semvec/pypi-packages huggingface dataset:
uspspy 0.0.2
Validate addresses with the USPS API.
Keywords: USPS Address Validation API XML

########################
USPS Address Validation
########################

``pyUSPS`` provides a simple client and interface for validating addresses
using the USPS API. It is mostly taken from 
https://github.com/BuluBox/usps-api/ 

The only reason this package exists is that not all environment can build
(or be made to build, or successfully install dated wheels for) ``lxml``.
So this version uses the native ElementTree implementation in the 
standard library - no compilation required.
atados-analytics-sdk 0.0.4b1
Analytics SDK
Requires Python: >=3.6

[![GitHub license](https://img.shields.io/github/license/Naereen/StrapDown.js.svg)](https://github.com/leojaimesson/typescript-package-boilerplate/blob/master/LICENSE.md)
[![python badge](https://img.shields.io/badge/type-python-green.svg)](https://python.org/en/)

# SDK Oficial para Python

Está SDK é utilizada para enviar eventos à API de Analytics da Atados

## Instalando

```
pip install @atados/analytics-sdk
```

## Antes de começar, entenda a função track

```typescript
track(
  /**
   * Tipo: String
   * Use para categorizar esta acao
   * Ex.:
   *   category = 'Apply',
   *   category = 'Notification',
   */
  category: string,
  /**
   * Tipo: String
   * Use para identificar esta ação dentro da categoria
   * Ex.:
   *   label = 'Cancel Apply',
   *   label = 'View Notification',
   */
  label: string,
  /**
   * Use para enriquecer esta ação dentro da categoria
   * Ex.:
   *   action = ID de inscricao
   *   action = { notificationId, notificationKind }
   */
  action: any,
  /**
   * Use este argumento para sobrepor as configurações de userId
   * ou meta
   */
  user_id?: number,
  /**
   * Use este argumento para sobrepor as configurações de meta
   */
  override_meta?: {}
): Promise<void>
```

## Iniciando

```python
from Analytics from analytics

def report_error(error):
  print(error.message)

analytics = Analytics(
  api_url=API_URL,
  api_token=API_TOKEN,
  meta=mock_meta,
  on_error=report_error,
  meta={ 'channelId': 1 }
)
track_result = analytics.track(
  category='Category',
  label='Label',
  action={ 'data': 1 },
  # (Opcional)
  user_id=1,
  # (Opcional)
  sessoin_id=1
)
```

## Utilizando

```typescript
analytics.track('Category', 'Label', 'Action')
```
AudioAugment 0.0.3
Audio data augmentation tool for machine learning projects
Requires Python: >=3.6

# Audio-Augment
#### Audio data augmentation tool for machine learning projects
This library augments audio training datasets by transforming provided WAV files in 10 different ways:
        
  1. Waveform Inversion
  2. Highpass Filter
  3. Lowpass Filter
  4. Bandpass Filter
  5. Add noise (normal, uniform)
  6. Pitch shift (low, high)
  7. Time shift (slow, fast)
   
## Directory Setup
Create two folders in your current working directory, one called `unprocessed` and another called `processed`.
Place all audio samples that you want to transform in the unprocessed folder (must be WAV format). 
Run this method and it will transform all audio files in your unprocessed folder, 
and save the new set of augmented samples in the processed folder.

## Requirements
- numpy
- pandas
- matplotlib
- soundfile
- librosa

        For questions about this library, email wesleylaurencetech@gmail.com