emdupre · November 30, 2017 20:02
diff --git a/maskadmask.py b/maskadmask.py
 def create_last_echo_mask(echo_list):
    """
    Make a map of longest echo that a voxel can be sampled with,
    with minimum value of map as X value of voxel that has median
    value in the 1st echo. N.B. larger factor leads to bias to lower TEs

    **Inputs**
        echo_list
            List of file names for all echos of interest

    **Outputs**
        last_echo_mask
            A numpy array whose values correspond to which echos
            a voxel can last be sampled with
    """
    # First, load each echo and average over time
    echos = []
    for e in echo_list:
        echos.append(np.mean(nib.load(e).get_data(), axis = -1))

    # In the first echo, find the 33rd percentile and the voxel(s)
    # whose average activity is equal to that value
    med_val = np.percentile(echos[0][np.nonzero(echos[0])],
                            33, interpolation="higher")
    vox = echos[0] == med_val

    # For each (averaged) echo, extract the max signal in the
    # identified voxels and divide it by 3-- save as a threshold
    thrs = np.empty(len(echos))
    for echo in echos:
        np.append(thrs, np.max(echo[vox]) / 3)

    # Let's stack the arrays to make this next bit easier
    echo_means = np.stack(echos, axis=-1)

    # Now, we want to find all voxels (in each echo) that show
    # absolute signal greater than our echo-specific threshold
    mthr = np.ones(echo_means.shape)
    for i in range(echo_means.shape[-1]):
        mthr[:, :, :, i] *= thrs[i]
    voxels = np.abs(echo_means) > mthr

    # We save those voxel indices out to an array
    last_echo_mask = np.array(voxels, dtype=np.int).sum(-1)

    return last_echo_mask
	def create_last_echo_mask(echo_list):
	"""
	Make a map of longest echo that a voxel can be sampled with,
	with minimum value of map as X value of voxel that has median
	value in the 1st echo. N.B. larger factor leads to bias to lower TEs

	Inputs
	echo_list
	List of file names for all echos of interest

	Outputs
	last_echo_mask
	A numpy array whose values correspond to which echos
	a voxel can last be sampled with
	"""
	# First, load each echo and average over time
	echos = []
	for e in echo_list:
	echos.append(np.mean(nib.load(e).get_data(), axis = -1))

	# In the first echo, find the 33rd percentile and the voxel(s)
	# whose average activity is equal to that value
	med_val = np.percentile(echos[0][np.nonzero(echos[0])],
	33, interpolation="higher")
	vox = echos[0] == med_val

	# For each (averaged) echo, extract the max signal in the
	# identified voxels and divide it by 3-- save as a threshold
	thrs = np.empty(len(echos))
	for echo in echos:
	np.append(thrs, np.max(echo[vox]) / 3)

	# Let's stack the arrays to make this next bit easier
	echo_means = np.stack(echos, axis=-1)

	# Now, we want to find all voxels (in each echo) that show
	# absolute signal greater than our echo-specific threshold
	mthr = np.ones(echo_means.shape)
	for i in range(echo_means.shape[-1]):
	mthr[:, :, :, i] *= thrs[i]
	voxels = np.abs(echo_means) > mthr

	# We save those voxel indices out to an array
	last_echo_mask = np.array(voxels, dtype=np.int).sum(-1)

	return last_echo_mask
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