semvec/pypi-packages huggingface dataset:
injected 0.3.0
Simple, type-safe dependency injection
Requires Python: >=3.12

<h1 align=center>injected</h1>

<p align=center>
    <a href=https://github.com/antonagestam/injected/actions?query=workflow%3ACI+branch%3Amain><img src=https://github.com/antonagestam/injected/actions/workflows/ci.yaml/badge.svg?branch=main alt="CI Build Status"></a>
    <a href=https://codecov.io/gh/antonagestam/injected><img src=https://codecov.io/gh/antonagestam/injected/branch/main/graph/badge.svg?token=GI8Z76HLYJ alt="Test coverage report"></a>
    <br>
    <a href=https://pypi.org/project/injected/><img src=https://img.shields.io/pypi/v/injected.svg?color=informational&label=PyPI alt="PyPI Package"></a>
    <a href=https://pypi.org/project/injected/><img src=https://img.shields.io/pypi/pyversions/injected.svg?color=informational&label=Python alt="Python versions"></a>
</p>

Simple, type-safe dependency injection in idiomatic Python, inspired by
[FastAPI][fastapi].

[fastapi]: https://fastapi.tiangolo.com/tutorial/dependencies/

#### Injecting dependencies

```python
from injected import depends, resolver


def get_a() -> int:
    return 13


def get_b() -> int:
    return 17


@resolver
def get_sum(
    a: int = depends(get_a),
    b: int = depends(get_b),
) -> int:
    return a + b


assert get_sum() == 30
```

#### Seeding the context of a resolver

It's sometimes useful to be able to provide an already resolved value, making it
available throughout the dependency graph. The canonical example of this is how FastAPI
makes things like requests and headers available to all dependencies.

To use this pattern, you specify a sentinel function, `get_global_value` in the example
below, and then map it to a resolved value in a context passed to `seed_context()`.

```python
from injected import depends, resolver, seed_context


def get_global_value() -> int: ...


@resolver
def calculate_value(a: int = depends(get_global_value)) -> int:
    return a + 13


seeded = seed_context(calculate_value, {get_global_value: 31})

assert seeded() == 44
```

#### Async dependencies and context managers

Dependencies can be any combination of async and non-async functions and context
managers. Async dependencies are resolved concurrently, and are scheduled at the optimal
time, as soon as their own dependencies are resolved.

Context managers are torn down upon exiting the entry-point function.

```python
import asyncio
from collections.abc import AsyncIterator
from contextlib import asynccontextmanager
from injected import depends, resolver


async def get_a() -> int:
    return 13


def get_b() -> int:
    return 17


@asynccontextmanager
async def get_c() -> AsyncIterator[int]:
    yield 23


@resolver
async def get_sum_async(
    a: int = depends(get_a),
    b: int = depends(get_b),
    c: int = depends(get_c),
) -> int:
    return a + b + c


@resolver
def get_sum_sync(
    a: int = depends(get_a),
    b: int = depends(get_b),
    c: int = depends(get_c),
) -> int:
    return a + b + c


assert asyncio.run(get_sum_async()) == 53
assert get_sum_sync() == 53
```
MEGnet-neuro 0.3.1
Package to calculate and classify ICAs using MEGNET deep learning architecture
Requires Python: <3.12

# MEGNET
[![megnet-tests](https://github.com/nih-megcore/MegNet/actions/workflows/megnet-actions.yml/badge.svg)](https://github.com/nih-megcore/MegNet/actions/workflows/megnet-actions.yml)

This repository is a fork of the code listed below in the original code reference.  This repository adds an automated processing wrapper and python package installation around the original codebase.  The current codebase utilizes mne python to preprocess the data, generate the infomax ICA components (n=20), circular topography maps, and timeseries outputs.  The architecture of neural net has been preserved, however, the weights have been reset to uniform distribution and retrained using repository data from MEGIN, CTF, 4D, and KIT systems.

## Install
```
conda create -n megnet 'mne>=1.6' 'python<3.12'
conda activate megnet
pip install git+https://github.com/nih-megcore/MegNet.git
```

## Post install initalization (downloading huggingface model weights)
```
megnet_init
```


## Original Code Repository
https://github.com/DeepLearningForPrecisionHealthLab/MegNET_2020 <br>
Manuscript available: https://pubmed.ncbi.nlm.nih.gov/34274419/ <br>
DOI: https://doi.org/10.1016/j.neuroimage.2021.118402 <br>
PMID: 34274419 <br>
cbpi4-KettleSensor 0.1.1
CraftBeerPi4 Plugin to add Sensor parameters for your Kettle and Fermenter

# CraftbeerpIi4 Plugin to monitor Kettle and Fermenter Parameters

KettleSensor extends the graphical output in craftbeerpi4. Your You can add target temperatur and powerlevel to your Kettle analytics.
The Fermentersonsor will add target temperature to your analytics

### Installation:

Please have a look at the [Craftbeerpi4 Documentation](https://openbrewing.gitbook.io/craftbeerpi4_support/readme/plugin-installation)

- Package name: cbpi4-kettlesensor
- Package link: https://github.com/PiBrewing/cbpi4-kettlesensor/archive/main.zip

### Changelog:

- 24.11.25: (0.1.1) Add pyproject.tonl to support pip 25.3+
- 17.06.22: (0.0.6) Fix to transfer also float values for fermenter target temp (required for ramp to temp step)
- 27.01.22: (0.0.5) Bug fixed for kettle sensor
- 16.01.22: (0.0.4) Adaption for cbpi 4.0.1.2
- 12.01.22: (0.0.3) Bug Fixes + Fermenter Status (-1: cooling, 0: no actor active, 1: heating)
- 03.01.21: (0.0.2) Added FermenterSensor
nexusai 0.5.37
Add your description here
Requires Python: >=3.9

## 🚧 Nexus TODO

change statu view to show queued and running jobs not per gpu stuff

- [ ] load dotenv
- [ ] broken history
- [ ] if job immedietly completes / fails and never attaches, show logs
- [ ] Filter job history/queue by user
- [ ] Git: clean up helper functions
- [ ] refactor
- [ ] Track per-job resource allocation in metadata
- [ ] Dependent jobs (run job B after job A completes)
- [ ] Better secrets management (e.g., encrypted `.env`)
- [ ] Multi-node support (DHT + RqLite for coordination/auth)
- [ ] Full job execution isolation (like slurm does it)
- [ ] Create a dedicated Linux user per Nexus user
- [ ] Documentation