semvec/pypi-packages huggingface dataset:
cace 2.9.0
Circuit Automatic Characterization Engine
Requires Python: >=3.8

<h1 align="center">CACE</h1>
<h2 align="center">Circuit Automatic Characterization Engine</h2>
<p align="center">
    <a href="https://opensource.org/licenses/Apache-2.0"><img src="https://img.shields.io/badge/License-Apache%202.0-blue.svg" alt="License: Apache 2.0"/></a>
    <img src="https://github.com/fossi-foundation/cace/actions/workflows/ci.yaml/badge.svg?branch=main" alt="GitHub Actions Status Badge" />
    <a href="https://www.python.org"><img src="https://img.shields.io/badge/Python-3.8-3776AB.svg?style=flat&logo=python&logoColor=white" alt="Python 3.8 or higher" /></a>
    <a href="https://github.com/grantjenks/blue"><img src="https://img.shields.io/badge/code%20style-blue-blue.svg" alt="Code Style: blue"/></a>
</p>
<p align="center">
    <a href="https://cace.readthedocs.io/"><img src="https://readthedocs.org/projects/cace/badge/?version=latest" alt="Documentation Build Status Badge"/></a>
    <a href="https://fossi-chat.org"><img src="https://img.shields.io/badge/Community-FOSSi%20Chat-1bb378?logo=element" alt="Invite to FOSSi Chat"/></a>
</p>

CACE is a framework for analog and mixed-signal circuits that enables automatic characterization under various conditions and with Monte Carlo and mismatch analysis. After all parameters have been run under the given conditions, CACE will generate a summary showing the circuit performance.

> [!NOTE]
> The latest documentation can be viewed online at [cace.readthedocs.io](https://cace.readthedocs.io/). 

## Installation

CACE currently supports two primary methods of installation for it and its dependencies.

Please read the installation instruction in the documentation under ["Installation Overview"](https://cace.readthedocs.io/en/latest/getting_started/index.html).

### Nix (Recommended)

Works for macOS and Linux (x86-64 and aarch64) as well for Windows via WSL2. Recommended, as it is more integrated with your filesystem and overall has less upload and download deltas.

See [Nix-based installation](https://cace.readthedocs.io/en/latest/getting_started/common/nix_installation/index.html) in the docs for more info.

### Python-only Installation

You'll need to bring your own compiled utilities, but otherwise, simply install CACE as follows:

```console
python3 -m pip install --upgrade cace
```

## Usage

### CLI:
To invoke the CLI:

```console
cace [datasheet] [output] [options]
```

### Web:
To invoke the web interface:
```console
cace-web
```

For more information about the usage of CACE please have a look at ["Usage Guides"](https://cace.readthedocs.io/en/latest/usage_guides/index.html) in the documentation.

## Examples

There exist already numerous designs that use CACE. We have assembled a list of different designs that you can use as reference: [Example Designs](https://cace.readthedocs.io/en/latest/examples/index.html). 

## License

[The Apache License, version 2.0](https://www.apache.org/licenses/LICENSE-2.0.txt).
niwrap-helper 0.7.0
Utility functions for working with NiWrap
Requires Python: >=3.11

# NiWrap Helper

Utility library to help make working with [NiWrap] + [`bids2table`] easier.

> [!IMPORTANT]
> Version `0.2.x` is incompatible with `0.1.x` due to breaking changes in 
> [`bids2table`]. Any previously created indices will **need** to be recreated.

<!-- Links -->
[`bids2table`]: https://github.com/childmindresearch/bids2table
[NiWrap]: https://styx-api.github.io/
album 0.12.1
Album is a framework for unifying computational tools across frameworks, languages, and environments.
Requires Python: <3.15,>=3.10
Keywords: virtual environments, imaging, data analysis

# album

Introduction: [https://album.solutions/](https://album.solutions/)

Documentation: [https://docs.album.solutions/](https://docs.album.solutions/)

Developer documentation: [https://gitlab.com/album-app/album/-/wikis/home](https://gitlab.com/album-app/album/-/wikis/home)

## Citation

Albrecht, J.P.\*, Schmidt, D.\*, and Harrington, K., 2021. Album: a
framework for scientific data processing with software solutions of
heterogeneous tools. arXiv preprint arXiv:2110.00601.  
https://arxiv.org/abs/2110.00601

## Developers

- Kyle Harrington, Max Delbrueck Center for Molecular Medicine in the
Helmholtz Association
- Jan Philipp Albrecht, Max Delbrueck Center for Molecular Medicine in
  the Helmholtz Association
- Deborah Schmidt, Max Delbrueck Center for Molecular Medicine in
  the Helmholtz Association
libtcrlm 1.1.2
TCR language modelling library using Pytorch.
Requires Python: >=3.9
Keywords: TCR, TR, T cell, transformer, bert, MLM, immunology, bioinformatics

# libtcrlm

This is the TCR language modelling library that powers SCEPTR.
It is a thin layer around PyTorch with some extra infrastructure.
Note that this repository only contains library code- for a readily-usable deployment of SCEPTR, or code used to train the models, please see the links below.

## Related links

|Link|Description|
|----|-----------|
|https://github.com/yutanagano/sceptr|Readily usable deployment of SCEPTR|
|https://github.com/yutanagano/tcrlm|Code used for training models|
gridx-connector 1.0.1
Connector for GridX to fetch live data from your Photovoltaic System

# Gridx Connector

### **<h3 style="text-align: center;">This is not an Offical Gridx Library</h3>**

Harness the power of your photovoltaic system with the GridboxConnector library. This versatile tool taps into the same REST API as the official dashboard and app, providing you with direct access to your data from the cloud.<br>

Whether you're a developer looking to integrate solar data into your own project, or a power user seeking command-line access, GridboxConnector has you covered. With its dual functionality, you can either embed it into your Python project as a library or use it as a standalone command-line interface (CLI) tool.<br>

**Take control of your solar data with GridboxConnector, and unlock the full potential of your photovoltaic system.**<br><br>
![Screenshot vom mygridbox](images/screenshot.png)

## Supported Realms
* Viessmann (will shutdown end of 2025)
* eon Home

## Installation

```script shell
pip install gridx-connector
```

## Usage

You can use the CLI to retrieve live data from the Viessmann Gridbox API or use the GridboxConnector class in your own code. <br>
Use your Login data from the App or from https://company.gridx.de/login

### CLI

```script shell
pip install gridx-connector
viessmann --username <username> --password <password>
```

### in your code

```python
from gridx_connector import GridboxConnector
from importlib.resources import files
import json
from supported_oem import SupportedOEM

config_file = files('gridx_connector').joinpath(f'{oem}.config.json')
with open(config_file, 'r') as file:
    data = json.load(file)
    data["login"]["username"] = username
    data["login"]["password"] = password
    connector = GridboxConnector(data)
    # Retrieve live data
    live_data = connector.retrieve_live_data()
    return live_data
```

### Example Output

```script json
{
    "consumption": 496,
    "directConsumption": 413,
    "directConsumptionEV": 0,
    "directConsumptionHeatPump": 0,
    "directConsumptionHeater": 0,
    "directConsumptionHousehold": 413,
    "directConsumptionRate": 1,
    "grid": 83,
    "gridMeterReadingNegative": 4318200000,
    "gridMeterReadingPositive": 14499360000,
    "measuredAt": "2023-08-04T11:29:43Z",
    "photovoltaic": 413,
    "production": 413,
    "selfConsumption": 413,
    "selfConsumptionRate": 1,
    "selfSufficiencyRate": 0.8326612903225806,
    "selfSupply": 413,
    "totalConsumption": 496
}
```

## Dependencies

- requests

## Contributing

If you'd like to contribute to gridx-connector, please follow these steps:

Fork the repository.
Create a new branch for your feature or bug fix.
Make your changes and write tests if possible.
Run tests and ensure they pass.
Submit a pull request.

## License

This project is licensed under the MIT License - see the LICENSE file for details.
umlmodel 3.4.0
The data model for UML Diagrams
Keywords: umldiagrammer, python, data model

[![CircleCI](https://dl.circleci.com/status-badge/img/gh/hasii2011/umlmodel/tree/master.svg?style=shield)](https://dl.circleci.com/status-badge/redirect/gh/hasii2011/umlmodel/tree/master)
[![PyPI version](https://badge.fury.io/py/umlmodel.svg)](https://badge.fury.io/py/umlmodel)
[![Maintenance](https://img.shields.io/badge/Maintained%3F-yes-green.svg)](https://GitHub.com/Naereen/StrapDown.js/graphs/commit-activity)

[![forthebadge made-with-python](http://ForTheBadge.com/images/badges/made-with-python.svg)](https://www.python.org/)

# Introduction

This project is the data model for the [UML Shapes module.](https://github.com/hasii2011/umlshapes)

# Installation

```bash
pip install umlmodel
```

___

Written by Humberto A. Sanchez II <mailto@Humberto.A.Sanchez.II@gmail.com>, (C) 2025


## Note
For all kind of problems, requests, enhancements, bug reports, etc., please drop me an e-mail.


------


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I am concerned about GitHub's Copilot project

I urge you to read about the [Give up GitHub](https://GiveUpGitHub.org) campaign from[the Software Freedom Conservancy](https://sfconservancy.org).

While I do not advocate for all the issues listed there I do not like that a company like Microsoft may profit from open source projects.

I continue to use GitHub because it offers the services I need for free.  But, I continue to monitor their terms of service.

Any use of this project's code by GitHub Copilot, past or present, is done without my permission.  I do not consent to GitHub's use of this project's code in Copilot.