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# satnogs-wut
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2020-01-02 16:44:03 -07:00
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The goal of satnogs-wut is to have a script that will take an
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observation ID and return an answer whether the observation is
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"good", "bad", or "failed".
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2020-01-02 16:52:23 -07:00
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## Good Observation
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## Bad Observation
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## Failed Observation
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# Machine Learning
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The system at present is build upon the following:
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* Debian
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2020-01-01 23:18:12 -07:00
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* Tensorflow
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* Keras
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Learning/Testing, results are inaccurate.
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2020-01-02 16:44:03 -07:00
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# wut?
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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-api-test` --- API Tests.
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* `wut-get-obs` --- Download the JSON for an observation ID.
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* `wut-get-staging` --- Download waterfalls to `data/staging` for review (deprecated).
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* `wut-get-train-bad` --- Download waterfalls to `data/train/bad` for review (deprecated).
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* `wut-get-train-good` --- Download waterfalls to `data/train/good` for review (deprecated).
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* `wut-get-validation-bad` --- Download waterfalls to `data/validation/bad` for review (deprecated).
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* `wut-get-validation-good` --- Download waterfalls to `data/validation/good` for review (deprecated).
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* `wut-get-waterfall` --- Download waterfall for an observation ID to `download/[ID]`.
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* `wut-get-waterfall-range` --- Download waterfalls for a range of observation IDs to `download/[ID]`.
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* `wut-ml` --- Main machine learning Python script using Tensorflow and Keras.
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* `wut-review-staging` --- Review all images in `data/staging`.
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2020-01-02 17:11:16 -07:00
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# Usage
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The main purpose of the script is to evaluate an observation,
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but to do that, it needs to build a corpus of observations to
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learn from. So many of the scripts in this repo are just for
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downloading and managing observations.
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The following steps need to be performed:
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1. Download waterfalls and JSON descriptions with `wut-get-waterfall-range`.
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These get put in the `downloads/[ID]/` directories.
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1. Organize downloaded waterfalls into categories (e.g. "good", "bad", "failed").
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Note: this needs a script written.
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Put them into their respective directories under:
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* `data/train/good/`
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* `data/train/bad/`
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* `data/train/failed/`
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* `data/validation/good/`
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* `data/validataion/bad/`
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* `data/validataion/failed/`
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1. Use machine learning script `wut-ml` to build a model based on
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the files in the `data/train` and `data/validation` directories.
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1. Rate an observation using the `wut` script.
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# Caveats
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This is the first machine learning script I've done,
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I know little about satellites and less about radio,
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and I'm not a programmer.
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2020-01-02 16:44:03 -07:00
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# Source License / Copying
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Main repository is available here:
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* https://spacecruft.org/spacecruft/satnogs-wut
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2020-01-02 16:55:08 -07:00
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License: CC By SA 4.0 International and/or GPLv3+ at your discretion. Other code licensed under their own respective licenses.
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2020-01-01 23:18:12 -07:00
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2020-01-02 16:55:08 -07:00
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Copyright (C) 2019, 2020, Jeff Moe
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