UnetUtils.jl provides commonly used utilities that are helpful when working with UnetStack and Julia. Currently, it contains utilities to work with UnetStack signal dumps (signals-*.txt files) and passband recordings (rec-*.dat files).
To install:
julia> # press ] for package mode
pkg> add UnetUtilsTo read a signals.txt file:
julia> using UnetUtils
julia> s = Signals.read("signals.txt")
5×11 DataFrame
Row │ time rxtime rssi preamble channels fc fs len lno filename dtype
│ ZonedDat… Int64? Float64? Int64 Int64 Float64 Float64 Int64 Int64 String DataType
─────┼──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
1 │ 2024-02-09T14:12:02.757+08:00 19320296667 -67.0 2 4 24000.0 24000.0 720 2 signals.txt ComplexF32
2 │ 2024-02-09T14:12:11.238+08:00 19328782709 -66.9 2 4 24000.0 24000.0 720 5 signals.txt ComplexF32
3 │ 2024-02-09T14:12:15.967+08:00 19333509458 -66.8 2 4 24000.0 24000.0 720 8 signals.txt ComplexF32
4 │ 2024-02-09T14:12:19.405+08:00 19336950750 -67.0 2 4 24000.0 24000.0 720 11 signals.txt ComplexF32
5 │ 2024-02-09T14:12:28.830+08:00 19346376417 -67.0 2 4 24000.0 24000.0 720 14 signals.txt ComplexF32We can read a signal from the file:
julia> x = Signals.read(s, 2) # read signal number 2
SampledSignal @ 24000.0 Hz, 720×4 Matrix{ComplexF64}:
-8.90469e-5-0.000147327im 0.000127849+0.0002874im 1.72211e-5+0.0001782im 0.000185837+0.000201513im
-7.16846e-5-0.000106024im -6.52411e-5+9.06558e-5im 0.000214363-1.82419e-5im -7.30673e-5+0.000159378im
-0.000169725-0.000221368im -5.35115e-5+0.000152804im 0.000218458+0.000330593im -0.000158725-4.01946e-5im
-0.000232667-0.000129752im -0.000293527+5.65732e-6im -0.000204773-8.53947e-5im -6.13353e-6-7.99393e-6im
⋮ ⋮ ⋮ ⋮In this example, the signal was a 4-channel baseband signal, and so it was extracted as a 4-column complex matrix. If the signal is a passband signal, it is returned as a real matrix.
To read a passband recording rec.dat:
julia> using UnetUtils
julia> r = Recordings.read("rec.dat")
SampledSignal @ 256000.0 Hz, 14126592×4 Matrix{Float32}:
-0.00142775 -0.00463575 -0.00311242 -0.00476095
-0.00155232 -0.00470167 -0.00364076 -0.00472483
-0.00146602 -0.00348717 -0.0026301 -0.00385222
-0.00153838 -0.00462216 -0.00315212 -0.00466463
-0.00173865 -0.00554317 -0.00420509 -0.00542805
-0.00148247 -0.00300187 -0.00201665 -0.00302324
⋮ ⋮ ⋮ ⋮If we have a directory full of recording files, we can also get an index of recordings to work with:
julia> r = Recordings.read("/my/recordings/")
15×5 DataFrame
Row │ time filename duration nchannels framerate
│ ZonedDat… String Float64 Int64 Float64
─────┼─────────────────────────────────────────────────────────────────────────
1 │ 2023-12-22T16:09:36.885+08:00 rec-1703… 55.182 4 256000.0
2 │ 2023-12-22T16:17:15.013+08:00 rec-1703… 53.837 4 256000.0
3 │ 2024-02-08T20:00:07.835+08:00 rec-1707… 58.5094 4 96000.0
4 │ 2024-02-08T20:24:11.641+08:00 rec-1707… 49.68 4 96000.0
5 │ 2024-02-08T20:32:19.897+08:00 rec-1707… 3.44535 4 96000.0
6 │ 2024-02-08T20:34:00.011+08:00 rec-1707… 2.41869 4 96000.0
7 │ 2024-02-08T20:35:20.049+08:00 rec-1707… 3.19735 4 96000.0
8 │ 2024-02-08T20:36:53.568+08:00 rec-1707… 44.864 4 96000.0
9 │ 2024-02-08T20:41:32.275+08:00 rec-1707… 24.056 4 96000.0
10 │ 2024-02-08T21:02:41.015+08:00 rec-1707… 5.94935 4 96000.0
11 │ 2024-02-08T21:05:44.358+08:00 rec-1707… 31.6934 4 96000.0
12 │ 2024-02-08T21:26:24.103+08:00 rec-1707… 28.4507 4 96000.0
13 │ 2024-02-08T21:28:13.746+08:00 rec-1707… 16.7814 4 96000.0
14 │ 2024-02-08T21:34:07.463+08:00 rec-1707… 27.832 4 96000.0
15 │ 2024-02-15T14:41:25.645+08:00 rec-1707… 5.46801 4 256000.0and then work with individual recordings:
julia> x = Recordings.read(r, 3) # load recording 3Recordings are always in passband and are returned as matrices of real numbers. The number of columns is equal to the number of channels in the recording.
We can also ask for a recording to be converted to a WAV file:
julia> Recordings.towav("rec.dat")This will create a rec.wav file in the same folder as the original recording.