semvec/pypi-packages huggingface dataset:
appmake 0.0.1
Define your database schema and API in a few lines of Python; export to a complete tested project in the language of your choice.
Requires Python: >=3.9

# App Make

Define your database schema and your API routes in a few lines of Python, and export to a complete tested project in your language of choice. After running the tool, you'll get:

- A complete suite of unit tests
- A browser-ready API client
- An Open API specificiation
- A Dockerfile

Working out of the box.
MITREAttackScrapper 0.1.5
A package for conveniently retrieving MITRE ATT&CK data
Requires Python: >=3.6

# MITREAttackScrapper
### A simple and easy data scrapper for **MITRE ATT&CK** information for threat intelligence and knowledge bases, written in Python3.
> Un scrapper de datos simple y f찼cil para la informaci처n de **MITRE ATT&CK** para inteligencia de amenazas y bases de conocimiento, escrito en Python3.

```py
# Need to MITRE ATT&CK data? How about using my package?
from MITREAttackScrapper.techniques.enterprise import MITREAttackEnterpriseTechniques

if __name__ == '__main__':
    detail = MITREAttackEnterpriseTechniques.get("T1548.001")
    print(f"Technique: {detail['name']}")
    print(f"Platforms affected by this technique: {detail['platforms']}")
    print(f"Number of mitigation suggested: {len(detail['mitigations'])}")

    # Technique: Abuse Elevation Control Mechanism
    # Platforms affected by this technique: ['Azure AD', 'Google Workspace', 'IaaS', 'Linux', 'Office 365', 'Windows', 'macOS']
    # Number of mitigation suggested: 1
```

### Note: Currently under development, not stable!!!

## How to use?
Refer to the **[documentation](https://knightchaser.github.io/MITREAttackScrapper/)**! >_<
- The documentation is based on **`sphinx-apidoc`**, which is an automated documentation tool for lazy Python and other language-based programmers.
- Locally build the Sphinx document if there are some changes on the codebase. The GitHub page action will read the refreshed page built results and the GitHub page will be updated accordingly.
```powershell
sphinx-apidoc -f -o docs/source .\MITREAttackScrapper\
./docs/make.bat html
```

## Coverage
- **TECHNIQUES**
  - [x] MITRE ATT&CK Enterprise Techniques
  - [ ] MITRE ATT&CK Mobile Techniques
  - [ ] MITRE ATT&CK ICS Techniques
- **TACTICS**
  - [x] MITRE ATT&CK Enterprise Tactics
  - [ ] MITRE ATT&CK Mobile Tactics
  - [ ] MITRE ATT&CK ICS Tactics
- **CTI**
  - [x] MITRE ATT&CK CTI Groups
  - [x] MITRE ATT&CK CTI Software
  - [x] MITRE ATT&CK CTI Campaigns
- **Defenses/Mitigations**
  - [x] MITRE ATT&CK Enterprise Mitigations(Defenses)
  - [ ] MITRE ATT&CK Mobile Mitigations(Defenses)
  - [ ] MITRE ATT&CK ICS Mitigations(Defenses)
- **ATT&CK MATRICES**
  - [x] MITRE ATT&CK Enterprise Matrices
  - [ ] MITRE ATT&CK Mobile Matrices
  - [ ] MITRE ATT&CK ICS Matrices
just-d4rl 0.2407.5
Easy-to-use D4RL offline dataset loader
Requires Python: >=3.8
Keywords: D4RL

# just-d4rl

Easy-to-use D4RL offline dataset loader, focused solely on downloading and providing D4RL datasets without dependencies on gym or gymnasium.


## Key Features

- Downloads and provides D4RL offline datasets
- No dependencies on gym or gymnasium
- Lightweight and focused functionality
- Easy integration with PyTorch


## Installation

Install from [PyPI](https://pypi.org/project/just-d4rl/):

```sh
pip install just-d4rl
```


## Usage

```python
from just_d4rl import D4RLDataset, d4rl_offline_dataset, D4RLScoreNormalizer

# Download and load a D4RL dataset
d4rl_dataset = d4rl_offline_dataset("hopper-medium-v2")

# Example datasets
d4rl_dataset = d4rl_offline_dataset("walker2d-random-v2")
d4rl_dataset = d4rl_offline_dataset("halfcheetah-medium-expert-v2")
d4rl_dataset = d4rl_offline_dataset("antmaze-umaze-v2")

dataset = d4rl_dataset
dataset['observations'].shape, dataset['actions'].shape, dataset['rewards'].shape, dataset['next_observations'].shape, dataset['terminals'].shape
# ((1000000, 11), (1000000, 3), (1000000,), (1000000, 11), (1000000,))

# Create a PyTorch Dataset
d4rl_dataset = d4rl_offline_dataset("hopper-medium-v2")
dataset = D4RLDataset(d4rl_dataset)

# Get a batch of data
batch = dataset[-16:]
batch["observation"].shape, batch["action"].shape, batch["reward"].shape, batch["next_observation"].shape, batch["terminal"].shape
# (torch.Size([16, 11]), torch.Size([16, 3]), torch.Size([16]), torch.Size([16, 11]), torch.Size([16]))


returns = np.random.rand(16, 1)
get_normalized_score = D4RLScoreNormalizer("hopper-medium-v2")
get_normalized_score(returns)
```
FashTextPy 1.0.0
Beautiful output to the python console/Красивый вывод в консоль python.

Р СѓСЃСЃРєР°СЏ РґРѕРєСѓРјРµРЅС‚Р°С†РёСЏ
РњРѕСЏ Р±РёР±Р»РёРѕС‚РµРєР° РїРѕР·РІРѕР»СЏРµС‚ РІС‹РІРѕРґРёС‚СЊ РєСЂР°СЃРёРІС‹Р№ С‚РµРєСЃС‚ РІ РєРѕРЅСЃРѕР»СЊ РЅР° РґРІСѓС… СЏР·С‹РєР°С…: СЂСѓСЃСЃРєРёР№/Р°РЅРіР»РёР№СЃРєРёР№.
Р’РµСЂСЃРёСЏ 1.0 - РёРјРµРµС‚ РІ СЃРµР±Рµ 2 С€СЂРёС„С‚Р° 'sharp' Рё 'stars'.

в†“РљР°Рє РїРѕР»СЊР·РѕРІР°С‚СЊСЃСЏв†“
import FashText
fprint(text='РўРµРєСЃС‚', font='РЁСЂРёС„С‚') # Р’ РїРѕР»Рµ text Р·Р°РїРёСЃС‹РІР°РµРј РЅСѓР¶РЅС‹Р№ РІР°Рј С‚РµРєСЃС‚. Р’ РїРѕР»Рµ font Р·Р°РїРёСЃС‹РІР°РµРј РІС‹Р±СЂР°РЅРЅС‹Р№ РІР°РјРё С€СЂРёС„С‚.

в†“РџСЂРёРјРµСЂ РёСЃРїРѕР»СЊР·РѕРІР°РЅРёСЏ Р±РёР±Р»РёРѕС‚РµРєРёв†“
from FashText import fprint
fprint(text= 'TEXT', font='stars')

Р”Р»СЏ РєРѕСЂСЂРµРєС‚РЅРѕРіРѕ РІС‹РІРѕРґР° РєРѕР»РёС‡РµСЃС‚РІРѕ СЃРёРјРІРѕР»РѕРІ РЅРµ РґРѕР»Р¶РЅРѕ РїСЂРµРІС‹С€Р°С‚СЊ РјРµСЃС‚Р° РІ РєРѕРЅСЃРѕР»Рё(С‡РµРј С€РёСЂРµ РјРѕРЅРёС‚РѕСЂ С‚РµРј Р±РѕР»СЊС€Рµ СЃРёРјРІРѕР»РѕРІ РјРѕР¶РЅРѕ РІРІРµСЃС‚Рё).
Р”Р»СЏ С‚РѕРіРѕ С‡С‚РѕР±С‹ РІС‹РІРѕРґ Р±С‹Р» РєРѕСЂСЂРµРєС‚РЅС‹Р№ РІС‹ РјРѕР¶РµС‚Рµ РёСЃРїРѕР»СЊР·РѕРІР°С‚СЊ РјРµРЅСЊС€Рµ СЃРёРјРІРѕР»РѕРІ, СѓРјРµРЅСЊС€РёС‚СЊ С…СѓРґ(РґР»СЏ VS Code - "Ctrl" + "-") РёР»Рё СЃРґРµР»Р°С‚СЊ С€СЂРёС„С‚ РјРµРЅСЊС€Рµ.
Р‘РёР±Р»РёРѕС‚РµРєР° С‚РѕР»СЊРєРѕ СЂР°Р·РІРёРІР°РµС‚СЃСЏ, РїРѕР¶Р°Р»СѓР№СЃС‚Р°, РЅРµ СЃСѓРґРёС‚Рµ СЃС‚СЂРѕРіРѕ).
Р’ Р±СѓРґСѓС‰РµРј СЏ Р±СѓРґСѓ СЂР°Р·РІРёРІР°С‚СЊ Р±РёР±Р»РёРѕС‚РµРєСѓ РїРѕСЃС‚Р°СЂР°СЋСЃСЊ РґРѕР±Р°РІРёС‚СЊ Р±РѕР»СЊС€Рµ РЅРѕРІС‹С… С€СЂРёС„С‚РѕРІ.
Р•СЃР»Рё РµСЃС‚СЊ РёРґРµРё РґР»СЏ СЂРµР°Р»РёР·Р°С†РёРё РёР»Рё РѕРїС‚РёРјРёР·Р°С†РёРё Р±РёР±Р»РёРѕС‚РµРєРё РјРѕР¶РµС‚Рµ СЃРєРёРґС‹РІР°С‚СЊ СЃРІРѕРё РїСЂРµРґР»РѕР¶РµРЅРёСЏ СЃСЋРґР° в†’virus.fash@gmail.comв†ђ

English documentation
My library allows you to output beautiful text to the console in two languages: Russian/English.
Version 1.0 - has 2 fonts 'sharp' and 'stars'.

в†“How to use в†“
import FashText
fprint(text='Text', font='Font') # In the text field write the text you want. In the font field write the font of your choice.

в†“Library usage example в†“
from FashText import fprint
fprint(text='TEXT', font='stars')

For correct output, the number of characters should not exceed the space in the console (the wider the monitor, the more characters can be entered).
To make the output correct you can use fewer characters, reduce the hud (for VS Code - "Ctrl" + "-") or make the font smaller.
The library is just developing, please do not judge harshly).
In the future I will develop the library will try to add more new fonts.
If you have ideas for implementation or optimization of the library you can send your suggestions here в†’virus.fash@gmail.comв†ђ.
chat-user 1.0.0
Streamlit component that styles user lists.
Requires Python: >=3.7

# chat-user

Streamlit component that allows you to do X

## Installation instructions

```sh
pip install chat-user
```

## Usage instructions

```python
import streamlit as st

from chat_user import chat_user

user1 = {
  "id": 1,
  "name": "user1",
  "socket": "",
  "admin": False,
}

user2 = {
  "id": 2,
  "name": "user2",
  "socket": "",
  "admin": False,
}

u1 = chat_user(user1, True)
print(u1)

u2 = chat_user(user2, False)
print(u2)
```
chat-message 1.0.0
Streamlit component that styles a chat bubble.
Requires Python: >=3.7

# chat-message

Streamlit component that allows you to style chat messages into chat bubbles. Uses daisyUI.

## Installation instructions

```sh
pip install chat-message
```

## Usage instructions

```python
import streamlit as st
from chat_message import chat_message

user = {
  "id": 1,
  "name": "user1",
  "socket": "",
  "admin": False,
}

# simulated database data
rows = [
  {
    'message_id': 1,
    'message_msg': 'This is a test.',
    'message_roomId': 1,
    'message_time': datetime.datetime(2024, 7, 10, 6, 55, 59),
    'author_id': 1,
    'author_name': 'user1',
    'author_email': 'user1@example.com',
    'author_socket': ''
  },
  {
    'message_id': 2,
    'message_msg': 'This is only a test.',
    'message_roomId': 1,
    'message_time': datetime.datetime(2024, 7, 10, 6, 55, 59),
    'author_id': 2,
    'author_name': 'user2',
    'author_email': 'user2@example.com',
    'author_socket': ''
  }
]

result = []
for row in rows:
  obj = {
    "id": row["message_id"],
    "message": row["message_msg"],
    "room": row["message_roomId"],
    "time": row["message_time"].isoformat(),
    "author": {
      "id": row["author_id"],
      "name": row["author_name"],
      "socket": row["author_socket"]
    }
  }
  result.append(obj)

i = 0
for row in result:
  prev = None
  next = None
  
  if len(result) > 1:
    prev = result[i-1]
  
  if len(result) > i+1:
    next = result[i+1]
  
  appendTime = True
  appendUser = True
                    
  if prev:
    if prev["author"]["id"] == row["author"]["id"]:
      diff = timeDiffInSecs(row["time"], prev["time"])
      if diff < 60:
        appendUser = False
  
  if next:
    if next["author"]["id"] == row["author"]["id"]:
      diff = timeDiffInSecs(next["time"], row["time"])
      if diff < 60:
        appendTime = False
  
  click = st_chat_message(row, user, appendUser, appendTime)
  print(click)
  
  i = i+1
```
lightning-watcher 0.0.1
Uses GOES data to monitor lightning strikes within a set radius and provide notifications.
Requires Python: >=3.9

# lightning_watcher
Goal is to monitor a given radius R around a point P for lightning strikes within the last X minutes.  Provide notifications to other services regarding the strikes.

# Getting Started
1. pip install -r requirements.txt
1. install pre-commit hooks
1. install pytest-cov