semvec/pypi-packages huggingface dataset:
scarfmatch 0.0.3
A package for stable matching problems with couples

# Scarf

Scarf is a python library for solving stable matching problems with couples, where one application is the [National Resident Matching Program](https://en.wikipedia.org/wiki/National_Resident_Matching_Program). The package is named after [Herbert Scarf](https://en.wikipedia.org/wiki/Herbert_Scarf), the inventor of Scarf's lemma and algorithm.

## Features

- Written in python3.
- Solve for a near feasible stable matching given the preference of doctors and hospitals, and hospital capacities.
- Generate random doctor hospital instances for simulation purposes.
- Accelerated with Numba. It is fast!

Accepted input format:
- Python Lists
- JSON

Algorithm:
- Scarf's algorithm with Iterative Rounding [[Nguyen and Vohra 2016]](https://web.ics.purdue.edu/~nguye161/e2sided.pdf)

## Installation

```
pip install scarfmatch
```

## Example Usage

```
import scarf

# An example with single doctors {s0, s1}, couples {c0=(c00, c01) c1=(c10, c11)}, and hospitals {h0, h1, h2} 
single_pref = [[0, 1, 2], # s0's preference on hospitals is h0 > h1 > h2 > unemployment
             [1, 0]] # s1's preference on hospitals is h1 > h0 > unemployment > h2
             # list only the hospitals preferred to unemployment 
couple_pref = [[(0, 0), (1, 1), (1, 0), (0, 1), (2, 2)], # c0's joint preference on hospital pairs
             [(1, 1), (2, 2), (1, -1)]] # c1's joint preference on joint plans for both members
             # for example, (1, 0) stands for "member 0 goes to hospital 1 and member 1 goes to hospital 0"
             # "-1" stands for the option of unemployment
             # list only the options preferred to (-1, -1) (i.e. both unemployed)
hospital_pref = [0, (0, 1), 1, (1, 0), (0, 0), (1, 1)] # all three hospitals have the same preference: s0 > c01 > s1 > c10 > c00 > c11 > vacancy
# If all hospital use the same preference, then hospital_pref is one list containing integers and tuples, where integers represent singles and tuples represent members of couples
# To specify different preference lists for different hospitals, please use a list of lists
# In each list, only specify acceptable doctors to the given hospital
hospital_cap = [2, 3, 1] # capacities of each hospital

S = scarf.create_instance(single_pref, couple_pref, hospital_pref, hospital_cap)
sol = scarf.solve(S) # solves for a possibly fractional stable matching
intsol = scarf.round(sol, S) # perform Nguyen and Vohra's IR algorithm to obtain a near feasible stable matching

print(intsol)

```

For more examples involving JSON input please refer to the Jupyter Notebooks in the notebooks folder. For more ways to access the matching result, please refer to the docstring of scarf.ScarfSolution.

## Support

## License

Released under MIT license

```
Copyright (c) 2019-2023 Dengwang Tang <dwtang@umich.edu>
```
pychromvar 0.0.4
A python package for chromVAR
Requires Python: >= 3.8

[![Stars](https://img.shields.io/github/stars/lzj1769/pychromVAR?logo=GitHub&color=yellow)](https://github.com/lzj1769/pychromVAR/stargazers)
[![PyPI](https://img.shields.io/pypi/v/pychromvar?logo=PyPI)](https://pypi.org/project/pychromvar/)
[![Docs](https://readthedocs.org/projects/pychromvar/badge/?version=latest)](https://pychromvar.readthedocs.io)

# pychromVAR 

pychromVAR is a python package for inferring transcription factor binding variability from scATAC-seq data by implmenting the algorithm proposed in [chromVAR](https://github.com/GreenleafLab/chromVAR). It is built on [anndata](https://anndata.readthedocs.io/en/latest/) and [mudata](https://mudata.readthedocs.io/en/latest/) therefore can work seamlessly with [Scanpy](https://scanpy.readthedocs.io/en/stable/index.html) and [Muon](https://gtca.github.io/muon/) pipeline. 

For more methodological details, please refer to the [paper](https://www.nature.com/articles/nmeth.4401). 

# Installation

The quickest and easiest way to get pychromvar is to to use pip:

```shell
pip install pychromvar
```

# Tutorial

You can find [here](https://pychromvar.readthedocs.io/en/latest/notebooks/multimodal_pbmc_3k.html) a tutorial how to use pychromVAR combined with Muon to analysis multimodal single-cell PBMC data.
Jianmu 0.0.12
A simple desktop app development framework combining Python, Vue.js, Element Plus and Electron.
Requires Python: >=3.7

<div align="center">
<br>
<img width="640" src="https://raw.githubusercontent.com/frederick-wang/jianmu/main/docs/images/jianmu-preview-without-border.png" alt="Jianmu Framework">
<br>
<br>
</div>

<p align="center" color="#262626">
A simple desktop app development framework combining Python, Vue.js, Element Plus and Electron.
</p>


<p align="center">
<img src="https://img.shields.io/pypi/pyversions/Jianmu" alt="pyversions"> <img src="https://img.shields.io/pypi/v/Jianmu" alt="version"> <img src="https://img.shields.io/pypi/l/Jianmu" alt="license"> <img src="https://img.shields.io/github/last-commit/frederick-wang/jianmu" alt="last-commit"> <img src="https://img.shields.io/github/commit-activity/m/frederick-wang/jianmu" alt="commit-activity">
</p>

## Installation

### Python Version

We recommend using the latest version of Python. Jianmu supports Python 3.6 and newer.

### Install Jianmu

To install the [Jianmu package](https://pypi.org/project/Jianmu/), use the following command:

```sh
python -m pip install jianmu -U
```

Jianmu is now installed. After installation, you will have access to the `jianmu` binary in your command line. You can verify that it is properly installed by simply running `jianmu` command or `python -m jianmu`, which should present you with a help message listing all available commands.

You can check you have the right version with this command:

```sh
jianmu --version
```

## Usage

To create a new project, run:

```sh
jianmu create <project-name>
```

To upgrade the template of your project, navigate to the project directory and run:

```sh
jianmu upgrade
```

To run your application in development mode, navigate to your project directory and run:

```sh
jianmu dev
```

To run your application in production mode, navigate to your project directory and run:

```sh
jianmu start
```

To build your application for a software release, navigate to your project directory and run:

```sh
jianmu build
```

To clean runtime temporary files in project directory, navigate to your project directory and run:

```sh
jianmu clean
```

## Screenshots

<p align="center">
<br>
<img width="640" src="https://raw.githubusercontent.com/frederick-wang/jianmu/main/docs/images/ui_screenshot.png" alt="Jianmu UI Screenshot" style="box-shadow: 0px 0px 12px rgba(0, 0, 0, .12);">
<br>
<br>
<img width="640" src="https://raw.githubusercontent.com/frederick-wang/jianmu/main/docs/images/console_screenshot.png" alt="Jianmu Console Screenshot" style="box-shadow: 0px 0px 12px rgba(0, 0, 0, .12);">
<br>
<br>
</p>