semvec/pypi-packages huggingface dataset:
ratio-genetic-py 0.4.0
Python bindings for the ratio-genetic Rust crate.
Requires Python: >=3.7
Keywords: genetic, algorithms, rust, mathematics, sga, clustering, sequencing

# Ratio Genetic Python Bindings

The bindings crate is currently just in a _proof of concept_ state and will for the
moment just follow the main crate's versioning.

This package provides a thin wrapper around the Ratio Genetic crate written in Rust. It
is built with Maturin utilizing PyO3 bindings for convenience.

To get up and running quickly, a Poetry section has been added to the `pyproject.toml`.
This installs a Poetry environment with Maturin and PyTest. After any changes to the
Rust code you should probably run `poetry run maturin develop` and `poetry run pytest`
using your preferred method.
swisshydrodata 0.3.1
A library to fetch data from the Swiss federal Office for Environment FEON
Requires Python: >=3.12

# swisshydrodata

swisshydrodata is a library that allow you to get data from the
[Swiss Federal Office for the Environment FOEN](https://www.hydrodaten.admin.ch/en/).
To find a station near to you, use the
[list of stations](https://www.hydrodaten.admin.ch/en/messstationen-vorhersage)
on the FEON website.

The library uses a REST API which hands out the data because the FEON
does not allow to use their data service as backend.

The data update interval is limited to onece every 10 minutes by FEON,
so thats how often the API has new data available.

## Example

```python
import aiohttp
import asyncio
from swisshydrodata import SwissHydroData


async def main():
    async with aiohttp.ClientSession() as session:
        shd = SwissHydroData(session)
        # returns a list of station numbers
        data = await shd.async_get_stations()
        print(data)

        # returns all data available for station #2143
        data = await shd.async_get_station(2143)
        print(data)

asyncio.run(main())
```
multiple-hypothesis-testing 0.2.1
Several methods of combining P-values
Requires Python: >=3.6

# MultiTest -- Global Tests for Multiple Hypothesis

This package is a Python implementation of the global tests for multiple hypothesis testing proposed in [1] and [2].

## News
- Changed the default normalization of ordered P-values when ealuating HC to use the mean and standard deviation of the beta distribution. To get the old behavior, initialize with `normalization = 'donoho-jin2008'`.
- 2024-01-15: Added the `MultiTest.fdr` method for False-discovery rate with optimized rate parameter.
- 2024-01-15: Added the `MultiTest.minp` method for minimal P-values as in Bonferroni style inference.

## The package includes several techniques for multiple hypothesis testing:
- ``MultiTest.hc`` Higher Criticism
- ``MultiTest.hcstar`` Higher Criticism with limited range proposed in [1]
- ``MultiTest.hc_jin`` Higher Criticism with limited range proposed as proposed in [3]
- ``MultiTest.berk_jones`` Berk-Jones statistic
- ``MultiTest.fdr`` False-discovery rate with optimized rate parameter
- ``MultiTest.minp`` Minimal P-values as in Bonferroni style inference
- ``MultiTest.fisher`` Fisher's method to combine P-values
In all cases, one should reject the null for large values of the test statistic.

## Example:
```
import numpy as np
from scipy.stats import norm
from multitest import MultiTest

p = 100
z = np.random.randn(p)
pvals = 2*norm.cdf(-np.abs(z)/2)

mtest = MultiTest(pvals)

hc, p_hct = mtest.hc(gamma = 0.3)
bj = mtest.berk_jones()

ii = np.arange(len(pvals))
print(f"HC = {hc}, Indices of P-values below HCT: {ii[pvals <= p_hct]}")
print(f"Berk-Jones = {bj}")
```

## Use cases: 
This package was used to obtain evaluations reported in [5] and [6].

## References:
[1] Donoho, David. L. and Jin, Jiashun. "Higher criticism for detecting sparse hetrogenous mixtures." The Annals of Statistics 32, no. 3 (2004): 962-994.
[2] Donoho, David L. and Jin, Jiashun. "Higher critcism thresholding: Optimal feature selection when useful features are rare and weak." proceedings of the national academy of sciences, 2008.
[3] Jin, Jiashun, and Wanjie Wang. "Influential features PCA for high dimensional clustering." The Annals of Statistics 44, no. 6 (2016): 2323-2359.
[4] Amit Moscovich, Boaz Nadler, and Clifford Spiegelman. "On the exact Berk-Jones statistics and their p-value calculation." Electronic Journal of Statistics. 10 (2016): 2329-2354.
[5] Donoho, David L., and Alon Kipnis. "Higher criticism to compare two large frequency tables, with sensitivity to possible rare and weak differences." The Annals of Statistics 50, no. 3 (2022): 1447-1472.
[6] Kipnis, Alon. "Unification of rare/weak detection models using moderate deviations analysis and log-chisquared p-values." Statistica Scinica 2025.
volcano-base 2.5.0
Download, find and manipulate volcano and climate related time series
Requires Python: >=3.12

# Core module for volcanic eruption simulation projects

<sup>Latest version: v2.5.0</sup> <!-- x-release-please-version -->

> [!WARNING]
>
> This README reflects the changes made to the main branch. For the most up to date
> documentation about the version you are using, see the README at the relevant tag.

This repository contains the core elements used across several independent volcanic
eruption studies. This include strategies for downloading necessary data, loading data
from files and doing simple general manipulations to the time series.

## Install

This project is published to [pypi](https://pypi.org). Install with you preferred
package manager, for example using `pip`:

```bash
pip install volcano-base
```

## Usage

The available modules are:

- `down` (download datasets)
- `load` (find and load local files)
- `manipulate` (make changes to time series)