User guide#

This section explains how to start a DPF server, load a signal from a WAV file, and perform operations on this signal.

Start a DPF server#

You use the connect_to_or_start_server() function to start either a remote or local DPF server, which is required to run PyAnsys Sound.

You can start the server with this code:

from ansys.sound.core.server_helpers import connect_to_or_start_server

my_server = connect_to_or_start_server()

If the ANSRV_DPF_SOUND_PORT environment variable is set, PyAnsys Sound attempts to connect to a server located in a Docker container. The default port is 6780.

If this environment variable is not set, PyAnsys Sound tries to start a local server.

For more information on local and remote DPF servers, see Install DPF Server in the PyDPF-Core documentation.

Load a signal#

Most of the processing done by PyAnsys Sound relies on temporal sound signals that are saved as WAV files.

To load a WAV file, you must use the LoadWav class. Once your signal is loaded, you can use all other PyAnsys Sound classes on this signal.

from ansys.sound.core.signal_utilities import LoadWav

# Load a WAV file and plot it
wav_loader = LoadWav(r"C:\path\to\my\wav\file.wav)
wav_loader.process()

# Obtain the output as a DPF fields container
fc_wav_signal = wav_loader.get_output()

# Plot the signal
wav_loader.plot()

For descriptions of all PyAnsys Sound classes and helpers, see API reference. These classes have four methods in common:

  • process(): Performs the operation that the class was made for. This method must be called explicitly every time an input parameter is changed.

  • plot(): Plots the output of the class. Depending on the nature of the output, the plot might be different.

  • get_output(): Gets the outputs as a DPF object (either a Field or a FieldsContainer object).

  • get_output_as_nparray(): Gets the output as a NumPy array.

A class might also have some additional methods.

For comprehensive information on using PyAnsys Sound classes and helpers, see Examples.