semvec/pypi-packages huggingface dataset:
GDX2py 2.2.0
Read and write GAMS Data eXchange (GDX) files using Python
License: MIT
Requires Python: >=3.8

# GDX2py

[![Documentation Status](https://readthedocs.org/projects/gdx2py/badge/?version=latest)](https://gdx2py.readthedocs.io/en/latest/?badge=latest)

Read and write GAMS Data eXchange (GDX) files using Python.

## Requirements

- GAMS: version 47.0 or higher
- Python: 3.8 or higher


### Installing GAMS

Get GAMS from [https://www.gams.com/download/](https://www.gams.com/download/) 
and install it to your system. No license is needed for the use of GDX libraries.


## Installation

Install with

    pip install gdx2py


## Usage

    >>> from gdx2py import GdxFile, GAMSSet, GAMSScalar, GAMSParameter
    >>> with GdxFile('/path/to/gdx/file.gdx', mode='w') as gdx:
    >>>     gdx['set1'] = ['a', 'b', 'c']  # Write a simple set
    >>>     set1 = gdx['set1']  # Read a symbol
    >>>     # Write a 2-dimensional set
    >>>     gdx['set2'] = [('a', 'foo'), ('b', 'bar'), ('c', 'baz')]
    >>>     # Write a scalar with explanatory text
    >>>     gdx['scalar'] = GAMSScalar(3.14, expl_text="Value of pi")  
    >>>     # Write a parameter with domain                                     
    >>>     gdx['par1'] = GAMSParameter({'a': 1, 'b': 2,  
                                         'c': 3, 'd': 4 }, 
                                        domain=['set1'])  

## Comparison to similar packages

Compared to other packages like [PyGDX](https://github.com/khaeru/py-gdx), [gdx-pandas](https://github.com/NREL/gdx-pandas), [gdxtools](https://github.com/boxblox/gdxtools) and [gdxpy](https://github.com/jackjackk/gdxpy), *GDX2py* relies 
only on the Python standard library and the low-level GDX API `gamsapi.core.gdx` module which is part of the GAMS 
Python API (`gamsapi`) available on PyPI.
 

<hr>
<center>
<table width=500px frame="none">
<tr>
<td valign="middle" width=100px>
<img src=https://europa.eu/european-union/sites/europaeu/files/docs/body/flag_yellow_low.jpg alt="EU emblem" width=100%></td>
<td valign="middle">This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 774629.</td>
</table>
</center>
mobio-mail-client-sdk-test 0.1.20
Mobio mail client SDK
License: MIT
Requires Python: >=3
Keywords: mobio, mail client, imap

##  Thư viện đọc email sử dụng core imap lib .


### Cài đặt:
```bash
 $ pip3 install mobio-mail-client-sdk
 ```


#### Log - 0.1.0
    - tạo sdk 

#### Log - 0.1.1
    - cập nhật hàm đọc email 

#### Log - 0.1.2
    - cập nhật hàm authen access token 

#### Log - 0.1.3
    - kiểm tra init folder mailbox  

#### Log - 0.1.4
    - Bỏ lru_cache của thư viện cũ  

#### Log - 0.1.5
    - fix seen email inbox
TiLearn 1.0.0
An open-source project for time management and scheduling solutions.
License: MIT
Requires Python: >=3.10.0

<a href="https://bancie.github.io/TiLearn/" target="_blank">
  <picture>
    <img alt="Bancie logo" src="https://github.com/Bancie/TiLearn/assets/144613141/9c35f828-8c29-444e-aafb-f298c2bdba93" width="400px">
  </picture>
</a>
<h3></h3>

[![homepage](https://img.shields.io/badge/powered%20by-TiLearn-%237072eb?style=for-the-badge&labelColor=%23555555)](https://bancie.github.io/TiLearn/)
[![PyPI](https://img.shields.io/badge/pypi%20package-1.0.0-%23177bbb?style=for-the-badge&logo=pypi&labelColor=white
)](https://pypi.org/project/TiLearn/)

Welcome to our open-source project focused on applying **Machine Learning (ML)** and **Deep Learning (DL)** techniques to **Machine Scheduling** and **Time Management**, often referred to as **Optimal Processing**. Our goal is to revolutionize the way tasks and processes are managed in various projects by leveraging advanced computational methods to optimize efficiency and productivity.

# Installation

To install TiLearn using PyPI, run the following command:

```
pip install TiLearn
```

Then, in the TiLearn repository that you cloned, simply run:

```
pip install .
```


# Documentation and Usage
For in-depth instructions on installation and building the documentation, see the [TiLearn Documentation Guide and Tutorial](https://bancie.github.io/TiLearn/).

Link: https://bancie.github.io/TiLearn/

# Project Goals

- **Optimized Scheduling**: Develop algorithms that can create optimal schedules for machines, minimizing downtime and maximizing throughput.
- **Predictive Maintenance**: Implement predictive models to foresee and mitigate potential machine failures, ensuring continuous and efficient operations.
- **Time Management**: Utilize deep learning models to enhance time management practices, helping businesses or individuals and team allocate resources more effectively and meet deadlines.
- **Scalability**: Design solutions that are scalable and adaptable to different industrial environments and varying sizes of operations.

# Responsibilities

As part of this open-source project, contributors are encouraged to:

- **Algorithm Development**: Create and refine machine learning and deep learning algorithms tailored to scheduling and time management.
- **Data Collection and Preprocessing**: Gather and preprocess data from various sources to train and validate models.
- **Model Training and Evaluation**: Train models using the collected data and evaluate their performance to ensure accuracy and reliability.
- **Integration and Testing**: Integrate developed models into real-world scheduling systems and conduct extensive testing to validate their effectiveness.
- **Documentation and Support**: Maintain comprehensive documentation of the project, providing clear guidelines for usage and contribution. Assist users and other contributors through forums and issue tracking.

For inquiries, please **contact** me at [chibangn1@gmail.com](https://mail.google.com/mail)