semvec/pypi-packages huggingface dataset:
bitmapmaker 1.0.3
A package for creating your own custom bitmaps from a windows GUI.
Requires Python: >=3.8

# bitmapmaker

bitmapmaker is a Python package that allows you to create custom bitmaps using a Windows GUI. You can toggle squares on and off, including bulk area selection, and visualize the resulting bitmap in a 2D array. The generated bitmap is also printed to the terminal for easy copying.

## Features

- Toggle individual squares on and off
- Bulk area selection
- Visualize the resulting bitmap in a 2D array
- Copy the generated bitmap from the terminal

## Installation

To install bitmapmaker, clone the repository and install the required dependencies:

```sh
git clone https://github.com/Ruxton07/bitmapmaker.git
cd bitmapmaker
pip install -r requirements.txt
```

## Usage

To use bitmapmaker, run the following command and enter the dimensions of the grid when prompted:

```sh
python -m src.bitmapmaker.bitmapgui
```

## Example

After running the command, a GUI window will open where you can toggle squares on and off. Once you submit, the generated bitmap will be displayed in a 2D array format and printed to the terminal.

## License

This project is licensed under the MIT License. See the LICENSE file for details.

## Contributing
 
Contributions are welcome! Please open an issue or submit a pull request on the [GitHub Repository](https://github.com/Ruxton07/bitmapmaker), or you can [contact me](##contact)

## Contact

For any questions or inquiries, please contact the author:
- **Name**: Ryan Kellar
- **Email**: rt.kellar@gmail.com

### Links
- [PyPI Homepage](https://pypi.org/project/bitmapmaker/)
- [GitHub Page](https://github.com/Ruxton07/bitmapmaker)
- [Issues](https://github.com/Ruxton07/bitmapmaker/issues)
fast-html2md 0.1.3
Convert HTML to Markdown for LLM input extraction
Requires Python: >=3.10

# fast-html2md [![PyPI version](https://badge.fury.io/py/fast-html2md.svg)](https://badge.fury.io/py/fast-html2md) [![Run Tests](https://github.com/ancs21/fast-html2md/actions/workflows/test.yml/badge.svg)](https://github.com/ancs21/fast-html2md/actions/workflows/test.yml) [![codecov](https://codecov.io/github/ancs21/fast-html2md/branch/main/graph/badge.svg?token=8KP9MXS92V)](https://codecov.io/github/ancs21/fast-html2md)

Convert HTML to Markdown for LLM input extraction.



## Installation

```bash

# use pip
pip install fast-html2md

# or use poetry
poetry add fast-html2md

# or use uv
uv add fast-html2md
```

## Usage

```python
from fast_html2md import HTMLToMarkdown

converter = HTMLToMarkdown()

html = """
<!DOCTYPE html>
<html>
<body>
  <h1 id="title" data-updated="20201101">Hi there</h1>
  <div class="post">
    Lorem Ipsum is simply dummy text of the printing and typesetting industry.
  </div>
  <div class="post">
    Lorem ipsum dolor sit amet, consectetur adipiscing elit.
  </div>
</body>
</html>
"""

markdown = converter.convert(html)

print(markdown)

# Count tokens
token_count = converter.count_tokens(markdown)
print(f"Token count: {token_count}")

# Compute cost
cost = converter.compute_cost(token_count)
print(f"Estimated cost: ${cost:.6f}")
```

## Features

- Fast HTML to Markdown conversion
- Optimized for LLM input processing
- Built-in token counting using tiktoken
- Clean and minimal output

## License

This project is licensed under the MIT License - see the [LICENSE](https://github.com/ancs21/fast-html2md/blob/main/LICENSE) file for details.

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.
pkdb-data 1.0.10
pkdb_data are python utilities for working with PK-DB.
Requires Python: >=3.10
Keywords: PK-DB, data, pharmacokinetics

# pkdb_data: python utilities for PK-DB data
[![Version](https://img.shields.io/pypi/v/pkdb_data.svg)](https://pypi.org/project/pkdb_data/)
[![MIT License](https://img.shields.io/pypi/l/pkdb_data.svg)](https://opensource.org/licenses/MIT)
[![Python Versions](https://img.shields.io/pypi/pyversions/pkdb_data.svg)](https://pypi.org/project/pkdb_data/)

This repository stores the curated study data for [https://pk-db.com](https://pk-db.com) and provides helpers for uploading studies to the database.

* [`./studies/`](./studies/): curated study data with subfolders based on substances
* [`./docs/CURATION.md`](./docs/CURATION.md): curation guidelines and documentation


# Documentation
Documentation such as installation instructions are available from
[`./docs/CURATION.md`](./docs/CURATION.md>)

## Functionality in a nutshell
The following provides a short overview of the main functionality.

### Upload study
To upload a study use
```bash
upload_study -s <study_dir>
```

### Upload studies
A set of studies can be uploaded via

```bash
upload_studies -s caffeine
```

# License
- Source Code: [MIT](https://opensource.org/license/MIT)
- Documentation: [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)

# Funding
Matthias König (MK) was supported by the Federal Ministry of Education and Research (BMBF, Germany) within the research network Systems Medicine of the Liver (LiSyM, grant number 031L0054). MK is supported by the Federal Ministry of Education and Research (BMBF, Germany) within ATLAS by grant number 031L0304B and by the German Research Foundation (DFG) within the Research Unit Program FOR 5151 QuaLiPerF (Quantifying Liver Perfusion-Function Relationship in Complex Resection - A Systems Medicine Approach) by grant number 436883643 and by grant number 465194077 (Priority Programme SPP 2311, Subproject SimLivA).

© 2017-2025 Jan Grzegorzewski & Matthias König
business-central-kon 1.0.0
Business Central SDK for KSPPL
Requires Python: >=3.12

Example Usage:

```python
from business_central_kon.client import BusinessCentralClient

username = "<username>"
password = "<password>"
base_url = "<base_url>"

client = BusinessCentralClient(username=username, password=password, base_url=base_url)

# CUSTOMER SERVICES
customers = client.customers.get_data(as_dataframe=True)
customer_ledger_entries = client.customer_ledger_entries.get_data(as_dataframe=True)

# VENDOR SERVICES
vendors = client.vendors.get_data(as_dataframe=True)
vendor_ledger_entries = client.vendor_ledger_entries.get_data(as_dataframe=True)

# ITEM SERVICES
items = client.items.get_data(as_dataframe=True)
item_ledger_entries = client.item_ledger_entries.get_data(as_dataframe=True)
ile_origin_entries = client.ile_origin_entries.get_data(as_dataframe=True)
product_groups = client.product_groups.get_data(as_dataframe=True)

# GENERAL LEDGER SERVICES
general_ledgers = client.general_ledgers.get_data(as_dataframe=True)
general_ledger_entries = client.general_ledger_entries.get_data(as_dataframe=True)

# SALES INVOICE SERVICES
sales_invoices = client.sales_invoices.get_data(as_dataframe=True)
sales_invoice_lines = client.sales_invoice_lines.get_data(as_dataframe=True)

# SALES ORDER SERVICES
sales_orders = client.sales_orders.get_data(as_dataframe=True)
sales_order_lines = client.sales_order_lines.get_data(as_dataframe=True)

# LOCATION SERVICES
locations = client.locations.get_data(as_dataframe=True)

# PRODUCTION BOM SERVICES
bom_headers = client.bom_headers.get_data(as_dataframe=True)
bom_lines = client.bom_lines.get_data(as_dataframe=True)

# RESPONSIBILITY MATRIX SERVICES
resp_matrix = client.responsibility_matrix.get_data(as_dataframe=True)
sales_people = client.sales_people.get_data(as_dataframe=True)

# SAMPLE ORDER SERVICES
sample_orders = client.sample_orders.get_data(as_dataframe=True)

# EXPORT ORDER SERVICES
export_orders= client.export_orders.get_data(as_dataframe=True)

# TRANSFER ORDER SERVICES
transfer_orders = client.transfer_orders.get_data(as_dataframe=True)
transfer_order_lines = client.transfer_order_lines.get_data(as_dataframe=True)
```
numpy_turtle 0.3
Turtle graphics with NumPy
Requires Python: >=3.11

# NumPy Turtle

Draw directly onto a NumPy array using very easy turtle graphics.
Inspired by the [turtle](https://docs.python.org/3.6/library/turtle.html) library.

## Examples

<table>
<tr>
<td>

Koch curve ([code](numpy_turtle/examples/koch_curve_c.py)):

![Koch curve](examples/images/koch_curve_c.png)

</td>
<td>

Sierpinski triangle ([code](numpy_turtle/examples/sierpinski_triangle.py)):

![Sierpinski triangle](examples/images/sierpinski_triangle.png)

</td>
</tr>
<tr>
<td>

Fractal plant ([code](numpy_turtle/examples/fractal_plant.py)):

![Fractal plant](examples/images/fractal_plant.png)

</td>
<td>

Fractal conifer ([code](numpy_turtle/examples/conifer.py)):

![Fractal conifer](examples/images/conifer.png)

</td>
</tr>
</table>



## Installation

```bash
pip install numpy-turtle
```