semvec/pypi-packages huggingface dataset:
weighted-buzzwords 0.1.0
Add your description here
Requires Python: >=3.11

# Plotting


# Weighted-buzzwords

## Buzzwords

``` python
buzzWords={ f"buzzword with weight {i}":i 
    for i in map(lambda x:x**2//3+x+5,range(15)) 
}

buzzWords
```

    {'buzzword with weight 5': 5,
     'buzzword with weight 6': 6,
     'buzzword with weight 8': 8,
     'buzzword with weight 11': 11,
     'buzzword with weight 14': 14,
     'buzzword with weight 18': 18,
     'buzzword with weight 23': 23,
     'buzzword with weight 28': 28,
     'buzzword with weight 34': 34,
     'buzzword with weight 41': 41,
     'buzzword with weight 48': 48,
     'buzzword with weight 56': 56,
     'buzzword with weight 65': 65,
     'buzzword with weight 74': 74,
     'buzzword with weight 84': 84}

``` python
from weighted_buzzwords.valuedbuzzwords import valued_buzzwords



valued_buzzwords(buzzWords)
```

![](README_files/figure-commonmark/cell-3-output-1.png)

``` python
valued_buzzwords(buzzWords,min_font=12,max_font=300)
```

![](README_files/figure-commonmark/cell-4-output-1.png)

``` python
valued_buzzwords(buzzWords,min_font=12,max_font=300, figsize=(25,4))
```

![](README_files/figure-commonmark/cell-5-output-1.png)

``` python
valued_buzzwords(buzzWords,min_font=12,max_font=300, figsize=(25,4),stretch=4)
```

![](README_files/figure-commonmark/cell-6-output-1.png)

``` python
valued_buzzwords(buzzWords,min_font=12,max_font=300, figsize=(25,4),stretch=4,cmap="jet")
```

![](README_files/figure-commonmark/cell-7-output-1.png)
ymrp 0.6.0
Yandex map resources parser library
Requires Python: >=3.12

# ymrp

## Requirements

YMRP stands on the shoulders of:
 - python 3.13
 - playwright
 - beautifulsoup4

## Installation

Create and activate a virtual environment and then install YMRP:

```sh
pip install ymrp
```

Install playwright dependencies

```sh
playwright install chromium
playwright install-deps
```

## Example

Get business reviews from yandex map

```python
from ymrp.parser import Parser

url = 'https://yandex.ru/maps/10875/lomonosov/?from=mapframe&ll=29.770779%2C59.913080&mode=poi&poi%5Bpoint%5D=29.770747%2C59.913078&poi%5Buri%5D=ymapsbm1%3A%2F%2Forg%3Foid%3D1245233699&tab=reviews&z=20.34'

parser = Parser()
reviews = parser.get_yandex_reviews(url)

for review in reviews:
    print(review)

```

Get business products and services from yandex map

```python
from ymrp.parser import Parser

url = 'https://yandex.ru/maps/10875/lomonosov/?from=mapframe&ll=29.770779%2C59.913080&mode=poi&poi%5Bpoint%5D=29.770747%2C59.913078&poi%5Buri%5D=ymapsbm1%3A%2F%2Forg%3Foid%3D1245233699&tab=prices&z=20.34'

parser = Parser()
products_and_services = parser.get_yandex_products_and_services(url)

for product_or_service in products_and_services:
    print(product_or_service)

```
sphinx-pdj-theme 0.7.3
A cool theme for sphinx documentation

PdJ Sphinx Theme
================

This theme is a theme for sphinx documentation based on the
read the docs theme.

It supports light and dark modes first using ``prefers-color-scheme`` with an option
to change the mode. If changed the decision will be remembered.

To install it, use pip:

```
$ pip install sphinx_pdj_theme
```

To enable it in your project documentation, set the following in
your conf.py file.

```python
import sphinx_pdj_theme
html_theme = 'sphinx_pdj_theme'
htm_theme_path = [sphinx_pdj_theme.get_html_theme_path()]
```

Logo and favicon
-----------------

To change the logo and the favicon use the following options:

```python
html_logo = 'path/to/logo.svg'
html_favicon = 'path/to/favicon.ico'
```

To set the logo's width and height use the ``logo_width`` and `logo_height`
theme options


```python
html_theme_options = {
    'logo_width': '200px',
    'logo_height': '150px',
}
```

Other options
-------------

To hide the search bar set the `search` option to `hide`

```python
html_theme_options = {
	'search': 'hide'
}
```

To hide the name of the project above the search bar set the `home_link` option
to `hide`

```python
html_theme_options = {
	'home_link': 'hide'
}
```

To include a navigation to the homepage in the sidebar, set the `home_menu_item` option
to `show`

```python
html_theme_options = {
	'home_menu_item': 'show'
}
```

By default the label of the navigation item will be the project name. To change it
set the `home_menu_item_label` option:

```python
html_theme_options = {
	'home_menu_item': 'show',
	'home_menu_item_label': 'Home',
}




Screenshots
-----------
![defaulf](https://raw.githubusercontent.com/jucacrispim/sphinx_pdj_theme/master/default.jpg)
![darker](https://raw.githubusercontent.com/jucacrispim/sphinx_pdj_theme/master/darker.jpg)
zipfile-helper-Nyx-o 0.0.2
Reading and extracting python files made easier
Requires Python: >=3.12.3

# Zipfile Helper
## Table of contents
### - [Project Description](#project-description)
### - [Version](#versions)
- [V0.0.1](#v001)
## Project Description
**Zipfile Helper** is a python package that makes reading and extracting data from a zipfile easier. 
## Versions
### V0.0.1
certbot-dns-mchost24 1.0.0
Certbot DNS Authenticator plugin for MCHost24
Requires Python: !=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7

# MCHost24 Certbot DNS Authentication Plugin

A plugin for certbot that enables performing DNS validation using the MCHost24 API.

## Usage

1. Obtain an MCHost24 API token (e.g. using the cli tool of the `mchost24` module found [here](https://github.com/JoeJoeTV/mchost24-api-python))
2. Install the plugin
3. Create a `mchost24.ini` config file that contains the `mchost24_dns_api_token` key with the value set to the previously obtained API token:
    ```ini
    # MCHost24 API token
    mchost24_dns_api_token=<insert obtained API token here>
    ```
4. Run `certbot` and tell it to use the plugin to perform dns authentication and the config file:
    ```sh
    certbot certonly --authenticator dns-mchost24 --dns-mchost24-credentials /etc/letsencrypt/mchost24/mchost24.ini -d domain.com
    ```

## Requirements

Requires the MCHost24 API python module, which can be found [here](https://github.com/JoeJoeTV/mchost24-api-python).
fastgam 1.0.2
A package implementing fast generalized additive models
Requires Python: >=3.9

# Fastgam

**Fastgam** is a Python package for constructing fast additive models.

## Installation

You can install the package via pip:

```bash
pip install fastgam
```

## Minimal example
In this example, we try to learn an additive model with $d = 5$ effects, with a regularity $s = 2$.
The data satisfies 
* $Y = f(X_1, \dots, X_5) + \varepsilon$,
* $f(x_1, \dots, x_5) = \sum_{j=1}^5 (\exp(x_j/(j+1))-1)$,
* $\varepsilon \sim \mathcal N(0, 1)$.

```python
import numpy as np
from fastgam import FastGAM

d = 5
s = 2
N = 10**2
N_val = 1000

lambda_N_list = [10**(-i/3) for i in range(10)]

x_train = np.random.uniform(size=(d, N))
f_train = np.zeros(N)
for i in range(d):
    f_train += np.exp(x_train[i]/(i+1)) - 1
y_train =  (f_train + np.random.normal(size=N)).astype(np.complex128)

x_val = np.random.uniform(size=(d, N_val))
f_val = np.zeros(N_val)
for i in range(d):
    f_val += np.exp(x_val[i]/(i+1)) - 1
y_val =  (f_val + np.random.normal(size=N_val)).astype(np.complex128)


lambda_n=N**(-(2*s)/(2*s+1))
m = 1+ int(N**(1/(2*s+1))/d)
threshold = lambda_n/10

model = FastGAM(d)
model.fit_grid(x_train, y_train, x_val, y_val,lambda_N_list, m, threshold)

N_test = 10**4
x_test = np.random.uniform(size=(d, N_test))
f_test = np.zeros(N_test)

for i in range(d):
    f_test += np.exp(x_test[i]/(i+1)) - 1
y_test =  f_test.astype(np.complex128)

model.predict(x_test)
model.mse(y_test)
```
stelpar 1.0.0
Stellar parameter estimation and analysis tool
Requires Python: >=3.6
Keywords: astronomy, astrophysics, stars, stellar parameters

# stelpar

[![github](https://img.shields.io/badge/GitHub-mjfields%2Fstelpar-purple)](https://github.com/mjfields/stelpar)
[![Documentation Status](https://readthedocs.org/projects/stelpar/badge/?version=latest)](https://stelpar.readthedocs.io/en/latest/?badge=latest)
[![DOI](https://img.shields.io/badge/DOI-10.1093%2Frasti%2Frzaf009-blue
)](https://doi.org/10.1093/rasti/rzaf009)
[![arxiv](https://img.shields.io/badge/arXiv-10.48550%2FarXiv.2504.02990-red
)](http://arxiv.org/abs/2504.02990)

A Python-based pipeline and analysis tool for estimating stellar parameters (particularly for young stars) including age, mass, radius, effective temperature, density, and others. The code works by downloading observed photometry and comparing to a stellar evolutionary model in an MCMC framework. 

## Documentation

Read the documentation at [stelpar.readthedocs.io](https://stelpar.readthedocs.io/en/latest/)

## Installation

The current preferred method to install stelpar is

`pip install stelpar`

If that does not work, try

`pip install git+https://github.com/mjfields/stelpar.git`

## Citation

If you use stelpar in your work, please cite [Fields et al. (2025)](https://doi.org/10.1093/rasti/rzaf009). DOI: 10.1093/rasti/rzaf009.
typically 0.1.8
(UNSTABLE) Collection of common types.
Requires Python: >=3.11

# typically

## Overview

When adding comprehensive type annotations to python projects, you often have to choose where to import identical types.

For instance, you might want to use a `TypedDict`, so you would naturally import:

```python
from typing import TypedDict
```

The issue is that now if you use your typed dictionary in a pydantic model, you will get an exception. This is because you actually "should have" imported:

```python
from typing_extensions import TypedDict
```

There's also `types`, `collections`, `collections.abc`, `annotated_types`, etc.

This package is intended to create a single source of core types for python projects with very few required dependencies, and maybe at most a few optional ones (eg: `pydantic`).

Suggested pattern:

```python
import typically as t
```

## Installation

With pip:

```sh
pip install typically
```

Or with `uv`:

```sh
uv add typically
```

## Notes

- Type annotations really shouldn't impose any overhead. Of course, that's possible to avoid, but I'm still aiming to keep the extra import time less than 100 ms. The goal is (maybe) to pack as much commonly used stuff into a single import without incurring overhead.
- All members of `builtins` are explicitly imported in the `__init__` file, as sometimes one might like to override them, but also be able to access original builtin type. 
- Various type variables are included. These are always named `T_...` where the ellipses is the type the type variable is bound to, if any.


## TODO

- [ ] Implement paramspec objects for functools caching.
- [ ] Add pydantic as submodule, as it imposes overhead and should be optional.
- [ ] Add improved decimal type.
text-to-identifier 0.1.0a1
A tool that converts arbitrary text (like user input or file names) into valid Python identifiers while preserving as much of the original meaning as possible.
Requires Python: >=2

A tool that converts arbitrary text (like user input or file names) into valid Python identifiers while preserving as much of the original meaning as possible.

## Installation

```bash
pip install text-to-identifier
```

## Usage

```python
# coding=utf-8
from text_to_identifier import text_to_identifier

assert text_to_identifier(u"") == u"_"
assert text_to_identifier(u" \r\n\t") == u"_"
assert text_to_identifier(u"123abc") == u"_123abc"
assert text_to_identifier(u"&abc 123") == u"_abc_123"
assert text_to_identifier(u"  hello  world  $") == u"_hello_world__"
assert text_to_identifier(u"测试@unicode") == u"测试_unicode"
```

## How It Works

First, `get_unicode_character_type()` categorizes each Unicode character into one of four types:

- `LETTER_OR_UNDERSCORE` (Unicode category starting with 'L' or underscore `_`)
- `DECIMAL_DIGIT` (Unicode category 'Nd')
- `SPACE_OR_CONTROL` (Unicode categories starting with 'Z' or 'Cc')
- `OTHER` (all other characters)

Then, it implements the following conversion rules using a state machine:

- The first character must be a letter/underscore. If the first character is a digit, prepend `_` to make it valid.
- Subsequent valid characters (letters/underscores/digits) are kept as-is.
- Other characters are replaced with underscores, but whitespace/control character sequences are collapsed into single underscores.
- Ensures the output is non-empty (appends `_` if empty).

## Contributing

Contributions are welcome! Please submit pull requests or open issues on the GitHub repository.

## License

This project is licensed under the [MIT License](LICENSE).