semvec/pypi-packages huggingface dataset:
requests-whaor 0.2.1
Requests With High Availability Onion Router. For the filthiest web scrapers that have no time for rate-limits.
License: MIT
Requires Python: >=3.8,<4.0
Keywords: requests,tor,haproxy,docker,rate-limit

# **requests-whaor** [[ri-kwests](https://www.dictionary.com/browse/requests) [hawr](https://www.dictionary.com/browse/whore)]

[**Requests**](https://requests.readthedocs.io) **+** [**Docker**](https://www.docker.com/) **+** [**HAproxy**](http://www.haproxy.org/) **+** [**Tor**](https://www.torproject.org/)

**Requests** **W**ith **H**igh **A**vailability **O**nion **R**outer. For the filthiest web scrapers that have no time for rate-limits.

[![Black](https://img.shields.io/badge/code%20style-black-black?style=for-the-badge&logo=appveyor)](https://github.com/psf/black)
[![GitHub](https://img.shields.io/github/license/dgnsrekt/requests-whaor?style=for-the-badge)](https://raw.githubusercontent.com/dgnsrekt/requests-whaor/master/LICENSE)


## Overview
**requests-whaor** proxies GET requests through a local **Docker** network of **TOR** circuits. It takes care of starting and stopping a pool of **TOR** proxies behind an **HAproxy** load balancer, which acts as a round robin reverse proxy network. This will give each request a new IP address.  If you start having issues with the initial pool of IPs, **requests-whaor** can gather a new pool of IP addresses by restarting all **TOR** containers.

## Install with pip
```
pip install requests-whaor
```

## Install with [Poetry](https://python-poetry.org/)
```
poetry add requests-whaor
```


## [>> **Quickstart** / **Docs** <<](https://dgnsrekt.github.io/requests-whaor/quickstart)

## Projects to highlight.
* [**dperson's**](https://hub.docker.com/u/dperson) - [torproxy docker container](https://hub.docker.com/r/dperson/torproxy)
* [**zet4's**](https://github.com/zet4) - [alpine-tor library](https://github.com/zet4/alpine-tor)
* [torproject](https://www.torproject.org/)
* [haproxy](https://hub.docker.com/_/haproxy)

## Useful Docker commands.
### If things get out of hand you may need these commands for debugging or killing containers.
```
docker ps -q --filter ancestor=osminogin/tor-simple | xargs -L 1 docker logs --follow

docker ps -q --filter ancestor=osminogin/haproxy | xargs -L 1 docker logs --follow

docker stop $(docker ps -q --filter ancestor=osminogin/tor-simple)

docker stop $(docker ps -q --filter ancestor=haproxy)

docker network rm $(docker network ls -q -f name=whaornet)
```

## TODO
* [ ] Testing.
* [ ] More request methods if requested.
* [ ] Options for using different Tor containers.
* [ ] Options for different load balancer containers.

## Contact Information
Telegram = Twitter = Tradingview = Discord = @dgnsrekt

Email = dgnsrekt@pm.me
cdstools 0.0.3
Tools designed for use with CDS data
License: MIT
Keywords: Credit Default Swap

This package offers users simple tools designed for Credit Default Swap (CDS) data. 
Included in this package is a hazard rate bootstrapping function which 
implements the JP Morgan model for determining hazard rate curves, and spread calculators
for both vanilla and binary CDSs. All functions linearly interpolate/extrapolate yield
curve values wherever necessary.

To be included in future releases will be a function that prices Credit Default 
Swaptions (options on CDSs). Also, later editions of this package will offer the users
options for interpolating/extrapolating yield curve values. 

Bootstrapping Function:
This function implements the so-called JP Morgan model. This model makes the 
assumptions that the interest rate process is independent of the default process
and that default leg pays at the end of each accrual period. Typically, regular
fee payments occur at the end of each period. To that end, this model assumes
that defaults occur midway buring each payment period. And finally, this model
assumes that the hazard rate is piecewise constant on the intervals that correspond
to the maturities of the CDS contracts.

CDS pricing function:
This function calculates the "fair" spread of a vanilla CDS, i.e. the spread that 
makes the value of the default/floating leg equal to the value of the payment/fixed 
leg. This is accomplished by using a given yield curve and given credit curve.

Binary CDS pricing function:
Similar to the previous function, this one calculates the "fair" spread of a binary CDS
by finding the spread that equates the value of the default/floating leg equal to
the value of the payment/fixed leg. This is also accomplished using a provided
yield curve and credit curve



Change Log
=========================

0.0.1 (10/08/2020)
-------------------------
- First Release


0.0.2 (10/10/2020)
-------------------------
- CDS spread calculator added. Some typos corrected.


0.0.3 (10/11/2020)
-------------------------
- Yield curve interpolation/extrapolation component was corrected.
- Binary CDS spread calculator added.
- Documentation updated
django-fluo-faq 0.1
Simple django faq application
License: MIT

# django-fluo-faq #

A simple faq system for django.


## Install ##

Add `faq.apps.FaqConfig` into your `INSTALLED_APPS`

```
#!python
INSTALLED_APPS = [
    ...
    "faq.apps.FaqConfig",
    ...
]
```

## CHANGES ##


0.1.0
=====


* initial release
django-fluo-posts 0.1
Integrate posts with django
License: MIT

# django-fluo-posts #

A generic extensible django application to handle posts, as news or blog applications.
Based on `django-fluo`.


## Install ##

Add `posts.apps.PostsConfig` into your `INSTALLED_APPS`

```
#!python
INSTALLED_APPS = [
    ...
    "posts.apps.PostsConfig",
    ...
]
```

## CHANGES ##


0.1.0
=====


* initial relase
django-fluo-post 0.1
Integrate posts with django
License: MIT

# django-fluo-posts #

A generic extensible django application to handle posts, as news or blog applications.
Based on `django-fluo`.


## Install ##

Add `posts.apps.PostsConfig` into your `INSTALLED_APPS`

```
#!python
INSTALLED_APPS = [
    ...
    "posts.apps.PostsConfig",
    ...
]
```

## CHANGES ##


0.1.0
=====


* initial relase
mnist-classifier 1.0.4
Basic mnist classifier example of a Reproducible Research Project in Python
License: MIT

![CircleCI](https://img.shields.io/circleci/build/github/sandrich/classifying_digits_mnist/master)
[![Coverage Status](https://coveralls.io/repos/github/sandrich/classifying_digits_mnist/badge.svg?branch=master)](https://coveralls.io/github/sandrich/classifying_digits_mnist?branch=master)
[![Generic badge](https://img.shields.io/badge/doc-latest-orange.svg)](https://sandrich.github.io/classifying_digits_mnist/)
![GitHub](https://img.shields.io/github/license/sandrich/classifying_digits_mnist)
![GitHub issues](https://img.shields.io/github/issues/sandrich/classifying_digits_mnist)

# M-NIST classification algorithm comparison

## Installation

You can just use
```bash
pip install mnist-classifier
```

## Documentation

You can find all the information you need on the [documentation page](https://sandrich.github.io/classifying_digits_mnist/index.html)

## Motivation for project

This project was realised in the scope of a course in Artificial Intelligence offered by [UniDistance](https://distanceuniversity.ch/artificial-intelligence/) and the [Idiap research Institute](https://github.com/idiap)

The hypothesis motivating the development of this package is the following:

 > Random Forests can give similar resulting prediction models to MLP Neural Networks on the M-NIST digit dataset in significantly less time.

With the code in this repository, we show that indeed, Random Forests *can* in fact produce similar (if not better) results with training times orders of magnitude smaller.

## License
MIT

## Authors
@sandrich - Christian Sandrini
@bigskapinsky - Calixte Mayoraz