semvec/pypi-packages huggingface dataset:
imgDedup 1.0.0
图像去重工具包.
Requires Python: >=3.11

# ImageDeduplication

图像去重
> 项目地址：https://github.com/firstelfin/ImageDeduplication
> PYPI地址：https://pypi.org/project/imgDedup/

## 自定义去重数据HashCode

数据类，包含了success、value（phash的hex值）、error、img_path属性。集成了`__sub__`、`__repr__`、`__bool__`、`to_dict`方法。这是去重的基本数据结构。

## 去重核心算法-phash

> 代码路径：`imgDedup/tools/imageFingerprint.py`

imageFingerprint:get_phash 基于`imagehash.phash`实现了感知哈希算法，计算图像的指纹。函数的输入可以是图像路径，也可以是图像ndarray。

## 去重管线1--单个数据集去重

> 代码路径：`imgDedup/utils/deduplication.py`

`deduplication:SelfDeduplication` 类实现了单个数据集的去重。去重逻辑是并发加载每个图片的HashCode, 然后初始化一个保存的空列表，循环这些HashCode，如果HashCode与列表中的HashCode都不相似，则将此HashCode加入列表。最后返回列表中的HashCode。

**使用案例：**

```python
>>> sd = SelfDeduplication(
...     src_dir=Path(f"xxxx"),
...     dst_dir=Path(f"xxxxx"),
...     use_link=True,
...     threshold=5,
...     hash_size=16
... )
>>> sd(save_json_path=Path(f"xxx/dedup测试/status/deduplication_record.json"))
```

## 去重管线2--多个数据集去重

> 代码路径：`imgDedup/utils/deduplication.py`

`deduplication:CrossDatasetDeduplication` 类实现了多个数据集的去重。去重逻辑是并发加载每个数据集的图片的HashCode, 然后初始化一个保存的空列表，循环这些HashCode，如果HashCode与列表中的HashCode都不相似，则将此HashCode加入列表。最后返回列表中的HashCode。

**使用案例：**

```python
>>> mdl = MultiDeduplication(
...     src_dir=Path("xxx/xxxx_deduplication_record.json"),
...     dst_dir=Path("xxxxx/images"),
...     targets=[
...         Path("xxxxxx/images"),
...         Path("aaaaaa/images"),
...         Path("ssssss/images"),
...         Path("wwwwww/images"),
...         Path("ffffff/xxxxxx_deduplication_record.json"),
...         Path("ccccccc/eeeeee_deduplication_record.json"),
...     ],
...     threshold=26,
...     use_link=False,
...     hash_size=16
... )
... mdl(save_json_path=Path("xxxss--202508_deduplication_record.json"))
```

## Install

源码安装：

```shell
>>> git clone https://github.com/firstelfin/ImageDeduplication.git
>>> cd &&pip install .
```

通过PYPI安装：

```shell
>>> pip install imgDedup
```
mstk 0.3.29
Molecular simulation toolkit

# mstk

_A toolkit to make molecular simulation less painful_

`mstk` is a Python toolkit designed to streamline the setup and management of molecular simulations, particularly geared
towards non-biological applications in material science and chemical engineering. It simplifies atom typing, force field
parameter assignment, and input file generation for major simulation engines.

## Features

* Assign atom types and force field parameters based on local chemical environment
* Generate input files for LAMMPS, GROMACS, NAMD and OpenMM
* Support Drude polarizable model, coarse-grained model, virtual site, linear angle...
* Read/write common topology (PSF, ZMAT, PDB, LAMMPS, ...) and trajectory (GRO, XTC, DCD, LAMMPS, ...) files
* Access local and remote job schedulers like Slurm

## Installation

```
conda install -c conda-forge numpy pandas rdkit openmm chemfiles packmol
pip install mstk
```

## Quick Example

Build a liquid mixture of 100 benzene and 1000 water molecules, ready for simulation.

```python
from mstk.topology import Molecule, Topology
from mstk.forcefield import ForceField, Typer
from mstk.simsys import System
from mstk.wrapper import Packmol

# Create topology from SMILES
benzene = Molecule.from_smiles('c1ccccc1')
water = Molecule.from_smiles('O')
top = Topology([benzene, water])

# Assign atom types as defined in `data/forcefield/primitive.smt`
typer = Typer.open('primitive.smt')
typer.type(top)

# Build a bulk liquid simulation box with Packmol
packmol = Packmol('packmol')
top.cell.set_box([4.0, 4.0, 4.0])
top.scale_with_packmol([100, 1000], packmol=packmol)

# Assign force field parameters as defined in `data/forcefield/primitive.zff`
ff = ForceField.open('primitive.zff')
ff.assign_charge(top)
system = System(top, ff)

# Generate input files for LAMMPS, GROMACS and NAMD
system.export_lammps()
system.export_gromacs()
system.export_namd()

# Generate OpenMM system and topology
omm_sys = system.to_omm_system()
omm_top = top.to_omm_topology()
```

__Important Note__:
*The __primitive__ atom typing and force field used above are for demonstration purpose only and are __not__ well
optimized for production use.*
*For reliable simulation, please prepare you own `smt` and `zff` files or get them from a validated source.*

## Documentation

https://mstk.readthedocs.io/en/latest/index.html

## TODO

- [ ] Refactor algorithms across `topology` and `analyzer` modules
- [ ] Separate `ommhelper` module into its own package
- [ ] Remove `analyzer` module
- [ ] Revise the implementation of Drude polarization and virtual sites
simple-calculator-baraah-mujahid 0.1.0
A simple calculator package for basic math operations
Requires Python: >=3.6

# تم تطوير هذه المكتبة بواسطة: براءة مجاهد الموفعي
# مكتبة بايثون بسيطة توفر وظائف العمليات الحسابية الأساسية:
# الجمع، الطرح، الضرب، والقسمة.

# طريقة التثبيت والاستخدام:
# إذا كنت تستخدم محرر Visual Studio Code، افتح الطرفية (Terminal)
# وتأكد أنك داخل مجلد المشروع الذي ترغب في استخدام المكتبة فيه.
# ثم نفّذ الأمر التالي لتثبيت المكتبة:

pip install simple-calculator-Baraah-Mujahed
botmail 0.2.7
Class to handle smpt and imap server for RPA apps
Requires Python: >=3.10

# BotMail

BotMail é uma ferramenta Python projetada para facilitar o gerenciamento de e-mails usando os protocolos SMTP e IMAP. Ideal para integrar em fluxos de trabalho de Automação de Processos Robóticos (RPA), permite o envio, leitura e manipulação de e-mails de forma simples e eficiente.

## Funcionalidades

- **Envio de E-mails (SMTP):** Configure e envie e-mails com facilidade.
- **Recebimento de E-mails (IMAP):** Leia e processe e-mails diretamente do servidor.
- **Manipulação de Anexos:** Faça download e gerenciamento de anexos.
- **Automação:** Integração com fluxos de RPA para tarefas repetitivas e automáticas.

## Requisitos

- Python 3.10 ou superior.
- Biblioteca padrão `smtplib`, `imaplib`, e `email`.

## Instalação

1. Instale o pacote:
   ```bash
    pip install botmail
   ```

## Exemplo de Uso

```python
from botmail.webmail import Email

if __name__ == "__main__":
    mail = Email(
        "smtp.example.com.br",
        "imap.example.com.br"
    )
    mail.send_email(
        "Nem Email Class",
        "example1@gmail.com",
        ["example2@gmail.com"],
        mail.generate_simple_html_body('Hi', 'BotMail', "What's up?"),
    )
```

No exemplo acima:
- Configuramos as credenciais do servidor SMTP e IMAP.
- Enviamos um e-mail simples usando `send_email`.

## Licença

Este projeto está licenciado sob a licença MIT. Consulte o arquivo [Licença MIT](LICENSE) para mais detalhes.

## Contribuições

Contribuições são bem-vindas! Caso tenha sugestões, melhorias ou correções, sinta-se à vontade para abrir uma issue ou enviar um pull request.

## Contato

Para dúvidas ou suporte, entre em contato com o mantenedor através do [GitHub](https://github.com/botlorien/botmail).
orbax-export 0.0.8
Orbax Export
Requires Python: >=3.10
Keywords: JAX machine learning, serialization, export

# Orbax Export

`pip install orbax-export` (latest PyPi release) OR

`pip install 'git+https://github.com/google/orbax/#subdirectory=export'` (from this repository, at HEAD)

`import orbax.export`

Orbax includes a serialization library for JAX users, enabling the exporting of JAX models to the TensorFlow SavedModel format.

Note that `orbax-export` requires TensorFlow, but does not include it by default to allow for flexibility in version choice. If you wish to install with standard TensorFlow, please use `pip install orbax-export[all]`.

To get started, check out our [documentation](https://orbax.readthedocs.io/en/latest/guides/export/orbax_export_101.html).