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fix bug with 2 json file for one level
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@Remi-Gau
Remi-Gau committed Aug 13, 2023
1 parent aaa0007 commit c8b4f25
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Showing 3 changed files with 266 additions and 146 deletions.
21 changes: 19 additions & 2 deletions +bids/+internal/get_meta_list.m
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Original file line number Diff line number Diff line change
Expand Up @@ -43,6 +43,15 @@
return
end

% in deepest level look for file with only a change in extension
basename = bids.internal.file_utils(filename, 'basename');
if strcmp(bids.internal.file_utils(basename, 'ext'), 'nii')
basename = bids.internal.file_utils(basename, 'basename');
end
ideal_metafile = bids.internal.file_utils('FPList', ...
pth, ...
sprintf(pattern, basename));

% Default assumes we are dealing with a file in the root directory
% like "participants.tsv".
% If the file has underscore separated entities ("sub-01_T1w.nii")
Expand All @@ -59,7 +68,7 @@

for n = 1:N

% List the potential metadata files associated with this file suffix type
% List the metadata files associated with this file
metafile = bids.internal.file_utils('FPList', pth, sprintf(pattern, p.suffix));

if isempty(metafile)
Expand All @@ -68,6 +77,14 @@
metafile = cellstr(metafile);
end

% in deepest level look for file with only a change in extension
if n == 1 && ~isempty(ideal_metafile)
metalist{end + 1, 1} = ideal_metafile; %#ok<*AGROW>
% Go up to the parent folder
pth = fullfile(pth, '..');
continue
end

% For all those files we find which one is potentially associated
% with the file of interest
% TODO: not more than one file per level is allowed
Expand Down Expand Up @@ -102,7 +119,7 @@

% append path to list
if ismeta
metalist{end + 1, 1} = metafile{i}; %#ok<AGROW>
metalist{end + 1, 1} = metafile{i};
end

end
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301 changes: 157 additions & 144 deletions tests/tests_layout/layout_timing.m
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Original file line number Diff line number Diff line change
Expand Up @@ -8,155 +8,168 @@

%% WITH SCHEMA
% data time (sec)
% qmri_tb1tfl 0.090
% qmri_qsm 0.090
% qmri_sa2rage 0.093
% pet004 0.105
% pet003 0.106
% asl003 0.106
% qmri_irt1 0.107
% qmri_vfa 0.110
% micr_SEMzarr 0.112
% qmri_mtsat 0.115
% hcp_example_bids 0.116
% pet001 0.117
% fnirs_tapping 0.117
% asl005 0.118
% qmri_mp2rage 0.121
% asl002 0.123
% asl004 0.124
% micr_SEM 0.128
% qmri_megre 0.138
% eeg_ds003654s_hed_inheritance 0.151
% ds000246 0.152
% pet002 0.159
% asl001 0.163
% pet005 0.166
% micr_SPIM 0.176
% ieeg_epilepsy_ecog 0.183
% ds000248 0.188
% ieeg_visual 0.191
% qmri_mp2rageme 0.192
% eeg_ds003654s_hed 0.201
% ieeg_epilepsy 0.204
% eeg_ds003654s_hed_library 0.212
% eeg_rest_fmri 0.224
% eeg_ds003654s_hed_longform 0.226
% fnirs_automaticity 0.229
% eeg_matchingpennies 0.308
% ds000247 0.321
% ieeg_filtered_speech 0.325
% eeg_face13 0.379
% ds003 0.410
% qmri_mese 0.473
% eeg_cbm 0.599
% ieeg_motorMiller2007 0.722
% synthetic 0.806
% ds101 0.807
% ds001 0.864
% ds005 0.865
% genetics_ukbb 0.882
% ieeg_visual_multimodal 0.907
% ds052 0.945
% ds105 0.961
% ds102 1.018
% qmri_mpm 1.174
% eeg_rishikesh 1.289
% ds011 1.310
% ds008 1.400
% ds109 1.462
% ds114 1.464
% ds051 1.526
% ds002 1.555
% ds116 1.749
% eeg_ds000117 1.777
% ds007 1.920
% ds107 2.171
% ds113b 2.232
% ds210 2.336
% ds009 2.476
% ds110 2.583
% ds006 2.866
% ds108 3.218
% ds000117 5.198
% 7t_trt 5.443
% asl001 0.084
% asl005 0.094
% asl003 0.095
% asl002 0.098
% asl004 0.109
% pet004 0.114
% pet003 0.127
% qmri_tb1tfl 0.137
% qmri_qsm 0.138
% qmri_irt1 0.139
% qmri_sa2rage 0.153
% micr_SEMzarr 0.159
% pet001 0.165
% qmri_mp2rage 0.174
% pet005 0.182
% qmri_vfa 0.185
% qmri_mtsat 0.187
% micr_SEM 0.198
% ds000246 0.215
% qmri_megre 0.217
% hcp_example_bids 0.244
% eeg_ds003645s_hed_inheritance 0.257
% pet002 0.259
% ds000248 0.292
% micr_SPIM 0.312
% qmri_mp2rageme 0.334
% eeg_ds003645s_hed_longform 0.383
% ieeg_visual 0.396
% eeg_ds003645s_hed_library 0.405
% ieeg_epilepsy_ecog 0.475
% fnirs_tapping 0.557
% eeg_rest_fmri 0.602
% ds000247 0.693
% ieeg_epilepsyNWB 0.752
% motion_systemvalidation 0.817
% eeg_matchingpennies 0.819
% qmri_mese 0.862
% eeg_face13 0.871
% ieeg_epilepsy 1.097
% ds004332 1.127
% eeg_ds003645s_hed 1.209
% ieeg_filtered_speech 1.783
% ds005 1.904
% ds003 2.030
% ds101 2.090
% qmri_mpm 2.333
% ds105 2.390
% motion_spotrotation 2.525
% ieeg_visual_multimodal 2.544
% ds052 2.627
% ds001 2.692
% genetics_ukbb 2.812
% synthetic 3.061
% ds011 3.139
% ds008 3.390
% eeg_cbm 3.691
% ds051 3.726
% ds114 3.818
% ds002 3.877
% ds102 4.276
% ds007 4.544
% ds109 4.726
% ds116 5.002
% eeg_rishikesh 5.175
% ieeg_motorMiller2007 5.342
% ds009 5.821
% ds006 6.574
% ds107 6.991
% ds110 7.362
% ds113b 7.766
% eeg_ds000117 8.033
% ds210 8.614
% motion_dualtask 8.633
% ds108 10.668
% ds000117 15.543
% 7t_trt 15.960
% fnirs_automaticity 19.642
% docs NaN
% ds000001-fmriprep NaN
% tools NaN

%% WITHOUT SCHEMA
% data time (sec)
% asl001 0.089
% asl003 0.092
% asl005 0.094
% asl002 0.097
% asl004 0.099
% qmri_qsm 0.103
% qmri_tb1tfl 0.106
% pet004 0.113
% qmri_sa2rage 0.113
% hcp_example_bids 0.116
% ds000248 0.117
% qmri_vfa 0.119
% pet003 0.120
% qmri_mp2rage 0.122
% ds000246 0.125
% micr_SEMzarr 0.128
% qmri_qsm 0.071
% qmri_tb1tfl 0.073
% qmri_sa2rage 0.081
% qmri_vfa 0.105
% qmri_irt1 0.108
% hcp_example_bids 0.113
% asl001 0.120
% pet004 0.122
% pet001 0.126
% qmri_mp2rage 0.128
% asl003 0.128
% qmri_mtsat 0.132
% pet001 0.133
% eeg_ds003654s_hed_inheritance 0.135
% qmri_irt1 0.141
% pet005 0.148
% micr_SEM 0.149
% eeg_ds003654s_hed_longform 0.174
% eeg_ds003654s_hed 0.176
% eeg_ds003654s_hed_library 0.176
% pet002 0.182
% qmri_megre 0.183
% micr_SPIM 0.185
% ieeg_epilepsy 0.195
% ieeg_visual 0.196
% ieeg_epilepsy_ecog 0.199
% qmri_mp2rageme 0.212
% eeg_rest_fmri 0.216
% fnirs_tapping 0.219
% ds000247 0.264
% eeg_matchingpennies 0.283
% eeg_face13 0.335
% ds003 0.375
% ieeg_filtered_speech 0.385
% eeg_cbm 0.476
% qmri_mese 0.592
% ds001 0.720
% ds005 0.789
% ieeg_motorMiller2007 0.808
% ds101 0.813
% ds105 0.829
% genetics_ukbb 0.902
% synthetic 0.963
% ds102 0.970
% ds052 0.993
% ds008 1.127
% eeg_rishikesh 1.144
% ds011 1.190
% ieeg_visual_multimodal 1.194
% ds114 1.213
% ds109 1.245
% ds002 1.338
% ds051 1.353
% qmri_mpm 1.358
% ds116 1.461
% eeg_ds000117 1.518
% ds007 1.586
% ds113b 1.774
% ds009 1.986
% ds107 2.048
% ds210 2.191
% ds006 2.359
% ds110 2.416
% ds108 2.917
% 7t_trt 3.905
% fnirs_automaticity 5.766
% ds000117 6.191
% asl002 0.133
% asl005 0.135
% pet003 0.145
% micr_SEMzarr 0.152
% qmri_megre 0.176
% asl004 0.177
% micr_SEM 0.183
% eeg_ds003645s_hed_inheritance 0.217
% pet005 0.220
% qmri_mp2rageme 0.243
% ds000248 0.264
% pet002 0.268
% micr_SPIM 0.292
% ds000246 0.307
% ieeg_epilepsyNWB 0.333
% eeg_ds003645s_hed_longform 0.335
% ieeg_visual 0.351
% eeg_ds003645s_hed_library 0.359
% eeg_ds003645s_hed 0.362
% ieeg_epilepsy 0.380
% fnirs_tapping 0.463
% ieeg_epilepsy_ecog 0.468
% motion_systemvalidation 0.508
% eeg_rest_fmri 0.523
% qmri_mese 0.716
% eeg_face13 0.767
% ieeg_filtered_speech 0.775
% eeg_matchingpennies 0.813
% ds003 1.049
% ds000247 1.092
% ds004332 1.406
% qmri_mpm 1.735
% ieeg_motorMiller2007 1.781
% motion_spotrotation 1.857
% genetics_ukbb 1.915
% ieeg_visual_multimodal 2.170
% ds005 2.202
% ds101 2.350
% synthetic 2.446
% ds052 2.544
% eeg_cbm 2.554
% ds001 2.638
% eeg_rishikesh 2.776
% ds102 2.939
% ds105 3.204
% ds109 3.461
% ds114 3.507
% ds011 4.585
% ds051 4.758
% ds002 4.927
% ds008 5.239
% ds116 5.307
% eeg_ds000117 5.373
% ds107 6.259
% ds113b 6.300
% ds210 7.619
% ds110 7.992
% motion_dualtask 8.380
% ds108 8.887
% ds007 10.000
% ds009 10.363
% ds006 10.371
% fnirs_automaticity 14.192
% 7t_trt 15.023
% ds000117 23.928
% docs NaN
% ds000001-fmriprep NaN
% tools NaN

pth_bids_example = get_test_data_dir();

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