semvec/pypi-packages huggingface dataset:
tcw 0.1.13
tiny contest winners application

# TCW: Tiny Contest Winners
A simple web app to create ephemeral raffle contests.

## Description
Allows someone to create a contest. When the contest has expired, the winners
will be randomly selected and emailed to the original contest creator.

## Contest Expiration
A contest is finished once its expiration time has passed, or once the max
number of entrants is reached.

## Contest Entrants
The contest creator is responsible for managing the signup link to the contest,
and distributing it to potential entrants. There is no search or recovery
option for a lost contest link.

## Notifications
This app does not notify the winners. The contest creator will be notified by
email of the winners once the contest expires. It is the responsibility of the
contest creator to notify entrants if they won.

## Storage of Contest data
Once a Contest has expired, and the creator of the contest has been notified
of the winners, all information about the contest is removed from the database.
This information cannot be recovered.
lmanage 0.3.36
LManage is a collection of useful tools for Looker admins to help curate and cleanup content and it's associated source LookML.
Requires Python: <3.12,>=3.8
Keywords: keyword, another_keyword

# LManage
## What is it.
LManage is a collection of useful tools for [Looker](https://looker.com/) admins to help curate and cleanup content and it's associated source [LookML](https://docs.looker.com/data-modeling/learning-lookml/what-is-lookml).

## How do i Install it.
Lmanage can be found on [pypi](#).
```
pip install lmanage
```

## How do I Use it.
### Commands
LManage will ultimately will have many different commands as development continues 
| Status  | Command    | Rationale                                                                                                                                                                                            |
|---------|------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Live | Object Migrator Tool | Migrate Looker Objects such as Content, Folders and Permissions, User Groups, Roles and Attributes between a Looker Instance or for Version Control [instructions](https://github.com/looker-open-source/lmanage/tree/main/instructions/looker_settings_capture.md)                                                                                                |
| Planned | scoper     | Takes in a model file, elminates the * includes, iterate through the explores and joins and creates a fully scoped model include list for validation performance and best practice code organization |
| Planned | removeuser | Based on last time logged in, prune Looker users to ensure a performant, compliant Looker instance                                                                                                   |
| Planned | [mapview](https://github.com/looker-open-source/lmanage/tree/main/instructions/mapview_README.md) | Find the LookML fields and tables that are associated with a piece of Looker content                          |

#### help and version
```
lmanage --help
Usage: lmanage [OPTIONS] COMMAND [ARGS]...

Options:
  --version  Show the version and exit.
  --help     Show this message and exit.

Commands:
  capturator
  configurator
```
#### Looker Object Migrator
The object migrator allows you to preserve a point in time representation of your Looker content (Looks and Dashboards), Folder structure, Content access settings, User groups, User roles, User Attributes and preserve these as a Yaml file. This tool then lets you configure a new instance based on that Yaml file.

[instructions](https://github.com/looker-open-source/lmanage/tree/main/instructions/looker_settings_capture.md)


**This is not an officially supported Google Product.**
subsampwinner 0.0.8
A package for feature selection using Subsampling Winner Algorithm
Requires Python: >=3.7

# subsamp Feature Selection with Subsampling Winner Algorithm

Subsampling Winner Algorithm (SWA)

SubsampWinner is a Python package that implements the Subsampling Winner Algorithm (SWA) for feature selection in high-dimensional datasets.
It includes a robust double assurance procedure to enhance stability and reliability in feature selection.

## Features

- Subsampling Winner Algorithm (SWA) for efficient feature selection;
- Double Assurance procedure for improved stability;
- Support for both homoskedastic and heteroskedastic data;
- Parallel processing capabilities for improved performance;
- Flexible parameter tuning and multiple testing correction methods.

## Installation

You can install SubsampWinner using pip:

```bash
pip install subsampwinner
```

## Quick Start

We start the experiment by generating a dataset with **80 samples and 100 features**.
We test the performance of the subsampling winner algorithm against different levels of signal strength.
The output includes the indices of the selected features and the summary of the final model.

Additionally, we run the double assurance procedure to further enhance the stability of the feature selection.

```python
### setup
import numpy as np
from subsampwinner.subsamp import subsamp
from subsampwinner.SubsampDoubleAssurance import SubsampDoubleAssurance
from subsampwinner.GenerateData import generate_heteroskedastic_data

# Generate sample data
n, p = 80, 100
beta0 = np.array([0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5])
beta0_index = np.arange(len(beta0))
beta = np.zeros(p)
beta[beta0_index] = beta0
gamma = np.zeros(p)

X, y, _, _ = generate_heteroskedastic_data(n, p, hetero_func=lambda x: 1.2,
    beta=beta, gamma=gamma, type='diagonal')

# Initialize and run SWA
swa = subsamp(s=25, m=1000, qnum=15)
swa.fit(X, y)
```

We obtain the following selected feature indices:

```python
# selected variables
selected_features = [selected_var + 1 for selected_var in swa.finalists]

print("Selected features:", selected_features)
```

and the following summary of the final model:

```python
# A summary of selected features
swa.final_model.summary()
```

We verify the stability of the feature selection by running the double assurance procedure.

```python
# Run Double Assurance procedure
sda = SubsampDoubleAssurance(m=1000)
results = sda.double_assurance(X, y, s0=26, T=0.9, I_max=20, init_range=0.3, r=0.75)
```
seismolab 1.1.4
Open-source python framework for downloading, analyzing, and visualizing data of variable stars from space-based surveys.
Requires Python: >=3.10

<div align="center">

# **Seismolab**

Open-source python framework for downloading, analyzing, and visualizing data of variable stars from space-based surveys.

[![pip](https://img.shields.io/badge/pip-install%20seismolab-blue.svg)](https://pypi.org/project/seismolab/)
[![License: MIT](https://img.shields.io/badge/License-MIT-orange.svg)](https://opensource.org/licenses/MIT)
[![](https://img.shields.io/badge/Homepage-Seismolab-brightgreen)](https://konkoly.hu/staff/lmolnar/seismolab/)
<a href="https://seismolab.readthedocs.io/en/latest/index.html"><img src="https://img.shields.io/badge/read-the_docs-4D827F.svg?style=flat"/></a>
[![Tests](https://github.com/konkolyseismolab/seismolab/actions/workflows/main.yml/badge.svg)](https://github.com/konkolyseismolab/seismolab/actions/workflows/main.yml)

</div>

--------------------------------------------------------------------------------

## Install seismolab with pip:

```bash
pip install seismolab
```

For more information on how to install and use seismolab, please refer to the [documentation](https://seismolab.readthedocs.io/en/latest/index.html).

## Contributing
Feel free to open PR / Issue, or contact me [here](bodi.attila@csfk.org).

## Acknowledgements
This project has been supported by the KKP-137523 'SeismoLab' Élvonal grant of the Hungarian Research, Development and Innovation Office (NKFIH) and the Lendület Program of the Hungarian Academy of Sciences under project No. LP2018-7.