Replace TFR plot with PSD, improved descriptions

examples/plot_simulate_alpha.py

@@ -6,11 +6,8 @@
 This example demonstrates how to simulate alpha and beta waves using
 HNN-core. Alpha activity can be produced with 10 Hz excitatory drive to the
 proximal or distal dendrites of pyramidal neurons. Providing proximal and
-distal drive simultaneously results in higher frequency beta activity [1]_, [2]_.
-
-Note that for plotting time-frequency representations, the code below calls
-the ``mne`` function :func:`~mne.time_frequency.tfr_array_morlet`. To install
-``mne``, simply ``pip install mne`` inside your python environment.
+distal drive simultaneously results in higher frequency beta activity [1]_,
+[2]_.
 """
 
 # Authors: Mainak Jas <mainak.jas@telecom-paristech.fr>
@@ -75,7 +72,7 @@
 # stochasticity of input spike timing, the proximal and distal spikes
 # occasionally arrive at the same time which will result in a beta frequency
 # (15-30 Hz) event. The higher frequency activity arises from biophysical
-# properties intrinsic to the cortical circuit.
+# properties intrinsic to the cortical neurons.
 location = 'distal'
 burst_std = 20
 weights_ampa_d = {'L2_pyramidal': 5.4e-5, 'L5_pyramidal': 5.4e-5}
@@ -86,23 +83,19 @@
     spike_isi=10, repeats=10, location=location, weights_ampa=weights_ampa_d,
     synaptic_delays=syn_delays_d, seedcore=16)
 
-dpl = simulate_dipole(net,postproc=False)
+dpl = simulate_dipole(net, postproc=False)
 
 ###############################################################################
-# It can be difficult to identify beta activity by inspecting the dipole
-# directly. One useful tool is to plot the time frequency spectrogram.
-# This requires creating a fixed-step tiling of frequencies from 5 to 50 Hz
-# in steps of 1 Hz
-import numpy as np
-from hnn_core.viz import plot_tfr_morlet
+# We can verify that beta frequency activity was produced by inspecting the PSD 
+# of the most recent simulation. While the 10 Hz alpha peak is still present, a 
+# much more prominent 20 Hz peak has appeared with the addition of rhythmic
+# distal inputs.
 trial_idx = 0  # single trial simulated
 dpl[trial_idx].scale(150000)
-tmin = 20
-fig, axes = plt.subplots(2, 1, sharex=True, figsize=(6, 6))
+fig, axes = plt.subplots(2, 1)
+tmin = 20  # exclude initial burn-in period
 plot_dipole(dpl[trial_idx], tmin=tmin, ax=axes[0], show=False)
-freqs = np.arange(5., 50., 1.)
-plot_tfr_morlet(dpl[trial_idx], freqs=freqs, n_cycles=freqs / 3,
-                tmin=tmin, ax=axes[1])
+plot_spectrogram(dpl[trial_idx], fmin=0., fmax=40., tmin=tmin, ax=axes[1])
 
 ###############################################################################
 # References