semvec/pypi-packages huggingface dataset:
spoonacular 3.0
A Python wrapper for the Spoonacular API
License: MIT
Requires Python: >=3.*
Keywords: spoonacular API food recipes ingredients cuisine groceries

# �� spoonacular ��
---
[![Build Status](https://travis-ci.org/johnwmillr/SpoonacularAPI.svg?branch=master)](https://travis-ci.org/johnwmillr/SpoonacularAPI)
[![PyPI version](https://badge.fury.io/py/spoonacular.svg)](https://pypi.org/project/spoonacular/)
[![Python version](https://img.shields.io/badge/python-3.x-brightgreen.svg)](https://pypi.org/project/spoonacular/)

Want to parse a list of [ingredients](https://rapidapi.com/spoonacular/api/Recipe%20-%20Food%20-%20Nutrition/functions/Parse%20Ingredients) within a recipe? Or are you looking for a random [food joke](https://rapidapi.com/spoonacular/api/Recipe%20-%20Food%20-%20Nutrition/functions/Get%20a%20Random%20Food%20Joke)? These are just two of many endpoints provided by the marvelous [Spoonacular](https://spoonacular.com/) food and recipes API. `spoonacular` provides a simple Python interface to this API.

## Installation
The easiest way to start using this package is to install it via [PyPI](https://pypi.python.org/pypi/spoonacular) using `pip`:

`$pip install spoonacular`

Thank you to [@sebbekarlsson](https://github.com/sebbekarlsson) for transferring ownership of the original PyPI package name.

If you'd prefer to clone and install the repository manually, follow these steps:

1. Clone this repo:
`$git clone https://github.com/johnwmillr/SpoonacularAPI.git`
2. Enter the cloned directory:
`$cd SpoonacularAPI`
3. Install:
    `$python setup.py install`

## Usage

### API Key

You'll need to sign up for an account on [RapidAPI](https://rapidapi.com/spoonacular/api/Recipe%20-%20Food%20-%20Nutrition/pricing) to start using `spoonacular`. Spoonacular is a freemium API, so you'll be able to start with 50 free calls a day. Registration requires a credit card number even for the free plan, because users are charged a small amount per call if they go over the daily limit. This package has a basic system in place to try and prevent overrage charges.

### Examples

```python
import spoonacular as sp
api = sp.API("your_api_key_here")

# Parse an ingredient
response = api.parse_ingredients("3.5 cups King Arthur flour", servings=1)
data = response.json()
print(data[0]['name'])
>>>"flour"

# Detect text for mentions of food
response = api.detect_food_in_text("I really want a cheeseburger.")
data = response.json()
print(data['annotations'][0])
>>>{"annotation": "cheeseburger", "tag":"dish"}

# Get a random food joke
response = api.get_a_random_food_joke()
data = response.json()
print(data['text'])
>>>"People are a lot less judgy when you say you ate an 'avocado salad' instead of a bowl of guacamole."
```

## Documentation
 - [Spoonacular website](https://spoonacular.com/food-api)
 - [RapidAPI](https://rapidapi.com/spoonacular/api/Recipe%20-%20Food%20-%20Nutrition)
 - [DocScraper](https://github.com/johnwmillr/DocScraper)

## Collaboration
Please feel free to collaborate with a pull request or by opening an issue.
restpass 1.2
Terminal based graphical utility for generating restorable passwords
License: MIT
Keywords: generator,password,passphrase,hash,restore

# restpass
Terminal based graphical utility for generating restorable passwords

[![Python Version](https://img.shields.io/pypi/pyversions/restpass.svg?color=yellow&style=flat-square)](https://www.python.org/downloads/)
[![GitHub Licence](https://img.shields.io/github/license/BananaLoaf/restpass.svg?color=blue&style=flat-square)](https://github.com/BananaLoaf/restpass/blob/master/LICENSE)
[![Package Version](https://img.shields.io/pypi/v/restpass.svg?color=green&style=flat-square)](https://pypi.org/project/restpass/)

![Demo](misc/demo.gif)
### Install
```bash
pip install restpass
```
### Usage
The core principle is simple - **for the same input you get the same output**. 
The input consist of input string, salt, length and alphabet.
Output is the generated password. In case of forgetting the password, it can always be restored (with the same input).
##### Recommendations
1. Use memorable phrases (favorite quotes, song lyrics, etc) for input string and salt (not required by default).
2. Use digits, lowercase and uppercase. Using symbols is the last thing to consider.
3. Salt is used in cases when you want to reuse input string, but have different password. For example, the same input string could be used for desktop and laptop, but used with different salt.
4. Minimum recommended length is 15. According to [howsecureismypassword.net](howsecureismypassword.net), it would take a computer about **558 QUADRILLION YEARS** to crack your password.
musicgen 0.0.1
A module for pre-processing steps in music generation
License: MIT
Keywords: LSTM,Sequence models,RNN,Neural network,Music generation

ï»¿# musicgen
musicgen is a module for pre-processing audio and to prepare the dataset for neural networks in genarating music. Built on top of Music21 and youtube_dl
### Features
  - Download the songs,videos from URL
  - Get the notes from music
  - Prepare the dataset for neural networks

### Installation



```sh
$ pip install musicgen
```
#### get_notes(path='*.mid')
Get all the notes and chords from the midi files  
##### Args:  
path : path for the song with midi extension. â€˜*.midâ€™ by default which gets the note of all the songs in the current working directory.  
##### Returns:  
List of notes obtained from the midi file   
#### download_song_with_url(url,audio_type='mp3',quality='192')
Downloads the audio of a video present in the given video URL to current working directory.  
##### Args:   
url : (string)  URL of a video  
audio_type :(string)"aac", "flac", "mp3", "m4a", "opus", "vorbis", or "wav". â€˜mp3â€™ by default  
#### create_midi(prediction_output,name='output')
convert the output from the prediction to notes and create a midi file from the notes in current working directory.  
##### Args:   
prediction_output : the output predictions of a trained model.  
name : (string) name of the generated file. output by default.  
#### prepare_sequences(notes, n_vocab,sequence_length = 100)
Prepare the sequences used by the Neural Network  
##### Args:  
notes : (list) notes of midi file   
n_vocab : (int) number of unique notes  
sequence_length : (int) number of time steps required. 100 by default.  
##### Returns:  
network_input, network_output   

#### download_video_with_url(url)
Downloads the video from the given URL into current working directory.  
##### Args:  
url : (string)  URL of a video  

#### download_videos(path)
Downloads the videos from the URLâ€™s present in a text file into current working directory.  
##### Args:  
path : (string)  path of a text file containing URLâ€™s  

#### download_songs(path)
Downloads the audio of videos from the URLâ€™s present in a text file into current working directory.  
##### Args:  
path : (string)  path of a text file containing URLâ€™s.  

#### song_notes_to_pickle(path,output)
saves the notes of a midi file as a pickle object.  
##### Args:  
path : (string) path  of the songs    
output : (string) name of the pickle file.  

#### generate_notes(model, network_input, pitchnames, n_vocab)
generates the notes from the trained keras model  
##### Args:  
model : Trained keras model for prediction  
network_input : input to the network  
pitchnames : set of items in the notes. It is found using pitchnames = sorted(set(item for item in notes))  
n_vocab : (int) number of unique notes  
##### Returns:  
predicted_output