script notes, etc.

README.md

@@ -30,37 +30,59 @@ observation ID and return an answer whether the  observation is
 <img src="satnogs-wut/media/branch/master/pics/waterfall-failed.png" width="300"/>
 </div>
 
+## 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/Observe
-* https://wiki.satnogs.org/Taxonomy_of_Observations
+* 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.
+Learning/testing, results are good.
-The main AI/ML development is now being done in Jupyter.
+The main AI/ML development is being done in Jupyter.
 
 # Jupyter
-There is a Jupyter Lab Notebook file.
+There Jupyter Lab Notebook files in the `notebooks/` subdirectory.
-This is producing real results at present, but has a long ways to go still...
+These are producing usable results.
 
-* `wut-ml.ipynb` --- Machine learning Python script using Tensorflow and Keras in a Jupyter Notebook.
+* `wut.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-predict.ipynb` --- Make prediction (rating) of observation from pre-existing model.
-* `wut-train.ipynb` --- ML Training file saved to `data/wut.h5`.
+* `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.