def run_fooof(freq_path,loc_path,save_report_path,save_variables_path,save_variable_prefix,save_variables = True
,save_report=True,freq_range=[1,30],channel = 'Oz'):
freqIterator = folderIterator(freq_path)
chan_inds_iterator = chanIndIterator(loc_path)
# Constant
bg_ppts= []# background parameters/ppts
peak_ppts = []
errors = []
for chan_ind, freq_var in zip(chan_inds_iterator,freqIterator)
if True: # if files[0].endswith(".mat"): TODO: set the conditions for safety
power,spectrum = getFreqValuesVec(freq_var)
if chan_ind != -1
power = 10**np.array(power[chan_ind])
spectrum=np.array(spectrum).flatten()
# Initialize FOOOF object
fm = FOOOF(peak_width_limits=[0.5,8])
# Model the power spectrum with FOOOF, and print out a report
fm.fit(spectrum, power, freq_range)
bg_ppts.append(fm.background_params_)
peak_ppts.append(fm.peak_params_)
errors.append(fm.error_)
if save_report:
fm.save_report(str(freq_var[-7:-4]),save_report_path)
if save_variables :
with cd(save_variables_path):
save_file(save_variable_prefix+'bg_ppts.npy',bg_ppts)
save_file(save_variable_prefix+'peak_ppts.npy',peak_ppts)
save_file(save_variable_prefix+'errors.npy',errors)
def plot_all_epochs(power_path,
cond,
loc_path,
color,
end_freq=30,
chan='Oz',
subplot=False):
'''
In the future, allow subplot optino and ppt option
'''
power_itr = folderIterator(power_path)
loc_itr = folderIterator(loc_path)
subplot_values = 1
for power_file, loc_file in zip(power_itr, loc_itr):
print(power_file)
features, freqVec = readOneFeatures(power_file, end_freq)
print('Dimension of all of the powers')
print(features.shape) # sanity check
plt.figure(subplot_values)
chanlocs = getChanLocs(loc_file)
if (chan in chanlocs):
chan_ind = chanlocs.index(chan)
# chan_powers = normalize_power(features[:,chan_ind,:])
chan_powers = features[:, chan_ind, :]
labels_count = features.shape[0]
mean = np.mean(chan_powers, 0)
std = np.std(chan_powers, axis=0)
plt.plot(freqVec,
mean,
color,
label=cond + ':' + str(labels_count) + 'Epochs')
plt.plot(freqVec, mean - std, color)
plt.plot(freqVec, mean + std, color)
plt.legend(loc='upper right', shadow=True, fontsize='x-large')
power_file = power_file.decode("utf-8")
plt.title('Participant: ' + power_file[-7:-4] + '\n Chan: ' + chan)
plt.xlabel('Frequency (Hz)')
plt.ylabel('Power')
else:
print(files[0], ' did not have ', chan)
input('understood?')
subplot_values += 1
def plot_all_ppt(cond_dir, conds, loc_path, color, label_itr, end_freq):
'''
Input:
cond_dir:
conds:
loc_path:
color:
label_itr:
end_freq:
Output:
Warnings. It can only do 2 conditions now
'''
loc_itr = folderIterator(loc_path)
c0_itr = folderIterator(cond_dir + '/' + conds[0])
c1_itr = folderIterator(cond_dir + '/' + conds[1])
for cond0, cond1 in zip(c0_itr, c1_itr):
pass
loc_dir = '/Volumes/SHard/Tsuchiya_Lab_Data/Probes/chanLocs/'
conds = ['Off', 'On']
##########
# Script #
##########
with cd(fooof_vars_path):
peak_ppts = np.load('basepeak_ppts.npy')
# Functions to apply for list
alpha = lambda arr: float('nan') if arr.size == 0 else findAlpha(arr)
peaks = list(map(alpha, peak_ppts))
peaks = [round(peak, 1) for peak in peaks] # round it to 0.1 scale
folder_iter_wrapper = lambda cond: folderIterator(freq_path + '/' + cond)
#TODO: ughhhh, generalize it... cond1 cond2...
# Get folder iterators for conditions
cond0_itr = folder_iter_wrapper(conds[0])
cond1_itr = folder_iter_wrapper(conds[1])
chan_inds_itr = chanIndIterator(loc_dir)
plt.figure()
# The difference of conditions with respect to alpha(cond1 - cond0)
alpha_diffs = getDiffs(cond0_itr,
cond1_itr,
chan_inds_itr,
peaks,
avoid_flickers=True)
plot_scatter(list(range(len(alpha_diffs))),