semvec/pypi-packages huggingface dataset:
directrix-pkg 0.0.2
A small example package

# Directrix Package

This is a simple example package. You can use
[Github-flavored Markdown](https://guides.github.com/features/mastering-markdown/)
to write your content.

This requires setuptools, wheel, and twine.

To create source and binary distributions in the dist/ folder execute:

```
python setup.py sdist bdist_wheel
```

To upload to PyPI test server execute:

```
twine upload --repository-url https://test.pypi.org/legacy/ dist/*
```

To install from PyPI test server execute:

```
pip install --index-url https://test.pypi.org/simple/ directrix_pkg
```

To upload to PyPI production server execute:

```
twine upload dist/*
```

To install from PyPI production server execute:

```
pip install directrix_pkg
```
gscapl 0.0.0.5
A package for geospatial context analysis
Requires Python: >=3.6

[![Build Status](https://travis-ci.com/lukeWaninger/GSCAP.svg?token=jrdsuo8zKWc9VxHgCViY&branch=master)](https://travis-ci.com/lukeWaninger/GSCAP)

# Geospatial Context Analysis Pipeline (GSCAP)
stopwords-guilannlp 13.2019.3.5
A comprehensive package for stopwords in NLP and text mining

## Stopwords Guilan NLP

This is  a comprehensive stopwords for natural language processing and text mining.

## Installation

[Now stopwords_guilannlp is on PyPI!](https://pypi.org/project/stopwords-guilannlp)
<br>
   * Download via PyPI: 
   ```
   $ pip3 install stopwords_guilannlp
   ```
   * Or clone the repo: 
   ```
   $ git clone --recursive git://github.com/JoyeBright/Sstopwords_guilannlp.git
   ```
   * Then install it: 
   ```
   $ python3 setup.py install
   ```

## Usage
 ```
 tokens = word_tokenize(s)
          filtered_tokens = []
          stopwords = stopwords_output("Persian", "nar")
          for w in tokens:
              if w not in stopwords:
                  filtered_tokens.append(w)
          return filtered_tokens
 ```
 * Note: Package does not support tokenizing process.
## Supported Languages
   * English
   * Persian

## Type of Outputs
   * Dataframe
   * Numpy Array
   * Set
   * List
deepdefacer 2.1.2
Automatic Removal of Facial Features from MRI Images

## DeepDefacer: Automatic Removal of Facial Features via Deep Learning.
 
 <div align="center">
<img style="float:center;margin:0px" src="images/diffsizeundefaced.png"> --------> <img style="float: center;" src="images/exampledefacedresized.png"> 
</div>

DeepDefacer is a MRI anonymization tool written in Python, on top of Tensorflow and Keras, that was developed in partnership with the Poldrack Lab at Stanford University. It can be used to quickly deface 3D MRI images of any resolution and size on commercial CPUs and GPUs. Its goal is to provide the community with an easy to use and efficient tool for defacing medical images that require anonymization for compliance with federal privacy laws (e.g HIPAA). 

### Referencing and citing DeepDefacer
If you use DeepDefacer in your work, please refer to this citation for the current version:

```
@article{khazane2019state,
  title={DeepDefacer: Automatic Removal of Facial Features from MR Scans Via Deep Learning},
  author={Anish Khazane, Julien Hoachuck, Dr. Chris Gorgowelski, Dr. Russell Poldrack},
  journal={in proceedings, arXiv preprint},
  year={2019}
}
```
If you use any of the architecture code from the [ARFF-CNN](https://github.com/AKhazane/ARFF-CNN.git), please also use the citation above to comply with its authors' instructions on referencing.


### Requirements 

* Any Python version between 2.7 and 3.6.
* If you are using the GPU version of this library, please ensure that your GPU drivers are correctly installed and up to date. Please reference [GPU Support for Tensorflow-GPU](https://www.tensorflow.org/install/gpu) for further details on GPU setup. 
* Input MRI images must have 3D structure and be saved in either .nii or .nii.gz format.

### Installation

Deepdefacer can be easily installed on any operating system via Pypi. There are two versions of this package; CPU or GPU support. Please enter **one** of the following commands into your terminal window to begin installation, depending on your system specifications and desired python version. 

CPU Support
```
pip install deepdefacer[tf_cpu] / pip3 install deepdefacer[tf_cpu]
```

GPU Support
```
pip install deepdefacer[tf_gpu] / pip3 install deepdefacer[tf_gpu]
```

**Note: If you are using a ZSH-type shell, you may need to wrap the package name in quotations in order to successfully initiate the Pip installation. (e.g ```pip install "deepdefacer[...]"```).**

### Usage and Documentation

Once installed, please enter `deepdefacer --help` into your terminal window to see a list of available options for this program. Defacing a 3D MRI image is extremely simple, and can be done with the following command:

 ```deepdefacer --input_file <input filename> ```
 
 The program will output a defaced image in the same directory as the input file, unless `--defaced_output_path` is specified. 
 
 You can also save the predicted mask for any MRI image by specifying the desired output filename and directory with the `--mask_output_path` option.