semvec/pypi-packages huggingface dataset:
frontivice 0.1
This package is designed to generate regular expression formats based on device names.


# FRONTIVICE

  

This package is designed to generate regular expression formats based on device names. It simplifies the process of validating and parsing device identifiers by providing pre-defined regex patterns for various device types.

  

## Installation

  

You can install `frontivice` via pip. Use the following command:

  

```markdown

pip install frontivice

```

  

## Usage

  

Generate regular expression formats based on device names.

```markdown

from frontivice import Device

try:
    device = Device(device_name="OVF-AAAAAAATEY", device_type="olt")
    print(device.get_device_format())
except ValueError as e:
    print(e)


```

  
  

## Supported Device Names

- OLT

- OTB

- CA1

- CA2

- FTSBS

- CPE
speedybottinker 0.1.8
Use googles-- highly experimental conversation design infra
Requires Python: >=3.6

## SpeedyBot-loco

SpeedyBot Loco is a Python library for handling Webex bots and adaptive cards, with an included CLI for easy setup and management.

Warning: Use googles-- highly experimental conversation design infra

```sh
pip install speedybot-loco
```

### Library

\```python
from speedybot import Bot, Cards

bot = Bot(token="your_token")
cards = Cards()
\```

## CLI Flags

| Command   | Flag            | Description                    |
| --------- | --------------- | ------------------------------ |
| `token`   | `-t`, `--token` | Set the bot token              |
| `webhook` | `-t`, `--token` | Set the bot token              |
|           | `-p`, `--port`  | Port to listen on              |
|           | `--path`        | Path to send POST requests     |
| `run`     | `-t`, `--token` | Set the bot token              |
|           | `-f`, `--file`  | Path to the Python file to run |

## Sample CLI Commands

### Set Bot Token

\```bash
speedybot-loco token -t abc123
\```

### Run Webhook Server

\```bash
speedybot-loco webhook -t abc123 -p 8080 --path /webhook
\```

### Run a Python File

\```bash
speedybot-loco run -t abc123 -f examples/example1/my_bot.py
\```

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Building and Publishing

### Build the distribution

\```bash
python setup.py sdist bdist_wheel
\```

### Upload to PyPI

\```bash
pip install twine

rm -rf dist build \*.egg-info

twine upload dist/\*

twine upload --skip-existing dist/\*

\```

## Trouble shooting

```sh

pip uninstall speedybot-loco

pip show speedybot-loco
pip list | grep speedybot-loco
which speedybot-loco
```
varstrat 0.1.1
A tool to annotate VCF files using genome stratification files, targeting difficult regions.
Requires Python: >=3.9

# VarStrat

A tool for annotating VCF files using genome stratification files, targeting difficult regions.

## Description
VarStrat is designed to annotate VCF (Variant Call Format) files using genome stratification files. It focuses on difficult regions, providing comprehensive annotations to aid in variant analysis.


## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install VarStrat.

```bash
pip install varstrat
```

## Usage
To use the tool from the command line:
```bash
varstrat --input_vcf input.vcf --output_vcf output.vcf --data_source data_source
```

## Command-line Options
```
usage: varstrat [-h] --input_vcf INPUT_VCF --output_vcf OUTPUT_VCF --data_source DATA_SOURCE

A tool for stratifying genetic variants.

optional arguments:
  -h, --help            show this help message and exit
  --input_vcf INPUT_VCF
                        Path to the input VCF file. The file can be in VCF or VCF.GZ format.
  --output_vcf OUTPUT_VCF
                        Path to the output VCF file. The output will be a .vcf file.
  --data_source DATA_SOURCE
                        Path to the data source directory containing stratification regions.
```

## Parameters Explanation
- input_vcf: Path to the input VCF file. The file can be in .vcf or .vcf.gz format.

- output_vcf: Path to the output VCF file. The output will be a .vcf file.

- data_source: Path to the data source directory containing stratification regions. The directory should have the following structure:
    ```
    data_source/
    ├── Region_name_1
    │   ├── sub_region_1.bed.gz
    │   ├── sub_region_2.bed.gz
    ├── Region_name_2
    │   ├── sub_region_1.bed.gz
    │   ├── sub_region_2.bed.gz
    ...
    ```
    Each region is represented by a folder containing one or more .bed.gz files that specify sub-regions.

    You can check at [genome-stratification](https://github.com/genome-in-a-bottle/genome-stratifications) for more information about the data source.
prk-scraping-bib 0.2.9
Primark scraping

# My Scraper

A Python library for scraping product details and availability from e-commerce websites.

Functions
checking_status(url)
Description: Checks the HTTP status code of a given URL.
Parameters: url (str) - The URL to check.
Returns: The URL if the status code is 200, otherwise None.
get_product_links(url, country_tag, dict)
Description: Retrieves product links and updates the dictionary with the new URL.
Parameters:
url (str) - The original URL.
country_tag (str) - The country code to replace in the URL.
dict (dict) - The dictionary to update with the new URL.
Returns: A tuple containing the BeautifulSoup object and the updated dictionary.
Get_driver(link)
Description: Initializes and returns a Selenium WebDriver.
Parameters: link (str) - The URL to navigate to.
Returns: Selenium WebDriver instance.
accept_cookies(driver)
Description: Accepts cookies on the webpage if the button is present.
Parameters: driver (WebDriver) - The Selenium WebDriver instance.
Returns: None
store_details(driver, dict_details, size_name)
Description: Extracts store details (name, address, availability) and updates the dictionary.
Parameters:
driver (WebDriver) - The Selenium WebDriver instance.
dict_details (dict) - The dictionary to update with store details.
size_name (str) - The size category to update in the dictionary.
Returns: Updated dictionary with store details.
get_categories(soup)
Description: Extracts product categories from a BeautifulSoup object.
Parameters: soup (BeautifulSoup) - The BeautifulSoup object containing the page content.
Returns: A tuple containing the main category, subcategory1, and subcategory2.
get_products(url)
Description: Retrieves product links from a sitemap URL.
Parameters: url (str) - The sitemap URL.
Returns: A list of product links.
get_sizes(link)
Description: Scrapes sizes and availability details for a product.
Parameters: link (str) - The product URL.
Returns: A dictionary with size and availability details.
get_product(url)
Description: Retrieves detailed product information from the given URL.
Parameters: url (str) - The product URL.
Returns: A dictionary containing product details (categories, name, price, description, image, etc.).
snnax 0.0.1
A library for spiking neural networks in JAX.
Requires Python: >=3.10

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   please update the URLs accordingly

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|

=====
SNNAX
=====


SNNAX is a lightweight library for implementing Spiking Neural Networks (SNN) is JAX. It leverages the excellent and intuitive [Equinox Library](https://docs.kidger.site/equinox/).
The core of SNNAX is a module that stacks layers of pre-defined or custom defined SNNs and Equinox neural network modules, and providing the functions to call them in a single *scan* loop. This mode of operation enables feedback loops across the layers of SNNs, while leveraging GPU acceleration as much as possible

A longer description of your project goes here...

.. _pyscaffold-notes:

Note
====

This project has been set up using PyScaffold 4.3.1. For details and usage
information on PyScaffold see https://pyscaffold.org/.