semvec/pypi-packages huggingface dataset:
great-expectations-zipcode-expectations 0.1.0
A collection of Expectations to validate zipcode data with Great Expectations.

## Great Expectations Zipcode Expectations

A collection of Expectations to validate zipcode data with Great Expectations.

Author: [Great Expectations](https://github.com/great_expectations/great_expectations)

[PyPi Link](https://python.org/pypi/great_expectations_zipcode_expectations)
fmdt-python 0.0.45
utlity functions for fast meteor detection toolbox
Requires Python: >=3.10

### Introduction

`fmdt` is a Python package used to analyze the performance of the [Fast Meteor Detection Toolbox](https://github.com/alsoc/fmdt)'s executables.

This branch contains the `fmdt`'s documentation and a series of scripts that showcase `fmdt`'s functionality.

### Installation

```
pip install fmdt-python
```

In order to use the functions `fmdt.detect` and `fmdt.visu` (to call `fmdt-detect` and `fmdt-visu`, respectively) you need to have `fmdt-detect` and `fmdt-visu` compiled and locatable via your system's 
`PATH`.

### Example Usage

In order to actually use `fmdt` you need to have installed the [Fast Meteor Detection Toolbox](https://github.com/alsoc/fmdt)
and have the executables `fmdt-detect`, `fmdt-visu`, and `fmdt-check` on your system `PATH`.

```
import fmdt

fmdt.detect("demo.mp4", trk_out_path="ex1_detect_tracks.txt")
fmdt.split_video_at_meteors("demo.mp4", "ex1_detect_tracks.txt")
```

### Documentation

Comprehensive documentation is hosted at [readthedocs](https://fmdt-python-clean.readthedocs.io/en/latest/)
great-expectations-semantic-types-expectations 0.1.0
A collection of Expectations to validate Semantically Typed Data with Great Expectations.

## Great Expectations Semantic Types Expectations
A collection of Expectations to validate Semantically Typed Data with Great Expectations.

### What Are Semantic Types?

Semantic Types categorize data by the type of information it represents. For example, consider a column with STRING data types. How do you know what the data means? You could be looking at a list of streets, cities, counties, etc. Without background information, it can be hard to know what you're looking at. That's where Semantic Types come in. The entity’s Semantic Type defines how it should be interpreted.

### Why should we test on Semantic Types?

Testing on these Semantic Types allows us to create more explicit, fit-for-purpose tests, unlocking questions like `Does this column contain US State abbreviations?` instead of asking `Does this column contain strings in the set [AL, AL, AR, AZ...]`

### How do you know when data entities are good candidates for Semantic Types testing?

Any data that you can tie to a real-world category or reference is ideal for this kind of test, e.g., phone numbers, ZIP codes, countries, coordinates, URLs, email addresses, etc.

This package contains a number of Expectations to support validation of Semantically Typed Data.

Author: [Great Expectations](https://github.com/great_expectations/great_expectations)

[PyPi Link](https://python.org/pypi/great_expectations_semantic_types_expectations)
great-expectations-geospatial-expectations 0.1.0
A collection of Expectations to validate Geospatial data with Great Expectations.

## Great Expectations Geospatial Expectations
A collection of Expectations to validate Geospatial data with Great Expectations.

### What Is Geospatial Data?

Geospatial data is information that describes objects, events, or phenomena with a location on or near the surface of the earth. It typically combines location information (usually in the form of coordinates) with attribute information (characteristics) and temporal information (denoting time or life span).

### How Can Great Expectations Contribute to Geospatial Data?

Geospatial data is most useful when it can be analyzed, shared, and integrated with other types of business data to build data visualizations. These visualizations, such as maps, graphs, cartograms, etc., show a more complete picture of the objects, events, or phenomena.

However, geospatial data is highly prone to error, as it usually involves robust datasets with data gathered from multiple sources in varying formats. This can cause multiple issues down the line. Before the data is used to create interactive maps and visualizations, it needs to be validated.

This package contains a number of Expectations to support validation of Geospatial Data.

Author: [Great Expectations](https://github.com/great_expectations/great_expectations)

[PyPi Link](https://python.org/pypi/great_expectations_geospatial_expectations)
great-expectations-ethical-ai-expectations 0.1.0
A collection of Expectations to validate for degradation, bias, and related Ethical Data concerns with Great Expectations.

## Great Expectations Ethical AI Expectations
A collection of Expectations to validate for degradation, bias, and related Ethical Data concerns with Great Expectations.

### What Is Ethical Data / Ethical AI and Why Is It Important?

The buzz around ethical AI has been growing louder and louder. Companies are collecting and using [more data than ever before](https://www.statista.com/statistics/871513/worldwide-data-created/). And the influence of that data is growing just as fast—with
data powering decisions and actions across every industry.

It's critical that the algorithms fueling these decisions and actions are ethical ones. That's where ethical AI comes in. Ethical AI is artificial intelligence that adheres to strict guidelines (or codes of conduct) regarding fundamental values to ensure automated systems respond to situations in an ethical way. For example, an [algorithm that determines credit limits shouldn't discriminate against women](https://www.washingtonpost.com/business/2019/11/11/apple-card-algorithm-sparks-gender-bias-allegations-against-goldman-sachs/) by granting them lower limits.

### How Can Great Expectations Contribute to Ethical AI

Great Expectations facilitates ethical AI by making it faster and easier to turn codes of conduct into actual code that can be quickly applied to data. For example, one common source of bad model performance is degradation in accuracy over time. For example, an Expectation like `expect_column_label_and_predicted_pair_accuracy_to_be_between` could be used to guard against model degradation.

Author: [Great Expectations](https://github.com/great_expectations/great_expectations)

[PyPi Link](https://python.org/pypi/great_expectations_ethical_ai_expectations)
nonebot-plugin-pokemonfusion 1.0.7
Nonebot2的Pokemon Infinite Fusion中文版融合计算器插件

# 简介
[Pokemon Infinite Fusion](https://www.pokecommunity.com/showthread.php?t=347883) 是 Schrroms 制作的宝可梦同人游戏，游戏中你可以融合任意两只宝可梦，目前共有 $420^2=176,400$ 种组合，其中150,444种由 [Japeal](https://japeal.com/pkm/) 生成，其余45,956种由作者和超过180k官方Discord和Reddit成员设计且不断更新。本仓库基于 [Nonebot2](http://v2.nonebot.dev/) 实现了由 [SDM0](https://twitter.com/SDM_0_) 创建，[Aegide](https://github.com/Aegide) 维护的 [融合计算器](https://aegide.github.io/) 的中文版插件。
# 安装
```pip install nonebot_plugin_pokemonfusion```
或
```nb plugin install nonebot_plugin_pokemonfusion```
# 配置
在.env中添加

```
enable_transparent = True
```

可以使机器人发送透明png（由于QQ的透明通道有bug所以不建议）
# 使用
```融合 [宝可梦A] [宝可梦B]```
参数留空则随机融合

![](/doc/1.png)
# 相关链接
作者及粉丝的原创精灵仓库 https://github.com/Aegide/custom-fusion-sprites

[Japeal](https://japeal.com/pkm/) 生成的精灵仓库 https://github.com/Aegide/autogen-fusion-sprites
eth-connect 1.0.1
ETH-Connect Python API
Requires Python: >=3.7

# ETH-Connect Python API

Ethereum accounts, signatures, and transactions have an additional layer of complexity over traditional cryptographic keys and signatures. [Zymbit](https://www.zymbit.com/)'s ETH-Connect API aims to abstract away this complexity, enabling you to seamlessly integrate with Ethereum and EVM compatible chains without having to deal with their technical intricacies.

**NOTE:** Only compatible with [HSM6](https://www.zymbit.com/hsm6/), [SCM](https://www.zymbit.com/scm/), and [SEN](https://www.zymbit.com/secure-compute-node/)