semvec/pypi-packages huggingface dataset:
mdu-listeria 0.1.12
MDU implementation of Listeria
Requires Python: >=3.6, <4
Keywords: Listeria bioinformatics microbial genomics publichealth Phylogenetic

# MDU-Listeria
MDU-Listeria Analysis

## Installation:
```
    pip3 install mdu-listeria==0.1.7
```
## Usage:
```
usage: mdu-listeria [-h] [--bohra BOHRA] [--jobid JOBID] [--reportid REPORTID]
                    [--skipfull] [--email EMAIL] [--cpu CPU] [--master]
                    [--noqc]
                    ST [ST ...]

positional arguments:
  ST                    A list of STs would like to Analysis, if only Full
                        analysis need, input with 'full'

optional arguments:
  -h, --help            show this help message and exit
  --bohra BOHRA, -b BOHRA
                        Model of Bohra Pipeline: preview, all, sa
  --jobid JOBID, -j JOBID
                        JOBID
  --reportid REPORTID   Report ID to create publich html folder
  --skipfull            Skip the full Listeria run
  --email EMAIL, -e EMAIL
                        Enable Email Notification while job is done
  --cpu CPU, -c CPU     Number of cpus to use (default as 16)
  --master              make a full Listeria run with isolates within a year
                        period only
  --noqc                disable the qc filter of Bohra and run with all
                        samples
```

## Example:
```
    #Normal Listeria Analysis Run (On ST2, ST5 and ST7):
    mdu-listeria -b sa --reportid Run_0527 --skipfull -e my@email.au 2 5 7

    #Ignore QC requirement of Bohra:
    mdu-listeria -b sa --reportid Run_0527 --skipfull --noqc -e my@email.au 2 5 7

    #Full run on all listeria samples in database:
    mdu-listeria -b sa --reportid Run_0527 -e my@email.au full

    #Master run(Listeria samples in most 1 year window):
    mdu-listeria -b sa --reportid Run_0527 --skipfull --master -e my@email.au full
```

## Contact
josh.zhang@Unimelb.edu.au
poi-clustering 0.0.6
Point-of-Interest clustering from GPS trajectories
Requires Python: >=3.6

# PoI Clustering with Stay Points/Stay Region Detection

Python implementation of PoI (Point-of-Interest) clustering algorithm based on:
* Yang Ye, Yu Zheng, Yukun Chen, Jianhua Feng, and Xing Xie. 2009. Mining Individual Life Pattern Based on Location History. In Proceedings of the 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware (MDM '09). http://dx.doi.org/10.1109/MDM.2009.11
* Raul Montoliu, Jan Blom, and Daniel Gatica-Perez. 2013. Discovering Places of Interest in Everyday Life from Smartphone Data. Multimedia Tools And Applications, 62, 1, 179-207. http://dx.doi.org/10.1007/s11042-011-0982-z
* Vincent W. Zheng, Yu Zheng, Xing Xie, and Qiang Yang. 2010. Collaborative Location and Activity Recommendations with GPS History Data. In Proceedings of the 19th International Conference on World Wide Web (WWW '10). https://doi.org/10.1145/1772690.1772795.


To find PoI, those studies propose a *stay point*, that is a micro cluster of temporal-spatial trajectories, and a *stay region*, that is a macro cluster of stay points. 

## Simple Description of Algorithm

* Find stay points regarding temporal and spatial distance between two trajectories. 
* Build grids that embodying stay points.
* Cluster neighboring grids and give labels. 

## Installation
```cmd
pip install poi-clustering
```

## How to Use
* This implementation follows scikit-learn's grammar; *fit* and *predict*. For more details, please see docstrings in codes. 
* [Example](https://github.com/woohyeok-choi/poi-clustering/blob/master/example.ipynb)
nomics-python 3.2.0
A python wrapper for the Nomics API

# nomics-python
A Python wrapper for the [Nomics Crypto Market Data API](http://docs.nomics.com/).  For some context, Nomics is a [crypto market cap and pricing data provider](https://nomics.com).

## Disclaimer
Although the api call descriptions are from the official documentation, this is an unofficial API wrapper. 

## Getting Started
Before using the Nomics API, sign up for a [free API key here](https://p.nomics.com/cryptocurrency-bitcoin-api).

To install the wrapper, enter the following into the terminal.
```bash
pip install nomics-python
```

Every api call requires this api key. Make sure to use this key when getting started. 
```python
from nomics import Nomics
nomics = Nomics("This-Is-A-Fake-Key-123")

markets = nomics.Markets.get_markets(exchange = 'binance')
```

## Wrapper Wiki
More information on all of the different API methods are available on the Github wiki page [here](https://github.com/TaylorFacen/nomics-python/wiki).

## Contributing
This project is open for contributions! Teamwork makes the dream work.
Cemotion-apple 0.3.3
基于NLP的中文情感倾向分析库

Cemotion是Python下的中文NLP库，可以进行 中文情感倾向分析。

Cemotion的模型经循环神经网络训练得到，会为 中文文本 返回 0～1之间的 情感倾向置信度。您可以批量分析中文文本的情感，并部署至Linux、macOS、Windows等生产环境中，无需关注内部原理。

该模块供Apple Silicon使用，已经过M1测试。请按该文档安装ARM Python、TensorFlow、scikit-learn环境。



### 安装方法

前提:
根据 [https://www.cyberlight.xyz/passage/tensorflow-apple-m1](https://www.cyberlight.xyz/passage/tensorflow-apple-m1) 此文方法安装ARM Python和TensorFlow（TensorFlow需要装到conda虚拟环境中，通读全文后，请使用文章末尾的方法安装TensorFlow）

此时，我们假定您已安装相关环境，并创建了名为py38的conda虚拟环境

1.进入命令窗口，激活conda虚拟环境，安装scikit-learn


```
conda activate py38 #激活虚拟环境 此处虚拟环境名称为py38（您可以自定义名称）
conda install scikit-learn #安装scikit-learn
```


之后输入以下命令安装Cemotion
```
pip install --upgrade pip
pip install cemotion-apple
```



### 使用方法
```
#按文本字符串分析
from cemotion import Cemotion

str_text1 = '配置顶级，不解释，手机需要的各个方面都很完美'
str_text2 = '院线看电影这么多年以来，这是我第一次看电影睡着了。简直是史上最大烂片！没有之一！侮辱智商！大家小心警惕！千万不要上当！再也不要看了！'

c = Cemotion()
print('"', str_text1 , '"\n' , '预测值:{:6f}'.format(c.predict(str_text1) ) , '\n')
print('"', str_text2 , '"\n' , '预测值:{:6f}'.format(c.predict(str_text2) ) , '\n')
```


```
#返回内容(该模块返回了这句话的情感置信度，值在0到1之间):
text mode
" 配置顶级，不解释，手机需要的各个方面都很完美 "
 预测值:0.999931 

text mode
" 院线看电影这么多年以来，这是我第一次看电影睡着了。简直是史上最大烂片！没有之一！侮辱智商！大家小心警惕！千万不要上当！再也不要看了！ "
 预测值:0.000001 
```




```
#使用列表进行批量分析
from cemotion import Cemotion

list_text = ['内饰蛮年轻的，而且看上去质感都蛮好，貌似本田所有车都有点相似，满高档的！',
'总而言之，是一家不会再去的店。']

c = Cemotion()
print(c.predict(list_text))
```


```
#返回内容(该模块返回了列表中每句话的情感置信度，值在0到1之间):
list mode
[['内饰蛮年轻的，而且看上去质感都蛮好，貌似本田所有车都有点相似，满高档的！', 0.999907], ['总而言之，是一家不会再去的店。', 0.049015]]
```