# Authors: Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
from mne.viz import plot_topomap

import mne
from mne.stats import spatio_temporal_cluster_test
from mne.datasets import sample
from mne.channels import read_ch_connectivity

print(__doc__)

# Set parameters
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
event_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'
event_id = {'Aud_L': 1, 'Aud_R': 2, 'Vis_L': 3, 'Vis_R': 4}
tmin = -0.2
tmax = 0.5

# Setup for reading the raw data
raw = mne.io.Raw(raw_fname, preload=True)
raw.filter(1, 30)
events = mne.read_events(event_fname)

"""
Explanation: .. _stats_cluster_sensors_2samp_spatial:
Spatiotemporal permutation F-test on full sensor data
Tests for differential evoked responses in at least
one condition using a permutation clustering test.
The FieldTrip neighbor templates will be used to determine
the adjacency between sensors. This serves as a spatial prior
to the clustering. Significant spatiotemporal clusters will then
be visualized using custom matplotlib code.
End of explanation
"""


picks = mne.pick_types(raw.info, meg='mag', eog=True)

reject = dict(mag=4e-12, eog=150e-6)
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,
                    baseline=None, reject=reject, preload=True)

epochs.drop_channels(['EOG 061'])
epochs.equalize_event_counts(event_id, copy=False)

condition_names = 'Aud_L', 'Aud_R', 'Vis_L', 'Vis_R'
X = [epochs[k].get_data() for k in condition_names]  # as 3D matrix
X = [np.transpose(x, (0, 2, 1)) for x in X]  # transpose for clustering

"""
Explanation: Read epochs for the channel of interest
End of explanation
"""


connectivity, ch_names = read_ch_connectivity('neuromag306mag')

print(type(connectivity))  # it's a sparse matrix!

plt.imshow(connectivity.toarray(), cmap='gray', origin='lower',
           interpolation='nearest')
plt.xlabel('{} Magnetometers'.format(len(ch_names)))
plt.ylabel('{} Magnetometers'.format(len(ch_names)))
plt.title('Between-sensor adjacency')

"""
Explanation: load FieldTrip neighbor definition to setup sensor connectivity
End of explanation
"""


# set cluster threshold
threshold = 50.0  # very high, but the test is quite sensitive on this data
# set family-wise p-value
p_accept = 0.001

cluster_stats = spatio_temporal_cluster_test(X, n_permutations=1000,
                                             threshold=threshold, tail=1,
                                             n_jobs=2,
                                             connectivity=connectivity)

T_obs, clusters, p_values, _ = cluster_stats
good_cluster_inds = np.where(p_values < p_accept)[0]

# Note. The same functions works with source estimate. The only differences
# are the origin of the data, the size, and the connectivity definition.
# It can be used for single trials or for groups of subjects.

"""
Explanation: Compute permutation statistic
How does it work? We use clustering to bind together features which are
similar. Our features are the magnetic fields measured over our sensor
array at different times. This reduces the multiple comparison problem.
To compute the actual test-statistic, we first sum all F-values in all
clusters. We end up with one statistic for each cluster.
Then we generate a distribution from the data by shuffling our conditions
between our samples and recomputing our clusters and the test statistics.
We test for the significance of a given cluster by computing the probability
of observing a cluster of that size. For more background read:
Maris/Oostenveld (2007), "Nonparametric statistical testing of EEG- and
MEG-data" Journal of Neuroscience Methods, Vol. 164, No. 1., pp. 177-190.
doi:10.1016/j.jneumeth.2007.03.024
End of explanation
"""


# configure variables for visualization
times = epochs.times * 1e3
colors = 'r', 'r', 'steelblue', 'steelblue'
linestyles = '-', '--', '-', '--'

# grand average as numpy arrray
grand_ave = np.array(X).mean(axis=1)

# get sensor positions via layout
pos = mne.find_layout(epochs.info).pos

# loop over significant clusters
for i_clu, clu_idx in enumerate(good_cluster_inds):
    # unpack cluster infomation, get unique indices
    time_inds, space_inds = np.squeeze(clusters[clu_idx])
    ch_inds = np.unique(space_inds)
    time_inds = np.unique(time_inds)

    # get topography for F stat
    f_map = T_obs[time_inds, ...].mean(axis=0)

    # get signals at significant sensors
    signals = grand_ave[..., ch_inds].mean(axis=-1)
    sig_times = times[time_inds]

    # create spatial mask
    mask = np.zeros((f_map.shape[0], 1), dtype=bool)
    mask[ch_inds, :] = True

    # initialize figure
    fig, ax_topo = plt.subplots(1, 1, figsize=(10, 3))
    title = 'Cluster #{0}'.format(i_clu + 1)
    fig.suptitle(title, fontsize=14)

    # plot average test statistic and mark significant sensors
    image, _ = plot_topomap(f_map, pos, mask=mask, axis=ax_topo,
                            cmap='Reds', vmin=np.min, vmax=np.max)

    # advanced matplotlib for showing image with figure and colorbar
    # in one plot
    divider = make_axes_locatable(ax_topo)

    # add axes for colorbar
    ax_colorbar = divider.append_axes('right', size='5%', pad=0.05)
    plt.colorbar(image, cax=ax_colorbar)
    ax_topo.set_xlabel('Averaged F-map ({:0.1f} - {:0.1f} ms)'.format(
        *sig_times[[0, -1]]
    ))

    # add new axis for time courses and plot time courses
    ax_signals = divider.append_axes('right', size='300%', pad=1.2)
    for signal, name, col, ls in zip(signals, condition_names, colors,
                                     linestyles):
        ax_signals.plot(times, signal, color=col, linestyle=ls, label=name)

    # add information
    ax_signals.axvline(0, color='k', linestyle=':', label='stimulus onset')
    ax_signals.set_xlim([times[0], times[-1]])
    ax_signals.set_xlabel('time [ms]')
    ax_signals.set_ylabel('evoked magnetic fields [fT]')

    # plot significant time range
    ymin, ymax = ax_signals.get_ylim()
    ax_signals.fill_betweenx((ymin, ymax), sig_times[0], sig_times[-1],
                             color='orange', alpha=0.3)
    ax_signals.legend(loc='lower right')
    ax_signals.set_ylim(ymin, ymax)

    # clean up viz
    mne.viz.tight_layout(fig=fig)
    fig.subplots_adjust(bottom=.05)
    plt.show()

"""
Exercises
----------

- What is the smallest p-value you can obtain, given the finite number of
   permutations?
- use an F distribution to compute the threshold by traditional significance
   levels. Hint: take a look at ```scipy.stats.distributions.f```
"""

"""
Explanation: Visualize clusters
End of explanation
"""


