semvec/pypi-packages huggingface dataset:
theohe-epias 0.0.13
Energy Exchange Istanbul (EXIST) or Enerji Piyasaları İşletme A.Ş. (EPİAŞ) by its Turkish name is an energy exchange company.
Requires Python: >=3.8

# Energy Exchange Istanbul (EXIST)

Energy Exchange Istanbul (EXIST) or Enerji Piyasaları İşletme A.Ş. (EPİAŞ) by its Turkish name is an energy exchange company.

## Services

 - Transparency Platform (Şeffaflık Platformu)
 - EMMS (Energy Markets Management System) - EPYS (Enerji Piyasaları Yönetim Sistemi)

### download via pip

```
!pip install theohe-epias
```

## Transparency Platform (Şeffaflık Platformu)

Transparency Platform provides necessary data for the transparent, reliable, fair and predictable operation of energy markets. You can access platform from [here](https://seffaflik.epias.com.tr/home).

Also, Transparency API is available for everyone. In the [Notebook](https://github.com/onurhakki/exist/blob/main/example-notebooks/Transparency%20WebService.ipynb), you can access example details.

### usage - (example: Market Clearing Price (MCP) - Piyasa Takas Fiyatı (PTF))

```
from theohe_epias.transparency.service import WebServiceTransparency
ws = WebServiceTransparency()
ws.dam.mcp()
```


### usage - between selected dates (example: Market Clearing Price (MCP) - Piyasa Takas Fiyatı (PTF))

```
from theohe_epias.transparency.service import WebServiceTransparency
ws = WebServiceTransparency()
ws.dam.mcp(startDate=(2023, 12, 1), endDate=(2023, 12, 31), function = "export")
```


### usage - (example: Market Clearing Price (MCP))

##### 2023 MCP hourly averages on monthly basis

In the [Notebook](https://github.com/onurhakki/exist/blob/main/example-notebooks/Transparency%20WebService%20(MCP%20-%20SMP%20-%20Direction).ipynb), you can access example details.


![mcp_2023](https://github.com/onurhakki/exist/assets/53830179/54c7390c-cc11-4230-9e40-ceda4a1f0592)


### usage - (example: System's Direction)

##### 2023 System's Direction averages on monthly basis

In the [Notebook](https://github.com/onurhakki/exist/blob/main/example-notebooks/Transparency%20WebService%20(MCP%20-%20SMP%20-%20Direction).ipynb), you can access example details.


![direction_2023](https://github.com/onurhakki/exist/assets/53830179/8f19d58e-344e-4253-aa69-8217f1b84113)


## EPYS (Enerji Piyasaları Yönetim Sistemi) - EMMS (Energy Markets Management System)

You can access platform from [here](https://epys.epias.com.tr/home).

Also, EPYS is only available for market participants. In the [Notebook](https://github.com/onurhakki/exist/blob/main/example-notebooks/EPYS%20WebService%20.ipynb), you can access example details.

### usage - login 

```
from theohe_epias.epys.service import WebServiceEPYS
import warnings

warnings.simplefilter("ignore")

ws = WebServiceEPYS(username = "USERNAME", organizationId = 123456789, test=None)
### None for prod
### "prp" for test
ws.login()
### or directly use below code
### ws.login("password")
```
unstable-baselines3 0.0.3100069
stable_baselines3 for pettingzoo multi agent algorithms
Requires Python: >=3.10

# Unstable Baselines 3

Bridge from [stable_baselines3](https://stable-baselines3.readthedocs.io/en/master/)
into [PettingZoo](https://pettingzoo.farama.org/) multi-agent algorithms

Trains agents completely independently (without shared parameters)

Designed for learning independent policies through Reinforcement learning in multi-agent team/adversarial games
imgease 0.2.0
A collection of CLI tools for image processing.
Requires Python: <4.0,>=3.12

# imgease - A Collection of Command-Line Tools for Image Processing

## Command-Line Tool: imgease-grayscale
Converts one or multiple images, or all images in a specified directory, to grayscale and saves them in the designated output directory. The script supports recursive processing of subdirectories within the specified directory.

### Features
- **Batch processing**:Can process one or multiple images, or all images in a directory.
- **Recursive processing**:Supports recursive processing of subdirectories within a directory.

### Installation
```bash
pipx install imgease
```

### Usage
#### Processing a single image
```bash
imgease-grayscale -i <input image path> -o <output directory>
```

#### Processing multiple images
```bash
imgease-grayscale -i <input image path> <input image path> -o <output directory>
```

#### Processing all images in a directory
```bash
imgease-grayscale -i <input directory path> -o <output directory>
```

#### Recursively processing subdirectories
```bash
imgease-grayscale -r -i <input directory path> -o <output directory>
```

##### Notes
- This script only processes image files in the following formats: `.png`, `.jpg`, `.jpeg`, `.bmp`, `.gif`, `.tiff`.
- Each converted image will have the `_grayscale` suffix added to its filename.

## Command-Line Tool: imgease-convert
converts common image formats (such as `.png`, `.bmp`, `.tiff`) to `.jpeg` format.

### Features
- **Batch processing**:Can process one or multiple images, or all images in a directory.
- **Recursive processing**:Supports recursive processing of subdirectories within a directory.

### Installation
```bash
pipx install imgease
```

### Usage
#### Processing a single image
```bash
imgease-convert -i <input image path> -o <output directory>
```

#### Processing multiple images
```bash
imgease-convert -i <input image path> <input image path> -o <output directory>
```

#### Processing all images in a directory
```bash
imgease-convert -i <input directory path> -o <output directory>
```

#### Recursively processing subdirectories
```bash
imgease-convert -r -i <input directory path> -o <output directory>
```

##### Notes
- This script only processes image files in the following formats: `.png`, `.bmp`, `.tiff`.
- Each converted image will have the `_converted` suffix added to its filename.

## License
This project is licensed under the GPL-3.0 License.
dynamodb-fsm 0.0.2
FSM storage on dynamo data bases for aiogram 3.10
Requires Python: >=3.9
Keywords: FSM, aiogram, asyncio, bot, dynamodb, finite-state-machine, ydb

# FSM-on-dynamodb

<a href="https://opensource.org/license/MIT"><img src="https://img.shields.io/badge/license-MIT-green.svg?logo=mit&logoColor=f5f5f5" alt="MIT-license"></a>
<a href="https://github.com/general-soldatov/FSM-on-dynamodb/"><img src="https://img.shields.io/badge/dynamo-fsm-blue.svg?logo=telegram&logoColor=f5f5f5" alt="dynamo-fsm"></a>
<img src="https://img.shields.io/badge/python-3.9%7C3.10%7C3.11-blue?logo=python&logoColor=f5f5f5" alt="Python" />
<a href="https://github.com/aiogram/aiogram/blob/dev-3.x/"><img src="https://img.shields.io/badge/aiogram-3.10-blue.svg?logo=telegram&logoColor=blue" alt="dynamo-fsm"></a>
<img src="https://img.shields.io/badge/boto3-dynamodb-green.svg?logo=" alt="dynamodb-boto3">

Надстройка над `aiogram` для организации FSM (finite-state-machine) на базе базы данных dynamodb с использованием библиотеки boto3 &amp; botocore. Может быть использована на serverless-решениях AWS и YandexCloud. Установить библиотеку можно с помощью команды:
```bash
pip install dynamodb-fsm
```
В GUI облачной панели управления или с помощью CLI создайте динамическую serverless базу данных и сервисный аккаунт с ключом. Для пользователей YandexCloud можете воспользоваться [документацией](https://yandex.cloud/ru/docs/ydb/quickstart).
Для корректной работы библиотеки `boto3` используется dataclass c переменными окружения, поэтому не забудьте внести следующую информацию в файл `.env`
```BASH
ENDPOINT='url_endpoint'
REGION_NAME='ru-central1'
AWS_ACCESS_KEY_ID='key_id'
AWS_SECRET_ACCESS_KEY='access_key_db'
```
В случае использования собственной конфигурации рекомендуется реализовать следующий код:

```python
from dynamodb_fsm import FSMDynamodb

...

@dataclass
class DatabaseConfig:
    endpoint_url: str = 'ENDPOINT'
    region_name: str = 'REGION_NAME'
    aws_access_key_id: str = 'AWS_ACCESS_KEY_ID'
    aws_secret_access_key: str = 'AWS_SECRET_ACCESS_KEY'


config = DatabaseConfig().__dict__
storage = FSMDynamodb(config=config)
```
Для подключения хранилища состояний к диспетчеру `aiogram` используйте следующий пример кода:

```python
from dynamodb_fsm import FSMDynamodb

...

storage = FSMDynamodb()
dp = Dispatcher(storage=storage)
```

Актуальная информация о функциях надстройки есть в репозитории [dynamodb-fsm](https://github.com/general-soldatov/FSM-on-dynamodb/tree/main)
orbithunter 1.3.3
Framework for Nonlinear Dynamics and Chaos
Requires Python: >=3.11
Keywords: pde, partial differential equation, numeric, numerical simulation, solver, framework, periodic orbit

orbithunter 1.3.3
=================
orbithunter serves as a framework for solving chaotic nonlinear partial differential equations.

Features
--------

- An object oriented approach to solutions of partial differential equations
- A general-purpose framework for finding, visualizing and manipulating these solutions
- High-level access to SciPy API, particularly its minimize and optimize packages.
- New spatiotemporal techniques such as 'clipping' and 'gluing'

Currently there is only an implementation of the Kuramoto-Sivashinsky equation (KSE).

External Links
--------------

Be sure to check out these resources for help and development!

- `Homepage and Documentation <https://orbithunter.readthedocs.io/en/latest/index.html>`_
- `Issues <https://orbithunter.readthedocs.io/en/latest/issues.html>`_
- `Bug reports <https://github.com/mgudorf/orbithunter/issues>`_
- `Source <https://github.com/mgudorf/orbithunter>`_
- `Developer Guide <https://orbithunter.readthedocs.io/en/latest/guide.html>`_
- `Tutorial Notebooks <https://github.com/mgudorf/orbithunter/tree/main/tutorial>`_
- `FAQ <https://orbithunter.readthedocs.io/en/latest/faq.html>`_

Support
-------

If you are having issues, please let us know.
We have a mailing list located at: orbithunter@googlegroups.com

License
-------

The project is licensed under the GNU General Public License v3 (GPLv3) license.
megaognezyum 1.1
Mega Ognezyum Module with Submodules
Requires Python: >=3.6

megaognezyum also known as rowanlibrary collection is a library collection used for web based network and data security. With pcap support, you will now be able to detect security and data in http ssh ftp networks or identify the person trying to access the systems in your own network through the firewall.

megaognezyum usage documents:https://azencompileropensourcefoundation.com/megaogzenyum-netcore/doc/,https://github.com/Goychay23/int/blob/main/Rowanlibrary/MEGAOGZENYUM.pdf
if you want to support, our github page:https://github.com/Goychay23/int
if you want to get technical support:https://azencompileropensourcefoundation.com/
# sample code and code provided for usage structure
```
import megaognezyum

# Create an instance of RowanObject
rowan = megaognezyum.RowanObject(
domain="example.com",
ip_address="192.168.1.1",
data="Sample data"
)

# Print the object's information
print("Printing RowanObject information:")
rowan.print_info()

```

to get specific data from ip address

```
# Track an HTTP link using the http submodule
print("\nTracking an HTTP link...")
megaognesium.http.track_http_link(
     url="http://example.com/page",
     trigger="User Click "
)
```

To check the content in the http url
animalapi 0.0.4
A simple Python wrapper for the some-random-api.ml API.
Keywords: animal, api, wrapper, images, facts


<h1 align="center">animalapi</h1>



<p align="center">

  A simple Python wrapper for the <a href="https://some-random-api.ml/">some-random-api.ml</a> API.

  <br />

  <a href="https://pypi.org/project/animalapi">

    <img src="https://img.shields.io/pypi/v/animalapi.svg" alt="PyPI version" />

  </a>

  <a href="https://github.com/dashutosh04/animalapi/blob/main/LICENSE">

    <img src="https://img.shields.io/github/license/dashutosh04/animalapi.svg" alt="License" />

  </a>

</p>



<p align="center">

  Easily access animal images and facts in your Python projects.

</p>



## Features



* **Random Animal Data:** Get randomized animal data (image and fact) with `rand_animals()`.

* **Specific Animal Data:** Fetch data for a specific animal using `animal_data(animal_name)`.

* **Supported Animals:**  Currently supports "dog", "cat", "bird" and "koala".

* **Error Handling:**  Provides informative error messages and handles invalid animal names and API request errors.



## Installation



```bash

pip install animalapi

```



## Usage

> Get random animal data



```Python

import animalapi as a



data = a.rand_animals()

img = data["image"]

fact = data["fact"]



print(img) 

print(fact) 

```



> Get data for a specific animal

```Python

import animalapi as a



data = a.animal_data("dog")

img = data["image"]

fact = data["fact"]



print(img)

print(fact)

```



### Contributing

Contributions are welcome! Feel free to open issues or pull requests.



### License

This project is licensed under the MIT License - see the LICENSE file for details.