wut-compare-txmode
parent
210957f770
commit
e4fd293b68
|
@ -30,6 +30,7 @@ The following scripts are in the repo:
|
|||
* `wut` --- Feed it an observation ID and it returns if it is a "good", "bad", or "failed" observation.
|
||||
* `wut-compare` --- Compare an observations' current presumably human vetting with a `wut` vetting.
|
||||
* `wut-compare-all` --- Compare all the observations in `download/` with `wut` vettings.
|
||||
* `wut-compare-txmode` --- Compare all the observations in `download/` with `wut` vettings using selected encoding.
|
||||
* `wut-dl-sort` --- Populate `data/` dir with waterfalls from `download/`.
|
||||
* `wut-dl-sort-txmode` --- Populate `data/` dir with waterfalls from `download/` using selected encoding.
|
||||
* `wut-ml` --- Main machine learning Python script using Tensorflow and Keras.
|
||||
|
|
|
@ -0,0 +1,45 @@
|
|||
#!/bin/bash
|
||||
# wut-compare-txmode
|
||||
#
|
||||
# Check the results of a prediction against vetted results
|
||||
# using a selected encoding.
|
||||
# Uses all files in download/ directory.
|
||||
# Available encodings:
|
||||
# AFSK AFSK1k2 AHRPT APT BPSK BPSK1k2 BPSK9k6 BPSK12k5 BPSK400 CERTO CW DUV
|
||||
# FFSK1k2 FM FSK1k2 FSK4k8 FSK9k6 FSK19k2 GFSK1k2 GFSK2k4 GFSK4k8 GFSK9k6
|
||||
# GFSK19k2 GFSK Rktr GMSK GMSK1k2 GMSK2k4 GMSK4k8 GMSK9k6 GMSK19k2 HRPT LRPT
|
||||
# MSK1k2 MSK2k4 MSK4k8 PSK PSK31 SSTV USB WSJT
|
||||
#
|
||||
# Usage:
|
||||
# wut-compare-txmode [Encoding]
|
||||
# Example:
|
||||
# wut-compare-txmode DUV
|
||||
|
||||
MAIN_DIR=`pwd`
|
||||
OBSENC="$1"
|
||||
|
||||
cd download/ || exit
|
||||
|
||||
CORRECT=0
|
||||
INCORRECT=0
|
||||
for OBSID in *
|
||||
do
|
||||
cd $MAIN_DIR
|
||||
# Get previous rating
|
||||
VET=`cat download/$OBSID/$OBSID.json | jq --compact-output '.[0] | {vetted_status}' | cut -f 2 -d ":" | sed -e 's/}//g' -e 's/"//g'`
|
||||
ENC=`cat download/$OBSID/$OBSID.json | jq --compact-output '.[0] | {transmitter_mode}' | cut -f 2 -d ":" | sed -e 's/}//g' -e 's/"//g'`
|
||||
if [ $OBSENC = $ENC ] ; then
|
||||
echo -n "$OBSID "
|
||||
echo -n "Vet: $VET "
|
||||
# Get Machine Learning Result
|
||||
WUT_VET=`./wut $OBSID | cut -f 2 -d " "`
|
||||
echo -n "Wut: $WUT_VET "
|
||||
if [ $VET = $WUT_VET ] ; then
|
||||
let CORRECT=$CORRECT+1
|
||||
else
|
||||
let INCORRECT=$INCORRECT+1
|
||||
fi
|
||||
echo "Correct: $CORRECT Incorrect: $INCORRECT"
|
||||
fi
|
||||
done
|
||||
|
|
@ -22,7 +22,7 @@
|
|||
# * File is randomly copied to either data/train or data/val directory.
|
||||
#
|
||||
# Possible vetted_status: bad, failed, good, null, unknown.
|
||||
set -x
|
||||
|
||||
OBSENC="$1"
|
||||
OBSIDMIN="$2"
|
||||
OBSIDMAX="$3"
|
||||
|
|
Loading…
Reference in New Issue