semvec/pypi-packages huggingface dataset:
blueair-api 1.47.3
Blueair Api Wrapper
Requires Python: <4,>=3.12.0
Keywords: hatch, rest-mini, api

[![GitHub Release][releases-shield]][releases]
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[![License][license-shield]](LICENSE)

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Api Wrapper for BlueAir API using async in python, this was inspired by [this guide](https://developers.home-assistant.io/docs/api_lib_index) to be a lightweight wrapper, with simple error handling.

a lot of this is based on [hass-blueair](https://github.com/aijayadams/hass-blueair).

***

[blueair_api]: https://github.com/dahlb/blueair_api
[commits-shield]: https://img.shields.io/github/commit-activity/y/dahlb/blueair_api.svg?style=for-the-badge
[commits]: https://github.com/dahlb/blueair_api/commits/main
[license-shield]: https://img.shields.io/github/license/dahlb/blueair_api.svg?style=for-the-badge
[maintenance-shield]: https://img.shields.io/badge/maintainer-Bren%20Dahl%20%40dahlb-blue.svg?style=for-the-badge
[releases-shield]: https://img.shields.io/github/release/dahlb/blueair_api.svg?style=for-the-badge
[releases]: https://github.com/dahlb/blueair_api/releases
[buymecoffee]: https://www.buymeacoffee.com/dahlb
[buymecoffeebadge]: https://img.shields.io/badge/buy%20me%20a%20coffee-donate-yellow.svg?style=for-the-badge
Prish 0.1.2
This Library is made for school CS students by which they do their simple tasks.

# Num To Words Hindi Style

This Library is made for school CS students by which they do their simple tasks Like as:- teversing, jod, ghato, guna, bhaag, num_toWords, friends_phone, friends_sallery, count_frequency.
## Installation

You can install this package using pip:

```bash
pip install Prish
speech-align-viz 0.1.1
Audio Text Alignment Visualization Tool
Requires Python: >=3.8

# SpeechAlignViz 🎙️📝

**SpeechAlignViz** is a web-based visualization tool for audio-text alignment. (🤗 [Hugging Face](https://huggingface.co/spaces/LoganLiu66/SpeechAlignViz)) It allows researchers and developers to verify and interact with speech transcription segments (SRT, VTT, TextGrid, JSON) directly on the audio waveform.

<p align="center">
  <img src="frontend/public/logo.svg" width="200" alt="SpeechAlignViz Logo" />
</p>

## Features

- **Unified Waveform View**: View text segments directly on the audio timeline.
- **Multi-Format Support**:
  - **JSON**: custom format `[{text, start_time, end_time}]`
  - **SRT**: SubRip Subtitle files
  - **WebVTT**: Web Video Text Tracks
  - **TextGrid**: Praat annotation files
- **Interactive Control**:
  - Click any segment to play its corresponding audio.
  - Zoom in/out for precise alignment checks.
  - Automatic audio-text synchronization.
- **Local & Upload Support**: Load files from your local filesystem or upload them directly via the browser.

<p align="center">
  <img src="images/image.png" alt="Demo" />
</p>

## Installation

### From PyPI
```bash
pip install speech-align-viz
```

### From Source
```bash
git clone https://github.com/LoganLiu66/SpeechAlignViz.git
cd SpeechAlignViz
pip install -e .
```

## Usage

Start the server using the CLI:
```bash
speech-align-viz serve --port 8001 --host 0.0.0.0
```
Then open [http://0.0.0.0:8001](http://0.0.0.0:8001) in your browser.

### CLI Figure Export
You can generate a static alignment figure directly from the command line:

```bash
speech-align-viz export \
  --audio ./examples/example.wav \
  --subtitle ./examples/example.json \
  --output-fig ./examples/example.png
```
*Note: The figure width is automatically calculated based on audio length to prevent overcrowding, but you can override it with `--width N`.*

### Loading Data
1. **Audio**: Upload an audio file (mp3, wav, ogg) or provide a local path.
2. **Transcript**: Upload a support transcript file (.json, .srt, .vtt, .TextGrid) or provide a local path.

## Development

The project structure is split into:
- `src/speech_align_viz`: FastAPI backend and CLI.
- `frontend`: React + Vite + WaveSurfer.js frontend.

To build the frontend:
```bash
cd frontend
npm run build
```
This will bundle the frontend into `src/speech_align_viz/static`.

## License
MIT
hdf5tools 0.5.0
Python hdf5 tools
Keywords: tethys

hdf5-tools
==================================

This git repository contains a python package with an H5 class to load and combine one or more HDF5 data files (or xarray datasets) with optional filters. The class will then export the combined data to an HDF5 file, file object, or xr.Dataset. This class is designed to be fast and safe on memory. This means that files of any size can be combined and saved even on a PC with low memory (unlike xarray).

Installation
------------
Using pip:

.. code::

    pip install hdf5tools


Or using conda/mamba from conda-forge:

.. code::

    conda install -c conda-forge hdf5tools


Usage
-------
Currently, only the **Combine** class is recommended for other to use.

First, initiate the class with one or many: paths to netcdf3, netcdf4, or hdf5 files; xr.Dataset objects (opened via xr.open_dataset); or h5py.File objects.

.. code:: python

    from hdf5tools import Combine

    ###############################
    ### Parameters

    path1 = '/path/to/file1.nc'
    path2 = '/path/to/file2.nc'

    ##############################
    ### Combine files

    c1 = Combine([path1, path2])



If you want to do some kind of selection via the coordinates or only select some of the data variables/coordinates then use the **.sel** method (like in xarray). Be sure to read the docstrings for additional info about the input parameters.

.. code:: python

    c2 = c1.sel({'time': slice('2001-01-01', '2020-01-01'), 'latitude': slice(0, 10)}, include_data_vars=['precipitation'])


And finally, save the combined data to a single hdf5/netcdf4 file using the **.to_hdf5** method. The only additional parameters that are important include the output which should be a path or a io.Bytes object, and the compression parameter. If you plan on using this file outside of the python environment, use gzip for compression, otherwise use lzf. The docstrings have more details.


.. code:: python

    output = '/path/to/output.nc'

    c2.to_hdf5(output, compression='gzip')


If you've passed xr.Dataset objects to Combine, it will be slower than passing the file as a path on disk. Only pass xr.Dataset objects to Combine if you don't want to write the intermediate file to disk before reading it into Combine.

The package also comes with a bonus function called **xr_to_hdf5**. It is a convenience function to convert a single xr.Dataset to an hdf5/netcdf4 file.

.. code:: python

    from hdf5tools import xr_to_hdf5

    xr_to_hdf5(xr_dataset, output, compression='gzip')