# SatFlow *Sat***ellite Optical ***Flow* with machine learning models.
The goal of this repo is to improve upon optical flow models for predicting future satellite images from current and past ones, focused primarily on EUMETSAT data.
## Installation
Clone the repository, then run `shell conda env create -f environment.yml conda activate satflow pip install -e . ``
Alternatively, you can also install a usually older version through `pip install satflow`
## Data
The data used here is a combination of the UK Met Office’s rainfall radar data, EUMETSAT MSG satellite data (12 channels), derived data from the MSG satellites (cloud masks, etc.), and numerical weather prediction data. Currently, some example transformed EUMETSAT data can be downloaded from the tagged release, as well as included under `datasets/`.
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Release files for satflow 0.3.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| satflow-0.3.1.tar.gz | 22.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| satflow-0.3.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 55.5 kB
Release files / satflow-0.3.1.tar.gz
| Download URL | satflow-0.3.1.tar.gz |
|---|---|
| Size | 22.4 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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Release files / satflow-0.3.1-py3-none-any.whl
| Download URL | satflow-0.3.1-py3-none-any.whl |
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| Size | 33.1 kB |
| Tags | Python 3 |
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