# Authors: Robert Luke <mail@robertluke.net>
#
# License: BSD (3-clause)
import numpy as np
import mne
[docs]
def make_first_level_design_matrix(raw, stim_dur=1.,
hrf_model='glover',
drift_model='cosine',
high_pass=0.01, drift_order=1,
fir_delays=[0], add_regs=None,
add_reg_names=None, min_onset=-24,
oversampling=50):
"""
Generate a design matrix based on annotations and model HRF.
This is a wrapper function for the nilearn :footcite:`abraham2014machine`
function ``make_first_level_design_matrix``. For detailed description
of the arguments see the nilearn documentation at http://nilearn.github.io
Parameters
----------
raw : instance of Raw
Haemoglobin data.
stim_dur : Number
The length of your stimulus.
hrf_model : {'glover', 'spm', 'spm + derivative', \
'spm + derivative + dispersion',\
'glover + derivative', 'glover + derivative + dispersion',\
'fir', None}, optional
Specifies the hemodynamic response function. Default='glover'.
drift_model : {'cosine', 'polynomial', None}, optional
Specifies the desired drift model. Default='cosine'.
high_pass : float, optional
High-pass frequency in case of a cosine model (in Hz).
Default=0.01.
drift_order : int, optional
Order of the drift model (in case it is polynomial).
Default=1.
fir_delays : array of shape(n_onsets) or list, optional
In case of FIR design, yields the array of delays used in the FIR
model (in scans). Default=[0].
add_regs : array of shape(n_frames, n_add_reg) or pandas DataFrame
Additional user-supplied regressors, e.g. data driven noise regressors
or seed based regressors.
add_reg_names : list of (n_add_reg,) str, optional
If None, while add_regs was provided, these will be termed
'reg_%i', i = 0..n_add_reg - 1
If add_regs is a DataFrame, the corresponding column names are used
and add_reg_names is ignored.
min_onset : float, optional
Minimal onset relative to frame_times[0] (in seconds)
events that start before frame_times[0] + min_onset are not considered.
Default=-24.
oversampling : int, optional
Oversampling factor used in temporal convolutions. Default=50.
Returns
-------
design_matrix : DataFrame instance,
Holding the computed design matrix, the index being the frames_times
and each column a regressor.
References
----------
.. footbibliography::
"""
from nilearn.glm.first_level import make_first_level_design_matrix
from pandas import DataFrame
frame_times = raw.times
# Create events for nilearn
conditions = raw.annotations.description
onsets = raw.annotations.onset - raw.first_time
duration = stim_dur * np.ones(len(conditions))
events = DataFrame({'trial_type': conditions,
'onset': onsets,
'duration': duration})
dm = make_first_level_design_matrix(frame_times, events,
drift_model=drift_model,
drift_order=drift_order,
hrf_model=hrf_model,
min_onset=min_onset,
high_pass=high_pass,
add_regs=add_regs,
oversampling=oversampling,
add_reg_names=add_reg_names,
fir_delays=fir_delays)
return dm
[docs]
def create_boxcar(raw, event_id=None, stim_dur=1):
"""
Generate boxcar representation of the experimental paradigm.
Parameters
----------
raw : instance of Raw
Haemoglobin data.
event_id : as specified in MNE
Information about events.
stim_dur : Number
The length of your stimulus.
Returns
-------
s : array
Returns an array for each annotation label.
"""
bc = np.ones(int(round(raw.info['sfreq'] * stim_dur)))
events, ids = mne.events_from_annotations(raw, event_id=event_id)
s = np.zeros((len(raw.times), len(ids)))
for idx, id in enumerate(ids):
id_idx = [e[2] == idx + 1 for e in events]
id_evt = events[id_idx]
event_samples = [e[0] for e in id_evt]
s[event_samples, idx] = 1.
s[:, idx] = np.convolve(s[:, idx], bc)[:len(raw.times)]
return s
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def longest_inter_annotation_interval(raw):
"""
Compute longest ISI per annotation.
Specifically, longest period between two trials of
the same condition.
Parameters
----------
raw : instance of Raw
Haemoglobin data.
Returns
-------
longest : list
Longest ISI per annotation.
annotation_name : list
Annotation name corresponding to reported interval.
"""
annotation_name = np.unique(raw.annotations.description)
longest = []
for desc in annotation_name:
mask = raw.annotations.description == desc
longest.append(np.max(np.diff(raw.annotations.onset[mask])))
return longest, annotation_name
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def drift_high_pass(raw):
"""
Compute cosine drift regressor high pass cut off.
Value computed according to Nilearn :footcite:`abraham2014machine`
`suggestion <http://nilearn.github.io/auto_examples/04_glm_first
_level/plot_first_level_details.html#changing-the-drift-model>`__.
Parameters
----------
raw : instance of Raw
Haemoglobin data.
Returns
-------
cutoff : number
Suggested high pass cut off.
References
----------
.. footbibliography::
"""
longest, annotation_name = longest_inter_annotation_interval(raw)
max_isi = np.max(longest)
return 1 / (2 * max_isi)