| Name | odrpack JSON |
| Version |
0.3.5
JSON |
| download |
| home_page | None |
| Summary | Package for weighted orthogonal distance regression (ODR). |
| upload_time | 2025-09-05 21:23:28 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | MIT License
Copyright (c) 2025 HugoMVale
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|
| keywords |
regression
statistics
mathematics
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# odrpack (-python)
[](https://github.com/HugoMVale/odrpack-python/actions)
[](https://codecov.io/gh/HugoMVale/odrpack-python)
[](https://img.shields.io/github/last-commit/HugoMVale/odrpack-python)
[](https://deepwiki.com/HugoMVale/odrpack-python)
## Description
This Python package provides bindings for the well-known weighted orthogonal distance regression
(ODR) solver [odrpack95]. This design ensures that users benefit from the performance and reliability
of the original Fortran implementation, while working within the modern Python ecosystem.
ODR, also known as [errors-in-variables regression], is designed primarily for instances when both
the explanatory and response variables have significant errors.
<p align="center">
<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/8/81/Total_least_squares.svg/220px-Total_least_squares.svg.png" width="250" alt="Deming regression; special case of ODR." style="margin-right: 10px;">
</p>
[errors-in-variables regression]: https://en.wikipedia.org/wiki/Errors-in-variables_models
[odrpack95]: https://github.com/HugoMVale/odrpack95
## Installation
You can install the package via pip:
```sh
pip install odrpack
```
## Documentation and Usage
The following example demonstrates a simple use of the package. For more comprehensive examples and explanations, please refer to the [documentation](https://hugomvale.github.io/odrpack-python/) pages.
```py
from odrpack import odr_fit
import numpy as np
xdata = np.array([0.982, 1.998, 4.978, 6.01])
ydata = np.array([2.7, 7.4, 148.0, 403.0])
beta0 = np.array([2.0, 0.5])
bounds = (np.array([0.0, 0.0]), np.array([10.0, 0.9]))
def f(x: np.ndarray, beta: np.ndarray) -> np.ndarray:
"Model function."
return beta[0] * np.exp(beta[1]*x)
sol = odr_fit(f, xdata, ydata, beta0, bounds=bounds)
print("beta:", sol.beta)
print("delta:", sol.delta)
```
```sh
beta: [1.63336897 0.9 ]
delta: [-0.36885696 -0.31272648 0.02929022 0.11031872]
```
Raw data
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"description": "# odrpack (-python)\n\n[](https://github.com/HugoMVale/odrpack-python/actions)\n[](https://codecov.io/gh/HugoMVale/odrpack-python)\n[](https://img.shields.io/github/last-commit/HugoMVale/odrpack-python)\n\n[](https://deepwiki.com/HugoMVale/odrpack-python)\n\n## Description\n\nThis Python package provides bindings for the well-known weighted orthogonal distance regression\n(ODR) solver [odrpack95]. This design ensures that users benefit from the performance and reliability\nof the original Fortran implementation, while working within the modern Python ecosystem. \n\nODR, also known as [errors-in-variables regression], is designed primarily for instances when both\nthe explanatory and response variables have significant errors. \n\n<p align=\"center\">\n <img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/8/81/Total_least_squares.svg/220px-Total_least_squares.svg.png\" width=\"250\" alt=\"Deming regression; special case of ODR.\" style=\"margin-right: 10px;\">\n</p>\n\n[errors-in-variables regression]: https://en.wikipedia.org/wiki/Errors-in-variables_models\n[odrpack95]: https://github.com/HugoMVale/odrpack95\n\n\n## Installation\n\nYou can install the package via pip:\n\n```sh\npip install odrpack\n```\n\n## Documentation and Usage\n\nThe following example demonstrates a simple use of the package. For more comprehensive examples and explanations, please refer to the [documentation](https://hugomvale.github.io/odrpack-python/) pages.\n\n```py\nfrom odrpack import odr_fit\nimport numpy as np\n\nxdata = np.array([0.982, 1.998, 4.978, 6.01])\nydata = np.array([2.7, 7.4, 148.0, 403.0])\n\nbeta0 = np.array([2.0, 0.5])\nbounds = (np.array([0.0, 0.0]), np.array([10.0, 0.9]))\n\ndef f(x: np.ndarray, beta: np.ndarray) -> np.ndarray:\n \"Model function.\"\n return beta[0] * np.exp(beta[1]*x)\n\nsol = odr_fit(f, xdata, ydata, beta0, bounds=bounds)\n\nprint(\"beta:\", sol.beta)\nprint(\"delta:\", sol.delta)\n```\n\n```sh\nbeta: [1.63336897 0.9 ]\ndelta: [-0.36885696 -0.31272648 0.02929022 0.11031872]\n```\n",
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