# Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)

import numpy as np
import matplotlib.pyplot as plt

import mne
from mne.time_frequency import tfr_morlet
from mne.stats import permutation_cluster_test
from mne.datasets import sample

print(__doc__)

"""
Explanation: Non-parametric between conditions cluster statistic on single trial power
This script shows how to compare clusters in time-frequency
power estimates between conditions. It uses a non-parametric
statistical procedure based on permutations and cluster
level statistics.
The procedure consists in:

extracting epochs for 2 conditions
compute single trial power estimates
baseline line correct the power estimates (power ratios)
compute stats to see if the power estimates are significantly different
    between conditions.
End of explanation
"""


data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
event_fname = data_path + '/MEG/sample/sample_audvis_raw-eve.fif'
tmin, tmax = -0.2, 0.5

# Setup for reading the raw data
raw = mne.io.read_raw_fif(raw_fname)
events = mne.read_events(event_fname)

include = []
raw.info['bads'] += ['MEG 2443', 'EEG 053']  # bads + 2 more

# picks MEG gradiometers
picks = mne.pick_types(raw.info, meg='grad', eeg=False, eog=True,
                       stim=False, include=include, exclude='bads')

ch_name = 'MEG 1332'  # restrict example to one channel

# Load condition 1
reject = dict(grad=4000e-13, eog=150e-6)
event_id = 1
epochs_condition_1 = mne.Epochs(raw, events, event_id, tmin, tmax,
                                picks=picks, baseline=(None, 0),
                                reject=reject, preload=True)
epochs_condition_1.pick_channels([ch_name])

# Load condition 2
event_id = 2
epochs_condition_2 = mne.Epochs(raw, events, event_id, tmin, tmax,
                                picks=picks, baseline=(None, 0),
                                reject=reject, preload=True)
epochs_condition_2.pick_channels([ch_name])

"""
Explanation: Set parameters
End of explanation
"""


decim = 2
freqs = np.arange(7, 30, 3)  # define frequencies of interest
n_cycles = 1.5

tfr_epochs_1 = tfr_morlet(epochs_condition_1, freqs,
                          n_cycles=n_cycles, decim=decim,
                          return_itc=False, average=False)

tfr_epochs_2 = tfr_morlet(epochs_condition_2, freqs,
                          n_cycles=n_cycles, decim=decim,
                          return_itc=False, average=False)

tfr_epochs_1.apply_baseline(mode='ratio', baseline=(None, 0))
tfr_epochs_2.apply_baseline(mode='ratio', baseline=(None, 0))

epochs_power_1 = tfr_epochs_1.data[:, 0, :, :]  # only 1 channel as 3D matrix
epochs_power_2 = tfr_epochs_2.data[:, 0, :, :]  # only 1 channel as 3D matrix

"""
Explanation: Factor to downsample the temporal dimension of the TFR computed by
tfr_morlet. Decimation occurs after frequency decomposition and can
be used to reduce memory usage (and possibly comptuational time of downstream
operations such as nonparametric statistics) if you don't need high
spectrotemporal resolution.
End of explanation
"""


threshold = 6.0
T_obs, clusters, cluster_p_values, H0 = \
    permutation_cluster_test([epochs_power_1, epochs_power_2],
                             n_permutations=100, threshold=threshold, tail=0)

"""
Explanation: Compute statistic
End of explanation
"""


times = 1e3 * epochs_condition_1.times  # change unit to ms
evoked_condition_1 = epochs_condition_1.average()
evoked_condition_2 = epochs_condition_2.average()

plt.figure()
plt.subplots_adjust(0.12, 0.08, 0.96, 0.94, 0.2, 0.43)

plt.subplot(2, 1, 1)
# Create new stats image with only significant clusters
T_obs_plot = np.nan * np.ones_like(T_obs)
for c, p_val in zip(clusters, cluster_p_values):
    if p_val <= 0.05:
        T_obs_plot[c] = T_obs[c]

plt.imshow(T_obs,
           extent=[times[0], times[-1], freqs[0], freqs[-1]],
           aspect='auto', origin='lower', cmap='gray')
plt.imshow(T_obs_plot,
           extent=[times[0], times[-1], freqs[0], freqs[-1]],
           aspect='auto', origin='lower', cmap='RdBu_r')

plt.xlabel('Time (ms)')
plt.ylabel('Frequency (Hz)')
plt.title('Induced power (%s)' % ch_name)

ax2 = plt.subplot(2, 1, 2)
evoked_contrast = mne.combine_evoked([evoked_condition_1, evoked_condition_2],
                                     weights=[1, -1])
evoked_contrast.plot(axes=ax2, time_unit='s')

plt.show()

"""
Explanation: View time-frequency plots
End of explanation
"""


