semvec/pypi-packages huggingface dataset:
hydrogapai 0.6.0
HydroGAP-AI: Hydro-Gap Artificial Intelligence
Requires Python: >=3.8

# HydroGAP-AI

TODO: Write a high level overview of the library

## Installation

You can install the library using the following command:

```bash
pip install hydrogapai
```

or directly from this repo using: 

```bash
pip install git+https://github.com/kperi/HydroGAP-AI.git
```

## Predict gaps in stations

### Usage

```python


from hydrogapai.gap_prediction import (
    predict_station_gaps,
)
station_file = "./data/lib/station_11.0_cleaned.csv"
results_folder = './output/test_run'

 
                


reall_combined_dfs, val_full, metrics_gaps, real_predictionst = predict_station_gaps(
    station_file,
    results_folder=results_folder,
    model_type=model_type,
    hyper_opt=False,
)
```

For a more detailed example of the library including outputs see this [notebook](./lib_demo.ipynb)
PSTransformer 2.3.0
`PSTransformer` is a Python package that provides two high-level features.
Keywords: python, transformer, chat-GPT Transformer




[![Build Status](https://github.com/raimon49/pip-licenses/workflows/Python%20package/badge.svg)](https://github.com/ProgramerSalar/PSTransformer) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pip-licenses.svg)](https://www.python.org/downloads/) [![PyPI version](https://badge.fury.io/py/pip-licenses.svg)](https://pypi.org/project/PSTransformer/) [![GitHub Release](https://img.shields.io/github/release/raimon49/pip-licenses.svg)](https://pypi.org/project/PSTransformer/#history)  [![MIT License](http://img.shields.io/badge/license-MIT-green.svg)](https://github.com/ProgramerSalar/PSTransformer/blob/master/LICENSE) [![PyPI - Downloads](https://img.shields.io/pypi/dm/pip-licenses)]()





Dump the software license list of Python packages installed with pip.



## Table of Contents



* [Description](#description)

* [Installation](#installation)

* [License](#license)

* [Dependencies](#Dependencies)

* [Reference](https://github.com/ProgramerSalar/PSTransformer/blob/master/Readme_File/REFERENCE.md)

* [Uninstallation](#uninstallation)

* [Contributing](#contributing)

* [CHANGELOG](#CHANGELOG)







## Description



`PSTransformer` is a Python package that provides two high-level features.





## installation

```

pip install PSTransformer==<version>



            OR



pip install PSTransformer

```













## license 

[MIT Licence](https://github.com/ProgramerSalar/PSTransformer/blob/master/LICENSE)



## Dependencies 

- ```torch``` 

- ```tqdm ``` this is progress bar library

- ```datasets ```  this is dataset libarary by huggingface

- ```tokenizers ```  this is tokenizers libary by huggingface

- ```tensorboard ```  TensorBoard is a visualization toolkit for machine learning experimentation. TensorBoard allows tracking and visualizing metrics such as loss and accuracy, visualizing the model graph, viewing histograms, displaying images and much more. In this tutorial we are going to cover TensorBoard installation, basic usage with PyTorch, and how to visualize data you logged in TensorBoard UI.





# Uninstallation

Uninstall package and dependent package with ```pip``` command .

```

pip uninstall PSTransformer torch tqdm datasets tokenizers tensorboard

```



# Contributing 

See [contribution guidelines](https://github.com/ProgramerSalar/PSTransformer/blob/master/CONTRIBUTING.md) .





# CHANGELOG



## 1.0.0



- First Implemention version

    - Add a function 



        - ```BilingualDataset()```

        - ```build_transformer()```





## 2.0.0

- solve some Error, I will Get the package error 



## 2.1.0 

- First Stable relased version



   



## 2.2.0

- Improve document

    - Update the README document

    - Add the Licence 

    - some Required Things 





## 2.3.0 

- Update the Libarary 

    - add the Diffusion model function 

    - update the Readme document
gen-bing 0.0.6
I'm From Indonesian, and I'm still learning.
Requires Python: >=3.9
Keywords: bing image generator gabut

# Gen Bing

A Python library for generating and saving images using Bing's Image Creator.

## Installation PyPi

```bash
pip3 install -U --no-cache-dir gen-bing
```

## Usage Async

```python
from Bing import AsyncImageGenerator

async def main():
    GEN = AsyncImageGenerator()
    images = await GEN.generate(prompt="A cute cat is playing on a bed. Digital Art 3D.", num_images=4)
    await GEN.save(images, output_dir="images/")

import asyncio
asyncio.run(main())
```

## Usage Sync

```python
from Bing import ImageGenerator

GEN = ImageGenerator()
images = GEN.generate(prompt="A cute cat is playing on a bed. Digital Art 3D.", num_images=4)
GEN.save(images, output_dir="images/")
```

## DON'T TRY THIS AT HOME 🏡 🙏🏻

## LuciferReborns
twinstop 0.2.2
twinstop identifies selenoproteins in close related transcriptomes
Requires Python: >=3.12

# twinstop
Twinstop identifies selenoproteins in close related transcriptomes

# Installation
In a new conda environment, run::

   conda install -c conda-forge -c bioconda -c mmariotti    twinstop

## Installation troubleshooting
If solving takes forever, we recommend switching to mamba instead.
That is, uninstall conda; install mamba; create a new environment; then run the command::

   mamba install -c conda-forge -c bioconda -c mmariotti    twinstop


As of this writing, the command above correctly installs twinstop in a linux64 system.
In case it fails of any unresolved conflicts, try to download the file requirements.txt,
and run this to create a copy of a verified conda environment::

  conda create --name twinstop --file requirements.txt

Then activate the new env twinstop, then run the install command above.

# Usage
To see usage, run::

  twinstop -h


# Authors
Sergio Sanchez Moragues and Marco Mariotti
uuid-lib 0.3.4
Python bindings for the UUID library
Requires Python: >=3.8
Keywords: uuid, uuid_lib, uuid1, uuid2, uuid3, uuid4, uuid5, uuid6, uuid7, uuid8


# python uuid-lib

Python library written in rust that lets you use uuid1..8 funcs.


## Available functions

##### **uuid1** - Generated based on a timestamp, a unique node identifier (typically a MAC address), and a random component.

##### **uuid2** - Similar to UUID1 but includes a group or user identifier, making it less popular and rarely used.

##### **uuid3** - Generated using the MD5 hash function based on a namespace and a unique string (e.g., a URL).

##### **uuid4** - A fully random UUID, potentially using cryptographically secure random number generators.

##### **uuid5** - Similar to UUID3 but uses SHA-1 instead of MD5 for hashing unique data and the namespace.

##### **uuid6** - A modified version of UUID1 with the timestamp in sequential order, improving database indexing performance.

##### **uuid7** - Generated based on a timestamp using Unix Epoch format and random bits to ensure uniqueness.

##### **uuid8** - Reserved for custom UUIDs with arbitrary generation schemes, leaving implementation details open. ***Accepts any UUID as bytes (If UUID generated by this lib, use object.bytes proprety)***

## Installation

Install uuid-lib with pip:

```bash
  pip install uuid-lib
```

Install uuid-lib with poetry:

```bash
  poetry install uuid-lib
```


## Usage/Examples

```python
...

from uuid_lib import uuid4, uuid7, uuid8(), ...


user_id = uuid7()  # <- Creates UUID object


# For uuid8(...) 

some_id = uuid8(user_id.bytes)
# or
some_id = uuid8(uuid4().bytes)
```
GDMO 0.1.1
GDMO Library for standardized actions and routines. Current options / functions are Forecast.Forecast, API.APIRequest, Tables.Landing, and Tables.Delta

# GDMO native classes for standardized interaction with data objects within Azure Databricks

This custom library allows our engineering team to use standardized packages that strip away a load of administrative and repetitive tasks from their daily object interactions. The current classes supported (V0.1.0) are:

## API - APIRequest
Class to perform a standard API Request using the request library, which allows a user to just add their endpoint / authentication / method data, and get the data returned without the need of writing error handling or need to understand how to properly build a request. 

## Tables - Landing
Class to land a dataframe or csv file to the databricks landing zone, and optionally convert this to the bronze layer data. Just say where to store it, and the class will take care of it with error handling associated and a normalized routine is followed. 

## Tables - Delta
No longer one needs to write a twelve-command notebook to create a table. Call this class once and see it happen. 

## Forecast - Forecast
Standardized way of forecasting a dataset. Input a dataframe with a Series, a Time, and a Value column, and see the function automatically select the right forecasting model and generate an output.
nonebot-plugin-QRrender 0.0.6
将文本转为二维码，可自定义样式
Requires Python: >=3.9

# nonebot-plugin-QRrender

_✨ 将文本转为二维码，可自定义样式 ✨_




## 📖 介绍

本插件使用[Amazing-QR](https://github.com/x-hw/amazing-qr)生成器生成二维码

可生成常见的二维码，以及有图片背景的艺术二维码

## 💿 安装


<summary>使用 nb-cli 安装</summary>
在 nonebot2 项目的根目录下打开命令行, 输入以下指令即可安装

    nb plugin install nonebot-plugin-QRrender



## ⚙️ 配置
请在 nonebot2 项目的`.env`文件中添加下表中的必填配置

| 配置项  | 必填 | 默认值 |          说明           |
| :----: | :--: | :----: | :--------------------: |
| QR_res |  否  |   5    | 二维码大小（范围为1~40） |



## 🎉 使用
插件自带指令文档，发送 `QR帮助` 即可获取指令文档

### 生成二维码
发送 `QR 文本` 即可生成二维码，若bot有设置命令前缀，需加命令前缀，如 `/`

支持的参数：模板

使用方法： `QR 文本 模板名` ，模板需自行上传或命名为`模板名.jpg`后添加至`data\QRrender\library`文件夹内

示例：
```
QR 114514 homo
QR 1919810
```
上述指令将会生成
```
内容为“114514”，背景是名为“homo”的模板图片的二维码
内容为“1919810”的普通二维码
```

### 上传图片模板
发送 `QR模板` 即可根据提示上传图片并命名

支持以`回复`、`附图`等方式直接上传图片并命名。例：
![Image_1726057638949](vx_images/76142920267411.jpg =216x)

## 可能遇到的问题

目前没发现（）


## 将来可能支持的功能

□支持扫码功能？

□支持gif动图

□...

## 还有些想说的
本仓库使用了nonebot-plugin-template的模板，谨向作者表示感谢

本人为普通大学生，并非计算机类的专业，水平有限，如有问题或建议请直接发issue，我会尽量解决
pyposolver 0.2
A Python library for mathematical and physics calculations.

# PyPoSolver

**PyPoSolver** is a Python library for performing mathematical and physics calculations, including numerical integration, root-finding, linear algebra operations, and basic mechanics.

## Installation

```bash
pip install pyposolver 
```

## Usage

### Mathematics

#### Integration

Use the `trapezoidal_rule` function to perform numerical integration:

```python
from pyposolver.maths.integration import trapezoidal_rule

def f(x):
    return x**2

result = trapezoidal_rule(f, 0, 1, 100)  # Integrate f(x) from 0 to 1 using 100 intervals
print(result)  # Expected: 0.333...
```

#### Root-Finding

- **Bisection Method**

Use `bisection_method` to find the root of a function within a specified interval:

```python
from pyposolver.maths.root_finding import bisection_method

def f(x):
    return x**2 - 4

root = bisection_method(f, 0, 3)  # Find root of f(x) in the interval [0, 3]
print(root)  # Expected: 2.0
```

- **Newton-Raphson Method**

Use `newton_raphson_method` for root-finding with an initial guess and the function's derivative:

```python
from pyposolver.maths.root_finding import newton_raphson_method

def f(x):
    return x**3 - 2*x - 5

def df(x):
    return 3*x**2 - 2

root = newton_raphson_method(f, df, 2)  # Initial guess: 2
print(root)  # Expected: 2.094...
```

#### Linear Algebra

- **Dot Product**

Calculate the dot product of two vectors:

```python
from pyposolver.maths.linear_algebra import dot_product

vector1 = [1, 2, 3]
vector2 = [4, 5, 6]

result = dot_product(vector1, vector2)
print(result)  # Expected: 32
```

- **Cross Product**

Compute the cross product of two 3D vectors:

```python
from pyposolver.maths.linear_algebra import cross_product

vector1 = [1, 2, 3]
vector2 = [4, 5, 6]

result = cross_product(vector1, vector2)
print(result)  # Expected: [-3, 6, -3]
```

- **Matrix Multiplication**

Multiply two matrices:

```python
from pyposolver.maths.linear_algebra import matrix_multiply

matrix1 = [[1, 2], [3, 4]]
matrix2 = [[5, 6], [7, 8]]

result = matrix_multiply(matrix1, matrix2)
print(result)  # Expected: [[19, 22], [43, 50]]
```

### Physics

- **Velocity**

Calculate velocity given initial velocity, acceleration, and time:

```python
from pyposolver.physics.mechanics import velocity

initial_velocity = 5
acceleration = 2
time = 3

result = velocity(initial_velocity, acceleration, time)
print(result)  # Expected: 11
```

- **Force**

Calculate force using mass and acceleration:

```python
from pyposolver.physics.mechanics import force

mass = 10
acceleration = 5

result = force(mass, acceleration)
print(result)  # Expected: 50
```