semvec/pypi-packages huggingface dataset:
mmos 0.1.0
AI记忆管理系统
Requires Python: >=3.6

# MMOS - AI记忆管理系统

MMOS是一个用于AI系统记忆管理的Python库，它提供了一系列工具来帮助人工智能代理存储、检索和管理记忆信息。

## 特点

- 高效的记忆存储机制
- 语义化检索功能
- 记忆优先级管理
- 长短期记忆集成
- 易于与其他AI系统集成

## 安装

```bash
pip install mmos
```

## 快速开始

```python
from mmos import MemoryManager

# 初始化记忆管理器
memory_manager = MemoryManager()

# 存储新记忆
memory_manager.store("用户喜欢蓝色", tags=["用户偏好", "颜色"])

# 检索记忆
results = memory_manager.retrieve("用户喜欢什么颜色?")
print(results)
```

## 贡献

欢迎提交Pull Request和Issue。

## 许可证

MIT
opsramp-network-backup-utils 1.0.4
OpsRamp Analytics SDK
Requires Python: >=3.6

## OpsRamp Analytics Utilities

This is the SDK for writing OpsRamp analytics apps. It is based on [dash](https://plotly.com/dash/), and it has a number of utility functions.

It contains [analysis wrapper project](https://github.com/opsramp/analysis-wrapper).

It is published on [Pypi](https://pypi.org/project/opsramp-analytics-utils/)

#### How to publish on Pypi

After make updates on SDK, modify the version in _setup.py_.

```
python setup.py sdist bdist_wheel
python -m twine upload dist/*

Note: if above command not works, then use below command
python -m twine upload --skip-existing dist/*
```

- To upgrade the sdk for your app
```
pip install --no-cache-dir --upgrade opsramp-analytics-utils
```
SaliencyMetrics 0.30
A collection of metrics for comparing saliency maps
Requires Python: >=3.10

# Saliency Metrics

![Tests](https://github.com/valevalerio/SaliencyMetrics/actions/workflows/test.yml/badge.svg)
[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)
[![PyPI version](https://img.shields.io/pypi/v/SaliencyMetrics)](https://pypi.org/project/SaliencyMetrics/)
![Documentation Status](https://readthedocs.org/projects/saliencymetrics/badge/?version=latest)

**Saliency Metrics** is a Python package that implements various metrics for comparing saliency maps generated by explanation methods. To ensure fair comparisons, metrics should be computed on the same saliency map and corresponding ground truth map.
The package includes the following metrics:
- **SSIM (Structural Similarity Index)**: A perceptual metric that quantifies the similarity between two images. It considers changes in structural information, luminance, and contrast.
- **PSNR (Peak Signal-to-Noise Ratio)**: A metric that measures the ratio between the maximum possible power of a signal and the power of corrupting noise. It is often used to assess the quality of reconstructed images.
- **EMD (Earth Mover's Distance)**: A metric that measures the distance between two probability distributions over a region D. It is often used in computer vision and image retrieval tasks.
```tutorial.ipynb``` is an oiginal way used to check and test the different metrics. 
# Installation

```pip install SaliencyMetrics```

### This module is a work in progress and is not yet complete.

# Usage

```python
from SaliencyMetrics import ssim, psnr, emd

import numpy as np
import matplotlib.pyplot as plt



# create a random saliency map
saliency_map = np.random.rand(28*28).reshape(28, 28)
# create a random ground truth map
ground_truth_map = np.random.rand(28*28).reshape(28, 28)
# create a random binary mask

# use all the metrics to compare the saliency map with the ground truth map
for metric in [ssim, psnr, emd]:
    
    print(f"{metric.__name__}: {metric(saliency_map, ground_truth_map)}")
    

```
airflow-ssis-provider 1.1.1
A provider package for Apache Airflow to run SSIS Packages
Requires Python: >=3.10

# Apache Airflow SSIS Connector
## Description
This is an operator and sensor that allows you to connect to SSIS and execute packges via Apache Airflow 2.8.0 or higher

## Special Thanks
- mike-hosseini [Original Work](https://github.com/mike-hosseini/airflow-sql-server-plugin)