semvec/pypi-packages huggingface dataset:
dlqhandler 0.8.0
A library for handling DLQ messages in AWS SQS
Requires Python: >=3.6

# DLQ Handler Library

A library for handling DLQ (Dead Letter Queue) messages in AWS SQS. This library allows you to reprocess messages from a DLQ with specified parameters such as the queue URL, original queue URL, and maximum number of attempts.

## Installation

To install the library, use `pip`:

```sh
pip install dlqhandler

from dlqhandler import ProcessMessage

# Initialize the DLQHandler with the required parameters
process_message_handler = ProcessMessage(
    dlq_queue_url='https://sqs.us-east-1.amazonaws.com/123456789012/my-dlq',
    original_queue_url='https://sqs.us-east-1.amazonaws.com/123456789012/my-queue',
    max_attempts=5,
    region_name='us-east-1',
    env=my_env_config,  # replace with your actual environment config
    nome_lambda='lambda-reprocessamento-dlq',
    namespace='DLQ-Mensageria'
)

# Process the messages from the DLQ
process_message_handler.execute()
CITIfile 0.1.6
To parse CITI format file to xarray.

# CITIfile

Read CITI format file by python.

## Introduction

`CITIfile` is a tiny library that allows you to read
CITI format file by using Python.

## Installation

```shell script
pip install -U CITIfile
```

## Tutorial

`CITIfile` is easy to use:

```python
from CITIfile import read_citifile


data = read_citifile("data.citi")
```

Then, it will parse the data file and
return a `xarray.Dataset` object. Like the following:

```
<xarray.Dataset>
Dimensions:   (C_d2: 3, L_load: 6, freq: 6)
Coordinates:
  * L_load    (L_load) float64 20.0 21.0 22.0 23.0 24.0 25.0
  * C_d2      (C_d2) float64 1.0 1.5 2.0
  * freq      (freq) float64 3e+07 3.1e+07 3.2e+07 3.3e+07 3.4e+07 3.5e+07
Data variables:
    S[1,1]    (L_load, C_d2, freq) complex128 (0.0517763713-0.00941470346j) ... (0.0593002475-0.0233189028j)
    S[1,2]    (L_load, C_d2, freq) complex128 (0.771829001+0.00291050315j) ... (0.774683786+0.0184790199j)
    S[2,1]    (L_load, C_d2, freq) complex128 (0.771829001+0.00291050315j) ... (0.774683786+0.0184790199j)
    S[2,2]    (L_load, C_d2, freq) complex128 (0.0517763713-0.00941470346j) ... (0.0593002475-0.0233189028j)
```

`CITIfile` parses and converts CITI format to `xarray.Dataset` object,
because CITI format file stores data in multidimensional array format
and `xarray` are designed to handle this kind of data.

See [xarray documents](http://xarray.pydata.org/en/stable/) to
learn that how to manipulate `xarray.Dataset` object.

### Displaying Coordinates and Data Vairables

```
print('Coordinates:')
for cname in data.coords:
    cdata = data.coords[cname].data
    print(f'- {cname}: {len(cname)} ({cdata}, {cdata.dtype})')

print('Data:')
for vname in data.data_vars:
    vdata = data.data_vars[vname]
    print(f'- {vname}: {vdata.dtype}')
```

This displays the names and types of the corrdinates and data variables, as well as the available corrdinate values (which are `numpy.ndarray`).

### Extracting Usable 2D Data

In this example, we extract `S[1,1]` vs. `freq`, at some given coordinates for `C_load` and `C_d2`:

```

# select the data variable at the given corrdinates
data_slice = data.data_vars["S[1,1]"].sel(L_load=20, C_d2=1)

# now there is only one coordinate (freq) left as independent variable
x = data_slice.coords["freq"].data

# dependent variable (Y-axis)
y = data_slice.data

for px, py in zip(x, y):
    print(f"freq={px} -> S[1,1]={py}")
```

Both `x` and `y` are `numpy.ndarray`.

## Websites

Main website:
https://github.com/TitorX/CITIfile

CITIfile Definitions:
http://literature.cdn.keysight.com/litweb/pdf/ads15/cktsim/ck2016.html

xarray document:
http://xarray.pydata.org/en/stable/

## Reports

Report bugs or ask questions at
https://github.com/TitorX/CITIfile/issues.

## Contact

Written by Shoukun Sun.

Email: titor.sun@gmail.com
pokepoke-miss 0.1.5
wmc的戳一戳回复插件，消息内容、概率都可以定制喔~
Requires Python: >=3.7

# pokepoke_miss

## 介绍

wmc的戳一戳回复插件，消息内容、概率都可以定制喔~

## 功能

- 对机器人执行戳一戳操作时，根据概率选择一个消息进行回复
- 预设：

  - `MISS` - 概率 10%
  - `GOOD` - 概率 10%
  - `GREAT` - 概率 30%
  - `PERFECT` - 概率 40%
  - `CRITICAL PERFECT` - 概率 10%

## 安装

### 使用 NB-CLI 安装

在 Nonebot 根目录执行

```bash
nb plugin install pokepoke_miss
```

### 手动安装

1. 将 `pokepoke_miss.py` 文件放入你的 Nonebot 插件目录中

2. 在 `bot.py`中写入以下代码：

   ```python
   nonebot.load_plugin("FOLDER-TO-PLUGIN.pokepoke_miss")

## 自定义

根据需求修改 `message_list` 中的消息内容和对应的概率，例如：

```python
message_list = [
    ("喵", 0.2),
    ("喵喵", 0.3),
    ("喵喵喵", 0.5)
]
```

## 许可

MIT License
ownlayer-sdk 0.1.0
Ownlayer Python SDK
Requires Python: <4.0,>=3.12

# Ownlayer Python SDK
Python SDK to integrate Ownlayer

## Usage

Install SDK

```shell
pip install ownlayer-sdk
```

Change openai import to use Ownlayer wrapper
```diff
- import openai
+ from ownlayer_py_sdk.openai import openai


def some_AI_func:
    # use AI as you did before
    return openai.chat.completions.create(
        ...
    )
```

Be sure to have add `OWNLAYER_API_KEY` to your `.env` file:

```dotenv
OWNLAYER_API_KEY=ey...xxx
```
Auto3D 2.3.1
Generating Low-energy 3D Conformers from SMILES/SDF
Requires Python: >=3.7

# **Auto3D**
<a href="https://pypi.org/project/Auto3D/" target="_blank"><img src="https://img.shields.io/badge/pypi-url-informational" alt="pypi_link"></a>
![PyPI](https://img.shields.io/pypi/v/Auto3D)
![PyPI - Downloads](https://img.shields.io/pypi/dm/Auto3D)
![PyPI - License](https://img.shields.io/pypi/l/Auto3D)

![auto3d-white](https://github.com/user-attachments/assets/3184d31b-fb21-42d5-a1e0-611ccbf66ad2)

**Auto3D** is a Python package for generating low-energy conformers from SMILES/SDF. Over the development process, we also added the APIs for computing single point energies, optimizing geometries, find stable tautomers. Auto3D can be imported as a Python library, or be excuted from the terminal.

Please check out the information at [**documentation**](https://auto3d.readthedocs.io/en/latest/index.html), including [installation](https://auto3d.readthedocs.io/en/latest/installation.html), [usage](https://auto3d.readthedocs.io/en/latest/usage.html), [API](https://auto3d.readthedocs.io/en/latest/api.html) and [citation](https://auto3d.readthedocs.io/en/latest/citation.html).

- **Jupyter notebook examples** can be found [here](https://github.com/isayevlab/Auto3D_pkg/tree/main/example)
- To-do list for **improvement and new features** can be found [here](https://github.com/isayevlab/Auto3D_pkg/discussions). You are welcomed to share your thoughts.
- Bugs go to the [issues](https://github.com/isayevlab/Auto3D_pkg/issues)
- **AIMNet2**: The default model in Auto3D is AIMNet2 since 2.2.1. If you specify optimizing_engine="AIMNET", it actually uses AIMNet2. The old AIMNet model has been deprecated since Auto3D 2.2.1, and every call to “AIMNET” refers to the AIMNet2 model.