#########################################################################################
##
## TIME DOMAIN NOISE SOURCES
## (blocks/noise.py)
##
## Milan Rother 2024/25
##
#########################################################################################
# IMPORTS ===============================================================================
import numpy as np
from ._block import Block
# NOISE SOURCE BLOCKS ===================================================================
[docs]
class WhiteNoise(Block):
"""White noise source with uniform spectral density. Samples from distribution
with 'sampling_rate' and holds noise values constant for time bins.
If no 'sampling_rate' (None) is specified, every simulation timestep
gets a new noise value. This is the default setting.
Parameters
----------
spectral_density : float
noise spectral density
noise : float
internal noise value
sampling_rate : float, None
frequency with which the noise is sampled
Attributes
----------
sigma : float
sqrt of spectral density -> signal amplitude
n_samples : int
internal sample counter
"""
#max number of ports
_n_in_max = 0
_n_out_max = 1
def __init__(self, spectral_density=1, sampling_rate=None):
super().__init__()
#clear all inputs because its a source block
self.inputs.clear()
self.spectral_density = spectral_density
self.sampling_rate = sampling_rate
self.sigma = np.sqrt(spectral_density)
self.n_samples = 0
self.noise = 0.0
def __len__(self):
return 0
[docs]
def reset(self):
super().reset()
#reset noise samples
self.n_samples = 0
self.noise = 0.0
[docs]
def sample(self, t):
"""Sample from a normal distribution after successful timestep
Parameters
----------
t : float
evaluation time for sampling
"""
if (self.sampling_rate is None or
self.n_samples < t * self.sampling_rate):
self.noise = np.random.normal(0, 1) * self.sigma
self.n_samples += 1
[docs]
def update(self, t):
"""update system equation for fixed point loop,
here just setting the outputs
Note
----
no direct passthrough, so the 'update' method
is optimized for this case
Parameters
----------
t : float
evaluation time
"""
self.outputs[0] = self.noise
[docs]
class PinkNoise(Block):
"""Pink noise (1/f) source using the Voss-McCartney algorithm.
Samples from distribution with 'sampling_rate' and generates noise
with a power spectral density inversely proportional to frequency.
Parameters
----------
spectral_density : float
Desired noise spectral density
num_octaves : int
Number of octaves (levels of randomness)
sampling_rate : float, None
Frequency with which the noise is sampled
Attributes
----------
sigma : float
sqrt of spectral density normalized to number of octaves
n_samples : int
internal sample counter
noise : float
internal noise value
octaves_values : array[float]
internal random numbers for octaves in the Voss-McCartney algorithm
"""
#max number of ports
_n_in_max = 0
_n_out_max = 1
def __init__(self, spectral_density=1, num_octaves=16, sampling_rate=None):
super().__init__()
#clear all inputs because its a source block
self.inputs.clear()
self.spectral_density = spectral_density
self.num_octaves = num_octaves
self.sampling_rate = sampling_rate
self.n_samples = 0
self.noise = 0.0
# Calculate the normalization factor sigma
self.sigma = np.sqrt(spectral_density/num_octaves)
# Initialize the random values for each octave
self.octave_values = np.random.normal(0, 1, self.num_octaves)
def __len__(self):
return 0
[docs]
def reset(self):
super().reset()
#reset counters and octave values
self.n_samples = 0
self.noise = 0.0
self.octave_values = np.random.normal(0, 1, self.num_octaves)
[docs]
def sample(self, t):
"""Generate a new pink noise sample at 't' using
the Voss-McCartney algorithm.
Parameters
----------
t : float
evaluation time for sampling
"""
if (self.sampling_rate is None or
self.n_samples < t * self.sampling_rate):
# Increment the counter
self.n_samples += 1
# Use bitwise operations to determine which octaves to update
mask, idx = self.n_samples, 0
while mask & 1 == 0 and idx < self.num_octaves:
mask >>= 1
idx += 1
# Update the selected octave with a new random value
if idx < self.num_octaves:
self.octave_values[idx] = np.random.normal(0, 1)
# Sum the octave values to produce the pink noise sample
pink_sample = np.sum(self.octave_values)
# Normalize by sigma to maintain consistent amplitude
self.noise = pink_sample * self.sigma
[docs]
def update(self, t):
"""update system equation for fixed point loop,
here just setting the outputs
Note
----
no direct passthrough, so the 'update' method
is optimized for this case
Parameters
----------
t : float
evaluation time
"""
self.outputs[0] = self.noise