semvec/pypi-packages huggingface dataset:
django-hstl 0.3
A Hostel Mannagement WEB APP
Requires Python: >=3.10

## Hostel Management WebApp using django

### Pre-requisite: ``git, python``

## Quick start
1. Install app.
	```bash
	pip install django-hstl
	```
1. Modify settings.py such that
	```python
	AUTH_USER_MODEL = 'django_hstl.MyUser'
	
	# Add the app in your INSTALLED_APPS
	
	INSTALLED_APPS = [
		# ...,
		"django_hstl",
	]
	```
1. Include the app URLconf in your project urls.py like this
	```python
	path("hstl/", include("django_hstl.urls")),
	```
1. Run `python manage.py migrate` to create the models.
1. Create Superuser `manage.py createsuperuser`
1. Visit the `/hstl/` URL to explore the WEB APP.

## Getting Started
1. Create a directory and cd into it.
	```bash
	mkdir ./hstl/
	cd ./hstl
	```
1. Create a virtual environment and activate it.
	* LINUX / MAC
	```bash
	python3 -m venv .venv
	source ./.venv/bin/activate
	```
	* WINDOWS
	```bash
	python -m venv .venv
	.\.venv\Scripts\activate
	```
1. Install app.
	```bash
	pip install django-hstl
	```
1. Create a django project
	```bash
	django-admin startproject MyApp .
	```
1. Modify "MyApp/setting.py" such that
	```python
	AUTH_USER_MODEL = 'django_hstl.MyUser'
	
	# Add the app in your INSTALLED_APPS
	
	INSTALLED_APPS = [
		# ...,
		"django_hstl",
	]
	```
1. Include the app URLconf in "MyApp/urls.py" like this
    ```python
	path("hstl/", include("django_hstl.urls")),
	```
1. Run `python manage.py migrate` to create the models.
1. Create Superuser `manage.py createsuperuser`
1. Visit the `/hstl/` URL to explore the WEB APP.
fedwrap 1.1.0
A library of API wrappers for US Federal Government datasets.
Requires Python: >=3.9

# fedwrap

A Python package providing convenient wrappers for accessing and working with US federal datasets, including the American Community Survey and CDC PLACES.

---

## Table of Contents

- [fedwrap](#fedwrap)
  - [Table of Contents](#table-of-contents)
  - [Overview](#overview)
  - [Features](#features)
  - [Installation](#installation)
  - [Quick Start](#quick-start)
  - [Widget](#widget)
  - [Modules](#modules)
  - [Dependencies](#dependencies)
  - [License](#license)
  - [Contact](#contact)
---

## Overview

`fedwrap` simplifies access to federal datasets by providing Pythonic wrappers for public APIs and data downloads. The package is designed for researchers, analysts, and developers who need streamlined access to high-value US government data.

---

## Features

- Easy access to the American Community Survey (ACS) and the CDC PLACES dataset 
- Data returned as pandas DataFrames for easy analysis

---

## Installation

```bash
pip install fedwrap
```

## Quick Start

```python
from fedwrap import get_acs_data, get_places_data, get_brfss_data

# Example: Fetch ACS data
acs_df = get_acs_data(
    measureid="HOUSEHOLD_TYPE", 
    year=2022, 
    geography="county"
)

# Example: Fetch CDC PLACES data
places_df = get_places_data(
    geo='county',
    year='2022',
    measureid='ARTHRITIS',
    datavaluetypid='CrdPrv'
)

# Example: Fetch BRFSS data
brfss_df = get_brfss_data(geo="state",
            measure="crude",
            year=2023,
            question_id="CHECKUP1",
            break_out_category="Sex")

```

## Widget 
To browse data using a widget with dropdown menus

```python 
from fedwrap.widget import display_explorer

display_explorer()
```

## Modules

- census_acs provides functions for accessing data from the American Community Survey API
- cdc_places provides functions for accessing data from the CDC PLACES dataset
- cdc_brfss provides functions for accessing data from the CDC BRFSS dataset

## Dependencies

- pandas
- requests
- beautifulsoup4

## License

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

## Contact

For any questions or suggestions, open an issue or contact Mark Aronson.
dunderdoc 0.4.1
A Documentation module for dunder methods
Requires Python: >=3.10

# dunderdoc
Documentation module for all python dunder (magic) methods

## Usage

```bash
uvx dunderdoc print
uvx dunderdoc find __add__  # search by dunder name
uvx dunderdoc list  # show all sections
uvx dunderdoc print {section}  # show all in a section
```

## Installing
If you prefer a persistent install:
```bash
pip install dunderdoc
```
scAnalysis 0.1.0
A toolkit for single-cell RNA-seq analysis.
Requires Python: >=3.10

# scAnalyzer: A Single-Cell Analysis Toolkit

A Python toolkit for single-cell RNA sequencing (scRNA-seq) analysis.

🚧 Warning this project is under heavy development and not ready for production. ABI changes can happen frequently until reach stable version 🚧


<p align="center">
  <img alt="GitHub" src="https://img.shields.io/github/license/ayyucedemirbas/scAnalyzer">
  <img alt="Black" src="https://img.shields.io/badge/code%20style-black-black"/>
  <img alt="isort" src="https://img.shields.io/badge/isort-checked-yellow"/>
</p>


## 🚀 Features

* **Core Data Structure**: `SingleCellDataset` (AnnData-like) for efficient handling of sparse matrices and metadata.
* **Preprocessing**: QC metrics, filtering (cells/genes), normalization, log-transformation, and highly variable gene (HVG) selection.
* **Dimensionality Reduction**: PCA, t-SNE, and UMAP implementations.
* **Clustering**: Graph-based (Leiden, Louvain), geometric (K-Means, Hierarchical), and density-based (DBSCAN) clustering.
* **Differential Expression**: Statistical testing (T-test, Wilcoxon) to identify marker genes.
* **Visualization**: Publication-ready plots (UMAP, t-SNE, Violin, Dotplot, Heatmap).
* **I/O**: Support for 10x Genomics (`.mtx`), H5AD (`.h5ad`), and CSV formats.

## 📦 Installation

Clone the repository and install the required dependencies:

```bash
git clone [https://github.com/demirbasayyuce/scAnalyzer.git](https://github.com/demirbasayyuce/scAnalyzer.git)
cd sc_analysis
pip install -r requirements.txt

## ⚡ Quick Start

Here is a minimal example of how to run a full analysis pipeline:

```python
import sc_io as io
import preprocessing as pp
import dimensionality as dim
import clustering as cl
import visualization as vis

# 1. Load Data
data = io.read_10x_mtx('./data/pbmc3k/')

# 2. Preprocess
pp.filter_cells(data, min_genes=200, max_pct_mito=5.0)
pp.normalize_total(data)
pp.log1p(data)
pp.highly_variable_genes(data, n_top_genes=2000)
pp.scale(data)

# 3. Embed & Cluster
dim.run_pca(data)
dim.neighbors(data)
dim.run_umap(data)
cl.cluster_leiden(data, resolution=0.5)

# 4. Visualize
vis.plot_umap(data, color='leiden', save='umap_clusters.png')

```

## 📂 Project Structure

* `core.py`: Main data structure (`SingleCellDataset`).
* `preprocessing.py`: Filtering, normalization, and scaling functions.
* `dimensionality.py`: PCA, Neighborhood Graph, t-SNE, UMAP.
* `clustering.py`: Community detection algorithms.
* `differential.py`: Marker gene identification.
* `visualization.py`: Plotting functions.
* `sc_io.py`: Input/Output handlers.
* `utils.py`: Helpers for merging and subsampling.

## 🧪 Running Tests

The project includes a comprehensive suite of unit tests. Run them using:

```bash
python -m unittest discover test

```

## 📄 License

MIT License.

```