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# Copyright 2016 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""fMRI Simulator
Simulate fMRI data for a single subject.
This code provides a set of functions necessary to produce realistic
simulations of fMRI data. There are two main steps: characterizing the
signal and generating the noise model, which are then combined to simulate
brain data. Tools are included to support the creation of different types
of signal, such as region specific differences in univariate
activity. To create the noise model the parameters can either be set
manually or can be estimated from real fMRI data with reasonable accuracy (
works best when fMRI data has not been preprocessed)
Functions:
generate_signal
Create a volume with activity, of a specified shape and either multivariate
or univariate pattern, in a specific region to represent the signal in the
neural data.
generate_stimfunction
Create a timecourse of the signal activation. This can be specified using event
onsets and durations from a timing file. This is the time course before
convolution and therefore can be at any temporal precision.
export_3_column:
Generate a three column timing file that can be used with software like FSL
to represent event event onsets and duration
export_epoch_file:
Generate an epoch file from the time course which can be used as an input to
brainiak functions
convolve_hrf
Convolve the signal timecourse with the HRF to model the expected evoked
activity
apply_signal
Combine the signal volume with the HRF, thus giving the signal the temporal
properties of the HRF (such as smoothing and lag)
calc_noise
Estimate the noise properties of a given fMRI volume. Prominently, estimate
the smoothing and SFNR of the data
generate_noise
Create the noise for this run. This creates temporal, spatial task and white
noise. Various parameters can be tuned depending on need
mask_brain
Create a mask volume that has similar contrast as an fMRI image. Defaults to
use an MNI grey matter atlas but any image can be supplied to create an
estimate.
compute_signal_change
Convert the signal function into useful metric units according to metrics
used by others (Welvaert & Rosseel, 2013)
Authors:
Cameron Ellis (Princeton & Yale) 2016-2018
Chris Baldassano (Princeton) 2016-2017
Mingbo Cai (Princeton) 2017
"""
import logging
from itertools import product
from statsmodels.tsa.arima_model import ARMA
import math
import numpy as np
# See pyflakes issue #248
# https://github.com/PyCQA/pyflakes/issues/248
import numpy.matlib # noqa: F401
from numpy.linalg import LinAlgError
from pkg_resources import resource_stream
from scipy import stats
from scipy import signal
import scipy.ndimage as ndimage
import copy
__all__ = [
"apply_signal",
"calc_noise",
"compute_signal_change",
"convolve_hrf",
"export_3_column",
"export_epoch_file",
"generate_signal",
"generate_stimfunction",
"generate_noise",
"mask_brain",
]
logger = logging.getLogger(__name__)
def _generate_feature(feature_type,
feature_size,
signal_magnitude,
thickness=1):
"""Generate features corresponding to signal
Generate a single feature, that can be inserted into the signal volume.
A feature is a region of activation with a specific shape such as cube
or ring
Parameters
----------
feature_type : str
What shape signal is being inserted? Options are 'cube',
'loop' (aka ring), 'cavity' (aka hollow sphere), 'sphere'.
feature_size : int
How big is the signal in diameter?
signal_magnitude : float
Set the signal size, a value of 1 means the signal is one standard
deviation of the noise
thickness : int
How thick is the surface of the loop/cavity
Returns
----------
signal : 3 dimensional array
The volume representing the signal
"""
# If the size is equal to or less than 2 then all features are the same
if feature_size <= 2:
feature_type = 'cube'
# What kind of signal is it?
if feature_type == 'cube':
# Preset the size of the signal
signal = np.ones((feature_size, feature_size, feature_size))
elif feature_type == 'loop':
# First make a cube of zeros
signal = np.zeros((feature_size, feature_size, feature_size))
# Make a mesh grid of the space
seq = np.linspace(0, feature_size - 1,
feature_size)
xx, yy = np.meshgrid(seq, seq)
# Make a disk corresponding to the whole mesh grid
xxmesh = (xx - ((feature_size - 1) / 2)) ** 2
yymesh = (yy - ((feature_size - 1) / 2)) ** 2
disk = xxmesh + yymesh
# What are the limits of the rings being made
outer_lim = disk[int((feature_size - 1) / 2), 0]
inner_lim = disk[int((feature_size - 1) / 2), thickness]
# What is the outer disk
outer = disk <= outer_lim
# What is the inner disk
inner = disk <= inner_lim
# Subtract the two disks to get a loop
loop = outer != inner
# Check if the loop is a disk
if np.all(inner is False):
logger.warning('Loop feature reduces to a disk because the loop '
'is too thick')
# If there is complete overlap then make the signal just the
# outer one
if np.all(loop is False):
loop = outer
# store the loop
signal[0:feature_size, 0:feature_size, int(np.round(feature_size /
2))] = loop
elif feature_type == 'sphere' or feature_type == 'cavity':
# Make a mesh grid of the space
seq = np.linspace(0, feature_size - 1,
feature_size)
xx, yy, zz = np.meshgrid(seq, seq, seq)
# Make a disk corresponding to the whole mesh grid
signal = ((xx - ((feature_size - 1) / 2)) ** 2 +
(yy - ((feature_size - 1) / 2)) ** 2 +
(zz - ((feature_size - 1) / 2)) ** 2)
# What are the limits of the rings being made
outer_lim = signal[int((feature_size - 1) / 2), int((feature_size -
1) / 2), 0]
inner_lim = signal[int((feature_size - 1) / 2), int((feature_size -
1) / 2),
thickness]
# Is the signal a sphere or a cavity?
if feature_type == 'sphere':
signal = signal <= outer_lim
else:
# Get the inner and outer sphere
outer = signal <= outer_lim
inner = signal <= inner_lim
# Subtract the two disks to get a loop
signal = outer != inner
# Check if the cavity is a sphere
if np.all(inner is False):
logger.warning('Cavity feature reduces to a sphere because '
'the cavity is too thick')
# If there is complete overlap then make the signal just the
# outer one
if np.all(signal is False):
signal = outer
# Assign the signal magnitude
signal = signal * signal_magnitude
# Return the signal
return signal
def _insert_idxs(feature_centre, feature_size, dimensions):
"""Returns the indices of where to put the signal into the signal volume
Parameters
----------
feature_centre : list, int
List of coordinates for the centre location of the signal
feature_size : list, int
How big is the signal's diameter.
dimensions : 3 length array, int
What are the dimensions of the volume you wish to create
Returns
----------
x_idxs : tuple
The x coordinates of where the signal is to be inserted
y_idxs : tuple
The y coordinates of where the signal is to be inserted
z_idxs : tuple
The z coordinates of where the signal is to be inserted
"""
# Set up the indexes within which to insert the signal
x_idx = [int(feature_centre[0] - (feature_size / 2)) + 1,
int(feature_centre[0] - (feature_size / 2) +
feature_size) + 1]
y_idx = [int(feature_centre[1] - (feature_size / 2)) + 1,
int(feature_centre[1] - (feature_size / 2) +
feature_size) + 1]
z_idx = [int(feature_centre[2] - (feature_size / 2)) + 1,
int(feature_centre[2] - (feature_size / 2) +
feature_size) + 1]
# Check for out of bounds
# Min Boundary
if 0 > x_idx[0]:
x_idx[0] = 0
if 0 > y_idx[0]:
y_idx[0] = 0
if 0 > z_idx[0]:
z_idx[0] = 0
# Max Boundary
if dimensions[0] < x_idx[1]:
x_idx[1] = dimensions[0]
if dimensions[1] < y_idx[1]:
y_idx[1] = dimensions[1]
if dimensions[2] < z_idx[1]:
z_idx[1] = dimensions[2]
# Return the idxs for data
return x_idx, y_idx, z_idx
def generate_signal(dimensions,
feature_coordinates,
feature_size,
feature_type,
signal_magnitude=[1],
signal_constant=1,
):
"""Generate volume containing signal
Generate signal, of a specific shape in specific regions, for a single
volume. This will then be convolved with the HRF across time
Parameters
----------
dimensions : 1d array, ndarray
What are the dimensions of the volume you wish to create
feature_coordinates : multidimensional array
What are the feature_coordinates of the signal being created.
Be aware of clipping: features far from the centre of the
brain will be clipped. If you wish to have multiple features
then list these as a features x 3 array. To create a feature of
a unique shape then supply all the individual
feature_coordinates of the shape and set the feature_size to 1.
feature_size : list, int
How big is the signal. If feature_coordinates=1 then only one value is
accepted, if feature_coordinates>1 then either one value must be
supplied or m values
feature_type : list, string
What feature_type of signal is being inserted? Options are cube,
loop, cavity, sphere. If feature_coordinates = 1 then
only one value is accepted, if feature_coordinates > 1 then either
one value must be supplied or m values
signal_magnitude : list, float
What is the (average) magnitude of the signal being generated? A
value of 1 means that the signal is one standard deviation from the
noise
signal_constant : list, bool
Is the signal constant across the feature (for univariate activity)
or is it a random pattern of a given magnitude across the feature (for
multivariate activity)
Returns
----------
volume_signal : 3 dimensional array, float
Creates a single volume containing the signal
"""
# Preset the volume
volume_signal = np.zeros(dimensions)
feature_quantity = round(feature_coordinates.shape[0])
# If there is only one feature_size value then make sure to duplicate it
# for all signals
if len(feature_size) == 1:
feature_size = feature_size * feature_quantity
# Do the same for feature_type
if len(feature_type) == 1:
feature_type = feature_type * feature_quantity
if len(signal_magnitude) == 1:
signal_magnitude = signal_magnitude * feature_quantity
# Iterate through the signals and insert in the data
for signal_counter in range(feature_quantity):
# What is the centre of this signal
if len(feature_size) > 1:
feature_centre = np.asarray(feature_coordinates[signal_counter, ])
else:
feature_centre = np.asarray(feature_coordinates)[0]
# Generate the feature to be inserted in the volume
signal = _generate_feature(feature_type[signal_counter],
feature_size[signal_counter],
signal_magnitude[signal_counter],
)
# If the signal is a random noise pattern then multiply these ones by
# a noise mask
if signal_constant == 0:
signal = signal * np.random.random([feature_size[signal_counter],
feature_size[signal_counter],
feature_size[signal_counter]])
# Pull out the idxs for where to insert the data
x_idx, y_idx, z_idx = _insert_idxs(feature_centre,
feature_size[signal_counter],
dimensions)
# Insert the signal into the Volume
volume_signal[x_idx[0]:x_idx[1], y_idx[0]:y_idx[1], z_idx[0]:z_idx[
1]] = signal
return volume_signal
def generate_stimfunction(onsets,
event_durations,
total_time,
weights=[1],
timing_file=None,
temporal_resolution=100.0,
):
"""Return the function for the timecourse events
When do stimuli onset, how long for and to what extent should you
resolve the fMRI time course. There are two ways to create this, either
by supplying onset, duration and weight information or by supplying a
timing file (in the three column format used by FSL).
Parameters
----------
onsets : list, int
What are the timestamps (in s) for when an event you want to
generate onsets?
event_durations : list, int
What are the durations (in s) of the events you want to
generate? If there is only one value then this will be assigned
to all onsets
total_time : int
How long (in s) is the experiment in total.
weights : list, float
What is the weight for each event (how high is the box car)? If
there is only one value then this will be assigned to all onsets
timing_file : string
The filename (with path) to a three column timing file (FSL) to
make the events. Still requires total_time to work
temporal_resolution : float
How many elements per second are you modeling for the
timecourse. This is useful when you want to model the HRF at an
arbitrarily high resolution (and then downsample to your TR later).
Returns
----------
stim_function : 1 by timepoint array, float
The time course of stimulus evoked activation. This has a temporal
resolution of temporal resolution / 1.0 elements per second
"""
# If the timing file is supplied then use this to acquire the
if timing_file is not None:
# Read in text file line by line
with open(timing_file) as f:
text = f.readlines() # Pull out file as a an array
# Preset
onsets = list()
event_durations = list()
weights = list()
# Pull out the onsets, weights and durations, set as a float
for line in text:
onset, duration, weight = line.strip().split()
# Check if the onset is more precise than the temporal resolution
upsampled_onset = float(onset) * temporal_resolution
# Because of float precision, the upsampled values might
# not round as expected .
# E.g. float('1.001') * 1000 = 1000.99
if np.allclose(upsampled_onset, np.round(upsampled_onset)) == 0:
warning = 'Your onset: ' + str(onset) + ' has more decimal ' \
'points than the ' \
'specified temporal ' \
'resolution can ' \
'resolve. This means' \
' that events might' \
' be missed. ' \
'Consider increasing' \
' the temporal ' \
'resolution.'
logger.warning(warning)
onsets.append(float(onset))
event_durations.append(float(duration))
weights.append(float(weight))
# If only one duration is supplied then duplicate it for the length of
# the onset variable
if len(event_durations) == 1:
event_durations = event_durations * len(onsets)
if len(weights) == 1:
weights = weights * len(onsets)
# Check files
if np.max(onsets) > total_time:
raise ValueError('Onsets outside of range of total time.')
# Generate the time course as empty, each element is a millisecond by
# default
stimfunction = np.zeros((int(round(total_time * temporal_resolution)), 1))
# Cycle through the onsets
for onset_counter in list(range(len(onsets))):
# Adjust for the resolution
onset_idx = int(np.floor(onsets[onset_counter] * temporal_resolution))
# Adjust for the resolution
offset_idx = int(np.floor((onsets[onset_counter] + event_durations[
onset_counter]) * temporal_resolution))
# Store the weights
stimfunction[onset_idx:offset_idx, 0] = [weights[onset_counter]]
return stimfunction
def export_3_column(stimfunction,
filename,
temporal_resolution=100.0
):
""" Output a tab separated three column timing file
This produces a three column tab separated text file, with the three
columns representing onset time (s), event duration (s) and weight,
respectively. Useful if you want to run the simulated data through FEAT
analyses. In a way, this is the reverse of generate_stimfunction
Parameters
----------
stimfunction : timepoint by 1 array
The stimulus function describing the time course of events. For
instance output from generate_stimfunction.
filename : str
The name of the three column text file to be output
temporal_resolution : float
How many elements per second are you modeling with the
stimfunction?
"""
# Iterate through the stim function
stim_counter = 0
event_counter = 0
while stim_counter < stimfunction.shape[0]:
# Is it an event?
if stimfunction[stim_counter, 0] != 0:
# When did the event start?
event_onset = str(stim_counter / temporal_resolution)
# The weight of the stimulus
weight = str(stimfunction[stim_counter, 0])
# Reset
event_duration = 0
# Is the event still ongoing?
while stimfunction[stim_counter, 0] != 0 & stim_counter <= \
stimfunction.shape[0]:
# Add one millisecond to each duration
event_duration = event_duration + 1
# Increment
stim_counter = stim_counter + 1
# How long was the event in seconds
event_duration = str(event_duration / temporal_resolution)
# Append this row to the data file
with open(filename, "a") as file:
file.write(event_onset + '\t' + event_duration + '\t' +
weight + '\n')
# Increment the number of events
event_counter = event_counter + 1
# Increment
stim_counter = stim_counter + 1
def export_epoch_file(stimfunction,
filename,
tr_duration,
temporal_resolution=100.0
):
""" Output an epoch file, necessary for some inputs into brainiak
This takes in the time course of stimulus events and outputs the epoch
file used in Brainiak. The epoch file is a way to structure the timing
information in fMRI that allows you to flexibly input different stimulus
sequences. This is a list with each entry a 3d matrix corresponding to a
participant. The dimensions of the 3d matrix are condition by epoch by
time. For the i-th condition, if its k-th epoch spans time points t_m to
t_n-1, then [i, k, t_m:t_n] are 1 in the epoch file.
Parameters
----------
stimfunction : list of timepoint by condition arrays
The stimulus function describing the time course of events. Each
list entry is from a different participant, each row is a different
timepoint (with the given temporal precision), each column is a
different condition. export_epoch_file is looking for differences in
the value of stimfunction to identify the start and end of an
epoch. If epochs in stimfunction are coded with the same weight and
there is no time between blocks then export_epoch_file won't be able to
label them as different epochs
filename : str
The name of the epoch file to be output
tr_duration : float
How long is each TR in seconds
temporal_resolution : float
How many elements per second are you modeling with the
stimfunction?
"""
# Cycle through the participants, different entries in the list
epoch_file = [0] * len(stimfunction)
for ppt_counter in range(len(stimfunction)):
# What is the time course for the participant (binarized)
stimfunction_ppt = np.abs(stimfunction[ppt_counter]) > 0
# Down sample the stim function
stride = tr_duration * temporal_resolution
stimfunction_downsampled = stimfunction_ppt[::int(stride), :]
# Calculates the number of event onsets. This uses changes in value
# to reflect different epochs. This might be false in some cases (the
# weight is non-uniform over an epoch or there is no break between
# identically weighted epochs).
epochs = 0 # Preset
conditions = stimfunction_ppt.shape[1]
for condition_counter in range(conditions):
weight_change = (np.diff(stimfunction_downsampled[:,
condition_counter], 1, 0) != 0)
# If the first or last events are 'on' then make these
# represent a epoch change
if stimfunction_downsampled[0, condition_counter] == 1:
weight_change[0] = True
if stimfunction_downsampled[-1, condition_counter] == 1:
weight_change[-1] = True
epochs += int(np.max(np.sum(weight_change, 0)) / 2)
# Get other information
trs = stimfunction_downsampled.shape[0]
# Make a timing file for this participant
epoch_file[ppt_counter] = np.zeros((conditions, epochs, trs))
# Cycle through conditions
epoch_counter = 0 # Reset and count across conditions
tr_counter = 0
while tr_counter < stimfunction_downsampled.shape[0]:
for condition_counter in range(conditions):
# Is it an event?
if tr_counter < stimfunction_downsampled.shape[0] and \
stimfunction_downsampled[
tr_counter, condition_counter] == 1:
# Add a one for this TR
epoch_file[ppt_counter][condition_counter,
epoch_counter, tr_counter] = 1
# Find the next non event value
end_idx = np.where(stimfunction_downsampled[tr_counter:,
condition_counter] == 0)[
0][0]
tr_idxs = list(range(tr_counter, tr_counter + end_idx))
# Add ones to all the trs within this event time frame
epoch_file[ppt_counter][condition_counter,
epoch_counter, tr_idxs] = 1
# Start from this index
tr_counter += end_idx
# Increment
epoch_counter += 1
# Increment the counter
tr_counter += 1
# Convert to boolean
epoch_file[ppt_counter] = epoch_file[ppt_counter].astype('bool')
# Save the file
np.save(filename, epoch_file)
def _double_gamma_hrf(response_delay=6,
undershoot_delay=12,
response_dispersion=0.9,
undershoot_dispersion=0.9,
response_scale=1,
undershoot_scale=0.035,
temporal_resolution=100.0,
):
"""Create the double gamma HRF with the timecourse evoked activity.
Default values are based on Glover, 1999 and Walvaert, Durnez,
Moerkerke, Verdoolaege and Rosseel, 2011
Parameters
----------
response_delay : float
How many seconds until the peak of the HRF
undershoot_delay : float
How many seconds until the trough of the HRF
response_dispersion : float
How wide is the rising peak dispersion
undershoot_dispersion : float
How wide is the undershoot dispersion
response_scale : float
How big is the response relative to the peak
undershoot_scale :float
How big is the undershoot relative to the trough
scale_function : bool
Do you want to scale the function to a range of 1
temporal_resolution : float
How many elements per second are you modeling for the stimfunction
Returns
----------
hrf : multi dimensional array
A double gamma HRF to be used for convolution.
"""
hrf_length = 30 # How long is the HRF being created
# How many seconds of the HRF will you model?
hrf = [0] * int(hrf_length * temporal_resolution)
# When is the peak of the two aspects of the HRF
response_peak = response_delay * response_dispersion
undershoot_peak = undershoot_delay * undershoot_dispersion
for hrf_counter in list(range(len(hrf) - 1)):
# Specify the elements of the HRF for both the response and undershoot
resp_pow = math.pow((hrf_counter / temporal_resolution) /
response_peak, response_delay)
resp_exp = math.exp(-((hrf_counter / temporal_resolution) -
response_peak) /
response_dispersion)
response_model = response_scale * resp_pow * resp_exp
undershoot_pow = math.pow((hrf_counter / temporal_resolution) /
undershoot_peak,
undershoot_delay)
undershoot_exp = math.exp(-((hrf_counter / temporal_resolution) -
undershoot_peak /
undershoot_dispersion))
undershoot_model = undershoot_scale * undershoot_pow * undershoot_exp
# For this time point find the value of the HRF
hrf[hrf_counter] = response_model - undershoot_model
return hrf
def convolve_hrf(stimfunction,
tr_duration,
hrf_type='double_gamma',
scale_function=True,
temporal_resolution=100.0,
):
""" Convolve the specified hrf with the timecourse.
The output of this is a downsampled convolution of the stimfunction and
the HRF function. If temporal_resolution is 1 / tr_duration then the
output will be the same length as stimfunction. This time course assumes
that slice time correction has occurred and all slices have been aligned
to the middle time point in the TR.
Be aware that if scaling is on and event durations are less than the
duration of a TR then the hrf may or may not come out as anticipated.
This is because very short events would evoke a small absolute response
after convolution but if there are only short events and you scale then
this will look similar to a convolution with longer events. In general
scaling is useful, which is why it is the default, but be aware of this
edge case and if it is a concern, set the scale_function to false.
Parameters
----------
stimfunction : timepoint by feature array
What is the time course of events to be modelled in this
experiment. This can specify one or more timecourses of events.
The events can be weighted or binary
tr_duration : float
How long (in s) between each volume onset
hrf_type : str or list
Takes in a string describing the hrf that ought to be created.
Can instead take in a vector describing the HRF as it was
specified by any function. The default is 'double_gamma' in which
an initial rise and an undershoot are modelled.
scale_function : bool
Do you want to scale the function to a range of 1
temporal_resolution : float
How many elements per second are you modeling for the stimfunction
Returns
----------
signal_function : timepoint by timecourse array
The time course of the HRF convolved with the stimulus function.
This can have multiple time courses specified as different
columns in this array.
"""
# Check if it is timepoint by feature
if stimfunction.shape[0] < stimfunction.shape[1]:
logger.warning('Stimfunction may be the wrong shape')
# How will stimfunction be resized
stride = int(temporal_resolution * tr_duration)
duration = int(stimfunction.shape[0] / stride)
# Generate the hrf to use in the convolution
if hrf_type == 'double_gamma':
hrf = _double_gamma_hrf(temporal_resolution=temporal_resolution)
elif isinstance(hrf_type, list):
hrf = hrf_type
# How many timecourses are there
list_num = stimfunction.shape[1]
# Create signal functions for each list in the stimfunction
for list_counter in range(list_num):
# Perform the convolution
signal_temp = np.convolve(stimfunction[:, list_counter], hrf)
# Down sample the signal function so that it only has one element per
# TR. This assumes that all slices are collected at the same time,
# which is often the result of slice time correction. In other
# words, the output assumes slice time correction
signal_temp = signal_temp[:duration * stride]
signal_vox = signal_temp[int(stride / 2)::stride]
# Scale the function so that the peak response is 1
if scale_function:
signal_vox = signal_vox / np.max(signal_vox)
# Add this function to the stack
if list_counter == 0:
signal_function = np.zeros((len(signal_vox), list_num))
signal_function[:, list_counter] = signal_vox
return signal_function
def apply_signal(signal_function,
volume_signal,
):
"""Combine the signal volume with its timecourse
Apply the convolution of the HRF and stimulus time course to the
volume.
Parameters
----------
signal_function : timepoint by timecourse array, float
The timecourse of the signal over time. If there is only one column
then the same timecourse is applied to all non-zero voxels in
volume_signal. If there is more than one column then each column is
paired with a non-zero voxel in the volume_signal (a 3d numpy array
generated in generate_signal).
volume_signal : multi dimensional array, float
The volume containing the signal to be convolved with the same
dimensions as the output volume. The elements in volume_signal
indicate how strong each signal in signal_function are modulated by
in the output volume
Returns
----------
signal : multidimensional array, float
The convolved signal volume with the same 3d as volume signal and
the same 4th dimension as signal_function
"""
# How many timecourses are there within the signal_function
timepoints = signal_function.shape[0]
timecourses = signal_function.shape[1]
# Preset volume
signal = np.zeros([volume_signal.shape[0], volume_signal.shape[
1], volume_signal.shape[2], timepoints])
# Find all the non-zero voxels in the brain
idxs = np.where(volume_signal != 0)
if timecourses == 1:
# If there is only one time course supplied then duplicate it for
# every voxel
signal_function = np.matlib.repmat(signal_function, 1, len(idxs[0]))
elif len(idxs[0]) != timecourses:
raise IndexError('The number of non-zero voxels in the volume and '
'the number of timecourses does not match. Aborting')
# For each coordinate with a non zero voxel, fill in the timecourse for
# that voxel
for idx_counter in range(len(idxs[0])):
x = idxs[0][idx_counter]
y = idxs[1][idx_counter]
z = idxs[2][idx_counter]
# Pull out the function for this voxel
signal_function_temp = signal_function[:, idx_counter]
# Multiply the voxel value by the function timecourse
signal[x, y, z, :] = volume_signal[x, y, z] * signal_function_temp
return signal
def _calc_fwhm(volume,
mask,
voxel_size=[1.0, 1.0, 1.0],
):
""" Calculate the FWHM of a volume
Estimates the FWHM (mm) of a volume's non-masked voxels
Parameters
----------
volume : 3 dimensional array
Functional data to have the FWHM measured.
mask : 3 dimensional array
A binary mask of the brain voxels in volume
voxel_size : length 3 list, float
Millimeters per voxel for x, y and z.
Returns
-------
fwhm : float, list
Returns the FWHM of each TR in mm
"""
# What are the dimensions of the volume
dimensions = volume.shape
# Iterate through the TRs, creating a FWHM for each TR
# Preset
v_count = 0
v_sum = 0
v_sq = 0