sbrl-github/research-rainfallradar
1
0
Fork 0
mirror of https://github.com/sbrl/research-rainfallradar synced 2024-11-22 01:12:59 +00:00
Code Issues Projects Releases Packages Wiki Activity

fix crash

Browse source
This commit is contained in:
Starbeamrainbowlabs 2022-08-31 16:25:27 +01:00
parent fe7a8b3fc0
commit f2312c1184
Signed by: sbrl
GPG key ID: 1BE5172E637709C2
2 changed files with 3 additions and 3 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

4
README.md
View file

@ -4,7 +4,7 @@
This is the 3rd major version of this model.
Unfortunately using this model is rather complicated and involves a large number of steps. This README (will) explain it the best I can though.
Unfortunately using this model is rather complicated and involves a large number of steps. There is no way around this. This README (will) explain it the best I can though.
## System Requirements
@ -24,7 +24,7 @@ The process of using this model is as follows.
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)
6. Use `rainfallwrangler` in this repository (finally!) to convert the output to .tfrecord files
6. Use `rainfallwrangler` in this repository (finally!) to convert the output to .json.gz then .tfrecord files
7. Pretrain a contrastive learning model
8. Encode the rainfall radar data with the contrastive learning model you pretrained
9. Train the *actual* model to predict water depth

2
aimodel/src/lib/dataset/dataset.py
View file

@ -7,7 +7,7 @@ from loguru import logger
import tensorflow as tf
from shuffle import shuffle
from .shuffle import shuffle
# TO PARSE: