| Name | scifit JSON |
| Version |
0.1.15
JSON |
| download |
| home_page | None |
| Summary | Scientific Fit for Python |
| upload_time | 2024-08-03 02:32:45 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | BSD 3-Clause License Copyright (c) 2020-2023, Jean Landercy (see AUTHORS and LICENSE_bundled files) All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| keywords |
scientific
curve-fitting
optimization
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
[](https://github.com/jlandercy/scifit/actions/workflows/pypi.yaml)
[](https://github.com/jlandercy/scifit/actions/workflows/docs.yaml)
# SciFit
> Comprehensive fits for scientists
Welcome to SciFit project the Python package for comprehensive fits for scientists
designed to ease fitting procedure and automatically perform the quality assessment.
The SciFit project aims to support your work by:
- Providing a clean, stable and compliant interface for each solver;
- Perform ad hoc transformations, processing and tests on each stage of a solver procedure;
- Render high quality figures summarizing solver solution and the quality assessment.
## Installation
You can install the SciFit package by issuing:
```commandline
python -m pip install --upgrade scifit
```
Which update you to the latest version of the package.
## Quick start
Let's fit some data:
```python
from scifit.solvers.scientific import *
# Select a specific solver:
solver = HillEquationFitSolver()
# Create some synthetic dataset:
data = solver.synthetic_dataset(
xmin=0.0, xmax=5.0, resolution=50,
parameters=[3.75, 0.21],
sigma=0.1, scale_mode="auto", seed=1234,
)
# Perform regression:
solution = solver.fit(data)
# Render results:
axe = solver.plot_fit()
```
We have a nice adjustments for such noisy data:
Or even better a full fit report to check each key points at once:
```python
solver.report("hill_report")
```
Which produces a PDF file called `hill_report.pdf`.
## Resources
- [Documentations][20]
- [Repository][21]
[20]: https://github.com/jlandercy/scifit/tree/main/docs
[21]: https://github.com/jlandercy/scifit
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