pyhrf.core.Condition(**kwargs)¶Bases: pyhrf.core.AttrClass
Represents an activation condition
FMRISessionSimulationData(onsets={'audio': array([ 15. , 20.7, 29.7, 35.4, 44.7, 48. , 83.4, 89.7,108. , 119.4, 135. , 137.7, 146.7, 173.7, 191.7, 236.7,251.7, 284.4, 293.4, 296.7]), 'video': array([ 0. , 2.4, 8.7, 33. , 39. , 41.7, 56.4, 59.7,75. , 96. , 122.7, 125.4, 131.4, 140.4, 149.4, 153. ,156. , 159. , 164.4, 167.7, 176.7, 188.4, 195. , 198. ,201. , 203.7, 207. , 210. , 218.7, 221.4, 224.7, 234. ,246. , 248.4, 260.4, 264. , 266.7, 269.7, 278.4, 288. ])}, durations={'audio': array([], dtype=float64), 'video': array([], dtype=float64)}, simulation_file='/home/docs/checkouts/readthedocs.org/user_builds/pyhrf/envs/latest/lib/python2.7/site-packages/pyhrf-0.5.0-py2.7-linux-x86_64.egg/pyhrf/datafiles/simu.pck')Bases: pyhrf.xmlio.Initable
FMRISessionSimulationData.to_dict()¶FMRISessionSurfacicData(onsets={'audio': array([ 15. , 20.7, 29.7, 35.4, 44.7, 48. , 83.4, 89.7,108. , 119.4, 135. , 137.7, 146.7, 173.7, 191.7, 236.7,251.7, 284.4, 293.4, 296.7]), 'video': array([ 0. , 2.4, 8.7, 33. , 39. , 41.7, 56.4, 59.7,75. , 96. , 122.7, 125.4, 131.4, 140.4, 149.4, 153. ,156. , 159. , 164.4, 167.7, 176.7, 188.4, 195. , 198. ,201. , 203.7, 207. , 210. , 218.7, 221.4, 224.7, 234. ,246. , 248.4, 260.4, 264. , 266.7, 269.7, 278.4, 288. ])}, durations={'audio': array([], dtype=float64), 'video': array([], dtype=float64)}, bold_file='/home/docs/checkouts/readthedocs.org/user_builds/pyhrf/envs/latest/lib/python2.7/site-packages/pyhrf-0.5.0-py2.7-linux-x86_64.egg/pyhrf/datafiles/real_data_surf_tiny_bold.gii')Bases: pyhrf.xmlio.Initable
FMRISessionSurfacicData.to_dict()¶FMRISessionVolumicData(onsets={'audio': array([ 15. , 20.7, 29.7, 35.4, 44.7, 48. , 83.4, 89.7,108. , 119.4, 135. , 137.7, 146.7, 173.7, 191.7, 236.7,251.7, 284.4, 293.4, 296.7]), 'video': array([ 0. , 2.4, 8.7, 33. , 39. , 41.7, 56.4, 59.7,75. , 96. , 122.7, 125.4, 131.4, 140.4, 149.4, 153. ,156. , 159. , 164.4, 167.7, 176.7, 188.4, 195. , 198. ,201. , 203.7, 207. , 210. , 218.7, 221.4, 224.7, 234. ,246. , 248.4, 260.4, 264. , 266.7, 269.7, 278.4, 288. ])}, durations={'audio': array([], dtype=float64), 'video': array([], dtype=float64)}, bold_file='/home/docs/checkouts/readthedocs.org/user_builds/pyhrf/envs/latest/lib/python2.7/site-packages/pyhrf-0.5.0-py2.7-linux-x86_64.egg/pyhrf/datafiles/subj0_bold_session0.nii.gz')Bases: pyhrf.xmlio.Initable
FMRISessionVolumicData.parametersComments = {'bold_file': 'Data file containing the 3D+time BOLD signal (nifti format)', 'durations': 'Durations of experimental simtuli in seconds.\nIt has to consistent with the definition of onsets', 'onsets': 'Onsets of experimental simtuli in seconds. \nDictionnary mapping stimulus name to the actual list of onsets.'}¶FMRISessionVolumicData.to_dict()¶pyhrf.core.FmriData(onsets, bold, tr, sessionsScans, roiMask, graphs=None, stimDurations=None, meta_obj=None, simulation=None, backgroundLabel=0, data_files=None, data_type=None, edge_lengths=None, mask_loaded_from_file=False, extra_data=None)¶Bases: pyhrf.xmlio.Initable
onsets¶a dictionary mapping a stimulus name to a list of session onsets. Each item of this list is a 1D numpy float array of onsets for a given session.
stimDurations¶same as ‘onsets’ but stores durations of stimuli
roiMask¶numpy int array of roi labels (0 stands for the background). Shape depends on the data form (3D volumic or 1D surfacic)
bold¶either a 4D numpy float array with axes [sag,cor,ax,scan] and then spatial axes must have the same shape as roiMask, or a 2D numpy float array with axes [scan, position] and position axis must have the same length as the number of positions within roiMask (without background). Sessions are stacked in the scan axis
sessionsScans¶a list of session indexes along scan axis.
tr¶Time of repetition of the BOLD signal
simulation¶if not None then it should be a list of simulation instance.
meta_obj¶extra information associated to data
average(flag=True)¶build_graphs(force=False)¶compute_average()¶discard_rois(roi_ids)¶discard_small_rois(min_size)¶from_simu_ui(sessions_data=None)¶from_simulation_dict(simulation, mask=None)¶from_surf_files(paradigm_csv_file='/home/docs/checkouts/readthedocs.org/user_builds/pyhrf/envs/latest/lib/python2.7/site-packages/pyhrf-0.5.0-py2.7-linux-x86_64.egg/pyhrf/datafiles/paradigm_loc_av.csv', bold_files=None, tr=2.4, mesh_file='/home/docs/checkouts/readthedocs.org/user_builds/pyhrf/envs/latest/lib/python2.7/site-packages/pyhrf-0.5.0-py2.7-linux-x86_64.egg/pyhrf/datafiles/real_data_surf_tiny_mesh.gii', mask_file=None)¶Return FmriData representation from surf files
from_surf_ui(sessions_data=None, tr=2.4, mask_file='/home/docs/checkouts/readthedocs.org/user_builds/pyhrf/envs/latest/lib/python2.7/site-packages/pyhrf-0.5.0-py2.7-linux-x86_64.egg/pyhrf/datafiles/real_data_surf_tiny_parcellation.gii', mesh_file='/home/docs/checkouts/readthedocs.org/user_builds/pyhrf/envs/latest/lib/python2.7/site-packages/pyhrf-0.5.0-py2.7-linux-x86_64.egg/pyhrf/datafiles/real_data_surf_tiny_mesh.gii')¶Convenient creation function intended to be used for XML I/O. ‘session_data’ is a list of FMRISessionVolumicData objects. ‘tr’ is the time of repetition. ‘mask_file’ is a path to a functional mask file.
This represents the following hierarchy:
- FMRIData:
- list of session data:
[ * data for session 1:
- onsets for session 1,
- durations for session 1,
- fmri data file for session 1 (gii)
* data for session 2:
- onsets for session 2,
- durations for session 2,
- fmri data file for session 2 (gii)
],
- time of repetition
- mask file
- mesh file
from_vol_files¶from_vol_files_rel¶from_vol_ui¶getSummary(long=False)¶get_condition_names()¶get_data_files()¶get_extra_data(label, default)¶get_graph()¶get_joined_durations()¶get_joined_onsets()¶get_nb_rois()¶Return the number of parcels (background id is discarded)
get_nb_vox_in_mask()¶get_roi_id()¶In case of FMRI data containing only one ROI, return the id of this ROI. If data contains several ROIs then raise an exception
get_roi_mask()¶keep_only_rois(roiIds)¶parametersComments = {'mask_file': 'Input n-ary mask file (= parcellation). Only positive integers are allowed. \nAll zeros are treated as background positions.', 'sessions_data': 'List of data definition for all sessions', 'tr': 'repetition time in seconds'}¶parametersToShow = ['tr', 'sessions_data', 'mask_file']¶roiMaskroi_split(mask=None)¶save(output_dir)¶Save paradigm to output_dir/paradigm.csv, BOLD to output_dir/bold.nii, mask to output_dir/mask.nii #TODO: handle multi-session
Return: tuple of file names in this order: (paradigm, bold, mask)
set_extra_data(label, value)¶store_mask_sparse(roiMask)¶pyhrf.core.FmriGroupData(list_subjects)¶Bases: pyhrf.xmlio.Initable
Used for group level hemodynamic analysis Encapsulates FmriData objects for all subjects All subjects must habe the same number of ROIs
build_graphs(force=False)¶getSummary(long=False)¶get_roi_id()¶roi_split()¶Retrieve a list of FmriGroupData object, each containing the data for all subject, in one ROI
pyhrf.core.get_data_file_name(filename)¶Return the path of a given filename.
pyhrf.core.get_roi_simulation(simu_sessions, mask, roi_id)¶Extract the ROI from the given simulation dict. :param - simu: dictionnary of simulated quantities :type - simu: dict :param - mask: binary mask defining the spatial extent of the ROI :type - mask: np.ndarray :param - roi_id: the id of the roi to extract :type - roi_id: int
| Returns: | dict of roi-specific simulation items |
|---|
pyhrf.core.get_src_doc_path()¶Return the documentation path of pyhrf.
pyhrf.core.get_src_path()¶Return the source path of pyhrf.
pyhrf.core.get_tmp_path(tag='pyhrf_')¶Return a temporary path.
pyhrf.core.list_data_file_names()¶List all the data filenames.
pyhrf.core.load_surf_bold_mask(bold_files, mesh_file, mask_file=None)¶pyhrf.core.load_vol_bold_and_mask(bold_files, mask_file)¶pyhrf.core.merge_fmri_sessions(fmri_data_sets)¶fmri_data_sets: list of FmriData objects. Each FmriData object is assumed to contain only one session
pyhrf.core.merge_fmri_subjects(fmri_data_sets, roiMask, backgroundLabel=0)¶fmri_data_sets: list of FmriData objects, for different subjects. In case of multisession data, merging of fmri data over sessions must be done for each subject before using this function. roiMask: multi_subject parcellation (nparray)