config.py

"""
===========
Config file
===========

Configuration parameters for the study. This should be in a folder called
``library/`` inside the ``processing/`` directory.
"""

import os
import numpy as np


###############################################################################
# DIRECTORIES
# -----------
# Let's set the `study path`` where the data is stored on your system
# study_path = '/Users/sophie/Dropbox/CBD_Hackaton_PreProc/MNE-sample-data/'
study_path = '../MNE-sample-data/'


# The ``subjects_dir`` and ``meg_dir`` for reading anatomical and MEG files.

subjects_dir = os.path.join(study_path, 'subjects')
meg_dir = os.path.join(study_path, 'MEG')

###############################################################################
# DEFINE SUBJECTS
# ---------------
#
# A list of ``subject names``
# These are the ``nips`` in neurospin lingo

# XXX how to write this if we want to process only one subject?
subjects = ['sample']

# ``bad subjects``  that should not be included in the analysis
exclude_subjects = []

# subjects = ('subject_01', 'subject_02', 'subject_03', 'subject_05', 'subject_06',
#             'subject_08', 'subject_09', 'subject_10', 'subject_11', 'subject_12',
#             'subject_14', 'subject_15', 'subject_16', 'subject_17', 'subject_18',
#             'subject_19', 'subject_23', 'subject_24', 'subject_25')

# # ``bad subjects``  that should not be included in the analysis
# exclude_subjects = {'subject_01', 'subject_09', 'subject_24'}

###############################################################################
# BAD CHANNELS
# ------------
#
# ``bad channels``, to be removed before maxfilter is applied
# you either get them from your recording notes, or from visualizing the data

bads = dict(sample=['MEG 2443', 'EEG 053'])

###############################################################################
# DEFINE ADDITIONAL CHANNELS
# --------------------------
#
# Here you name/ replace  extra channels that were recorded, for instance EOG, ECG
# ``set_channel_types`` defines types of channels
# example :
# set_channel_types = {'EEG061': 'eog', 'EEG062': 'eog', 'EEG063': 'ecg', 'EEG064': 'misc'}
set_channel_types = None

# ``rename_channels`` rename channels
#
# example :
# rename_channels = {'EEG061': 'EOG061', 'EEG062': 'EOG062', 'EEG063': 'ECG063'}
rename_channels = None

###############################################################################
# FREQUENCY FILTERING
# -------------------
#
# ``h_freq``  : the high-frequency cut-off in the lowpass filtering step.
# Keep it None if no lowpass filtering should be applied.
h_freq = None

# ``l_freq``  : the low-frequency cut-off in the highpass filtering step.
# Keep it None if no highpass filtering should be applied.
l_freq = 45.


###############################################################################
# MAXFILTER PARAMETERS
# -------------------
#
# Download the ``cross talk file`` and ``calibration file`` (these are machine specific)
# path:
# and place them in the study folder
mf_ctc_fname = os.path.join(study_path, 'SSS', 'ct_sparse_mgh.fif')
mf_cal_fname = os.path.join(study_path, 'SSS', 'sss_cal_mgh.dat')

# ``mf_reference_run `` : defines the reference run used to adjust the head position for
# all other runs
mf_reference_run = 0  # take 1st run as reference for head position

# Set the origin for the head position
mf_head_origin = (0., 0., 0.04)

# ``mf_st_duration `` : if None, no temporal-spatial filtering is applied during MaxFilter,
# otherwise, put a float that speficifies the buffer duration in seconds,
# Elekta default = 10s, meaning it acts like a 0.1 Hz highpass filter
mf_st_duration = 30.

###############################################################################
# RESAMPLING
# ----------
#
# ``resample_sfreq``  : a float that specifies at which sampling frequency
# the data should be resampled. If None then no resampling will be done.
resample_sfreq = 256.


# ``decim`` : integer that says how much to decimate data at the epochs level.
# It is typically an alternative to the `resample_sfreq` parameter that
# can be used for resampling raw data. 1 means no decimation.
decim = 1

###############################################################################
# AUTOMATIC REJECTION OF ARTIFACTS
# --------------------------------
#
#  ``reject`` : the default rejection limits to make some epochs as bads.
# This allows to remove strong transient artifacts.
# **Note**: these numbers tend to vary between subjects.
reject = dict(grad=4000e-13, mag=4e-12, eog=150e-6)

###############################################################################
# EPOCHING
# --------
#
# ``tmin``: float that gives the start time before event of an epoch.
tmin = -0.2

#  ``tmax`` : float that gives the end time after event of an epochs.
tmax = 0.5

# ``baseline`` : tuple that specifies how to baseline the epochs; if None,
# no baseline is applied

baseline = (None, 0.)

#  `event_id`` : python dictionary that maps events (trigger/marker values)
# to conditions. E.g. `event_id = {'Auditory/Left': 1, 'Auditory/Right': 2}`

event_id = {'Auditory/Left': 1, 'Auditory/Right': 2,
            'Visual/Left': 3, 'Visual/Right': 4}
conditions = ['Auditory', 'Visual', 'Right', 'Left']

###############################################################################
# ICA parameters
# --------------
# ``runica`` : boolean that says if ICA should be used or not.
runica = True

###############################################################################
# SOURCE SPACE PARAMETERS
# -----------------------
#

spacing = 'oct6'
mindist = 5
smooth = 10

fsaverage_vertices = [np.arange(10242), np.arange(10242)]

if not os.path.isdir(study_path):
    os.mkdir(study_path)

if not os.path.isdir(subjects_dir):
    os.mkdir(subjects_dir)

###############################################################################
# ADVANCED
# --------
#
# ``l_trans_bandwidth`` : float that specifies the transition bandwidth of the
# highpass filter. By default it's `'auto'` and uses default mne parameters.
l_trans_bandwidth = 'auto'

#  ``h_trans_bandwidth`` : float that specifies the transition bandwidth of the
# lowpass filter. By default it's `'auto'` and uses default mne parameters.
h_trans_bandwidth = 'auto'

#  ``N_JOBS`` : an integer that specifies how many subjects you want to run in parallel.
N_JOBS = 1