semvec/pypi-packages huggingface dataset:
micropyLMS 1.0.3
Dependency lite and micropython friendly library for interacting with Lyrion Music Server systems
Requires Python: >=3.9

# micropyLMS

A dependency lite and micropython friendly library for interacting with Lyrion Music Server (LMS) systems

# Overview

This library is intended to help build micropython based LMS controller projects. LMS allows for multiple players to simultaneously play separate streams and most commands and queries have to be represented as coming from one of the players connected to the server. Because of this, the bulk of the library is the player class that allows the creation of an object to hold information about and interaction methods for an LMS player. 

A player object only needs to be created once. Any commands can be issued via the class methods. The various class attributes store some useful information on the last updated status of the player. Other information can be retrieved with the generic ``player_query()`` method. To insure that the information is current the ``status_update()`` method should be called. So for example, if the song playing has moved on since the last ``status_update()`` call the ``player.title``, ``player.artist``, and ``player.album`` information will all refer to that previous song until ``status_update()`` is called again.

# Installation

``pip install micropyLMS``

# Example Setup

The following code snippet shows how to initialize and use a simple setup
```
import micropyLMS

host = "192.168.1.5" # Change this to the specific local URL for your LMS server.
player_name = "Lounge" # Change to the name of the specific player. 

server_url = micropyLMS.build_url(host)
player = micropyLMS.get_player(server_url,player_name)
player.status_update()

player.set_volume(50) # Sets the volume to half the maximum.
player.play() # Starts the playback. Enjoy your music.
...
```

# Advanced Usage

There are a wide varied of commands and queries that could be issued that are less commonly used than the ones implemented in this library. For that reason, the ``Player`` class has a generic ``player_query()`` method and the library has a generic query function ``core_query()`` for non-player queries. Constructing valid arguments for these function is a little more complicated. For full information on how to structure the argument or arguments that go in the place of ``*command`` for these functions see https://lyrion.org/reference/cli/using-the-cli/ under the jsonrpc.js section and the command part of the body of the request.
navis-fastcore 0.1.0
NAVis core functions reimplemented in Rust.
Requires Python: >=3.9

[![test](https://github.com/schlegelp/fastcore-rs/actions/workflows/test.yml/badge.svg)](https://github.com/schlegelp/fastcore-rs/actions/workflows/test.yml) [![docs](https://github.com/schlegelp/fastcore-rs/actions/workflows/docs.yaml/badge.svg)](https://schlegelp.github.io/fastcore-rs/)

# navis-fastcore

Re-implementation of `navis` core functions in Rust.

## Install

We provide precompiled binaries for all major Python versions, CPU architectures and operating systems.

From [PyPI](https://pypi.org/project/navis-fastcore):

```bash
pip install navis-fastcore
```

If that fails, try building from source (see below).

## Usage

`navis-fastcore` itself does not depend on `navis`. This is to allow
using `fastcore` in libraries other than `navis`. Please see the
[docs](https://schlegelp.github.io/fastcore-rs/) for examples.

`navis` will automatically use `fastcore` if it is available.
The integration is still work in progress, so for now you
should install `navis` from Github to make sure you have the
latest version.

## Building from source

1. [Install rust](https://www.rust-lang.org/tools/install)
2. Clone this repository
3. `cd` into `fastcore-rs/py` directory
4. Create a virtual environment: `python3 -m venv .venv`
5. Activate virtual environment: `source .venv/bin/activate`
6. Compile via either:
   - `maturin develop --release` which will compile the
     extension into the `fastcore/` directory
   - `maturin build --release` to build wheel in `/target/wheels`
7. To install the Python package either do:
  -  `pip install -e .` to install in editable mode
  - `pip install targets/wheels/navis_fastcore....whl`

Note that unless you compiled with the `--release` flag,
timings will be much slower (up to 10x) than in a release build!

## Test
First make sure `pytest` is installed:
```
pip install pytest -U
```

Then run the test-suite like so:
```
pytest --verbose -s
```
tscat 0.4.4
A library which stores, loads and filters time-series-events and catalogues.
Requires Python: >=3.8
Keywords: satellite, plasma-physics, nasa-data, amda, cdpp, CDF

# Time Series Catalogues

![Test Status](https://github.com/SciQLop/tscat/actions/workflows/test_main.yml/badge.svg)
![Coverage Status](https://codecov.io/gh/SciQLop/tscat/branch/main/graph/badge.svg)


A library which stores, loads and filters time-series-events and associates them catalogues and
dynamic catalogues (filter-based).

## Usage

You can install the package using pip:
```bash
pip install tscat
```
You can also install the package from source:
```bash
git clone https://github.com/SciQLop/tscat
cd tscat
pip install -e .
```

## Examples

Let's create a simple catalogue with some events and save it:

```python
from tscat import create_catalogue, create_event, add_events_to_catalogue, save
from datetime import datetime
# Create a catalogue
catalogue = create_catalogue("my_catalogue", author="John Doe", description="A sample catalogue", tags=["example", "sample"])
# Create some events
events = [
    create_event(start=datetime(2023, 1, 1), stop=datetime(2023, 1, 2), author="Alice", tags=["tag1", "tag2"]),
    create_event(start=datetime(2023, 1, 3), stop=datetime(2023, 1, 4), author="Bob", tags=["tag3"], some_custom_attribute="value1"),
]
# Add events to the catalogue
add_events_to_catalogue(catalogue, events)
# Save changes to the catalogue database
save()

```

Let's create a catalogue from a pandas DataFrame:
```python
import pandas as pd
from tscat import create_catalogue, create_event, add_events_to_catalogue, save

# Let's fetch some catalogue on GitHub
df = pd.read_pickle("https://helioforecast.space/static/sync/icmecat/HELIO4CAST_ICMECAT_v23_pandas.p")[0]

# Create events from the DataFrame
events = [
    create_event(
        start=row['mo_start_time'],
        stop=row['mo_end_time'],
        author="Christian Möstl",
        tags=["HELIO4CAST", "ICMECAT"],
        **{k: v for k, v in row.items() if k not in ['mo_start_time', 'mo_end_time']}
    )
    for _, row in df.iterrows()
]

# Create a catalogue from the DataFrame
catalogue = create_catalogue("icmecat", author="Christian Möstl", description="HELIO4CAST ICMECAT catalogue", tags=["HELIO4CAST", "ICMECAT"], events=events)

# save the catalogue
save()
```

## Features

- Store and manage time-series events in catalogues
- Filter events using dynamic catalogue queries
- Load and export catalogue data
- Associate events with metadata and attributes
- Time-based filtering and search capabilities
- Python API for programmatic access


* Free software: GNU General Public License v3
datasette-checkbox 0.1a4
Add interactive checkboxes to columns in Datasette
Requires Python: >=3.10

# datasette-checkbox

[![PyPI](https://img.shields.io/pypi/v/datasette-checkbox.svg)](https://pypi.org/project/datasette-checkbox/)
[![Changelog](https://img.shields.io/github/v/release/datasette/datasette-checkbox?include_prereleases&label=changelog)](https://github.com/datasette/datasette-checkbox/releases)
[![Tests](https://github.com/datasette/datasette-checkbox/actions/workflows/test.yml/badge.svg)](https://github.com/datasette/datasette-checkbox/actions/workflows/test.yml)
[![License](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/datasette/datasette-checkbox/blob/main/LICENSE)

Add interactive checkboxes to columns in Datasette. Requires Datasette 1.0 alpha or higher.

![Animated demo showing checkboxes in columns for is_done, should_be_deleted and is_happy - checking the checkboxes shows an updated message next to each one which then fades away.](https://raw.githubusercontent.com/datasette/datasette-checkbox/main/datasette-checkbox.gif)

## Installation

Install this plugin in the same environment as Datasette.
```bash
datasette install datasette-checkbox
```
## Usage

This plugin adds checkboxes to the table and row pages in Datasette for any column with a name that starts `is_*` or `should_*` or `has_*` and that is of type `integer`.

Toggling those checkboxes updates the underlying column to a `1` or a `0`.

The checkbox interface will only be shown for users who have `update-row` permission for the table.

The easiest way to try this plugin is using the `--root` Datasette option:

```bash
datasette data.db --root
```
Or use the [Datasette permission system](https://docs.datasette.io/en/latest/authentication.html#permissions) to grant `update-row` to specific users.

## Development

To set up this plugin locally, first checkout the code. Then create a new virtual environment:
```bash
cd datasette-checkbox
python3 -m venv venv
source venv/bin/activate
```
Now install the dependencies and test dependencies:
```bash
pip install -e '.[test]'
```
To run the tests:
```bash
pytest
```