semvec/pypi-packages huggingface dataset:
roboflex.audio-sdl 0.1.8
Roboflex Audio input library using Simple Directmedia Layer (SDL2)
License: MIT
Requires Python: >=3.6
Keywords: sdl,sdl2,audio,robotics,middleware,flexbuffers,python,c++,c++20

# roboflex.audio_sdl

Support for reading audio data using Simple Directmedia Layer.

## System dependencies

Requires SDL to be installed. More than likely, it already is installed in your distro. But if not:

    apt-get install libsdl2-dev

    # or maybe on mac:

    brew install sdl2

## pip install

    pip install roboflex.audio_sdl

## Import

    import roboflex.audio_sdl as ras

## Nodes

There is only one: **AudioSensor**

    # all parameters optional: below are the defaults
    audio_sensor = ras.AudioSensor(
        capture_id = -1,
        channels = 1,
        sampling_rate = 48000,
        capture_samples = 512,
        format = ras.BitDepth.F32,
        name = "AudioSensor",
        data_key = "data",
        debug = False,
    )

    # must be started (like all sensors)!
    audio_sensor.start()

    # you can get the realized channels and sampling rate (might be different from what you requested):
    audio_sensor.sample_rate
    audio_sensor.channels

    # you can print the available detected devices, through a static method
    ras.AudioSensor.show_devices()

This sensor publishes a TensorMessage, with the audio data encoded into a tensor of shape (C, S), where C is num channels, and S is capture_samples, under the key <data_key>.


## Other

Available BitDepths:

    # wrapped from SDL_AudioFormat: https://wiki.libsdl.org/SDL2/SDL_AudioFormat

    enum ras.BitDepth:
        S8,
        U8,
        S16LSB,
        S16MSB,
        S16SYS,
        S16,
        U16LSB,
        U16MSB,
        U16SYS,
        U16,
        S32LSB,
        S32MSB,
        S32SYS,
        S32,
        F32LSB,
        F32MSB,
        F32SYS,
        F32
frame-model 0.1.2
Módulo para distribuição de Modelos Numéricos.
License: MIT
Requires Python: >=3.10
Keywords: recuperação de dados climáticos

Framework

Descrição
É um pacote in Python para a distribuição de dados brutos dos Modelos Numéricos de forma segmentada/particionada. Com esse pacote o usuário não necessita fazer o Download de todo o volume bruto o pacote auxilia a manipular somente a sua necessidade.

support Python >= 3.10.

Instalação
conda create -n  python=3.10

conda activate 

conda install -c conda-forge matplotlib pycurl cfgrib netCDF4 pynio xarray dask esmpy scipy mpi4py xesmf

conda install -c anaconda ipykernel

pip install -i https://test.pypi.org/simple/ 

Como Usar
import cptecmodel.CPTEC_BAM as BAM

bam = BAM.model()

date = '2023010800'

vars = ['t', 'u10m']

levels = [1000, 850]

steps = 1

f = bam.load(date=date, var=vars,level=levels, steps=steps)