exponent = -2 # Simulate powerlaw activity, specifically brown noise times = create_times(n_seconds, fs) br_noise = sim.sim_powerlaw(n_seconds, fs, exponent) ################################################################################################### # Plot the simulated data, in the time domain plot_time_series(times, br_noise) ################################################################################################### # Plot the simulated data, in the frequency domain freqs, psd = spectral.compute_spectrum(br_noise, fs) plot_power_spectra(freqs, psd) ################################################################################################### # # Simulate Filtered 1/f Activity # ------------------------------ # # The powerlaw simulation function is also integrated with a filter. This can be useful # if one wants to filter out the slow frequencies, as is often done with neural signals, # to remove the very slow drifts that we see in the pure 1/f simulations. # # To filter a simulated powerlaw signal, simply pass in a filter range, and the filter will # be applied to the simulated data before being returned. Here we will apply a high-pass filter. # # We can see that the resulting signal has much less low-frequency drift than the first one. #
################################################################################################### # # We can also compare these signals in the frequency. # # Notice that the asymmetric oscillation has strong harmonics resulting from the # non-sinusoidal nature of the oscillation. # ################################################################################################### # Plot the simulated data, in the frequency domain freqs_sine, psd_sine = compute_spectrum(osc_sine, fs) freqs_shape, psd_shape = compute_spectrum(osc_shape, fs) plot_power_spectra([freqs_sine, freqs_shape], [psd_sine, psd_shape]) ################################################################################################### # Simulate a Bursty Oscillation # ----------------------------- # # Sometimes we want to study oscillations that come and go, so it can be useful to simulate # oscillations with this property. # # You can simulate bursty oscillations with :func:`~neurodsp.sim.periodic.sim_bursty_oscillation`. # # To control the bursitness of the simulated signal, you can control the probability # that a burst will start or stop with each new cycle. # ###################################################################################################
###################################################################################################
# Simulate an oscillation over an aperiodic component
signal = sim.sim_combined(n_seconds, fs, components)
###################################################################################################
# Plot the simulated data, in the time domain
plot_time_series(times, signal)
###################################################################################################
# Plot the simulated data, in the frequency domain
freqs, psd = spectral.compute_spectrum(signal, fs)
plot_power_spectra(freqs, psd)
###################################################################################################
#
# We can switch out any components that we want, for example trading the stationary oscillation
# for a bursting oscillation, also with an aperiodic component.
#
# We can also control the relative proportions of each component, by using a parameter called
# `component_variances` that specifies the variance of each component.
#
###################################################################################################
# Define the components of the combined signal to simulate
components = {
'sim_synaptic_current': {
#%% Welch frequency distribution
from neurodsp.plts.spectral import plot_power_spectra
plot_power_spectra(freq_mean[:200], psd_mean[:200], 'Welch')
#%% Simple plot
from neurodsp.plts.time_series import plot_time_series
times = create_times(len(datastruct[subj][ch])/fs, fs)
plot_time_series(times, datastruct[subj][ch], xlim=[0, .8])
#%%
with pd.option_context('display.max_rows', 20
, 'display.max_columns', None):
print(df)
#%%
import matplotlib.pyplot as plt
plt_time = [0, 5000]
plt.figure(figsize = (20,8));
plt.plot((sig[plt_time[0]:plt_time[1]]),label= 'Raw Signal')
avg_type='median',
nperseg=fs * 2)
# Median filtered spectrum
freq_mf, psd_mf = compute_spectrum(sig, fs, method='medfilt')
###################################################################################################
#
# You can plot power spectra with :func:`~.plot_power_spectra`.
#
###################################################################################################
# Plot the power spectra
plot_power_spectra([freq_mean[:200], freq_med[:200], freq_mf[100:10000]],
[psd_mean[:200], psd_med[:200], psd_mf[100:10000]],
['Welch', 'Median Welch', 'Median Filter FFT'])
###################################################################################################
# Aside: Fitting 1/f and oscillations in power spectra
# ----------------------------------------------------
#
# You might notice in the above power spectra that there are regions of band-specific power,
# reflecting oscillations, as well as regions that show linear decrease when plotted in
# log-log scale, in particular the frequency region between 30-100Hz.
#
# As well as a large body of work investigating oscillations, this "1/f-like" aperiodic
# components has been found to correlate with aging, memory, and cognitive control in several
# previous publications, and has been proposed to reflect the physiological balance between
# excitation and inhibition.
#
n_seconds = 10 fs = 1000 exponent = -2 times = create_times(n_seconds, fs) br_noise = sim.sim_powerlaw(n_seconds, fs, exponent) ################################################################################################### # Plot the simulated data, in the time domain plot_time_series(times, br_noise) ################################################################################################### # Plot the simulated data, in the frequency domain freqs, psd = spectral.compute_spectrum(br_noise, fs) plot_power_spectra(freqs, psd) ################################################################################################### # # Simulate filtered brown noise # ----------------------------- # # However, brown noise has a lot of power in very slow frequnecies, # whereas these slow frequencies are often not present or filtered out # in neural signals. Therefore, we may desire our brown noise to be # high-pass filtered. See that the resulting signal has much less # low-frequency drift. # # Note this might not be ideal because it creates an "oscillation" at # the cutoff frequency. #