cedalion.io package
Submodules
cedalion.io.anatomy module
- cedalion.io.anatomy.cell_coordinates(mask, affine, units='mm')
- cedalion.io.anatomy.read_segmentation_masks(basedir, mask_files={'csf': 'csf.nii', 'gm': 'gm.nii', 'scalp': 'scalp.nii', 'skull': 'skull.nii', 'wm': 'wm.nii'})
- Return type:
DataArray
cedalion.io.bids module
- cedalion.io.bids.read_events_from_tsv(fname)
cedalion.io.forward_model module
- cedalion.io.forward_model.load_Adot(fn)
Load Adot from a netCDF file.
- Parameters:
fn (
str) – str File name to load the data from.- Returns:
- xr.DataArray
Data loaded from the file.
- Return type:
Adot
cedalion.io.photogrammetry module
- cedalion.io.photogrammetry.opt_fid_to_xr(fiducials, optodes)
Convert OrderedDicts fiducials and optodes to cedalion LabeledPoints objects.
- Parameters:
fiducials – OrderedDict The fiducials as an OrderedDict.
optodes – OrderedDict The optodes as an OrderedDict.
- Returns:
- cedalion.LabeledPoints
The fiducials as a cedalion LabeledPoints object.
- optodescedalion.LabeledPoints
The optodes as a cedalion LabeledPoints object.
- Return type:
fiducials
- cedalion.io.photogrammetry.read_einstar(fn)
Read optodes and fiducials from einstar devices.
- Parameters:
fn – The filename of the einstar photogrammatry output file.
- Returns:
- OrderedDict
The fiducials as an OrderedDict.
- optodesOrderedDict
The optodes as an OrderedDict.
- Return type:
fiducials
- cedalion.io.photogrammetry.read_photogrammetry_einstar(fn)
Read optodes and fiducials from photogrammetry pipeline.
This method reads the output file as returned by the photogrammetry pipeline using an einstar device.
- Parameters:
fn – the filename of the einstar photogrammatry output file
- Returns:
- cedalion.LabeledPoints
The fiducials as a cedalion LabeledPoints object.
- optodescedalion.LabeledPoints
The optodes as a cedalion LabeledPoints object.
- Return type:
fiducials
cedalion.io.probe_geometry module
- cedalion.io.probe_geometry.read_digpts(fname, units='mm')
- Return type:
DataArray
- cedalion.io.probe_geometry.read_einstar_obj(fname)
Read a textured triangle mesh generated by Einstar devices.
- Return type:
- cedalion.io.probe_geometry.read_mrk_json(fname, crs)
- Return type:
DataArray
cedalion.io.snirf module
- class cedalion.io.snirf.DataType(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)
Bases:
Enum- CW_AMPLITUDE = 1
- CW_FLUORESCENCE_AMPLITUDE = 51
- DCS_BFI = 410
- DCS_G2 = 401
- FD_AC_AMPLITUDE = 101
- FD_FLUORESCENCE_AMPLITUDE = 151
- FD_FLUORESCENCE_PHASE = 152
- FD_PHASE = 102
- PROCESSED = 99999
- TDG_AMPLITUDE = 201
- TDG_FLUORESCENCE_AMPLITUDE = 251
- TDM_AMPLITUDE = 301
- TDM_FLUORESCENCE_AMPLITUDE = 351
- class cedalion.io.snirf.DataTypeLabel(value, names=None, *, module=None, qualname=None, type=None, start=1, boundary=None)
Bases:
StrEnum- BFI = 'BFi'
- DMEAN = 'dMean'
- DOD = 'dOD'
- DSKEW = 'dSkew'
- DVAR = 'dVar'
- H2O = 'H2O'
- HBO = 'HbO'
- HBR = 'HbR'
- HBT = 'HbT'
- HRF_BFI = 'HRF BFi'
- HRF_DMEAN = 'HRF dMean'
- HRF_DOD = 'HRF dOD'
- HRF_DSKEW = 'HRF dSkew'
- HRF_DVAR = 'HRF dVar'
- HRF_HBO = 'HRF HbO'
- HRF_HBR = 'HRF HbR'
- HRF_HBT = 'HRF HbT'
- LIPID = 'Lipid'
- MUA = 'mua'
- MUSP = 'musp'
- RAW_NIRX = 'raw-DC'
- RAW_SATORI = 'RAW'
- class cedalion.io.snirf.Element(data=None, stim=None, geo3d=None, geo2d=None, aux=None, meta_data=None, measurement_lists=None)
Bases:
object
- cedalion.io.snirf.denormalize_measurement_list(df_ml, nirs_element)
- cedalion.io.snirf.geometry_from_probe(nirs_element)
- cedalion.io.snirf.labels_and_positions(probe)
- cedalion.io.snirf.measurement_list_from_stacked(stacked_array, data_type, trial_types, stacked_channel='snirf_channel')
- cedalion.io.snirf.measurement_list_to_dataframe(measurement_list, drop_none=False)
- cedalion.io.snirf.meta_data_tags_to_dict(nirs_element)
- cedalion.io.snirf.parse_data_type(value)
- cedalion.io.snirf.parse_data_type_label(value)
- cedalion.io.snirf.read_aux(nirs_element, opts)
- cedalion.io.snirf.read_data_elements(data_element, nirs_element, stim)
- cedalion.io.snirf.read_nirs_element(nirs_element, opts)
- cedalion.io.snirf.read_snirf(fname, squeeze_aux=False)
- cedalion.io.snirf.reduce_ndim_sourceLabels(sourceLabels)
Deal with multidimensional source labels.
snirf supports multidimensional source labels but we don’t. This function tries to reduce n-dimensional source labels to a unique common prefix to obtain only one label per source
- Return type:
list
- cedalion.io.snirf.stim_to_dataframe(stim)
- cedalion.io.snirf.write_snirf(fname, data_type, timeseries, geo3d, stim, aux={}, meta_data={})