semvec/pypi-packages huggingface dataset:
lignum 0.0.3
A teensy structured JSON logger for python.

# lignum
A teensy structured JSON logger for python.

## Usage
Install lignum with `pip`:

```bash
pip install lignum
```

Initialize the logger and use the level functions to start logging messages:

```python
import lignum
log = lignum.Logger()
log.info('Application started.') # Prints {"timestamp":1724129120,"message":"Application started.","level":"info"} to STDERR
```

By default, lignum prints to `sys.stderr`. You can use another stream type by specifying it in the
arguments:

```python
import lignum
log = lignum.Logger(sys.stdout)
log.error('The system is down.') # Prints {"timestamp":17241236120,"message":"The system is down.","level":"error"} to STDOUT
```

You can use a `File` as a stream to log to files on disk:

```python
import lignum
with open('log.log','w') as f:
  log = lignum(f)
  log.warn('I will haunt your dreams.') # Prints {"timestamp":17241336120,"message":"I will haunt your dreams.","level":"warn"} to ./log.log
```

Add additional fields using `extra` dictionaries:

```python
import lignum
log = lignum()
log.debug('Unknown value.', { 'value': mystery }) # Prints {"timestamp":1724149120,"message":"Unknown value.","level":"debug","value":-32768} to STDERR
```
mailsafeguard 0.3.0
A robust Python package for validating and filtering disposable email addresses to enhance email integrity and security.
Requires Python: >=3.6
Keywords: email validation, disposable email, email filtering, email security, Python package, email verification, spam prevention, email integrity, DNS resolution, email address validation, email protection, cybersecurity, email checker, email legitimacy, email hygiene, domain validation, email domain check, email format, email quality, email verification API, email address checker, email list management, email validation tool, email address validation tool, email blacklist, email whitelist, domain verification, DNS lookup, email fraud prevention, email validation library, disposable email checker, email validation service, email validation Python, secure email validation, email validation package, email validation software, email data quality, email validation script, Python email validation, email validation algorithm, email domain lookup, email verification tool, email protection tool, email security tool, email integrity tool

# MailsaFeGuard - Ultimate Email Validation and Filtering Tool 📧🚫

Welcome to **MailsaFeGuard**, the powerful Python package designed to ensure the integrity and legitimacy of email addresses. With MailsaFeGuard, you can effortlessly filter out disposable email addresses often used for spam or fraudulent activities. Say goodbye to unreliable email addresses and hello to cleaner, more trustworthy communications! 🌟📩

## Features

### 1. Comprehensive Email Validation 📜✅

- **Syntax Validation**: Ensures that the email format follows standard conventions.
- **Whitelist Check**: Cross-references the email domain with a whitelist of trusted providers like Gmail, Outlook, and Yahoo.
- **Disposable Domain Check**: Compares the domain against a list of known disposable email providers.
- **DNS Resolution**: Verifies the existence and resolution of the email domain via DNS.

### 2. Easy-to-Use Interface 🖥️👌

- Simple and intuitive API for seamless integration into your applications.
- Clear and informative output for each email validation.

### 3. Reliable and Up-to-Date Information 🔄📅

- Regularly updated list of disposable domains from a GitHub repository.
- Robust DNS checks for domain verification.

## How It Works

MailsaFeGuard follows a multi-step approach to ensure email legitimacy:

- **Syntax Validation**: Verifies that the email address adheres to standard formatting rules.
- **Whitelist Check**: Compares the email domain against a list of well-known, trusted email providers.
- **Disposable Domain Check**: Fetches an updated list of disposable email domains from a GitHub repository to check against.
- **DNS Resolution**: Ensures that the domain associated with the email address can be resolved through DNS, including its subdomains.

## Installation

To get started with MailsaFeGuard, install it via pip:

```bash
pip install mailsafeguard


from mailsafeguard import is_disposable_email

email = 'test@example.com'
print(is_disposable_email(email))
Dirlin 0.1.17
Handy functions for local files
Requires Python: <4.0,>=3.11

# Dirlin Project
Dirlin is a python script that has some handy tools for dealing with local files.

Some of the use cases include:
1. Getting the most recently downloaded files into a DataFrame
2. Finding files with the same naming conventions into a Dataframe
3. Creating mappings for fast column index-matching

# Installation
You can install this project via pip:

```bash
pip install dirlin
```

# Uses
This will make it easier to pull files from your local directory to work on different projects.

1. Import Path and Folder Objects
2. Define the Helper Object
3. Create the folder attributes
4. Define a function to create new folders

```python
# object.py
from dirlin import Path, Folder  # 1 - import path and folder objects
class LocalHelper:  # 2 - defining the Helper Object
    _base_path = Path("path to directory")
    
    DOWNLOADS = Folder(_base_path / "Folder1")  # 3 - creating the folder attributes
    DESKTOP = Folder(_base_path / "Folder2")  # 3 - defining the folder attributes

    @classmethod
    def new_folder(cls, folder: str | Path):  # 4 - defining a function to create new folders
        if isinstance(folder, str):
            folder = Path(folder)
        return Folder(folder)
```
## Getting the most recently downloaded file
```python
from dirlin import Folder, Path

def get_most_recent(filename):
    _base_path = Path("path to directory")
    folder = Folder(_base_path / "Folder1")
    
    df = folder.open_recent(filename)  # returns a dataframe of the most recent file with that pattern
    return df
```

## Combining Multiple Excel documents into a single file
```python
from dirlin import Folder, Path

def get_most_recent(filename):
    _base_path = Path("path to directory")
    folder = Folder(_base_path / "Folder1")
    
    combined_df = folder.find_and_combine(filename_pattern=filename)  # combines documents 
    return combined_df
fennet 0.0.1
Fennet
Requires Python: >=3.8
Keywords: deep learning, protein language model, proteomics, multi-dimensional proteomics

# Fennet
Deep learning for multi-dimensional proteomics


# mhc

For development:
In linux, if you clone the project in local and work in conda virtual environment,
when encountering *ModuleNotFoundError: No module named 'fennet'*，
you can execute *export PYTHONPATH=$PYTHONPATH:/path/to/Fennet/* in terminal and retry fennet command line tools.