semvec/pypi-packages huggingface dataset:
pytire 0.4.1
Python library to handle tire attributes.
License: MIT
Requires Python: >=3.7,<3.11

# pytire
[![Documentation Status](https://readthedocs.org/projects/pytire/badge/?version=latest)](https://pytire.readthedocs.io/en/latest/?badge=latest)
![Build Status](https://img.shields.io/github/workflow/status/girotobial/pytire/test)
[![codecov](https://codecov.io/gh/girotobial/pytire/branch/main/graph/badge.svg?token=FRVK7M9PLQ)](https://codecov.io/gh/girotobial/pytire)
[![PyPI Version](https://img.shields.io/pypi/v/pytire)](https://pypi.org/project/pytire/)
[![Licence](https://img.shields.io/github/license/girotobial/pytire)](https://github.com/girotobial/pytire/blob/main/LICENSE)
[![Code Style: Black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)

A library to make interpreting tire attributes and calculations easier.

### Table of Contents
  * [Table of Contents](#table-of-contents)
  * [Getting Started](#getting-started)
  * [Dev Setup](#dev-setup)
## Getting Started
To use this library install it via pip

```sh
$ pip install pytire
```

Usage
```python
>>> from pytire import Tire
>>> tire = Tire("34x10.75-16")
>>> tire.diameter
0.8636...

>>> tire.width
0.27305...

>>> tire.inner_diameter
0.4064...

>>> tire.volume()
0.203642044328
```
[The Docs are here.](https://pytire.readthedocs.io/en/latest/)
## Dev Setup

Clone from github
```
$ git clone 
```

Install using poetry
```sh
$ poetry install
```
set up pre-commit
```sh
$ pre-commit install
```

Alternatively use the dev container.
nb-prep 1.0.2
Prepares jupyter notebooks for storing stripped versions in git and sharing results with stakeholders
License: MIT
Requires Python: >=3.7
Keywords: precommit nbconvert nbstripout jupyter notebook python

[![Unit tests](https://github.com/allianz-direct/nb_prep/actions/workflows/unit_tests.yml/badge.svg)](https://github.com/allianz-direct/nb_prep/actions/workflows/unit_tests.yml)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/nb-prep)
![PyPI](https://img.shields.io/pypi/v/nb-prep)
![PyPI - Downloads](https://img.shields.io/pypi/dm/nb-prep)
![GitHub contributors](https://img.shields.io/github/contributors-anon/allianz-direct/nb_prep)
![PyPI - License](https://img.shields.io/pypi/l/nb-prep)

# nb_prep

`nb_prep` automates preparing jupyter notebooks for sharing and storage.

You can use the `nb_prep` CLI to:

- Convert jupyter notebooks to HTML (using [`nbconvert`](https://nbconvert.readthedocs.io/)) and:
    - add a date prefix to the filename.
    - add a git hash suffix to the filename.
    - move the HTML file to a configured output directory
- Strip jupyter notebooks of all cell outputs (using [`nbstripout`](https://github.com/kynan/nbstripout))

You can also configure `nb_prep` once as a pre- and post-commit hook and have notebook output automatically prepared every time you `git commit`.

`nb_prep` is a useful automation tool when you have a lot of analysis notebooks in git that you want to share with stakeholders.
If your main interest is preparing clean notebooks for storing in git commits, consider using [jupytext](https://github.com/mwouts/jupytext) instead.

## Installation

```bash
pip install nb_prep
```

## Documentation

See [allianz-direct.github.io/nb_prep](https://allianz-direct.github.io/nb_prep).
skgpytorch 0.2.0
example description
License: MIT
Requires Python: >=3.6

# skgpytorch
[![Coverage Status](https://coveralls.io/repos/github/patel-zeel/skgpytorch/badge.svg?branch=main)](https://coveralls.io/github/patel-zeel/skgpytorch?branch=main)

[GPyTorch](https://gpytorch.ai/) Models in [Scikit-learn](https://scikit-learn.org/stable/) wrapper.

# Example

```py
import torch
from skgpytorch.models import ExactGPRegressor
from skgpytorch.metrics import mean_squared_error, negative_log_predictive_density
from gpytorch.kernels import RBFKernel, ScaleKernel

# Define a model
train_x = torch.rand(10, 1)
train_y = torch.rand(10)
test_x = torch.rand(10, 1)
test_y = torch.rand(10)

kernel = ScaleKernel(RBFKernel(ard_num_dims=train_x.shape[1]))
gp = ExactGPRegressor(train_x, train_y, kernel)

# Fit the model (This supports batch training of GP models as well)
gp.fit(n_epochs=2, verbose=True, n_restarts=1, verbose_gap=2, batch_size=10, lr=0.1, random_state=0)

# Get the predictions
pred_dist = gp.predict(test_x)

# Access properties of predictive distribution
pred_mean = pred_dist.mean # Mean
pred_var = pred_dist.variance # Variance
pred_stddev = pred_dist.stddev # Standard deviation
lower, upper = pred_dist.confidence_region() # 95% confidence region

# Calculate metrics (Soon this will be implemented in gpytorch itself)
print("MSE:", mean_squared_error(pred_dist, test_y))
print("NLPD:", negative_log_predictive_density(pred_dist, test_y))
```

```bash
Restart: 0, Iter: 0, Loss: 1.0135, Best Loss: inf
Restart: 0, Iter: 2, Loss: 0.9371, Best Loss: inf
Restart: 0, Iter: 4, Loss: 0.8644, Best Loss: inf
Restart: 0, Iter: 6, Loss: 0.7978, Best Loss: inf
Restart: 0, Iter: 8, Loss: 0.7382, Best Loss: inf
Restart: 1, Iter: 0, Loss: 0.9626, Best Loss: 0.6819
Restart: 1, Iter: 2, Loss: 0.8948, Best Loss: 0.6819
Restart: 1, Iter: 4, Loss: 0.8239, Best Loss: 0.6819
Restart: 1, Iter: 6, Loss: 0.7537, Best Loss: 0.6819
Restart: 1, Iter: 8, Loss: 0.6880, Best Loss: 0.6819
MSE: 0.08736331760883331
NLPD: 0.49492106437683103
```