semvec/pypi-packages huggingface dataset:
kass-flow 0.2.6
Library to manage payments using the KASS API
License: MIT
Requires Python: >=3.6,<3.9


Kass Flow
=========

Helper for the `KASS <https://www.kass.is/>`_ payment gateway. Written for python 3.8.5 (probably works on versions >= 3.6).

See the `KASS API docs <https://kass.github.io/api/>`_ for more info.

This module is not affiliated with KASS.

TODO
----


* [x] Create payment
* [ ] Retreive payment info
* [ ] Retreive payment status
* [ ] Cancel payment
* [ ] Add concurrency with RQ when dispatching multiple payments

Usage
-----

Here is a short guide to get you started.

This will dispatch a payment to the KASS API.

.. code-block:: sh

   poetry add kass-flow
   # or
   pip install kass-flow

.. code-block:: python

   from kass_flow.kass import KassBilling
   from kass_flow.interfaces import KassRequestPaymentDict

   kass_token: str = "some-token"
   kass_url: str = "https://api.kass.is/v1/payments"
   instance = KassBilling(kass_token, kass_url)

   payload: KassRequestPaymentDict = {
       "amount": 2199,
       "description": "Kass bolur",
       "image_url": "https://photos.kassapi.is/kass/kass-bolur.jpg",
       "order": "ABC123",
       "recipient": "7798217",
       "terminal": 1,
       "expires_in": 90,
       "notify_url": "https://example.com/callbacks/kass",
   }

   result, is_valid = instance.dispatch(payload)

When the recipient cancels or pays the requested order KASS will send a callback to the ``notify_url``\ , which you need to catch on your server and probably validate the signature that is included in the POST payload.

At some point in time the user will pay/reject the requested order. Here is an example on how to validate the payload.

.. code-block:: python

   from kass_flow.kass import KassBilling
   from kass_flow.interfaces import KassRequestPaymentDict

   def some_view_that_handles_kass_callback(request):
       payload = request.data
       kass_token: str = "some-token"
       kass_url: str = "https://api.kass.is/v1/payments"
       instance = KassBilling(kass_token, kass_url)

       if instance.is_signature_valid(payload):
           # process the payload.

Development
-----------

.. code-block:: sh

   pip install poetry
    # to manage envs yourself
   poetry config virtualenvs.create false
   poetry install
   pytest tests

If you are using VSCode for development there is a ``.vscode/settings.example.json`` for sensible defaults. Since mypy is used you need to install the ``mypyls`` language server.

.. code-block::

   poetry install "https://github.com/matangover/mypyls/archive/master.zip#egg=mypyls[default-mypy]"
   # or
   pip install "https://github.com/matangover/mypyls/archive/master.zip#egg=mypyls[default-mypy]"
mako2cli 0.1.1
Mako Template Command-Line Tool
License: MIT
Requires Python: >=3.7,<4.0
Keywords: mako,template,cli

[![Tests](https://github.com/leaningdiggers/mako2cli/workflows/Tests/badge.svg)](https://github.com/leaningdiggers/mako2cli/actions?workflow=Tests)
[![Codecov](https://codecov.io/gh/leaningdiggers/mako2cli/branch/master/graph/badge.svg)](https://codecov.io/gh/leaningdiggers/mako2cli)
[![PyPI](https://img.shields.io/pypi/v/mako2cli.svg)](https://pypi.org/project/mako2cli/)
[![Read the Docs](https://readthedocs.org/projects/mako2cli/badge/)](https://mako2cli.readthedocs.io/)

# mako2cli

This project aims to port Mako Template to a simple usage on command line

## Installation

To install the mako2cli Python project,
run this command in your terminal:

```
$ pip install mako2cli
```
cordial 0.226
comprehensive recommender systems
License: MIT
Keywords: machine learning,recommender systems,automation

# cordial
Coridal is an intuitive light-weight API that allows developers to seamlessly use different recommender systems for their needs. 
In just three lines of a code, one can built a powerful content recommender system. Currently, Coridal only provides content recommenders,
but collaborative filtering is coming soon!

# Installation
`pip install cordial`

# Example usage:

```python

# An example with Cordial's BasicRecommender
from cordial.content_recommenders import GraphRecommender
recommender = BasicRecommender('disney')
print(recommender.recommend('Toy Story')['result'])

# An example with Cordial's GraphRecommender
from cordial.content_recommenders import GraphRecommender
recommender = GraphRecommender('netflix')
print(recommender.recommend('Toy Story')['result'])

# It's as simple as pie!
```

Full documentation can be viewed [here](https://github.com/Vagif12/cordial/wiki)
counterix 1.2.2
Generating count-based Distributional Semantic Models
License: MIT
Keywords: count-based,distributional semantics

# counterix
[![GitHub release][release-image]][release-url]
[![PyPI release][pypi-image]][pypi-url]
[![Build][build-image]][build-url]
[![MIT License][license-image]][license-url]


[release-image]:https://img.shields.io/github/release/akb89/counterix.svg?style=flat-square
[release-url]:https://github.com/akb89/counterix/releases/latest
[pypi-image]:https://img.shields.io/pypi/v/counterix.svg?style=flat-square
[pypi-url]:https://pypi.org/project/counterix/
[build-image]:https://img.shields.io/github/workflow/status/akb89/counterix/CI?style=flat-square
[build-url]:https://github.com/akb89/counterix/actions?query=workflow%3ACI
[license-image]:http://img.shields.io/badge/license-MIT-000000.svg?style=flat-square
[license-url]:LICENSE.txt

A small toolkit to generate count-based PPMI-weighed SVD Distributional Semantic Models.

## Install
```shell
pip install counterix
```

or, after a git clone:
```shell
python3 setup.py install
```

## Use

### Generate
To generate a raw count matrix from a tokenized corpus, run:
```shell
counterix generate \
  --corpus /abs/path/to/corpus/txt/file \
  --min-count frequency_threshold \
  --win-size window_size
```

If the `--output` parameter is not set, the output files will be saved to the corpus directory.

### Weigh
To weigh a raw count model with PPMI, run:
```
counterix weigh --model /abs/path/to/raw/count/npz/model
```

### SVD
To apply SVD on a PPMI-weighed model, with k=10000, run:
```
counterix svd \
  --model /abs/path/to/ppmi/npz/model \
  --dim singular_vectors_final_dim
```

To control the number of threads used during SVD, run counterix with `env OMP_NUM_THREADS=1`
copulabayesnet 0.1.6
Test different copulas and use multivariate Gaussian copulas
License: MIT
Requires Python: >=3.6
Keywords: Copulas,Bayesian Networks,Multivariate normal

# copulabayesnet

* Load data
* Test Copulas
* Predict from multivariate normal copulas
* Generate several useful plots

## Documentation

[Online documentation](https://copulabayesnet.readthedocs.io/en/latest/ "ReadTheDocs copulabayesnet")

## Installation
Install basic

`pip install copulabayesnet`

If you are looking for latest updates, consider installation directly from sources.

```
git clone https://github.com/SjoerdGn/copulabayesnet.git
cd copulabayesnet
python setup.py install
```

## Functionalities


-- make table here


## Gaussian copula

![Gaussian copula](https://latex.codecogs.com/gif.latex?c_R%5E%7B%5Ctext%7BGa%7D%7D%28u%29%20%3D%20%5Cfrac%7B1%7D%7B%5Csqrt%7B%7C%7BR%7D%7C%7D%7D%5Cexp%5Cleft%28-%5Cfrac%7B1%7D%7B2%7D%20%5Cbegin%7Bpmatrix%7D%5CPhi%5E%7B-1%7D%28u_1%29%5C%5C%20%5Cvdots%20%5C%5C%20%5CPhi%5E%7B-1%7D%28u_d%29%5Cend%7Bpmatrix%7D%5ET%20%5Ccdot%20%5Cleft%28R%5E%7B-1%7D-I%5Cright%29%20%5Ccdot%20%5Cbegin%7Bpmatrix%7D%5CPhi%5E%7B-1%7D%28u_1%29%5C%5C%20%5Cvdots%20%5C%5C%20%5CPhi%5E%7B-1%7D%28u_d%29%5Cend%7Bpmatrix%7D%20%5Cright%29%2C)