semvec/pypi-packages huggingface dataset:
compass-school 1.1.0
This Python implementation tries to model school choice and resulting school segregation based on the work of Schelling (1971) and Stoica & Flache (2014).
Requires Python: >=3.10

# Computational Modelling of Primary School Segregation (COMPASS)

[![DOI](https://zenodo.org/badge/483226861.svg)](https://zenodo.org/badge/latestdoi/483226861)

This Python implementation tries to model school choice and resulting school segregation based on the work of Schelling (1971) and Stoica & Flache (2014).

## Usage

### Install and Run
It's required to firstly install [GDAL](https://gdal.org/index.html) on your computer. 

To install the package, run `pip install compass-school`. 

To run a demo with web-based GUI, run `python run.py` from the root directory of the project. This will start a local server to automatically open your web browser and show the program as a webpage. If not seeing the webpage open, manually input `http://localhost:5004/` in the link bar. 

### Update Documentation
Install pdoc3 if you haven't already done so. Browse to the compassproject folder in your terminal and run `pdoc3 --html --force --output-dir docs compass`. The documentation should be updated now.

### Overview
The repository consists of:
* **run.py:** a script that runs the model interactively with a visualisation (browser)
* **testrun.py:** a test script (work in progress)
* **household.py:** the household class
* **student.py:** the student class
* **neighbourhood.py:** the neighbourhood class
* **school.py** the school class
* **allocator.py:** allocates the students to their school of choice
* **agents_base.py:** overarching agent used for inheritance
* **model.py:** initialises the entire system and all of its components
* **parameters.py:** contains all the parameter values for the simulation
* **scheduler.py:** takes care of the activation, sequence and placement of all agents
* **visualisation.py:** browser based visualisation
* **utils.py:** containing all measurements
* **functions.py:** containing some math functions to be used by the classes

### Simulations
Information on how to run the code here.

### Testing and development

Setup a virtualenv with the required dependencies.
```bash
$ python -m venv env
$ . env/bin/activate
$ pip install -r requirements.txt
```

Install the package locally (in developement, or editing mode):
```bash
$ pip install -e .
```

Then run the tests with:
```bash
$ pytest
```

### Profiling

Some profiling result can be found in this [notebook](https://github.com/ODISSEI-School-Choice/school-choice/blob/jisk-v2/profile.ipynb). Also, some scaling graphs can be found in this [notebook](https://github.com/ODISSEI-School-Choice/school-choice/blob/jisk-v2/scaling_graph.ipynb).
pulsar-request-response 0.1.5
Common python package
Requires Python: >=3.6

# pulsar-request-response

#### 介绍
这个项目是让pulsar支持请求响应模式，就是发送一条消息后能接收到响应，支持异步 

#### 软件架构
软件架构说明


#### 安装教程

1.  xxxx
2.  xxxx
3.  xxxx

#### 使用说明

1.  xxxx
2.  xxxx
3.  xxxx

#### 参与贡献

1.  Fork 本仓库
2.  新建 Feat_xxx 分支
3.  提交代码
4.  新建 Pull Request


#### 特技

1.  使用 Readme\_XXX.md 来支持不同的语言，例如 Readme\_en.md, Readme\_zh.md
2.  Gitee 官方博客 [blog.gitee.com](https://blog.gitee.com)
3.  你可以 [https://gitee.com/explore](https://gitee.com/explore) 这个地址来了解 Gitee 上的优秀开源项目
4.  [GVP](https://gitee.com/gvp) 全称是 Gitee 最有价值开源项目，是综合评定出的优秀开源项目
5.  Gitee 官方提供的使用手册 [https://gitee.com/help](https://gitee.com/help)
6.  Gitee 封面人物是一档用来展示 Gitee 会员风采的栏目 [https://gitee.com/gitee-stars/](https://gitee.com/gitee-stars/)
exoclasma-pipe 0.9.1
Exo-C Data Quality Check, Mapping, and Variant Calling, part of ExoClasma Suite
Requires Python: >=3.8
Keywords: adapters,bioinformatics,calling,mapping,variant

# exoclasma-pipe

## Description

exoclasma-pipe is a tool for reads quality control, adapters removing, mapping, and variant calling, a part of upcoming ExoClasma Suite.

Features:

- Pool FastQ quality control via FastQC
- Mapping pipeline: cutadapt + BWA + GATK MarkDuplicates
- Variant calling pipeline: GATK BQSR + GATK HaplotypeCaller

**This is a pre-release. Use it at your own risk!**

## Installation

```bash
python3 -m pip install exoclasma-pipe
```

## Command-line dependencies

- [SAMtools](https://github.com/samtools/samtools)
- [BWA](https://github.com/lh3/bwa)
- [BEDtools](https://github.com/arq5x/bedtools2)
- [FastQC](https://github.com/s-andrews/FastQC)
- [cutadapt](https://github.com/marcelm/cutadapt)
- [GATK](https://github.com/broadinstitute/gatk)

First five are available at Ubuntu repos:

```bash
apt install samtools samtools bwa bedtools fastqc cutadapt
```

GATK should be installed into Miniconda environment [as described by the developer](https://github.com/broadinstitute/gatk#requirements).

## Usage

### FastQC

```bash
exoclasma-pipe FastQC -f ${file_1} ${mask_1} ${file_2} -d ${output_folder}
```

### Align

```bash
exoclasma-pipe Align -u ${unit_json_file}
```

Unit JSON describes a job for pipeline.
It must have the following format:

```json
{
	"ID": "Scc3-WXS",
	"Description": "Extensive sampling of Saccharomyces cerevisiae in Taiwan reveals ecology and evolution of predomesticated lineages (SRX11913458)",
	"ParentDir": "testoutput",
	"Input": [
		{
			"Files": {
				"R1": "testdata/test_scc3_R1.fastq.gz",
				"R2": "testdata/test_scc3_R2.fastq.gz",
				"Unpaired": null
			},
			"RG": {
				"Sample": "Scc3",
				"Library": "LY0200-I",
				"Platform": "ILLUMINA",
				"Instrument": "IlluminaHiSeq2500",
				"Lane": "1",
				"Barcode": "ACGTAT"
			},
			"Adapter": "illumina"
		}
	],
	"Reference": {
		"GenomeInfo": "testdata/testReference/testReference.info.json",
		"Capture": "testCapture"
	},
	"Config": {
		"Threads": 4,
		"RemoveTempFiles": true
	}
}
```

Reference with GenomeInfo file and Captures can be prepared with [exoclasma-index](https://github.com/genomech/exoclasma-index).

### Call

```bash
exoclasma-pipe Call -u ${unit_json_file} -d ${dbSNP_vcf_gz}
```

Here you will need a Unit JSON file which was created with `exoclasma-pipe Align`.
dbSNP will be used for BQSR stage.