#########################################################################################
##
## RANDOM NUMBER GENERATOR BLOCK (rng.py)
##
## Milan Rother 2024
##
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# IMPORTS ===============================================================================
import numpy as np
from ._block import Block
# NOISE SOURCE BLOCKS ===================================================================
[docs]
class RNG(Block):
"""
Generates a random output value between -1 and 1
from a uniform distribution.
If no `sampling_rate` (None) is specified, every
simulation timestep gets a random value.
Parameters
----------
sampling_rate : float, None
number of random samples per time unit
Attributes
----------
n_samples : int
internal sample counter
val : float
internal random number state
"""
def __init__(self, sampling_rate=None):
super().__init__()
self.sampling_rate = sampling_rate
self.n_samples = 0
self.val = 0.0
def __len__(self):
return 0
[docs]
def reset(self):
super().reset()
#reset noise samples
self.n_samples = 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.val = 2.0*np.random.rand() - 1.0
self.n_samples += 1
[docs]
def update(self, t):
"""update system equation for fixed point loop,
here just setting the outputs
Parameters
----------
t : float
evaluation time
Returns
-------
error : float
absolute error to previous iteration for convergence
control (always '0.0' because source-type)
"""
self.outputs[0] = self.val
return 0.0