semvec/pypi-packages huggingface dataset:
sitemapa 0.1.4
Create advanced sitemaps easily
Keywords: python,sitemap,

# Sitemapa
#### _Create advanced sitemaps easily._

![Create sitemaps with Sitemapa](https://abstractkitchen.com/static/assets/sitemapa-image.jpg "Create sitemaps with Sitemapa")

Sitemapa is a small package to reduce your work while generating sitemaps. You describe your sitemaps with JSON-structure. Sitemapa is framework agnostic and not indexing your website — it's just generating sitemaps from your description. Noting more. I use it to generate sitemaps for millions of URLs on my websites. 

Keep in mind that it's your job to validate your urls and lastmod dates. [Learn more about sitemaps](https://developers.google.com/search/docs/advanced/sitemaps/overview) — it's important for your website SEO.

## Features
- No extra dependencies
- Create regular sitemaps. URLs, Images, Videos and News are supported.
- Create index sitemap to combine your regular sitemaps
- Create extra attributes for your tags like <video:restriction relationship="allow"> [video section](https://developers.google.com/search/docs/advanced/sitemaps/video-sitemaps#example-sitemap)
- Use JSON to describe your sitemaps. Don't waste your time with XML.
- Compress sitemaps with gzip

## Installation

```sh
pip install sitemapa

# import in your script
from sitemapa import Sitemap, IndexSitemap
```

## Usage

[Read about usage on my website](https://abstractkitchen.com/blog/sitemaps-for-devs/#sitemapa-library).

## Contacts
- [visit abstractkitchen.com](https://abstractkitchen.com)
- drop me a line: dmitry@abstractkitchen.com
- [connect with me on reddit](https://www.reddit.com/user/denisberezovsky)

## License

MIT
besimilarity 1.0.1
A package to calculate and find the best similarity between two binary vectors
Requires Python: >=3.6

[![Sphinx build](https://github.com/dwikiramdhani53/besimilarity/actions/workflows/sphinx.yml/badge.svg)](https://github.com/dwikiramdhani53/besimilarity/actions/workflows/sphinx.yml)

# Besimilarity

Besimilarity is a Python library for computing your suitable similarity matrix from your binary data.

### Installation
To use Besimilarity, first install it using pip:
```console
   (.venv) $ pip install best-binary-similarity
```
### Get started
To get started, you can import the library and use the `Besimilarity` class:
```python
   import besimilarity

   # Instatntiate a Besimlarity object
   aggbs = besimilarity.AgglomerativeBestSimilarity()
   
   # Call the fit function
   aggbs.fit(X)

   # Print out the result
   print(aggbs.get_result())
```
orm2pydantic 0.1.1
Convert SQLAlchemy models to Pydantic models
Requires Python: <4.0,>=3.9
Keywords: pydantic,sql,sqlalchemy,orm,model,schema

# orm2pydantic

Convert SQLAlchemy models to Pydantic models

## Usage

...

## Installation

`pip install orm2pydantic`

## Dependencies

Python Version ..., OS ...

## Building from source

Run `python -m build`
TimezoneTimeFinder 0.0.1
For finding timezones by the current time there
Requires Python: >=3.7

# TimezoneTimeFinder

TimezoneTimeFinder is a package that lets you know in what timezones it's a specific time

* 24 hour version 

    * getTimezones
        
        </br>

        GetTimezones is a function that lets you input the current time and it will respond with a list of time zones where it currently is that specific time.


        `from TimezoneTimeFinder import TimezoneTimeFinder24h
        print(TimezoneTimeFinder24h.getTimezone("13:29"))`


        RESULT:

        `
        ['Africa/Blantyre', 'Africa/Bujumbura', 'Africa/Cairo', 'Africa/Ceuta', 'Africa/Gaborone', 'Africa/Harare', 'Africa/Johannesburg', 'Africa/Juba', 'Africa/Khartoum', 'Africa/Kigali', 'Africa/Lubumbashi', 'Africa/Lusaka', 'Africa/Maputo', 'Africa/Maseru', 'Africa/Mbabane', 'Africa/Tripoli', 'Africa/Windhoek']`
        
        </br>
        </br>



    * getTimezone

        </br>
        GetTimezone is a function that lets you input the current time and it will respond with a one time zone where it currently is that specific time.
        
        
            
        `
        from TimezoneTimeFinder import TimezoneTimeFinder24h
        print(TimezoneTimeFinder24h.getTimezone("13:29"))`


        RESULT:

        `Atlantic/Cape_Verde`
    
    **Inputs:**

    * Both functions takes an input of the time as a string with a colon deviding hours from minutes.

    
    * Both has a minuteCeck input that is turned on by default but by setting it as False it skips the checks for minutes and only looks for hours, wich means that you don't need to input the **current** minute.  
    </br>
    </br>
    </br>




* 12 hour version

    * getTimezones
        
        </br>

        `from TimezoneTimeFinder import TimezoneTimeFinder12h
        print(TimezoneTimeFinder12h.getTimezones(time="00:20", AmPm="am"))`
        
        
        <br/>RESULT: <br/> `['Antarctica/Casey', 'Asia/Magadan', 'Asia/Sakhalin', 'Asia/Srednekolymsk', 'Pacific/Bougainville', 'Pacific/Efate', 'Pacific/Guadalcanal', 'Pacific/Kosrae', 'Pacific/Norfolk', 'Pacific/Noumea', 'Pacific/Pohnpei']` 
        
        <br/>
        <br/>



    * getTimezone

        </br>`from TimezoneTimeFinder import TimezoneTimeFinder12h
    print(TimezoneTimeFinder12h.getTimezone(time="00:15", AmPm="am"))`
    
    
        </br>RESULT: </br> `Antarctica/Casey`  
            

    
    </br></br>
    Everything is the same as the 24h version exccept that you need to specify AM or PM usigng the AmPm input in the functions
memacs 2022.8.6
Visualize your (digital) life in Emacs Org mode by converting data to Org mode format
Keywords: quantified self,emacs,org-mode,org mode

This Python framework converts data from various sources to Org mode format
which may then included in Org mode agenda (calendar). This way, you get a 360-degree-view of your
digital life.

Each Memacs module converts a different input format into Org mode files.

- Target group: users of Emacs Org mode who are able to use command line tools
- Hosted and documented on github: https://github.com/novoid/memacs
dsnd-probability-M3hzhats 0.6
Gaussian and Binomial distributions

# DSND Probability Distribution

This package enables you to perform basic analysis on Gaussian and Binomial distributions, such as calculating the mean and standard deviation, visualising the distributions as histograms, and adding two Gaussian or two Binomial distributions for identical p values.

---

## How To Install
```
pip install dsnd_probability_M3hz
```