semvec/pypi-packages huggingface dataset:
terraformlintingcli 0.1.0
A cli to lint and check file positioning for terraform resources
License: MIT
Keywords: terraformlintingcli terraform linting position checking cli

===================
terraformlintingcli
===================

A cli to lint and check file positioning for terraform resources


* Documentation: http://terraformlintingcli.readthedocs.io/en/latest/

Features
--------

* Lints terraform resources based on provided rules
* Enforces filename positioning for terraform resources based on provided rules




History
-------

0.1 (2018-05-24)
---------------------

* First release
carpedm 0.1.7
Character shapes image metadata manager for machine learning.
License: MIT
Requires Python: ~=3.4
Keywords: machine learning framework deep learning image-to-text japanese character

*******************************
CarpeDM: Sieze the Data Manager
*******************************

.. image:: https://pypip.in/version/carpedm/badge.png
    :target: https://pypi.python.org/pypi/carpedm/
    :alt: Latest Version

.. image:: https://travis-ci.org/SimulatedANeal/carpedm.png
    :target: https://travis-ci.org/SimulatedANeal/carpedm
    :alt: Continuous Integration Testing

.. image:: https://pypip.in/license/carpedm/badge.png
    :target: https://pypi.python.org/pypi/carpedm/
    :alt: License

.. image:: https://readthedocs.org/projects/carpedm/badge/?version=latest
    :target: http://carpedm.readthedocs.io/en/latest/?badge=latest
    :alt: Documentation Status

`Docs <http://carpedm.readthedocs.io/en/latest/>`_
| `Install Guide <http://carpedm.readthedocs.io/en/latest/install.html>`_
| `Tutorial <http://carpedm.readthedocs.io/en/latest/guides/usage.html>`_

Description
===========
*CarpeDM* is a general library for downloading, viewing, and manipulating image data.
Originally developed as a ChARacter shaPE Data Manager, CarpeDM aims to make Japanese character shape (字形) data
and other image datasets more accessible to machine learning researchers.

.. csv-table:: Datasets Currently Available for Download
    :header: "ID", "Dataset"
    :widths: 10, 80

    "pmjtc", "| `Pre-Modern Japanese Text Character Shapes Dataset (日本古典籍字形データセット) <http://codh.rois.ac.jp/char-shape/>`_,
    | provided by the Center for Open Data in the Humanities (CODH)."

Though still in the early stages of development, a high-level interface is also provided for

* Automatic model-ready data generation.
* Flexible training of models with a variety of deep learning frameworks.

Currently supported deep learning frameworks:

* `TensorFlow <https://www.tensorflow.org/>`_
lukis 0.18.5
Generate a device independent vector graphic stream that can be converted to pdf, svg, or other vector graphic format.
License: MIT
Requires Python: >=3
Keywords: lukis vector graphic pdf svg

=====
Lukis
=====

Description:
~~~~~~~~~~~~
* Generate device independent vector graphic stream
  that can be converted to pdf, svg, or other vector graphic format.

* The output stream is in pure txt, and csv format.

* A simple stream looks like this::

    MoveTo,50,50
    LineTo,150,150
    Stroke

* "Lukis" is the Malay word for "Draw"

* show(GStream) converts the graphic stream to pdf, svg or other
  graphic file, and open it using the default viewer.

Example:
--------
::

    #To make a line from (0,0) to (300,300) and display the graphics:
    S = moveto(0,0)\\
      + lineto(300,300)\\
      + stroke()
    show(S)


Installation
~~~~~~~~~~~~
Python 3 as default Python::

	pip install lukis

Python as Python3::

	pip3 install kopi
ai-eye 0.0.7
get the eye state from the landmarks of the face in the image
License: MIT

Info
====
`ai_eye.py 2018-05-25`

`Author: Zhao Mingming <471106585@qq.com>`

`Copyright: This module has been placed in the public domain.`

`version:0.0.7`


Functions:

- `has_closed_eye`: eye's open degree


How To Use This Module
======================
.. image:: funny.gif
   :height: 100px
   :width: 100px
   :alt: funny cat picture
   :align: center

1. when u use pip install ldm==0.0.2

.. code:: python

    from ldm import landmarks
    from ai_eye import has_closed_eye
    from skimage import io
    imagepath="closed_eye/10.jfif"
    img=io.imread(imagepath)
    ldl,helptxt=landmarks(img)
    print helptxt
    for ld in ldl:
        print has_closed_eye(ld)




2. when u use  pip install ldm==0.0.4


.. code:: python

    import  ldm
    from ai_eye import has_closed_eye
    from skimage import io

    imagepath="closed_eye/10.jfif"
    ldmer=ldm.LDM()
    img=io.imread(imagepath)
    ldl,facel,helptxt=ldmer.landmarks(img)
    print helptxt
    for ld in ldl:
        print has_closed_eye(ld)
quickslice 1.1
Extract slices from compressed volumetric data
License: MIT

# quickslice

A simple python package to compress large volumetric data and dynamically extract slices from the compressed volume.


## Usage

Build
    
    quickslice build path/to/directory
    quickslice build path/to/directory --size 256 256 256

Slice

    quickslice slice file.cct -z 0
    quickslice slice file.cct -z 0.5
    quickslice slice file.cct -x 0.7
    quickslice slice file.cct -y 1
    



## Files

### quickslice

[quickslice](https://github.com/agu3010/cct/blob/master/quickslice) is the main binary file


### build.py

[build.py](https://github.com/agu3010/cct/blob/master/core/build.py) takes input of a directory containing slices in alphanumeric order and converts it to a CCT (Compressed Computed Tomography) file of size 128 x 128 x 128


### slice.py

[slice.py](https://github.com/agu3010/cct/blob/master/core/slice.py) is used to extract a slice from a cct file. The following command shows the xy plane at z = 0 of [file.cct](https://github.com/agu3010/cct/blob/master/file.cct)


## Additional Stuff

### The z=0 plane output

![-z 0 plane](https://github.com/agu3010/cct/blob/master/output.png?raw=true)


### More examples

More examples and commands with options can be found in [examples](https://github.com/agu3010/cct/tree/master/examples)




# CCT File format

Compressed Computed Tomography Files

CCT files represent GBs of Tomographic Datasets in a single compressed file. The file size depends on the output size. The default size is 128 x 128 x 128 and the size of the output file is between 7 MB to 12 MB

Note: Refrain from using CCT files for in-detail visualisation. CCT files are built using a super-lossy conversion technique and are meant to be used for a small quick preview of planes extracted from 3d volumes.


### Representation:


Line 1:                       sizeX,sizeY,sizeZ

Line 2 to Line (sizeZ + 1):   Z-Slice representation

The following voxels:

    [
        [
            [0,0,1],
            [0,0,1],
            [0,1,1],
            [0,1,1],
            [1,1,1]
        ],
        [
            [0,0,1],
            [0,0,1],
            [0,1,1],
            [0,1,1],
            [1,1,1]
        ],
        [
            [0,0,1],
            [0,0,1],		
            [0,1,1],
            [0,1,1],
            [1,1,1]
        ],
        [
            [0,0,1],
            [0,0,1],
            [0,1,1],
            [0,1,1],
            [1,1,1]
        ] 
    ]


will be represented as


    3,5,4

    0,0,1,0,0,1,0,1,1,0,1,1,1,1,1

    0,0,1,0,0,1,0,1,1,0,1,1,1,1,1

    0,0,1,0,0,1,0,1,1,0,1,1,1,1,1

    0,0,1,0,0,1,0,1,1,0,1,1,1,1,1


### Example
[file.cct](https://github.com/agu3010/cct/blob/master/file.cct) is a 128 x 128 x 128 grayscale tomographic model
parc-reader 0.1.5
Work with the PARC dataset in Python
License: MIT
Keywords: NLP natrual language processing computational linguistics Penn Attribution Relation Corpus PARC PARC3

# corenlp-xml-reader
A utility for easily working with CoreNLP-annotated text in Python.

## About this package
Other than reading the xml file into a python representation, this utility
does some necessary "fixing up" of the data in CoreNLP.
[to be described]

## Quickstart

### Install
Install from the Python Package Index:
```bash
pip install corenlp-xml-reader
```

Alternatively, install a version you can hack on:
```bash
git clone https://github.com/enewe101/corenlp-xml-reader.git
cd corenlp-xml-reader
python setup.py develop
```

### Use

[Add basic usage examples.]