semvec/pypi-packages huggingface dataset:
l0n0l2fa 2.0.2
TOTP 2FA
Requires Python: >=3.8

# 1. 介绍
TOTP 2FA动态密码服务器

# 2. 安装
```bash
pip install l0n0l2fa
```

# 3. 配置文件介绍(tokens_otp.json)
```json
[
  {
    "token": "token", // 自定义, 唯一访问凭证
    "user": "动态密码", // 用户名
    "secrets": [
      {
        "name": "user1", // 密码的标签, 自定义
        "secret": "user1密码" // 密码, 由需要2fa的服务提供(github, pypi ...)
      },
      {
        "name": "user2", // 密码的标签, 自定义
        "secret": "user2密码" // 密码, 由需要2fa的服务提供(github, pypi ...)
      }
    ]
  }
]
```
# 4. 命令介绍
```bash
l0n0l2fa -h
usage: l0n0l2fa [-h] [--host HOST] [--port PORT] [--certfile CERTFILE] [--keyfile KEYFILE] [--tokens TOKENS]

OTP 管理服务

options:
  -h, --help           show this help message and exit
  --host HOST          监听地址 (默认: 127.0.0.1)
  --port PORT          监听端口 (默认: 8080)
  --certfile CERTFILE  SSL 证书文件路径 (可选)
  --keyfile KEYFILE    SSL 私钥文件路径 (可选)
  --tokens TOKENS      Token 配置文件路径 (默认: tokens_otp.json)
```
# 5. 用例
1. [github配置方法(pypi需点击HomePage再访问)](docs/github.md)
pylhc_submitter 2.0.6
pylhc-submitter contains scripts to simplify the creation and submission of jobs to HTCondor at CERN
Requires Python: >=3.10

# <img src="https://raw.githubusercontent.com/pylhc/pylhc.github.io/master/docs/assets/logos/OMC_logo.svg" height="28"> PyLHC Submitter

[![Cron Testing](https://github.com/pylhc/submitter/workflows/Cron%20Testing/badge.svg)](https://github.com/pylhc/submitter/actions?query=workflow%3A%22Cron+Testing%22)
[![Coverage](https://raw.githubusercontent.com/pylhc/submitter/python-coverage-comment-action-data/badge.svg)](https://github.com/pylhc/submitter/tree/python-coverage-comment-action-data)
<!-- [![GitHub last commit](https://img.shields.io/github/last-commit/pylhc/submitter.svg?style=popout)](https://github.com/pylhc/submitter/) -->
[![PyPI Version](https://img.shields.io/pypi/v/pylhc_submitter?label=PyPI&logo=pypi)](https://pypi.org/project/pylhc_submitter/)
[![GitHub release](https://img.shields.io/github/v/release/pylhc/submitter?logo=github)](https://github.com/pylhc/submitter/)
[![Conda-forge Version](https://img.shields.io/conda/vn/conda-forge/pylhc_submitter?color=orange&logo=anaconda)](https://anaconda.org/conda-forge/pylhc_submitter)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4818455.svg)](https://doi.org/10.5281/zenodo.4818455)

This package contains scripts to simplify the creation, parametrization and submission of simulation jobs to HTCondor clusters at CERN.

See the [API documentation](https://pylhc.github.io/submitter/) for details.

## Installing

**Note**: This package is available

- through `PyPI` on `Linux`, `Windows` and `macOS`
- through `conda-forge` on `Linux` and `macOS`

Installation is easily done via `pip`:

```bash
python -m pip install pylhc-submitter
```

One can also install in a `conda` environment via the `conda-forge` channel with:

```bash
conda install -c conda-forge pylhc_submitter
```

After installing, scripts can be run with either `python -m pylhc_submitter.SCRIPT --FLAG ARGUMENT` or by calling the Python files directly.

See the [API documentation](https://pylhc.github.io/submitter/) for details.

## Functionality

- `HTCondor Job Submitter` - Allows to generate jobs based on a templates and submit them to HTCondor. ([**job_submitter.py**](pylhc_submitter/job_submitter.py))
- `AutoSix` - Allows to generate and submit parametric SixDesk studies easily. ([**autosix.py**](pylhc_submitter/autosix.py))

## License

This project is licensed under the `MIT` License - see the [LICENSE](LICENSE) file for details.
pywright-gpt-tts 0.0.5
Python Module for Free ChatGPT TTS
Requires Python: >=3.10

# Pywright GPT TTS
Python Module for Free ChatGPT TTS.  
ChatGPT Automation for Text-To-Speech programmatically.  

*Easy to use - No need for extra documentation !*  
*Learn as you go ! Try it out !*
sdds 0.4.3
SDDS file handling.
Requires Python: >=3.10
Keywords: SDDS

# SDDS

[![Tests](https://github.com/pylhc/sdds/actions/workflows/coverage.yml/badge.svg?branch=master)](https://github.com/pylhc/sdds/actions/workflows/coverage.yml)
[![GitHub last commit](https://img.shields.io/github/last-commit/pylhc/sdds.svg?style=popout)](https://github.com/pylhc/sdds/)
[![PyPI Version](https://img.shields.io/pypi/v/sdds?label=PyPI&logo=pypi)](https://pypi.org/project/sdds/)
[![GitHub release](https://img.shields.io/github/v/release/pylhc/sdds?logo=github)](https://github.com/pylhc/sdds/)
[![Conda-forge Version](https://img.shields.io/conda/vn/conda-forge/sdds?color=orange&logo=anaconda)](https://anaconda.org/conda-forge/sdds)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.5705820.svg)](https://doi.org/10.5281/zenodo.5705820)

This package provides reading and writing functionality for [**self describing data sets (sdds)**](https://ops.aps.anl.gov/SDDSIntroTalk/slides.html) files.
On the python side, the data is stored in a class structure with attributes corresponding to the sdds-format itself (see [sdds-format](https://ops.aps.anl.gov/manuals/SDDStoolkit/SDDStoolkitsu2.html)).

See the [API documentation](https://pylhc.github.io/sdds/) for details.

## Installing

Installation is easily done via `pip`:

```bash
python -m pip install sdds
```

One can also install in a `conda` environment via the `conda-forge` channel with:

```bash
conda install -c conda-forge sdds
```

## Example Usage

```python
import sdds

sdds_data = sdds.read("path_to_input.sdds")
sdds.write(sdds_data, "path_to_output.sdds")
```

### Read files with different endianness

By default the endianness (byte order) of the file is determined either by
a comment `!# little-endian` or `!# big-endian` in the header of the file.
If this comment is not found, the endianness of the running machine is assumed.

One can force a certain kind of endianness to the reader by supplying it to
the read function:

```python
import sdds

sdds_data = sdds.read("path_to_input_with_big_endian.sdds", endianness="big")
sdds_data = sdds.read("path_to_input_with_little_endian.sdds", endianness="little")
```

Be aware that `sdds.write` will always write the file in big-endian order and
will also leave a comment in the file, so that the reader can determine the
endianness and there is no need to supply it when reading
a file written by this package.

## Known Issues

- Can't read binary columns
- No support for `&include` tag

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
ewha-anomaly-detection 0.1.9
An Pedestrian Trajectory Anomaly Detection for Python.
Requires Python: >=3.8

# ewha_anomaly_detection

A Python package for detecting abnormal pedestrian trajectory with LSTM-VAE model.


## How to use

'''python
from ewha_anomaly_detection.anomaly_detection import Anomaly_Detection as ad

weights_path = "/your_directory/vae_lstm_weights.weights.h5"

anmly = ad(df=df, weights_path=weights_path)
anomaly_1, anomaly_2 = anmly.call()
'''

ad.call() automatically predicts (1) the abnormal trajectory IDs from your pedestrian trajectory dataset and (2) the abnormal trajectory ratio for each CCTV road segment from which the trajectories were extracted.

## Requirements

numpy == 1.24.3
pandas
geopandas
shapely
tensorflow

## License
This project is licensed under the JuyeonCho License.

## Contact
whwndus13@naver.com
ewha-anomaly-dynamicP 0.1.1
An Pedestrian Trajectory Anomaly Detection for Python.
Requires Python: >=3.8

# ewha_anomaly_dynamicP

A Python package for detecting abnormal pedestrian trajectory with LSTM-VAE model.
Specifically, for dynamic points table of a Ian DataBase


## How to use

'''python
from ewha_anomaly_dynamicP.anomaly_dynamicP import Anomaly_Detection as ad

weights_path = "/your_directory/vae_lstm_weights.weights.h5"

anmly = ad(df=df, weights_path=weights_path)
anomaly_1, anomaly_2 = anmly.call()
'''

ad.call() automatically predicts (1) the abnormal trajectory IDs from your pedestrian trajectory dataset and (2) the abnormal trajectory ratio for each CCTV road segment from which the trajectories were extracted.

## Requirements

numpy == 1.24.3
pandas
geopandas
shapely
tensorflow

## License
This project is licensed under the JuyeonCho License.

## Contact
whwndus13@naver.com