semvec/pypi-packages huggingface dataset:
ncdu-dropbox 0.1
Generates ncdu compatible json file for Dropbox
License: MIT

ncdu-dropbox
============

Generates `ncdu <http://dev.yorhel.nl/ncdu>`__ compatible JSON file for
Dropbox.

You can use this script (with ncdu) to visualize which files and folders
are taking up all the space in your dropbox.

Usage
=====

First, generate an oauth token from Dropbox's `app
console <http://dropbox.com/developers/apps>`__.

.. code:: bash

    $ ncdu-dropbox --token $TOKEN -o dropbox.json
    $ ncdu -f dropbox.json

Caution: Due to limitations in Dropbox API, it takes about 10s to fetch
the information of ~5,000 files and folders. So, if you have millions of
files in your Dropbox, this script may be running for hours.

Related projects
================

See `ncdu-s3 <https://github.com/EverythingMe/ncdu-s3>`__.
websauna.magiclogin 1.0a2
Webasauna magic login addon
License: MIT
Keywords: web websauna pyramid

This is a Python package for magiclogin, an addon for `Websauna framework <https://websauna.org>`_.

To run this package you need Python 3.5.2+, PostgresSQL and Redis.

Features
========

* Passwordless login with OAuth and email link options.

* Throttle protection against brute forcing and spam

* Interstitial page to require user to login and then continue what ever HTTP GET/POST action user was performing

Screenshots

.. image:: https://github.com/websauna/websauna.magiclogin/raw/master/screenshots/login.png
    :width: 400px

.. image:: https://github.com/websauna/websauna.magiclogin/raw/master/screenshots/email.png
    :width: 400px

Installation
============

Setup OAuth credential in ``development.ini`` and ``development-secrets.ini`` according to Websauna documentation.

Example ``development.ini``::

    websauna.social_logins =
        facebook
        google

Example ``development-secrets.ini`` bits::

    [facebook]
    class = authomatic.providers.oauth2.Facebook
    consumer_key = xxx
    consumer_secret = yyy
    scope = user_about_me, email
    mapper = websauna.system.user.social.FacebookMapper

    [google]
    class = websauna.system.user.googleoauth.Google
    consumer_key = xxx
    consumer_secret = yyy
    mapper = websauna.system.user.social.GoogleMapper
    scope = profile email

Copy bits from ``demo.py`` to your application initializer.

Settings
========

Available INI settings::

    # Throttle email login endpoints to this window
    magiclogin.login_email_throttle = 50/3600

    # How fast email login link dies
    magiclogin.email_token_expiration_seconds = 300

    # Text shown on the login panel
    magiclogin.login_slogan = Your login text goes here

You might also have long, secure, sessions in production::

    # Set session length to one year
    redis.sessions.cookie_max_age = 31536000
    redis.sessions.cookie_secure = True
    redis.sessions.cookie_httponly = True

Running the development website
===============================

Local development machine
-------------------------

Example (OSX / Homebrew)::

    createdb magiclogin_dev
    ws-sync-db  ws://websauna/magiclogin/conf/development.ini
    pserve ws://websauna/magiclogin/conf/development.ini --reload


You can visit::

    http://localhost:6543/login

    http://localhost:6543/require_login_example_page

Running the test suite
======================

First create test database::

    # Create database used for unit testing
    psql create magiclogin_test

Install test and dev dependencies (run in the folder with ``setup.py``)::

    pip install -e ".[dev,test]"

Run test suite using py.test running::

    py.test

More information
================

Please see https://websauna.org/


Changelog for Websauna Magic Login
==================================

1.0a2 (2018-04-22)
------------------

- Fix missing 'namespace_packages'.


1.0a1 (2018-03-06)
------------------

- Initial Release
nbne 0.81
Method to create representations for nodes in a graph, using Neighbor Based Node Embeddings (NBNE) method.
License: MIT
Keywords: graphs node embeddings machine learning link prediction

# NBNE

Code to use Neighbor Based Node Embeddings (NBNE) method to create representations to nodes in a graph.


### Installation

You can install NBNE directly from PyPI:

`pip install nbne`

Or from source:

```
git clone https://github.com/tiagopms/nbne.git
cd nbne
pip install .
```
#### Dependencies

NBNE has the following requirements:

* [NetworkX](https://networkx.github.io/)
* [Gensim](https://radimrehurek.com/gensim/)

### Usage

#### Basic Usage

The libraries gensim and networkx should be installed. Then run:

```bash
    $ nbne --input examples/data/watts_strogatz.graph --output examples/data/watts_strogatz.emb
```

#### Using in other Applications

Import nbne module in your application and train model with:

```python
    from nbne import train_model
    train_model(graph, num_permutations)
```

Where graph should be a networkx graph. To save the model in an output file:
Import nbne module in your application and train model with:

```python
    from nbne import train_model
    import networkx as nx
    graph = nx.watts_strogatz_graph(1000, 50, 0.2)
    train_model(graph, num_permutations, output_name)
```

### Input

Input should be a edgelist with format:

```
    node1_id node2_id
    node1_id node3_id
    node2_id node3_id
```

### Output

The output is a document with `n+1` lines. The first has format:

```
    num_nodes embeddings_size
```

And the other:

```
    node_id embedding
```

Where `embedding` is a space separated vector with dimension `d`, i.e. `d1 d2 d3 ... dn`.