semvec/pypi-packages huggingface dataset:
F1-2021-Telemetry 0.1
A python package to parse telemetry data from the F1 game into python objects
Requires Python: >=3.10

# F1 Game Telemetry
A python package to parse telemetry data from the F1 game into python objects. Currently works for F1 2021.

## Enabling UDP Telemetry in the game
In the F1 game navigate to Telemetry Settings and set the following values:
  - UDP Telemetry: On
  - UDP IP Address: 127.0.0.1
  - UDP Port: 20777
  - UDP Send Rate: Higher is better
  - UDP Format: 2021
FactoryOS-Lib 0.0.2
A generic and reusabe package of machine learning models to use for Valiot prodicts
Requires Python: >=3.7

# Valiot Data Science Library

This is a repository of data science algorithms using Valiot Worker jobs as base

# Current list of algorithms

1. MovingAverage: Uses the average of the last n elements
2. Stats: Calculate the standard deviation and variance anc store in a variable

# Installation
```
pip install factoryos_lib
```

# Getting started

Create a job and call the execute function

```
@vw.job(name='TEST_DATA_STATS',
        alias='TEST_DATA_STATS',
        description="t",
        queueType=QueueType.ON_DEMAND,
        schedule="*/60 * * * *",
        enabled=True,
        lockRequired=True
        )

def stats_executor(job, log, update_job, get_job_status, context, update_context, **kwargs):
    f = Stats()
    kwargs = {"input": {
        "variable": "TEST_DATA"
    }}
    f.execute(job, log, update_job, get_job_status, context, update_context, **kwargs)
```
flatpy 0.0.4
A library for computing samplings in arbitrary dimensions
Requires Python: >=3.7

=======
flatpy
=======

.. badges

.. image:: https://img.shields.io/pypi/v/flatpy.svg
        :target: https://pypi.python.org/pypi/flatpy
        :alt: PyPi
.. .. image:: https://travis-ci.org/maljovec/flatpy.svg?branch=master
..         :target: https://travis-ci.org/maljovec/flatpy
..         :alt: Travis-CI
.. .. image:: https://coveralls.io/repos/github/maljovec/flatpy/badge.svg?branch=master
..         :target: https://coveralls.io/github/maljovec/flatpy?branch=master
..         :alt: Coveralls
.. .. image:: https://readthedocs.org/projects/flatpy/badge/?version=latest
..         :target: https://flatpy.readthedocs.io/en/latest/?badge=latest
..         :alt: ReadTheDocs
.. .. image:: https://pyup.io/repos/github/maljovec/flatpy/shield.svg
..         :target: https://pyup.io/repos/github/maljovec/flatpy/
..         :alt: Pyup

.. end_badges

.. logo

.. .. image:: docs/_static/flatpy.svg
..    :align: center
..    :alt: flatpy

.. end_logo

.. introduction

Function Library for Analysis and Testing in Python - A collection of
scalar functions (many of which generalize to arbitrary dimensions)
typically used in global optimization problems, but also suitable for
various other test cases.

.. LONG_DESCRIPTION

Working on several different applications with scalar field topology, active learning, and optimization, I have found myself programming and reprogramming a core set of often cited test functions. In addition, I have found the need to modify or tweak some of these functions while also generating completely novel functions that exhibit some specified behavior. This work is an attempt to consolidate those functions and offer them in a reusable fashion in a simple python library with some nice recipes/utilities such as generating a test 2D grid of data, adding noise to the functions, etc.

.. END_LONG_DESCRIPTION

.. end_introduction

.. install

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

A preliminary version is available on PyPI::

    pip install flatpy

Otherwise, you can download the repository for the most cutting edge additions::

    git clone https://github.com/maljovec/flatpy.git
    cd flatpy
    python setup.py [build|develop|install]

.. end-install

.. usage

Usage
=====

::

    import flatpy
    import matplotlib.pyplot as plt

    X = flatpy.utils.generate_test_grid_2d(40)
    x, y = flatpy.utils.unpack2D(X)
    z = flatpy.nD.schwefel(X)
    plt.figure()
    img = plt.tricontourf(x, y, z, cmap="cividis")
    plt.colorbar(img)
    plt.show()


.. image:: images/schwefel.png
    :align: center
    :alt: flatpy

.. end-usage


.. testing

.. Testing
.. =====

.. TODO

.. end-example

.. todo

.. What's Next
.. ======

.. end-todo
better-feediverse 2.1.0
Connect an RSS Feed to Mastodon
Requires Python: >=3.3

This repo is a copy and updated version of [edsu/feediverse](https://github.com/edsu/feediverse)

You can always view changes here: https://github.com/edsu/feediverse/compare/master...xopez:better_feediverse:master

## Description

*better_feediverse* will read RSS/Atom feeds and send the messages as Mastodon posts.
It's meant to add a little bit of spice to your timeline from other places.
Please use it responsibly.

## Install

    pip install better_feediverse

## Run

The first time you run *better_feediverse* you'll need to tell it your Mastodon
instance and get an access token which it will save in a configuration file. If
you don't specify a config file it will use `~/.better_feediverse`:

    better_feediverse

Once *better_feediverse* is configured you can add it to your crontab:

    */15 * * * * /usr/local/bin/better_feediverse    

Run `better_feediverse --help` to show the command line options.

## Post Format

You can customize the post format by opening the configuration file (default is
~/.better_feediverse) and updating the *template* property of your feed. The default
format is:

    {title} {url}

If you want you can use `{summary}` in your template, and add boilerplate text
like so:

    Bookmark: {title} {url} {summary}

`{hashtags}` will look for tags in the feed entry and turn them into a space
separated list of hashtags. For some feeds (e.g. youtube-rss) you should use `{link}` instead of `{url}`.

`{content}` is the whole content of the feed entry (with html-tags
stripped). Please be aware that this might easily exceed Mastodon's
default limit of 500 characters.
boots 0.1.2
A tiny statistical bootstraping library.
Requires Python: >=3.8,<4.0

# 🥾 Boots 👢 - A Tiny Bootstrapping Library

This is a tiny library for doing bootstrap sampling and estimating. It pulls together various tricks to make the process as fast and painless as possible. The tricks included are:

- Parallel execution with [`joblib`](https://joblib.readthedocs.io/en/latest/parallel.html) 
- [The Bayesian bootstrap](https://matteocourthoud.github.io/post/bayes_boot/) with two-level sampling.
- The [Vose method](https://github.com/MaxHalford/vose) for fast weighted sampling with replacement

**Install**

```bash
pip install boots
```

For development:

```bash
pip install git+https://github.com/pmbaumgartner/boots
```

## Example

```python
from boots import bootstrap
import numpy as np

x = np.random.pareto(2, 100)

samples = bootstrap(
    data=x,
    statistic=np.median,
    n_iterations=1000,
    seed=1234,
    n_jobs=-1
)

# bayesian two-level w/ 4 parallel jobs
samples = bootstrap(
    data=x,
    statistic=np.median, 
    n_iterations=1000, 
    seed=1234, 
    n_jobs=4, 
    bayesian=True
)

# do something with it
import pandas as pd
posterior = pd.Series(samples)
posterior.describe(percentiles=[0.025, 0.5, 0.975])
```

**Paired Statistics**

```python
from boots import bootstrap
import numpy as np


# generate some fake correlated data by sorting two arrays and adding some noise
a = np.sort(np.random.normal(0, 1, 100)) + np.random.normal(0, 1, 100)
b = np.sort(np.random.normal(0, 1, 100)) + np.random.normal(0, 1, 100)
pairs = list(zip(a, b))

# for paired (or row-wise) metrics you might need to
# create a wrapper function that unpacks
# each row's values into array arguments for your metric function
def corr_unwrap(pairs):
    a1, a2 = zip(*pairs)
    corr = np.corrcoef(a1, a2)[0, 1]
    return corr


samples = bootstrap(
    data=pairs,
    statistic=corr_unwrap,
    n_iterations=1000,
    seed=1234,
    n_jobs=-1,
    bayesian=True
)

import pandas as pd
posterior = pd.Series(samples)
posterior.describe(percentiles=[0.025, 0.5, 0.975])
```