semvec/pypi-packages huggingface dataset:
opsmatcal 1.2
A python package for calibrating opensees uniaxial materials using genetic algorithm
Requires Python: >=3.9

## opsmatcal

```
A python package for calibrating opensees uniaxial materials using genetic algorithm.
```

---

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install opsmatcal.

```
pip install opsmatcal
```
---
## Usage

Import opsmatcal in your Python code like this:

```python
import opsmatcal.matcalibrate as omc
```

---
## Support
- [Email](mailto:ok.programss@gmail.com)

- [Telegram Channel](https://t.me/OKprograms)

- [Instagram](https://www.instagram.com/opensees_apps/?hl=en)


## Documentation

1- Download from: [Documentation](https://drive.google.com/file/d/1qUoTewxzERZKUqCiSyMkAUqFCrTL1BQK/view?usp=sharing)

2- Use getDoc() method of opsmatcal.matcalibrate module:
```python
omc.getDoc()
```

---
## Author
```
Oktay Alizadeh Arasi
Master of Science in Structural Engineering
```

---
## License
```
OSI Approved : MIT License
```
---
econfiguration 0.5.3
A simple log module
Requires Python: >=3.7

# pyeconfiguration

简易配置对象, 配置文件导入和保存.



## 安装

使用 pip 安装 econfiguration

`pip install econfiguration`



## 快速上手

```Python
import econfiguration

# 创建一个配置对象
config = econfiguration.Configuration()

# 使用对象属性创建配置条目
config.attr_value = 1

# 使用 key 创建配置条目
config['item_value'] = 2

# 使用对象属性访问配置条目
print(config.attr_value)
print(config.item_value)

# 使用 item 访问配置条目
print(config['attr_value'])
print(config['item_value'])
```


### 嵌套

```Python
import econfiguration

# 创建配置对象
config = econfiguration.Configuration()

# 嵌套配置对象
config.abc = econfiguration.Configuration()

# 设置配置条目
config.abc.value = 1
```


### 导入和保存文件

```Python
import econfiguration

# 在初始化时导入配置文件
config = econfiguration.Configuration('config.json', _type='json')

# 清除所有数据
config._con_clear_data()

# 从 json 文件中更新数据
config._con_update_from_json('config.json')

# 保存配置文件
config._con_asve_as_json('config.json')
```



## Configuration 配置对象

```Python
Configuration(_file: Union[str, list, tuple] = ...,
              _type: str = 'json', read_only: bool = False)
# 创建配置对象
# _file: 需要导入的配置文件路径
# _type: 文件类型 ( 目前只接受 json )
# read_only: 设定这个对象为只读


._con_clear_data() -> None
# 清除所有条目数据

._con_set_read_only(value: bool) -> None
# 设置配置对象为只读模式

._con_is_read_only() -> bool
# 获取配置对象的只读模式描述

._con_update_from_data(data: dict) -> None
# 更新条目数据
# 当某项条目的数据类型为字典且键 ".con_is_configuration" 的值为 True 时将会初始化成一个 Configuration 对象.

._con_update_from_json(_file: Union[str, list, tuple] = ...) -> None
# 从 json 文件中更新数据

._con_get_data() -> dict
# 以字典的形式获取所有条目数据
# 数据类型为 Configuration 的条目也会分解成字典

._con_asve_as_json(_path: Union[str, list, tuple]) -> None
# 保存数据到 json 文件

._con_get_value(__key: str) -> Union[int, float, str, bool, list, tuple, dict]
# 获取指定条目的数据
# 当 key 不存在时返回 None

._con_set_value(__key: str, __value: Union[int, float, str, bool, list, tuple, dict]) -> None
# 设置条目数据
# key 不允许使用 "_con_" 字段开头
# value 不允许使用复杂数据类型
```


## 其它

### 字典重载

使用 Configuration._con_update_from_data 更新数据时
当某项数据的类型为 dict 且其中包含 '.con_is_configuration' 且值为 True, 那么这个字典会被重载成 Configuration 对象
billarchive 0.4
Command-line automated bills downloading
Requires Python: >=3.6

# billarchive

billarchive allows you to download bills from popular websites, so you don't
have to periodically visit every website where you buy products or services
and download bills.

It relies on modules implemented by [woob](https://woob.tech/modules).

[PyPI page](https://pypi.org/project/billarchive/)

## Configuration

The `~/.config/woob/backends` file must be configured for you to choose the
desired modules and their credentials.

Then `~/.config/woob/billarchive` can be configured to specify various
options, e.g. how to name downloaded files, whether to force conversion to pdf,
date until which new documents are searched, etc.

See [`config_example`](config_example) file in repository.

## Installation

### Normal setup

- pip install billarchive

### Dev setup

You need to have pip installed first. You can look at [pip's manual](https://pip.pypa.io/en/stable/installation/) for more information.

#### If woob is already installed

```sh
git clone https://gitlab.com/hydrargyrum/billarchive/
cd billarchive
pip install --no-deps -e .
```

#### If woob is not installed already

```sh
git clone https://gitlab.com/hydrargyrum/billarchive/
cd billarchive
pip install -e .
```

## Usage

Run `billarchive download` to download all documents on all backends, as
specified by config.

Or run `billarchive -b backend download` to use only one backend at a time.
pyHC128 1.0.2
pyHC128 is a Python package for the HC-128 stream cipher. It uses a 128-bit key and IV to generate a keystream.
Requires Python: >=3.6

pyHC128 is a Python package for the HC-128 stream cipher.
It uses a 128-bit key and IV to generate a keystream.

For more Documentation visit:
https://github.com/StaticStub/pyHC128
torchninja 0.1.3
Tools for training PyTorch models

# torchninja

torchninja is a Python package that provides tools for training PyTorch models. It offers a flexible and easy-to-use training framework with built-in functionality for logging, metrics tracking, and model checkpointing.

## Features

- Simplifies the process of training PyTorch models.
- Flexible and customizable trainer class.
- Automatic logging of training progress and metrics.
- Support for model checkpointing to save and load model states.
- Easy integration with TensorBoard for visualizing training metrics.
- Provides utility functions for common tasks in PyTorch model training.

## Installation

You can install torchninja using pip:

```shell
pip install torchninja
```

torchninja has the following dependencies:

- torch
- torchvision
- tensorboard
- tqdm
- numpy
- matplotlib

Make sure these dependencies are installed in your environment before using torchninja.

## Getting Started

To get started with torchninja, you can refer to the `examples` directory in the project repository. It contains example scripts that demonstrate how to use the torchninja trainer class to train a PyTorch model. You can modify these examples according to your specific use case.

## Documentation

The documentation for torchninja is available on the project's GitHub repository:

- [torchninja GitHub repository](https://github.com/silvaan/torchninja)

The documentation provides detailed information on the usage of the torchninja package, including the trainer class, available functionality, and usage examples.

## Contributing

Contributions to torchninja are welcome! If you find any issues, have suggestions for improvements, or would like to add new features, please open an issue or submit a pull request on the project's GitHub repository.

## License

torchninja is released under the MIT License. See the [LICENSE](LICENSE) file for details.

## Contact

For any questions or inquiries, you can reach out to the project author:

- Author: Silvan Ferreira
- Email: silvanfj@gmail.com

Feel free to contact the author for any assistance or collaboration opportunities related to torchninja.