caching, downloads ...
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README.md
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README.md
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@ -119,6 +119,16 @@ The following steps need to be performed:
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1. Rate an observation using the `wut` script.
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1. Rate an observation using the `wut` script.
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# Data Caching Downloads
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The scripts are designed to not download a waterfall or make a JSON request
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for an observation it has already requested. The first time an observation
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is requested, it is downloaded from the SatNOGS network to the `download`
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directory. That `download` directory is the download cache.
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The `data` directory is just temporary files,mostly linked from the
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`downloads` directory. Files in the `data` directory are deleted by many
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scripts, so don't put anything you want to keep in there.
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# Caveats
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# Caveats
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This is the first machine learning script I've done,
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This is the first machine learning script I've done,
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@ -21,9 +21,9 @@ OBSIDMIN="$1"
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OBSIDMAX="$2"
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OBSIDMAX="$2"
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OBSID=$OBSIDMIN
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OBSID=$OBSIDMIN
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# First make sure we have the range all downloaded:
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# Enable the following if you want to download waterfalls in this range:
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echo "Downloading Waterfalls"
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#echo "Downloading Waterfalls"
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./wut-water-range $OBSIDMIN $OBSIDMAX
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#./wut-water-range $OBSIDMIN $OBSIDMAX
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# XXX remove data/train and data/val directories XXX
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# XXX remove data/train and data/val directories XXX
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echo "Removing data/ subdirectories"
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echo "Removing data/ subdirectories"
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12
wut-ml
12
wut-ml
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@ -1,5 +1,17 @@
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#!/usr/bin/python3
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#!/usr/bin/python3
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# wut-ml
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# wut-ml
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#
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# Vet a SatNOGS image using machine learning (guessing).
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# It will vet the image located at test/unvetted/waterfall.png.
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#
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# Note, there is an issue to fix where it will vet everything
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# under the data/test directory, so fix that. For now, just deleted
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# everything else. :)
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#
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# Usage:
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# wut-ml
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# Example:
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# wut-ml
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import os
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import os
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import numpy as np
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import numpy as np
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