semvec/pypi-packages huggingface dataset:
queuing-hub 0.0.0
Multi-cloud Queuing Hub
Requires Python: >=3.6, <3.10
Keywords: aws,gcp,queue,development

# queuing-hub

Multi-cloud Queuing Hub for Python

![queuing-hub](https://github.com/tosh223/py-queuing-hub/blob/main/drawio/queuing_hub.svg)

## Description

- This is a wrapper tool for AWS SQS and Google Cloud PubSub(Topic and pull subscription) with transparent interface.
- Easy messaging redundancy.
    - Improve fault tolerance by avoiding queues becoming SPOFs
    - Duplicate production messages to test environment for debugging

## Install

## Requirements

- python = "^3.6"
- google-cloud-pubsub = "^2.4.0"
- google-cloud-monitoring = "^2.0.1"
- boto3 = "^1.17.18"

## Usage

### Publisher

```py
from queuing_hub.publisher import Publisher

pub = Publisher()
# Send a message to all queues accessible by default
response = pub.push(topic_list=pub.topic_list, body='Hello world!')
```

### Subscriber

```py
from queuing_hub.subscriber import Subscriber

sub = Subscriber()
# Receive messages with list ascending priority from queues accessible by default
response = sub.pull(sub_list=sub.sub_list, max_num=1, ack=True)
```

### Forwarder

```py
from queuing_hub.forwarder import Forwarder

fwd = Forwarder(sub=sub.sub_list[0], topic=pub.topic_list[0], max_num=1)
# copy message
response_0 = fwd.pass_through()
# move message
response_1 = fwd.transport()
```
hawkes 1.0.0
a python package for simulation and inference of Hawkes processeses
Requires Python: >=3.6

# Hawkes
a python package for simulation and inference (maximum likelihood estimation) of Hawkes processes.

# Install
```
pip install hawkes
```

# Tutorial
https://colab.research.google.com/github/omitakahiro/Hawkes/blob/master/notebooks/tutorial.ipynb

# Feature
This package provides the following kernel functions:
- exponential function  
- sum of multiple exponential functions  
- power-law function
- non-parametric function

This package provides the following classes of a baseline intensity:
- constant baseline
- piecewise constant baseline model
- piecewise linear baseline model
- log-linear baseline model
- custom baseline function

# Contact
Takahiro Omi  
https://sites.google.com/view/omitakahiro/home-english  
takahiro.omi.em[at]gmail.com