import mne

"""
Explanation: Basic MEG and EEG data processing

MNE-Python reimplements most of MNE-C's (the original MNE command line utils)
functionality and offers transparent scripting.
On top of that it extends MNE-C's functionality considerably
(customize events, compute contrasts, group statistics, time-frequency
analysis, EEG-sensor space analyses, etc.) It uses the same files as standard
MNE unix commands: no need to convert your files to a new system or database.
What you can do with MNE Python

Raw data visualization to visualize recordings, can also use
     mne_browse_raw for extended functionality (see ch_browse)
Epoching: Define epochs, baseline correction, handle conditions etc.
Averaging to get Evoked data
Compute SSP projectors to remove ECG and EOG artifacts
Compute ICA to remove artifacts or select latent sources.
Maxwell filtering to remove environmental noise.
Boundary Element Modeling: single and three-layer BEM model
     creation and solution computation.
Forward modeling: BEM computation and mesh creation
     (see ch_forward)
Linear inverse solvers (MNE, dSPM, sLORETA, eLORETA, LCMV, DICS)
Sparse inverse solvers (L1/L2 mixed norm MxNE, Gamma Map,
     Time-Frequency MxNE)
Connectivity estimation in sensor and source space
Visualization of sensor and source space data
Time-frequency analysis with Morlet wavelets (induced power,
     intertrial coherence, phase lock value) also in the source space
Spectrum estimation using multi-taper method
Mixed Source Models combining cortical and subcortical structures
Dipole Fitting
Decoding multivariate pattern analysis of M/EEG topographies
Compute contrasts between conditions, between sensors, across
     subjects etc.
Non-parametric statistics in time, space and frequency
     (including cluster-level)
Scripting (batch and parallel computing)

What you're not supposed to do with MNE Python
- **Brain and head surface segmentation** for use with BEM
  models -- use Freesurfer.

<div class="alert alert-info"><h4>Note</h4><p>This package is based on the FIF file format from Neuromag. It
          can read and convert CTF, BTI/4D, KIT and various EEG formats to
          FIF.</p></div>

Installation of the required materials
See install_python_and_mne_python.
<div class="alert alert-info"><h4>Note</h4><p>The expected location for the MNE-sample data is
    ``~/mne_data``. If you downloaded data and an example asks
    you whether to download it again, make sure
    the data reside in the examples directory and you run the script from its
    current directory.

    From IPython e.g. say::

        cd examples/preprocessing


    %run plot_find_ecg_artifacts.py</p></div>

From raw data to evoked data
Now, launch ipython_ (Advanced Python shell) using the QT backend, which
is best supported across systems::
$ ipython --matplotlib=qt
First, load the mne package:
<div class="alert alert-info"><h4>Note</h4><p>In IPython, you can press **shift-enter** with a given cell
          selected to execute it and advance to the next cell:</p></div>
End of explanation
"""


mne.set_log_level('WARNING')

"""
Explanation: If you'd like to turn information status messages off:
End of explanation
"""


mne.set_log_level('INFO')

"""
Explanation: But it's generally a good idea to leave them on:
End of explanation
"""


mne.set_config('MNE_LOGGING_LEVEL', 'WARNING', set_env=True)

"""
Explanation: You can set the default level by setting the environment variable
"MNE_LOGGING_LEVEL", or by having mne-python write preferences to a file:
End of explanation
"""


mne.get_config_path()

"""
Explanation: Note that the location of the mne-python preferences file (for easier manual
editing) can be found using:
End of explanation
"""


from mne.datasets import sample  # noqa
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
print(raw_fname)

"""
Explanation: By default logging messages print to the console, but look at
:func:mne.set_log_file to save output to a file.
Access raw data
^^^^^^^^^^^^^^^
End of explanation
"""


raw = mne.io.read_raw_fif(raw_fname)
print(raw)
print(raw.info)

"""
Explanation: <div class="alert alert-info"><h4>Note</h4><p>The MNE sample dataset should be downloaded automatically but be
          patient (approx. 2GB)</p></div>

Read data from file:
End of explanation
"""


print(raw.ch_names)

"""
Explanation: Look at the channels in raw:
End of explanation
"""


start, stop = raw.time_as_index([100, 115])  # 100 s to 115 s data segment
data, times = raw[:, start:stop]
print(data.shape)
print(times.shape)
data, times = raw[2:20:3, start:stop]  # access underlying data
raw.plot()

"""
Explanation: Read and plot a segment of raw data
End of explanation
"""


picks = mne.pick_types(raw.info, meg=True, eeg=False, stim=True,
                       exclude='bads')
raw.save('sample_audvis_meg_raw.fif', tmin=0, tmax=150, picks=picks,
         overwrite=True)

"""
Explanation: Save a segment of 150s of raw data (MEG only):
End of explanation
"""


events = mne.find_events(raw, stim_channel='STI 014')
print(events[:5])

"""
Explanation: Define and read epochs
^^^^^^^^^^^^^^^^^^^^^^
First extract events:
End of explanation
"""


mne.set_config('MNE_STIM_CHANNEL', 'STI101', set_env=True)

"""
Explanation: Note that, by default, we use stim_channel='STI 014'. If you have a different
system (e.g., a newer system that uses channel 'STI101' by default), you can
use the following to set the default stim channel to use for finding events:
End of explanation
"""


event_id = dict(aud_l=1, aud_r=2)  # event trigger and conditions
tmin = -0.2  # start of each epoch (200ms before the trigger)
tmax = 0.5  # end of each epoch (500ms after the trigger)

"""
Explanation: Events are stored as a 2D numpy array where the first column is the time
instant and the last one is the event number. It is therefore easy to
manipulate.
Define epochs parameters:
End of explanation
"""


raw.info['bads'] += ['MEG 2443', 'EEG 053']

"""
Explanation: Exclude some channels (original bads + 2 more):
End of explanation
"""


picks = mne.pick_types(raw.info, meg=True, eeg=True, eog=True, stim=False,
                       exclude='bads')

"""
Explanation: The variable raw.info['bads'] is just a python list.
Pick the good channels, excluding raw.info['bads']:
End of explanation
"""


mag_picks = mne.pick_types(raw.info, meg='mag', eog=True, exclude='bads')
grad_picks = mne.pick_types(raw.info, meg='grad', eog=True, exclude='bads')

"""
Explanation: Alternatively one can restrict to magnetometers or gradiometers with:
End of explanation
"""


baseline = (None, 0)  # means from the first instant to t = 0

"""
Explanation: Define the baseline period:
End of explanation
"""


reject = dict(grad=4000e-13, mag=4e-12, eog=150e-6)

"""
Explanation: Define peak-to-peak rejection parameters for gradiometers, magnetometers
and EOG:
End of explanation
"""


epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=True, picks=picks,
                    baseline=baseline, preload=False, reject=reject)
print(epochs)

"""
Explanation: Read epochs:
End of explanation
"""


epochs_data = epochs['aud_l'].get_data()
print(epochs_data.shape)

"""
Explanation: Get single epochs for one condition:
End of explanation
"""


from scipy import io  # noqa
io.savemat('epochs_data.mat', dict(epochs_data=epochs_data), oned_as='row')

"""
Explanation: epochs_data is a 3D array of dimension (55 epochs, 365 channels, 106 time
instants).
Scipy supports read and write of matlab files. You can save your single
trials with:
End of explanation
"""


epochs.save('sample-epo.fif')

"""
Explanation: or if you want to keep all the information about the data you can save your
epochs in a fif file:
End of explanation
"""


saved_epochs = mne.read_epochs('sample-epo.fif')

"""
Explanation: and read them later with:
End of explanation
"""


evoked = epochs['aud_l'].average()
print(evoked)
evoked.plot(time_unit='s')

"""
Explanation: Compute evoked responses for auditory responses by averaging and plot it:
End of explanation
"""


max_in_each_epoch = [e.max() for e in epochs['aud_l']]  # doctest:+ELLIPSIS
print(max_in_each_epoch[:4])  # doctest:+ELLIPSIS

"""
Explanation: .. topic:: Exercise

Extract the max value of each epoch
End of explanation
"""


evoked_fname = data_path + '/MEG/sample/sample_audvis-ave.fif'
evoked1 = mne.read_evokeds(
    evoked_fname, condition='Left Auditory', baseline=(None, 0), proj=True)

"""
Explanation: It is also possible to read evoked data stored in a fif file:
End of explanation
"""


evoked2 = mne.read_evokeds(
    evoked_fname, condition='Right Auditory', baseline=(None, 0), proj=True)

"""
Explanation: Or another one stored in the same file:
End of explanation
"""


contrast = mne.combine_evoked([evoked1, evoked2], weights=[0.5, -0.5])
contrast = mne.combine_evoked([evoked1, -evoked2], weights='equal')
print(contrast)

"""
Explanation: Two evoked objects can be contrasted using :func:mne.combine_evoked.
This function can use weights='equal', which provides a simple
element-by-element subtraction (and sets the
mne.Evoked.nave attribute properly based on the underlying number
of trials) using either equivalent call:
End of explanation
"""


average = mne.combine_evoked([evoked1, evoked2], weights='nave')
print(contrast)

"""
Explanation: To do a weighted sum based on the number of averages, which will give
you what you would have gotten from pooling all trials together in
:class:mne.Epochs before creating the :class:mne.Evoked instance,
you can use weights='nave':
End of explanation
"""


epochs_eq = epochs.copy().equalize_event_counts(['aud_l', 'aud_r'])[0]
evoked1, evoked2 = epochs_eq['aud_l'].average(), epochs_eq['aud_r'].average()
print(evoked1)
print(evoked2)
contrast = mne.combine_evoked([evoked1, -evoked2], weights='equal')
print(contrast)

"""
Explanation: Instead of dealing with mismatches in the number of averages, we can use
trial-count equalization before computing a contrast, which can have some
benefits in inverse imaging (note that here weights='nave' will
give the same result as weights='equal'):
End of explanation
"""


import numpy as np  # noqa
n_cycles = 2  # number of cycles in Morlet wavelet
freqs = np.arange(7, 30, 3)  # frequencies of interest

"""
Explanation: Time-Frequency: Induced power and inter trial coherence
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Define parameters:
End of explanation
"""


from mne.time_frequency import tfr_morlet  # noqa
power, itc = tfr_morlet(epochs, freqs=freqs, n_cycles=n_cycles,
                        return_itc=True, decim=3, n_jobs=1)
power.plot([power.ch_names.index('MEG 1332')])

"""
Explanation: Compute induced power and phase-locking values and plot gradiometers:
End of explanation
"""


from mne.minimum_norm import apply_inverse, read_inverse_operator  # noqa

"""
Explanation: Inverse modeling: MNE and dSPM on evoked and raw data
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Import the required functions:
End of explanation
"""


fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'
inverse_operator = read_inverse_operator(fname_inv)

"""
Explanation: Read the inverse operator:
End of explanation
"""


snr = 3.0
lambda2 = 1.0 / snr ** 2
method = "dSPM"

"""
Explanation: Define the inverse parameters:
End of explanation
"""


stc = apply_inverse(evoked, inverse_operator, lambda2, method)

"""
Explanation: Compute the inverse solution:
End of explanation
"""


stc.save('mne_dSPM_inverse')

"""
Explanation: Save the source time courses to disk:
End of explanation
"""


fname_label = data_path + '/MEG/sample/labels/Aud-lh.label'
label = mne.read_label(fname_label)

"""
Explanation: Now, let's compute dSPM on a raw file within a label:
End of explanation
"""


from mne.minimum_norm import apply_inverse_raw  # noqa
start, stop = raw.time_as_index([0, 15])  # read the first 15s of data
stc = apply_inverse_raw(raw, inverse_operator, lambda2, method, label,
                        start, stop)

"""
Explanation: Compute inverse solution during the first 15s:
End of explanation
"""


stc.save('mne_dSPM_raw_inverse_Aud')

"""
Explanation: Save result in stc files:
End of explanation
"""


print("Done!")

"""
Explanation: What else can you do?
^^^^^^^^^^^^^^^^^^^^^
- detect heart beat QRS component
- detect eye blinks and EOG artifacts
- compute SSP projections to remove ECG or EOG artifacts
- compute Independent Component Analysis (ICA) to remove artifacts or
  select latent sources
- estimate noise covariance matrix from Raw and Epochs
- visualize cross-trial response dynamics using epochs images
- compute forward solutions
- estimate power in the source space
- estimate connectivity in sensor and source space
- morph stc from one brain to another for group studies
- compute mass univariate statistics base on custom contrasts
- visualize source estimates
- export raw, epochs, and evoked data to other python data analysis
  libraries e.g. pandas
- and many more things ...

Want to know more ?
^^^^^^^^^^^^^^^^^^^
Browse the examples gallery &lt;auto_examples/index.html&gt;_.
End of explanation
"""


