semvec/pypi-packages huggingface dataset:
paxtools 6.0.0.post1
Python wrapper for Paxtools.
Requires Python: >=3.13
Keywords: paxtools, cli

# PAXTools

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

Python wrapper for Paxtools. This requires Java to run, as this is a pip wrapper for a Java executable jar.

_This currently is only a CLI wrapper. This only contains a python utility to convert BioPAX tools to SIF._
_This satisfies my needs, though I may extend this in the future, and for some time I also plan to review and accept incoming PRs._
_If this turns out to be a false claim, feel free to email me and I'll gladly move the package name to the motivated individual!_

## CLI

Running this package directly opens the `paxtools` jar, which requires Java.
JVM arguments can be passed in with the universal environment variable `JDK_JAVA_OPTIONS`.

## Versioning

We care very little about breaking API changes in the actual API, since these largely are reflective
of Paxtools changes. Instead, the version is `<paxtools-version>-<incremented-number>`.

## Development

After cloning this repository, run:

```sh
git submodule update --init --recursive
```

which will clone the Paxtools repository under `paxtools/paxtools`.
Install [maven](https://maven.apache.org/), then run `mvn install` under `paxtools/paxtools`, which will build
the desired (and shadowed!) `paxtools/paxtools/paxtools-console/target/paxtools.jar`.

The python code under `paxtools` (`cli.py`) provides a small installation-invariant wrapper
for this tool which can be installed using `pip`.
asyncvictoria 0.7.1
Write data to VictoriaMetrics
Requires Python: >=3.10
Keywords: iot, logging

AsyncVictoria
=============

Welcome to `AsyncVictoria <https://github.com/M-o-a-T/asyncvictoria>`__!

This library contains a small async wrapper for submission of data to,
and (rudimentary) reading from, `VictoriaMetrics <https://victoriametrics.com>`__.

Also included:

* A basic unit test
* Test scaffolding, to easily test your own code
* a utility to read the last timestamp, as VictoriaMetrics supports efficient queries
* helper code to interpret RRD / collectd input so that you can feed those
  to VictoriaMetrics
* a couple of example scripts
* a migration tool to migrate data from Akumuli to VictoriaMetrics

Tests and collectd support currently require Trio because (a) anyio doesn't
support subprocesses, (b) UDP socket support in anyio is kindof limited.
rqrisk 1.0.13
risk calc
Requires Python: >=3.7

# RQRISK
simple and fast indicator calculator


## Install

pip install rqrisk [--upgrade]


## Usage

import packages

`
import rqrisk
`
* create Risk object

`
risk = rqrisk.Risk(portfolio_returns, benchmark_returns, risk_free_rate, period)
`

* get result

`
result = risk.all() # dict
`

## Example

```jupyter
In [1]: import rqrisk

In [2]: import numpy

In [3]: risk = rqrisk.Risk(
   ...:     # 组合日收益率
   ...:     daily_returns=numpy.array([0.0006, 0.0213, -0.0155, -0.017, -0.0025, -0.0173, -0.0006, -0.0094, 0.0108, -0.0151]),
   ...:     # 基准日收益率
   ...:     benchmark_daily_returns=numpy.array([0.0024, 0.0048, -0.0036, -0.013, 0.0107, -0.0203, 0.0097, 0.0018, 0.0083, -0.0089]),
   ...:     # 无风险利率
   ...:     risk_free_rate=0.02
   ...: )
   
In [4]: risk.all()
Out[4]: 
{'return_rate': -0.04454119145160703,
 'annual_return': -0.6827938423763955,
 'benchmark_return': -0.008557443372075783,
 'benchmark_annual_return': -0.19472831925009826,
 'alpha': -0.9467029842994686,
 'beta': 0.8912056741947679,
 'volatility': 0.013005985801417234,
 'annual_volatility': 0.2064636239147226,
 'benchmark_volatility': 0.010414781162687322,
 'benchmark_annual_volatility': 0.1653295254937847,
 'max_drawdown': 0.06502900516774743,
 'tracking_error': 0.009180922248518028,
 'annual_tracking_error': 0.14574262245479186,
 'information_ratio': -0.4113963917368945,
 'sharpe': -5.55274570048971,
 'excess_sharpe': -6.328416385440682,
 'downside_risk': 0.011371747671783752,
 'annual_downside_risk': 0.1805208978703054,
 'sortino': -6.350732870959105,
 'calmar': -10.499835275275627,
 'excess_return_rate': -0.036369224165807304,
 'excess_annual_return': -0.6068586934419611,
 'excess_volatility': 0.009180922248518028,
 'excess_annual_volatility': 0.14574262245479186,
 'excess_max_drawdown': 0.050301584825935344,
 'var': 0.025161857442751474,
 'win_rate': 0.3,
 'correlation': 0.7136492542281812}
```
dbgpt-sandbox 0.7.3
A secure sandbox execution environment for DB-GPT Agent
Requires Python: >=3.10

# DB-GPT Sandbox

背景：AI Agent 逐步成为使用 AI 解决真实环境中各类问题的有力工具，然而真实环境的任务隔离性和安全性是企业落地中必然要考虑的问题。 DB-GPT Agent 目前不支持统一、可扩展的安全沙箱环境。 

#### 预期目标：

 为 DB-GPT Agent 实现一个安全的沙箱执行环境（支持 Agent、工具的运行和多语言代码的执行）。 分三个部分：

1. 基于 DB-GPT Agent + Docker 容器实现安全的代码执行环境，支持 Python、Shell、Node.js 等代码的执行，改造 DB-GPT 现有的代码执行智能体。 
2. 支持有状态的沙箱环境，多次代码执行可以在相同的环境中，并且上次环境的变更能影响下次的执行（例如第一次执行安装 pypi 依赖，第二次执行安装后的依赖能正常使用） 
3. 插件化的安全沙箱环境实现，设计统一的沙箱环境接口，支持 Docker、Podman、本地进程（基Cgroup/Namespace/WebAssembly等）等沙箱环境的实现。

#### 产出要求：

1. 项目设计文档（含架构图、原理图、实现细节等）
2. 实现安全沙箱环境的核心模块（统一沙箱环境接口，Docker 实现和本地进程的实现）
3. 提供完整的使用教程文档说明
4. 基于沙箱环境，开发一个支持 Python 等代码执行的 Agent 案例
dbgpt 0.7.5
DB-GPT is an experimental open-source project that uses localized GPT large models to interact with your data and environment. With this solution, you can beassured that there is no risk of data leakage, and your data is 100% private and secure.
Requires Python: >=3.10

# dbgpt-core

Package that contains modules and utilities that can be used across packages and services.