semvec/pypi-packages huggingface dataset:
audioanalytics 0.1.3
A python package to simplify the process of performing basic exploratory audio analysis and emotion detection
License: MIT

Components of the AudioAnalytics Package: 1) Extract Feature - It will return the feature values in a Dataframe. 2) Model_Load - It loads the model from backend.3) Predict - Predicts the Emotion by conducting the analysis of the provided audio.4) Summary Report - A report where probabilities are displayed of 4 Emotions.NOTE : THE AUDIO FILE INPUT FORMAT SHOULD BE .wav EXTENSION. ANY OTHER AUDIO FILE FORMAT IS NOT SUPPORTED FOR 0.1 VERSION.
asynckafka 0.2.0
Fast python kafka client for asyncio.
License: MIT
Keywords: asyncio,kafka,cython

**********
Asynckafka
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.. image:: https://travis-ci.com/jmf-mordis/asynckafka.svg?branch=master
    :target: https://travis-ci.com/jmf-mordis/asynckafka

Fast python Kafka client for asyncio.
Asynckafka is written in Cython_ on top of Rdkafka_ as Kafka driver.

.. _Cython: cython.org
.. _Rdkafka: https://github.com/edenhill/librdkafka

The documentation can be found here_.

.. _here: https://jmf-mordis.github.io/asynckafka/

Features
========

* Consumer using a balanced group
* Producer

The library was born as a project to learn Cython, right now it only has the basic
features implemented, some of the most important missing features are:

* Offset management in the consumer
* Precise partition management

Examples
========

Simple consumer
---------------

How to use a consumer::

    consumer = Consumer(
        brokers='localhost:9092',
        topics=['my_topic'],
        group_id='my_group_id',
    )
    consumer.start()

    async for message in consumer:
        print(f"Received message: {message.payload}")

Simple producer
---------------

How to use a producer::

    producer = Producer(brokers="localhost:9092")
    producer.start()
    await producer.produce("my_topic", b"my_message")

Benchmark
#########

The test was performed in June of 2018 using a single Kafka broker without replication.
The purpose of the benchmark was only to have an idea of the order of magnitude of the
library's performance under these conditions.

Comparison between asynckafka and aiokafka in production and consumption:

Production
**********

.. image:: https://github.com/jmf-mordis/asynckafka/raw/master/docs/benchmark/graphs/producer.png
   :width: 800

Consumption
***********

.. image:: https://github.com/jmf-mordis/asynckafka/raw/master/docs/benchmark/graphs/consumer.png
   :width: 800

The benchmark used for asynckafka is in benchmark directory while the
benchmark used for aiokafka is in its own repository, also in the benchmark folder.
finder-sidebar-db 1.0.1
Module for editing the Favorites entries of the Finder sidebar.
License: MIT

# FinderSidebarEditor
Python module for easily adding, removing, and moving favorites on the Finder sidebar in the context of the logged in user.

Example Usage:
```python
from finder_sidebar_editor import FinderSidebar                # Import the module

sidebar = FinderSidebar()                                      # Create a Finder sidebar instance to act on.

sidebar.remove("All My Files")                                 # Remove 'All My Files' favorite from sidebar
sidebar.remove("iCloud")                                       # Remove 'iCloud' favorite from sidebar
sidebar.add("/Library")                                        # Add '/Library' favorite to sidebar
sidebar.add("/SomeShare", uri="smb://shares")                  # Mount 'smb://shares/SomeShare' to '/Volumes/SomeShare' and add as favorite to sidebar
sidebar.add("/SomeOtherShare", uri="afp://username:pw@server") # Mount pw protected 'afp://server/SomeOtherShare' to '/Volumes/SomeOtherShare' and add as favorite to sidebar
sidebar.move("Library", "Applications")                        # Move 'Library' favorite to slot just below 'Applications'
```
kagcolabserver 0.1.3
Automate the kaggle data import portion and run codeserver in that directory
License: MIT

# Library for importing kaggle data into colab and run vscode on the folder
_This is already implemented by ML Genius Abhishek Thakur, just a litte bit refinement and I have added methods for importing kaggle data straight into your_ _Drive folder and the vscode will open in your kaggle folder_

## Installation:
_pip install kagcolabserver_

# Basics optional params for the Code:

## Primary params

* -- port : for hosting the code server locally and tunnel using the ngrok
* -- drive : for mouting the drive
* -- folder_tree : Folder structure which will be created in the drive Ex: titanic/ or kaggle/titanic etc...
* -- kaggle : for working with kaggle user api and data api

## Secondary params

**Note :**  _On clicking API key in your profile, a file named kaggle.json with username and api key will be downloaded, kindly use them for below params_

* -- username : Your Kaggle username
* -- kag_api_key : API key found in your profile
* -- kag_data_api : On each kaggle competition, a data api will be given should pass that one!


# How to Use the library?

## For just starting the code server from google colab

```
* from kagcolabserver import kagColabServer
* kagColabServer(port=7000, drive=True, kaggle=False)
```

## For starting vscode in particular foler

```
* from kagcolabserver import kagColabServer
* kagColabServer(port=7000, drive=True, kaggle=False, folder_tree="kaggle")
```

## For importing Kaggle as well as starting the code-server in the folder location

```
* from kagcolabserver import kagColabServe
* kagColabServer(port=4000, drive=True)
* kolab = kagColabServer(port=4000,drive=True, kaggle=True)
* kolab.import_kaggle_data(username='XXXX', kag_api_key='XXXXX', kag_data_api='kaggle competitions download -c titanic', folder_tree='titanic/')
```