semvec/pypi-packages huggingface dataset:
pyhkc 0.5.0
A Python client for HKC SecureComm API

# HKC Alarm API Python Wrapper

Python module for interacting with HKC's Alarm API, allowing for easy interactions with the alarm system.

**Note**: This uses a private API, which means it is subject to change without notice and can break at any time. Always be cautious and respectful when using private APIs. To mimic the behavior of the app and reduce the chance of being rate-limited or blocked, it is recommended to limit requests to the API to one every 5-10 seconds, especially when fetching logs or inputs, similar to HKC's new v2 App.

## Features

- Retrieve system status.
- Fetch all inputs.
- View recent logs.
- Arm and disarm the alarm in various modes.

## Installation

1. Clone this repository:
```
git clone https://github.com/jasonmadigan/pyhkc
```

2. Navigate to the repository's directory:
```
cd pyhkc
```

3. Install the required packages:
```
pip install -r requirements.txt
```

## Dependencies

- `requests==2.31.0`
- `tabulate==0.9.0`

## Example Usage

```python
from hkc_alarm import HKCAlarm

# Initialize the system with your credentials.
panel_id = [your-panel-id]  # replace with your panel ID
panel_password = "[your-panel-password]"  # replace with your panel password
user_code = [your-user-code]  # replace with your user code

alarm_system = HKCAlarm(panel_id, panel_password, user_code)

# Retrieve system status.
status = alarm_system.get_system_status()
print("System Status:", status)

# Fetch all inputs.
inputs = alarm_system.get_all_inputs()
print("All Inputs:", inputs)

# View recent logs.
logs = alarm_system.fetch_logs()
print("Recent Logs:", logs)

# Arm the system.
# alarm_system.arm_fullset()

# Disarm the system.
# alarm_system.disarm()
```

## Publishing

```bash
python3 setup.py sdist bdist_wheel
twine upload dist/*
```
ddmail-validators 0.0.6
Python package for validating input for the DDMail project
Requires Python: >=3.8

# What is ddmail_validators
Python package for validating input for the DDMail project.

## What is DDMail
DDMail is a e-mail system/service that prioritizes security. A current production example can be found at www.ddmail.se

## Operating system
Developt for and tested on debian 12.

## Installing using pip
`pip install ddmail-validators`

## Building and installing from source using hatchling.
Step 1: clone github repo<br>
`git clone https://github.com/drzobin/ddmail_validators [code path]`<br>
`cd [code path]`<br>
<br>
Step 2: Setup python virtual environments<br>
`python -m venv [venv path]`<br>
`source [venv path]/bin/activate`<br>
<br>
Step 3: Install required dependencies<br>
`pip install -r requirements.txt`<br>
<br>
Step 4: Build package<br>
`python -m pip install --upgrade build`<br>
`python -m build `<br><br>
Packages is now located under dist folder<br>
<br>
Step 5: Install package<br>
`pip install dist/[package name].whl`

## Testing
`cd [code path]`<br>
`pytest --cov=ddmail_validators tests/`

## Coding
Follow PEP8 and PEP257. Use Ruff or Flake8 with flake8-docstrings for linting. Strive for 100% test coverage.
bayes-traj 1.0.7
bayes_traj

# Introduction

**bayes_traj** is a software package written in Python that provides
routines for performing Bayesian trajectory
modeling of longitudinal data. Multiple, longitudinally observed target
variables -- continuous, binary, or a combination -- can be modeled
simultaneously. Per-trajectory random effects can also be modeled for
continuous target variables. This package also provides command-line tools
that facilitate spefication of Bayesian priors, enable visualization
of trajectory modeling results, and compute summary and model
fit statistics. 

# Installation

In order to install the package, type the folowing in the terminal:

    $ pip install bayes_traj

# Overview

**bayes_traj** provides several command-line tools: 

* `generate_prior` -- used to speficy Bayesian priors for use the trajectory
  modeling
* `viz_data_prior_draws` -- provides visualization of random draws from the
  prior
* `bayes_traj_main` -- performs Bayesian trajectory modeling using a prior file
* `viz_model_trajs` -- provides visualization of trajectories fit using
  `bayes_traj_main`
* `sumarize_traj_model` -- prints model summary and fit statistics given a model
  file produce by `bayes_traj_main`
* `assign_trajectory` -- writes a data file with appended trajectory assignment
  information given an input data file and a model file generated by the
  `bayes_traj_main` tool  	     

Each of these tools can be run with the -h flag for additional usage information.

For additional documentation, see https://acil-bwh.github.io/bayes_traj/index.html

# Tests

To run all unit tests, type the following in the package root directory:

    $ pytest


# Contribute

Please read our [contribution guidelines](./CONTRIBUTING.md).
pyrena 1.4.3
Python wrapper for Arena QMS API.
Requires Python: >=3.7

# pyrena

API wrapper for interacting with Arena QMS.

Pyrena is not associated with or approved by Arena Solutions or PTC.

## Installation

`pip install pyrena`

## Docs

Documentation generated by Pyrena self-introspecting its own code. This goes into more detail than this readme.

```python
import pyrena
pyrena.docs()
```

## Usage

### Create client

```python
import pyrena

client = pyrena.Arena("username", "password")
```

Users on the government server should add the parameter `arenagov=True` during client creation.

Users on the Europe server should add the parameter `europe=True` during client creation. 

### Change users

```python
client.logout()
client.login("different_username", "different_password")
```

### Search

Define an object type and search parameters to get a list of results. Functions equivalently to top bar search on Arena website; however searching based on custom attributes must use the custom attribute GUID. See Arena help documentation on app.bom.com

```python
search_results = client.Listing(client.QualityProcess, number="NCMR-*")
```

### Retrieve specific object

Option 1 - get object by object GUID, if known

```python
my_change_order = client.Change("object_guid")
```

Option 2 - get object by its "user-friendly" ID (known as `number` in the API)

```python
my_capa = client.find(client.QualityProcess, number="CAPA-000001")
```
drf-nested-model-serializer 0.0.9
Adds support for writable nested serializers to the Django REST framework.
Requires Python: >=3.9

# DRF Nested Serializer

Adds support for writable nested serializers to the Django REST framework.

Find out more with the [Getting Started](https://gniludio.github.io/drf-nested-model-serializer/getting_started) guide.

## Features

- All relationship types
    - Including through model
- Drop-in replacement
- Create and update support
- Flexible field in- and exclusion

## Example

```python hl_lines="10"
from rest_framework.serializers import ModelSerializer
from drf_nested_model_serializer.serializer import NestedModelSerializer
from .models import MyChildModel, MyParentModel

class MyChildSerializer(ModelSerializer):
    class Meta:
        model = MyChildModel
        fields = ("id", "")

class MyParentSerializer(NestedModelSerializer):
    nested = MyChildSerializer()

    class Meta:
        model = MyParentModel
        fields = ("id", "nested")

```

```python
data = {
    "child": {
        "name": "John Doe"
    }
}
serializer = MyParentSerializer(data=data)
if serializer.is_valid():
    instance = serializer.save()
```

```python
print(MyParentSerializer(instance=instance).data)
{
    "id": 1,
    "child": {
        "id": 1,
        "name": "John Doe"
    }
}
```

## Build and publish
> Increase version number in `pyproject.toml`.
```shell
stubgen -m drf_nested_model_serializer -o .
py -m build
py -m twine upload dist/*
```
gits 1.0.5
Gits is a Python command-line application designed to help you to handle multiple GitHub and GitLab repositories on your machine. With using Gits, you can start cloning from and pushing to multiple GitHub and GitLab accounts without any additional configurations - It automatically sets up everything needed to prevent conflicts between your accounts.

# Gits v1.0

**Project**: Gits
<br>**Version**: 1.0.5
<br>**OS**: Microsoft / Windows
<br>**Author**: Irakli Gzirishvili
<br>**Mail**: gziraklirex@gmail.com

**Gits** is a Python command-line interface application, designed to help you to work with multiple GitHub and GitLab account repositories on your development machine.

Using **Gits**, you can start cloning from and pushing to multiple GitHub and GitLab accounts without any additional configurations - It automatically sets up everything needed to prevent conflicts between your accounts.

Disclaimer: Gits CLI app is an independent open-source project (created by Irakli Gzirishvili) and is not affiliated with, endorsed by or officially associated with Git, GitHub or GitLab.

## Installation

To use **Gits**, follow these steps:

- Open CMD and run the following command to install `pip install gits` then restart your CMD
- To check if **Gits** is installed correctly, run the following command `gits`

## Commands

These are the available commands you can use:

- `gits` - To list available commands
- `gits connect` - Setup new ssh connection
- `gits clone (ssh-url)` - Clone project from GitHub / GitLab
- `gits show` - Show existing connections
- `gits drop` - Select connection and drop it

## NOTE

- App will create and edit file 'C:/Users/(username)/.ssh/config' to setup SSH request options.
- App will create SSH key files in folder 'C:/Users/(username)/.gits' to separate them from others (if you have any).
- By default, app will generate and store SSH keys with ssh-keygen rsa 4096 configured command.
VTKio 0.1.0.dev9
A simple package for converting data between VTK files (XML and HDF5) and NumPy arrays.
Requires Python: >=3.10
Keywords: Converter, HDF5, NumPy, VTK, Visualisation, XML

# VTKio
![](https://img.shields.io/badge/Python-3776AB?style=flat&logo=python&logoColor=white)  
A simple python package for reading and writing Visualization Tool Kit (VTK) files.



## Supported features
Legacy ascii `.vtk` files are not supported and only the newer **XML** and **HDF5** based formats are supported.

XML files can be written in `ascii`, `base64` binary and appended `base64` binary formats. 
This means all files remain valid `XML` documents. 
Support for reading and writing `raw` appended data is provided, but discouraged.

XML and VTKHDF files can also be read using the associated file readers. 

Data is returned in appropriate VTK classes with arrays stored in `numpy` formats. 

> [!WARNING]
> VTKHDF files can only be opened in supported software.  
> For example, ParaView has full VTKHDF support in 5.13 and above.


## VTK File Formats
All file formats have been developed based on VTK's [documentation](https://docs.vtk.org/en/latest/index.html) where 
the [XML](https://docs.vtk.org/en/latest/design_documents/VTKFileFormats.html#xml-file-formats) formats and newer [VTKHDF](https://docs.vtk.org/en/latest/design_documents/VTKFileFormats.html#vtkhdf-file-format) formats are described in detail.
Additional information can be found in [Chapter 5](https://book.vtk.org/en/latest/VTKBook/05Chapter5.html#) of the **VTK Book**.

Further information regarding the VTK data model can be found in the [ParaView documentation](https://docs.paraview.org/en/latest/UsersGuide/understandingData.html#vtk-data-model).

Example datafiles can be found for various filetypes at the [VTK Examples Repository](https://gitlab.kitware.com/vtk/vtk-examples/-/tree/master/src/Testing/Data?ref_type=heads).

## Documentation
For full documentation visit [jpmorr.gitlab.io/vtkio](https://jpmorr.gitlab.io/vtkio).

## Related packages
The following packages have some overlap in functionality with VTKio: 
 - [PyEVTK](https://github.com/paulo-herrera/PyEVTK)
 - [meshio](https://github.com/nschloe/meshio)
 - [uvw](https://github.com/prs513rosewood/uvw)
 - [vtk-hdf](https://github.com/jmag722/vtk-hdf)
scda-dash-auth 0.3.8
wrapper for scda dash authentication qdt auth service and Microsoft Identity Platform
Requires Python: >=3.9

# Introduction

Dash authorization using Microsoft Azure Active Directory and QDT Authentication framework.
This projects requires the authentication service running.

## Installation

Install the package using pip:

```bash
pip install scda_dash_auth
```

## Usage

To add authentication, add the following to your Dash app:

```python
from scda_dash_auth import SCDAAuth

auth = SCDAAuth(
    app,
    app_name = 'my-app',
    secret_key = 'some-secret-key-for-session',
    auth_url = 'http://0.0.0.0:8000',
)
```

This will add the authentication to your Dash app. The `auth_url` should point to the authentication service.