semvec/pypi-packages huggingface dataset:
cosim-toolbox 1.0.1
Python support for the CoSimulation Toolbox
Requires Python: >=3.10
Keywords: power system, transactive, energy

============
Introduction
============

Co-Simulation Toolbox to push advances in the field.

Installation
============

cosim_toolbox can be installed using pip_::

  $ pip install cosim_toolbox

However, in order to be useful, cosim_toolbox needs custom versions of
GridLAB-D and other federates that work with HELICS.  It also requires Python 3.10 or later, with
HELICS_, NumPy_, MongoDB_, Pandas_, and Psycopg_.  There is reposistory of the complete
Co-Simulation for Windows, Linux and Mac OS X on GitHub.  A Docker_ version is also available for users.

Development Work Flow for cosim_toolbox
=======================================

* From this directory, 'pip install -e .' points Python to this cloned repository for any calls to cosim_toolbox functions
* See the https://github.com/pnnl/cst for a roadmap of existing Python source files, and some documentation.  Any changes or additions to the code need to be made in this directory.
* Run tests from any other directory on this computer
* When ready, edit the cosim_toolbox version number and dependencies in setup.py
* To deploy follow the instructions in the Python Packaging Guide:
    1. Create an account on PyPI if you haven't yet.
    2. Install twine and build: pip install twine build
    3. Create the source distribution, change to 'src' directory execute: python3 -m build .
    4. Check your distribution files for errors: twine check dist/*
    5. (Optional) Upload to the PyPI test server first (note: separate user registration required): twine upload --repository-url https://test.pypi.org/legacy/ dist/*
    6. Upload to PyPI: twine upload dist/*
* Any user gets the changes with 'pip install cosim_toolbox --upgrade'
* Use 'pip show cosim_toolbox' to verify the version and location on your computer

Using Co-Simulation Toolbox
===========================

This is a developer's platform for electric power grid research.  See
http://cosimtoolbox.readthedocs.io/en/latest/ for user instructions, and
https://[github]/copper for source code.

Links to Dependencies
=====================

* Docker_
* GridLAB-D_
* HELICS_
* NumPy_
* MongoDB_
* Pandas_
* Psycopg_
* Python_

Subdirectories
==============

- *cosim_toolbox*; utilities for building and running co-simulations using HELICS federates.
- *test*; scripts that support testing the package; not automated.

License & Copyright
===================

- Copyright (c) 2022-2025 Battelle Memorial Institute

.. _Docker: https://www.docker.com
.. _GridLAB-D: http://gridlab-d.shoutwiki.com
.. _HELICS: https://helics.org
.. _NumPy: https://www.numpy.org
.. _MongoDB: https://www.mongodb.com
.. _Pandas: https://pandas.pydata.org
.. _Psycopg: https://www.psycopg.org/docs
.. _pip: https://pip.pypa.io/en/stable
.. _Python: https://www.python.org
friendly-sequences 1.14
Friendly sequences made in Python with :love:
Requires Python: >=3.10

# Friendly Sequences

Inspired by Scala Sequence class [1] and iterchain [2],
but with a good typing support.

[1] https://alvinalexander.com/scala/seq-class-methods-examples-syntax/
[2] https://github.com/Evelyn-H/iterchain

## Motivation

It is possible to compose functions in python with many functional
programming primitives, like map, filter, reduce etc. But, in my opinion,
looks a bit ugly and you need to get use to this structure. For example, you
can write something like this:

```python
import itertools

from functools import reduce

assert (
    reduce(
        lambda left, right: f"{left}{right}",
        map(
            str,
            sorted(
                filter(
                    lambda x: x != 2,
                    map(
                        lambda x: x + 1,
                        itertools.chain.from_iterable(
                            zip(
                                (1, 2),
                                (3, 4),
                            )
                        ),
                    ),
                )
            ),
        ),
        "",
    )
    == "345"
)
```

or even this:

```python
import itertools

assert (
    "".join(
        sorted(
            str(x)
            for x in (
                x
                for x in (
                    x + 1
                    for x in itertools.chain.from_iterable(
                        zip(
                            (1, 2),
                            (3, 4),
                        )
                    )
                )
                if x != 2
            )
        )
    )
    == "345"
)
```

but with the friendly-sequences it is just this:

```python
from friendly_sequences import Seq

assert (
    Seq[int]((1, 2))
    .zip(Seq[int]((3, 4)))
    .flat_map(lambda x: x + 1)
    .filter(lambda x: x != 2)
    .sort()
    .map(str)
    .fold(lambda left, right: f"{left}{right}", "")
) == "345"
```

## Installation

```bash
$ pip install friendly-sequences
```
cloudisk 0.1.0
A decentralized content distribution system. Run your own cloud.
Requires Python: >=3.10

# cloudisk

[![Coverage Status](https://coveralls.io/repos/github/kerudev/cloudisk/badge.svg?branch=main)](https://coveralls.io/github/kerudev/cloudisk?branch=main)
[![Build Status](https://github.com/kerudev/cloudisk/workflows/Lint/badge.svg)](https://github.com/kerudev/cloudisk/actions)
[![PyPI - Version](https://img.shields.io/pypi/v/cloudisk?label=version)](https://pypi.org/project/cloudisk/)
[![PyPI - Python](https://img.shields.io/pypi/pyversions/cloudisk?label=python)](https://pypi.org/project/cloudisk/)

> [!WARNING]
> This library is still on a pre-alpha state.
> Keep your eyes open for future releases!

A decentralized content distribution system. Run your own cloud.

## The idea

cloudisk is born from the need of a simple server to share pictures and videos
with friends that doesn't depend on file size or device storage.

We decided to program our own, kind of like CLI `Jellyfin`, where we can add
the features we need: an intuitive and private cloud that is easy to use and
easy to develop.

## Getting started

To install cloudisk, use `pip install cloudisk`.

After installing, you can run `cloudisk -h` to get the full commands list and
a their description. Use `cloudisk <command> -h` to get help about their flags.

Use `cloudisk run` to start the web server at `127.0.0.1:8000`.

## Environment variables

The following env vars are used by cloudisk:

- `CLOUDISK_STATIC`: the path of the static files that will be served.
reflex-suneditor 0.1.0.post1
Reflex suneditor
Requires Python: >=3.10

```reflex-suneditor``` is [suneditor](http://suneditor.com/sample/index.html) based package.

package is avaiable at https://pypi.org/project/reflex-suneditor

Installation:

```pip3 install reflex-suneditor```
phonexia-referential-deepfake-detection-client 2.0.1
Referential Deepfake Detection Client
Requires Python: >=3.9
Keywords: grpc, voice, voice-biometry, speech, referential, deepfake, detection

![](https://www.phonexia.com/wp-content/uploads/phonexia-logo-transparent-500px.png)

# Phonexia referential deepfake detection client

This module contains client for communication with [Referential Deepfake Detection microservice](https://hub.docker.com/repository/docker/phonexia/referential-deepfake-detection/general) developed by [Phonexia](https://phonexia.com).

To use this client you will first need a running instance of any *Phonexia Referential Deepfake Detection microservice*. If you don't yet have any running instance, don't hesitate to [contact our sales department](mailto:info@phonexia.com).

You can learn more about the speaker identification technology [here](https://docs.phonexia.com/technologies/speaker-identification/).

On [this page](https://docs.phonexia.com/products/speech-platform-4/grpc/api/phonexia/grpc/technologies/referential_deepfake_detection/v1/referential_deepfake_detection.proto), you will find a *gRPC API* reference for *Referential Deepfake Detection microservice*.
pudu-ui 0.1.3
UI library for Python based in pyglet
Requires Python: >=3.11

# pudu-ui

UI library for creating desktop apps with Python based in pyglet. It has 
different components available.

## Examples

There are many examples using the UI components that you can find in the 
_examples_ folder.

## Testing

To run them:

`python -m unittest -v tests`

This will run every test in the "_tests_" module, if you add new tests make sure to
import them into the "_\_\_init\_\_.py_" file of the "_tests_" folder.

## Dependencies

- pyglet

You can install the dependencies by running:

`python -m pip install -r requirements.txt`
dbt-strict-undefined 0.1.2
dbt plugin to raise a compilation error when accessing an undefined variable
Requires Python: >=3.8

# dbt-subprocess-plugin

This is a [dbt plugin]() to raise a compilation error if a dbt Jinja macro refers to an undefined variable.

Relates to [dbt-labs/dbt-core#1389: dbt should use jinja's StrictUndefined setting](https://github.com/dbt-labs/dbt-core/issues/1389).

[dbt-plugin]: https://github.com/dbt-labs/dbt-core/blob/fa96acb15f79ae4f10b1d78f311f5ef2f4ed645e/core/dbt/plugins/manager.py

## Usage

1. Install `dbt-subprocess-plugin` into the same Python environment as dbt itself.
2. Start using it!