semvec/pypi-packages huggingface dataset:
autopypack 1.3.0
Automatically installs missing Python libraries when running your code.
License: MIT
Requires Python: >=3.8

# AutoPyPack

**AutoPyPack** is a zero-config tool that automatically installs missing Python packages in your project — whether you're running a script, a notebook, or from the command line.

> ✨ Works as both a CLI tool, an importable module, and supports magic commands inside Jupyter notebooks.

---

## 🚀 Features

- 📦 Automatically installs missing packages from `import` statements
- 🧠 Smart mapping from import names to PyPI package names
- 🛠️ Use it via CLI, Python, or inside **Jupyter notebooks** with `%%autopypack` magic
- 🔍 Detects local modules and standard libraries — no false installs
- 🧪 Includes dev tools for testing and linting

---

## 📦 Installation

Install from PyPI:

```bash
pip install autopypack
```

Or install from source:

```bash
git clone https://github.com/harshRaj1601/AutoPyPack
cd AutoPyPack
pip install -e .
```

---

## 🧠 Usage

### ✅ Method 1: As an importable module (auto-scans your script)

```python
import AutoPyPack  # 👈 Automatically scans and installs packages

# Your usual imports
import numpy as np
import pandas as pd
```

#### Manual usage

```python
import AutoPyPack

AutoPyPack.install()                        # Scan current directory
AutoPyPack.install("/path/to/project")      # Scan specific directory
AutoPyPack.scan_file("example.py")          # Scan a specific file
```

---

### 🧪 Method 2: Inside a Jupyter Notebook

Enable magic with a single import:

```python
import AutoPyPack
```

Use it in a cell:

```python
%%autopypack
import pandas as pd
import sklearn
print("All imported successfully!")
```

✨ AutoPyPack will:
- Detect missing packages in that cell
- Install them with pip
- Execute the cell code

---

### 🖥️ Method 3: As a CLI tool

#### Install missing packages in your project

```bash
autopypack install
autopypack i  # Short form
```

#### List all external packages

```bash
autopypack list
autopypack l  # Short form
```

#### Options

- `--dir`, `-d`: Target a specific directory (default: `.`)
- `--quiet`, `-q`: Suppress detailed logs (useful for scripts)

#### Example: generate `requirements.txt`

```bash
autopypack list --quiet > requirements.txt
```

---

## ⚙️ How it works

1. Scans `.py` or notebook cell for `import` statements
2. Identifies unique modules
3. Uses `mappings.json` to resolve correct package names
4. Installs anything missing via `pip`
5. Skips local modules and stdlib

---


## 🪪 License

MIT © Harsh Jaiswal

---

## 🔗 Links

- GitHub: [AutoPyPack](https://github.com/harshRaj1601/AutoPyPack)
MIT

[![PyPI Downloads](https://static.pepy.tech/badge/autopypack)](https://pepy.tech/projects/autopypack)
epipackpy 1.0.2
EpiPack: scATAC-seq reference mapping, label transfer and OOR detection
License: MIT
Requires Python: <3.12,>=3.8

<p align="left">
  <img src="docs/source/_static/epipack_logo8.svg" width="200">
</p>

# EpiPack ![version](https://img.shields.io/badge/version-1.0.1-blue.svg)

---

## Description
EpiPack is a modular deep learning toolkit for **single-cell ATAC-seq reference mapping, cell label annotation, and out-of-reference (OOR) detection**.  
By introducing heterogeneous transfer learning and peak-informed variational inference (PEIVI), EpiPack enables scalable construction of harmonized reference atlases and robust query mapping across diverse scATAC-seq datasets. It further provides **global-local OOR detection frameworks** for discovering novel cell types or perturbed cellular states with interpretable uncertainty estimation. Please see our manuscript for more details.

<p align="center">
  <img src="docs/source/_static/workflow.jpg" alt="Main figure" width="600">
</p>

---

## Installation
The package is available on **PyPI** and can be installed with all required dependencies via:
```bash
pip install epipackpy
```

---

## Tutorial
Please refer to our full documentation and tutorials at  
👉 [epipack.readthedocs.io](https://epipack.readthedocs.io/)

Demo datasets used in the tutorial can be downloaded from our [Google Drive folder](https://drive.google.com/drive/folders/1wyuJZF87lMvhXUZQqRroUlNHLD9OfYdv?usp=drive_link). The names of the datasets are aligned with the names that we used in the tutorial for better reproduction.

---

## Dependencies
```
- Python >= 3.9  
- PyTorch >= 2.0.1  
- PyTorch-CUDA >= 11.8  
- NumPy >= 1.26.4  
- Pandas >= 1.5.3  
- SciPy >= 1.10.0  
- Scikit-learn >= 1.5.2  
- tqdm >= 4.66.1  
- Matplotlib >= 3.9.4  
- Seaborn >= 0.12.2  
```
---

For **PyTorch installation**, we recommend users to follow the official [PyTorch installation guide](https://pytorch.org/get-started/locally/) to select the correct build based on their CUDA version.

---
langchain-chroma 1.1.0
An integration package connecting Chroma and LangChain.
License: MIT
Requires Python: <4.0.0,>=3.10.0

# langchain-chroma

[![PyPI - Version](https://img.shields.io/pypi/v/langchain-chroma?label=%20)](https://pypi.org/project/langchain-chroma/#history)
[![PyPI - License](https://img.shields.io/pypi/l/langchain-chroma)](https://opensource.org/licenses/MIT)
[![PyPI - Downloads](https://img.shields.io/pepy/dt/langchain-chroma)](https://pypistats.org/packages/langchain-chroma)
[![Twitter](https://img.shields.io/twitter/url/https/twitter.com/langchainai.svg?style=social&label=Follow%20%40LangChainAI)](https://twitter.com/langchainai)

Looking for the JS/TS version? Check out [LangChain.js](https://github.com/langchain-ai/langchainjs).

## Quick Install

```bash
pip install langchain-chroma
```

## 🤔 What is this?

This package contains the LangChain integration with Chroma.

## 📖 Documentation

View the [documentation](https://docs.langchain.com/oss/python/integrations/providers/chroma) for more details.
legume-nutrient-dashboard 0.1.1
Interactive dashboard for analyzing legume nutrient data from USDA FoodData Central
License: MIT
Requires Python: >=3.11
Keywords: nutrition, legumes, USDA, dashboard, data-analysis

# Legume Nutrient Dashboard

The Legume Nutrient Dashboard is an interactive Streamlit app created for a STAT 386 final project. It collects Foundation-food data from the USDA FoodData Central API (category: "Legumes and Legume Products"), extracts and cleans nutrient information, and provides interactive visualizations for exploration.

---

## Quickstart

Prerequisites:
- A working Python environment (see `pyproject.toml` / `myenv`).
- A USDA API key saved in a file named `api.txt` at the project root (one-line file containing the key).

1) Sync environment & run tests (project uses `uv` for environment management):

```bash
uv sync
uv run pytest
```

2) Generate the cleaned dataset

The main script fetches data from the USDA API, extracts nutrients, and writes `legus_cleaned.csv`.

```bash
uv run python main.py
```

3) Launch the Streamlit dashboard

```bash
uv run streamlit run src/legume_nutrient_dashboard/streamlit_app.py
```

**Important:** Always run from the project root directory so the app can locate `legus_cleaned.csv`.

## Notes
- The pipeline filters for Data Type: `Foundation` and Category: `Legumes and Legume Products`.
- Ensure `api.txt` exists in the project root before running `main.py`.

## Dashboard Features

- **Legume Selector:** pick a legume category from the sidebar to filter results.
- **Radar Chart:** compares average nutrient content across groups (e.g., Protein, Fat, Carbs, Starch, and selected minerals).
- **Correlation Heatmap:** interactive Plotly heatmap showing nutrient correlations across the dataset.
- **Dataset Preview:** table view of the cleaned `legus_cleaned.csv` for inspection.

## Technologies Used

- **Python:** 3.11+ (project environment in `myenv`)
- **Streamlit:** dashboard UI
- **pandas:** data manipulation
- **requests:** USDA API calls
- **regex:** nutrient and category extraction
- **seaborn / matplotlib:** static charts
- **plotly:** interactive visualizations
- **uv:** environment & dependency management

## Files of interest

- `main.py` — main pipeline to fetch, clean, and save data
- `legus_cleaned.csv` — cleaned dataset output
- `src/legume_nutrient_dashboard/streamlit_app.py` — Streamlit dashboard