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https://github.com/ecmwf/ecpoint-calibrate
Interactive GUI (developed in Python) for calibration and conditional verification of numerical weather prediction model outputs.
https://github.com/ecmwf/ecpoint-calibrate
calibration decision-trees ecmwf meteorology metview python weather-forecast
Last synced: 2 months ago
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Interactive GUI (developed in Python) for calibration and conditional verification of numerical weather prediction model outputs.
- Host: GitHub
- URL: https://github.com/ecmwf/ecpoint-calibrate
- Owner: ecmwf
- License: gpl-3.0
- Created: 2018-05-11T16:15:36.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2023-07-20T09:49:09.000Z (12 months ago)
- Last Synced: 2024-04-13T17:17:45.489Z (3 months ago)
- Topics: calibration, decision-trees, ecmwf, meteorology, metview, python, weather-forecast
- Language: JavaScript
- Homepage:
- Size: 44.9 MB
- Stars: 21
- Watchers: 9
- Forks: 8
- Open Issues: 29
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- open-sustainable-technology - ecPoint-Calibrate - A software that uses conditional verification tools to compare numerical weather prediction model outputs against point observations and, in this way, anticipate sub-grid variability and identify biases at grid scale. (Atmosphere / Meteorological Observation and Forecast)
README
# ecPoint-Calibrate
[](https://codecov.io/gh/esowc/ecPoint-Calibrate)
[](https://www.python.org/)
ecPoint-Calibrate is a software that uses conditional verification tools to compare numerical weather prediction (NWP) model outputs against point observations and, in this way, anticipate sub-grid variability and identify biases at grid scale.
It provides a dynamic and user-friendly environment to post-process NWP model parameters (such as precipitation, wind, temperature, etc.) and produce probabilistic products for geographical locations (everywhere in the world, and up to medium-range forecasts).
The development of this project was sponsored by the project "ECMWF Summer of Weather Code (ESoWC)"
[@esowc_ecmwf](https://twitter.com/esowc_ecmwf?lang=en)
[ECMWF](https://www.ecmwf.int).
## Build with Docker
```
docker build -f Dockerfile.core -t ecmwf/ecpoint-calibrate-core:dev .
```
## Deploy new versions of the Docker containers
```
./deploy.sh
```
## Create a production AppImage
```
yarn dist
```
The appimage won't work on modern machines without manually adding the `--no-sandbox` electron
option and re-packaging.
### Install `appimagetool`
```
sudo wget https://github.com/AppImage/AppImageKit/releases/download/continuous/appimagetool-x86_64.AppImage -O /usr/local/bin/appimagetool
sudo chmod +x /usr/local/bin/appimagetool
```
### Repackage the AppImage
```
cd pkg
./ecPoint-Calibrate-0.30.0.AppImage --appimage-extract
```
This will extract the image into the `squashfs-root` directory.
Open `squashfs-root/AppRun` and change the `exec` lines to have the `--no-sandbox` argument.
e.g. `exec "$BIN" --no-sandbox`
Then repackage:
```
appimagetool squashfs-root ecPoint-Calibrate-0.30.0.AppImage
```
## Python Backend
We need `metview-batch` from conda-forge so unfortunately need to use `conda` with `poetry`.
### Creating the environment
```
conda create --name ecpoint_calibrate_env --file conda-linux-64.lock
conda activate ecpoint_calibrate_env
poetry install
```
### Activating the environment
```
conda activate ecpoint_calibrate_env
```
### Updating the environment
#### Poetry (strongly preferred)
Installing a new package with poetry will update the poetry lockfile.
```
poetry install $DEP
```
#### Conda
You should very rarely need to add a new conda dep.
```
conda-lock -k explicit --conda mamba
mamba update --file conda-linux-64.lock
poetry update
```
### Run tests
First activate the conda env, then run `pytest`.
## Electron Frontend
You'll need node v 14.5.0.
### Installing deps
```
yarn
```
### Run the app
```
yarn start
```
### Run tests
```
npm run test
```