semvec/pypi-packages huggingface dataset:
scgen 2.1.0
ScGen - Predicting single cell perturbations.
License: MIT
Requires Python: >=3.7,<4.0

# scGen [![PyPI version](https://badge.fury.io/py/scgen.svg)](https://badge.fury.io/py/scgen) [![Build Status](https://travis-ci.com/theislab/scGen.svg?branch=master)](https://travis-ci.com/theislab/scGen) [![Documentation Status](https://readthedocs.org/projects/scgen/badge/?version=latest)](https://scgen.readthedocs.io/en/latest/?badge=latest) [![Downloads](https://pepy.tech/badge/scgen)](https://pepy.tech/project/scgen)




<img align="center" src="./sketch/sketch.png?raw=true">

## Introduction
scGen is a generative model to predict single-cell perturbation response across cell types, studies and species
  [(Nature Methods, 2019)](https://www.nature.com/articles/s41592-019-0494-8). scGen is implemented using the [scvi-tools framework](https://scvi-tools.org/).

## Getting Started
What you can do with scGen:

* Train on a dataset with multiple cell types and conditions and predict the perturbation effect on the cell type
which you only have in one condition. This scenario can be extended to multiple species where you want to predict
the effect of a specific species using another or all the species.

* Train on a dataset where you have two conditions (e.g. control and perturbed) and predict on second dataset
with similar genes.

* Remove batch effect on labeled data. In this scenario you need to provide cell_type and batch labels to
the method. Note that `batch_removal` does not require all cell types to be present in all datasets (batches). If
you have dataset specific cell type it will preserved as before.

* We assume there exist two conditions in you dataset (e.g. control and perturbed). You can train the model and with
your data and predict the perturbation for the cell type/species of interest.

* We recommend to use normalized data for the training. A simple example for normalization pipeline using scanpy:

``` python
import scanpy as sc
adata = sc.read(data)
sc.pp.normalize_total(adata)
sc.pp.log1p(adata)
```
* We further recommend to use highly variable genes (HVG). For the most examples in the paper we used top ~7000
HVG. However, this is optional and highly depend on your application and computational power.




## Installation

### Installation with pip
To install the latest version scGen via pip:
```bash
pip install scgen
```

or install the development version via pip:
```bash
pip install git+https://github.com/theislab/scgen.git
```

## Examples

See examples at our [documentation site](https://scgen.readthedocs.io/).

## Reproducing paper results
In order to reproduce paper results visit [here](https://github.com/M0hammadL/scGen_reproducibility).

## References

Lotfollahi, Mohammad and Wolf, F. Alexander and Theis, Fabian J.
**"scGen predicts single-cell perturbation responses."**
Nature Methods, 2019. [pdf](https://rdcu.be/bMlbD)
zhihu-crawler 0.0.2
知乎关键词搜索、热榜、用户信息、回答、专栏文章、评论等信息的抓取程序
License: MIT
Keywords: zhihu zhihu_crawler zhihu_scraper crawler scraper

zhihu_crawler
=============

本程序支持关键词搜索、热榜、用户信息、回答、专栏文章、评论等信息的抓取

项目目录
-------
- __init__.py 为程序的对外统一入口
- constants.py 常量
- exceptions.py 自定义异常
- extractors.py 数据清洗
- page_iterators.py 简单的页面处理
- zhihu_scraper.py 页面请求、cookie设置
- zhihu_types.py 类型提示、检查。项目自定义类型
- 注意事项: 项目内有部分异步操作，在模块引用之前需要使用猴子补丁; 同时该项目没有对ip限制、登录做针对性处理


安装
----
.. code:: bash
    pip install zhihu_crawler


使用
----
.. code:: python

    if __name__ == '__main__':

        # 设置代理; 如采集量较大，建议每次请求都切换代理
        set_proxy({'http': 'http://127.0.0.1:8125', 'https': 'http://127.0.0.1:8125'})

        # 设置cookie
        set_cookie({'d_c0': 'AIBfvRMxmhSPTk1AffR--QLwm-gDM5V5scE=|1646725014'})

        # 搜索采集使用案例:
        for info in search_crawl(key_word='天空', count=10):
            print(info)

        # 可传入data_type 指定搜索类型
        for info in search_crawl(key_word='天空', count=10, data_type='answer'):
            print(info)

        # 用户信息回答列表使用案例(采集该用户信息及50条回答信息,每条回答包含50条评论):
        for info in user_crawler('wo-men-de-tai-kong',
                                 answer_count=50,
                                 comment_count=50
                                 ):
            print(info)

        # 用户信息提问列表使用案例(采集该用户信息及10条问题信息,每条问题包含10条回答，每条回答包含50条评论):
        for info in user_crawler('wo-men-de-tai-kong',
                                 question_count=10,
                                 drill_down_count=10,
                                 comment_count=50):
            print(info)

        # 热点问题采集使用案例
        # 采集 前10个问题, 每个问题采集10条回答
        for info in hot_questions_crawl(question_count=10, drill_down_count=10):
            print(info)

        # 可传入period 指定热榜性质。如小时榜、日榜、周榜、月榜
        # 传入domains 采集指定主题的问题
        for info in hot_questions_crawl(question_count=10, period='day', domains=['1001', 1003]):
            print(info)
shape 0.1.1
2D shapes abstraction
License: MIT
Requires Python: >=3.6

[![Build Status](https://travis-ci.com/smidm/shape.svg?branch=main)](https://travis-ci.com/smidm/shape)

`shape` is a Python package for 2D shapes handling.

# Features

- objects: 
  - abstract shape
  - point
  - polygon
  - bounding box
  - rotated bounding box
  - ellipse
- relations: intersection, closeness
- manipulation: move and rotate
- visualization using matplotlib

# Installation

`pip install shape`

# Documentation

See tests for now.

# Alternatives

https://github.com/varunagrawal/bbox
- bounding box only
- lightweight

https://github.com/shapely/shapely
- complex
- based on C++ GEOS library
nbabattle 0.0.7
A lightweight python library to generate two nba team battle image.
License: MIT
Keywords: pypi,nbabattle,nba_battle

![](src/nbabattle/demo/demo.png)
# Generate NBA battle Picture Generator
A lightweight python library to generate two nba team battle image.


## Installation
```shell
$ pip install nbabattle
```

## Usage
```python
from nbabattle import NbaBattle

Photo = NbaBattle('GSW', 'DET')
battle = Photo.create_battle_image()
# Show an image
battle.show()
# Save to the folder
battle.save("generate/gsw-det.png")
```

### Notice
Please use the team's abbreviation as the input.