semvec/pypi-packages huggingface dataset:
odoo-addon-energy-communities-service-invoicing 16.0.0.7.2
Short (1 phrase/line) summary of the module's purpose, used as subtitle on modules listing or apps.openerp.com
Requires Python: >=3.10

# Energy Communities Service Invoicing

## Changelog

## Changelog

### 2026-02-10 (v16.0.0.7.2)

- Fix creating invoice cron
- Fix correct language when duplicating contracts

### 2026-01-15 (v16.0.0.7.1)

- Apply new formula for count members

### 2025-11-03 (v16.0.0.7.0)

- Adjustments for new public form for new community creation: Create bank accounts and
  journals

### 2025-10-22 (v16.0.0.6.1)

- Remove duplicated Voluntary Share product category (from energy_communities)

### 2025-10-22 (v16.0.0.6.0)

- Improved MulltiCompanyEasyCreationWizard

### 2025-10-01 (v16.0.0.5.9)

- fix access permisions to wizards
- fix sequence in new menus
- refactor validation excpetions to standard python raise exception

### 2025-09-24 (v16.0.0.5.8)

- Make AssignPackToPartner and PackProductCreator wizards available for all admins

### 2025-09-24 (v16.0.0.5.7)

- added field `service_type` in AccountMove model

### v16.0.0.5.6

- Fix journal propagation

### 2025-05-21

- Added Readme
nvidia-cublasmp-cu12 0.8.0.2023
cuBLASMp - runtime libraries
Keywords: cuda, nvidia, runtime, blas, multi-node, gemm

# NVIDIA cuBLASMp library

NVIDIA cublasMp is a high performance, multi-process, GPU accelerated library for distributed basic dense linear algebra.

cuBLASMp is compatible with 2D block-cyclic data layout and provides PBLAS-like C APIs.

- [cuBLASMp Documentation](https://docs.nvidia.com/cuda/cublasmp/)
- [cuBLASMp Downloads (dev-zone)](https://developer.nvidia.com/cublasmp-downloads)
nvidia-cublasmp-cu13 0.8.0.2023
cuBLASMp - runtime libraries
Keywords: cuda, nvidia, runtime, blas, multi-node, gemm

# NVIDIA cuBLASMp library

NVIDIA cublasMp is a high performance, multi-process, GPU accelerated library for distributed basic dense linear algebra.

cuBLASMp is compatible with 2D block-cyclic data layout and provides PBLAS-like C APIs.

- [cuBLASMp Documentation](https://docs.nvidia.com/cuda/cublasmp/)
- [cuBLASMp Downloads (dev-zone)](https://developer.nvidia.com/cublasmp-downloads)
enapter 0.15.1
Enapter Python SDK

# Enapter Python SDK

[![CI](https://github.com/Enapter/python-sdk/actions/workflows/ci.yml/badge.svg)](https://github.com/Enapter/python-sdk/actions/workflows/ci.yml)
[![PyPI version](https://img.shields.io/pypi/v/enapter.svg)](https://pypi.org/project/enapter)
[![Code Style](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/python/black)

A software development kit (SDK) for building applications and integrations
with Enapter using Python.

## Features

- [Standalone
  Devices](https://v3.developers.enapter.com/docs/standalone/introduction)
  framework
- [MQTT
  API](https://v3.developers.enapter.com/reference/device_integration/mqtt_api/)
  client
- [HTTP API](https://v3.developers.enapter.com/reference/http/intro) client

## Installation

> [!IMPORTANT]
> Requires **Python 3.11+**.

> [!WARNING]
> The API is still under development and may change at any time. It is
> recommended to pin the package version when installing.

Install from PyPI:

```bash
pip install enapter==0.15.1
```

## Usage

Explore the examples:

- [Standalone Devices](examples/standalone)
- [MQTT API](examples/mqtt/api)
- [HTTP API](examples/http/api)

These provide a good overview of the available features and should give you
enough to get started.

> [!TIP]
> Don't hesitate to peek into the source code - it's meant to be easy to
> follow.

## Help

If you feel lost or confused, reach us on
[Discord](https://discord.com/invite/TCaEZs3qpe) or just [file a
bug](https://github.com/Enapter/python-sdk/issues/new). We'd be glad to help.
rockpool 3.0.4
A Python package for developing, simulating and training spiking neural networks, and deploying on neuromorphic hardware
Requires Python: >=3.8
Keywords: spiking neural network SNN neuromorphic machine learning ML

# Rockpool

![PyPI - Package](https://img.shields.io/pypi/v/rockpool) ![Conda](https://img.shields.io/conda/v/conda-forge/rockpool) [![Documentation Status](https://img.shields.io/badge/docs-ok-green)](https://rockpool.ai) ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/rockpool?logo=python) ![Black - formatter](https://img.shields.io/badge/code_style-black-black)[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3773845.svg)](https://doi.org/10.5281/zenodo.3773845)

![Noodle](https://rockpool.ai/_static/noodle-small.png)

Rockpool is a Python package for developing signal processing applications with spiking neural networks. Rockpool allows you to build networks, simulate, train and test them, deploy them either in simulation or on event-driven neuromorphic compute hardware. Rockpool provides layers with a number of simulation backends, including Brian2, Torch, JAX, Numba and raw numpy. Rockpool is designed to make machine learning based on SNNs easier. It is not designed for detailed simulation of biological networks.

# Documentation and getting started

The best place to start with Rockpool is the [documentation](https://rockpool.ai), which contains tutorials and getting started guides.

The documentation is hosted online: [https://rockpool.ai/](https://rockpool.ai/), or can be built and hosted locally with Sphinx. Much of the documentation is available as Jupyter notebooks, to be used interactively.

# Installation instructions

Use `pip` to install Rockpool and required dependencies

```bash
$ pip install rockpool --user
```

The `--user` option installs the package only for the current user.

If you want to install all the extra dependencies required for Brian, PyTorch and Jax layers, use the command

```bash
$ pip install rockpool[all] --user
```

# License

Rockpool is released under a AGPL license. Commercial licenses are available on request.

# Contributing

Fork the public repository at https://github.com/SynSense/rockpool, then clone your fork.

```bash
$ git clone https://github.com/your-fork-location/rockpool.git rockpool
```

Install the package in development mode using `pip`

```bash
$ cd rockpool
$ pip install -e . --user
```

or

```bash
$ pip install -e .[all] --user
```

The main branch is `development`. You should commit your modifications to a new feature branch.

```bash
$ git checkout -b feature/my-feature develop
...
$ git commit -m 'This is a verbose commit message.'
```

Then push your new branch to your repository

```bash
$ git push -u origin feature/my-feature
```

When you're finished with your modifications, make a merge request on github.com, from your branch in your fork to https://github.com/SynSense/rockpool.