https://github.com/i4ds/flaresense
ML-Powered Solar Flare Classification with e-Callisto.
https://github.com/i4ds/flaresense
Last synced: about 1 year ago
JSON representation
ML-Powered Solar Flare Classification with e-Callisto.
- Host: GitHub
- URL: https://github.com/i4ds/flaresense
- Owner: i4Ds
- License: gpl-3.0
- Archived: true
- Created: 2023-09-25T07:25:43.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-01-18T16:09:31.000Z (over 2 years ago)
- Last Synced: 2025-02-22T12:42:46.469Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 11.4 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Pro5D - Klassifikation von Sonneneruptionen mittels Machine Learning und e-Callisto Netzwerk
Dieses Projekt wird im Rahmen der Studiengangs B.Sc. Data Science der Fachhochschule Nordwestschweiz bearbeitet.
## Projektarbeiter
Dieses Projekt wird von folgenden Studenten bearbeitet:
| Name | GitHub Handle |
| :------------------- | :----------------------------------------------------------- |
| Patrick Schürmann | [@patschue](https://github.com/patschue) |
| Gabriel Torres Gamez | [@gabrieltorresgamez](https://github.com/gabrieltorresgamez) |
## Taskboard
Hier geht es zum Projekt Taskboard mit allen Tasks und Issues:
[Link zum Taskboard](https://github.com/orgs/i4Ds/projects/11)
## Zotero
Hier geht es zu unserer Zotero Gruppe:
[Link zu Zotero](https://www.zotero.org/groups/5202251/pro5d_23hs_i4ds22/library)
## Dataset
Hier geht es zum 15 Minuten Dataset:
[Link zum Dataset](https://huggingface.co/datasets/StellarMilk/ecallisto-bursts)
Notiz: Der Datensatz wird bei einigen Notebooks automatisch heruntergeladen.
## DagsHub
Hier geht es zu unserer DagsHub Organisation:
[Link zur Organisation](https://dagshub.com/org/FlareSense/home)
## Repository Setup Instructions
1. Clone the repo.
2. Run `make reqs` to install required python packages.
### If you want to use DagsHub:
3. Setup the DVC credentials using DagsHub.
4. Run `make pull` to pull the data from DagsHub.
5. You're ready to start developing!
### Datamanagement with DagsHub
The data in this repo is managed via DVC. Here are some useful commands:
- `make pull` - Pulls the data from DagsHub.
- `make relink` - After changes in the DVC Folders (data) this command relinks the files in the repo.
- `make push` - Pushes the data to DagsHub.
Before committing changes to the data folder, make sure to run `make relink` to update the links to the data.
Afterwards, add, commit and push the changes to the repo (using git).
Finally, run `make push` to push the data to DagsHub.
## Projekt Organisation
├── .dvc <- DVC Settings, don't touch.
├── data
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering)
│ and a short `-` delimited description, e.g.
│ `01-initial-data-exploration`.
├── references <- Data dictionaries, manuals, and all other explanatory materials.
├── slurm <- Slurm scripts for running the code on the i4Ds cluster.
├── src <- Source code for use in this project.
├── .dvcignore <- Files and directories to ignore by DVC.
├── .gitignore <- Files and directories to ignore by Git.
├── data.dvc <- DVC data/ folder registry.
├── LICENSE <- GNU General Public License v3.0.
├── Makefile <- Makefile with commands.
├── params.yml <- The parameters for the data pipeline.
├── README.md <- The top-level README for developers using this project.
└── requirements.txt <- The requirements file for reproducing the analysis environment.