Source code for sfs.td.source

"""Compute the sound field generated by a sound source.

The Green's function describes the spatial sound propagation over time.

.. include:: math-definitions.rst

.. plot::
:context: reset

import matplotlib.pyplot as plt
import numpy as np
from scipy.signal import unit_impulse
import sfs

xs = 1.5, 1, 0  # source position
rs = np.linalg.norm(xs)  # distance from origin
ts = rs / sfs.default.c  # time-of-arrival at origin

# Impulsive excitation
fs = 44100
signal = unit_impulse(512), fs

grid = sfs.util.xyz_grid([-2, 3], [-1, 2], 0, spacing=0.02)

"""
import numpy as _np

from .. import default as _default
from .. import util as _util

[docs]def point(xs, signal, observation_time, grid, c=None): r"""Source model for a point source: 3D Green's function. Calculates the scalar sound pressure field for a given point in time, evoked by source excitation signal. Parameters ---------- xs : (3,) array_like Position of source in cartesian coordinates. signal : (N,) array_like + float Excitation signal consisting of (mono) audio data and a sampling rate (in Hertz). A DelayedSignal object can also be used. observation_time : float Observed point in time. grid : triple of array_like The grid that is used for the sound field calculations. See sfs.util.xyz_grid(). c : float, optional Speed of sound. Returns ------- numpy.ndarray Scalar sound pressure field, evaluated at positions given by *grid*. Notes ----- .. math:: g(x-x_s,t) = \frac{1}{4 \pi |x - x_s|} \dirac{t - \frac{|x - x_s|}{c}} Examples -------- .. plot:: :context: close-figs p = sfs.td.source.point(xs, signal, ts, grid) sfs.plot2d.level(p, grid) """ xs = _util.asarray_1d(xs) data, samplerate, signal_offset = _util.as_delayed_signal(signal) data = _util.asarray_1d(data) grid = _util.as_xyz_components(grid) if c is None: c = _default.c r = _np.linalg.norm(grid - xs) # If r is +-0, the sound pressure is +-infinity with _np.errstate(divide='ignore'): weights = 1 / (4 * _np.pi * r) delays = r / c base_time = observation_time - signal_offset points_at_time = _np.interp(base_time - delays, _np.arange(len(data)) / samplerate, data, left=0, right=0) # weights can be +-infinity with _np.errstate(invalid='ignore'): return weights * points_at_time
[docs]def point_image_sources(x0, signal, observation_time, grid, L, max_order, coeffs=None, c=None): """Point source in a rectangular room using the mirror image source model. Parameters ---------- x0 : (3,) array_like Position of source in cartesian coordinates. signal : (N,) array_like + float Excitation signal consisting of (mono) audio data and a sampling rate (in Hertz). A DelayedSignal object can also be used. observation_time : float Observed point in time. grid : triple of array_like The grid that is used for the sound field calculations. See sfs.util.xyz_grid(). L : (3,) array_like Dimensions of the rectangular room. max_order : int Maximum number of reflections for each image source. coeffs : (6,) array_like, optional Reflection coeffecients of the walls. If not given, the reflection coefficients are set to one. c : float, optional Speed of sound. Returns ------- numpy.ndarray Scalar sound pressure field, evaluated at positions given by *grid*. Examples -------- .. plot:: :context: close-figs room = 5, 3, 1.5 # room dimensions order = 2 # image source order coeffs = .8, .8, .6, .6, .7, .7 # wall reflection coefficients grid = sfs.util.xyz_grid([0, room], [0, room], 0, spacing=0.01) p = sfs.td.source.point_image_sources( xs, signal, 1.5 * ts, grid, room, order, coeffs) sfs.plot2d.level(p, grid) """ if coeffs is None: coeffs = _np.ones(6) positions, order = _util.image_sources_for_box(x0, L, max_order) source_strengths = _np.prod(coeffs**order, axis=1) p = 0 for position, strength in zip(positions, source_strengths): if strength != 0: p += strength * point(position, signal, observation_time, grid, c) return p