Skip to content

Commit

Permalink
Misc style & doc updates.
Browse files Browse the repository at this point in the history
  • Loading branch information
@TomDonoghue
TomDonoghue authored Apr 19, 2019
1 parent 58da814 commit bf3618d
Showing 1 changed file with 34 additions and 58 deletions.
92 changes: 34 additions & 58 deletions examples/plot_mne_example.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -2,8 +2,7 @@
Using fooof with MNE
====================
This examples illustrates how to use fooof with MNE
and create topographical plots
This examples illustrates how to use fooof with MNE and create topographical plots.
This tutorial does require that you have `MNE
<https://mne-tools.github.io/>`_ installed. If you don't already have
Expand All @@ -13,8 +12,7 @@

###################################################################################################


#General imports
# General imports
import numpy as np
import pandas as pd
from matplotlib import cm, colors, colorbar
Expand All @@ -30,7 +28,6 @@
from fooof import FOOOF, FOOOFGroup
from fooof.analysis import *


###################################################################################################
# Load & Check MNE Data
# ---------------------
Expand Down Expand Up @@ -74,65 +71,61 @@
reject = dict(eeg=180e-6)
event_id = {'left/auditory': 1}
events = mne.read_events(event_fname)
epochs = mne.Epochs(raw, events=events, event_id=event_id,
tmin=5, tmax=125, baseline=None, preload=True)
epochs = mne.Epochs(raw, events=events, event_id=event_id, tmin=5, tmax=125,
baseline=None, preload=True, verbose=False)

###################################################################################################

# Creating Power Spectra Densities
psd, freqs = mne.time_frequency.psd_welch(epochs, fmin=1., fmax=50., n_fft=2000,
n_overlap=250, n_per_seg=500)
spectra, freqs = mne.time_frequency.psd_welch(epochs, fmin=1., fmax=50., n_fft=2000,
n_overlap=250, n_per_seg=500)

###################################################################################################
# fooofgroup
# FOOOFGroup
# ----------
#
# The FOOOFGroup object is used to fit
# FOOOF models across the power spectra. A list of FOOOFGroup objects is returned.
# The FOOOFGroup object is used to fit FOOOF models across the power spectra.
#

###################################################################################################

# Initialize a FOOOF group object, with desired settings
# Initialize a FOOOFGroup object, with desired settings
fg = FOOOFGroup(peak_width_limits=[1, 6], min_peak_height=0.075,
max_n_peaks=6, peak_threshold=1, verbose=False)

###################################################################################################

# Shaping the PSDs so that we can run fooofgroup
fooof_psd = np.squeeze(psd[0,:,:])
n_channels, n_freq = fooof_psd.shape
# Selecting the first epoch of data to FOOOF
spectra = np.squeeze(spectra[0,:,:])
n_channels, n_freq = spectra.shape
num_blocks = len(mne.read_events(event_fname))

# Setting frequency range
freq_range = [3, 35]

# This returns a list of FOOOFGRoup objects
fg.fit(freqs, fooof_psd, freq_range)
# Fit the FOOOF model across all channels
fg.fit(freqs, spectra, freq_range)
fg.plot()

###################################################################################################

# Periodic features
# Define labels for periodic & aperiodic features
feats = ["CFS", "PWS", "BWS"]

# Aperiodic features
aperiodic_feats = ["Offset","Exponent"]

# Define bands of interest
bands = {'theta': [3,7],
'alpha': [7,14],
'beta': [15,30]}
bands = {'theta': [3, 7],
'alpha': [7, 14],
'beta': [15, 30]}

# Creating dictionaries to store all the aperiodic properties across frequencies
# Create dictionaries to store all the periodic properties across frequencies
results = {}
for band_name in bands.keys():
results[band_name] = np.zeros(shape=[ num_blocks, n_channels, len(feats)])
results[band_name] = np.zeros(shape=[num_blocks, n_channels, len(feats)])

# Creating dictionaries to store all the periodic properties across frequencies
# Creating dictionaries to store all the aperiodic properties across frequencies
exponent_results = np.zeros(shape=[num_blocks, n_channels, len(aperiodic_feats)])


###################################################################################################

# Populating periodic and aperiodic values
Expand All @@ -142,23 +135,18 @@
for band_label, band_range in bands.items():
results[band_label][block, ind, :] = get_band_peak(res.peak_params, band_range, True)


###################################################################################################
# Plotting topographies
# Plotting Topographies
# ---------------------
#
# The following will illustrate the process of exploring the previously generated
# oscillatory values
# Now we can plot the extracted FOOOF features across all channels.
#

###################################################################################################
# This is the function required to create color bars for topographies

def plot_topo_colorbar(vmin, vmax, label):
"""
Create a colorbar for the topography plots
vmin: int
vmax: int
label: str
"""
"""Helper function to create colorbars for the topography plots."""

fig = plt.figure(figsize=(2, 3))
ax1 = fig.add_axes([0.9, 0.25, 0.15, 0.9])

Expand All @@ -168,18 +156,21 @@ def plot_topo_colorbar(vmin, vmax, label):
cb1 = colorbar.ColorbarBase(plt.gca(), cmap=cmap,
norm=norm, orientation='vertical')

###################################################################################################
#
# In this example, we will be plotting the alpha center frequency and oscillatory exponent.

###################################################################################################

# Settings: In this example, we will be plotting the Alpha Center Frequency
# and oscillatory exponent
# Settings to grab the alpha center frequency
band = 'alpha'
cur_data = results[band]

topo_dat = np.mean(cur_data,0)

###################################################################################################

# Looking at the alpha Center Frequeuncy
# Looking at the alpha center frequeuncy
print('CURRENT FEATURE:', feats[0])
disp_dat = topo_dat[:,0]

Expand All @@ -195,7 +186,7 @@ def plot_topo_colorbar(vmin, vmax, label):

###################################################################################################

# Looking at the Oscillatory
# Looking at the aperiodic exponent
cur_data = exponent_results

topo_dat = np.mean(cur_data,0)
Expand All @@ -212,18 +203,3 @@ def plot_topo_colorbar(vmin, vmax, label):
fig, ax = plt.subplots()
plot_topo_colorbar(vmin, vmax, exponent_results[1])
mne.viz.plot_topomap(disp_dat, raw.info, vmin=vmin, vmax=vmax, cmap=cm.viridis, contours=0, axes=ax)


###################################################################################################
# Alternative epoching methods
# ----------------------------
#
# Antother way to epoch your data based on time by avaeraging across events as suppose to
# selecting specifc time sections

###################################################################################################

tmin, tmax = -0.2, 0.5
epochs_params = dict(events=events, event_id=event_id, tmin=tmin, tmax=tmax,
reject=reject)
epochs = mne.Epochs(raw, **epochs_params).average()

0 comments on commit bf3618d

Please sign in to comment.