semvec/pypi-packages huggingface dataset:
tgapp-tgcomments 0.2.6
TurboGears2 pluggable application for comments to entities with facebook sharing
Keywords: turbogears2.application

About tgcomments
-------------------------

tgcomments is a Pluggable Comments application for TurboGears2.
Comments can be added to any webpage by using the ``comments_for`` partial.

TGComments supports Facebook for avatar if the user logged using tgapp-fbauth
or if the User model provides a similar interface. Otherwise will fallback to
Gravatar.

Installing
-------------------------------

tgcomments can be installed both from pypi or from bitbucket::

    pip install tgapp-tgcomments

should just work for most of the users

Plugging tgcomments
----------------------------

In your application *config/app_cfg.py* import **plug**::

    from tgext.pluggable import plug

Then at the *end of the file* call plug with tgcomments::

    plug(base_config, 'tgcomments')


To expose comments support for an entity, just call
the ``comments_for(entity)``  partial for
that entity inside your templates as explained in the
**Exposed Partials** section.

Exposed Partials
----------------------

tgcomments exposes a bunch of partials which can be used
to render pieces of the blogging system anywhere in your
application:

- ``tgcomments.partials:comments_for(entity)``
    Given any SQLAlchemy or Ming entity (instance) which is available inside your application ``model`` module
    it will display a list of comments for that entity with a box to add a new comment.

Provided Options
--------------------

tgcomments supports a bunch of options that can be passed to the plug call
to change its behavior:

- *notify_facebook* (default:True) automatically notify on facebook comments that
    the user wrote if he has logged using Facebook
- *allow_anonymous* (default:True) allow anonymous users to comment

- *pretty_date* a function that will be used to properly format dates (example: "5 minutes ago")

- *get_user_avatar* a function that will be used to get the avatar (by default searches
  for the ``avatar`` property, then fallbacks for fb information, then fallbacks gravatar)

Exposed Templates
--------------------

The templates used by registration and that can be replaced with
*tgext.pluggable.replace_template* are:

- ``tgcomments.templates.comments_partial``

Available Hooks
---------------
TGComments exposes some hooks to configure it's behavior, The hooks that can be
used with TurboGears2 register_hook are:

- ``tgcomments.before_add(entity, user, kw)`` - called before adding a comment. kw['body'] can be modified here


Changelog
---------

- ``0.2.6`` - support turbogears 2.4
- ``0.2.3`` - added ``tgcomments.before_add`` hook
- ``0.2.2`` - fixed anon comment creation broken by v0.2.1, used pre instead of div to
    display the comment body, so it keeps newlines
- ``0.2.1`` - fixed ming relation with User and added author_username
- ``0.2.0`` - introduced compatibility with ming
sphinx-examples 0.0.5
A lightweight example directive to make it easy to demonstrate code / results.
Requires Python: >=3.7
Keywords: documentation,sphinx

# sphinx-examples

A small Sphinx extension to create examples of source markup and the result of rendering it in your documentation.
This is useful if you wish to demonstrate the functionality of a new directive or role in Sphinx.

🚨**This is very alpha software**🚨: It is packaged primarily for convience and not heavily tested. Use at your own risk!

## Installation

You can install `sphinx-examples` with `pip`:

```bash
pip install sphinx-examples
```

## Usage

See [the `sphinx-examples` documentation](https://ebp-sphinx-examples.readthedocs.io/en/latest/) for more information.

## Develop

To develop this extension, follow these steps:

1. **Clone the repository locally**

   ```console
   $ git clone https://github.com/executablebooks/sphinx-examples
   $ cd sphinx-examples
   ```
2. **Install pre-commit hooks**

   ```console
   $ pre-commit install
   ```
3. **Install development and documentation dependencies**

   ```console
   $ pip install -e .[sphinx]
   ```

The codebase for this package is in `src/sphinx_examples`.
Documentation is in `docs/`.

This package has no testing infrastructure, so be careful when using it!
If it becomes more complex or with more features, we may add more testing around it in the future.
For not it is packaged for convenience and re-use across EBP repositories.

## Build the documentation

The documentation is currently not hosted anywhere, so if you'd like to preview the documentation "live", run the following command:

```console
pip install -e .[sphinx]
sphinx-build docs docs/_build/html
```

Then browse the HTML files in `docs/_build/html` to see the rendered documentation.

## Inspiration

This project takes heavy inspiration from the [Bootstrap v5 documenation](https://getbootstrap.com/docs/5.0/utilities/borders/#border).
viscars 0.0.10
VisCARS: Knowledge Graph-based Context-Aware Recommender System for Visualizations
Requires Python: >=3.7,<4.0
Keywords: recommender system,visualizations,context-aware

# VisCARS: Graph-Based Context-Aware Visualization Recommendation System

![version](https://img.shields.io/pypi/v/viscars)

## Installation

```
pip install viscars
```

## Citation
neps-academy 0.0.1
This is a placeholder for Neps Academy.

# Python Project Creator

This project generates Python code to start a new project.

In the current state it supports well the following templates:

- [x] **basic**: A basic project with a main.py file
- [x] **cp**: Contains the files used to solve competitive programming problems.

Still need some testing:

- [ ] **pip**: A template to create a package that will be uploaded to PIP.

In the future, I expect to create templates that need more sophisticated code generation, such as:

- [ ] **flask**: A template to be used when starting a Flask project.

# Build and Upload

python setup.py sdist
python setup.py bdist_wheel

twine upload dist/\*
neps 0.0.1
This is a placeholder for Neps Academy

# Python Project Creator

This project generates Python code to start a new project.

In the current state it supports well the following templates:

- [x] **basic**: A basic project with a main.py file
- [x] **cp**: Contains the files used to solve competitive programming problems.

Still need some testing:

- [ ] **pip**: A template to create a package that will be uploaded to PIP.

In the future, I expect to create templates that need more sophisticated code generation, such as:

- [ ] **flask**: A template to be used when starting a Flask project.

# Build and Upload

python setup.py sdist
python setup.py bdist_wheel

twine upload dist/\*
juce-rsa-python 0.0.4
A juce RSA algorithm implementation without compiling C++ source code

# Juce RSA Python
This is a juce RSA algorithm implementation without compiling C++ source code

# Install 
```shell
pip3 install juce-rsa-python
```

# Usage

```python
from juce_rsa_python import JuceRSA

public_key = '11,76e78a6f1f8158f8bbc85d7737218096953fedfa823a4ed45e884c3b7e7d3d0fe99cfdeae8fc5abdc1451fce4894fac9b19ae0b30973041b531753153f6f29ab'
private_key = '5aed4bbe6362e9af262fcf00cfce5345f9a95ba172a50f1adee0b2c4156ed4566ad34d099ef3157a74cd8b43b1b3072e44aeb65db623a66976435b59edbe0069,76e78a6f1f8158f8bbc85d7737218096953fedfa823a4ed45e884c3b7e7d3d0fe99cfdeae8fc5abdc1451fce4894fac9b19ae0b30973041b531753153f6f29ab'

text = 'Hello World!'

# encrypt
public_juce_rsa = JuceRSA(public_key)
encrypted = public_juce_rsa.encrypt(text)

# print(encrypted) 
# e72a9ff9b0b36bf96e5fa6a6820a9f86f11b2a82594185731368c01e466ccb24fd2c29a87ac9cd025a4f45e6b1cacf39cc2c5906d139f21f09cbcaa0f7277fb

# decrypt
private_juce_rsa = JuceRSA(private_key)
decrypted = private_juce_rsa.decrypt(encrypted)

# print(decrypted)
# Hello World!

```
distributions-for-dsnd 0.5
Gaussian and Binomial distributions

# Distributions
This package calculates Gaussian and binomial distributions and is part of an exercise of Udacity's nano degree program in data science. 

## Installation
You can use pip to install the package:
'''python
pip install -i https://test.pypi.org/simple/ distributions-for-dsnd
'''

## Usage
'''python
from distributions_for_dsnd import Gaussian, Binomial

# returns a Gaussian distribution with a mean and standard deviation equal to the input
Gaussian(10, 7)

# returns a Binomial distribution with a probability and number of samples equal to the input
Binomial(0.4, 25)
'''