| Name | discontinuum JSON |
| Version |
0.5.1
JSON |
| download |
| home_page | None |
| Summary | Estimate discontinuous timeseries from continuous covariates. |
| upload_time | 2024-12-18 22:53:09 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | License ======= Unless otherwise noted, this project is in the public domain in the United States because it contains materials that originally came from the United States Geological Survey, an agency of the United States Department of Interior. For more information, see the official USGS copyright policy at https://www.usgs.gov/information-policies-and-instructions/copyrights-and-credits Additionally, we waive copyright and related rights in the work worldwide through the CC0 1.0 Universal public domain dedication. CC0 1.0 Universal Summary ------------------------- This is a human-readable summary of the [Legal Code (read the full text)][1]. ### No Copyright The person who associated a work with this deed has dedicated the work to the public domain by waiving all of his or her rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law. You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. ### Other Information In no way are the patent or trademark rights of any person affected by CC0, nor are the rights that other persons may have in the work or in how the work is used, such as publicity or privacy rights. Unless expressly stated otherwise, the person who associated a work with this deed makes no warranties about the work, and disclaims liability for all uses of the work, to the fullest extent permitted by applicable law. When using or citing the work, you should not imply endorsement by the author or the affirmer. [1]: https://creativecommons.org/publicdomain/zero/1.0/legalcode |
| keywords |
timeseries
gaussian processes
discontinuous
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# discontinuum
> [!WARNING]
> Experimental.
## Overview
`discontinuum` is a middleware for developing Gaussian process (GP) models.
Why might we want a middleware?
GP's are a flexible and elegant approach to modeling dynamical systems
for which we have sparse and uncertain observations.
In this arena, simple GP models, specified in several lines of math,
can often achieve state-of-the-art predictive performance.
However, fitting GP's is numerically intense, $\mathcal{O}(n^3)$ complexity.
They have several optimizations that take advantage of simplifying assumptions,
different algorithms, or GPUs, but each has tradeoffs.
Ideally, we could quickly write mathematical models, then run them on whichever
"engine" is best suited for a particular problem.
Furthermore, most models include a lot of relatively standard utility functions
for plotting, managing metadata, data pre-processing, and other "boiler plate."
`discontinum` packages engines and utilities within a single ecosystem,
such that creating a new model is just a matter of writing a little math without
too much boilerplate.
## Installation
```
pip install discontinuum
```
## Models
### loadset-gp
`loadest-gp` is Gaussian-process model for estimating river constituent time series,
which borrows its namesake from the venerable LOAD ESTimator (LOADEST) software program.
However, LOADEST has several serious limitations
---it's essentially a linear regression---and it has been all but replaced by
the more flexible Weighted Regression on Time Discharge and Season (WRTDS),
which allows the relation between target and covariate to vary through time.
`loadest-gp` takes the WRTDS idea and reimplements it as a GP.
Try it out in the [loadest-gp demo](https://github.com/thodson-usgs/discontinuum/blob/main/notebooks/loadest-gp-demo.ipynb).
### rating-gp
`rating-gp` is a Gaussian-process model for estimate river flow from stage time series.
Try it out in the [rating-gp demo](https://github.com/thodson-usgs/discontinuum/blob/main/notebooks/rating-gp-demo.ipynb).
## Engines
Currently, the only supported engines are the marginal likelihood implementation in `pymc` and `gpytorch`.
Latent GP implementations could be added in the future.
In general, the `gpytorch` implementation is faster and provides a lot a powerful features,
like GPU support, whereas `pymc` is a more complete probabilistic-programming framework,
which can be "friendlier" for certain use cases.
## Roadmap
```mermaid
mindmap
root((discontinuum))
data providers
USGS
etc
engines
PyMC
PyTorch
utilities
pre-processing
post-processing
plotting
models
loadest-gp
rating-gp
```
Raw data
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"description": "# discontinuum\n\n\n\n> [!WARNING] \n> Experimental.\n\n## Overview\n`discontinuum` is a middleware for developing Gaussian process (GP) models.\nWhy might we want a middleware? \nGP's are a flexible and elegant approach to modeling dynamical systems\nfor which we have sparse and uncertain observations.\nIn this arena, simple GP models, specified in several lines of math,\ncan often achieve state-of-the-art predictive performance.\nHowever, fitting GP's is numerically intense, $\\mathcal{O}(n^3)$ complexity.\nThey have several optimizations that take advantage of simplifying assumptions,\ndifferent algorithms, or GPUs, but each has tradeoffs.\nIdeally, we could quickly write mathematical models, then run them on whichever\n\"engine\" is best suited for a particular problem.\n\nFurthermore, most models include a lot of relatively standard utility functions\nfor plotting, managing metadata, data pre-processing, and other \"boiler plate.\"\n`discontinum` packages engines and utilities within a single ecosystem,\nsuch that creating a new model is just a matter of writing a little math without \ntoo much boilerplate.\n\n## Installation\n```\npip install discontinuum\n```\n\n## Models\n\n### loadset-gp\n`loadest-gp` is Gaussian-process model for estimating river constituent time series,\nwhich borrows its namesake from the venerable LOAD ESTimator (LOADEST) software program.\nHowever, LOADEST has several serious limitations\n---it's essentially a linear regression---and it has been all but replaced by\nthe more flexible Weighted Regression on Time Discharge and Season (WRTDS),\nwhich allows the relation between target and covariate to vary through time.\n`loadest-gp` takes the WRTDS idea and reimplements it as a GP.\nTry it out in the [loadest-gp demo](https://github.com/thodson-usgs/discontinuum/blob/main/notebooks/loadest-gp-demo.ipynb).\n\n### rating-gp\n`rating-gp` is a Gaussian-process model for estimate river flow from stage time series.\nTry it out in the [rating-gp demo](https://github.com/thodson-usgs/discontinuum/blob/main/notebooks/rating-gp-demo.ipynb).\n\n## Engines\nCurrently, the only supported engines are the marginal likelihood implementation in `pymc` and `gpytorch`.\nLatent GP implementations could be added in the future.\nIn general, the `gpytorch` implementation is faster and provides a lot a powerful features,\nlike GPU support, whereas `pymc` is a more complete probabilistic-programming framework,\nwhich can be \"friendlier\" for certain use cases.\n\n\n## Roadmap\n```mermaid\nmindmap\n root((discontinuum))\n data providers\n USGS\n etc\n engines\n PyMC\n PyTorch\n utilities\n pre-processing\n post-processing\n plotting\n models\n loadest-gp\n rating-gp\n```\n",
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