semvec/pypi-packages huggingface dataset:
Flask-KVSession-fork 0.6.4
Transparent server-side session support for flask
License: MIT

Flask-KVSession
===============

Flask-KVSession is an `MIT-licensed
<https://github.com/mbr/flask-kvsession/blob/master/LICENSE>`_ server-side
session replacement for `Flask <http://flask.pocoo.org>`_'s signed
client-based session management. Instead of storing data on the client, only a
securely generated ID is stored on the client, while the actual session data
resides on the server.

This has two major advantages:

* Clients no longer see the session information
* It is possible to securely destroy sessions to protect against replay
  attacks.

Other things are possible with server side session that are impossible with
clients side sessions, like inspecting and manipulating data in absence of the
client.

Flask-KVSession uses the `simplekv <http://github.com/mbr/simplekv>`_-package
for storing session data on a variety of backends, including `redis
<http://redis.io>`_, `memcached <http://memcached.org>`_, SQL databases using
`SQLAlchemy <http://sqlalchemy.org>`_, `mongoDB <http://www.mongodb.org/>`_ or
just flat files.

Integration with Flask is seamless, once the extension is loaded for a Flask
application, it transparently replaces Flask's own Session management. Any
application working with sessions should work the same with Flask-KVSession
(if it does not, file a bug!).


Documentation and development
-----------------------------

Development `happens on github <https://github.com/mbr/flask-kvsession/>`_,
you can find the `documentation on PyPI
<http://pythonhosted.org/Flask-KVSession>`_.
visualize3d 1.0.1
A library to make 3d visualizations easier.
License: MIT

# Visualize3D

> A small library to help with 3d visualizing (pointclouds).

## Getting Started

Please make sure you have pytorch installed properly as a first step.

```bash
pip install visualize3d
```

## Usage

In your code you can create visualizations:
```python
import numpy as np
import visualize3d as v3d

# First create a list of geometries to show.
N=42
geometries = [
    # Pointcloud (N, 3)
    v3d.create_pointcloud_from_numpy(
        pointcloud=np.random.rand(N, 3),
    ),
    # Add random lines (e.g. for a box or trajectory)
    v3d.create_lines_from_numpy(
        points=np.random.rand(N, 3),
        lines=np.array([i, i + 1] for i in range(N - 1)),
    ),
    # Add the origin arrows (X=red, Y=green, Z=blue).
    v3d.create_origin()
]

# Save visualization to disk (usefull in remote work or when batch processing)
v3d.save("output.v3d.npy", geometries)

# or show directly (if you work locally or interactive)
v3d.show(geometries)
```

To show a visualization that was saved to disk, you can write your own code or simply use the `visualize3d` commandline tool.
```bash
# Show visualization from above
visualize3d /path/to/output.v3d.npy

# Show a pcd file directly
visualize3d /path/to/filename.pcd

# Show all supported files in a folder after another
visualize3d /path/to/folder
```

## Contributing

Currently there are no guidelines on how to contribute, so the best thing you can do is open up an issue and get in contact that way.
In the issue we can discuss how you can implement your new feature or how to fix that nasty bug.

To contribute, please fork the repositroy on github, then clone your fork. Make your changes and submit a merge request.

## License

This repository is under MIT License. Please see the [full license here](https://github.com/penguinmenac3/visualize3d/blob/main/LICENSE).