semvec/pypi-packages huggingface dataset:
cwinpy 1.4.2
A Python module for Bayesian inference with continuous gravitational-wave sources
Requires Python: <4,>=3.9

<p align="left">
  <img src="./logo/logo.png" width="350" title="CWInPy logo">
</p>

# CW Inference in Python

A Python module for performing Bayesian inference on continuous gravitational-wave signals from pulsars

* [Documentation](https://cwinpy.readthedocs.io)

[![Documentation Status](https://readthedocs.org/projects/cwinpy/badge/?version=latest)](https://cwinpy.readthedocs.io/en/latest/?badge=latest)
[![Build Status](https://github.com/cwinpy/cwinpy/workflows/build/badge.svg)](https://github.com/cwinpy/cwinpy/actions?query=workflow%3Abuild)
[![codecov](https://codecov.io/gh/cwinpy/cwinpy/branch/master/graph/badge.svg)](https://codecov.io/gh/cwinpy/cwinpy)
[![PyPI version](https://badge.fury.io/py/cwinpy.svg)](https://badge.fury.io/py/cwinpy)
[![Anaconda-Server Badge](https://anaconda.org/conda-forge/cwinpy/badges/version.svg)](https://anaconda.org/conda-forge/cwinpy)
[![DOI](https://joss.theoj.org/papers/10.21105/joss.04568/status.svg)](https://doi.org/10.21105/joss.04568)
water-column-sonar-resampling 25.11.2
Water Column Sonar Data Reprocessing Project for Warren Tech Capstone 2026
Requires Python: >=3.12
Keywords: reprocessing, sonar, water column, zarr

# water-column-sonar-resampling
Water Column Sonar Data Reprocessing Project for Warren Tech Capstone 2026

## Reminders
Please update the patch number in `pyproject.toml` !!!

## UV Commands
To get the most fresh copy of the project run `uv venv` and then run the source command given `source .venv/bin/activate` (for Linux) -- opens a venv with all of the most up to date packages and code. Be sure to run `uv sync` afterwards if it's a brand new enviorment.

To add a new package to the enviorment you can install it inside of the venv or use `uv add <package name>` followed by `uv sync` instead.

To sync packages like Pytest that are dev dependencies use `uv sync --all-extras` after your `uv sync`

To run Pytest as it would appear in Github make sure all packages are synced and then run `uv run pytest`

## Tagging Commands

`git tag -a v25.11.x -m "Releasing v25.11.x"`

`git push origin --tags`

_Make sure to increase x by one every time. Will reset when a new month starts (December will reset to 25.12.0)_

If you forget to tag a push use the following command:

`git tag -a v25.9.x [(part of or all) commit checksum] -m "Releasing v25.9.x"`
rainduck 0.5.0
A simple BrainFuck extension with macros
Requires Python: >=3.11

# RainDuck

A simple BrainFuck extension with macros transpiled to BrainFuck.

## Installation

Use `pip` to install RainDuck.
```bash
pip install rainduck
```

## Usage

```bash
# transpiles code to BrainFuck
rainduck my-program.rd

# use your favourite BrainFuck compiler or interpreter
brainfuck my-program.bf

# or use pipe
rainduck my-program.rd --stdout | brainfuck
```

Run `rainduck --help` for more options.

### Basic Syntax

```rainduck
// comments are created with '//'
let // if you want to define macros, you must start with the word 'let'
#import(../folder/file.rd) // imports all global macros in ../folder/file.rd
clear = {[-]} // macro without arguments
five_left = {5<} // integer repeats given string
ten_right = {-2five_left} // negative integer reverses string and turns > <-> < and + <-> -
error = {-3[+]} // loop cannot be inverted, this raises an error...
no_error = {-1 error} // ...but double iversion doesn't have effect, this raises no error
// using ?<element>:<element> syntax, you can specify normal and inverted version of some code:
move_left_to_zero = {?{<[<]}:{>[>]}}
move_right_to_zero = {-1move_left_to_zero} // >[>]
move_cell(
    from;
    to = < // default value for this argument
) = {from [- -1from to + -1to from] -1from}

// most whitespace characters are optional
copy_cell(from={};to={};storage=<)={from[--1from storage+-1storage to+-1to from]move(storage)-1from}
in // end of macro definitions
// follows executed code
move( // macro call
    { // code blocks are created with curly brackets...
        let five_left={5{<<>}} in // ...and can contain local macro definition...
        3 {five_left >} // ...but don't have to
    };
    >
)

// `@<character>` is treated as a number equal to ASCII/UNICODE code of the character.
@B+ . @B- @F+ . @F- @! .
#comment(This comment will be also in final brainfuck code.)
```

## Contributing

Pull requests are welcome. I also welcome remarks about anything I've done incorrectly or improperly.

## License

[MIT](https://choosealicense.com/licenses/mit/)
gpder 1.0.1
Bayesian Experimental Design for Minimizing the Uncertainty of Gaussian Processes
Requires Python: >=3.10

# GPder 

This package offers an implementation of the Gaussian Process (GP) Regression 
algorithm with and without derivative information. 

## Description 
The following kernels can be used:
- RegularKernel: Kernel for regular GP regression

    $k(x_i, x_j) = \alpha^2 \mathrm{exp} \left( -\frac{\mid \mid x_i - x_j \mid \mid^2 }{2 \ell^2} \right) + \sigma^2 I$

- DerivativeKernel: Kernel for GP regression with derivative observations. Has the same form as the regular kernel but the covariance term is expanded to include derivative observations. The added noise is also expanded with the derivative noise parameter $\sigma^2_{\nabla}$.

    $k({x}_i, {x}_j) = \alpha^2 \mathrm{exp} \left( -\frac{\mid \mid {x}_i - {x}_j \mid \mid^2 }{2{\ell}^2} \right) _{\mathrm{expanded}} + \sigma^2 _{\mathrm{expanded}} I$


See "Efficient Estimation of Unfactorizable Systematic Uncertainties".

### Install

```
pip install gpder
```

### References

Efficient Estimation of Unfactorizable Systematic Uncertainties