ak9im/p2
2023-02-27 02:53:01 +01:00
..
data p2: correct impulse func data 2023-02-27 02:28:20 +01:00
matlab p2: add p2.m 2023-02-25 22:22:46 +01:00
res p2(visualise): use 2nd order fit 2023-02-27 02:53:01 +01:00
stats p2: make autocorrelation always positive 2023-02-27 01:36:36 +01:00
.golangci.yml add .golangci-lint 2023-01-28 19:30:33 +01:00
data.go p2: calculate impulse func estimate 2023-02-26 21:20:40 +01:00
go.mod p2: implement file reading+basic data operations 2023-02-24 23:50:13 +01:00
go.sum p2: implement file reading+basic data operations 2023-02-24 23:50:13 +01:00
main.go p2: implement file reading+basic data operations 2023-02-24 23:50:13 +01:00
README.md readme: update instructions 2023-02-26 21:26:58 +01:00
run.go p2: add a way to run visualise.py cmd 2023-02-27 02:16:43 +01:00
visualise.py p2(visualise): use 2nd order fit 2023-02-27 02:53:01 +01:00

p2

this is a Go subproject containing code for task no. 2. Python is used for visualisation.

compile

go build -v .

run

to compute correlations, impulse function:

# pass a csv data file.
./p2 -datafile=./data/m.csv

to visualise the computed data:

python visualise.py