def load(filename):
"""
load available file
available exts: .nii(.gz), .mha, .xls(x), .csv, .tsv, .json
:param filename: file want to load
:type filename: str
:return: object
"""
if '.nii' in filename:
from nibabel import Nifti1Image
img = Nifti1Image.load(filename)
else:
import pandas as pd
if '.xls' in filename:
img = pd.read_excel(filename)
elif '.csv' in filename:
img = pd.read_csv(filename)
elif '.tsv' in filename:
img = pd.read_table(filename)
elif '.1D' in filename:
img = pd.read_csv(filename, header=None, sep='\s+')
elif '.json' in filename:
import json
img = json.load(open(filename))
else:
raise Exception #TODO: Exception message handler
return img
def __init__(self, data, affine=None, **kwargs):
from .load import load, datadict
from .brain import Brain
from .model import Model
if isinstance(data, six.string_types):
if data in datadict.keys():
data = load(data)
else:
image = Nifti1Image.load(data)
super(Nifti, self).__init__(image.dataobj, image.affine)
if isinstance(data, np.ndarray):
if affine is None:
raise IOError(
"If data is provided as array, affine must also be provided"
)
else:
super(Nifti, self).__init__(data, affine, **kwargs)
elif isinstance(data, Nifti):
super(Nifti, self).__init__(data.dataobj, data.affine)
elif isinstance(data, Brain):
data = data.to_nii(**kwargs)
super(Nifti, self).__init__(data.dataobj, data.affine)
elif isinstance(data, Model):
bo = Brain(data)
data = bo.to_nii(**kwargs)
super(Nifti, self).__init__(data.dataobj, data.affine)
def combine_atlas(path):
"""
:param path:
:return:
"""
import os
from nibabel import Nifti1Image as ImageObj
affine = list()
label = dict()
atlasdata = None
list_of_rois = [img for img in os.listdir(path) if '.nii' in img]
rgbs = np.random.rand(len(list_of_rois), 3)
label[0] = 'Clear Label', [.0, .0, .0]
for idx, img in enumerate(list_of_rois):
imageobj = ImageObj.load(os.path.join(path, img))
affine.append(imageobj.affine)
if idx == 0:
atlasdata = np.asarray(imageobj.dataobj)
else:
atlasdata += np.asarray(imageobj.dataobj) * (idx + 1)
label[idx + 1] = split_nifti(img), rgbs[idx]
atlas = ImageObj(atlasdata, affine[0])
return atlas, label
def get_data():
_package_directory = os.path.dirname(
os.path.abspath(ibc_public.utils_data.__file__))
subject_session = ibc_public.utils_data.get_subject_session(
'rsvp-language')
ibc = '/storage/store/data/ibc/derivatives/'
data = []
for subject, session in subject_session:
# fetch the data
data_path = os.path.join(ibc, subject, session, 'func')
imgs = sorted(glob.glob(os.path.join(data_path, 'w*RSVPLanguage*')))
n_scans = [Nifti1Image.load(img).shape[3] for img in imgs]
confounds = sorted(
glob.glob(os.path.join(data_path, 'rp_*RSVPLanguage*')))
events = sorted(
glob.glob(os.path.join(data_path, '*RSVPLanguage*_events.tsv')))
dmtxs = [
make_dmtx(these_events, n_scans=these_scans, t_r=2)
for these_events, these_scans in zip(events, n_scans)
]
data.append((imgs, confounds, dmtxs))
return data
