# satnogs-wut

The goal of satnogs-wut is to have a script that will take an
observation ID and return an answer whether the  observation is
"good", "bad", or "failed".

## Good Observation
![Good Observation](pics/waterfall-good.png)

## Bad Observation
![Bad Observation](pics/waterfall-bad.png)

## Failed Observation
![Failed Observation](pics/waterfall-failed.png)

# Machine Learning
The system at present is build upon the following:

* Debian
* Tensorflow
* Keras


Learning/Testing, results are inaccurate.


# wut?
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 observation IDs' presumably human vetting with a `wut` vetting.
* `wut-compare-all` --- Compare all the observations in `download/` with `wut` vettings.
* `wut-dl-sort` --- Populate `data/` dir with waterfalls from `download/`.
* `wut-ml` --- Main machine learning Python script using Tensorflow and Keras.
* `wut-obs` --- Download the JSON for an observation ID.
* `wut-review-staging` --- Review all images in `data/staging`.
* `wut-water` --- Download waterfall for an observation ID to `download/[ID]`.
* `wut-water-range` --- Download waterfalls for a range of observation IDs to `download/[ID]`.


# Installation
Most of the scripts are simple shell scripts with few dependencies.

## Setup
The scripts use files that are ignored in the git repo.
So you need to create those directories:

```
mkdir -p download
mkdir -p data/train/good
mkdir -p data/train/bad
mkdir -p data/train/failed
mkdir -p data/val/good
mkdir -p data/val/bad
mkdir -p data/val/failed
mkdir -p data/staging
mkdir -p data/test/unvetted
```

## Debian Packages
You'll need `curl` and `jq`, both in Debian's repos.

```
apt update
apt install curl jq
```

## Machine Learning
For the machine learning scripts, like `wut-ml`, both Tensorflow
and Keras need to be installed. The versions of those in Debian
didn't work for me. IIRC, for Tensorflow I built a `pip` of
version 2.0.0 from git and installed that. I installed Keras
from `pip`. Something like:

```
# XXX These aren't the exact commands, need to check...
# Install bazel or whatever their build system is
# Install Tensorflow
git clone tensorflow...
cd tensorflow
./configure
# run some bazel command
dpkg -i /tmp/pkg_foo/*.deb
apt update
apt -f install
# Install Keras
pip3 install --user keras
# A million other commands....
```


# Usage
The main purpose of the script is to evaluate an observation,
but to do that, it needs to build a corpus of observations to
learn from. So many of the scripts in this repo are just for
downloading and managing observations.


The following steps need to be performed:

1. Download waterfalls and JSON descriptions with `wut-get-waterfall-range`.
   These get put in the `downloads/[ID]/` directories.

1. Organize downloaded waterfalls into categories (e.g. "good", "bad", "failed").
   Use `wut-dl-sort` script.
   The script them into their respective directories under:
	* `data/train/good/`
	* `data/train/bad/`
	* `data/train/failed/`
	* `data/val/good/`
	* `data/val/bad/`
	* `data/val/failed/`

1. Use machine learning script `wut-ml` to build a model based on
   the files in the `data/train` and `data/val` directories.

1. Rate an observation using the `wut` script.


# Caveats
This is the first machine learning script I've done,
I know little about satellites and less about radio,
and I'm not a programmer.


# Source License / Copying
Main repository is available here:

* https://spacecruft.org/spacecruft/satnogs-wut


License: CC By SA 4.0 International and/or GPLv3+ at your discretion. Other code licensed under their own respective licenses.

Copyright (C) 2019, 2020, Jeff Moe