semvec/pypi-packages huggingface dataset:
wemeet-openapi-sdk-python 1.0.10
OpenAPI SDK for Python
Requires Python: >=3.7
Keywords: Wemeet, OpenAPI SDK

# 简介
欢迎使用腾讯会议开发者工具套件（SDK），为方便 Python 开发者调试和接入腾讯云会议 API，这里向您介绍适用于 Python 的腾讯会议开发工具包，并提供首次使用开发工具包的简单示例。让您快速获取腾讯会议 Python SDK 并开始调用。

# 依赖环境
1. 依赖环境：Python 3.7 版本及以上。
2. 购买腾讯会议企业版获取 SecretID、SecretKey。SecretID 是用于标识 API 调用者的身份，SecretKey 是用于加密签名字符串和服务器端验证签名字符串的密钥 SecretKey 必须严格保管，避免泄露。

# 获取安装
有以下两种方式获取安装。
## 通过pip安装（推荐）
您可以通过 pip 安装方式将腾讯会议 OpenAPI Python SDK 安装到您的项目中。
请在命令行中执行以下命令:

```bash
pip install --upgrade wemeet-openapi-sdk-python
```
请注意，如果同时有 python2 和 python3 环境， python3 环境需要使用 pip3 命令安装。
## 通过源码包安装
1. 前往 [Github 代码托管地址](https://github.com/TencentCloud/wemeet-openapi-sdk-python) 下载源码压缩包；
2. 解压源码包到您项目合适的位置；
3. 使用如下命令安装：
```bash
cd wemeet-openapi-sdk-python
pip install .
```

# 示例

以创建会议接口为例：
```Python
import random
import time

import wemeet_openapi

# create_meeting_demo 创建会议请求demo
def create_meeting_demo():

    # 1.构造 client 客户端(jwt 鉴权需要配置 appId sdkId secretID 和 secretKey)
    client = wemeet_openapi.Client(app_id="2****46", sdk_id="2****50",
                                   secret_id="Zk*****J8h",
                                   secret_key="Y2z*****WRsVksn")

    # 2.构造请求体
    request = wemeet_openapi.ApiV1MeetingsPostRequest(
        body=wemeet_openapi.V1MeetingsPostRequest(
            instanceid=2,
            meeting_type=0,
            subject="测试会议",
            type=1,
            userid="userid",
            start_time="1651334400",
            end_time="1651377600"
        )
    )

    # 3.构造 JWT 鉴权器
    authenticator_builder = wemeet_openapi.JWTAuthenticator(
        nonce=random.randrange(0, 2 ** 64),
        timestamp=str(int(time.time()))
    ).options_build()

    # 4.发送对应的请求
    try:
        response = client.meetings_api.v1_meetings_post(
            request=request,
            authenticator_options=[authenticator_builder])

        print(f"Response from `MeetingsApi.V1MeetingsPost`: {vars(response)}\n")
    except wemeet_openapi.ClientException as e:
        print(f"Error when calling `MeetingsApi.V1MeetingsPost`: {e}\n")

    except wemeet_openapi.ServiceException as svrErr:
        print(f"Error when calling `MeetingsApi.V1MeetingsPost`: {svrErr}\n")
        print(f"Full HTTP response: {str(svrErr.api_resp.raw_body)}\n")


# 调用入口点函数
if __name__ == "__main__":
    create_meeting_demo()
```
md-insights-client 0.3.1
OPSWAT MetaDefender InSights API client

# md-insights-client-api

API client for MetaDefender InSights threat intelligence feeds.

## Installation

The app has been tested on Python 3.

It's best to install the program into a Python virtual environment. The
recommended way to install it is using [pipx](https://pypa.github.io/pipx/):

    pipx install md-insights-client

It can also be installed using [pip](https://pip.pypa.io/en/stable/) into a
target virtualenv.

    /path/to/environment/bin/python3 -m pip install md-insights-client

## Configuration

A configuration file must be populated with an API key. If only querying the API
to perform lookups, this configuration setting is all that is required. If
retrieving snapshots, a list of feed names to retrieve must also be specified.

A sample configuration file can be copied from `config/dot.md-insights.yml` and
installed at `$HOME/.md-insights.yml`. Update the configuration file to make
the following changes:

1. Set your API key.
2. Uncomment feed names for the MetaDefender InSights feeds you will access
   (if applicable).

Don't forget to set a restrictive mode on the file:

```
chmod 0600 ~/.md-insights.yml
```

## Usage

When installed, two commands are available.

### md-insights-query-client

The `md-insights-query-client` command can be used to query the MD InSights API
to look up artifacts against one or more threat intelligence collections.

See `-h/--help` output for help.

To use this command, provide multiple positional arguments to the script.

- The first argument is the **query type**, such as `c2-dns`, `c2-ip`,
  `reputation` or `all`. The special `all` type autodetects the artifact
  format(s) to query all relevant collections.
- The second and subsequent arguments are the artifacts for which to query.
  One or more artifacts such as IP addresses or domain names may be specified.

For example:

```
md-insights-query-client all appleprocesshub.com apimonger.com
```

By default, response data is output in tabular format, one indicator per row
that is found in MD InSights collections. If you prefer to see the raw JSON
response format from the API, use the `-j/--json` option.

### md-insights-snapshot-client

The `md-insights-snapshot-client` command can be used to download feed
snapshots. To retrieve feed snapshots, your API key must be provisioned with
access to the selected feeds.

See `-h/--help` output for help.

When the command is called, the client script downloads feed snapshots from the
API service. As the compressed snapshots are downloaded, they are decompressed
and the feeds are written to disk.

## Documentation

For information about MetaDefender InSights threat intelligence feeds, see the
documentation site:

<https://www.opswat.com/docs/mdinsights>
pyfoma 1.0.7
Python Finite-State Toolkit
Requires Python: >=3.6

# PyFoma

[![PyPI version](https://badge.fury.io/py/pyfoma.svg)](https://badge.fury.io/py/pyfoma)

## Quickstart

```bash
pip install pyfoma
```

```python
from pyfoma import FST
myfst = FST.re("(cl|n|s)?e(ve|a)r")
myfst.view()
```

<img src="./docs/examples/images/quickstart1.png" width="722">

Next, check out the [documentation](https://mhulden.github.io/pyfoma/)!

## Contributing

<!-- TODO: Include details on how to open PRs -->

### Rebuilding the documentation

```bash
# Update automatically-scraped docs
sphinx-apidoc -o docs src/pyfoma/

# Rebuild docs
cd docs
make html
```

## Citation

```
@inproceedings{hulden-etal-2024-pyfoma,
    title = "{P}y{F}oma: a Python finite-state compiler module",
    author = "Hulden, Mans and Ginn, Michael and Silfverberg, Miikka and Hammond, Michael",
    editor = "Cao, Yixin and Feng, Yang and Xiong, Deyi",
    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)",
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.acl-demos.24",
    doi = "10.18653/v1/2024.acl-demos.24",
    pages = "258--265"
}
```
weread2notionpro 0.6.8
自动将微信读书笔记和阅读记录同步到Notion
Requires Python: >=3.6

## 更新日志

### 0.6.5

1. 划线和笔记数据库支持修改标题
2. 划线和笔记数据库支持删除属性

### 0.6.7

1. 修复重复插入问题（微信读书bookId不一致导致）
2. 删除已经重复插入的数据
chemprep 0.1.0
A Python library for molecular fingerprinting and similarity calculations.
Requires Python: >=3.7

# ChemPrep

A Python library and command-line tool for molecular fingerprinting, similarity calculations, and descriptor generation using RDKit.

## Installation

```bash
pip install chemprep
```

## Usage

### As a Library

```python
from chemprep.fingerprints import generate_fingerprint, get_available_fingerprints
from chemprep.similarity import calculate_similarity, get_available_similarity_metrics
from chemprep.descriptors import get_all_descriptors

# Get available fingerprints and similarity metrics
print(get_available_fingerprints())
print(get_available_similarity_metrics())

# Generate a fingerprint
smiles = "CCO"
fp = generate_fingerprint(smiles, fingerprint_type="Morgan")
print(fp)

# Calculate similarity
smiles1 = "CCO"
smiles2 = "CCN"
similarity = calculate_similarity(smiles1, smiles2, fingerprint_type="Morgan", similarity_metric="Tanimoto")
print(similarity)

# Generate descriptors
descriptors = get_all_descriptors(smiles)
print(descriptors)
```

### Command-Line Interface

```bash
# Get help
chemprep --help
chemprep fingerprint --help
chemprep similarity --help
chemprep descriptors --help

# Generate a fingerprint for a single SMILES
chemprep fingerprint "CCO"

# Generate fingerprints from a file
chemprep fingerprint -i smiles.txt -t RDKit -o fingerprints.csv

# Calculate similarity
chemprep similarity "CCO" "CCN" -t Morgan -m Tanimoto

# Generate descriptors for a single SMILES
chemprep descriptors "CCO"

# Generate descriptors from a file
chemprep descriptors -i smiles.csv -o descriptors.csv
```

## Development

### Setup

```bash
git clone https://github.com/santuchal/ChemPrep.git
cd chemprep
pip install -e .[dev]
```

### Running Tests

```bash
python -m unittest discover tests
```
tp-shared-schemas 0.2.23
Pydantic cхемы для всех проектов
Requires Python: >=3.12

# 🧩 tp-shared-schemas

Общий репозиторий схем для использования в нескольких проектах.

---

## Установка:
`poetry add tp-shared-schemas`

## Очистка при обновлении
- `poetry cache clear --all PyPI`
- `poetry add tp-shared-schemas`
- `poetry update`



## Публикация:
Собирает и загружает собранный пакет в PyPI.

`poetry publish --build`

## Структура проекта

**messages** 
-------------------------
Схемы сообщений от сервисов

Пример импорта  

from tp_shared_schemas.messages import GibddDcResultsStreamMessage

В каждой папке лежат соответствующие Pydantic-схемы, сгруппированные по функционалу.
--------------------------
---

## Как подключить репозиторий к существующему проекту

Если у вас есть локальный проект и вы хотите добавить репозиторий с общими схемами, выполните команды:
в файле pyproject.toml прописать зависимость:
1) [tool.poetry.dependencies]
tp-shared-schemas = { git = "https://gitlab.8525.ru/modules/tp-shared-schemas.git", rev = "main" }
2) Выполнить команду poetry install или poetry update


## Репозиторий
```
cd existing_repo
git remote add origin https://gitlab.8525.ru/modules/tp-shared-schemas.git
git branch -M main
git push -uf origin main
```