semvec/pypi-packages huggingface dataset:
doit-graph 0.3.0
doit cmd plugin: create task's dependency-graph image
License: MIT
Keywords: doit graph graphviz

# doit-graph

Generates a graph (using graphviz's dot) of [doit](http://pydoit.org) tasks.

Sample for [doit tutorial](http://pydoit.org/tutorial_1.html) tasks:

![Sample output](/tasks.png)


## install

pip install doit-graph


## usage

```
$ doit graph
$ dot -Tpng tasks.dot -o tasks.png
```

- By default sub-tasks are hidden. Use option `--show-subtasks` to display them.

- By default all tasks are included in graph.
  It is possible to specify which tasks should be included in the graph (note dependencies will be automatically included).

- To draw tasks in execution order (i.e. reverse of dependency direction), use option `--reverse`

```
$ doit graph --reverse
```

- To draw tasks from left-to-right instead of the default top-to-bottom, use option `--horizontal` or `-h`

```
$ doit graph --horizontal
```

### legend

![Legend](/legend.png)

- group-tasks have double bondary border in the node
- `task-dep` arrow have a solid head
- `setup-task` arrow have an empty head



### limitations

`calc_dep` and `delayed-tasks` are not supported.



## DEV notes

http://graphviz.org/doc/info/attrs.html
pycopy-uasyncio.udp 0.1.2
UDP support for Pycopy's uasyncio
License: MIT

This is a module for the standard library of Pycopy project:
https://github.com/pfalcon/pycopy . This module was implemented from
scratch specifically to target Pycopy, with efficient and lean design
in mind. Note that this module is likely work in progress and likely
supports just a subset of CPython’s corresponding module. Please help
with the development if you are interested in this module.
textda 0.1.0.6
this is data augmentation for chinese text
License: MIT
Keywords: classification,expansion,augmentation,addition,data,text,chinese

# This is Data Augmentation for Chinese text for Python3

## Usage
### you have two func for Chinese text Data Augmentation 

### Install textda
pip install:

```bash
pip install textda
```

1. you can expansion data use **data_expansion**
```python
from textda.data_expansion import *
print(data_expansion('生活里的惬意，无需等到春暖花开')) 

```
output:

```python
['生活里面的惬意，无需等到春暖花开', 
'生活里的等到春暖花开',
'生活里无需惬意，的等到春暖花开', 
'生活里的惬意，无需等到春暖花开', 
'生活里的惬意，并不需要等到春暖花开', 
'生活无需的惬意，里等到春暖花开', 
'生活里的惬意，等到无需春暖花开']

```

param explain：

    :param sentence: input sentence text
    :param alpha_sr: Replace synonym control param. bigger means more words are Replace
    :param alpha_ri: Random insert. bigger means more words are Insert
    :param alpha_rs: Random swap. bigger means more words are swap
    :param p_rd: Random delete. bigger means more words are deleted
    :param num_aug: How many times do you repeat each method

- you can use parameters alpha_sr, alpha_ri, alpha_rs, p_rd, num_aug can control ouput.

    if you set alpha_ri and alpha_rs is 0 that means use **linear classifier** for it, and insensitive to word location

    like this:
  ```python

  from textda.data_expansion import *

  print(data_expansion('生活里的惬意，无需等到春暖花开', alpha_ri=0, alpha_rs=0))

  ```
  output:

  ```python
  ['生活里的惬意，无需等到春暖花开', 
      '，无需春暖花开', 
      '生活里面的惬意，无需等到春暖花开', 
      '生活里的惬意，需等到春暖花开']

  ```



2. you can use **translate_batch** like this:

```python
from textda.youdao_translate import *
dir = './data'
translate_batch(os.path.join(dir, 'insurance_train'), batch_num=30)

```

```
# translate results:  chinese->english and english -> chinese

颜色碰掉了一个角不延迟,但事情或他们不赠送,或发送,眉笔打开已经破碎,磨山楂,也不打破一只手,轻轻刷掉,持久性不长,
这个用户没有填写评价内容
颜色非常不喜欢它
不说话,缓慢的新领域
不太容易染好骑吗
不是很好我喜欢!
没有颜色的眼影
应该有大礼物盒眼影,礼物不礼物盒,没有一起破碎粉碎好的眼影不买礼物清洁剂脏就像商品是压力
没有生产日期,我不知道是否真实,总是觉得有点奇怪
是一个小飞粉吗
但是一些混合的颜色
有几次,现在这个东西,笔是空的
眼影有点小,少一点。
不好的颜色,粉红色
明星不想买,坏了,不容易,不要在乎太多!
一开始我已经联系快递,快递一直拖,说他将返回将联系快递服务
画不是,是不好的
物理和照片有很大的区别
不要把眼影刷不是很方便
感觉好干,颜色更暗
打破了在运输途中,有点太脆弱…
盒子有点坏了,还没有发送。

```

param explain：

    :param file_path: src file path
    :param batch_num: default 30
    :param reWrite: default True. means you can rewrite file , False means you can append data after this file.
    :param suffix: new file suffix



## Reference:

https://github.com/jasonwei20/eda_nlp

Code for the ICLR 2019 Workshop paper: Easy data augmentation techniques for boosting performance on text classification tasks. https://arxiv.org/abs/1901.11196


## License

[MIT](./LICENSE)
Sparks 0.1.0
A CLI tool that creates and updates project folders from templates to spark faster project bootstrapping.
License: MIT
Requires Python: >=3.7

# Sparks
<!-- insert badges here -->
Sparks is a minimal command-line tool to manage customizable project folders from templates.

## Features
* Simple command line usage thanks to [Click](https://click.palletsprojects.com).
* Customizable folder, files, and file contents thanks to [Jinja2](http://jinja.pocoo.org).

<!--
## Table of Contents
Optionally, include a table of contents in order to allow other people to quickly navigate especially long or detailed READMEs.
-->
## Installation
Run the following to install:
```bash
pip install sparks
```

## Usage

### Creating a new project
```bash
sparks create --help
Usage: sparks create [OPTIONS]

Options:
  --template TEXT  Template folder to generate from
  --output TEXT    Output folder to create files in
  --skip-prompt
  --help           Show this message and exit
```

<!--
## Contributing
For guidance on setting up a development environment and how to make a contribution to Sparks, see the [contributing guidelines](https://github.com/binaryart/sparks).
-->
<!--
## Credits
Include a section for credits in order to highlight and link to the authors of your project.
-->

## License
Sparks is released under the [MIT License](https://opensource.org/licenses/MIT).
envrun-python 0.0.3
Run command with specified environment-variable file
License: MIT

envrun-python
=============

.. image:: https://img.shields.io/pypi/v/envrun-python.svg
    :target: https://pypi.python.org/pypi/envrun-python
    :alt: Latest PyPI version

.. image:: https://travis-ci.org/ericgj/envrun-python.png
   :target: https://travis-ci.org/ericgj/envrun-python
   :alt: Latest Travis CI build status

Run command with specified environment-variable file

Install
-------

::

  pip install envrun-python


Usage
-----

Run specifying environment variables in YAML format::

  envrun -f env.yaml somecmd arg1 arg2


Or JSON, or .env format::

  envrun -f .env somecmd arg1 arg2


Run within a python virtualenv::

  envrun -e .venv pytest


That's basically it.

Note the .env format parser is taken from
`python-dotenv <https://github.com/theskumar/python-dotenv>`_.


Contact
-------

`Eric Gjertsen <ericgj72@gmail.com>`_.