Update motivations

motivations/plot_BandRatios.py

@@ -2,16 +2,36 @@
 Band Ratio Measures
 ===================
 
-Showing why band ratio measures are bad.
+Exploring band ratio measures in the context of the FOOOF model.
 """
 
 ###################################################################################################
 #
-# Band ratios measures are a
+# Band Ratios Project
+# -------------------
 #
-# In this notebook
+# Note: this example is quick demonstration of how band ratios measures relate
+# to periodic and aperiodic activity.
 #
-# This topic is the
+# This topic has been the topic of a full research project, available
+# `here <https://github.com/voytekresearch/BandRatios>`_,
+#
+
+###################################################################################################
+#
+# Introduction
+# ------------
+#
+# Band ratios measures are a relatively common measure, proposed to measure oscillatory,
+# or periodic, activity.
+#
+# They are typically calculated as:
+#
+# .. math::
+#    BR = \frac{avg(low band power)}{avg(high band power)}
+#
+# In this notebook we will explore this measure in the context of conceptualizing
+# neural power spectra as a combination of both aperiodic and periodic activity.
 #
 
 ###################################################################################################
@@ -22,12 +42,7 @@
 
 from fooof.utils import trim_spectrum
 
-###################################################################################################
-
-# Simulation Settings
-nlv = 0
-f_res = 0.5
-f_range = [3, 30]
+from fooof.bands import Bands
 
 ###################################################################################################
 
@@ -45,7 +60,36 @@ def calc_band_ratio(freqs, powers, low_band, high_band):
 
 ###################################################################################################
 
-fs, ps = gen_power_spectrum(f_range, [0, 1.5], [[10, 0.31, 1], [22, 0.2, 2]], nlv=nlv, freq_res=f_res)
+# Simulation Settings
+nlv = 0
+f_res = 0.5
+f_range = [3, 30]
+
+# Set our band definitions
+bands = Bands({'theta' : [4, 8], 'alpha' : [8, 13], 'beta' : [13, 30]})
+
+###################################################################################################
+
+# Simulate a power spectrum
+freqs, pows = gen_power_spectrum(f_range, [0, 1.5], [[10, 0.31, 1],
+                                 [22, 0.2, 2]], nlv=nlv, freq_res=f_res)
+
+###################################################################################################
+
+#
+
+
+###################################################################################################
+
+# Calculate a band ratio measure
+tbr = calc_band_ratio(freqs, pows, bands.theta, bands.beta)
+print('Calculate theta / beta ratio is :\t {:1.2f}'.format(tbr))
+
+###################################################################################################
+
+###################################################################################################
+
+###################################################################################################
 
 ###################################################################################################
 

motivations/plot_BandsVsAperiodic.py

@@ -123,7 +123,7 @@
 # Periodic & Aperiodic Model
 # --------------------------
 #
-# An alternative posibility is that what is different between age groups is predominantly the 1/f.
+# An alternative possibility is that what is different between age groups is predominantly the 1/f.
 #
 # In this simulation, we simulate child and adult power spectra as having different 1/f properties.
 #

motivations/plot_DoYouEvenOscillate.py

@@ -180,7 +180,7 @@
 # --------------------------------------------------
 #
 # In principle, signals that display power across all frequencies need not arise from, or be
-# best descriped as, signals with rhythmic components.
+# best described as, signals with rhythmic components.
 #
 # Of course, it could be the case that such a signal does indeed arise from the summation across
 # many oscillations. Here we will explore that.
@@ -284,7 +284,7 @@
 # rhythmic components, the question becomes what is the best / most likely description for
 # neural signals.
 #
-# Neural signals clearly containt 1/f activity, as seen in their frequency representations.
+# Neural signals clearly contain 1/f activity, as seen in their frequency representations.
 #
 # They also sometimes do contain clear rhythmic components, visible as clear and relatively
 # consistent rhythms in the time series, and regions of extra power, or peaks, in the power
@@ -313,7 +313,7 @@
 # 3.2: Local Field Potentials
 # ~~~~~~~~~~~~~~~~~~~~~~~~~~~
 #
-# Simulate a local field potential as aggregrate activity of post-synaptic potentials.
+# Simulate a local field potential as aggregate activity of post-synaptic potentials.
 #
 
 ###################################################################################################

motivations/plot_IfYouFilterTheyWillCome.py

@@ -19,9 +19,9 @@
 ###################################################################################################
 
 import numpy as np
-from neurodsp import sim, filt
 
 # Imports from NeuroDSP to simulate time series
+from neurodsp import sim, filt
 from neurodsp.utils import create_times
 from neurodsp.plts import plot_time_series
 

motivations/plot_UnderdeterminedChanges.py

@@ -1,5 +1,5 @@
 """
-Underdetermined measures
+Underdetermined Measures
 ========================
 
 Figure 1 from the FOOOF paper, in quantitatively exact code.
@@ -65,7 +65,8 @@ def plot_style(ax):
 for cur_ax, (title, cur_dat) in zip(ax.reshape(-1), ps_comps.items()):
 
     # Create COMMENT
-    plot_spectra_shading(fs, [ps_base, cur_dat], [8, 12], log_freqs=True, log_powers=True, ax=cur_ax)
+    plot_spectra_shading(fs, [ps_base, cur_dat], [8, 12],
+        log_freqs=True, log_powers=True, ax=cur_ax)
 
     # Add the title, and do some plot styling
     cur_ax.set_title(title, {'fontsize' : 20})