semvec/pypi-packages huggingface dataset:
pyfastpfor 1.4.0
Python bindings for the FastPFor library (fast integer compression)

Pythong bindings for FastPFor: A research library with integer compression schemes. FastPFor is broadly applicable to the compression of arrays of 32-bit integers where most integers are small. The library seeks to exploit SIMD instructions (SSE) whenever possible. This library can decode at least 4 billions of compressed integers per second on most desktop or laptop processors. That is, it can decompress data at a rate of 15 GB/s. This is significantly faster than generic codecs like gzip, LZO, Snappy or LZ4.
portfolio-stats 1.0.3
Calculate fundamental metrics of a portfolio's individual assets.

# `PortfolioStats`

A python class to compute key portfolio statistics for assets based on their price data.

## Introduction

`PortfolioStats` provides an organized method to calculate and view various portfolio metrics such as the Sharpe Ratio, cumulative returns, annualized returns, annualized volatility, and maximum drawdown.

## Parameters

- **returns** (`pd.DataFrame`): Return data for assets.
- **log** (`bool`): Indicator to use logarithmic returns. Default is `False`.
- **annualization_factor** (`int`): Factor to annualize returns and risk. Default is `252` (number of trading days in a year).

## Calculations

### Sharpe Ratio

$$\frac{E\bigl[r\bigl] \times A}{\sigma{\bigl[r\bigl]} \times \sqrt{A}}$$

Where:
- $E\bigl[r\bigl]$ is the expected return of the asset.
- $\sigma{\bigl[r\bigl]}$ is the standard deviation (risk) of the returns.

### Cumulative Returns

Using simple returns:
$$\text{Cumulative Return} = \prod^T_{i=1}(1+r_{i}) - 1$$

Using logarithmic returns:
$$\text{Cumulative Return} = \sum^T_{i=1} r_{i}$$

### Annualized Return

$$\text{Annualized Return} = E\bigl[r\bigl] \times \text{ }A$$

### Annualized Volatility

$$\text{Annualized Volatility} = \sigma{\bigl[r\bigl]} \times \sqrt{A}$$

### Maximum Drawdown

Using simple returns:

$$\text{MDD} = \min \left( \frac{\text{CR}_{\text{trough}}}{\text{CR}_{\text{peak}}} - 1 \right)$$

Using logarithmic returns:

$$\text{MDD} = \min \left( \text{CLR} - \text{CLR (max up to now)} \right)$$

Where:
- $\text{CR}$ is the cumulative simple return series.
- $\text{CLR}$ is the cumulative logarithmic return series.

## Usage

Initialize the class with return data and optional parameters. Access the `.info` attribute to view the calculated portfolio statistics.
pychromium 1.2
chromium 浏览器文件包
Keywords: pychromium,chromium,chrome

# 项目描述

chromium 浏览器文件包。

注：此 pypi 包非 chromium 发行，但内含从 chromium 官网下载的浏览器文件。

# 作者信息

昵称：lcctoor.com

域名：lcctoor.com

邮箱：lcctoor@outlook.com

[主页](https://lcctoor.github.io/arts/) \| [微信](https://lcctoor.github.io/arts/arts/static/static-files/WeChatQRC.jpg) \| [Python交流群](https://lcctoor.github.io/arts/arts/static/static-files/PythonWeChatGroupQRC.jpg) \| [捐赠](https://lcctoor.github.io/arts/arts/static/static-files/DonationQRC-1rmb.jpg)

# Bug提交、功能提议

您可以通过 [Github-Issues](https://github.com/lcctoor/arts/issues)、[微信](https://lcctoor.github.io/arts/arts/static/static-files/WeChatQRC.jpg) 与我联系。

# 安装

```
pip install pychromium
```

# 教程

```python
from pychromium import (
    chromium_dir,  # chromium 文件夹路径
    bootstrap_file  # 浏览器启动文件路径, 即 chrome.exe 的路径
)
```
BOTANY 0.0.9
Various calculator tools




<b>BOTANY</b>

<p>CAUTION: A lot of the functions pass strings to subprocesses.</p>

</p>Various calculator tools</p>

<section>
<header>Directory Equalizer</header>
<code>
import BOTANY.FS.DIRECTORY.EQUALIZE as EQUALIZE
EQUALIZE.MULTIPLE ({
	"DIRECTORY 1": "",
	"DIRECTORY 2": "",
	
	"DIRECTORIES": [
		
	],
	
	"START": "yes"	
})
</code>
</section>
npis-api-utils 1.0.0
NPIS api related libraries.

# NPIS UTILS

![Python](https://img.shields.io/badge/python-3.9-blue) ![Flask](https://img.shields.io/badge/Flask-2.3.2-blue) ![postgres](https://img.shields.io/badge/postgres-11.0-blue)
[![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)[![linting: pylint](https://img.shields.io/badge/linting-pylint-yellowgreen)](https://github.com/PyCQA/pylint)


This Python package include all the libraries and utils needed for NPIS related services.

## Usage

Install using pip
RdbEX 0.2.4
Replit DataBase EXtended
Requires Python: >=3.10.0,<3.12

# RdbEX
## Replit DataBase EXtended

Replit database extended is a package designed to expand the abilities of the default replit database.

it adds things such as:
<br>
**emulated folders**
<br>
**ease of access**
<br>
**value sharing**
<br>
**safe secret storage**
<br>
*and much more...*

## how to use: a basic rundown

### (NOTE: does NOT support versions below 0.2)

after installing the package, import using <code>import rdbex</code>.

to create a new key in the root directory, use <code> rdbex.set("keyname")</code>.

to delete a key, use <code> rdbex.delete("keyname")</code>.

to create a new key within a folder, use <code> rdbex.set("keyname", "folderA")</code>. (same thing for deleting keys within folders)

to create keys within nested folders, use <code> rdbex.set("keyname", "folderA|folderB")</code>. (same thing for deleting keys within folders)

to list keys and list keys within a folder(s), use <code> rdbex.list("folderA|folderB (optional)")</code>.

and to drop your database (with an optional path), use <code> rdbex.drop("folderpath (optional)")</code>

### key sharing

to share a key with another value, use <code>rdbex.reference("FROM", "key", "path (optional)"</code> in the <code>value</code> section of <code>rdbex.set()</code>

### secret storage

to safely store replit secrets and os environment variables within your database, use <code>rdbex.reference("ENV", "variable name")</code> in the <code>value</code> section of <code>rdbex.set()</code>

## Recovery.

### warning!

to repair anything without reinstalling the package, you need to make sure the "recovery.py" and "__internal/_recovery.py" files are accessible.
<br>
<br>
if you delete a required file from the package or the package gets corrupted and if fails to load on import, you will get an ImportError that says *"The package has been installed incorrectly and/or has been corrupted. please reinstall the package."*, to fix this, just uninstall & reinstall the package using the package manager of your choice.

if the database is corrupted, you will get an ImportError saying *"critical damage to rdbex detected. a repair has been ran, please restart. if the error persists, use "from rdbex import recovery" and run recovery.rebuild() to attempt to fix the issue"*, before doing anything, try rerunning the code, as it attempts to repair the database. if the error still persists, run <code>from rdbex import recovery</code>. after importing recovery, run <code>recovery.rebuild()</code> to completely rebuild the database metadata. 
<br>
***this WILL wipe ALL user-set config, but NOT the db***

<br>
<br>
<br>
<sub>*keep in mind that by default, "|" is the spacing character for folders, this can be changed via the <code>configure()</code> command in utils*

<sub>*this is not full documentation, and it will be coming soon.*

<sub>*converting databases is currently not implemented, but might happen in the future.*
ballpackage-ubiloctech 0.0.1
A package to simulate bouncing balls between 4 walls
Requires Python: >=3.9

# Example Package that simulates a set of bouncing balls in a closed room

Simulates a set of bouncing balls in a closed room. This package uses law of conservation of momentum and energy to simulate different levels of elastic collisions by setting the elastic coefficient (eCoefft).

The number of balls and ball color can also be set.

The library can also save and re-load states.
quipt-cli 0.1.5
The PC Client for the Quipt App.
Requires Python: >=3.10

# Quipt CLI
A connection between the Quipt Smartphone App and the PC Qupit Client
It is used to create and push scripts to the remote server. 
For more information read [Getting Started](https://quipt.app/docs/getting-started) at [quipt.app](https://quipt.app)
pythreadex 0.1
Utility for retrieving and plotting ThreadEx data
Requires Python: >=3.6
Keywords: meteorology,weather

# pythreadex
Pythreadex is a simple Python utility to fetch long-term station data for the United States from ThreadEx, courtesy of ACIS (https://www.rcc-acis.org/). This utility includes a few plotting and climatology functions, as well as converting data to CSV format.

## Installation


### Pip

Installation is available via pip:

```sh
pip install pythreadex
```

### From source

pythreadex can also be installed from source by cloning the GitHub repository:

```sh
git clone https://github.com/tomerburg/pythreadex
cd pythreadex
python setup.py install
```

## Dependencies
- matplotlib >= 2.2.2
- numpy >= 1.14.3
- scipy >= 1.1.0
- pandas >= 1.3.0

## Sample Usage
For full documentation and examples, please refer to [Tropycal Documentation](https://tropycal.github.io/tropycal/).

As of v0.3, the documentation is up-to-date following a bug that started with v0.2.5 where the documentation was not updated with each release.

## Sample Usage
This sample code shows how to search through a dataset, retrieve station data, make a plot and convert the data to CSV format:

```python
from pyhreadex import Dataset
import matplotlib.pyplot as plt
            
# Create an instance of Dataset
dataset = Dataset()

# Retrieve all stations in New Jersey
print(stations.search_by_state('NJ'))

# Search for Newark, NJ's station ID
station_id = dataset.search_by_name('Newark, NJ')

# Create an instance of a Station object with this station ID
station = dataset.get_station(station_id)

# Make a plot of 2023 maximum temperatures, from January to May
station.plot_temp_time_series('max', year=2023, date_range=('1/1','5/31'))

# Convert data to a CSV file
station.to_csv('newark.csv')
```