semvec/pypi-packages huggingface dataset:
legitindicators 0.0.76
Legit indicators to be used in trading strategies.

# Legit Indicators

![Publish Python 🐍 distributions 📦 to PyPI and TestPyPI](https://github.com/aticio/legitindicators/workflows/Publish%20Python%20%F0%9F%90%8D%20distributions%20%F0%9F%93%A6%20to%20PyPI%20and%20TestPyPI/badge.svg)

Collection of indicators that I used in my strategies.

## List of indicators: ##

* Simple Moving Average
* Exponential Moving Average
* Average True Range
* Smoothed ATR - Super Smoother applied Average True Range
* Rolled Moving Average
* Atrlimit - Average True Range implementation with a limit for using as a volatility indicator
* Smoothed Atrlimit - Smoothed Average True Range implementation with a limit for using as a volatility indicator
* Roofing Filter - Python implementation of the Roofing Filter indicator created by John Ehlers
* Super Smoother - Python implementation of the Super Smoother indicator created by John Ehlers
* SZLADX - A low lagging upgrade of ADX indicator.
* True Range
* Decycler - Python implementation of Simple Decycler indicator created by John Ehlers
* Decycler Oscillator - Python implementation of Decycler Oscillator created by John Ehlers
* High Pass Filter
* Damiani Volatmeter
* Voss - Python implementation of Voss indicator created by John Ehlers
* Hurst Coefficient
* Kaufman Efficiency Ratio
* Even Better Sine Wave - Python implementation of Even Better Sine Wave indicator created by John Ehlers
* Cube Transform - Python implementation of Cube Transform created by John Ehlers
* Simple Harmonic Oscillator
* Smoothed Simple Harmonic Oscillator - Super Smoother applied Simple Harmonic Oscillator
* Kaufman Adaptive Moving Average
* Linear Regression Curve
* Linear Regression Slope
* TrendFlex - Python implementation of TrendFlex indicator created by John Ehlers
* Custom TrendFlex - Python implementation of TrendFlex indicator with customizable super smoother length, created by John Ehlers
* Automatic Gain Control - Python implementation of Automatic Gain Control created by John Ehlers
* Smoothed SSL - Super Smoother Applied SSL indicator
* Bollinger Bands %B
* Volume Heat - A custom volume indicator
* Double Super Smoother - Two Super Smoother indicators for checking line crosses
* EMA Trailing - A trailing stop implementation using Exponantial Moving Average
* Momentum Normalized - Normalized Momentum Indicator; Cube Transform applied


## Installation

Run the following to install:

```python
pip install legitindicators
```
aminpy 1.0.3
A Python library - rubino
Requires Python: >=3.7
Keywords: rubika, api, bot, aminpy

from aminpy import rubino
import asyncio
from termcolor import colored
#AminEbrahimi
async def main():
    async with rubino("YOUR_AUTH_TOKEN") as bot:
        # Upload an image post
        post = await bot.add_post(
            "image.jpg",
            text="Hello from aminpy!",
            type="Picture"
        )
        print(colored("Post Created:", "green"), post)

asyncio.run(main())
TESTITSA 0.0.1
ISTerre Time Series Analysis (ITSA) is a tool developed by Lou Marill.  This tool, developed in Python, produces a trajectory model for GNSS time series.
Requires Python: >=3.7
Keywords: gnss, gps, Time Series, Analysis, trajectory model

## ITSA = ISTerre Time Series Analysis

ITSA was developped by Lou Marill during her PhD, as a member of the ISTerre Cycle team.
This tool developped in python produces a trajectory model for GNSS time series. 

Inputs:
- the time series in PBO pos format
- the dates and types of discontinuities (antenna, earthquakes, slow-slip events, swarms)
- station coordinates (used to get earthquakes-station distance)
- optionally, a list of position outliers

ITSA calculates:
- the 3 dimensional offset for each equipment discontinuity
- post-seismic relaxation, currently without inversion (tau is an input parameter)
- linear, seasonal, and acceleration on the full time series

Outputs:
- Final time series
- Intermediate time series
- All modelled and applied corrections

ITSA is distributed under the licence CC-BY-NC: https://creativecommons.org/licenses/by-nc/4.0/

Documentation is available on the gitlab wiki: https://gricad-gitlab.univ-grenoble-alpes.fr/isterre-cycle/itsa/-/wikis/home
zagen 0.1.2
Agentic package to build your next AI Workflows
Requires Python: >=3.12
Keywords: agent, agentic workflow, mcp, multi mcp client

![zagen](./asset/zagen.png)

Accessible and customizable agent components that you can copy and paste into your apps. Free. Open Source. Use this to build your own agentic workflows.

### 👉 Getting Started

#### Install using uv:

```bash
uv add zagen
```

#### Install using pip:

```bash
pip install zagen
```

### 🤩 Feel The Magic

```python
from zagen.agent import OpenAIAgent, OpenAIAgentConfig
from zagen.mcp import MCPServer

def get_weather(loc: str) -> Literal["Sunny", "Cloudy", "Rainy"]:
    """Get the weather of current locations"""
    return "Sunny"

triage_agent = OpenAIAgent(
    name="Triage Agent",
    instructions="""You are the Triage Agent.
    """,
    mcp={
        "weather": MCPServer(
            type="lambda",
            function=[get_weather],
        ),
        "slack": MCPServer(
            type="sse",
            url="https://"
        ),
    },
    config=OpenAIAgentConfig(
        model="gpt-4o",
        base_url="",
        api_key="",
    ),
)
```
wnpoly 2.0.2
Package to compute useful polynomials for Webnucleo projects.
Requires Python: >=3.0
Keywords: polynomials, Bell, symmetric

Overview
========

.. image:: https://zenodo.org/badge/603141720.svg
  :target: https://doi.org/10.5281/zenodo.15113409

The module computes various polynomials useful for `webnucleo <https://webnucleo.readthedocs.io>`_ projects.  In particular, the package has modules to compute symmetric and Bell polynomials for input sets of variables.  For the complete Bell polynomials, the package also has a routine to invert an appropriate set of Bell polynomials to find the variables that would give rise to those polynomials.

|pypi| |doc_stat| |license| |test| |lint-test| |black|

Installation
------------

Install from `PyPI <https://pypi.org/project/wnpoly>`_ with pip by
typing in your favorite terminal::

    $ pip install wnpoly

Usage
-----

To learn how to use the package, follow the Jupyter notebook
`tutorial <https://github.com/mbradle/wnpoly/blob/main/tutorial/>`_.

Authors
-------

- Bradley S. Meyer <mbradle@g.clemson.edu>
- Ian Reistroffer <ireistr@g.clemson.edu>

Contribute
----------

- Issue Tracker: `<https://github.com/mbradle/wnpoly/issues/>`_
- Source Code: `<https://github.com/mbradle/wnpoly/>`_

License
-------

The project is licensed under the GNU Public License v3 (or later).

Documentation
-------------

The project documentation is available at `<https://wnpoly.readthedocs.io>`_.

.. |pypi| image:: https://badge.fury.io/py/wnpoly.svg 
    :target: https://badge.fury.io/py/wnpoly
.. |license| image:: https://img.shields.io/github/license/mbradle/wnpoly
    :alt: GitHub
.. |doc_stat| image:: https://readthedocs.org/projects/wnpoly/badge/?version=latest 
    :target: https://wnpoly.readthedocs.io/en/latest/?badge=latest
    :alt: Documentation Status
.. |test| image:: https://github.com/mbradle/wnpoly/actions/workflows/test.yml/badge.svg?branch=main&event=push
        :target: https://github.com/mbradle/wnpoly/actions/workflows/test.yml
.. |lint| image:: https://img.shields.io/badge/linting-pylint-yellowgreen
    :target: https://github.com/pylint-dev/pylint
.. |lint-test| image:: https://github.com/mbradle/wnpoly/actions/workflows/lint.yml/badge.svg?branch=main&event=push
        :target: https://github.com/mbradle/wnpoly/actions/workflows/lint.yml
.. |black| image:: https://img.shields.io/badge/code%20style-black-000000.svg
    :target: https://github.com/psf/black
mathlib-Ninja-22 0.1
Basit matematik işlemleri yapan kütüphane
Requires Python: >=3.6

# Mathlib

Basit matematik iÅŸlemleri yapan Python kÃ¼tÃ¼phanesi.

## Kurulum

```bash
pip install mathlib-Ninja-22
```

## KullanÄ±m

```python
from mathlib import topla, carp

# Toplama iÅŸlemi
sonuc = topla(5, 3)  # 8

# Ã‡arpma iÅŸlemi
sonuc = carp(4, 2)  # 8
```

## Ã–zellikler

- Toplama iÅŸlemi
- Ã‡arpma iÅŸlemi

## Lisans

MIT License
easycnnpro 1.0.1
EasyCnnPro: Intuitive Python library for creating, training and visualizing convolutional neural networks. Features simplified CNN layer definition, automated training workflows, model visualization, and seamless Keras-to-ONNX conversion. Includes 15 pre-configured popular models for immediate use.
Keywords: deep-learning, cnn, neural-networks, tensorflow, keras, machine-learning, ai, computer-vision, image-processing, model-training, visualization, easy-to-use, python, convolutional-networks


# EasyCNN - Easy Creating and Visualizing CNN Model

EasyCNN is a Python library designed to simplify the creation, training, and visualization of convolutional neural networks (CNNs). It provides an intuitive interface for deep learning enthusiasts and developers who want to work with CNN models without the complexity often associated with neural network frameworks.


## Features

- Straightforward definition of CNN layers with intuitive syntax
- Streamlined training and model evolution capabilities
- Visual representation of model architecture
- 15 pre-configured popular Keras application models ready for immediate use
- Seamless conversion of Keras models to ONNX format



## Documentation

[Documentation](https://github.com/Gabrli/EasyCNN---docs)



## Authors

- [@Gabrli](https://github.com/Gabrli)


## Tech Stack

**Languages:** Python
  
**Libraries:** Tensorflow, Matplotlib, Numpy, OpenCv

## License

[MIT](https://choosealicense.com/licenses/mit/)


## FAQ

#### what are the advantages ?

- Very easy and comfortable syntax
- Full control for developer
- Automatic data preparation and visualization processes
- Compatibility of model: option to convert to onnx type file.

#### What functionalities are under construction?

- Presets for popular models
- Exporter and Converter for files with models
- Special Visualizer to display training process


## Contributing

Contributions are always welcome!

See `contributing.md` for ways to get started.

Please adhere to this project's `code of conduct`.


## Basic Usage/Example

```python
from easycnn.core import EasyCNN
from easycnn.visualizer import TrainingVisualizer
import os
from tensorflow.keras.datasets import cifar10

class_names = ['car', 'plane', 'cat', 'dog', 'bird', 'deer', 'horse', 'frog', 'ship', 'truck']

(x_train, y_train), (x_test, y_test) = cifar10.load_data()

my_file = os.path.join(os.path.dirname(__file__), 'car.jpg')

x_train = x_train[:2000]
y_train = y_train[:2000]
x_test = x_test[:400]
y_test = y_test[:400]
x_train = x_train / 255
x_test = x_test / 255

model = EasyCNN()
model.add_conv(32, 3)
model.add_max_pool(2)
model.add_conv(64, 3)
model.add_max_pool(2)
model.add_conv(128, 3)
model.add_max_pool(2)
model.add_flatten()
model.add_dense(10, activation='softmax')
model.compile()
history = model.train(x_train, y_train, x_test, y_test, epochs=5)
prediction = model.predict(my_file)

visualizer = TrainingVisualizer()
visualizer.plot_training(history)
```
nlptoolkit-informationretrieval-cy 1.0.7
Information Retrieval Library

Information Retrieval
============

Video Lectures
============

[<img src=https://github.com/StarlangSoftware/InformationRetrieval/blob/master/video1.jpg width="50%">](https://youtu.be/DhjZPVrvdnE)[<img src=https://github.com/StarlangSoftware/InformationRetrieval/blob/master/video2.jpg width="50%">](https://youtu.be/rfNoyFw-_g8)[<img src=https://github.com/StarlangSoftware/InformationRetrieval/blob/master/video3.jpg width="50%">](https://youtu.be/sYHVpTZL6o4)[<img src=https://github.com/StarlangSoftware/InformationRetrieval/blob/master/video4.jpg width="50%">](https://youtu.be/bRckCK9VcKQ)[<img src=https://github.com/StarlangSoftware/InformationRetrieval/blob/master/video5.jpg width="50%">](https://youtu.be/ZX4zTT69ll0)[<img src=https://github.com/StarlangSoftware/InformationRetrieval/blob/master/video6.jpg width="50%">](https://youtu.be/AVoLka-LDXY)[<img src=https://github.com/StarlangSoftware/InformationRetrieval/blob/master/video7.jpg width="50%">](https://youtu.be/5GOyBTeSJwo)[<img src=https://github.com/StarlangSoftware/InformationRetrieval/blob/master/video8.jpg width="50%">](https://youtu.be/-iu6N8KZslw)[<img src=https://github.com/StarlangSoftware/InformationRetrieval/blob/master/video9.jpg width="50%">](https://youtu.be/LwQYHFyDd8U)[<img src=https://github.com/StarlangSoftware/InformationRetrieval/blob/master/video10.jpg width="50%">](https://youtu.be/Y_jS03r6GMI)[<img src=https://github.com/StarlangSoftware/InformationRetrieval/blob/master/video11.jpg width="50%">](https://youtu.be/msRT2yx0yms)[<img src=https://github.com/StarlangSoftware/InformationRetrieval/blob/master/video12.jpg width="50%">](https://youtu.be/B5RProYhMvk)[<img src=https://github.com/StarlangSoftware/InformationRetrieval/blob/master/video13.jpg width="50%">](https://youtu.be/dxc3ONoW63E)

For Developers
============

You can also see [Python](https://github.com/starlangsoftware/InformationRetrieval-Py), [Java](https://github.com/starlangsoftware/InformationRetrieval), [C++](https://github.com/starlangsoftware/InformationRetrieval-CPP), [Js](https://github.com/starlangsoftware/InformationRetrieval-Js), [C](https://github.com/starlangsoftware/InformationRetrieval-C), or [C#](https://github.com/starlangsoftware/InformationRetrieval-CS) repository.
fengluB 0.0.4a1
牛逢路的开发工具包
Requires Python: >=3.6

# Fenglu's Utils for Python
## 如何使用
* `$ pip3 install fengluB`
* 在py文件中引入 `from fengluU import n2u`
* 调用 `n2u.number2upper(123456789)`
## n2u number2upper 
* 将输入的数字按照中文的书写方式返回(支持的数字长度是65536位数字)
    > 例如：<br/>
        传入：123456789<br/>
        传出：一亿二千三百四十五万六千七百八十九 
* 算法如下：
   * 算法采用的是上数法，数穷则变，即：万万曰亿，亿亿曰兆，兆兆曰京...
   * 数字从右往左数，可以获取到每个数字的编号，起始编号为0
   * 用该位数字编号和4取模,若有余，余1为十，余2为百，余3为千
   * 若整除，则用该位数字编号与4整除，若结果为奇数，则该位单位为万
   * 若结果为偶数:
        1. :  先判断该结果是否是2的整数次方,若是，该位单位是`CN_UNIT[次方]`
        2. ：若不是:从亿位开始作为检测标记位，用结果和(2^检测标记位下标)取余
           1. 若能整除，并且商为奇数则单位是 `CN_INIT[标记检测位下标]`
           2. 否则标记检测位+1
* 另外，我这个方法支持的数字真的非常大，我可以不判断数字长度吗？
    每添加一个更大的单位在上面的`CN_UNIT`列表的最后面，您就可以将当前的长度扩充一倍
    
    > 添加方法: `n2u.append_unit('更大的单位')`                                    
* 参数：
    
    > :param num: int 一个整型数字
* 返回值 
    > :return: 返回数字的中文书写方式
* 用法
    > ```python
    > from fengluU import n2u
    > print(n2u(123456789))
    > $ 一亿二千三百四十五万六千七百八十九
    > ```

## rmb2u rmb2upper
* 将输入的数字按照人民币大写的书写方式返回(支持16位)
    > 例如： 
        123456789<br/>
        壹亿贰仟叁佰肆拾伍万陆仟柒佰捌拾玖元整
* 参数
    > :param num: str 人民币小写金额
* 返回值
    > :return: 返回人民币大写的形式
* 用法
    > ```python
    > from fengluU import rmb2u
    > print(rmb2u('1234567.89'))
    > $ 壹佰贰拾叁万肆仟伍佰陆拾柒元捌角玖分
    > ```

## MySQLHelper
* MySQLHelper MySQL工具类，便于数据库连接池及连接的管理和增删改查

## MySQL Alter
* MySQLAlter MYSQL对比表不同工具类，便于比较不同环境的数据库表，并生成改表语句