diff --git a/README.md b/README.md index d4e0076..0c43fe2 100644 --- a/README.md +++ b/README.md @@ -30,37 +30,59 @@ observation ID and return an answer whether the observation is +## wut Web + +Main site: +* https://wut.spacecruft.org/ + +Source code: +* https://spacecruft.org/spacecruft/satnogs-wut + +Beta (test) site: + +* https://wut-beta.spacecruft.org/ + +Alpha (development) site: + +* https://wut-alpha.spacecruft.org/ + ## Observations See also: * https://wiki.satnogs.org/Operation -* https://wiki.satnogs.org/Rating_Observations -* https://wiki.satnogs.org/Taxonomy_of_Observations +* https://wiki.satnogs.org/Observe +* https://wiki.satnogs.org/Observations +* https://wiki.satnogs.org/Category:RF_Modes * Sample observation: https://network.satnogs.org/observations/1456893/ # Machine Learning The system at present is built upon the following: * Debian Buster. -* Tensorflow 2.1 with built-in Keras. +* Tensorflow 2 with Keras. * Jupyter Lab. +* Voila. -Learning/testing, results are ~~inaccurate~~ getting closer. -The main AI/ML development is now being done in Jupyter. +Learning/testing, results are good. +The main AI/ML development is being done in Jupyter. # Jupyter -There is a Jupyter Lab Notebook file. -This is producing real results at present, but has a long ways to go still... +There Jupyter Lab Notebook files in the `notebooks/` subdirectory. +These are producing usable results. -* `wut-ml.ipynb` --- Machine learning Python script using Tensorflow and Keras in a Jupyter Notebook. -* `wut-predict.ipynb` --- Make prediction (rating) of observation, using `data/wut.h5`. -* `wut-train.ipynb` --- ML Training file saved to `data/wut.h5`. +* `wut.ipynb` --- Machine learning Python script using Tensorflow and Keras in a Jupyter Notebook. +* `wut-predict.ipynb` --- Make prediction (rating) of observation from pre-existing model. +* `wut-train.ipynb` --- Train models to be using by prediction engine. +* `wut-web.ipynb` --- Website: https://wut.spacecruft.org/ +* `wut-web-beta.ipynb` --- Website: https://wut-beta.spacecruft.org/ +* `wut-web-alpha.ipynb` --- Website: https://wut-alpha.spacecruft.org/ # wut scripts -The following scripts are in the repo: +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-audio-archive` --- Downloads audio files from archive.org. +* `wut-audio-sha1` --- Verifies sha1 checksums of files downloaded from archive.org. * `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-tx` --- Compare all the observations in `download/` with `wut` vettings using selected transmitter UUID. @@ -69,19 +91,30 @@ The following scripts are in the repo: * `wut-dl-sort` --- Populate `data/` dir with waterfalls from `download/`. * `wut-dl-sort-tx` --- Populate `data/` dir with waterfalls from `download/` using selected transmitter UUID. * `wut-dl-sort-txmode` --- Populate `data/` dir with waterfalls from `download/` using selected encoding. +* `wut-dl-sort-txmode-all` --- Populate `data/` dir with waterfalls from `download/` using all encodings. * `wut-files` --- Tells you about what files you have in `downloads/` and `data/`. +* `wut-files-data` --- Tells you about what files you have in `data/`. +* `wut-img-ck.py` --- Validate image files are not corrupt with PIL. * `wut-ml` --- Main machine learning Python script using Tensorflow and Keras. +* `wut-ml-auto` --- Machine learning Python script using Tensorflow and Keras, auto. * `wut-ml-load` --- Machine learning Python script using Tensorflow and Keras, load `data/wut.h5`. * `wut-ml-save` --- Machine learning Python script using Tensorflow and Keras, save `data/wut.h5`. * `wut-obs` --- Download the JSON for an observation ID. * `wut-ogg2wav` --- Convert `.ogg` files in `downloads/` to `.wav` files. +* `wut-rm-random` --- Randomly deletes stuff. Very bad. * `wut-review-staging` --- Review all images in `data/staging`. +* `wut-tf` --- Shell script to set variables when launching `wut-tf.py`. +* `wut-tf.py` --- Distributed learning script to be run on multiple nodes. * `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]`. +* `wut-worker` --- Shell script to set variables when launching `wut-worker.py`. +* `wut-worker.py` --- Distributed training script to run on multiple nodes. +* `wut-worker-mas` --- Shell script to set variables when launching `wut-worker-mas.py`. +* `wut-worker-mas.py` --- Distributed training script to run on multiple nodes, alt version. # Installation -Most of the scripts are simple shell scripts with few dependencies. +Installation notes... ## Setup The scripts use files that are ignored in the git repo. @@ -354,7 +387,7 @@ Alpha and Beta development and test servers are here: * https://wut-beta.spacecruft.org # Caveats -This is nearly the first machine learning script I've done, +This is the first artificial intelligence script I've done, I know little about radio and less about satellites, and I'm not a programmer.