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https://github.com/ecmwf/polytope
A library for extracting polytope "features" from datacubes
https://github.com/ecmwf/polytope
datacubes feature-extraction polytope
Last synced: 5 days ago
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A library for extracting polytope "features" from datacubes
- Host: GitHub
- URL: https://github.com/ecmwf/polytope
- Owner: ecmwf
- License: apache-2.0
- Created: 2023-02-03T17:23:05.000Z (almost 2 years ago)
- Default Branch: develop
- Last Pushed: 2024-12-17T11:43:10.000Z (5 days ago)
- Last Synced: 2024-12-17T12:24:39.579Z (5 days ago)
- Topics: datacubes, feature-extraction, polytope
- Language: Python
- Homepage: https://www.ecmwf.int
- Size: 209 MB
- Stars: 33
- Watchers: 15
- Forks: 4
- Open Issues: 17
-
Metadata Files:
- Readme: readme.md
- Contributing: CONTRIBUTING.rst
- License: LICENSE
Awesome Lists containing this project
README
Concept •
Installation •
Example •
Testing •
Documentation
Polytope is a library for extracting complex data from datacubes. It provides an API for non-orthogonal access to data, where the stencil used to extract data from the datacube can be any arbitrary *n*-dimensional polygon (called a *polytope*). This can be used to efficiently extract complex features from a datacube, such as polygon regions or spatio-temporal paths.
Polytope is designed to extend different datacube backends:
* XArray dataarrays
* FDB object stores (through the GribJump software)
Polytope supports datacubes which have branching, non-uniform indexing, and even cyclic axes. If the datacube backend supports byte-addressability and efficient random access (either in-memory or direct from storage), **Polytope** can be used to dramatically decrease overall I/O load.
> [!WARNING]
> This project is BETA and will be experimental for the foreseeable future. Interfaces and functionality are likely to change, and the project itself may be scrapped. DO NOT use this software in any project/software that is operational.
## Concept
Polytope is designed to enable extraction of arbitrary extraction of data from a datacube. Instead of the typical range-based bounding-box approach, Polytope can extract any shape of data from a datacube using a "polytope" (*n*-dimensional polygon) stencil.
The Polytope algorithm can for example be used to extract:
- 2D cut-outs, such as country cut-outs, from a datacube
- timeseries from a datacube
- more complicated spatio-temporal paths, such as flight paths, from a datacube
- and many more high-dimensional shapes in arbitrary dimensions...
For more information about the Polytope algorithm, refer to our [paper](https://arxiv.org/abs/2306.11553).
If this project is useful for your work, please consider citing this paper.
## Installation
Install the polytope software with Python 3 (>=3.7) from GitHub directly with the command
python3 -m pip install git+ssh://[email protected]/ecmwf/polytope.git@develop
or from PyPI with the command
python3 -m pip install polytope-python
## Example
Here is a step-by-step example of how to use this software.
1. In this example, we first specify the data which will be in our Xarray datacube. Note that the data here comes from the GRIB file called "winds.grib", which is 3-dimensional with dimensions: step, latitude and longitude.
```Python
import xarray as xr
array = xr.open_dataset("winds.grib", engine="cfgrib")
```
We then construct the Polytope object, passing in some additional metadata describing properties of the longitude axis.
```Python
options = {"longitude": {"cyclic": [0, 360.0]}}
from polytope.polytope import Polytope
p = Polytope(datacube=array, axis_options=options)
```
2. Next, we create a request shape to extract from the datacube.
In this example, we want to extract a simple 2D box in latitude and longitude at step 0. We thus create the two relevant shapes we need to build this 3-dimensional object,
```Python
import numpy as np
from polytope.shapes import Box, Select
box = Box(["latitude", "longitude"], [0, 0], [1, 1])
step_point = Select("step", [np.timedelta64(0, "s")])
```
which we then incorporate into a Polytope request.
```Python
from polytope.polytope import Request
request = Request(box, step_point)
```
3. Finally, extract the request from the datacube.
```Python
result = p.retrieve(request)
```
The result is stored as an IndexTree containing the retrieved data organised hierarchically with axis indices for each point.
```Python
result.pprint()
Output IndexTree:
↳root=None
↳step=0 days 00:00:00
↳latitude=0.0
↳longitude=0.0
↳longitude=1.0
↳latitude=1.0
↳longitude=0.0
↳longitude=1.0
```
## Testing
#### Additional Dependencies
The Polytope tests and examples require additional Python packages compared to the main Polytope algorithm.
The additional dependencies are provided in the requirements_test.txt and requirements_examples.txt files, which can respectively be found in the tests and examples folders.
Moreover, Polytope's tests and examples also require the installation of eccodes and GDAL.
It is possible to install both of these dependencies using either a package manager or manually.
## Contributing
The main repository is hosted on GitHub; testing, bug reports and contributions are highly welcomed and appreciated.
Please see the [Contributing](./CONTRIBUTING.rst) document for the best way to help.
Main contributors:
- Mathilde Leuridan - [ECMWF](https://www.ecmwf.int)
- James Hawkes - [ECMWF](https://www.ecmwf.int)
- Simon Smart - [ECMWF](www.ecmwf.int)
- Emanuele Danovaro - [ECMWF](www.ecmwf.int)
- Tiago Quintino - [ECMWF](www.ecmwf.int)
See also the [contributors](https://github.com/ecmwf/polytope/contributors) for a more complete list.
## License
Copyright 2021 European Centre for Medium-Range Weather Forecasts (ECMWF)
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at [http://www.apache.org/licenses/LICENSE-2.0](http://www.apache.org/licenses/LICENSE-2.0).
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
In applying this licence, ECMWF does not waive the privileges and immunities
granted to it by virtue of its status as an intergovernmental organisation nor
does it submit to any jurisdiction.
## Citing
If this software is useful in your work, please consider citing our [paper](https://arxiv.org/abs/2306.11553) as
> Leuridan, M., Hawkes, J., Smart, S., Danovaro, E., and Quintino, T., “Polytope: An Algorithm for Efficient Feature Extraction on Hypercubes”, arXiv e-prints, 2023. doi:10.48550/arXiv.2306.11553.
## Acknowledgements
Past and current funding and support for **Polytope** is listed in the adjoining [Acknowledgements](./ACKNOWLEDGEMENTS.rst).