demo does not work
Remi-Gau opened this issue · comments
Had some issues when downloading the data:
Had to add double quotes around file names to make it run: probably because I am using zsh as my shell.
Note that this may make it hard for windows users to run the demo because curl has a different syntax on windows I think.
!curl --create-dirs "https://s3.amazonaws.com/openneuro.org/ds002785/derivatives/fmriprep/sub-0001/func/sub-0001_task-restingstate_acq-mb3_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz?versionId=UfCs4xtwIEPDgmb32qFbtMokl_jxLUKr" -o "sample_data/sub-0001_task-restingstate_acq-mb3_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz"
!curl --create-dirs "https://s3.amazonaws.com/openneuro.org/ds002785/derivatives/fmriprep/sub-0001/func/sub-0001_task-restingstate_acq-mb3_desc-confounds_regressors.tsv?versionId=biaIJGNQ22P1l1xEsajVzUW6cnu1_8lD" -o "sample_data/sub-0001_task-restingstate_acq-mb3_desc-confounds_regressors.tsv"I am then getting an error at this step:
# load sub-0001 data from nifti file
BOLD = data_loader.nifti2timeseries(
nifti_file='sample_data/sub-0001_task-restingstate_acq-mb3_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
n_rois=100, Fs=1/0.75,
subj_id='sub-0001',
confound_strategy='no_motion', # no_motion, no_motion_no_gsr, or none
standardize=False,
TS_name=None,
session=None,
)
BOLD.visualize( start_time=0, end_time=1000, nodes_lst=range(10)){
"name": "IndexError",
"message": "only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices",
"stack": "---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
Cell In[6], line 2
1 # load sub-0001 data from nifti file
----> 2 BOLD = data_loader.nifti2timeseries(
3 nifti_file='sample_data/sub-0001_task-restingstate_acq-mb3_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz',
4 n_rois=100, Fs=1/0.75,
5 subj_id='sub-0001',
6 confound_strategy='no_motion', # no_motion, no_motion_no_gsr, or none
7 standardize=False,
8 TS_name=None,
9 session=None,
10 )
12 BOLD.visualize( start_time=0, end_time=1000, nodes_lst=range(10))
File ~/github/origami/dFC/pydfc/data_loader.py:243, in nifti2timeseries(nifti_file, n_rois, Fs, subj_id, confound_strategy, standardize, TS_name, session)
225 def nifti2timeseries(
226 nifti_file,
227 n_rois, Fs,
(...)
232 session=None,
233 ):
234 '''
235 this function is only for single subject and single session data loading
236 it uses nilearn maskers to extract ROI signals from nifti files
(...)
241 {100, 200, 300, 400, 500, 600, 700, 800, 900, 1000}
242 '''
--> 243 time_series, labels, locs = nifti2array(
244 nifti_file=nifti_file,
245 confound_strategy=confound_strategy,
246 standardize=standardize,
247 n_rois=n_rois
248 )
250 assert type(locs) is np.ndarray, 'locs must be a numpy array'
251 assert type(labels) is list, 'labels must be a list'
File ~/github/origami/dFC/pydfc/data_loader.py:205, in nifti2array(nifti_file, confound_strategy, standardize, n_rois)
199 elif confound_strategy=='no_motion':
200 confounds_simple, sample_mask = load_confounds(
201 nifti_file,
202 strategy=[\"high_pass\", \"motion\", \"wm_csf\"],
203 motion=\"basic\", wm_csf=\"basic\"
204 )
--> 205 time_series = masker.fit_transform(
206 nifti_file,
207 confounds=confounds_simple,
208 sample_mask=sample_mask
209 )
210 elif confound_strategy=='no_motion_no_gsr':
211 confounds_simple, sample_mask = load_confounds(
212 nifti_file,
213 strategy=[\"high_pass\", \"motion\", \"wm_csf\", \"global_signal\"],
214 motion=\"basic\", wm_csf=\"basic\", global_signal=\"basic\"
215 )
File ~/miniconda3/lib/python3.11/site-packages/sklearn/utils/_set_output.py:140, in _wrap_method_output.<locals>.wrapped(self, X, *args, **kwargs)
138 @wraps(f)
139 def wrapped(self, X, *args, **kwargs):
--> 140 data_to_wrap = f(self, X, *args, **kwargs)
141 if isinstance(data_to_wrap, tuple):
142 # only wrap the first output for cross decomposition
143 return (
144 _wrap_data_with_container(method, data_to_wrap[0], X, self),
145 *data_to_wrap[1:],
146 )
File ~/github/nilearn/nilearn/nilearn/maskers/nifti_labels_masker.py:531, in NiftiLabelsMasker.fit_transform(self, imgs, confounds, sample_mask)
500 def fit_transform(self, imgs, confounds=None, sample_mask=None):
501 \"\"\"Prepare and perform signal extraction from regions.
502
503 Parameters
(...)
529
530 \"\"\"
--> 531 return self.fit(imgs).transform(
532 imgs, confounds=confounds, sample_mask=sample_mask
533 )
File ~/miniconda3/lib/python3.11/site-packages/sklearn/utils/_set_output.py:140, in _wrap_method_output.<locals>.wrapped(self, X, *args, **kwargs)
138 @wraps(f)
139 def wrapped(self, X, *args, **kwargs):
--> 140 data_to_wrap = f(self, X, *args, **kwargs)
141 if isinstance(data_to_wrap, tuple):
142 # only wrap the first output for cross decomposition
143 return (
144 _wrap_data_with_container(method, data_to_wrap[0], X, self),
145 *data_to_wrap[1:],
146 )
File ~/github/nilearn/nilearn/nilearn/maskers/base_masker.py:267, in BaseMasker.transform(self, imgs, confounds, sample_mask)
264 else:
265 all_confounds.append(confounds)
--> 267 return self.transform_single_imgs(
268 imgs, confounds=all_confounds, sample_mask=sample_mask
269 )
File ~/github/nilearn/nilearn/nilearn/maskers/nifti_labels_masker.py:703, in NiftiLabelsMasker.transform_single_imgs(self, imgs, confounds, sample_mask)
700 region_ids[i] = ids[i]
702 if self.labels is not None:
--> 703 self.region_names_ = {
704 key: self.labels[region_id]
705 for key, region_id in region_ids.items()
706 if region_id != self.background_label
707 }
708 else:
709 self.region_names_ = None
File ~/github/nilearn/nilearn/nilearn/maskers/nifti_labels_masker.py:704, in <dictcomp>(.0)
700 region_ids[i] = ids[i]
702 if self.labels is not None:
703 self.region_names_ = {
--> 704 key: self.labels[region_id]
705 for key, region_id in region_ids.items()
706 if region_id != self.background_label
707 }
708 else:
709 self.region_names_ = None
IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices"
}
@mtorabi59 : did you have a look at @Remi-Gau issue ? I think this needs to fix that before the paper is released
Which issue can be solved by users ? the curl download on Windows ?
@jbpoline yes, if I change it (add double quotations) then it won't work for mac. So I think Windows users can change the curl syntax and then run it for now. I will address this in future.
My suggestion would be to rely either on the AWS CLI or datalad as suggested in the openneuro download page : https://openneuro.org/datasets/ds002785/versions/1.0.0/download
this way you are sure that you don't have an OS dependent issue.