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README.md

@@ -36,15 +36,13 @@ The process of using this model is as as illustrated:
 
 ![Flowchart illustrating the data flow for using the code in this repository to make predictions water depth](./research-rainfallradar%20overview.png)
 
-TODO fix this flowchart.
-
 More fully:
 
 1. Apply for access to [CEDA's 1km rainfall radar dataset](https://catalogue.ceda.ac.uk/uuid/27dd6ffba67f667a18c62de5c3456350)
 2. Download 1km rainfall radar data (use [`nimrod-data-downloader`](https://www.npmjs.com/package/nimrod-data-downloader))
 3. Obtain a heightmap (or *Digital Elevation Model*, as it's sometimes known) from the Ordnance Survey (can't remember the link, please PR to add this)
 4. Use [`terrain50-cli`](https://www.npmjs.com/package/terrain50-cli) to slice the the output from steps #2 and #3 to be exactly the same size [TODO: Preprocess to extract just a single river basin from the data]
-5. Push through [HAIL-CAESAR](*https://github.com/sbrl/HAIL-CAESAR) (this fork has the ability to handle streams of .asc files rather than each time step having it's own filename)
+5. Push through [HAIL-CAESAR](https://github.com/sbrl/HAIL-CAESAR) (this fork has the ability to handle streams of .asc files rather than each time step having it's own filename)
 6. Use `rainfallwrangler` in this repository (finally!) to convert the output to .json.gz then .tfrecord files
 7. Train a DeepLabV3+ prediction model
 
@@ -63,12 +61,28 @@ This tool was also written me, [@sbrl](https://starbeamrainbowlabs.com/) - the p
 
 Full documentation on this tool is available at the above link.
 
-<------ WRITING HERE
+**Heightmap:** Anything will do, but I used the [Ordnance Survey Terrain50](https://www.ordnancesurvey.co.uk/products/os-terrain-50) heightmap, since it is in the OS National Grid format (eww >_<), same as the aforementioned rainfall radar data.
 
+### Running the simulation
+Once you have your data, ensure it is in a format that the HAIL-CAESAR model will understand. For the rainfall radar data, this is done using the `radar2caesar` command of `nimrod-data-downloader`, as mentioned above.
 
-TODO document the next steps.
+before running the simulation, the heightmap and rainfall radar will need cropping to match one another. For this the tool [`terrain50-cli`](https://www.npmjs.com/package/terrain50-cli) was developed.
+
+Once this is done, the next step is to run HAIL-CAESAR. Details on this can be found here:
+
+<https://github.com/sbrl/HAIL-CAESAR/>
+
+....unfortunately, due to the way HAIL-CAESAR is programmed, it reads *all* the rainfall radar data into memory first before running the simulation. From memory for data from 2006 to 2020 it used approximately 350GiB - 450GiB RAM.
+
+Replacing this simulation with a better one is on the agenda for moving forwards with this research project - especially since I need to re-run a hydrological simulation anyway when attempting a tile-based approach.
+
+### Preparing to train the model
+Once the simulation has run to completion, all 3 pieces are now in place to prepare to train an AI model. The AI model training process requires that data is stored in `.tfrecord` files for efficiency given the very large size of the dataset in question.
+
+This is done using the `rainfallwrangler` tool in the eponymous directory in this repository. Full documentation on `rainfallwrangler` can be found in the README in that directory:
+
+[rainfallwrangler README](./rainfallwrangler/README.md)
 
-## rainfallwrangler
 `rainfallwrangler` is a Node.js application to wrangle the dataset into something more appropriate for training an AI efficiently. The rainfall radar and water depth data are considered temporally to be regular time steps. Here's a diagram explaining the terminology:
 
 ```
@@ -90,5 +104,16 @@ Note to self: 150.12 hashes/sec on i7-4770 4c8t, ???.?? hashes/sec on Viper comp
 After double checking, rainfallwrangler does NOT mess with the ordering of the data.
 
 
+
+### Training the model
+After all of the above steps are completed, a model can now be trained.
+
+The current state of the art (that was presented in the above paper!) is based on DeepLabV3+. A note of caution: this repository contains some older models, so it can be easy to mix them up. Hence this documentation :-)
+
+<------ WRITING HERE
+
+TODO: Continue the guide here.
+
+
 ## License
 All the code in this repository is released under the GNU Affero General Public License unless otherwise specified. The full license text is included in the [`LICENSE.md` file](./LICENSE.md) in this repository. GNU [have a great summary of the licence](https://www.gnu.org/licenses/#AGPL) which I strongly recommend reading before using this software.

rainfallwrangler/README.md

@@ -0,0 +1,24 @@
+# rainfallwrangler
+
+> Wrangles rainfall radar and water depth data into something sensible.
+
+
+This Node.js-based tool is designed for wrangling rainfall, heightmap, and water depth data into something that the image semantic segmentation model that is the main feature of this repository can understand.
+
+The reason for this is efficiency: nothing less than a set of `.tfrecord` files for reading in parallel is sufficient if one wants the model to train in a reasonable length of time.
+
+
+TODO: Write a guide for this tool here.
+
+## System requirements
+
+
+## Getting started
+
+
+
+## Contributing
+
+
+## Licence
+Same as  that of the main repository. TODO expand on this.