semvec/pypi-packages huggingface dataset:
newbookapi 0.1.0
A Python client for the Newbook API.
Requires Python: >=3.6

# Py-Newbook API Interaction

This project is about interacting with the Newbook API. It provides methods to authenticate, test the API, manage guests, sites and bookings.

## Class: NewbookApi

This class is used to interact with the Newbook API.

### Methods

#### __init__(self, base_url: str,username: str, password: str , region: str) -> None

Initialize the NewbookApi class.

Parameters:
- username (str): The username for the API.
- password (str): The password for the API.
- region (str): The region for the API.

#### get_api_keys(self) -> dict

Get the API keys.

Returns:
- dict: The response from the API.

#### auth_test(self, api_key: str) -> dict

Test the authentication with the API.

Parameters:
- api_key (str): The API key to test.

Returns:
- dict: The response from the API.

#### get_guests_lists(self) -> dict

Get the guests lists.

Returns:
- dict: The response from the API.

#### create_new_guest(self, new_guest) -> dict

Create a new guest.

Parameters:
- new_guest (dict): The new guest details.

Returns:
- dict: The response from the API.

#### update_existing_guest(self, updated_guest) -> dict

Update an existing guest.

Parameters:
- updated_guest (dict): The updated guest details.

Returns:
- dict: The response from the API.

#### get_sites_list(self) -> dict

Get the sites list.

Returns:
- dict: The response from the API.

#### get_bookings_list(self, booking) -> dict

Get the bookings list.

Parameters:
- booking (dict): The booking details.

Returns:
- dict: The response from the API.

#### create_new_booking(self, new_booking) -> dict

Create a new booking.

Parameters:
- new_booking (dict): The new booking details.

Returns:
- dict: The response from the API.

#### update_existing_booking(self, existing_booking) -> dict

Update an existing booking.

Parameters:
- existing_booking (dict): The existing booking details.

Returns:
- dict: The response from the API.
cognitivepersonalizationtaskfingerprinting 1.0.3
The source code and more information are published in the following repository: https://github.com/dennispaulino/TaskFingerprintingForCognitivePersonalizationOfWebTasksDesignPackage
Requires Python: >=3.8

# TaskFingerprinting Package For Cognitive Personalization of Task Design

Source code for the  framework built for our study on assessing crowd workers behaviors for task design personalization in crowdsourcing. Beside crowdsourcing, this package can be used to perform cognitive personalization seamlessly in web tasks.
If you use our work in your research, please cite the following paper published at Sensors 2023.

> Dennis Paulino, Diogo Guimarães, António Correia, José Ribeiro, João Barroso and Hugo Paredes. A Model for Cognitive Personalization of Microtask Design. Sensors 2023, 23, 3571. https://doi.org/10.3390/s23073571 

Example of installing and using the package:

    pip install cognitivepersonalizationtaskfingerprinting
    import cptf.cog_personalization_micro_task_fingerprinting as taskfingerprinting
    import cptf.cog_personalization_deep_learning_model as dl
    
    #It is performed the task fingerprinting technique based on prompt user to enter the path of the interaction log files, and then it will be generated a csv file
    taskfingerprinting.main()
    
    #Based on the task fingerprintings' csv file, the user will be prompted to select the file and develop a deep learning model
    #CAUTION: It is best to look at the source code and adapt the respective parameters to better ajust for each specific dataset
    dl.main()

        
    
    



### Contact

Please feel free to contact Dennis Paulino (dpaulino@utad.pt) for further questions.