semvec/pypi-packages huggingface dataset:
immersivepoints 1.0.0
Render point clouds inline in Jupyter notebooks and in VR
Requires Python: >=3.7
Keywords: point cloud, visualization, 3D, VR, jupyter

# ImmersivePoints

## What is it?
ImmersivePoints is a web-application for virtual reality devices to explore 3D data in the most natural way possible. <br>
To view the data you need to either attach a virtual reality device attached to your PC, or a stand-alone one, such as the oculus quest.

## Explore examples
Take a look at the following already uploaded examples: 
* [A frame from the AEV dataset](https://immersivepoints.com/oculus.html?name=e2652aab-4ace-4a09-86f4-374b23cb677b.xyzi)
* [A frame from the AEV dataset with semantic information](https://immersivepoints.com/oculus.html?name=816ca9a5-1eec-4e23-a34e-f409dbed1ff0.xyzi)
* [A brain with some information of a clustering algorithm](https://immersivepoints.com/oculus.html?name=77b04781-5d7c-445f-9e6a-65956758d644.xyzi)
* [The notre dame](https://immersivepoints.com/oculus.html?name=d6263c4a-7121-432f-8712-b0de530a78ff.xyzrgb)

## Upload your own
Want to see your own pointcloud in virtual reality? Upload your binary file [here](https://immersivepoints.com/upload.html)!
The binary file is expected to be in float32, and consist of XYZH values.
X, Y, and Z are floating point numbers in any range, but it's wise to position them around the point of origin of the virtual reality environment. 
The H-value signifies the hue of that point, which should be between 0.0 and 1.0 according to [this specification](https://threejs.org/docs/#api/en/math/Color.setHSL>this specification). 
Note that saturation and lightness are currently always set to 1.0.

## Export examples
Want to see some examples on how to export pointclouds? Take a look at:
* [this subsampling example](https://github.com/rmeertens/ImmersivePoints/blob/master/export_subsample.ipynb)
* [this example to export a CSV file](https://github.com/rmeertens/ImmersivePoints/blob/master/export_csv.ipynb)
* [this export of a PLY file](https://github.com/rmeertens/ImmersivePoints/blob/master/export_ply.ipynb)
* [this export of self driving car data](https://github.com/rmeertens/ImmersivePoints/blob/master/export_AEV_data.ipynb)

## All rights reserved.
dmgbuild 1.6.7
macOS command line utility to build disk images
Requires Python: >=3.10
Keywords: dmg

========
dmgbuild
========

.. |pyversions| image:: https://img.shields.io/pypi/pyversions/dmgbuild.svg
   :target: https://pypi.python.org/pypi/dmgbuild
   :alt: Python Versions

.. |version| image:: https://img.shields.io/pypi/v/dmgbuild.svg
   :target: https://pypi.python.org/pypi/dmgbuild
   :alt: PyPI Version

.. |maturity| image:: https://img.shields.io/pypi/status/dmgbuild.svg
   :target: https://pypi.python.org/pypi/dmgbuild
   :alt: Maturity

.. |license| image:: https://img.shields.io/pypi/l/dmgbuild.svg
   :target: https://github.com/dmgbuild/dmgbuild/blob/main/LICENSE
   :alt: MIT License

.. |ci| image:: https://github.com/dmgbuild/dmgbuild/workflows/CI/badge.svg?branch=main
   :target: https://github.com/dmgbuild/dmgbuild/actions
   :alt: Build Status

|pyversions| |version| |maturity| |license| |ci|

What is this?
-------------

``dmgbuild`` is a command line tool to create macOS disk images (aka
``.dmg`` files).  While it is possible to create disk images easily enough
from the command line using the ``hdiutil`` program that ships with macOS,
there is no easy way to configure the appearance of the resulting disk image
when the user opens it.

``dmgbuild`` allows for full customization of the resulting disk image,
without relying on Finder, and without using deprecated APIs (like the
Alias Manager functions).

``dmgbuild`` is a wrapper around macOS specific tools, so it can't be used on
Windows or Linux.

See the documentation_ for more information.

.. _documentation: http://dmgbuild.readthedocs.io
django-jbank 4.9.5
Collection of small utilities for Django and Django REST framework projects.
Requires Python: >=3.8

django-jbank
============

Basic (Finnish) bank file format support for Django projects. Django 4 support and unit test coverage 51%.

Features
========

* Finnish banks:
  * Parsing TO/TITO files
  * Parsing SVM/KTL files
  * Parsing camt.053.001.02 files
  * Parsing camt.054.001.02 files
  * Parsing pain.002.001.03 files
  * Generating pain.001.001.03 files
* Spanish banks:
  * Parsing AEB43 statement files


Pre-Requisites
==============

* sudo apt install -y xmlsec1 libxmlsec1-dev libssl-dev libffi-dev


Install
=======

* pip install django-jbank


Tested Banks
============

* POP-Pankki
* Danskebank Finland
* Säästöpankki
* Aktia
* Nordea


Converting p12 to PEM formats
=============================

1. openssl pkcs12 -in WSNDEA1234.p12 -out WSNDEA1234.pem
2. openssl rsa -in WSNDEA1234.pem -outform PEM -out prod_private_key.pem
3. openssl x509 -in WSNDEA1234.pem -outform PEM -out prod_public_key_cert.pem


Changes
=======

4.0.1:
+ Upgraded component requirements to latest lxml (4.9.1), etc.

3.2.0:
+ Django 3.0 support, prospector fixes