from sklearn.model_selection import KFold

def examplar_epoch_data(epochs_in, N, navg):
    epo = epochs_in.copy()[range(N)]
    fold_num = N // navg
    if N % navg > 0: fold_num += 1

    X1 = epo.get_data()  # signals: n_epochs, n_channels, n_times
    y1 = epo.events[:, 2]

    n_trl, n_ch, n_times = epo.get_data().shape

    kf = KFold(n_splits=fold_num, shuffle= True)
    kf_idx = [x for x in kf.split(X1, y1)]
    train_y = [y1[x[0]] for x in kf_idx] # get y labels of each training-fold
    test_y = [x[1] for x in kf_idx] # get y labels of each testing-fold

    exampler_data = np.zeros((fold_num, n_ch, n_times))
    for i in range(fold_num):
        temp = epo[test_y[i]].average().data
        exampler_data[i,:,:] = temp
        pass
    y = np.ones(fold_num)
    return exampler_data, y

def decode_logit(epo1, epo2, navg):
    n_c1 = epo1.get_data().shape[0]
    n_c2 = epo2.get_data().shape[0]

    N = min([n_c1, n_c2])


    examplar_data_c1, y1 = examplar_epoch_data(epo1, N, navg)
    examplar_data_c2, y2 = examplar_epoch_data(epo2, N, navg)

    X = np.concatenate((examplar_data_c1, examplar_data_c2), axis = 0)
    y = [1] * len(y1) + [2] * len(y2)

    clf = make_pipeline(StandardScaler(),
                        SVC(kernel='linear',gamma='auto'))
                        #LinearModel(LogisticRegression(solver='lbfgs')))

    time_decod = SlidingEstimator(clf, n_jobs=1, scoring='roc_auc', verbose = False)
    scores = cross_val_multiscore(time_decod, X, y, cv=5, n_jobs=-1)

    # Mean scores across cross-validation splits
    scores = np.mean(scores, axis=0)
    
    fig, ax = plt.subplots()
    ax.plot(epo1.times, scores, label='score')
    ax.axhline(.5, color='k', linestyle='--', label='chance')
    ax.set_xlabel('Times')
    ax.set_ylabel('AUC')  # Area Under the Curve
    ax.legend()
    ax.axvline(.0, color='k', linestyle='-')
    ax.set_title('Sensor space decoding')

    return scores

epo1 = epochs['Auditory/Left']
epo2 = epochs['Visual/Left']
scores = decode_logit(epo1, epo2, 4)

# dementia project, envelop connectivity

conda install -c conda-forge mne-connectivity

# MNE python 維護 (2022/06/28, Mac M1 Max)
https://mne.tools/stable/install/manual_install.html

conda install --channel=conda-forge --name=base mamba
mamba create --override-channels --channel=conda-forge --name=mne

## (old macbook pro)Upgrading all packages

Generally speaking, if you want to upgrade your whole software stack including all the dependencies, the best approach is to re-create it as a new virtual environment, because neither conda nor pip are fool-proof at making sure all packages remain compatible with one another during upgrades.

Here we’ll demonstrate renaming the old environment first, as a safety measure. We’ll assume that the existing environment is called mne and you want to rename the old one so that the new, upgraded environment can be called mne instead. Unfortunately conda doesn’t have a “rename” command so we’ll first clone the old one with a new name (old_mne), then delete the original, then create the new, updated environment re-using the original name. In the first step we’ll also use conda in --offline mode so that it uses cached copies of all the packages instead of re-downloading them.

conda create --name old_mne --clone mne --offline  # copy with new name,
conda env remove --name mne --all                  # remove original,
conda create --name mne --channel conda-forge mne  # replace with updated

conda install --name=base nb_conda_kernels # to use Jupyter notebook
conda install -c conda-forge notebook

To use Alice dataset for Eelbrain

conda install -c conda-forge eelbrain
conda install -c conda-forge jupytext
pip install https://github.com/christianbrodbeck/TRF-Tools/archive/win.zip
pip install https://github.com/christianbrodbeck/gammatone/archive/fmax.zip

pip install fathon # DFA
pip install emd # 

Added `ipywidgets` to avoid `ImportError: IProgress not found. Please update jupyter and ipywidgets

conda install -c conda-forge ipywidgets

# using WSL
issues about launching X Display
https://ripon-banik.medium.com/run-x-display-from-wsl-f94791795376

# TO DO
Install libeemd, pyemd, emd