semvec/pypi-packages huggingface dataset:
configuration-master 0.0.1
A package for configuration management

# config-master

**config-master** is a Python package designed for easy configuration management. It offers flexibility in storing and retrieving configuration data through global variables or environment variables.

## Installation

You can install **config-master** via pip:

```sh
pip install config-master
```

## Basic Usage

```python
from config_master import ConfigMaster

# Set a configuration value
ConfigMaster.set_config("my_key", "my_value")

# Get a configuration value
value = ConfigMaster.get_config_value("my_key")
print(value)
```

## Configuration Methods

You can manage your configuration in one of two ways based on your coding style.

1. **Using Environment Variables**
   By default, configuration values are stored as environment variables. This is recommended for security and encapsulation.
   ```python
   ConfigMaster.set_config("my_key", "my_value")
   ```

2. **Using Global Variables**
   If you prefer to manage configuration values using global variables, set the `use_global` parameter to True. Note that with global variables, you can only access them using the `ConfigMaster.get_config_value()` method.
   ```python
   ConfigMaster.use_global = True
   ConfigMaster.set_config("my_key", "my_value")
   ```

## Working with JSON Files

You can load configuration data from a JSON file:

```python
# Set configuration from a JSON file
ConfigMaster.set_config_from_json_file("config.json")
```

## Working with Dictionaries

Set multiple configuration values using a dictionary. The keys in the dictionary should match the variable names you intend to set.

```python
data = {
    "my_key": "my_value",
    "another_key": "another_value"
}

ConfigMaster.set_config_from_dict(data)

# Get a configuration value
value = ConfigMaster.get_config_value("my_key")
print(value)
```

**config-master** makes configuration management in Python a breeze, allowing you to choose the approach that best fits your project's needs.
symbolic-learn 0.1.4
A sklearn-compatible package that implements a symbolic regression model
Requires Python: >=3.7

Welcome to symbolic-learn's repository!
========================================

symbolic-learn is a sklearn-compatible package that implements a symbolic regression model.


What is symbolic regression?
------------------------------

Symbolic regression is a type of regression model that combines mathematical blocks to find the function that best fits the data. Here each function is represented as a binary tree like this one:

.. image:: https://raw.githubusercontent.com/vinpap/symbolic-learn/master/docs/_static/genetic_program_tree.png
   :alt: Function tree representation : image not found
   :align: center

The model initially generates a random population of such functions. It then uses genetic programming techniques on it to find out the function that best fits our dataset.
As this model is based on `scikit-learn's <http://scikit-learn.org>`_ base estimator, it can be used the same way you would use any sklearn model. Thus, you can use it in pipelines or apply fine-tuning techniques such as GridSearchCV on it.

Symbolic regression is best used when you want to take a naive approach to solving a regression problem. Unlike most existing models, it does not come with an **a priori** specification of a model. Therefore it is a good idea to use it when you want to find out and understand the mathematical structures in your data. 

Example
-----------------------------

Here is how to instantiate and train a symbolic regression model::

    from sblearn.models import SymbolicRegressor
    model = SymbolicRegressor()
    model.fit(X_train, y_train)

After training your model, you can use access the fitted function's formula and function tree through the model's attributes `formulas` and `trees`. `Read the doc <https://symbolic-learn.readthedocs.io/en/latest/>`_ for more information.


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

In order to install the package, use this command::

   pip install symbolic-learn

*Note for Windows users*: Microsoft Visual C++ 2014 or higher is required!
encrypt256 1.8.6.2
str 型和 bytes 型数据加密器
Keywords: encrypt256,encrypt,AES,pycryptodome,Crypto

# 项目描述

str 型和 bytes 型数据加密器。

1、底层加密算法为 AES-CBC-256。

2、加密时，会自动创建随机 salt、随机 iv、原始明文的校验值，并把校验值添加到密文中。

3、解密时，会自动根据校验值校验“解密得到的明文”与“原始明文”是否一致。

# 作者信息

昵称：lcctoor.com

[主页](https://lcctoor.github.io/arts/) \| [微信](https://lcctoor.github.io/arts/arts/static/static-files/WeChatQRC.jpg) \| [Github](https://github.com/lcctoor) \| [PyPi](https://pypi.org/user/lcctoor) \| [Python交流群](https://lcctoor.github.io/arts/arts/static/static-files/PythonWeChatGroupQRC.jpg) \| [邮箱](mailto:lcctoor@outlook.com) \| [域名](http://lcctoor.com) \| [捐赠](https://lcctoor.github.io/arts/arts/static/static-files/DonationQRC-0rmb.jpg)

# Bug提交、功能提议

您可以通过 [Github-Issues](https://github.com/lcctoor/arts/issues)、[微信](https://lcctoor.github.io/arts/arts/static/static-files/WeChatQRC.jpg) 与我联系。

# 安装

```
pip install encrypt256
```

# 教程 ([查看美化版](https://lcctoor.github.io/arts/?pk=encrypt256)👈)

#### 导入

```python
from encrypt256 import Encrypt256
```

#### 创建加密器

```python
password1 = 123456789  # 支持int型密钥
password2 = '黄河之水天上来'  # 支持str型密钥
password3 = '床前明月光'.encode('utf8')  # 支持bytes型密钥

enctool = Encrypt256(password1)  # 创建加密器
```

#### 加密

```python
p1 = '人生自古谁五死'  # 可加密str型数据
p2 = '莎士比亚'.encode('utf8')  # 可加密bytes型数据

c1 = enctool.encrypt(p1)
c2 = enctool.encrypt(p2)
```

#### 解密

```python
r1 = enctool.decrypt(c1)
r2 = enctool.decrypt(c2)

assert p1 == r1
assert p2 == r2
assert type(p1) is type(r1)
assert type(p2) is type(r2)
```

当发生以下情况时，会解密失败并报错：

1、密钥错误。

2、由于密文被篡改，导致 AES 算法解密失败。

3、由于密文被篡改，虽然 AES 算法解密成功，但校验值错误。
bframe 0.0.17
A simple python web server frame
Requires Python: >=3.8

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</svg>
</div>


`bframe`是一个基于`WSGI`的协议的`web`框架。它非常像`flask`，但也会有`django restframework`的味道。

`bframe`的初衷是为了学习`python`的`web`框架，加深对`WSGI`及周边生态的了解。当然，若您想更深入的了解`python web`开发，请参与进来吧！


### 安装

```shell
pip install -U bframe
```

### 快速入门

```python
# app.py
from bframe import Frame

app = Frame(__name__)


@app.get("/")
def home():
    return "hello world"


if __name__ == "__main__":
    app.run()
```

### 启动项目

```shell
python app.py
```

### LINKS

项目文档: [https://bean-jun.github.io/bframe-docs/](https://bean-jun.github.io/bframe-docs/)

项目DEMO: [https://github.com/PyWebBuilders/Plats.git](https://github.com/PyWebBuilders/Plats.git)
DeepGapSeq 0.0.1
An smFRET analysis package, including deeplearning for gap state identification
Requires Python: >=3.9

# DeepGapSeq

## Installing DeepGapSeq From GitHub

    conda create –-name DeepGapSeq python==3.9
    conda activate DeepGapSeq
    conda install -c anaconda git
    conda update --all

    pip install https://github.com/piedrro/DeepGapSeq.git

### To install **MATLAB** engine (Windows):

    pip install matlabengine

    This will likely fail due to a MATLAB version issue. 
    Read the traceback, and install the recomended verison. 
    Then try again: 'pip install matlabengine==XXXX'