semvec/pypi-packages huggingface dataset:
ham2spec 0.6.2
A Python extension for computing spectra from Hamiltonians
Requires Python: >=3.6

# ham2spec

This is a Python extension written in Rust using [PyO3](https://github.com/PyO3/pyo3) and [maturin](https://github.com/PyO3/maturin).

This module computes absorption and circular dichroism (CD) spectra from a Hamiltonian and a set of pigment positions and transition dipole moments. It's primarily used by the [fmo_analysis](https://github.com/savikhin-lab/fmo_analysis) tool.

## Installation
Right now this only works on macOS because I only have a macOS system to test on. The primary hurdle to building on other systems is the dependency on LAPACK. Your system will need to have a LAPACK implementation installed, and you'll need to set the correct `lapack_src` feature.

## Development
The Python extension module requires a `crate-type` of `"cdylib"`, but running examples and requires a `crate-type` of `"rlib"`. In order to accommodate both you'll need to run tests via
```
$ cargo test --lib
```
and examples via
```
$ cargo run --no-default-features --example example_name
```

## License

Licensed under either of

 * Apache License, Version 2.0, ([LICENSE-APACHE](LICENSE-APACHE) or http://www.apache.org/licenses/LICENSE-2.0)
 * MIT license ([LICENSE-MIT](LICENSE-MIT) or http://opensource.org/licenses/MIT)

at your option.

### Contribution

Unless you explicitly state otherwise, any contribution intentionally
submitted for inclusion in the work by you, as defined in the Apache-2.0
license, shall be dual licensed as above, without any additional terms or
conditions.
pyroarc 1.1.2
This package performs transformation
Requires Python: >=3.6

# pyroar Transofrmation Package
When Py roars, it produces such powerful package
This package scans any pandas datafram and gives recommendation about the required data engineering and data wrangling for the data using statistical methods
You can performs the follwoing:
- Missing Values treatment (dropping and imputation)
- Outliers detection (using Tuckey Inter Quartile Range (IQR), z-score test for normaly distributed data and skewed data)
- Feature transformations ( reciprocal, squared, and log)
- Scaling for Normal and Noraml Distribuated Featrures.
- Statistical testing using Shapiro and Jaque-bera for numerical features
- Statistical testing using Chai-square for categorical features.
- Recommendation for re-grouping classes of categorical variables 

The following features will be implemented in the future realeases:
- Grouping and binning using statistical testing for ordinal features
- Statistical testing for numerical variables (poission test)
. The source code is available at 
[Github-flavored Markdown](https://github.com/IE-Group-C/pyroar.git)
.
async-case 10.1.0
Backport of Python 3.8's unittest.async_case

# async_case
[![PyPi Version](https://img.shields.io/pypi/v/async_case.svg)](https://pypi.python.org/pypi/async_case/)

Backport of Python 3.8's unittest.IsolatedAsyncioTestCase

See upstream [docs](https://docs.python.org/3/library/unittest.html#unittest.IsolatedAsyncioTestCase)
and [source](https://github.com/python/cpython/blob/3.8/Lib/unittest/async_case.py) for more.

```
pip install async_case
```
tmg-biomechanics 0.0.2
Time series analysis of tensiomyography (TMG) data
Requires Python: >=3.6

# TMG
Utilities for time-series analysis of tensiomyography (TMG) measurement data.
TMG is used to non-invasively measure muscle belly displacement with respect to time during electrically induced isometric contraction, and is used in both sports diagnostics and medical contexts.
To quickly visualize a TMG measurement, [see this ~90 second animation](https://www.youtube.com/watch?v=RwsBNEcN6PA).
For more information about TMG measurements, see [the TMG website's description of the TMG measurement device](https://www.tmg-bodyevolution.com/sports/tmg-measuring-device/).

The Python script `src/tmg/tmg.py` does the following:
- Compute the standard TMG signal parameters `Dm`, `Td`, `Tc`, `Ts`, `Tr`.
- Compute the time derivative of a TMG signal.
- Compute parameters describing the derivative of the TMG signal:
  `rdd_max`, `rdd_min`, `rdd_peak_to_peak`, `rdd_max_time`, and `rdd_min_time`.