semvec/pypi-packages huggingface dataset:
pySNPCall 0.1
Python implementation of Java SNP calling pipeline (https://github.com/DSGlab/SNPCallingPipeline/)
License: MIT

ABOUT
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Python implementation of Java SNP calling pipeline (https://github.com/DSGlab/SNPCallingPipeline).
Usage is descriped below. For consistency the configuration file is exactly as described in the Java code.
Descriptions of the various options in the Java code is given at https://github.com/DSGlab/SNPCallingPipeline.
An example configuration file is included in this directory.
                                       USAGE
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
usage: pySNPCallingPipeline.py [-h] -c CONF_FILE [--aln_only] [--no_aln]
                               [--local] [--submit] [--subset SUBSET]
                               [--def_run]

Run pySNPCallingPipeline

optional arguments:
  -h, --help            show this help message and exit
  -c CONF_FILE, --conf CONF_FILE
                        A configuration file is required to run
                        pySNPCallingPipeline.
  --aln_only            Alignment only, default is FALSE.
  --no_aln              Run all analysis using pre-run alignments, default is
                        FALSE.
  --local               Is this a LOCAL run or should SLURM files be created?
                        Default is True.
  --submit              If running a supercomputing cluster, should only slurm
                        files be created or should the jobs be submitted as
                        well.
  --subset SUBSET       Provide a comma seperated list of a subset of
                        "alignment, getHQSNPs, intraClonalSNPs, checkSNPs,
                        filterSNPs"
  --def_run             Default: run entire calculation locally.
markr 0.0.1
Package and script allowing marks to be associated with files
License: MIT
Requires Python: >=3.4.0


# Markr

> A library and script for associating marks/labels/key-values with a given
> file. Markr lets you perform operations on the marks for files.

Associating metadata with files/resources can be used to organize, search and
perform other operations on files. Markr provides a library and script to do
this using external attributes.

## Getting Started

Install the python3 package

```shell
pip3 install markr
```

Test setting and getting `marks`.

```shell
$ touch me.txt
$ markr set me.txt foo bar
$ markr set me.txt alice bob
$ markr get me.txt
foo : bar
alice : bob
```

Creating a mark directory

```shell
$ mkdir -p test/dir
$ touch test/dir/me.txt
$ markr set me.txt foo bar
$ markr set me.txt alice bob
$ markr get me.txt
alice : bob
foo : bar
$ markr dir test
$ tree marks
marks/
├── alice
│   └── me.txt -> ../../test/dir/me.txt
└── foo
    └── me.txt -> ../../test/dir/me.txt

2 directories, 2 files
```

## Advanced Features

The `dir` command scans all files underneath a given directory for marks, and
creates a new directory structure where each subdirectory is a given label. This
allows you to view what files have the same marks


## Licensing

MIT License.
gym-embedding-world 0.0.4.0
Two word embedding mapping compatible with OpenAI gym.
License: MIT
Requires Python: >=3.5,<3.7
Keywords: embedding_world

OpenAI gym Embedding world
==========================

<div align="center">
  <img width="40%" src="https://raw.githubusercontent.com/SamirMoustafa/gym-embedding-world/master/assets/9-dimensional-hypercube.gif"><br><br>
  <h6>An eight-dimensional hypercube graph.</h6>
</div>

[![Build Status](https://travis-ci.org/SamirMoustafa/gym-embedding-world.svg?branch=master)](https://travis-ci.org/SamirMoustafa/gym-embedding-world)

Two word embedding mapping compatible with [OpenAI gym](https://github.com/openai/gym>).

Requirements:
- Python 3.5+
- OpenAI Gym
- NumPy
- Gensim

Install environment on anaconda
-------------------------------

    $ conda env create -f gym-embedding-world/environment.yml
    $ source embedding-world
    $ pip install -e gym-embedding-world/.

Install environment on colab
----------------------------

    !git clone "https://github.com/SamirMoustafa/gym-embedding-world.git"
    !pip install -e gym-embedding-world/.
    !mv gym-embedding-world gym-embedding-world-org
    !cp -r gym-embedding-world-org/embedding_world /content
    !ls embedding_world
    
Usage
-----

    $ python >>> import gym
    $ python >>> import embedding_world
    $ python >>> env = gym.make('embedding_world-v0')
    $ python >>> env.set_paths(embedding_from_file="... YOUR EMBEDDING PATH TO MAP FROM IT  ...",
                               embedding_to_file  ="... YOUR EMBEDDING PATH TO MAP TO IT  .....")
    $ python >>> env.production_is_off()
    $ python >>> env.set_sentences('... YOUR SENTENCE TO TRANSLATE FROM IT ...', 
                                   '... YOUR SENTENCE TO TRANSLATE TO IT .....')
    $ python >>> state, reward, done, info = env.step('dim(0)+1')

``embedding_world-v0``
----------------------

Embedding world is a simple environment based on OpenAI gym, that loads two-word embedding e.g. [Stanfrod GloVe](https://nlp.stanford.edu/projects/glove/) or [facebook fastText models](https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md)  with N-dimension and moves from one word(s) embedding-location to the other embedding using an agent actions such that actions that could be taken are `2N + 1` actions `{dimension(i)+1, dimension(i)-1}` ∪ ` {pickup}` ∀ `i` in range from 1 to N

which deterministically cause the corresponding state transitions
but actions that would take an agent of the grid leave a state unchanged.
The reward is negative for all transition until the goal is reached.
The terminal state(goal) is represented in a vector/s.

This environment has been built as part of a graduation project at [University of Alexandria, Department of Computer Science](http://sci.alexu.edu.eg/index.php/en/)

Please use this bibtex if you want to cite this repository in your publications:

```
@misc{embedding_world,
    author = {Samir Moustafa},
    title = {Embedding Environment for OpenAI Gym},
    year = {2019},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/SamirMoustafa/gym-embedding-world}}
}
```