semvec/pypi-packages huggingface dataset:
pecoret-default-templates 0.0.1
PeCoReT default templates.

# PeCoReT - Default Templates

This repo contains a set of example templates for PeCoReT.

This includes:
- Vulnerability Templates
- Example Report Template


## Vulnerability Templates
The current focus for development is on the core and frontend part.
The vulnerability templates are mainly taken from other sources.

The main sources for the vulnerability templates are:

- https://knowledge-base.secureflag.com/
- https://github.com/Probely/vulnerabilities-knowledge-base
- https://github.com/Ostorlab/KB
- https://www.acunetix.com/vulnerabilities


## Localization
To Be Done
news-nlp 1.0.22
This package aims to collect news data via Google News API, preporcessing, implement pretrained news type classifer, sentiment classifier and perform statistical hierarchy predictive model for news event and then implememnt multiple geolocator.
Requires Python: >=3.8,<4.0


<!-- ![](/home/jason/Documents/data/newplot.png) -->
# News NLP for Global Geopolitical Risk
=======================================

This is a Python client for the news NLP.

Usage
-----
First install it

```
pip install news-nlp -U
```

Then instantiate and use it like this:

```python
import News_NLP
from News_NLP.GNews import GNews
from News_NLP.preprocessing import Preprocessing_Clf_SA, Preprocessing_GEO,Combine_Col
from News_NLP.gpr_classifier import GPR_Clf
from News_NLP.sentiment_clf import Sen_Clf
from News_NLP.geolocator import Get_CSC_Prob, CSC_Prob
```

* Fetch news articles from Google News API, `from News_NLP.GNews import GNews`
* Preproces news data for sentiment analysis and news type classifier
   `from News_NLP.preprocessing import Preprocessing_Clf_SA`
* Preproces news data for multiple-geolocator
  `from News_NLP.preprocessing import Preprocessing_GEO`
* Implement pretrained deep leanring model to caterise news article 
  `from News_NLP.gpr_classifier import GPR_Clf`
* Perform sentiment classifier `from News_NLP.sentiment_clf import Sen_Clf`
* Implement multiple geolocator `form News_NLP.geolocator import Get_CSC_Prob, CSC_Prob`,

# GitHub
For a demonstration in Jupyter notebook see https://github.com/HigherHoopern/News_NLP/NewsApp.ipynb

download sample news data
https://github.com/HigherHoopern/News_NLP/blob/main/data/SampleNews.csv
scene-graph-predictor-pc 0.1.1
3D Scene Graph Models
Requires Python: >=3.8

# 点云场景图预测使用说明

# 1. 环境依赖

CUDA版本: 11.3
其他依赖库的安装命令如下：

```bash
conda create -n py38 python=3.8
```

```bash
pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113
pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.12.1+cu113.html
pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-1.12.1+cu113.html
pip install torch-spline-conv -f https://pytorch-geometric.com/whl/torch-1.12.1+cu113.html
pip install torch-geometric
```

# 2. 下载安装

```bash
pip install scene-graph-predictor-pc
```

# 3. 使用说明
```python
import scene_graph_predictor_pc
import trimesh

plydata=trimesh.load('scene/scene0/labels.instances.align.annotated.v2.ply', process=False)
model = scene_graph_predictor_pc.SceneGraphPredictor()
# model 加载数据
model.load('checkpoint')
# 模型预测

```
margay 0.2.0
SDK for Margay Gateway - provide tooling and examples of usage.
Requires Python: >=3.8
Keywords: api,geteway,websocket,tools,margay

# Margay Gateway - toolkit
Provide set of tooling for building reliable websocket gateway system. 
Architecture part described in Margay Gateway core [repository](https://github.com/moaddib666/wss-api-gateway.go#readme)

## Quickstart

- Install sdk `pip install margay`
- Deploy `Margay Gateway` with RMQ locally part see [instruction](https://github.com/moaddib666/wss-api-gateway.go#local-stand)
- Setup Subscriber or use example `examples/vanilla_worker.py` or `examples/vanilla_publisher.py` 
- Setup client or use example `examples/vanilla_client.py`
  - You can generate auth token by using [cli](https://github.com/moaddib666/wss-api-gateway.go/blob/main/cmd/indentety_provider/encoder.go)
  - By using sdk auth
  - By using external indentety provider
- Connect your backend
  - Basically that means:
    - You subscribe RMQ topic with your `CustomQueue` and listen for events
    - You are ready to publish new events for your clients
- Connect websocket client to `Margay Gateway`
- That's it now you are ready to send/receive messages trough `Margay Gateway`

## Configure
You are able to setup sdk for your custom needs

Example of change of default subscriber queue name
```python
from margay.sdk.config import Config

Config.set("queue", "VanillaWorkerQueue")
```

## Subscriber
Example custom subscriber creation 
```python
from margay.sdk.subscriber import Subscriber
from margay.sdk.protocol import RawMessage

class JsonMessage(RawMessage):
    """ Your message protocol """

class Router:
    """ Your event router """

class Handler:
    """ Your event handler """
    
class SimpleWorker(Subscriber):
    Message = JsonMessage
    resolve = Router.resolve
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

    def process(self, message: JsonMessage):
        self.resolve(message)

```

## Publisher
Example custom publisher creation

```python
from margay.sdk.publisher import Publisher

class Event:
    """Your custom event protocol"""
    
class EventPublisher(Publisher):
    origin = "MyAwsomeMss"
    def publish(self, event: Event, recipient: str):
        self.publish_raw(event.serialize(), self.origin, recipient)
```

## Auth
### JWT
```python
from margay.sdk.auth import JWTAuth
user = "John Snow"
secret = "SuperSecret"
identity_provider = JWTAuth(secret)
token = identity_provider.issue_token(user)
payload = identity_provider.verify_token(token, user)
print(payload)
```

## Debugging
Set debug for SDKLogger
```python
import logging
from margay.sdk.logger import SDKLogger
import sys
logging.basicConfig(
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
    level=logging.INFO,
    handlers=[logging.StreamHandler(sys.stdout)]
)
SDKLogger.setLevel(logging.DEBUG)
```