semvec/pypi-packages huggingface dataset:
djangocms-filer-video 0.1.1
Video extension for Django filer.
Keywords: django,djangocmsfiler,video

djangocms-filer-video
==================

Video extension for Django filer.

.. image:: https://travis-ci.org/Air-Mark/djangocms-filer-video.svg?branch=master
    :target: https://travis-ci.org/Air-Mark/djangocms-filer-video
.. image:: https://coveralls.io/repos/Air-Mark/djangocms-filer-video/badge.svg?branch=master&service=github
    :target: https://coveralls.io/github/Air-Mark/djangocms-filer-video?branch=master


Quick start
************

#. Install ``djangocms-filer-video``:

   ::

      pip install djangocms-filer-video


   or from sources

   ::

      pip install git+https://github.com/Air-Mark/djangocms-filer-video.git


#. Add ``djangocms_filer_video`` to ``INSTALLED_APPS`` in your Django settings.

#. Add video field to filer settings into settings file. Note: the order is important.

   ::

      FILER_FILE_MODELS = (
            'filer.Image',
            'djangocms_filer_video.VideoFile',
            'filer.File',
      )


#. Usage of template tags:

   ::

        {% load filer_video_tags %}
        {% video_thumbnail video '1280x0' 'mp4' as video_large_mp4 %}
        {% video_thumbnail video '640x0' 'mp4' as video_small_mp4 %}
        <div class="product__video-in">
            <video style="width: 50%" loop="loop" data-width="{{ video_small_mp4.meta_width }}" data-height="{{ video_small_mp4.meta_height }}" muted="muted" poster="{{ poster }}">
                <source src="{{ video_small_mp4.file.url }}" data-large="{{ video_large_mp4.file.url }}" type="video/{{ video_small_mp4.video_format }}">
            </video>
        </div>

#. Usage of model fields

   ::

      from django.db import models
      from djangocms_filer_video.fields.video import FilerVideoFileField


      class ExampleModel(models.Model):
         main_video = FilerVideoFileField(verbose_name=_('Main video'), blank=True, null=True,
                                          on_delete=models.SET_NULL,
                                          related_name='post_properties_video')


Release 0.1 (Januar 17, 2019)
--------------------------

- Initial release
beewars 0.1.8
BeeWars (Game)
Requires Python: >=3.2

# BeeWars 

## A simple to learn, hard to master game.

[Github(Disabled)](https://github.com/poyynt/beewars/)  
[PyPI](https://pypi.org/project/beewars)

### To execute:  
Run `beewars` in a shell.  

*Game tutorial will be added soon.*
cvesearch 1.2
Search references, exploit modules and pocs for any CVE ID
Requires Python: >=3
Keywords: cve cveid cvesearch cvelinks cvepoc cveurl vulnerability exploits references poc search

**CVESearch** is a tool to simplify searching for exploits, references and proof-of-concept codes fairly simple from sources like Metasploit, Exploit-DB etc. All you need is a Linux/Windows box with ***Python3*** installed. Internally, this tool works as a thin wrapper around: https://cve.circl.lu/ without which, this project might have not been possible!


## Requirements

* **Python 3.x**
    * requests
    * termcolor
    * ujson (optional, but good to have!)


## Install

```
* pip3 install cvesearch
```


## Usage

```
* cvesearch -h           # Show help and exit
* cvesearch -s 2017-0145 # Show all found matches
```


## Feedback

Should you have any feedback or suggestion, kindly drop an email to the author (Sujit Ghosal, **thesujit** **[@]** **gmail** **[.]** **com**) mentionining your inputs.



## Disclaimer

Please, use this tool at your own risk. The author shall not be responsible for any misuse done through usage of this tool. Any actions and or activities related to this tool is solely your responsibility. The misuse of this tool might result in criminal charges brought against the persons in question. Please pay respect to the third-party website owners and bandwidth, who makes these type of structured information available easily to the public.
CrowdGO 1.0
Gene ontology prediction merging and improvement, using semantic ontology and a random forest algorithm

pip3 install sklearn
pip3 install pandas
pip3 install numpy

python3 CrowdGO.py -i example_input/input.tab -r example_output_training/randomForest.pkl -o example_output

python3 CrowdGO_training.py -i example_input/training.tab -o example_output_training