# Import filter function from neurodsp.filt import filter_signal # Import simulation code for creating test data from neurodsp.sim import sim_combined from neurodsp.utils import set_random_seed, create_times # Import utilities for loading and plotting data from neurodsp.utils.download import load_ndsp_data from neurodsp.plts.time_series import plot_time_series ################################################################################################### # Set the random seed, for consistency simulating data set_random_seed(0) ################################################################################################### # General settings for simulations fs = 1000 n_seconds = 5 # Set the default aperiodic exponent exp = -1 # Generate a times vector, for plotting times = create_times(n_seconds, fs) ################################################################################################### # Bandpass filters
# sphinx_gallery_thumbnail_number = 3 # Import numpy import numpy as np # Use NeuroDSP for time series simulations & analyses from neurodsp import sim from neurodsp.utils import create_times, set_random_seed from neurodsp.spectral import compute_spectrum_welch from neurodsp.plts import plot_time_series, plot_power_spectra ################################################################################################### # Set random seed, for consistency generating simulated data set_random_seed(21) # Simulation Settings n_seconds = 2 s_rate = 1000 # Compute an array of time values, for plotting, and check length of data times = create_times(n_seconds, s_rate) n_points = len(times) ################################################################################################### # Frequency Representations of Aperiodic Signals # ---------------------------------------------- # # Let's start with aperiodic signals, and examine how different types of aperiodic # signals are represented in the frequency domain.
