semvec/pypi-packages huggingface dataset:
go2scope 0.0.1
go2scope support functions for python: data I/O
Requires Python: >=3.7

Copyright (c) 2019 The Python Packaging Authority
# **go2Scope** Python Package
[go2Scope](https://go2scope.com) is a microscopy automation programming environment, based on the popular Open Source microscopy software [micro-manager](https://micro-manager.org). **go2Scope** mainly solves issues with controlling multiple microscopes in a highly automated distributed environment.

Python package is intended to provide utilities for interfacing to **go2scope** which is mainly written in C++ and Java.

## **dataio** sub-package ##
Reading and writing of "classic" micromanager datasets. Classic dataset is a folder with sub-folders containing metadata and individual image files. Each image can be accessed with four integer coordinates: position, channel, slice and time-point.

This package does not work with OME compatible "image stack files".
letterpress 0.0.1
batch docx generation
Requires Python: >=3

# Letterpress

Letterpress取意于活字印刷，是帮需要批量生成Word的朋友写的一个小工具。

每一套信息需要生成大量文件，Word自带的邮件生成也帮不了他。

## Installation

```bash
python -m pip install letterpress
```

## Usage

假设你有一个Word文件，起名为template.docx，其中的内容是这样的：

```
标题：{{ topic }}
正文: {{ content }}
```

这些内容可以在正文，可以在表格，甚至可以在页眉页脚或者嵌套的表格中。

当然，也可以在页眉页脚嵌套的表格中。

这些内容也可以是任何格式，任何大小，甚至大括号与变量名都不需要统一格式。

唯一需要注意的是，需要用两个大括号和一个空格包围你的变量名。

然后运行下面的Python代码：

```python
from letterpress import TemplateDocument

vm = {
    'topic': 'my-topic',
    'content': 'my-content',
}
template = TemplateDocument('template.docx', vm.keys())
template.parse_document()
template.replace_and_save(vm, 'outcome.docx')

vm = {
    'topic': 'my-another-topic',
    'content': 'my-another-content',
}
template.replace_and_save(vm, 'another-outcome.docx')
```

就根据给定的内容生成了两个Word文件。

## FAQ

Q: Word文件无法读取，显示检测到不是Word文件。
A: python-docx读取的方式不适用于老版本Word，升级到docx就好了。
easycfg 1.0.2
A simple package for easy work with cfg files
Requires Python: >=3.4

# Let's work with a config files easy!
[Usage](https://github.com/nMagic/easycfg/wiki)

Fast example:
#### **`config.yml`**
```yaml config.yml
servers:
  - host: example1.com
    login: login1
    password: password1
  - host: example2.com
    login: login2
    password: password2
  - host: example3.com
    login: login3
    password: password3
env: prod
```

```python
>>> from easycfg import Config
>>> cfg = Config('config.yml')
>>> cfg.servers
[{'host': 'example1.com', 'login': 'login1', 'password': 'password1'}, {'host': 'example2.com', 'login': 'login2', 'password': 'password2'}, {'host': 'example3.com', 'login': 'login3', 'password':
'password3'}]
>>> cfg.servers[2]
{'host': 'example3.com', 'login': 'login3', 'password': 'password3'}
>>> cfg.servers[:2]
[{'host': 'example1.com', 'login': 'login1', 'password': 'password1'}, {'host': 'example2.com', 'login': 'login2', 'password': 'password2'}]
>>> cfg.servers.login
['login1', 'login2', 'login3']
```

> easycfg supports json and yaml formats now
fuzzy-text-classifier 1.5.0
A fuzzy classifier for natural language text
Requires Python: >=3.6

# FTC - Fuzzy Text Classifier

A fuzzy classifier library for natural language text.
[Github-Repo](https://github.com/raffaellopaletta/ftc)


### Simple usage example

```python
#import library
from fuzzy_text_classifier import TextClassifier  

#instantiate class
tc = TextClassifier()

#train the classifier with some documents
doc1 = 'some text document about sport'
doc2 = 'another text document about cinema'
doc3 = 'another one text document music'

tc.train(doc1, 'sport')
tc.train(doc2, 'cinema')
tc.train(doc3, 'music')

#classify a document
doc = 'a document about sport to classify'
ranking = tc.classify(doc)
```

**ranking** will be an object like 

`[{'sport': 1.0}, {'cinema': 0.6666666666666666}, {'music': 0.3333333333333333}]
`

a list ordered by similarity where the key is the category and the value is the degree of similarity between 
the document and the category.