semvec/pypi-packages huggingface dataset:
jesseprusso-hello-world-lib 0.1.0
Uma biblioteca simples para gerenciar tarefas
Requires Python: >=3.7

# hello-world-lib 📝

Uma biblioteca Python simples de gerenciamento de tarefas, desenvolvida com boas práticas e estrutura modular. Ideal para estudos e projetos iniciais.

## 🚀 Funcionalidades

- Adicionar tarefas
- Listar tarefas
- Remover tarefas por índice
- Uso de context manager para controle de escopo
- Estrutura de projeto com boas práticas
ml-insights 1.1.0
Package to calibrate and understand ML Models
Requires Python: >=3.8

## ML Insights


Welcome to ML-Insights!

This package contains two main sets of tools:

- SplineCalib: Spline-based Probability Calibration
- ModelXRay: Model Interpretability

## Probability Calibration

For probability calibration, use the SplineCalib class.  Detailed documentation is available here:
[https://ml-insights.readthedocs.io](https://ml-insights.readthedocs.io)

Find more detailed examples here:
[https://github.com/numeristical/introspective/tree/master/examples](https://github.com/numeristical/introspective/tree/master/examples)

## Model Interpretation

For understanding black-box models, the main entry point is the `ModelXRay` class.  Instantiate it with the model and data.  The data can be what the model was trained with, but intended to be used for out of bag or test data to see how the model performs when one feature is changed, holding everything else constant.

    >>> import ml_insights as mli
    >>> xray = mli.ModelXRay(model, data.sample(500))
    >>> xray.feature_dependence_plots()

![mli_screenshot](mli_screenshot.png)

Find more detailed examples here:
[https://github.com/numeristical/introspective/tree/master/examples](https://github.com/numeristical/introspective/tree/master/examples)


## Other Documentation

[https://ml-insights.readthedocs.io](https://ml-insights.readthedocs.io)

Disclaimer
==========

We have tested this tool to the best of our ability, but understand that it may have bugs.  It was developed on Python 3.  Use at your own risk, but feel free to report any bugs to our github. <https://github.com/numeristical/introspective>

Installation
=============

    $ pip install ml_insights


Source
======

Find the latest version on github: https://github.com/numeristical/introspective

Feel free to fork and contribute!

License
=======

Free software: `MIT license <LICENSE>`_

Developed By
============

- Brian Lucena
- Ramesh Sampath

References
==========

Lucena, B. 2018. Spline-Based Probability Calibration. https://arxiv.org/abs/1809.07751

Alex Goldstein, Adam Kapelner, Justin Bleich, and Emil Pitkin. 2014. Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation. Journal of Computational and Graphical Statistics (March 2014)
pywaiwera 1.5.1
Python package for the Waiwera simulator
Requires Python: >=2.7

Waiwera Python Package
======================

PyWaiwera is the official Python package for use with the parallel open-source geothermal flow simulator, [Waiwera](https://waiwera.github.io/).

# Installation

The easiest way to install PyWaiwera is using `pip`:

    pip install pywaiwera

or if you do not have permissions to install system-wide Python packages, you can install it locally in your own user account:

    pip install --user pywaiwera

During the installation it may warn you that executable scripts are being installed to a directory that is not listed in your system's ``PATH`` environment variable. This means that you need to add this directory to your ``PATH`` if you want to be able to run the supplied scripts from anywhere on your machine.

PyWaiwera can be upgraded to the latest version at any time by running:

    pip install --upgrade pywaiwera

# Running Waiwera using Docker

PyWaiwera provides a console executable script `waiwera-dkr` for running Waiwera via [Docker](https://www.docker.com/).  It should be accessible directly as a console command.

To run a Waiwera model using Docker, please navigate to the input file's location and run the command:

    waiwera-dkr filename.json

where `filename.json` is the name of the model JSON input file.

Waiwera example problems to try can be obtained via the command:

    waiwera-dkr --examples

which will download a directory of example Waiwera benchmark test problems.

For more detailed help on the options available with `waiwera-dkr`, run:

    waiwera-dkr --help

# Using PyWaiwera in a script

It is also possible to use the PyWaiwera package from within Python scripts. For example, the following script imports PyWaiwera, creates a Docker environment and uses it to run Waiwera on the model input file `input.json`:

    import pywaiwera
    print(pywaiwera.__version__)
    env = pywaiwera.docker.DockerEnv()
    env.run_waiwera('input.json')
ScriptSignals 0.1.8
esta libreria te permitira comunicar varios scripts de python
Requires Python: >=3.6

# ScriptSignals
1. # instalar
    -usá ```pip install ScriptSignals``` para instalar ScriptSignals
2. # importar
    -usa ````import ScriptSignals```` para importar ScriptSignals
3. # usar
    1. # send:
       - funcion: enviar informacion a otro script
       
       - argumentos: servidor, mensaje, datos, nombre, funcion
       1. servidor, es el servidor a enviar
       2. mensaje, es el mensaje a enviar
       3. datos, son datos extra en formato personalizado
       4. nombre, es el nombre del cliente
       5. funcion, es una funcion que se ejecutara si un servidor responde al mensaje
          
          -argumentos:
          1. info, es una lista con 3 elementos, el mensaje, datos extra sobre el y el servidor que lo envio
      2. # respond
         - funcion, es una funcion exclusiva del servidor para responder a mensajes
         - argumentos
         1. cliente
         2. mensaje
         3. datos
         4. nombre
         - cliente es el cliente a responder
         - mensaje es el mensaje a enviar
         - datos son datos extras a enviar
         - nombre es el nombre del servidor
      2. # create_server
         - funcion, es para convertir tu script en un servidor en un hilo aparte de manera que no bloquea el flugo principal del programa
         - argumentos
         1. funcion
         2. nombre
         - funcion es una funcion con los argumentos info que debe ser una lista de 3 elementos, un elemento es el mensaje recibido, el otro la informacion extra del mensaje, y el otro es el nombre del cliente que lo envio
         - nombre, es el nombre del servidor, los clientes deberan referenciarlo para que llegue el mensaje