semvec/pypi-packages huggingface dataset:
libm-ext 0.0.2
A Python package wrapping the essential mathematical special functions in libm-ext with vectorized support.
Requires Python: >=3.11

<div align=center>
<h1>
libm-ext
</h1>
<p align="center">
A Python package wrapping the essential mathematical special functions in <a href="https://crates.io/crates/libm-ext">libm-ext</a> with vectorized support.
</p>
<p align="center">
<img alt="License: MIT OR Apache-2.0" src="https://img.shields.io/crates/l/libm-ext?style=for-the-badge">
<a href="https://pypi.org/project/libm-ext/"> <img alt="PyPI" src="https://img.shields.io/pypi/v/libm-ext?style=for-the-badge"> </a>
</p>
</div>

## Trigonometric functions

- `sinpi` / `sinpif` for $\sin(\pi x)$ (double-precision / single-precision)
- `cospi` / `cospif` for $\cos(\pi x)$ (double-precision / single-precision)
- `sincospi` / `sincospif` for $\sin(\pi x)$ and $\cos(\pi x)$ (double-precision / single-precision)

## Usage
### Scalar functions

```python
from libm_ext import sinpi, cospi, sincospi

sinpi(1.0)
cospi(1.0)
sincospi(1.0)
```
### Vectorized functions with NumPy ndarray

```python
import numpy as np
from libm_ext.vectorized import sinpi, cospi

x = np.random.standard_normal(16000)
sinpi(x)
cospi(x)
```

## License

Licensed under either of:

 * Apache License, Version 2.0, ([LICENSE-APACHE](LICENSE-APACHE) or <https://www.apache.org/licenses/LICENSE-2.0>)
 * MIT license ([LICENSE-MIT](LICENSE-MIT) or <https://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.
xraylib 4.2.1
pip-installable xraylib
Requires Python: >=3.10

xraylib: a library for interactions of X-rays with matter 
=========================================================

Welcome to the __xraylib__ Github project page!

For all information, consult the **[official documentation](http://github.com/tschoonj/xraylib/wiki)**

[![PyPI - Downloads](https://img.shields.io/pypi/dm/xraylib?label=PyPI%20Downloads)](https://pypi.org/project/xraylib/) [![CI](https://github.com/tschoonj/xraylib/actions/workflows/ci.yml/badge.svg?branch=master&event=push)](https://github.com/tschoonj/xraylib/actions/workflows/ci.yml) [![Build and upload to PyPI](https://github.com/tschoonj/xraylib/actions/workflows/build-publish-pypi.yml/badge.svg?branch=master&event=push)](https://github.com/tschoonj/xraylib/actions/workflows/build-publish-pypi.yml) [![Maven Central](https://img.shields.io/maven-central/v/com.github.tschoonj/xraylib.svg?label=Maven%20Central)](https://search.maven.org/search?q=g:%22com.github.tschoonj%22%20AND%20a:%22xraylib%22) [![CodeQL](https://github.com/tschoonj/xraylib/actions/workflows/codeql.yml/badge.svg?branch=master&event=push)](https://github.com/tschoonj/xraylib/actions/workflows/codeql.yml)

Get in touch using Gitter [![Gitter](https://badges.gitter.im/xraylib/community.svg)](https://gitter.im/xraylib/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
quannengbao 0.0.14
all kinds of function inside
Requires Python: >=3.7

# quannengbao
### 需要显示安装全能包指定路径
- pip install --updgrade quannengbao 
### quannengbao安装完成后另外安装bytedtqs
- 内网web环境安装新版 bytedtqs
  - pip install --upgrade bytedtqs --index-url=https://bytedpypi.byted.org/simple/
- 本地环境安装旧版 bytedtqs
  - pip install bytedtqs==0.0.50 --index-url=https://bytedpypi.byted.org/simple/
spharapy 1.3.0
SPHARA-based spatial harmonic analysis and filtering
Requires Python: >=3.10

.. image:: https://readthedocs.org/projects/spharapy/badge/?version=latest
   :target: https://spharapy.readthedocs.io/en/latest/
   :alt: Read the Docs

.. image:: https://img.shields.io/pypi/v/spharapy.svg
   :target: https://pypi.org/project/spharapy/
   :alt: PyPI version

.. image:: https://img.shields.io/pypi/pyversions/spharapy.svg
   :target: https://pypi.org/project/spharapy/
   :alt: Supported Python versions

.. image:: https://img.shields.io/pypi/l/spharapy.svg
   :target: https://pypi.org/project/spharapy/
   :alt: License

.. image:: https://img.shields.io/badge/tests-passing-brightgreen.svg
   :alt: Tests

.. image:: https://img.shields.io/badge/coverage-high-brightgreen.svg
   :alt: Coverage


	 
Sphara Implementation in Python
===============================

SpharaPy is a Python implementation of the SPHARA framework
(Spatial Harmonic Analysis), an extension of classical spatial Fourier
analysis to non-uniformly positioned samples on arbitrary surfaces in
``R^3``. For details, see Graichen et al. (2015).

The basis functions used by SPHARA are obtained from the eigenanalysis
of a discrete Laplace–Beltrami operator defined on a triangular mesh
of the spatial sample locations.

The SpharaPy toolbox provides:

- Tools to compute SPHARA basis functions
- Routines to perform SPHARA analysis and synthesis (SPHARA transform)
- Methods for spatial filtering in the SPHARA domain
- Additional helper functions to design SPHARA-domain filters
  (ideal, Gaussian and Butterworth; low-pass, high-pass and band-pass)

These filters can be used directly with the class
``spharapy.spharafilter.SpharaFilter``.

Requirements and Installation
-----------------------------

Required software and packages:

- Python >= 3.10
- NumPy >= 1.23.5
- SciPy >= 1.14
- Matplotlib >= 3.6

Installation via pip:

``pip install spharapy``

Examples and Usage
------------------

Minimal examples are included within the source code.
More detailed tutorial examples are provided in the documentation.

Citing SpharaPy
---------------

If you find this toolbox useful and publish results obtained using it,
please consider citing the following publications:

- **Graichen, U., Eichardt, R., & Haueisen, J. (2019).**
  *SpharaPy: A Python toolbox for spatial harmonic analysis of non-uniformly
  sampled data.* SoftwareX, 10, Article 100289.  
  https://doi.org/10.1016/j.softx.2019.100289

- **Graichen, U., Eichardt, R., Fiedler, P., Strohmeier, D., Zanow, F.,
  & Haueisen, J. (2015).**
  *SPHARA – A generalized spatial Fourier analysis for multi-sensor
  systems with non-uniformly arranged sensors: Application to EEG.*  
  PLOS ONE, 10(4), e0121741.  
  https://doi.org/10.1371/journal.pone.0121741
pastdays 0.0.1
Find past market days similar to today using historical data
Requires Python: >=3.8
Keywords: trading, backtesting, market-analysis, time-series, finance

# pastdays

> Find **past market days similar to today** and see what actually happened next.

`pastdays` is a Python library that compares **current partial market data**
with **historical data** to identify **similar past days** and analyze
their outcomes.

It does **not predict**, **does not trade**, and **does not generate signals**.
It simply answers one question:

> “When the market looked like this before, what happened afterwards?”

---

## ✨ Why this library exists

Most backtests lie because they assume:
    - perfect execution
    - known future
    - curve-fitted parameters

Traders, however, think differently:

    > “Have I seen this kind of day before?”

    `pastdays` is built around that mindset.

---

## 🧠 What it does (v0)

    - Accepts **user-provided historical data**
    - Accepts **current-day partial data**
    - Extracts simple, explainable features
    - Finds **K most similar historical days**
    - Reports **what happened next** on those days

No machine learning. No black box.

---

## 📦 Installation

    pip install pastdays