semvec/pypi-packages huggingface dataset:
Easys-Decorator 0.1.2
A library to simplify decorators in Python
Requires Python: >=3.6

## Easys-Decorator API 中文版

**Easys-Decorator** 是一个 Python 库，旨在简化创建和使用装饰器的过程。它提供了一个用户友好的接口，用于定义和应用装饰器，消除了传统装饰器中常见的样板代码。

**核心概念：**

* **装饰器函数：** Easys-Decorator 允许你使用简单的语法定义装饰器函数。
* **装饰器参数：** 你可以向你的装饰器传递可选参数，使它们更加灵活和可重用。
* **装饰器应用：** 使用 `@decorate` 语法，以声明式的方式应用装饰器变得非常直观。

**API 参考：**

**1. 定义装饰器：**

```python
from easys_decorator import decorate

@decorate
def my_decorator(func, *args, **kwargs):
  """
  这是一个简单的装饰器，会在装饰的函数前后打印一条消息。
  """
  print(f"调用 {func.__name__}，参数：{args}，关键字参数：{kwargs}")
  result = func(*args, **kwargs)
  print(f"{func.__name__} 返回：{result}")
  return result
```

**2. 应用装饰器：**

```python
@my_decorator
def my_function(a, b, c=10):
  """
  这是一个简单函数，用于将三个数字相加。
  """
  return a + b + c

# 调用装饰后的函数
result = my_function(2, 3, c=5)
```

**3. 带参数的装饰器：**

```python
@decorate(param1="value1", param2="value2")
def my_parameterized_decorator(func, param1, param2, *args, **kwargs):
  """
  这是一个带参数的装饰器，用于打印其参数的值。
  """
  print(f"param1: {param1}, param2: {param2}")
  result = func(*args, **kwargs)
  return result

@my_parameterized_decorator
def another_function(x, y):
  """
  这是另一个演示函数。
  """
  return x * y
```

**4. 装饰器链：**

```python
@my_decorator
@my_parameterized_decorator(param1="value3", param2="value4")
def chained_function(a, b):
  """
  此函数演示了如何将多个装饰器链接在一起。
  """
  return a - b
```

**5. 装饰器组：**

```python
from easys_decorator import DecoratorGroup

group = DecoratorGroup(
    my_decorator,
    my_parameterized_decorator(param1="value5", param2="value6"),
)

@group
def grouped_function(x, y, z):
  """
  此函数使用装饰器组，一次性应用多个装饰器。
  """
  return x + y + z
```

**注意：** `@decorate` 语法需要 Python 3.7 或更高版本。对于旧版本，你可以使用 `decorate()` 函数，如下所示：

```python
decorated_function = decorate(my_decorator)(my_function)
```

**Easys-Decorator** 通过提供清晰直观的 API 简化了装饰器的使用。它消除了对复杂嵌套函数的需求，让你可以专注于装饰器的逻辑，而无需担心实现细节。
errorhook 1.5
Hook your Error.

# ErrorHook

自动全局捕捉错误，并
- 输出错误到控制台
- 输出错误到文件（可选，默认开启）
- 显示错误窗口（可选，默认开启）
Code by: cc1287

### 示例

```python
import ErrorHook
# 然后...就什么都不用做了，会自动捕捉错误

# 下面有一个错误
raise BaseException('I am a Error.')
```

![错误窗口样式](https://gitee.com/cc1287/error-hook/raw/master/assets/image-20240715103626507.png)

### 安装

```bash
pip install ErrorHook
```

### License

```
MIT License

Copyright (c) 2024 cc1287

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
```

#### 更新日志
- 1.2
> 让导入更便捷，更改导入名称从`ErrorHook`为`errorhook`
- 1.1
> 更新界面-退出按钮
- 1.0
> update
liveramp-clean-room 0.3.3
This is a python client that provides high level functions for interacting with liveramp's clean room.
Requires Python: <4.0.0,>=3.7.1

# Liveramp clean room
This library provides high level functions for interacting with liveramp's clean room API. Whilst users can directly interact with the API, we highly recommend the use of this client as it provides the following benefits:
- Automatic fetching and efficient caching of access token
- Automatic fetching of query status with exponential backoff
- Provides a simple interface for fetching query result as a pandas dataframe
- Provides user friendly interfaces for interacting with assets and associated permissions
- Provides clear and intuitive error messages


## Pre-Requisites


### Credentials File


Use of the client requires an account configured with access to an organization within Data Hub. LiveRamp customers must use a service account for that purpose. A service account credential, in the form of a JSON file, can be generated by a LiveRamp employee and shared securely.


### Installation


The Client is hosted on https://pypi.org/project/liveramp-clean-room/ and thus can be installed via


```bash
pip install liveramp_clean_room
```


Once installed the client be instantiated and added to your code as the example below shows:


```python
from liveramp_clean_room import datahub


## Initialize a client
org_id = "00XXX0XXX0XXX00X0XXX000XXX"
credentials_file = "Path to service account file"
proxies = {
    'http': 'http://10.10.1.10:3128',
    'https': 'http://10.10.1.10:1080',
}

client = datahub.Client(org_id, credentials_file, proxies=proxies)
```
Where `org_id` is the id of an organization within DataHub and `credentials_file` is the JSON file location of your service account

`proxies` is an optional parameter that can be used to specify a proxy server for the client to use. If not specified, the client will not use a proxy server.



## Running Queries


To run a query utilize the run_query function. DataHub supports the following query types:


- Data Manipulation Language (DML)


    `SELECT query`


- Data Definition Language (DDL)


    `CREATE OR REPLACE FUNCTION/DROP FUNCTION)`, used to create or drop UDFs, a createdAssetID will be returned in the response. 


Example: Run a query and get results
---
```python
from liveramp_clean_room import datahub


org_id = "00XXX0XXX0XXX00X0XXX000XXX"
credentials_file = "Path to service account file"
client = datahub.Client(org_id, credentials_file)
query_id = client.run_query("select * from schema.mytable")


## Fetch result as a pandas dataframe
dataframe = client.get_result(query_id)
```


> Note that the client returns a Pandas Dataframe
telegram-tdata-decrypter 1.1.2
decrypter tdata to session
Requires Python: <4,>=3.7

# Telegram Desktop tdata folder decrypter written in python

Inspired by [telegram-desktop-decrypt](https://github.com/atilaromero/telegram-desktop-decrypt)

# install

```

pip install  telegram_tdata_decrypter

```

# origin

```
thinks  https://github.com/ntqbit/tdesktop-decrypter

```