semvec/pypi-packages huggingface dataset:
dside 2.3.3
Design space identification tool for plotting and analysing design spaces (2D and 3D).
License: MIT
Requires Python: >=3.9

# dside

[![Documentation Status](https://readthedocs.org/projects/dside/badge/?version=latest)](https://dside.readthedocs.io/en/latest/?badge=latest)
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Design space identification tool for plotting and analysing design spaces (2D and 3D). Constraints with respect to key performance indicators (KPIs) are used to categorize the samples. Concave hulls (alpha shape) are used to identify design space (DSp) and quantify the size of the space. Given nominal operating point (NOP), an acceptable operating region (AOR) can be quantified to find the maximum multivariate allowable disturbance that the process can still handle while satisfying all constraints (multivariate proven acceptable range - MPAR).


## Installation
Currently, dside requires pandas, numpy, and matplotlib. dside can be installed with the following commands.
```bash
pip install dside
```

## Quick Overview
Use this tool to visualize 2D and 3D design spaces, obtain mathematical representations of the design space boundary in the form of alpha shapes, calculate the size of the design space, and investigate nominal operating points in terms of performance and flexibility (acceptable ranges). Identification of multi-region spaces is possible.

```
import dside
# 1. Create instance of ds with data from DataFrame df
ds = dside.DSI(df)
# 2. Screen the points using the constraints (dictionary)
ds.screen(constraints)
# 3. Find DSp boundaries based on vnames (list of variable names)
ds.find_DSp(vnames)
# 4. Plot the design space and the samples
ds.plot(vnames)
# 5. Plot the nominal point and AOR based on point x (list/numpy array)
ds.find_AOR(x)
# 6. Save the results in detailed output.txt file
ds.send_output('output')
```

![image](https://raw.githubusercontent.com/stvsach/dside/main/Fig/2D.svg)
![image](https://raw.githubusercontent.com/stvsach/dside/main/Fig/3D.svg)

## Citation

Please cite the following <a href="https://doi.org/10.1016/j.cherd.2023.08.016" target="_blank">publication</a> if you are using dside for your own research.

```text
@article{sachio2023dsp,
    title={A model-based approach towards accelerated process development: A case study on chromatography},
    author = {Steven Sachio and Cleo Kontoravdi and Maria M. Papathanasiou},
    journal = {Chemical Engineering Research and Design},
    volume = {197},
    pages = {800-820},
    year = {2023},
    issn = {0263-8762},
    doi = {https://doi.org/10.1016/j.cherd.2023.08.016},
    url = {https://www.sciencedirect.com/science/article/pii/S0263876223005166},
}
```
GeocodingX 1.1.1
地址标准化工具
License: MIT
Requires Python: >=3.6

# geocoding

#### 介绍
* 本项目旨在将不规范(或者连续)的文本地址进行尽可能的**标准化**, 以及对两个地址进行**相似度的计算**。
* 该模块为 [bitlap/geocoding](https://github.com/bitlap/geocoding) 项目的Python实现，原项目为Kotlin开发
* 安装命令 pip install GeocodingX

地理编码技术, 主要分为如下步骤
 * 地址标准库
 * 地址标准化
 * 相似度计算


# 1. 数据测试

方法调用: `Geocoding` 类
 * normalizing: 标准化
 * analyze: 解析成分词文档
 * similarity: 相似度计算
 * similarityWithResult: 相似度计算, 返回包含更多丰富的数据
 
## 1.1 标准化

```markdown
>> 输入: 上海上海宝山区宝山区【新沪路58弄11-802  水韵华庭 】 (水韵华庭附近)号大都会3号楼2单元1303
>> 输出:
Address(
	provinceId=310000000000, province=上海, 
	cityId=310100000000, city=上海市, 
	districtId=310113000000, district=宝山区, 
	streetId=None, street=None, 
	townId=None, town=None, 
	villageId=None, village=None, 
	road=新沪路, 
	road_num=58弄, 
	building_num=11-802, 
	text=水韵华庭水韵华庭附近
)
```
* 返回的对象解释
    * province相关: 省
    * city相关: 市
    * district相关: 区、县
    * street相关: 街道
    * town相关: 乡镇
    * village相关: 村
    * road: 道路
    * roadNum: 路号
    * buildingNum: 建筑物号
    * text: 标准化后为匹配的地址。一般包含小区, 商场名称等信息

> 注: 如果对text的结果不是很满意, 比如出现重复或不准确, 可以通过分词的手段解决

```java
>> 输入:
  浙江金华义乌市南陈小区8幢2号
  浙江金华义乌市稠城街道浙江省义乌市宾王路99号后面南陈小区8栋2号
>> 输出: 
  0.6795216660765248
```

```java
>> 输入:
  山东省沂水县四十里堡镇东艾家庄村206号
  浙江金华义乌市南陈小区8幢2号
>> 输出:
  0.0
 ```

## 感谢
* 感谢[原作者](https://github.com/bitlap/geocoding)的辛苦付出！