semvec/pypi-packages huggingface dataset:
arvd-ap 0.2.0
My description will help you to pass time and understand my ability of writing codes
Requires Python: >=3.6

# arvd_ap

This project is done for fun and to learn how python modules are created. Since I'm spending some of my valuable resource called "Time" 
I've slao included some useful functions inside this.The following text will tell you what are the functions that I've initiased under this.
They are: avg(),sign(),perfect_num(),armstrong_num(),palindrome_num(),primality(),fib(),vowel_count(),consonant_count(),space_count(),
palindrome_str(),word_count(),alpha_count(),int_count() and div_it().

## Installation

To install the package, use the following command:

Or else go to PyPI.com and serch for the module named rvd_ap. So you'll find it there.

```sh
pip install arvd_ap
benchmarkCR 0.1.0
benchmarkCR (systematic CRISPR screen benchmarking framework) is a benchmarking toolkit for evaluating CRISPR screen results against biological gold standards. The toolkit computes gene-level and complex-level performance metrics, helping researchers systematically assess the biological relevance and resolution of their CRISPR screening data.
Requires Python: >=3.8

# benchmarkCR

🧬 **benchmarkCR** is a benchmarking toolkit for evaluating CRISPR screen results against biological gold standards. It provides precision-recall analysis using reference gene sets from CORUM protein complexes, Gene Ontology Biological Processes (GO-BP), KEGG pathways, and other curated resources. The toolkit computes gene-level and complex-level performance metrics, helping researchers systematically assess the biological relevance and resolution of their CRISPR screening data.


---

## 🔧 Features

- Precision-recall curve generation for ranked gene lists

- Evaluation using CORUM complexes, GO terms, pathways

- Complex-level resolution analysis and visualization

- Easy integration into CRISPR screen workflows

---

## 📦 Installation

Suggested to use Python version `3.10` with `virtual env`.

Create `venv`

```bash
conda create -n p310 python=3.10
conda activate p310
pip install uv
```

Install benchmarkCR

```bash
uv pip install -e .
```

---

## 🚀 Quickstart

```python
import benchmarkcr

inputs = {
    "Melanoma (63 Screens)": {
        "path": benchmarkcr.get_example_data_path("melanoma_cell_lines_500_genes.csv"), 
        "sort": "high"
    },
    "Liver (24 Screens)": {
        "path": benchmarkcr.get_example_data_path("liver_cell_lines_500_genes.csv"), 
        "sort": "high"
    },
    "Neuroblastoma (37 Screens)": {
        "path": benchmarkcr.get_example_data_path("neuroblastoma_cell_lines_500_genes.csv"), 
        "sort": "high"
    },
}

config = {
    "min_complex_size": 3,
    "min_complex_size_for_percomplex": 3,
    "output_folder": "output",
    "gold_standard": "CORUM",
    "color_map": "RdYlBu",
    "jaccard": True,
    "plotting": {
        "save": {
            "save_plot": True,
            "output_type": "png",
            "output_folder": "./output",
        }
    },
    "preprocessing": {
        "normalize": False,
        "fill_na": False,
        "drop_na": True,
    }
}

# Initialize logger, config, and output folder
benchmarkcr.initialize(config)

# Load datasets and gold standard terms
data, _ = benchmarkcr.load_datasets(inputs)
terms, genes_in_terms = benchmarkcr.load_gold_standard()

# Run analysis
for name, dataset in data.items():
    df, pr_auc = benchmarkcr.pra(name, dataset)
    pc = benchmarkcr.pra_percomplex(name, dataset)
    #fpc = benchmarkcr.fast_pra_percomplex(name, dataset) # not tested yet.
    cc = benchmarkcr.complex_contributions(name)

# Generate plots
benchmarkcr.plot_auc_scores()
benchmarkcr.plot_precision_recall_curve()
benchmarkcr.plot_percomplex_scatter()
benchmarkcr.plot_complex_contributions()
benchmarkcr.plot_significant_complexes()
```

---

## 📂 Examples

- [examples/basic_usage.py](examples/basic_usage.py)
- [notebooks/demo.ipynb](notebooks/demo.ipynb)

---

## 📃 License

MIT
certifyGUI 0.2.1
Certificate Designer is a Python GUI tool that lets you visually design and generate personalized certificates from Excel data using a drag-and-drop interface. Built with Tkinter and Pillow, it supports live preview, font customization, and bulk certificate creation with zero coding required.
Requires Python: >=3.6

Certify
🎓 Certificate Designer - Python GUI Tool

![License](https://img.shields.io/badge/license-MIT-blue.svg)
![Built with](https://img.shields.io/badge/built%20with-Tkinter%20%2B%20Pillow-orange)

> A no-code, drag-and-drop certificate generator powered by Python! Create professional certificates from Excel in minutes. 🎉

---


✨ Features

- 🖼️ **Interactive Canvas**: Draw text boxes on your certificate template with real-time mouse input.
- 📄 **Excel Integration**: Auto-map certificate fields from Excel sheet columns.
- 🖍️ **Custom Fonts & Styles**: Set font style, size, and color for each field.
- 👁️ **Live Preview**: Preview the first certificate before batch generation.
- 🖨️ **Bulk Export**: Generate personalized certificates for hundreds of participants.
- 📁 **Save as PNG**: Certificates saved to a `certificates/` folder for easy access.

---

🚀 Quick Start

1️⃣ Requirements


pip install pillow pandas openpyxl
2️⃣ Run the App
bash
Copy
Edit
python certificate_gui.py
🧠 How It Works
Load your certificate template image (JPG/PNG).

Load your Excel file (.xlsx) with participant data.

Draw a rectangle for each column (e.g., Name, Event, Date).

Choose your font style, size, and color per field.

Preview the output.

Hit "Generate All" to export certificates!

📁 File Structure

certificate_gui.py     # Main GUI logic
certificates/          # Auto-created folder with generated PNGs
preview.png            # Sample preview image
🧩 Tech Stack
Python 3.x

Tkinter – GUI interface

Pillow – Image processing

pandas – Excel (.xlsx) reading

openpyxl – Backend engine for .xlsx files

🎯 Use Cases
College and school event certifications 🏫

Hackathon participation awards 👨‍💻

Webinar/Workshop attendance proof 🎤

Volunteer appreciation 🎗️

🙌 Contributing
Pull requests are welcome. Feel free to open an issue to discuss improvements or ideas 💡.

📜 License
This project is licensed under the MIT License – see the LICENSE file for details.

🧑‍💻 Developed by
Muthukumaran S
✨ Follow me on GitHub: @MuthuKumaran-Dev-10000 (https://github.com/MuthuKumaran-Dev-10000)


---

Let me know if you want:
- 🎨 Badge styles for dark themes
- 📌 Sample certificate template and Excel
- 📦 To package it into a PyPI installable package

Happy shipping your library! 🚀
pdf-highlight-extractor 0.1.2
Extract and summarize highlights from PDF files.
Requires Python: >=3.7
Keywords: pdf highlights extraction annotation pymupdf research notes text

# 📘 pdf_highlight_extractor

Extract highlighted text from PDF files using PyMuPDF.

This lightweight utility reads highlights from PDFs, along with the associated page number and highlight color. Perfect for summarizing annotated documents, research papers, or ebooks.

---

## 🔧 Installation

Install from PyPI:

```bash
pip install pdf-highlight-extractor
```

---

## 🚀 Usage

```python
from pdf_highlight_extractor.reader import extract_highlights

highlights = extract_highlights("sample.pdf")

for h in highlights:
    print(f"Page {h['page']} | Color: {h['color']} | Text: {h['text']}")
```

### 📝 Output Example

```text
Page 2 | Color: (1.0, 1.0, 0.0) | Text: This is a highlighted phrase
Page 5 | Color: (0.0, 1.0, 0.0) | Text: Another important note
```

---

## 🧠 Features

- ✅ Extract text from highlights
- ✅ Get page number and highlight color
- ✅ Fallback extraction if highlight text is not directly stored
- ✅ Simple API for automation or personal use

---

## 🧪 Example PDF

You can test the tool using any PDF with highlights created in:
- Adobe Acrobat Reader
- Preview (macOS)
- Xodo or other PDF apps

---

## 📦 Requirements

- Python 3.7+
- PyMuPDF (automatically installed)

> Only needed for development:

```bash
pip install -e .
```
django-run-context 1.0
Detetermine the context under which a Django project is run (provides a get_run_context function)
Requires Python: >=3.7

# django-run-context

Provides a simple function that returns the run context that Django finds itself in.

The following values are currently supported:

- "runserver_reloader"
- "runserver_noreloader"
- "runserver_reloaded"
- "not runserver"

To use this just add to your settings.py:*

```python
from django_run_context import get_run_context

RUN_CONTEXT = get_run_context()
```

and then anywhere else in you Django project you can access it:

    ```python
    import django.conf.settings
    
    print(settings.RUN_CONTEXT)
    ```

and use it to alter the behaviour of your app based on that context.

The immediate need it addressed was a desire to dump project diagnostics in debug mode, in the `settings.py`. For example a common paradigm I employ:

```python
if DEBUG:
    log.debug(f"Django version: {django.__version__}")
    log.debug(f"Python version: {sys.version}")
    log.debug(f"Django loaded from: {django.__file__}")
    log.debug(f"Using Path: {sys.path}")
    log.debug(f"Process Info: {pinfo()}")
    log.debug(f"Static root: {STATIC_ROOT}")
    log.debug(f"Static file dirs: {locals().get('STATICFILES_DIRS', globals().get('STATICFILES_DIRS', []))}")
    log.debug(f"Installed apps: {INSTALLED_APPS}")
    log.debug(f"Database: {DATABASES['default']}")
    log.debug(f"Command line: {sys.argv}")
```

But this is spewed out twice, because by default runserver loads `settings.py` then starts a new instance of runserver that provides a web interface (at http://127.0.0.1:8000 by default) which again loads `settings.py`  (the supervising instance of runserver does not act as a webserver at all, but watches the filesystem for changes and reruns the actual webserver instance). So, we can suppress the output under the first instance (the reloader)

```python
if DEBUG and RUN_CONTEXT != "runserver_reloader":
    log.debug(f"Django version: {django.__version__}")
    log.debug(f"Python version: {sys.version}")
    log.debug(f"Django loaded from: {django.__file__}")
    log.debug(f"Using Path: {sys.path}")
    log.debug(f"Process Info: {pinfo()}")
    log.debug(f"Static root: {STATIC_ROOT}")
    log.debug(f"Static file dirs: {locals().get('STATICFILES_DIRS', globals().get('STATICFILES_DIRS', []))}")
    log.debug(f"Installed apps: {INSTALLED_APPS}")
    log.debug(f"Database: {DATABASES['default']}")
    log.debug(f"Command line: {sys.argv}")
```

and it is spewed put just once when the debug project starts up whether using reloader (the default) or not.

This can of course easily be extended for tests that are more specific than "not runserver", possibly telling us if uwsgi, gunicorn or daphne or some other context. Feel free to expand on it if you like (PRs gladly accepted).
mermaid-doc-mcp-server 0.2.0
A Model Context Protocol (MCP) server that provides access to Mermaid diagram documentation.
Requires Python: >=3.10

# Mermaid Doc MCP Server

Mermaid Doc MCP Server is a server for generating Mermaid documents.

## Features

- **List Diagrams**: List all available Mermaid diagram names in the documentation.
- **Retrieve Documentation**: Retrieve the documentation content for a specific Mermaid diagram.


## Prerequisites

### Installation Requirements

1. Install `uv` from [Astral](https://docs.astral.sh/uv/getting-started/installation/) or the [GitHub README](https://github.com/astral-sh/uv#installation)
2. Install Python 3.10 or newer using `uv python install 3.10` (or a more recent version)

## Installation

To add this MCP server to your Amazon Q or Claude, add the following to your MCP config file. With Amazon Q, create (if does not yet exist) a file named `.amazonq/mcp.json` under the same directory that is running `q chat`. Then add the following config:

```json
{
  "mcpServers": {
    "mermaid-doc-mcp-server": {
        "command": "uvx",
        "args": ["mermaid-doc-mcp-server@latest"]
    }
  }
}
```

## Tools

### list_diagrams

List all available Mermaid diagram names in the documentation.

```python
def list_diagrams() -> list:
```

### get_diagram_doc

Retrieve the documentation content for a specific Mermaid diagram.

```python
def get_diagram_doc(diagram_name: str) -> str:
```
aspubsub 0.1.3
Asynchronous PubSub interface for python web frameworks.
Requires Python: >=3.9

# AsPubSub

Asynchronous PubSub interface for Python.

Currently Supported Back-End:
- InMemory
- Redis PubSub (support `psubscribe` and `punsubscribe`)


## Credits

- [broadcaster](https://github.com/encode/broadcaster)
sr.comp 1.12.0
Reliable software for running robotics competitions
Requires Python: >=3.9

SRComp
======

|Build Status| |Docs Status|

Reliable software for running robotics competitions, primarily used by
`Student Robotics <https://studentrobotics.org>`__.

The `SRComp wiki <https://github.com/PeterJCLaw/srcomp/wiki>`__ provides
an overview of the suite as a whole.

This repository provides a python API to accessing information about the
state of the competition. That *compstate* is stored as a collection of
YAML files in a git repository. This allows the state of the competition
to be managed in isolation from the software while still providing
consistent representations of that state.

Usage
-----

Python clients should install the library using:

.. code:: shell

    pip install sr.comp

Only the ``SRComp`` is class directly exposed, and it should be constructed
around the path to a local working copy of a *compstate repo*.

.. code:: python

    from srcomp import SRComp
    comp = SRComp('/path/to/compstate')

**Web clients** should look at using the HTTP API provided by
`srcomp-http <https://github.com/PeterJCLaw/srcomp-http>`__
rather than implementing their own intermediary.

There is also a **command line** interface which provides utilities for
managing a *compstate repo*:
`srcomp-cli <https://github.com/PeterJCLaw/srcomp-cli>`__.

See the
`dummy-comp <https://github.com/PeterJCLaw/dummy-comp>`__
for an example of the structure and values expected in a *compstate
repo*.

Development
-----------

**Install**:
``pip install -e . -r dev-requirements.txt``

**Checks**:
``./script/check``

.. |Build Status| image:: https://circleci.com/gh/PeterJCLaw/srcomp/tree/main.svg?style=svg
   :target: https://circleci.com/gh/PeterJCLaw/srcomp/tree/main

.. |Docs Status| image:: https://readthedocs.org/projects/srcomp/badge/?version=latest
   :target: https://srcomp.readthedocs.org/