ak9im/p2
2023-02-27 04:27:46 +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): better titles 2023-02-27 04:05:29 +01:00
stats p2: add alternative way to get matrix product 2023-02-27 04:10:44 +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 p2: update readme 2023-02-27 04:27:46 +01:00
run.go p2: calculate impulseFunc using autocorrels 2023-02-27 04:09:01 +01:00
visualise.py p2(visualise): better titles 2023-02-27 04:05:29 +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

alternatively, from current folder you could do both of the above in a single step:

# compute stuff and visualise.
-./p2 -datafile=./data/m.csv
+./p2 -datafile=./data/m.csv -vis