semvec/pypi-packages huggingface dataset:
hydra-py 0.5.3
Hydra for Python. A light-weight library for building distributed applications such as microservices.
Requires Python: >=3.7

![](./assets/hydrapy-logo.png)

# Hydra-Py
Hydra for Python. A light-weight library for building distributed applications such as microservices.

```diff
- NOTE THIS PROJECT IS IN VERY EARLY DEVELOPMENT
- From version 0.3.0 HydraPy only supports the use of short-form UMF see: https://github.com/pnxtech/umf/blob/master/umf.md#Short-form-syntax
- Starting from version 0.5.0 HydraPy expects a Redis URL formatted connection string in the following format, where unused fields may be ignored:  redis://[user]:[password]@[host:port]/[database_number]
```
- Hydra-Py is changing quickly and not recommended for serious use at this time
* As a microservices approach, Hydra-Py depends on the presence of a Redis database server as its single infrastructual dependency
* Hydra-Py requires Python 3.7+ and aioredis==1.3.1 or greater

## Introduction
Hydra is an approach to building light-weight microservices by leveraging the awesome power of the Redis database platform.

> Hydra-Py is a nextgen port of the NodeJS implementation of Hydra with a goal of offering the same level of ease of use to the Python community.  We hope this will empower data scientists to build containerized microservices for their AI/ML applications.

## Documentation
Visit the [project documentation](https://github.com/pnxtech/HydraPy/tree/master/documentation) for a quick start guide and additional documentation.
json-pattern-validator 0.1
Utility for evaluate matching json with a template
Keywords: json,schema,validation,validator,payload

Validation schema based of a json string or a dict

Schema:
=======
* You must define a dict which will match with a json/dict under test
* add **!** at begin fo key for to indicate a required field
* In value of that key you will need specify a validator for such field
* That validator have to be a valid default validator or you can add your own
```
    EXAMPLE_SCHEMA = {
        "!version": "string",
        "!email": "email",
        "!data": {
            "!Id": "number",
            "title": "string"  # optional
            },
        }
 ```
 

Usage:
======
    Example:
```
    ok_json = {
        "version": "1.0.1a",
        "email": "test@test.com",
        "data": {
            "Id": 231,
            "title": "description"
        }
    }
    e = JSONEvaluator()
    e.set_schema(EXAMPLE_SCHEMA)
    e.evaluate(ok_json)
    if e.ok:
       print("Dict validate")
    else:
       print("errors found:")
       print(e.errors)
        
```
hierchical-quantifier 0.1.0
computig hierarchical quantifier value from music pieces
Requires Python: >=3.6

# Quantify Hierarchicity in Western Classical Music
[![codecov](https://codecov.io/gh/DCMLab/hierarchicity-in-music/branch/master/graph/badge.svg?token=LKO7M8A35X)](https://codecov.io/gh/DCMLab/hierarchicity-in-music)

Python library to compute Information-theoretical based measures on music data. It contains
the implementation 

- Mutual information based quantifier to compute the long-term and short-term relationship
in a music corpus.

- Kullback-Leibler divergence based quantifer to compute the Information gain given a section
of a piece relative to the hole music piece.