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