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 aField
or aFieldsContainer
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.