def createSampleVolume( outputfile ):
'''
Create a sample volume which has a different value for each voxel.
Then, save the volume.
'''
print Colors.PURPLE + 'Creating a sample volume (d:' + Colors.CYAN + str( SAMPLE_VOLUME_DIMENSION_X ) + ',' + str( SAMPLE_VOLUME_DIMENSION_Y ) + ',' + str( SAMPLE_VOLUME_DIMENSION_Z ) + Colors.PURPLE + ' s:' + Colors.CYAN + str( SAMPLE_VOLUME_SPACING_X ) + ',' + str( SAMPLE_VOLUME_SPACING_Y ) + ',' + str( SAMPLE_VOLUME_SPACING_Z ) + '): ' + Colors.ORANGE + outputfile + Colors.PURPLE + '..' + Colors._CLEAR
testArr = numpy.zeros( ( SAMPLE_VOLUME_DIMENSION_X, SAMPLE_VOLUME_DIMENSION_Y, SAMPLE_VOLUME_DIMENSION_Z ) )
r = 0
for i in range( testArr.shape[0] ):
for j in range( testArr.shape[1] ):
for k in range( testArr.shape[2] ):
r += 1
testArr[i, j, k] = r
img = Nifti1Image( testArr, None )
hdr = img.get_header()
hdr.set_zooms( [SAMPLE_VOLUME_SPACING_X, SAMPLE_VOLUME_SPACING_Y, SAMPLE_VOLUME_SPACING_Z] );
hdr.set_xyzt_units( 1.0, 1.0 )
save( img, outputfile )
return testArr
def atlas_from_freesurfer_masks(sub,
mask_dir,
roi_ids,
rh_constant=200,
overwrite=False):
'''
Create atlas from individual binary masks produced by freesurfer's 'mri_label2vol' tool
Args:
sub (int):
subject number
mask_dir (str):
path to subject's folder with freesurfer output
roi_ids (dict):
{target_ROI: Left Hemisphere ROI index}
rh_constant (int):
Right Hemisphere ROI index := Left Hemisphere ROI index + rh_constant
overwrite (bool):
If true: delete old atlas and create new one
Returns:
atlas (Nifti1Image):
T1w Atlas with voxel values indicative of ROI identity (as defined in 'roi_ids')
'''
atlas_path = os.path.join(
mask_dir, "sub-" + str(sub).zfill(2) + "_Mask_T1w_Glasser.nii.gz")
if os.path.isfile(atlas_path) and not overwrite:
atlas = nifti1.load(atlas_path)
else:
# create atlas
hemi_dict = {'L': 'lh.L_', 'R': 'rh.R_'}
hemi_add = {'L': 0, 'R': rh_constant}
mask_array = []
mask_array_tmp = []
for roi in roi_ids.keys():
for hemi in ['L', 'R']:
mask_raw = nifti1.load(
os.path.join(mask_dir,
hemi_dict[hemi] + roi + '_ROI.fbr.nii.gz'))
if len(mask_array) == 0:
mask_array_tmp = mask_raw.get_fdata()
mask_array_tmp[
mask_array_tmp == 1] = roi_ids[roi][0] + hemi_add[
hemi] # convert binary mask by assigning roi_id to masked voxels
mask_array = mask_array_tmp.copy()
freesurfer_affine = mask_raw.affine.copy()
else:
mask_array_tmp = mask_raw.get_fdata()
mask_array[mask_array_tmp ==
1] = roi_ids[roi][0] + hemi_add[hemi]
atlas = nifti1.Nifti1Image(mask_array, freesurfer_affine)
nifti1.save(atlas, atlas_path)
return atlas
def rawToNifti(infile, sizefile=None, outfile=None, dimorder=None, diskorder='F', dtype=None, split4=False):
"""Converts a raw file to a NIfTI file.
Arguments:
infile: Filename of a raw file.
sizefile: Filename of a size_info config file. If None, attempts to find
this file in the same directory as infile.
outfile: Filename (including .nii) of the NIfTI file to generate.
If None, it will be generated from infile.
dimorder: Four-character string that is a permutation of "XYZI",
indicating the dimension order of the image in "infile".
The purpose of this argument is to rearrange the order of the
dimensions in the infile to match the NIfTI canonical order of
(X, Y, Z, I), where I is the dimension along which multiple
acquisitions are concatenated.
The default value, None, is equivalent to "XYZI".
Note that this argument will be overridden if the size_info
file contains a "dimension_order" value.
diskorder: A string, 'F' or 'C', representing the order in which the data
values are stored in the raw file.
dtype: The numpy dtype for the infile. If None, it is inferred from
infile's extension.
split4: If True, output numbered 3-D images from 4-D input.
"""
(raw, cfg) = readRawWithSizeInfo(infile, sizefile=sizefile, dtype=dtype,
dimorder=dimorder, diskorder=diskorder,
memorder='C')
vox_sz = cfg['voxel_size_(mm)']
# Rearrange dimensions.
try:
dimorder = cfg['dimension_order']
except KeyError:
if dimorder is None:
dimorder = _miraw_helpers.DIM_DEFAULT_ORDER
if not _miraw_helpers.isValidDimorder(dimorder):
raise ValueError('"%s" is not a valid dimorder argument.'%repr(dimorder))
raw_transp = raw.transpose(_miraw_helpers.dimorderToReverseDimmap(dimorder))
if split4 and len(raw_transp.shape) == 4:
raw_stack = [raw_transp[:,:,:,i] for i in range(raw_transp.shape[3])]
else:
raw_stack = [raw_transp]
i = 0
for img in raw_stack:
nii = nifti1.Nifti1Pair(img, np.diag(vox_sz + [0.0]))
nii.get_header().set_xyzt_units('mm')
outfname = outfile
if outfname is None:
outfname = os.path.splitext(infile)[0] + '.nii'
if split4:
outfname = os.path.splitext(outfname)[0] + ('_%03i.nii' % i)
i += 1
nifti1.save(nii, outfname)
def test_save_load():
shape = (2, 4, 6)
npt = np.float32
data = np.arange(np.prod(shape), dtype=npt).reshape(shape)
affine = np.diag([1, 2, 3, 1])
affine[:3,3] = [3,2,1]
img = ni1.Nifti1Image(data, affine)
img.set_data_dtype(npt)
with InTemporaryDirectory() as pth:
pth = mkdtemp()
nifn = pjoin(pth, 'an_image.nii')
sifn = pjoin(pth, 'another_image.img')
ni1.save(img, nifn)
re_img = nils.load(nifn)
yield assert_true(isinstance(re_img, ni1.Nifti1Image))
yield assert_array_equal(re_img.get_data(), data)
yield assert_array_equal(re_img.get_affine(), affine)
# These and subsequent del statements are to prevent confusing
# windows errors when trying to open files or delete the
# temporary directory.
del re_img
try:
import scipy.io
except ImportError:
# ignore if there is no matfile reader, and restart
pass
else:
spm2.save(img, sifn)
re_img2 = nils.load(sifn)
yield assert_true(isinstance(re_img2, spm2.Spm2AnalyzeImage))
yield assert_array_equal(re_img2.get_data(), data)
yield assert_array_equal(re_img2.get_affine(), affine)
del re_img2
spm99.save(img, sifn)
re_img3 = nils.load(sifn)
yield assert_true(isinstance(re_img3,
spm99.Spm99AnalyzeImage))
yield assert_array_equal(re_img3.get_data(), data)
yield assert_array_equal(re_img3.get_affine(), affine)
ni1.save(re_img3, nifn)
del re_img3
re_img = nils.load(nifn)
yield assert_true(isinstance(re_img, ni1.Nifti1Image))
yield assert_array_equal(re_img.get_data(), data)
yield assert_array_equal(re_img.get_affine(), affine)
del re_img
def test_save_load():
shape = (2, 4, 6)
npt = np.float32
data = np.arange(np.prod(shape), dtype=npt).reshape(shape)
affine = np.diag([1, 2, 3, 1])
affine[:3, 3] = [3, 2, 1]
img = ni1.Nifti1Image(data, affine)
img.set_data_dtype(npt)
with InTemporaryDirectory() as pth:
pth = mkdtemp()
nifn = pjoin(pth, 'an_image.nii')
sifn = pjoin(pth, 'another_image.img')
ni1.save(img, nifn)
re_img = nils.load(nifn)
yield assert_true(isinstance(re_img, ni1.Nifti1Image))
yield assert_array_equal(re_img.get_data(), data)
yield assert_array_equal(re_img.get_affine(), affine)
# These and subsequent del statements are to prevent confusing
# windows errors when trying to open files or delete the
# temporary directory.
del re_img
try:
import scipy.io
except ImportError:
# ignore if there is no matfile reader, and restart
pass
else:
spm2.save(img, sifn)
re_img2 = nils.load(sifn)
yield assert_true(isinstance(re_img2, spm2.Spm2AnalyzeImage))
yield assert_array_equal(re_img2.get_data(), data)
yield assert_array_equal(re_img2.get_affine(), affine)
del re_img2
spm99.save(img, sifn)
re_img3 = nils.load(sifn)
yield assert_true(isinstance(re_img3, spm99.Spm99AnalyzeImage))
yield assert_array_equal(re_img3.get_data(), data)
yield assert_array_equal(re_img3.get_affine(), affine)
ni1.save(re_img3, nifn)
del re_img3
re_img = nils.load(nifn)
yield assert_true(isinstance(re_img, ni1.Nifti1Image))
yield assert_array_equal(re_img.get_data(), data)
yield assert_array_equal(re_img.get_affine(), affine)
del re_img
def img_transform(img_file_name,save_dir):
img_hdr = nitool.load(img_file_name)
img_data = img_hdr.get_data()
img_shape = img_data.shape
t = img_data.flatten()
t1 = np.zeros(t.size)
t1[np.where(t>100)] = t[np.where(t > 100)]+3000
t1[np.where(np.logical_and(t >= -1000, t <= -100))]=t[np.where(np.logical_and(t >= -1000,t <= -100))]+1000
t1[np.where(np.logical_and(t >= -99, t <= 100))]=(t[np.where(np.logical_and(t >= -99, t <= 100))]+99)*11+911
trans_img = t1.reshape(img_shape)
trans_pair = nitool.Nifti1Pair(trans_img,img_hdr.affine)
save_name = os.path.join(save_dir, 'itrans'+'.nii.gz')
nitool.save(trans_pair, save_name)
return save_name
def test_save_load():
shape = (2, 4, 6)
npt = np.float32
data = np.arange(np.prod(shape), dtype=npt).reshape(shape)
affine = np.diag([1, 2, 3, 1])
affine[:3,3] = [3,2,1]
img = ni1.Nifti1Image(data, affine)
img.set_data_dtype(npt)
with InTemporaryDirectory() as pth:
nifn = 'an_image.nii'
sifn = 'another_image.img'
ni1.save(img, nifn)
re_img = nils.load(nifn)
yield assert_true(isinstance(re_img, ni1.Nifti1Image))
yield assert_array_equal(re_img.get_data(), data)
yield assert_array_equal(re_img.get_affine(), affine)
# These and subsequent del statements are to prevent confusing
# windows errors when trying to open files or delete the
# temporary directory.
del re_img
if have_scipy: # skip we we cannot read .mat files
spm2.save(img, sifn)
re_img2 = nils.load(sifn)
yield assert_true(isinstance(re_img2, spm2.Spm2AnalyzeImage))
yield assert_array_equal(re_img2.get_data(), data)
yield assert_array_equal(re_img2.get_affine(), affine)
del re_img2
spm99.save(img, sifn)
re_img3 = nils.load(sifn)
yield assert_true(isinstance(re_img3,
spm99.Spm99AnalyzeImage))
yield assert_array_equal(re_img3.get_data(), data)
yield assert_array_equal(re_img3.get_affine(), affine)
ni1.save(re_img3, nifn)
del re_img3
re_img = nils.load(nifn)
yield assert_true(isinstance(re_img, ni1.Nifti1Image))
yield assert_array_equal(re_img.get_data(), data)
yield assert_array_equal(re_img.get_affine(), affine)
del re_img
def test_save_load():
shape = (2, 4, 6)
npt = np.float32
data = np.arange(np.prod(shape), dtype=npt).reshape(shape)
affine = np.diag([1, 2, 3, 1])
affine[:3, 3] = [3, 2, 1]
img = ni1.Nifti1Image(data, affine)
img.set_data_dtype(npt)
with InTemporaryDirectory() as pth:
nifn = 'an_image.nii'
sifn = 'another_image.img'
ni1.save(img, nifn)
re_img = nils.load(nifn)
yield assert_true(isinstance(re_img, ni1.Nifti1Image))
yield assert_array_equal(re_img.get_data(), data)
yield assert_array_equal(re_img.get_affine(), affine)
# These and subsequent del statements are to prevent confusing
# windows errors when trying to open files or delete the
# temporary directory.
del re_img
if have_scipy: # skip we we cannot read .mat files
spm2.save(img, sifn)
re_img2 = nils.load(sifn)
yield assert_true(isinstance(re_img2, spm2.Spm2AnalyzeImage))
yield assert_array_equal(re_img2.get_data(), data)
yield assert_array_equal(re_img2.get_affine(), affine)
del re_img2
spm99.save(img, sifn)
re_img3 = nils.load(sifn)
yield assert_true(isinstance(re_img3, spm99.Spm99AnalyzeImage))
yield assert_array_equal(re_img3.get_data(), data)
yield assert_array_equal(re_img3.get_affine(), affine)
ni1.save(re_img3, nifn)
del re_img3
re_img = nils.load(nifn)
yield assert_true(isinstance(re_img, ni1.Nifti1Image))
yield assert_array_equal(re_img.get_data(), data)
yield assert_array_equal(re_img.get_affine(), affine)
del re_img
def createSampleVolumeWithSelectedLabels( outputfile, values ):
'''
Create a sample volume which has a randomly chosen value from the values list.
Then, save the volume.
'''
print Colors.PURPLE + 'Creating a sample volume (d:' + Colors.CYAN + str( SAMPLE_VOLUME_DIMENSION_X ) + ',' + str( SAMPLE_VOLUME_DIMENSION_Y ) + ',' + str( SAMPLE_VOLUME_DIMENSION_Z ) + Colors.PURPLE + ' s:' + Colors.CYAN + str( SAMPLE_VOLUME_SPACING_X ) + ',' + str( SAMPLE_VOLUME_SPACING_Y ) + ',' + str( SAMPLE_VOLUME_SPACING_Z ) + '): ' + Colors.ORANGE + outputfile + Colors.PURPLE + '..' + Colors._CLEAR
testArr = numpy.zeros( ( SAMPLE_VOLUME_DIMENSION_X, SAMPLE_VOLUME_DIMENSION_Y, SAMPLE_VOLUME_DIMENSION_Z ) )
for i in range( testArr.shape[0] ):
for j in range( testArr.shape[1] ):
for k in range( testArr.shape[2] ):
testArr[i, j, k] = random.sample( values, 1 )[0]
img = Nifti1Image( testArr, None )
hdr = img.get_header()
hdr.set_zooms( [SAMPLE_VOLUME_SPACING_X, SAMPLE_VOLUME_SPACING_Y, SAMPLE_VOLUME_SPACING_Z] );
hdr.set_xyzt_units( 1.0, 1.0 )
save( img, outputfile )
return testArr
# Set directories, specify ROIs and load dictionary for labels
TR = 3
ds_dir = "/home/alex/Datasets/ds001246/"
spm_dir = os.path.join(ds_dir, "derivatives", spm_type)
n_subs = len(glob.glob(ds_dir + os.sep + "sub*"))
##############################################################################
for sub in range(1, n_subs + 1):
img_output_dir = os.path.join(spm_dir, "sub-" + str(sub).zfill(2))
# For each run, get averaged condition-specific residuals
pooled_residuals_array, generic_affine, fourth_dimension_descriptors = \
pooled_residuals_pipeline(sub, spm_dir, spm_type, ses_type, TR,
drop_first_res)
# Make subject-specific nifti image of block-averaged residuals
pooled_residuals = nifti1.Nifti1Image(pooled_residuals_array,
generic_affine)
nifti_filename = os.path.join(
img_output_dir,
"sub-" + str(sub).zfill(2) + "_V_" + stimulus_set + ".nii.gz")
nifti1.save(pooled_residuals, nifti_filename)
csv_filename = os.path.join(
img_output_dir,
"sub-" + str(sub).zfill(2) + "_uq_conditions_" + stimulus_set + ".csv")
df = pd.DataFrame({'descriptor': fourth_dimension_descriptors})
df.to_csv(csv_filename, header=False)
def save_nii(self, person, nii, nii_prefix='mri_smoothed_removed/{}.nii'):
path = os.path.join(self.file_dir, nii_prefix.format(person.filename))
nifti1.save(nii, path)
data_folder = sys.argv[1]
gm_filename = r'c1reg.nii'
wm_filename = r'c2reg.nii'
csf_filename = r'c3reg.nii'
sublist_filename = sys.argv[2]
with open(sublist_filename, 'rt') as sublistfile:
lines = sublistfile.readlines()
sublist = [k.rstrip() for k in lines]
#%%
for i in sublist:
print(i)
gm_file = nitool.load(os.path.join(data_folder, i, gm_filename))
gm_img = gm_file.get_data()
wm_file = nitool.load(os.path.join(data_folder, i, wm_filename))
wm_img = wm_file.get_data()
csf_file = nitool.load(os.path.join(data_folder, i, csf_filename))
csf_img = csf_file.get_data()
tiv_img = gm_img + wm_img + csf_img
tiv_file = nitool.Nifti1Image(tiv_img, gm_file.affine)
tiv_filename = os.path.join(data_folder, i, 'tivmask_image.nii.gz')
print(tiv_filename)
nitool.save(tiv_file, tiv_filename)
es_array = np.asarray(es.dataobj)
p = nib.load(p_path)
p_array = np.asarray(p.dataobj)
for p in ps:
corrected_array = voxelwise_correction(es_array,
p_array,
voxel_count,
thres=p)
affine = es.affine
header = es.header
corrected_niis = nib.Nifti1Image(corrected_array, affine, header)
new_f = os.path.join(voxel_path, 'es_bon_{}.nii'.format(str(p)[2:]))
print(new_f)
print(len(corrected_array[corrected_array != 0]))
nifti1.save(corrected_niis, new_f)
# %%
# ROI GMV correction
import numpy as np
from meta_analysis import utils
def load_nii_array(filepath):
nii = nib.load(filepath)
return np.asarray(nii.dataobj), nii
def roi_correction(value_path, p_path, count, out_path, p=0.001, top=1):
v_array, v_nii = load_nii_array(value_path)
p_array, _ = load_nii_array(p_path)
import os
import sys
#%%
data_folder = sys.argv[1]
gm_filename = r'c1reg.nii'
wm_filename = r'c2reg.nii'
sublist_filename = sys.argv[2]
with open(sublist_filename, 'rt') as sublistfile:
lines = sublistfile.readlines()
sublist = [k.rstrip() for k in lines]
#%%
for i in sublist:
print(i)
gm_file = nitool.load(os.path.join(data_folder, i, gm_filename))
gm_img = gm_file.get_data()
wm_file = nitool.load(os.path.join(data_folder, i, wm_filename))
wm_img = wm_file.get_data()
tbv_img = gm_img + wm_img
tbv_file = nitool.Nifti1Image(tbv_img, gm_file.affine)
tbv_filename = os.path.join(data_folder, i, 'tbvmask_image.nii.gz')
print(tbv_filename)
nitool.save(tbv_file, tbv_filename)
