semvec/pypi-packages huggingface dataset:
wxcadm 4.6.1
A Python 3 library for Webex Calling administrators
Requires Python: >=3.8

# wxcadm
Python Library for Webex Calling Administration

# Purpose
wxcadm is a Python 3 library to simplify the API calls to Webex in order to manage and report on users of Webex Calling.
Although the primary focus is Webex Calling, many of the other Webex admin functions are included. This library is not
meant to be an interface to the Meetings and Messaging capabilities of Webex....there are plenty of other modules that
provide that.

# Installation
**wxcadm** is available as a [PIP Package](https://pypi.org/project/wxcadm/)

```
$ python -m pip install wxcadm
```
# Quickstart
By creating a Webex instance with a valid API Access Token, the module will pull the Webex Organization information as
well as all the People within the Organization. The Org instance will contain all People, whether they have the 
Webex Calling service or not. An Org method ```get_webex_people()``` makes it easy to get only the People that have
Webex Calling.

You can obtain a 12-hour access token by logging into https://developer.webex.com and visiting the **Getting Started**
page.

Once you have the access token, the following will initialize the API connection and pull data

```python
import wxcadm

access_token = "Your API Access Token"
webex = wxcadm.Webex(access_token)
```
Since most administrators only have access to a single Webex Organization, you can access that Organization with the
**org** attribute. If the administrator has access to more than one Organization, they can be accessed using the
**orgs** attribute, which is a list of the organizations that can be managed. See the "Regarding Multiple 
Organizations" section below for further information.

You can see all the attributes with
```python
vars(webex.org)
```
Note that, by default, all the People are pulled when the Org is initialized. For large organizations, this may take
a while, but then all the People are stored as Person objects.

To iterate over the list of people, simply loop through the **people** attribute of the Org. For example:
```python
for person in webex.org.people:
    # Print all of the attributes of the Person
    print(vars(person))
    # Or access the attributes directly
    email = person.email
```
# Documentation
**wxcadm** documentation is housed at [Read The Docs](https://wxcadm.readthedocs.io/en/latest/).
django-svg-sprite 2025.12.3
A Django template tag for easy use of SVG sprites in templates.
Requires Python: <4.0,>=3.10

# django-svg-sprite

A Django template tag for easy use of SVG sprites in templates.

## Quick start

1. Add `django_svg_sprite` to your `INSTALLED_APPS` setting like this:

    ```python
    INSTALLED_APPS = [
        ...,
        'django_svg_sprite',
    ]
    ```

2. Set the `SVG_SPRITE` setting to the SVG sprite file in your static files to be used, e.g.:

    ```python
    SVG_SPRITE = 'bootstrap-icons.svg'
    ```

3. Use the tag in your template, e.g.:

    ```django
    {% load svg_sprite %}
    {% svg_sprite 'hand-thumbs-up' fill='yellow' class='bi' %}
    ```

See [settings](https://tmb.codeberg.page/django-svg-sprite/settings/) and [template tags documentation](https://tmb.codeberg.page/django-svg-sprite/templatetags/) for details.

## Commercial support

Commercial support, including feature development, is available. [Get in contact.](https://boelz.eu/kontakt.html)

## License

This project is licensed under the MIT license.
brainspace 0.1.22
Cortical gradients and beyond
Requires Python: >=3.5
Keywords: brain cortex gradient manifold

====================
BrainSpace
====================

BrainSpace is a lightweight cross-platform toolbox primarily intended 
for macroscale gradient mapping and analysis of 
neuroimaging and connectome level data. The current version 
of BrainSpace is available in Python and MATLAB, programming 
languages widely used by the neuroimaging and network neuroscience 
communities. The toolbox also contains several maps that allow for 
exploratory analysis of gradient correspondence with other 
brain-derived features, together with tools to generate spatial null models.

For installation instructions, examples and documentation of BrainSpace see
our `documentation <https://brainspace.readthedocs.io>`_.

Happy gradient analysis! 

License
-----------

The BrainSpace source code is available under the BSD (3-Clause) license.

Support
-----------

If you have problems installing the software or questions about usage 
and documentation, or something else related to BrainSpace, 
you can post to the Issues section of our `repository <https://github.com/MICA-MNI/BrainSpace/issues>`_.

Paper
-----------

If you consider using BrainSpace, please cite our manuscript: 
Vos de Wael R, Benkarim O, Paquola C, Lariviere S, Royer J, Tavakol S, Xu T, Hong S, Langs G, Valk S, Misic B, Milham M, Margulies D, Smallwood J, Bernhardt B (2020). BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets. Commun Biol 3, 103.

Core development team
-----------------------

* Reinder Vos de Wael, MICA Lab - Montreal Neurological Institute
* Oualid Benkarim, MICA Lab - Montreal Neurological Institute
* Boris Bernhardt, MICA Lab - Montreal Neurological Institute
sdi-python 1.0.0
Synthetic Digital Immunity SDK for Python - AI-powered cybersecurity for microservices
Requires Python: >=3.7
Keywords: sdi cybersecurity anomaly-detection microservices security ai

# SDI Python SDK

Synthetic Digital Immunity SDK for Python - AI-powered cybersecurity for microservices.

## Installation

```bash
pip install sdi-python
```

## Quick Start

```python
from sdi import SdiClient

client = SdiClient(api_key=os.getenv('SDI_API_KEY'))

analysis = client.analyze(
    method='GET',
    path='/api/users/123',
    headers={},
    service_id='my-service'
)

if analysis['anomaly_detected']:
    print(f'Anomaly detected: {analysis["anomaly_score"]}')
```

## Documentation

Full documentation: https://github.com/skesani/sdi

## License

Apache-2.0
pydre 25.3
Driving simulation data processing engine
Requires Python: >=3.12
Keywords: driving simulation

# Pydre Overview

Pydre (pronounced *pie-dray*) is a Python software package designed to analyze the raw driving data taken from real-time driving simulation research studies. 

Pydre was originally based on the output file format from the [SimObserver](https://www.faac.com/realtime-technologies/products/simobserver-pro/) system sold by Realtime Technologies for recording data from their SimCreator simulation system. However, it can also input data from Scanner software and any similar tabular time-series data.

# Documentation

Full documentation can be found on [GitHub pages](https://osudsl.github.io/pydre/).

# Support 

If you find a bug in Pydre, please report it using the [issues](https://github.com/OSUDSL/pydre/issues) feature on GitHub. For contribution information, please view the [contributing](https://osudsl.github.io/pydre/contrib/) page in the documentation.

# Credits

Pydre was developed by The Ohio State University [Driving Simulation Lab](https://drivesim.osu.edu/).


If you use Pydre in your research, please cite [Pydre: A Python package for driving simulation data reduction](https://doi.org/10.21105/joss.07870) in JOSS.

[![DOI](https://joss.theoj.org/papers/10.21105/joss.07870/status.svg)](https://doi.org/10.21105/joss.07870)
juham-watermeter 0.1.6
Web-camera based watermeter for Juham
Requires Python: >=3.8
Keywords: object-oriented, plugin, framework, watermeter, home automation

Watermeter plugin for Juham™
=============================

Description
-----------

A web camera and AI-based water meter solution for Juham™ home automation.

This package includes two different water meter implementations:

* A simple class that compares subsequent images to measure differences. The greater the difference between images,
  the more the arrows and digits on the water meter have changed, indicating water consumption.
  This solution also uploads the images to a specified FTP site when water consumption is detected,
  allowing homeowners to inspect the water meter visually. While this solution doesn’t provide exact water
  consumption readings, it is highly reliable for detecting leaks. Just ensure that spiders or other potentially moving
  objects don't obstruct the camera's view of the water meter.  
  
* Tesseract OCR and OpenCV solution for reading and interpreting water meter digits.
  This class requires further work to be truly useful. My Raspberry Pi didn't have enough disk
  space, so I decided to set this aside for now.


.. image:: _static/images/watermeter_diff.png
   :alt: Web camera based water meter leak detector based on comparison of subsequent images
   :width: 640px
   :align: center  


Getting Started
---------------

To install:

   .. code-block:: bash

      pip install juham-watermeter

Which installs everything needed for reading water meters with the simple image comparison based solution.


If you want to play with the Tesseract OCR and OpenCV solution for reading and interpreting water meter digits,
please read on.


Installing Tesseract OCR to read the digits:

.. code-block:: bash

      sudo apt install tesseract-ocr

If you are on Windows, visit the Tesseract GitHub repository, or Download a precompiled Windows binary from UB Mannheim.

2. Install 

   .. code-block:: bash

      pip install juham-watermeter


2. Configure

To adjust update interval and other attributes edit `WaterMeter.json` configuration file.


### Note about OpenCV and contributors

The Python coverage failed with the No source for code: 'juham_watermeter\config-3.py'
error, when all the other Python projects with precisely identical structures worked fine.
Traced down the bug to opencv package, and added a .coveragerc file with the following content:

  .. code-block:: bash

    [run]
    omit = 
      config.py
      config-3.py

as a solution. This will of course blow up sooner or later, when some OpenCV developer decides
to rename those files.