Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

[FIX] Remove nifti requirement in selcomps() #130

Merged
merged 1 commit into from
Sep 14, 2018
Merged

[FIX] Remove nifti requirement in selcomps() #130

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
24 changes: 6 additions & 18 deletions tedana/selection/select_comps.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,11 +1,9 @@
"""
Functions to identify TE-dependent and TE-independent components.
"""
import os
import json
import logging
import pickle
import pkg_resources

from nilearn._utils import check_niimg
import numpy as np
Expand All @@ -17,7 +15,6 @@
getelbow_aggr, do_svm)

LGR = logging.getLogger(__name__)
RESOURCES = pkg_resources.resource_filename('tedana', 'tests/data')


def selcomps(seldict, mmix, mask, ref_img, manacc, n_echos, t2s, s0, olevel=2,
Expand Down Expand Up @@ -350,33 +347,24 @@ def selcomps(seldict, mmix, mask, ref_img, manacc, n_echos, t2s, s0, olevel=2,
"""
LGR.debug('Computing 3D spatial FFT of beta maps to detect high-spatial frequency artifacts')
# spatial information is important so for NIFTI we convert back to 3D space
if utils.get_dtype(ref_img) == 'NIFTI':
dim1 = np.prod(check_niimg(ref_img).shape[:2])
else:
dim1 = mask.shape[0]
dim1 = np.prod(check_niimg(ref_img).shape[:2])
fproj_arr = np.zeros([dim1, len(all_comps)])
fproj_arr_val = np.zeros([dim1, len(all_comps)])
spr = []
fdist = []
for comp_num in all_comps:
# convert data back to 3D array
if utils.get_dtype(ref_img) == 'NIFTI':
tproj = utils.new_nii_like(ref_img, utils.unmask(seldict['PSC'],
mask)[:, comp_num]).get_data()
else:
tproj = utils.unmask(seldict['PSC'], mask)[:, comp_num]
tproj = utils.new_nii_like(ref_img, utils.unmask(seldict['PSC'],
mask)[:, comp_num]).get_data()
fproj = np.fft.fftshift(np.abs(np.fft.rfftn(tproj)))
fproj_z = fproj.max(axis=-1)
fproj[fproj == fproj.max()] = 0
spr.append(np.array(fproj_z > fproj_z.max() / 4, dtype=np.int).sum())
fproj_arr[:, comp_num] = stats.rankdata(fproj_z.flatten())
fproj_arr_val[:, comp_num] = fproj_z.flatten()
if utils.get_dtype(ref_img) == 'NIFTI':
fprojr = np.array([fproj, fproj[:, :, ::-1]]).max(0)
fdist.append(np.max([utils.fitgaussian(fproj.max(jj))[3:].max() for
jj in range(fprojr.ndim)]))
else:
fdist = np.load(os.path.join(RESOURCES, 'fdist.npy'))
fprojr = np.array([fproj, fproj[:, :, ::-1]]).max(0)
fdist.append(np.max([utils.fitgaussian(fproj.max(jj))[3:].max() for
jj in range(fprojr.ndim)]))
if type(fdist) is not np.ndarray:
fdist = np.array(fdist)
spr = np.array(spr)
Expand Down