semvec/pypi-packages huggingface dataset:
touchbar-lyric 0.8.2
Display lyrics on your touchbar with BTT
License: MIT
Requires Python: <4.0,>=3.7

<center><h1>Synced Lyric on TouchBar</h1></center>

:warning: I no longer have a macbook with TouchBar, so I won't be able to update this project as often.

[![Codacy Badge](https://api.codacy.com/project/badge/Grade/77de523131f9441997db18c608b3c54e)](https://app.codacy.com/manual/mouchenghao/touchbar-lyric?utm_source=github.com&utm_medium=referral&utm_content=ChenghaoMou/touchbar-lyric&utm_campaign=Badge_Grade_Dashboard) [![Build Status](https://travis-ci.com/ChenghaoMou/touchbar-lyric.svg?branch=master)](https://travis-ci.com/ChenghaoMou/touchbar-lyric) [![Codacy Badge](https://app.codacy.com/project/badge/Coverage/aadeca6117a14aa6b655e21d5bbc09ea)](https://www.codacy.com/manual/mouchenghao/touchbar-lyric?utm_source=github.com&utm_medium=referral&utm_content=ChenghaoMou/touchbar-lyric&utm_campaign=Badge_Coverage) [![PyPI version](https://badge.fury.io/py/touchbar-lyric.svg)](https://badge.fury.io/py/touchbar-lyric)

Show synced lyric in the touch-bar with BetterTouchTool and NetEase/QQ Music APIs. Based on the idea of [Kashi](https://community.folivora.ai/t/kashi-show-current-song-lyrics-on-touch-bar-spotify-itunes-youtube/6301).

![Preview](./lyric_chinese.png)
![Preview](./lyric_english.png)

## Features

-   **Synced lyrics** from QQ Music and NetEase Music APIs;
-   Support **Spotify** (Recommended) & **Music** (Only songs in your playlists);
-   Support for **English/Spanish/Chinese(Simplified/Traditional)/Japanese** and more;

## Instruction

**If you are not familiar with command line, python ecosystem or having problems understanding this tutorial, find a friend to help you. Issues/DMs are not actively monitored for this project.**

### 1. Installation
```shell
pip3 install touchbar_lyric --upgrade
```

### 2. Configuration in BetterTouchTool

Same as Kashi:

1.  Copy&paste the content in `lyric.json` in _Meun Bar > Touch Bar_;
2.  Change the python path `$PYTHONPATH` to your own python path in the script area;

```shell
$PYTHONPATH -m touchbar_lyric --app Music
```

or use Spotify(default)

```shell
$PYTHONPATH -m touchbar_lyric --app Spotify
```

Show Traditional Chinese lyrics

```shell
$PYTHONPATH -m touchbar_lyric --app Spotify --traditional
```

**Be careful with typing double hyphens in BTT. It automatically change it to an em slash. Use copy & paste instead!**

## Acknowledgement

1. Inspired by [Kashi](https://community.folivora.ai/t/kashi-show-current-song-lyrics-on-touch-bar-spotify-itunes-youtube/6301) by [Jim Ho](https://github.com/jimu-gh).
2. Supported by wonderful projects like [qq-music-api](https://github.com/Rain120/qq-music-api) by [Rain120](https://github.com/Rain120) and [spotifylyrics](https://github.com/SimonIT/spotifylyrics) by [SimonIT](https://github.com/SimonIT).

## Disclaimer

This project is not affiliated with Apple, Spotify, QQ Music, NetEase Music, BetterTouchTool or any other third party. This project is not intended to violate any terms of service of the aforementioned parties. This project is for educational purposes only.
alertist 0.1.0
A general purpose library for sending alerts
License: MIT
Requires Python: <4.0,>=3.11

# alertist

Alertist is a library that makes it as easy as possible to send a message to a target, without rewriting code. All configuration happens via environment variables.

-----

## Motivation

Sending alerts just needs to be easy. Code shouldn't need rewriting so that you can change the destination for a message, a deployment shouldn't have to happen, and CI changed just to send an email, when an SNS message will do.  Enter alertist.

Alertist makes is simple to send alerts. Configure environment variables, and run your python application. But maybe that doesn't work for you - alertist loads a .env by default, from your current working directory, making it as painless as possible to trigger alert sending, without application overhead.

## Installation

`pip install alertist`

## Usage

Create an alertist object, and call the `alert` method with the target and message. The alertist object, must be instantiated with the target, or alert type you'd like to send.

```python

from alertist import Alertist
from alertist.targets import Pushover

a = Alertist(Pushover)
a.alert("Hello, world!")
```

If you'd like to send a message asynchronously, you use the exact same code, except that the `alert` method is now called `aalert`, and must be awaited.

```python
from alertist import Alertist
from alertist.targets import Pushover

a = Alertist(Pushover)
await a.alert("Hello, world!")
```

Supported alert types can be found in the [targets folder](alertist/targets).

-----

## Contributing

### Writing a new alert target

Bug, features, or integrations? Feel free to contribute in whatever way you can! But below, let's highlight how to create a new alert target.

1. Create a new class subclassing `ABCTarget`
1. Implement the synchronous `alert` method, and asynchronous `aalert` method
1. Add all environment variables that can be configured to `CONFIG_KEYS` in your class.
1. Add your class to `targets.__all__` in `__init__.py`
1. Write two unit tests, probably mocking your connecting.
ras-stac 0.1.1b1
Create SpatioTemporal Asset Catalog (STAC) objects from HEC-RAS model data.
License: MIT
Requires Python: >=3.9
Keywords: hec-ras, catalog, STAC

# ras-stac
[![CI](https://github.com/fema-ffrd/rashdf/actions/workflows/continuous-integration.yml/badge.svg?branch=main)](https://github.com/fema-ffrd/ras-stac/actions/workflows/continuous-integration.yml)
[![Release](https://github.com/fema-ffrd/ras-stac/actions/workflows/release.yml/badge.svg)](https://github.com/fema-ffrd/ras-stac/actions/workflows/release.yml)
[![PyPI version](https://badge.fury.io/py/ras-stac.svg)](https://badge.fury.io/py/ras-stac)

Utilities for making SpatioTemporal Asset Catalogs of HEC-RAS models

This repository contains code for developing STAC items from HEC-RAS models. Current activities focus on creating items for geometry files `g**.hdf` and plan files `p**.hdf stored in AWS S3. More to come. 

*Source code largely ported from [ffrd-stac](https://github.com/arc-pts/ffrd-stac/blob/204e1ec85068936856b317fa9446da3c4da5d8d4/ffrd_stac/rasmeta.py).*


## Getting Started

1. For local development, create a `.env` file using the `example.env` file.

2. Start a minio service and load data using the [cloud-mock](https://github.com/fema-ffrd/cloud-mock) repository.

3. Run the `populate-sample-data.sh` script to test set-up, connetivity, and view a sample stac catalog created using this library.


**NOTE** It is recommended that ras-stac not be run in a container for testing and development due to networking issues that complicate use of these tools, when using minio.
penguindashboard 1.0.13
PEngUIN: Protein Engineering Using Independent Networks
License: MIT
Requires Python: >=3.7

# PEngUIN - Protein Engineering Using Independent Networks

PEngUIN (Protein Engineering Using Independent Networks) is a comprehensive protein engineering dashboard designed to streamline the process of protein analysis and engineering using advanced deep learning models. By inputting a protein sequence or fasta file, users can leverage powerful tools such as BLAST, ESM-2, SaProt, MutCompute, ProteinMPNN, and AntiBERTa to generate suggested mutations for protein engineering.

## Installation

To get started with PEngUIN, you can install the package using pip:

```bash
pip install penguindashboard
```


### Prerequisites

(Required packages and setup instructions will be listed here)

## Usage

To use PEngUIN, simply input your protein sequence or fasta file. The dashboard will handle the rest, running various deep learning models to analyze the input and suggest potential modifications.

(Commands and detailed usage instructions will be provided here)

## Features

PEngUIN offers a range of functionalities centered around protein engineering:

- **BLAST**: Provides mutational analysis based on homologues sequences from an API BLAST call.
- **ESM-2**: Mutation residue prediction across all positions in a sequence using ESM-2 (650M).
- **SaProt**: Mutation residue prediction using the Steinnegar Lab's structure aware language model, SaProt.
- **MutCompute**: Structure-based masked prediction using Ellington Lab's MutCompute model.
- **ProteinMPNN**: In-painting of single residues across the entire sequence using Baker Lab's ProteinMPNN.
- **AntiBERTa**: Mutation prediction the Antibody specific protein language model AntiBERTa.

## Output

The output from PEngUIN consists of npy logits files, which contain detailed predictions in a specific amino acid order. 

Sequence order is as follows:
```bash
amino_acids = ['A', 'R', 'N', 'D', 'C', 'Q', 'E', 'G', 'H', 'I', 'L', 'K', 'M', 'F', 'P', 'S', 'T', 'W', 'Y', 'V']
```

## License

PEngUIN is available under the MIT License. See the LICENSE file for more details.