semvec/pypi-packages huggingface dataset:
teslagenerator-ok 0.0.5
Generator infomation package. Faker infomation
Keywords: Generator, Tesla, TeslaGenerator, Infomation, Tesla Information, Faker

# HOW TO USE

## Step 1: Install Required Libraries
Firstly, you need to install the unidecode library. You can do this by running the following command:

Copy code
```python
pip install teslagenerator-ok
```
## Step 2: Import the Code into Your Project
Add the code to your project by copying it and saving it to a Python file in your project directory.

## Step 3: Use the Code
Once you have imported the code into your project, you can use the functions and classes defined in the provided code. Below is an example of how to use some of the functions:

```python
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from faker import Faker

# Create a Faker object
faker = Faker()

# Generate a username
username = faker.username()
print("Username:", username)

# Generate an email
email = faker.email()
print("Email:", email)

# Generate user information
information = faker.generateInformation()
print("User information:", information)
```
## Step 4: Test and Adjust
Run your code and check if it works as expected. You can also adjust the functions and classes in the code according to your needs.

### Note:
**Make sure you have installed unidecode before using the code.**
**Check and ensure that you understand how the functions and classes in the code work before integrating them into your project.**
**Customize the code to fit your project requirements and make sure it meets the needs of your project.**
py-checksum 0.5.1
Create unified checksum from all the files and directories within a certain path
Requires Python: <4.0,>=3.5

# PyChecksum

A simple CLI tool to create unified checksum from all the files and directories within a certain path. You can use it within your CI/CD pipeline and automations to detect change or deterministically address a specific artifact.

# How to use?

To view help:

```bash
./pychecksum -h
```

Get the checksum of current directories files and sub-directories:

```bash
./pychecksum
```

Create a checksum of all the files and sub-directoreis within `src` directory:

```bash
./pychecksum -d ./src
```

Exclude `node_modules`, `__pycache__` directories and `.env` files:

```bash
./pychecksum -d . -e node_modules __pycache__ .env
```

Limit the size of the checksum to 10 characters:

```bash
./pychecksum -s 10
```
nonlinear-clock 0.0.1
Nonlinear Clock, provides visualizations that eliminate the need for multiple plots to inspect the influence of original variables in the latent space. Nonlinear Clock enhances the explainability and compactness of visualizations of embedded data.
Requires Python: >=3.11

# Nonlinear Clock

It is difficult for humans to perceive high-dimensional data. Therefore, high-dimensional data is projected into lower dimensions to visualize it. 
Many applications benefit from complex nonlinear dimensionality reduction techniques (e.g., UMAP, t-SNE, PHATE, and autoencoders), but the effects of individual high-dimensional features are hard to explain in the latent spaces. 

Most solutions use multiple two-dimensional plots to analyze the effect of every variable in the embedded space, but this is not scalable, leading to k plots for k different variables. 

Our solution, Nonlinear Clock, provides novel visualizations that eliminate the need for multiple plots to inspect the influence of original variables in the latent space. Nonlinear Clock enhances the explainability and compactness of visualizations of embedded data and is available in an open-source Python library.
backstage_catalog_client 0.1.0
a python client for the Backstage catalog API
Requires Python: <4.0,>=3.8

# backstage-catalog-client

[![Release](https://img.shields.io/github/v/release/mspiegel31/backstage-catalog-client)](https://img.shields.io/github/v/release/mspiegel31/backstage-catalog-client)
[![Build status](https://img.shields.io/github/actions/workflow/status/mspiegel31/backstage-catalog-client/main.yml?branch=main)](https://github.com/mspiegel31/backstage-catalog-client/actions/workflows/main.yml?query=branch%3Amain)
[![codecov](https://codecov.io/gh/mspiegel31/backstage-catalog-client/branch/main/graph/badge.svg)](https://codecov.io/gh/mspiegel31/backstage-catalog-client)
[![Commit activity](https://img.shields.io/github/commit-activity/m/mspiegel31/backstage-catalog-client)](https://img.shields.io/github/commit-activity/m/mspiegel31/backstage-catalog-client)
[![License](https://img.shields.io/github/license/mspiegel31/backstage-catalog-client)](https://img.shields.io/github/license/mspiegel31/backstage-catalog-client)

A python client for the Backstage catalog API. Only uses native python datatypes.

- **Github repository**: <https://github.com/mspiegel31/backstage-catalog-client/>
- **Documentation** <https://mspiegel31.github.io/backstage-catalog-client/>

# Installation

backstage_catalog_client is available [on PyPi](https://pypi.org/project/backstage_catalog_client/). Requires python 3.8+

```
python3 -m pip install backstage_catalog_client
```

# Usage

to use a ready-made client, import it and make requests

```python
import asyncio
import json
from backstage_catalog_client import HttpxClient


async def main():
    catalog = HttpxClient("https://demo.backstage.io/")
    data = await catalog.get_entities()
    for entity in data.items[:1]:
        print(json.dumps(entity, indent=2))


if __name__ == "__main__":
    asyncio.run(main())
```

# Documentation

project documentation is available at the [main documentation site](https://mspiegel31.github.io/backstage-catalog-client/)

# Prior Art

1. JS Catalog Client: [backstage-catalog-client](https://www.npmjs.com/package/@backstage/catalog-client)
1. Go Catalog Client: [go-backstage](https://github.com/tdabasinskas/go-backstage)

---

Repository initiated with [fpgmaas/cookiecutter-poetry](https://github.com/fpgmaas/cookiecutter-poetry).