg86BUcIsw+tC8QINga1CtkL1QjSDx4E2AbxFCYOJQRqDIoFZwelC14LUgjiHLYoUhqZBAMPYwXaDHId3Rq3DmEOOBIUCiQL8gR0HzYoPCMjDRYGdAm3CIMVaSQwHZgIPgwwE00FXxPtBfEN6hNaGrQVdAarBGELZwazEvwahhAADzwgaxxhEqsP4wPHFO8V1ggNHB8TqwyUEbwEbB6/M2AjvATkEPgHVhQfFqIFmBZfGvMZjSK6M9w3qyFAFwkLQB9ABaoU0BsbHl8jjSCxDgEiQh0KE8QbmS7JOi8wsxR0NZcjtRPtOwtLgDnDGvAdzRNjCu8XQirHOoZDUSV5E2QcbyzHKb4PaS0+QKBECTf5B+sj1xa0CtYjZzknNhYt+xoPElYFuhYwFmcM5hg0HBQlQBoHDeweCSfeLJkzR0FJMmQg1SUoH60TehmAJOYdQjWMI4AGUxjhKeEm3A0JG2Amhx8DJBwryht3DiMpdjU4Qjw0yDXCKWUXjBfTAbQUOyvGMVwWbQ9MHBUJuip7POhMpW9BZCUmOTiSJh4M+SN6M3QqqxZLHGYosSvbKOIkHg7wKOxf0T5gDzgk0x5vHO1Bu2usdnNxSlU2PIgckiG8PRU7qjgxGK8anhgeC3INhwvqPd9xDHt7eFtVexmPCpYTBiGYEF0cfQlHKsYIUyZOJt4yzDATJ3QyGjepIgUU0hjMOitFnBRWLQAd2iLELBgz3ivCCBUO1iBYLRMpuieCES8RqxmVAYwhXS6WJf8SqA4YAioRARejE+wYkjKmOXAtUEMXOwgkqwZGJgsnVi2yGMcSxAk6CRgNkh8ZGJY1Vy3+Bycf/jYJM74R4AZzIFcKzySqJgYe2yMOHzInPBtlJ7QXexa5De8JPQWfDrISQBZRHgcPAx5YKfEeRj51NBgUwxwiAREfwxUUDZofLSX9Bw8tURskCmwr5EmXM9sXNgUEEIUtNSG7GmIE3BfOG34gUhaoJEEVrQQJGZwVPiv7HvkqxipvGx4wPCnYATExxGEgdME3cR5bMpwzJguESG5KOzL9G5oncy5jHCodFicZKHI9wB5CElAjcSBgLkwjxSj6GmgTrg4nEjUNmCYeNBQhrCFpG50OkCsKQIVAAS0eNesxyTm4N58P1QznK08RXyERJiwRhBxPJkQuMwYrOolGiR1RH8kZWxikJkgtcD6nSVA4GiL9EKgczDHfEc8kKENFNgocoR3LAhQkciZ2Cwoimib4HXItngQkMP8ruy94RTg0LQ90CRMRfSSIOp0ubCjUIaQLsS8UHHoPsAt4H9gYnlM1QfohYBsQGi5MLV02fvJXD2nWLWwgcFF6XNJkB2qEnum+TSF9Sbo2QGMYrTGarJIKi/uV96HIaBpD0x2ahYGI13gVP8YNKWO+i3eQbnp3kKZlr1EKfO545WIPKx4ktEQ+HuMkFBNgPeF6lKiUjIlGYzYdF3Ewh2ENg9t8Fz/MDsYOehAjJ/Ac4XNbduQmLhLcH6MyuDfGT5FkIBnoM4tjoX3WURpb4mtCMHMwXCOLK3pHRi1uM7I1pCw/RoRMzjt9RRIU8DQ/RrlN2EL8QyojqUzNUdYvjye9HZc0yVcoTOwxT0IfKfMgNx0mOYEloDQYQPg+ox4UIg0cWD+sLEAN9BzlKNdCfzYiJDNJSixvI/ghYiq1MdguphRmMSE/0DFIPaEZpQuWHXMSXyhRK84Z6AvSC7UlBitvK90ydR7lKXQQNgdGEYkMwxCzCToQxBpZH9kHIhEvFbc3KyAIGFQh2Q91H94OYBTLIbQlFBCgEssT1xVjCocg8hbuHHMiFhQ3EqYLwiMdLVceXxCxEREVzxwsDO4QfhsrKB4gTA2LCXwUbhBTFtIeVxmNG3seyyJ0H/4RQBLGDwkPSBWaFjUOpA9RDJYPDhYxB3cNMg2hDMEJUh5nGzAXKhKDCfcY6h/3IJ0WNwluBMkI8AzoHlM8hE/NNpoejQ3GGFgWLx/mHEwiiysJGz4i2TWlKXQb9hcyL6QiHD0eKl4mGlRrTjwlDyXNQj8fxEZ4ejhY50otOtYHzi6mJh5LeF/DKEAnRB2nJcsRGTjYQd1MCyucLG5DL4hhRFEZ3wvqHPAbAyMIJ8AmQGbNg+xexQsXEugUZmlldxl2E5JplfZgm2JVP7g4Ay94nPl85VZoXLhWZ0tTXBU+I4JSfJs4WVBrWbFMBTByag6grYPANCNHgG6uUbw91Cj0HoMcFkTgI9Mc0Btgcj9xmT1HJWglRjmIVqtTAxR9GZMgdijuIIYHmh9SLJAYnQEGGZsVERa3HAMd1yLLKWAENCCVLLEu0yDlLUAhcQCxGv0Xtx66INcXyhimEYkmPBEHDnJAjEh7I0oS8RRmMgIuixj/IhYhRQ6HIbou8TK+IusTbQ9EQL00FBqPOJwyPD1cFlAmJjdZIm0l1ifAEhIV1yJKKboNtRKpIawVHy8BYD47RCynR2MJHE8KSNInSwl2AyoJ1x1tPY44jSxBD2U1ZBgkG+gnsCXTB6kS8yA8FAQxM0yJKXo0HVwTEfA2OxfzLK4ftlAWWXU65ixWMkY1QzZqXwVwK0GKIGkgDxbyMxhdvgR7Qk4ocCxpKO559nPBIyoa5C7HJIwrQx6WMfsrZ0c4RnYuLmJoNuspkBjWCVkOZzSDEPyLHIRHajUjxDLkHCIe2zZlVgk6nyNgQKUZEER9T+4SUkzyVHA62iYDFoMyzjAvBWlF7kCkJXUsCDWjHV4yd0BjOfYrOQZKJpcG6BcIK/ZSg0M+Uy1DJhfdD/4VJxFkBTka/ho+FYIjCxccEDAatRX8BJcSZiciHFQKgBgzCaczWiArJ4sw3DuWEPI0XhGrGjQQjRYoD3cLYBuCIi4aRiJpNwkr/C6DCZggihr+GJk/txVYFf8iCAxAHgsRpg7QE1Ei5SWJI8ItViJEFCQOExFVJJIObiL0F9wdAix+LrYx+itIGGkZOTQzLoFANDNvEWsnMhRhFCQ4STsQEzQkYSlaEdoWoztiLSMI1xBMKXNb8EjBIosu7DVvEN4zpxx/C44f8x9IKaUqWS3rJLwLlRA5G/4sCxR2J2AqaSxOPVkenylkChITag/4GdUDSwYhGqYhDhCZKT0p/CF3HKEkYRYxEaAcWwvvGawvEy4tEXoTkgvVG2kRxhVmHk0eXklxMMYkNE39QwYG5CREL7UtUSMlKpYq6BsJEKYb4RkiOCYxFB2HRURYay4OHEcaqAUkJRAxkyi5D0knkCptGKsQGw9nHCsTOg+dGO4oTi6xIdMQBCFwHMkrNU8cJucj5DToMcIXAiRcMsVVzFM="  

>>> a = base64.standard_b64decode(raw)  
>>> b = struct.unpack("<1600H", a)  
>>> c = []  
>>> for x in b:  
...   c.append(x/100000)  
...  
>>> c  
[0.02852, 0.00013, 0.00015, 0.00014, 0.00038, 0.00038, 0.00036, 0.00057, 0.00101, 0.0016, 0.0023, 0.00193, 0.00514, 0.01301, 0.01195, 0.00617, 0.00908, 0.01072, 0.00583, 0.00534, 0.00273, 0.00139, 0.02491, 0.04337, 0.01989, 0.00189, 0.00227, 0.00607, 0.0067, 0.00203, 0.00124, 0.0001, 0.00574, 0.01567, 0.01394, 0.00703, 0.00537, 0.00418, 0.00373, 0.01041, 0.0178, 0.0253, 0.0253, 0.03589, 0.02476, 0.02424, 0.02537, 0.01269, 0.01748, 0.00911, 0.00676, 0.00461, 0.00143, 0.0046, 0.00351, 0.00328, 0.00091, 0.00353, 0.00255, 0.0006, 0.00695, 0.01038, 0.00554, 0.00529, 0.00856, 0.00733, 0.03101, 0.01968, 0.04992, 0.048, 0.0025, 0.01346, 0.03317, 0.04018, 0.02406, 0.01009, 0.03348, 0.03575, 0.02149, 0.00486, 0.0146, 0.0376, 0.06009, 0.01695, 0.04772, 0.0585, 0.03423, 0.00504, 0.01909, 0.04176, 0.05484, 0.15287, 0.16236, 0.1137, 0.15194, 0.04881, 0.0182, 0.00868, 0.04595, 0.05188, 0.03606, 0.02529, 0.03797, 0.00608, 0.02156, 0.02389, 0.00788, 0.01336, 0.01457, 0.04892, 0.08745, 0.06103, 0.01698, 0.00701, 0.02998, 0.03331, 0.06909, 0.16156, 0.20205, 0.13292, 0.07975, 0.09828, 0.12794, 0.0902, 0.06014, 0.06761, 0.07712, 0.04387, 0.08293, 0.06197, 0.04163, 0.01863, 0.10031, 0.09712, 0.022, 0.02584, 0.03882, 0.09484, 0.13546, 0.05447, 0.03098, 0.021, 0.03269, 0.05611, 0.03392, 0.03457, 0.02262, 0.07888, 0.1425, 0.14968, 0.06414, 0.19465, 0.2847, 0.04484, 0.34534, 0.33448, 0.09644, 0.10468, 0.11864, 0.30389, 0.51424, 0.17757, 0.08613, 0.06738, 0.00988, 0.10569, 0.11427, 0.04388, 0.10924, 0.09527, 0.12335, 0.15078, 0.02822, 0.04562, 0.03782, 0.05814, 0.22914, 0.22811, 0.05424, 0.03487, 0.01218, 0.04935, 0.06722, 0.03638, 0.07583, 0.08716, 0.08138, 0.03826, 0.05836, 0.03737, 0.02439, 0.00824, 0.02916, 0.07897, 0.07817, 0.01445, 0.0659, 0.00783, 0.03374, 0.02657, 0.02298, 0.07877, 0.10422, 0.11901, 0.1514, 0.08948, 0.03082, 0.06796, 0.00615, 0.148, 0.1839, 0.07376, 0.03328, 0.07436, 0.02855, 0.09068, 0.1633, 0.13497, 0.07772, 0.01108, 0.16664, 0.19068, 0.06089, 0.07165, 0.02977, 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