| Name | discontinuum JSON |
| Version |
0.2
JSON |
| download |
| home_page | None |
| Summary | Estimate discontinuous timeseries from continuous covariates. |
| upload_time | 2024-05-24 15:31:30 |
| 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) timeseries models.
Why might we want a middleware?
GP's are an elegant way to model timeseries with uncertainty.
In many cases, we can represent a complex timeseries as a GP with only a few lines of math.
However, fitting GP's is numerically intense, $\mathcal{O}(n^3)$ complexity.
There are several optimizations that take advantage of simplifying assumptions, different algorithms, or GPUs,
but each has different tradeoffs.
Ideally, we could write the mathematical model once, then run it on whichever "engine" is best suited for a particular problem.
With every model comes a lot of standard utility functions,
and the goal of `discontinuum` is to package these different model applications, engines, and utilities into a single ecosystem.
## Installation
```
pip install discontinuum
```
## Models
Only one for now.
### loadset-gp
LOAD ESTimator (LOADEST) is a software program for estimating river constituent timeseries using surrogate variables (covariates).
For example, estimating nitrate concentration based on date and streamflow.
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.
```python
from loadest_gp import LoadestGP()
model = LoadestGP()
model.fit(target, covariates)
model.plot(covariates)
```
## Engines
Currently, the only engine is `pymc`'s marginal likelihood implementation.
## Roadmap
```mermaid
mindmap
root((discontinuum))
data providers
USGS
EPA
etc
engines
PyMC
Tensorflow
PyTorch
utilities
pre-processing
post-processing
plotting
models
loadest-gp
your own
```
Raw data
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"description": "# discontinuum\n> [!WARNING] \n> Experimental.\n\n## Overview\n`discontinuum` is a middleware for developing Gaussian process (GP) timeseries models.\nWhy might we want a middleware? \nGP's are an elegant way to model timeseries with uncertainty.\nIn many cases, we can represent a complex timeseries as a GP with only a few lines of math.\nHowever, fitting GP's is numerically intense, $\\mathcal{O}(n^3)$ complexity.\nThere are several optimizations that take advantage of simplifying assumptions, different algorithms, or GPUs,\nbut each has different tradeoffs.\nIdeally, we could write the mathematical model once, then run it on whichever \"engine\" is best suited for a particular problem.\nWith every model comes a lot of standard utility functions,\nand the goal of `discontinuum` is to package these different model applications, engines, and utilities into a single ecosystem.\n\n## Installation\n```\npip install discontinuum\n```\n\n## Models\nOnly one for now.\n\n### loadset-gp\nLOAD ESTimator (LOADEST) is a software program for estimating river constituent timeseries using surrogate variables (covariates).\nFor example, estimating nitrate concentration based on date and streamflow.\nHowever, LOADEST has several serious limitations---it's essentially a linear regression---\nand it has been all but replaced by the 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.\n\n```python\nfrom loadest_gp import LoadestGP()\n\nmodel = LoadestGP()\nmodel.fit(target, covariates)\nmodel.plot(covariates)\n```\n\n\n## Engines\nCurrently, the only engine is `pymc`'s marginal likelihood implementation.\n\n## Roadmap\n```mermaid\nmindmap\n root((discontinuum))\n data providers\n USGS\n EPA\n etc\n engines\n PyMC\n Tensorflow\n PyTorch\n utilities\n pre-processing\n post-processing\n plotting\n models\n loadest-gp\n your own\n \n```\n",
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