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Added the plotting option for log-scaling of the tf spectrum

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Julius Voggesberger
2021-03-29 16:25:38 +02:00
parent c6a6fd7012
commit d09480c5bb
2 changed files with 17 additions and 6 deletions
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13
decoding_tf_analysis.py
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@@ -183,7 +183,7 @@ def create_tfr(raw, condition, freqs, n_cycles, response='induced', baseline=Non
return power
def time_frequency(dataset, filename, compute_tfr=True):
def time_frequency(dataset, filename, scaling='lin', compute_tfr=True):
"""
Runs time frequency analysis
@@ -191,10 +191,13 @@ def time_frequency(dataset, filename, compute_tfr=True):
:param filename: Filename of either the file from which the TFRs will be loaded
or to which they will be saved
:param compute_tfr: If True the TFRs will be created, else the TFRs will be loaded from a precomputed file
:param scaling: default 'lin' for linear scaling, else can be 'log' for logarithmic scaling
"""
# Parameters
# freqs = np.linspace(0.1, 50, num=50) # Use this for linear space scaling
freqs = np.logspace(*np.log10([0.1, 50]), num=50)
if scaling == 'lin':
freqs = np.linspace(0.1, 50, num=50) # Use this for linear space scaling
else:
freqs = np.logspace(*np.log10([0.1, 50]), num=50)
n_cycles = freqs / 2
cond1 = []
cond2 = []
@@ -245,11 +248,11 @@ def time_frequency(dataset, filename, compute_tfr=True):
F, clusters, cluster_p_values, h0 = mne.stats.permutation_cluster_test(
[mne.grand_average(cond1).data, mne.grand_average(cond2).data], n_jobs=4, verbose='INFO',
seed=123)
plot_tf_cluster(F, clusters, cluster_p_values, freqs, times)
plot_tf_cluster(F, clusters, cluster_p_values, freqs, times, scaling)
if __name__ == '__main__':
mne.set_log_level(verbose=VERBOSE_LEVEL)
ds = 'N170'
decoding(ds, 'faces_vs_cars', True)
time_frequency(ds, 'face_intact_vs_all_0.1_50hz_ncf2', True)
time_frequency(ds, 'face_intact_vs_all_0.1_50hz_ncf2', 'log', False)