SciPy

pynibs package

Subpackages

Submodules

pynibs.coil module

All functions to operate on TMS coils go here, for example to create .xdmf files to visualize coil positions.

pynibs.coil.calc_coil_position_pdf(fn_rescon=None, fn_simpos=None, fn_exp=None, orientation='quaternions', folder_pdfplots=None)

Determines the probability density functions of the transformed coil position (x’, y’, z’) and quaternions of the coil orientations (x’’, y’’, z’’)

Parameters:

  • fn_rescon (str) – Filename of the results file from TMS experiments (results_conditions.csv)

  • fn_simpos (str) – Filename of the positions and orientation from TMS experiments (simPos.csv)

  • fn_exp (str) – Filename of experimental.csv file from experiments

  • orientation (str) – Type of orientation estimation: ‘quaternions’ or ‘euler’

  • folder_pdfplots (str) – Folder, where the plots of the fitted pdfs are saved (omitted if not provided)

Returns:

  • pdf_paras_location (list of list of np.ndarray) –

    [n_conditions] Pdf parameters (limits and shape) of the coil position for x’, y’, and z’ for each:

    • beta_paras … [p, q, a, b] (2 shape parameters and limits)

    • moments … [data_mean, data_std, beta_mean, beta_std]

    • p_value … p-value of the Kolmogorov Smirnov test

    • uni_paras … [a, b] (limits)

  • pdf_paras_orientation_euler (list of np.ndarray) –

    [n_conditions] Pdf parameters (limits and shape) of the coil orientation Psi, Theta, and Phi for each:

    • beta_paras … [p, q, a, b] (2 shape parameters and limits)

    • moments … [data_mean, data_std, beta_mean, beta_std]

    • p_value … p-value of the Kolmogorov Smirnov test

    • uni_paras … [a, b] (limits)

  • OP_mean (List of [3 x 4] np.ndarray) – [n_conditions] List of mean coil position and orientation for different conditions (global coordinate system)

    \begin{bmatrix} | & | & | & | \\ ori_x & ori_y & ori_z & pos \\ | & | & | & | \\ \end{bmatrix}

  • OP_zeromean (list of [3 x 4 x n_con_each] np.ndarray [n_conditions]) – List over conditions containing zero-mean coil orientations and positions

  • V (list of [3 x 3] np.ndarrays [n_conditions]) – Transformation matrix of coil positions from global coordinate system to transformed coordinate system

  • P_transform (list of np.ndarray [n_conditions]) – List over conditions containing transformed coil positions [x’, y’, z’] of all stimulations (zero-mean, rotated by SVD)

  • quaternions (list of np.ndarray [n_conditions]) – List over conditions containing imaginary part of quaternions [x’’, y’’, z’’] of all stimulations

pynibs.coil.calc_coil_transformation_matrix(LOC_mean, ORI_mean, LOC_var, ORI_var, V)

Calculate the modified coil transformation matrix needed for SimNIBS based on location and orientation variations observed in the framework of uncertainty analysis

Parameters:

  • LOC_mean (np.ndarray of float) – (3), Mean location of TMS coil

  • ORI_mean (np.ndarray of float) –

    (3 x 3) Mean orientations of TMS coil

    \begin{bmatrix} | & | & | \\ x & y & z \\ | & | & | \\ \end{bmatrix}

  • LOC_var (np.ndarray of float) –

    1. Location variation in normalized space (dx’, dy’, dz’), i.e. zero mean and projected on principal axes

  • ORI_var (np.ndarray of float) –

    1. Orientation variation expressed in Euler angles [alpha, beta, gamma] in deg

  • V (np.ndarray of float) – (3x3) V-matrix containing the eigenvectors from _,_,V = numpy.linalg.svd

Returns:

  • mat (np.ndarray of float)

  • (4, 4) Transformation matrix containing 3 axis and 1 location vector –

    \begin{bmatrix} | & | & | & | \\ x & y & z & pos \\ | & | & | & | \\ 0 & 0 & 0 & 1 \\ \end{bmatrix}

pynibs.coil.check_coil_position(points, hull)

Check if magnetic dipoles are lying inside head region

Parameters:

  • points (np.ndarray of float) – (N_points, 3) Coordinates (x,y,z) of magnetic dipoles

  • hull (Delaunay object or np.ndarray of float) – (N_surface_points, 3) Head surface data

Returns:

valid – Validity of coil position: TRUE: valid FALSE: unvalid

Return type:

bool

pynibs.coil.create_stimsite_from_exp_hdf5(fn_exp, fn_hdf, datanames=None, data=None, overwrite=False)

This takes an experiment.hdf5 file and creates an .hdf5 + .xdmf tuple for all coil positions for visualization.

Parameters:

  • fn_exp (str) – Path to experiment.hdf5

  • fn_hdf (str) – Filename for the resulting .hdf5 file. The .xdmf is saved with the same basename. Folder should already exist.

  • datanames (str or list of str, optional) – Dataset names for data

  • data (np.ndarray, optional) – Dataset array with shape = (len(poslist.pos), len(datanames()).

  • overwrite (bool, default: False) – Overwrite existing files.

pynibs.coil.create_stimsite_from_list(fn_hdf, poslist, datanames=None, data=None, overwrite=False)

This takes a list of matsimnibs-style coil position and orientations and creates an .hdf5 + .xdmf tuple for all positions.

Centers and coil orientations are written to disk, with optional data for each coil configuration.

Parameters:

  • fn_hdf (str) – Filename for the .hdf5 file. The .xdmf is saved with the same basename. Folder should already exist.

  • poslist (list of np.ndarray) – (4,4) Positions.

  • datanames (str or list of str, optional) – Dataset names for data.

  • data (np.ndarray, optional) – Dataset array with shape = (len(poslist.pos), len(datanames()).

  • overwrite (bool, defaul: False) – Overwrite existing files.

pynibs.coil.create_stimsite_from_matsimnibs(fn_hdf, matsimnibs, datanames=None, data=None, overwrite=False)

This takes a matsimnibs array and creates an .hdf5 + .xdmf tuple for all coil positions for visualization.

Centers and coil orientations are written disk.

Parameters:

  • fn_hdf (str) – Filename for the .hdf5 file. The .xdmf is saved with the same basename. Folder should already exist.

  • matsimnibs (np.ndarray) –

    (4, 4, n_pos) Matsimnibs matrices containing the coil orientation (x,y,z) and position (p)

    \begin{bmatrix} | & | & | & | \\ x & y & z & p \\ | & | & | & | \\ 0 & 0 & 0 & 1 \\ \end{bmatrix}

  • datanames (str or list of str, optional) – Dataset names for data.

  • data (np.ndarray, optional) – (len(poslist.pos), len(datanames).

  • overwrite (bool, default: False) – Overwrite existing files.

pynibs.coil.create_stimsite_from_tmslist(fn_hdf, poslist, datanames=None, data=None, overwrite=False)

This takes a :py:class:simnibs.sim_struct.TMSLIST from simnibs and creates an .hdf5 + .xdmf tuple for all positions.

Centers and coil orientations are written to disk, with optional data for each coil configuration.

Parameters:

  • fn_hdf (str) – Filename for the .hdf5 file. The .xdmf is saved with the same basename. Folder should already exist.

  • poslist (simnibs.sim_struct.TMSLIST) – poslist.pos[*].matsimnibs have to be set.

  • datanames (str or list of str, optional) – Dataset names for data.

  • data (np.ndarray, optional) – Dataset array with shape = (len(poslist.pos), len(datanames()).

  • overwrite (bool, default: False) – Overwrite existing files

pynibs.coil.create_stimsite_hdf5(fn_exp, fn_hdf, conditions_selected=None, sep='_', merge_sites=False, fix_angles=False, data_dict=None, conditions_ignored=None)

Reads results_conditions and creates an hdf5/xdmf pair with condition-wise centers of stimulation sites and coil directions as data.

Parameters:

  • fn_exp (str) – Path to results.csv.

  • fn_hdf (str) – Path where to write file. Gets overridden if already existing.

  • conditions_selected (str or list of str, optional) – List of conditions returned by the function, the others are omitted. If None, all conditions are returned.

  • sep (str, default: "_") – Separator between condition label and angle (e.g. M1_0, or M1-0).

  • merge_sites (bool) – If true, only one coil center per site is generated.

  • fix_angles (bool) – rename 22.5 -> 0, 0 -> -45, 67.5 -> 90, 90 -> 135.

  • data_dict (dict ofnp.ndarray of float [n_stimsites] (optional), default: None) – Dictionary containing data corresponding to the stimulation sites (keys).

  • conditions_ignored (str or list of str, optional) – Conditions, which are not going to be included in the plot.

Returns:

<Files> – Contains information about condition-wise stimulation sites and coil directions (fn_hdf)

Return type:

hdf5/xdmf file pair

Example

pynibs.create_stimsite_hdf5('/exp/1/experiment_corrected.csv',
                            '/stimsite', True, True)
pynibs.coil.get_coil_dipole_pos(coil_fn, matsimnibs)

Apply transformation to coil dipoles and return position.

Parameters:

  • coil_fn (str) – Filename of coil .ccd file.

  • matsimnibs (np.ndarray of float) – Transformation matrix.

Returns:

dipoles_pos – (N, 3) Cartesian coordinates (x, y, z) of coil magnetic dipoles.

Return type:

np.ndarray

pynibs.coil.get_invalid_coil_parameters(param_dict, coil_position_mean, svd_v, del_obj, fn_coil, fn_hdf5_coilpos=None)

Finds gpc parameter combinations, which place coil dipoles inside subjects head. Only endpoints (and midpoints) of the parameter ranges are examined.

get_invalid_coil_parameters(param_dict, pos_mean, v, del_obj, fn_coil, fn_hdf5_coilpos=None)

Parameters:

  • param_dict (dict) – Dictionary containing dictionary with 'limits' and 'pdfshape'. keys: 'x', 'y', 'z', 'psi', 'theta', 'phi'.

  • coil_position_mean (np.ndarray) – (3, 4) Mean coil positions and orientations.

  • svd_v (np.ndarray) – (3, 3) SVD matrix V.

  • del_obj (scipy.spatial.Delaunay) – Skin surface.

  • fn_coil (str) – Filename of coil .ccd file.

  • fn_hdf5_coilpos (str) – Filename of .hdf5 file to save coil_pos in (incl. path and .hdf5 extension).

Returns:

fail_params – Index and combination of failed parameter.

Return type:

list of int

pynibs.coil.random_walk_coil(start_mat, n_steps, fn_mesh_hdf5, angles_dev=3, distance_low=1, distance_high=4, angles_metric='deg', coil_pos_fn=None)

Computes random walk coil positions/orientations for a SimNIBS matsimnibs coil pos/ori.

Parameters:

  • start_mat (np.ndarry) – (4, 4) SimNIBS matsimnibs.

  • n_steps (int) – Number of steps to walk.

  • fn_mesh_hdf5 (str) – .hdf5 mesh filename, used to compute skin-coil distances.

  • angles_dev (float or list of float, default: 3) – Angles deviation,`` np.random.normal(scale=angles_dev)``. If list, angles_dev = [alpha, beta, theta].

  • distance_low (float, default: 1) – Minimum skin-coil distance.

  • distance_high (float, default: 4) – Maximum skin-coil distance.

  • angles_metric (str, default: 'deg') – One of ('deg', 'rad').

  • coil_pos_fn (str, optional) – If provided, .hdf5/.xdmf tuple is written with coil positions/orientations.

Returns:

  • walked_coils (np.ndarray) – (4, 4, n_steps + 1) coil positions / orientations.

  • <file> (.hdf5/.xdmf file tupel with n_steps + 1 coil positions/orientations.)

pynibs.coil.sort_opt_coil_positions(fn_coil_pos_opt, fn_coil_pos, fn_out_hdf5=None, root_path='/0/0/', verbose=False, print_output=False)

Sorts coil positions according to Traveling Salesman problem

Parameters:

  • fn_coil_pos_opt (str) – Name of .hdf5 file containing the optimal coil position indices

  • fn_coil_pos (str) – Name of .hdf5 file containing the matsimnibs matrices of all coil positions

  • fn_out_hdf5 (str) – Name of output .hdf5 file (will be saved in the same format as fn_coil_pos_opt)

  • verbose (bool, default: False) – Print output messages

  • print_output (bool or str, default: False) – Print output image as .png file showing optimal path

Return type:

<file> .hdf5 file containing the sorted optimal coil position indices

pynibs.coil.test_coil_position_gpc(parameters)

Testing valid coil positions for gPC analysis

pynibs.coil.write_coil_pos_hdf5(fn_hdf, centers, m0, m1, m2, datanames=None, data=None, overwrite=False)

Creates a .hdf5 + .xdmf file tuple for all coil positions. Coil centers and coil orientations are saved, and - optionally - data for each position if data and datanames are provided.

Parameters:

  • fn_hdf (str) – Filename for the .hdf5 file. The .xdmf is saved with the same basename. Folder should already exist.

  • centers (np.ndarray of float) – (n_pos, 3) Coil positions.

  • m0 (np.ndarray of float) – (n_pos, 3) Coil orientation x-axis (looking at the active (patient) side of the coil pointing to the right).

  • m1 (np.ndarray of float) – (n_pos, 3) Coil orientation y-axis (looking at the active side of the coil pointing up away from the handle).

  • m2 (np.ndarray of float) – (n_pos, 3) Coil orientation z-axis (looking at the active (patient) side of the coil pointing to the patient).

  • datanames (str or list of str, optional) – (n_data) Dataset names for data

  • data (np.ndarray, optional) – (n_pos, n_data) Dataset array with (len(poslist.pos), len(datanames()).

  • overwrite (bool, default: False) – Overwrite existing files.

pynibs.freesurfer module

This holds methods to interact with FreeSurfer ([1]), for example to translate FreeSurfer files into Paraview readable .vtk files.

References

[1]

Dale, A.M., Fischl, B., Sereno, M.I., 1999. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9, 179-194.

pynibs.freesurfer.data_sub2avg(fn_subject_obj, fn_average_obj, hemisphere, fn_in_hdf5_data, data_hdf5_path, data_label, fn_out_hdf5_geo, fn_out_hdf5_data, mesh_idx=0, roi_idx=0, subject_data_in_center=True, data_substitute=-1, verbose=True, replace=True, reg_fn='sphere.reg')

Maps the data from the subject space to the average template. If the data is given only in an ROI, the data is mapped to the whole brain surface.

Parameters:

  • fn_subject_obj (str) – Filename of subject object .hdf5 file (incl. path), e.g. .../probands/subjectID/subjectID.hdf5

  • fn_average_obj (str) – Filename of average template object .pkl file (incl. path), e.g. .../probands/avg_template/avg_template.hdf5.

  • hemisphere (str) – Define hemisphere to work on ('lh' or 'rh' for left or right hemisphere, respectively).

  • fn_in_hdf5_data (str) – Filename of .hdf5 data input file containing the subject data.

  • data_hdf5_path (str) – Path in .hdf5 data file where data is stored (e.g. '/data/tris/').

  • data_label (str or list of str) – Label of datasets contained in hdf5 input file to map.

  • fn_out_hdf5_geo (str) – Filename of .hdf5 geo output file containing the geometry information.

  • fn_out_hdf5_data (str) – Filename of .hdf5 data output file containing the mapped data.

  • mesh_idx (int) – Index of mesh used in the simulations.

  • roi_idx (int) – Index of region of interest used in the simulations.

  • subject_data_in_center (bool, default: True) – Specify if the data is given in the center of the triangles or in the nodes.

  • data_substitute (float) – Data substitute with this number for all points outside the ROI mask

  • verbose (bool) – Verbose output (Default: True)

  • replace (bool) – Replace output files (Default: True)

  • reg_fn (str) – Sphere.reg fn

Returns:

<Files> – Geometry and corresponding data files to plot with Paraview:

  • fn_out_hdf5_geo.hdf5: geometry file containing the geometry information of the average template

  • fn_out_hdf5_data.hdf5: geometry file containing the data

Return type:

.hdf5 files

pynibs.freesurfer.freesurfer2vtk(in_fns, out_folder, hem='lh', surf='pial', prefix=None, fs_subject='fsaverage', fs_subjects_dir=None)

Transform multiple FreeSurfer .mgh files into one .vtk file. This can be read with Paraview and others.

Parameters:

  • in_fns (list of str or str) – Input filenames.

  • out_folder (str) – Output folder.

  • hem (str, default: 'lh') – Which hemisphere: 'lh' or 'rh'.

  • surf (str, default: 'pial') – Which FreeSurfer surface: 'pial', 'inflated', …

  • prefix (str, optional) – Prefix to add to each filename.

  • fs_subject (str, default: 'fsaverage') – FreeSurfer subject.

  • fs_subjects_dir (str, optional) – FreeSurfer subjects directory. If not provided, read from environment.

Returns:

<File> – One .vtk file with data as overlays from all .mgh files provided

Return type:

out_folder/{prefix}_{hem}_{surf}.vtk

pynibs.freesurfer.make_average_subject(subjects, subject_dir, average_dir, fn_reg='sphere.reg')

Generates the average template from a list of subjects using the FreeSurfer average.

Parameters:

  • subjects (list of str) – Paths of subjects directories, where the FreeSurfer files are located, e.g. for simnibs mri2mesh .../fs_SUBJECT_ID.

  • subject_dir (str) – Temporary subject directory of FreeSurfer (symlinks of subjects will be generated in there and average template will be temporarily stored before it is copied to average_dir).

  • average_dir (str) – path to directory where average template will be stored, e.g. probands/avg_template_15/mesh/0/fs_avg_template_15.

  • fn_reg (str, default: 'sphere.reg' --> ?h.sphere.reg>) – Filename suffix of FreeSurfer registration file containing registration information to template.

Returns:

<Files> – Average template in average_dir and registered curvature files, ?h.sphere.reg in subjects/surf folders.

Return type:

.tif and .reg files

pynibs.freesurfer.make_group_average(subjects=None, subject_dir=None, average=None, hemi='lh', template='mytemplate', steps=3, n_cpu=2, average_dir=None)

Creates a group average from scratch, based on one subject. This prevents for example the fsaverage problems of large elements at M1, etc. This is an implemntation of [2] ‘Creating a registration template from scratch (GW)’.

References

Parameters:

  • subjects (list of str) – List of FreeSurfer subjects names.

  • subject_dir (str) – Temporary subject directory of FreeSurfer (symlinks of subjects will be generated in there and average template will be temporarily stored before it is copied to average_dir).

  • average (str, default: subjects[0]) – Which subject to base new average template on.

  • hemi (str, default: 'lh') –

    Which hemisphere: lh or rh.

    Deprecated since version 0.0.1: Don’t use any more.

  • template (str, default: 'mytemplate') – Basename of new template.

  • steps (int, default: 2) – Number of iterations.

  • n_cpu (int, default: 4) – How many cores for multithreading.

  • average_dir (str) – Path to directory where average template will be stored, e.g. probands/avg_template_15/mesh/0/fs_avg_template_15.

Returns:

  • <File> (.tif file) – SUBJECT_DIR/TEMPLATE*.tif, TEMPLATE0.tif based on AVERAGE, rest on all subjects.

  • <File> (.myreg file) – SUBJECT_DIR/SUBJECT*/surf/HEMI.sphere.myreg*.

  • <File> (.tif file) – Subject wise sphere registration based on TEMPLATE*.tif.

pynibs.freesurfer.read_curv_data(fname_curv, fname_inf, raw=False)

Read curvature data provided by FreeSurfer with optional normalization.

Parameters:

  • fname_curv (str) – Filename of the FreeSurfer curvature file (e.g. ?h.curv), contains curvature data in nodes can be found in mri2mesh proband folder: proband_ID/fs_ID/surf/?h.curv.

  • fname_inf (str) – Filename of inflated brain surface (e.g. ?h.inflated), contains points and connectivity data of surface can be found in mri2mesh proband folder: proband_ID/fs_ID/surf/?h.inflated.

  • raw (bool) – If raw-data is returned or if the data is normalized to -1 for neg. and +1 for pos. curvature.

Returns:

curv – Curvature data in element centers.

Return type:

np.ndarray of float or int

pynibs.hdf5_io module

pynibs.hdf5_io.create_fibre_geo_hdf5(fn_fibres_hdf5, overwrite=True)

Reformats geometrical fibre data and adds a /plot subfolder containing geometrical fibre data including connectivity

Parameters:

  • fn_fibres_hdf5 (str) – Path to fibre.hdf5 file containing the original fibre data

  • overwrite (bool) – Overwrites existing /plot subfolder in .hdf5 file

pynibs.hdf5_io.create_fibre_xdmf(fn_fibre_geo_hdf5, fn_fibre_data_hdf5=None, overwrite=True, fibre_points_path='fibre_points', fibre_con_path='fibre_con', fibre_data_path='')

Creates .xdmf file to plot fibres in Paraview

Parameters:

  • fn_fibre_geo_hdf5 (str) – Path to fibre_geo.hdf5 file containing the geometry (in /plot subfolder created with create_fibre_geo_hdf5())

  • fn_fibre_data_hdf5 (str (optional) default: None) – Path to fibre_data.hdf5 file containing the data to plot (in parent folder)

  • fibre_points_path (str (optional) default: fibre_points) – Path to fibre point array in .hdf5 file

  • fibre_con_path (str (optional) default: fibre_con) – Path to fibre connectivity array in .hdf5 file

  • fibre_data_path (str (optional) default: "") – Path to parent data folder in data.hdf5 file (Default: no parent folder)

Returns:

<File>

Return type:

.xdmf file for Paraview

pynibs.hdf5_io.create_position_path_xdmf(sorted_fn, coil_pos_fn, output_xdmf, stim_intens=None, coil_sorted='/0/0/coil_seq')

Creates one .xdmf file that allows paraview plottings of coil position paths.

A set of coil positions plotted to show the path of coil movement.

Paraview can be used to visualize the order of realized stimulation positions.

Parameters:

  • sorted_fn (str) – .hdf5 filename with position indices, values, intensities from pynibs.sort_opt_coil_positions().

  • coil_pos_fn (str) – .hdf5 filename with original set of coil positions. Indices from sorted_fn are mapped to this. Either ‘/matsimnibs’ or ‘m1’ and ‘m2’ datasets.

  • output_xdmf (str) –

  • stim_intens (int, optional) – Intensities are multiplied by this factor.

  • coil_sorted (str) – Path to coil positions in sorted_fn

Returns:

output_xdmf

Return type:

<file>

pynibs.hdf5_io.data_superimpose(fn_in_hdf5_data, fn_in_geo_hdf5, fn_out_hdf5_data, data_hdf5_path='/data/tris/', data_substitute=-1, normalize=False)

Overlaying data stored in .hdf5 files except in regions where data_substitute is found. These points are omitted in the analysis and will be replaced by data_substitute instead.

Parameters:

  • fn_in_hdf5_data (list of str) – Filenames of .hdf5 data files with common geometry, e.g. generated by pynibs.data_sub2avg(…).

  • fn_in_geo_hdf5 (str) – Geometry .hdf5 file, which corresponds to the .hdf5 data files.

  • fn_out_hdf5_data (str) – Filename of .hdf5 data output file containing the superimposed data.

  • data_hdf5_path (str) – Path in .hdf5 data file where data is stored (e.g. '/data/tris/').

  • data_substitute (float or np.NaN, default: -1) – Data substitute with this number for all points in the inflated brain, which do not belong to the given data set.

  • normalize (bool or str, default: False) –

    Decide if individual datasets are normalized w.r.t. their maximum values before they are superimposed.

    • ’global’: global normalization w.r.t. maximum value over all datasets and subjects

    • ’dataset’: dataset wise normalization w.r.t. maximum of each dataset individually (over subjects)

    • ’subject’: subject wise normalization (over datasets)

Returns:

<File> – Overlayed data.

Return type:

.hdf5 file

pynibs.hdf5_io.hdf_2_ascii(hdf5_fn)

Prints out structure of given .hdf5 file.

Parameters:

hdf5_fn (str) – Filename of .hdf5 file.

Returns:

h5 – Structure of .hdf5 file

Return type:

items

pynibs.hdf5_io.load_mesh_hdf5(fname)

Loading mesh from .hdf5 file and setting up TetrahedraLinear class.

Parameters:

fname (str) – Name of .hdf5 file (incl. path)

Returns:

obj – TetrahedraLinear object

Return type:

pynibs.mesh.mesh_struct.TetrahedraLinear

Example

.hdf5 file format and contained groups. The content of .hdf5 files can be shown using the tool HDFView (https://support.hdfgroup.org/products/java/hdfview/)

mesh
I---/elm
I    I--/elm_number          [1,2,3,...,N_ele]           Running index over all elements starting at 1,
                                                            triangles and tetrahedra
I    I--/elm_type            [2,2,2,...,4,4]             Element type: 2 triangles, 4 tetrahedra
I    I--/node_number_list    [1,5,6,0;... ;1,4,8,9]      Connectivity of triangles [X, X, X, 0] and tetrahedra
                                                                    [X, X, X, X]
I    I--/tag1                [1001,1001, ..., 4,4,4]     Surface (100X) and domain (X) indices with 1000 offset
                                                                     for surfaces
I    I--/tag2                [   1,   1, ..., 4,4,4]     Surface (X) and domain (X) indices w/o offset
I
I---/nodes
I    I--/node_coord          [1.254, 1.762, 1.875;...]   Node coordinates in (mm)
I    I--/node_number         [1,2,3,...,N_nodes]         Running index over all points starting at 1
I    I--/units               ["mm"]                      .value is unit of geometry
I
I---/fields
I    I--/E/value             [E_x_1, E_y_1, E_z_1;...]   Electric field in all elms, triangles and tetrahedra
I    I--/J/value             [J_x_1, J_y_1, J_z_1;...]   Current density in all elms, triangles and tetrahedra
I    I--/normE/value         [normE_1,..., normE_N_ele]  Magnitude of electric field in all elements,
                                                                    triangles and tetrahedra
I    I--/normJ/value         [normJ_1,..., normJ_N_ele]  Magnitude of current density in all elements,
                                                                    triangles and tetrahedra

/data
I---/potential               [phi_1, ..., phi_N_nodes]   Scalar electric potential in nodes (size N_nodes)
I---/dAdt                    [A_x_1, A_y_1, A_z_1,...]   Magnetic vector potential (size 3xN_nodes)
pynibs.hdf5_io.load_mesh_msh(fname)

Loading mesh from .msh file and return TetrahedraLinear object.

Parameters:

fname (str) – .msh filename (incl. path)

Returns:

obj

Return type:

pynibs.mesh.mesh_struct.TetrahedraLinear

pynibs.hdf5_io.msh2hdf5(fn_msh=None, skip_roi=False, skip_layer=True, include_data=False, approach='mri2mesh', subject=None, mesh_idx=None)

Transforms mesh from .msh to .hdf5 format. Mesh is read from subject object or from fn_msh.

Parameters:

  • fn_msh (str, optional) – Filename of .msh file.

  • skip_roi (bool, default: False) – Skip generating ROI in .hdf5

  • skip_layer (book, default: True) – Don’t create gm layers.

  • include_data (bool, default: False) – Also convert data in .msh file to .hdf5 file

  • subject (pynibs.Subject, optional) – Subject information, must be set to use skip_roi=False.

  • mesh_idx (int or list of int or str or list of str, optional) – Mesh index, the conversion from .msh to .hdf5 is conducted for.

  • approach (str) –

    Approach the headmodel was created with (“mri2mesh” or “headreco”).

    Deprecated since version 0.0.1: Not supported any more.

Returns:

<File> – .hdf5 file with mesh information

Return type:

.hdf5 file

pynibs.hdf5_io.print_attrs(name, obj)

Helper function for hdf_2_ascii(). To be called from h5py.Group.visititems()

Parameters:

  • name (str) – Name of structural element

  • obj (object) – Structural element

Returns:

<Print>

Return type:

Structure of .hdf5 file

pynibs.hdf5_io.read_arr_from_hdf5(fn_hdf5, folder)

Reads array from and .hdf5 files and returns as list: Strings are returned as np.bytes_ to str and ‘None’ to None

Parameters:

  • fn_hdf5 (str) – Filename of .hdf5 file

  • folder (str) – Folder inside .hdf5 file to read

Returns:

data_from_hdf5 – List containing data from .hdf5 file

Return type:

list

pynibs.hdf5_io.read_data_hdf5(fname)

Reads phi and dA/dt data from .hdf5 file (phi and dAdt are given in the nodes).

Parameters:

fname (str) – Filename of .hdf5 data file

Returns:

  • phi (np.ndarray of float [N_nodes]) – Electric potential in the nodes of the mesh

  • da_dt (np.ndarray of float [N_nodesx3]) – Magnetic vector potential in the nodes of the mesh

pynibs.hdf5_io.read_dict_from_hdf5(fn_hdf5, folder)

Read all arrays from from hdf5 file and return them as dict

Parameters:

  • fn_hdf5 (str) – Filename of .hdf5 file

  • folder (str) – Folder inside .hdf5 file to read

Returns:

d – Dictionary from .hdf5 file folder

Return type:

dict

pynibs.hdf5_io.simnibs_results_msh2hdf5(fn_msh, fn_hdf5, S, pos_tms_idx, pos_local_idx, subject, mesh_idx, mode_xdmf='r+', n_cpu=4, verbose=False, overwrite=False, mid2roi=False)

Converts simnibs .msh results file(s) to .hdf5 / .xdmf tuple.

Parameters:

  • fn_msh (str list of str) – Filenames (incl. path) of .msh results files from SimNIBS.

  • fn_hdf5 (str or list of str) – Filenames (incl. path) of .hdf5 results files.

  • S (Simnibs Session object) – Simnibs Session object the simulations are conducted with.

  • pos_tms_idx (list of int) – Index of the simulation w.r.t. to the simnibs TMSList (inside Session object S) For every coil a separate TMSList exists, which contains multiple coil positions.

  • pos_local_idx (list of int) – Index of the simulation w.r.t. to the simnibs POSlist in the TMSList (inside Session object S) For every coil a separate TMSList exists, which contains multiple coil positions.

  • subject (pynibs.subject.Subject) – Subject object.

  • mesh_idx (int or str) – Mesh id.

  • mode_xdmf (str, default: "r+") – Mode to open hdf5_geo file to write xdmf. If hdf5_geo is already separated in tets and tris etc., the file is not changed, use “r” to avoid IOErrors in case of parallel computing.

  • n_cpu (int) – Number of processes.

  • verbose (bool, default: False) – Print output messages.

  • overwrite (bool, default: False) – Overwrite .hdf5 file if existing.

  • mid2roi (bool or string, default: False) – If the mesh contains ROIs and the e-field was calculated in the midlayer using simnibs (S.map_to_surf = True), the midlayer results will be mapped from the simnibs midlayer to the ROIs (takes some time for large ROIs).

Returns:

<File> – .hdf5 file containing the results. An .xdmf file is also created to link the results with the mesh .hdf5 file of the subject

Return type:

.hdf5 file

pynibs.hdf5_io.simnibs_results_msh2hdf5_workhorse(fn_msh, fn_hdf5, session, pos_tms_idx, pos_local_idx, subject, mesh_idx, mode_xdmf='r+', verbose=False, overwrite=False, mid2roi=False)

Converts simnibs .msh results file to .hdf5 (including midlayer data if desired)

Parameters:

  • fn_msh (list of str) – Filenames of .msh results files from SimNIBS.

  • fn_hdf5 (str or list of str) – Filenames of .hdf5 results files.

  • session (Simnibs Session object) – Simnibs session the simulations were conducted with.

  • pos_tms_idx (list of int) – Index of the simulation w.r.t. to the simnibs TMSList (inside session). For every coil a separate TMSList exists, which contains multiple coil positions.

  • pos_local_idx (list of int) – Index of the simulation w.r.t. to the simnibs POSlist in the TMSList (inside session). For every coil a separate TMSList exists, which contains multiple coil positions.

  • subject (Subject object) – pynibs.Subject.

  • mesh_idx (int or str) – Mesh index or id.

  • mode_xdmf (str, default: "r+") – Mode to open hdf5_geo file to write xdmf. If hdf5_geo is already separated in tets and tris etc., the file is not changed, use “r” to avoid IOErrors in case of parallel computing.

  • verbose (bool, default: False) – Print output messages.

  • overwrite (bool, default: False) – Overwrite .hdf5 file if existing.

  • mid2roi (bool, list of string, or string, default: False) – If the mesh contains ROIs and the e-field was calculated in the midlayer using SimNIBS (S.map_to_surf = True), the midlayer results will be mapped from the simnibs midlayer to the ROIs (takes some time for large ROIs).

Returns:

<File> – .hdf5 file containing the results. An .xdmf file is also created to link the results with the mesh .hdf5 file of the subject.

Return type:

.hdf5 file

pynibs.hdf5_io.split_hdf5(hdf5_in_fn, hdf5_geo_out_fn='', hdf5_data_out_fn=None)

Splits one hdf5 into one with spatial data and one with statistical data. If coil data is present in hdf5_in, it is saved in hdf5Data_out. If new spatial data is added to file (curve, inflated, whatever), add this to the geogroups variable.

Parameters:

  • hdf5_in_fn (str) – Filename of .hdf5 input file

  • hdf5_geo_out_fn (str) – Filename of .hdf5 .geo output file

  • hdf5_data_out_fn (str) – Filename of .hdf5 .data output file (ff none, remove data from hdf5_in)

Returns:

  • <File> (.hdf5 file) – hdf5Geo_out_fn (spatial data)

  • <File> (.hdf5 file) – hdf5Data_out_fn (data)

pynibs.hdf5_io.write_arr_to_hdf5(fn_hdf5, arr_name, data, overwrite_arr=True, verbose=False, check_file_exist=False)

Takes an array and adds it to an hdf5 file.

If data is list of dict, write_dict_to_hdf5() is called for each dict with adapted hdf5-folder name Otherwise, data is casted to np.ndarray and dtype of unicode data casted to '|S'.

Parameters:

  • fn_hdf5 (str) – Filename of .hdf5 file.

  • arr_name (str) – Complete path in .hdf5 file with array name.

  • data (ndarray, list or dict) – Data to write.

  • overwrite_arr (bool, default: True) – Overwrite existing array.

  • verbose (bool, default: False) – Print information.

pynibs.hdf5_io.write_coil_hdf5(tms_coil, fn)

Creates .hdf5/.xdmf file tuples with information to visualize SimNIBS .tcd coil information.

Can be visualized with ParaView (use Glyph plugin to view wires).

Example coil visualization

SimNIBS .tcd coil model from create_two_stimulator_coil.py visualized with ParaView.

Parameters:

  • tms_coil (SimNIBS.simulation.tms_coil.tms_coil or str) – The coil (object or .tcd file) that shall be written to disk as an .hdf5 file.

  • fn (str) – Filename where to write the files.

Returns:

  • fn.hdf5/fn.xdmf (<file>) – Paraview file tuple with casing data.

  • fn_wires.hdf5/fn_wires.xdmf (<file>) – Paraview file tuple with wiring data.

pynibs.hdf5_io.write_coil_sequence_xdmf(coil_pos_fn, data, vec1, vec2, vec3, output_xdmf)
pynibs.hdf5_io.write_data_hdf5(out_fn, data, data_names, hdf5_path='/data', mode='a')

Creates a .hdf5 file with data.

Parameters:

  • out_fn (str) – Filename of output .hdf5 file containing the geometry information

  • data (np.ndarray or list of nparrays of float) – Data to save in hdf5 data file

  • data_names (str or list of str) – Labels of data

  • hdf5_path (str) – Folder in .hdf5 geometry file, where the data is saved in (Default: /data)

  • mode (str, default: "a") – Mode: “a” append, “w” write (overwrite)

Returns:

<File> – File containing the stored data

Return type:

.hdf5 file

Example

File structure of .hdf5 data file

data
|---/data_names[0]          [data[0]]           First dataset
|---/    ...                   ...                  ...
|---/data_names[N-1]        [data[N-1]]         Last dataset
pynibs.hdf5_io.write_data_hdf5_surf(data, data_names, data_hdf_fn_out, geo_hdf_fn, replace=False, replace_array_in_file=True, datatype='tris')

Saves surface data to .hdf5 data file and generates corresponding .xdmf file linking both. The directory of data_hdf_fn_out and geo_hdf_fn should be the same, as only basenames of files are stored in the .xdmf file.

Parameters:

  • data (np.ndarray or list) – (N_points_ROI, N_components) Data to map on surfaces.

  • data_names (str or list) – Names for datasets.

  • data_hdf_fn_out (str) – Filename of .hdf5 data file.

  • geo_hdf_fn (str) – Filename of .hdf5 geo file containing the geometry information (has to exist).

  • replace (bool, default: False) – Replace existing .hdf5 and .xdmf file completely.

  • replace_array_in_file (bool, default: True) – Replace existing array in file.

  • datatype (str, default: 'tris') – Triangle or node data.

Returns:

  • <File> (.hdf5 file) – data_hdf_fn_out.hdf5 containing data

  • <File> (.xdmf file) – data_hdf_fn_out.xdmf containing information about .hdf5 file structure for Paraview

Example

File structure of .hdf5 data file

/data
|---/tris
|      |---dataset_0    [dataset_0]    (size: N_dataset_0 x M_dataset_0)
|      |---   ...
|      |---dataset_K   [dataset_K]     (size: N_dataset_K x M_dataset_K)
pynibs.hdf5_io.write_data_hdf5_vol(data, data_names, data_hdf_fn_out, geo_hdf_fn, replace=False, replace_array_in_file=True)

Saves surface data to .hdf5 data file and generates corresponding .xdmf file linking both. The directory of data_hdf_fn_out and geo_hdf_fn should be the same, as only basenames of files are stored in the .xdmf file.

Parameters:

  • data (np.ndarray or list) – (N_points_ROI, N_components) Data to map on surfaces.

  • data_names (str or list) – Names for datasets.

  • data_hdf_fn_out (str) – Filename of .hdf5 data file.

  • geo_hdf_fn (str) – Filename of .hdf5 geo file containing the geometry information (has to exist).

  • replace (bool, default: False) – Replace existing .hdf5 and .xdmf file completely.

  • replace_array_in_file (bool, default: True) – Replace existing array in file.

Returns:

  • <File> (.hdf5 file) – data_hdf_fn_out.hdf5 containing data

  • <File> (.xdmf file) – data_hdf_fn_out.xdmf containing information about .hdf5 file structure for Paraview

Example

File structure of .hdf5 data file

/data
|---/tris
|      |---dataset_0    [dataset_0]    (size: N_dataset_0 x M_dataset_0)
|      |---   ...
|      |---dataset_K   [dataset_K]     (size: N_dataset_K x M_dataset_K)
pynibs.hdf5_io.write_dict_to_hdf5(fn_hdf5, data, folder, check_file_exist=False, verbose=False)

Takes dict (from subject.py) and passes its keys to write_arr_to_hdf5()

fn_hdf5:folder/
              |--key1
              |--key2
              |...
Parameters:

  • fn_hdf5 (str) –

  • data (dict or pynibs.Mesh) –

  • folder (str) –

  • verbose (bool) –

  • check_file_exist (bool) –

pynibs.hdf5_io.write_geo_hdf5(out_fn, msh, roi_dict=None, hdf5_path='/mesh')

Creates a .hdf5 file with geometry data from mesh including region of interest(s).

Parameters:

  • out_fn (str) – Output hdf5 filename for mesh’ geometry information.

  • msh (pynibs.mesh.mesh_struct.TetrahedraLinear) – Mesh to write to file.

  • roi_dict (dict of (RegionOfInterestSurface or RegionOfInterestVolume)) – Region of interest (surface and/or volume) information.

  • hdf5_path (str, default: '/mesh') – Path in output file to store geometry information.

Returns:

<File> – File containing the geometry information

Return type:

.hdf5 file

Example

File structure of .hdf5 geometry file

mesh
I---/elm
I    I--/elm_number             [1,2,3,...,N_ele]        Running index over all elements starting at 1
                                                            (triangles and tetrahedra)
I    I--/elm_type               [2,2,2,...,4,4]          Element type: 2 triangles, 4 tetrahedra
I    I--/tag1                [1001,1001, ..., 4,4,4]     Surface (100X) and domain (X) indices with 1000
                                                                    offset for surfaces
I    I--/tag2                [   1,   1, ..., 4,4,4]     Surface (X) and domain (X) indices w/o offset
I    I--/triangle_number_list   [1,5,6;... ;1,4,8]       Connectivity of triangles [X, X, X]
I    I--/tri_tissue_type        [1,1, ..., 3,3,3]        Surface indices to differentiate between surfaces
I    I--/tetrahedra_number_list [1,5,6,7;... ;1,4,8,12]  Connectivity of tetrahedra [X, X, X, X]
I    I--/tet_tissue_type        [1,1, ..., 3,3,3]        Volume indices to differentiate between volumes
I    I--/node_number_list       [1,5,6,0;... ;1,4,8,9]   Connectivity of triangles [X, X, X, 0] and
                                                            tetrahedra [X, X, X, X]
I
I---/nodes
I    I--/node_coord          [1.254, 1.762, 1.875;...]   Node coordinates in (mm)
I    I--/node_number         [1,2,3,...,N_nodes]         Running index over all points starting at 1
I    I--/units               ['mm']                      .value is unit of geometry

roi_surface
I---/0                                                           Region of Interest number
I    I--/node_coord_up              [1.254, 1.762, 1.875;...]    Coordinates of upper surface points
I    I--/node_coord_mid             [1.254, 1.762, 1.875;...]    Coordinates of middle surface points
I    I--/node_coord_low             [1.254, 1.762, 1.875;...]    Coordinates of lower surface points
I    I--/tri_center_coord_up        [1.254, 1.762, 1.875;...]    Coordinates of upper triangle centers
I    I--/tri_center_coord_mid       [1.254, 1.762, 1.875;...]    Coordinates of middle triangle centers
I    I--/tri_center_coord_low       [1.254, 1.762, 1.875;...]    Coordinates of lower triangle centers
I    I--/node_number_list           [1,5,6,0;... ;1,4,8,9]       Connectivity of triangles [X, X, X]
I    I--/delta                      0.5                          Distance parameter between GM and WM surface
I    I--/tet_idx_tri_center_up      [183, 913, 56, ...]          Tetrahedra indices where triangle center of
                                                                    upper surface are lying in
I    I--/tet_idx_tri_center_mid     [185, 911, 58, ...]          Tetrahedra indices where triangle center of
                                                                    middle surface are lying in
I    I--/tet_idx_tri_center_low     [191, 912, 59, ...]          Tetrahedra indices where triangle center of
                                                                    lower surface are lying in
I    I--/tet_idx_node_coord_mid     [12, 15, 43, ...]            Tetrahedra indices where the node_coords_mid
                                                                    are lying in
I    I--/gm_surf_fname              .../surf/lh.pial             Filename of GM surface from segmentation
I    I--/wm_surf_fname              .../surf/lh.white            Filename of WM surface from segmentation
I    I--/layer                      3                            Number of layers
I    I--/fn_mask                    .../simnibs/mask.mgh         Filename of region of interest mask
I    I--/X_ROI                      [-10, 15]                    X limits of region of interest box
I    I--/Y_ROI                      [-10, 15]                    Y limits of region of interest box
I    I--/Z_ROI                      [-10, 15]                    Z limits of region of interest box
I
I---/1
I    I ...

roi_volume
I---/0                                                           Region of Interest number
I    I--/node_coord                 [1.254, 1.762, 1.875;...]    Coordinates (x,y,z) of ROI nodes
I    I--/tet_node_number_list       [1,5,6,7;... ;1,4,8,9]       Connectivity matrix of ROI tetrahedra
I    I--/tri_node_number_list       [1,5,6;... ;1,4,8]           Connectivity matrix of ROI triangles
I    I--/tet_idx_node_coord         [183, 913, 56, ...]          Tetrahedra indices where ROI nodes are
I    I--/tet_idx_tetrahedra_center  [12, 15, 43, ...]            Tetrahedra indices where center points of
                                                                    ROI tetrahedra are
I    I--/tet_idx_triangle_center    [12, 15, 43, ...]            Tetrahedra indices where center points of
                                                                    ROI triangles are

I---/1
I    I ...
pynibs.hdf5_io.write_geo_hdf5_surf(out_fn, points, con, replace=False, hdf5_path='/mesh')

Creates a .hdf5 file with geometry data from midlayer.

Parameters:

  • out_fn (str) – Filename of output .hdf5 file containing the geometry information.

  • points (np.ndarray) – (N_points, 3) Coordinates of nodes (x,y,z).

  • con (np.ndarray) – (N_tri, 3) Connectivity list of triangles.

  • replace (bool) – Replace .hdf5 geometry file (True / False).

  • hdf5_path (str, default: '/mesh') – Folder in .hdf5 geometry file, where the geometry information is saved in.

Returns:

<File> – File containing the geometry information.

Return type:

.hdf5 file

Example

File structure of .hdf5 geometry file:

mesh
|---/elm
|    |--/triangle_number_list   [1,5,6;... ;1,4,8]      Connectivity of triangles [X, X, X]
|    |--/tri_tissue_type        [1,1, ..., 3,3,3]       Surface indices to differentiate between surfaces
|
|---/nodes
|    |--/node_coord             [1.2, 1.7, 1.8; ...]    Node coordinates in (mm)
pynibs.hdf5_io.write_geo_hdf5_vol(out_fn, points, con, replace=False, hdf5_path='/mesh')

Creates a .hdf5 file with geometry data from midlayer.

Parameters:

  • out_fn (str) – Filename of output .hdf5 file containing the geometry information.

  • points (np.ndarray) – (N_points, 3) Coordinates of nodes (x,y,z).

  • con (np.ndarray) – (N_tri, 3) Connectivity list of triangles.

  • replace (bool) – Replace .hdf5 geometry file (True / False).

  • hdf5_path (str, default: '/mesh') – Folder in .hdf5 geometry file, where the geometry information is saved in.

Returns:

<File> – File containing the geometry information.

Return type:

.hdf5 file

Example

File structure of .hdf5 geometry file:

mesh
|---/elm
|    |--/triangle_number_list   [1,5,6;... ;1,4,8]      Connectivity of triangles [X, X, X]
|    |--/tri_tissue_type        [1,1, ..., 3,3,3]       Surface indices to differentiate between surfaces
|
|---/nodes
|    |--/node_coord             [1.2, 1.7, 1.8; ...]    Node coordinates in (mm)
pynibs.hdf5_io.write_temporal_xdmf(hdf5_fn, data_folder='c', coil_center_folder=None, coil_ori_0_folder=None, coil_ori_1_folder=None, coil_ori_2_folder=None, coil_current_folder=None, hdf5_geo_fn=None, overwrite_xdmf=False, verbose=False, xdmf_fn=None)

Creates .xdmf markup file for given ROI hdf5 data file with 4D data. This was written to be able to visualize data from the permutation analysis of the regression approach It expects an .hdf5 with a data group with (many) subarrays. The N subarrays name should be named from 0 to N-1 Each subarray has shape (N_elemns, 1)

Not tested for whole brain.

hdf5:/data_folder/0
                 /1
                 /2
                 /3
                 /4
                 ...
Parameters:

  • hdf5_fn (str) – Filename of hdf5 file containing the data.

  • data_folder (str or list of str) – Path within hdf5 to group of dataframes.

  • hdf5_geo_fn (str, optional) – Filename of hdf5 file containing the geometry.

  • overwrite_xdmf (bool) – Overwrite existing .xdmf file if present.

  • coil_center_folder (str) –

  • coil_ori_0_folder (str) –

  • coil_ori_1_folder (str) –

  • coil_ori_2_folder (str) –

  • coil_current_folder (str) –

  • xdmf_fn (str, optional) – Filename of the temporal xdmf file. If not given, created from hdf5 hdf5_fn.

  • verbose (bool) – Print output or not.

Returns:

<File> – hdf5_fn[-4].xdmf

Return type:

.xdmf file

pynibs.hdf5_io.write_xdmf(hdf5_fn, hdf5_geo_fn=None, overwrite_xdmf=False, overwrite_array=False, verbose=False, mode='r+')

Creates .xdmf markup file for given hdf5 file, mainly for paraview visualization. Checks if triangles and tetrahedra already exists as distinct arrays in hdf5_fn. If not, these are added to the .hdf5 file and rebased to 0 (from 1). If only hdf5_fn is provided, spatial information has to be present as arrays for tris and tets in this dataset.

Parameters:

  • hdf5_fn (str) – Filename of hdf5 file containing the data

  • hdf5_geo_fn (str, optional) – Filename of hdf5 file containing the geometry

  • overwrite_xdmf (bool, default: False) – Overwrite existing xdmf file if present.

  • overwrite_array (bool, default: False) – Overwrite existing arrays if present

  • verbose (bool) – Print output.

  • mode (str, default: "r+") – Mode to open hdf5_geo file. If hdf5_geo is already separated in tets and tris etc., nothing has to be written, use “r” to avoid IOErrors in case of parallel computing.

Returns:

  • fn_xml (str) – Filename of the created .xml file

  • <File> (.xdmf file) – hdf5_fn[-4].xdmf (only data if hdf5Geo_fn provided)

  • <File> (.hdf5 file) – hdf5_fn changed if neccessary

  • <File> (.hdf5 file) – hdf5geo_fn containing spatial data

pynibs.hdf5_io.write_xdmf_surf(data_hdf_fn_out, data_names, data_xdmf_fn, geo_hdf_fn, data_dims, data_in_tris=True, data_prefix='/data/tris/')

Write XDMF files for surfaces, such as ROIs.

Parameters:

  • data_hdf_fn_out (str) –

  • data_names (list of str) –

  • data_xdmf_fn (str) –

  • geo_hdf_fn (str) –

  • data_dims (list of int) – The data dimensions.

  • data_in_tris (bool, default: True.) –

  • data_prefix (str, default: '/data/tris/') –

Returns:

<File> – Descriptor file pointing to geo and data .hdf5 files

Return type:

.xdmf file

pynibs.muap module

pynibs.muap.calc_mep_wilson(firing_rate_in, t, Qvmax=900, Qmmax=300, q=8, Tmin=14, N=100, M0=42, lam=0.002, tau0=0.006)

Determine motor evoked potential from incoming firing rate

Parameters:

  • firing_rate_in (ndarray of float [n_t]) – Input firing rate from alpha motor neurons

  • t (ndarray of float [n_t]) – Time axis in s

  • Qvmax (float, optional, default: 900) – Max of incoming firing rate [1/s]

  • Qmmax (float, optional, default: 300) – Max of MU firing rate [1/s]

  • q (float, optional, default: 8) – Min firing rate of MU [1/s]

  • Tmin (float, optional, default: 14) – Min MU threshold [1/s]

  • N (float, optional, default: 100) – Number of MU

  • M0 (float, optional, default: 42) – Scaling constant of MU amplitude [mV/s]

  • lam (float, optional, default: 0.002) – MUAP timescale of first order Hermite Rodriguez function [s]

  • tau0 (float, optional, default: 0.006) – Standard shift of MUAP to ensure causality [s]

Returns:

mep – Motor evoked potential at surface electrode

Return type:

ndarray of float [n_t]

pynibs.muap.compute_signal(signal_matrix, sensor_matrix)

Determine average signal from one single muscle fibre on all point electrodes

Parameters:

  • signal_matrix (ndarray of float [n_time x n_fibre]) – Signal matrix containing the action potential values for each time step in the rows

  • sensor_matrix (ndarray of float [n_fibre x n_ele]) – Sensor matrix containing the inverse distances weighted with the anisotropy of muscle tissue

Returns:

signal – Average signal detected all point electrodes

Return type:

ndarray of float [n_time]

pynibs.muap.create_electrode(l_x, l_z, n_x, n_z)

Creates electrode coordinates

Parameters:

  • l_x (float) – X-extension of electrode in mm

  • l_z (float) – Z-extension of electrode in mm

  • n_x (int) – Number of point electrode in x-direction

  • n_z (int) – Number of point electrodes in z-direction

Returns:

electrode_coords – Coordinates of point electrodes (x, y, z)

Return type:

ndarray of float [n_ele x 3]

pynibs.muap.create_muscle_coords(l_x, l_y, n_x, n_y, h)

Create x and y coordinates of muscle fibres in muscle

Parameters:

  • l_x (float) – X-extension of muscle in mm

  • l_y (float) – Y-extension of muscle in mm

  • n_x (int) – Number of muscle fibres in x-direction

  • n_y (int) – Number of muscle fibres in y-direction

  • h (float) – Offset of muscle from electrode plane in mm

Returns:

muscle_coords – Coordinates of muscle fibres in x-y plane (x, y, z)

Return type:

ndarray of float [n_muscle x 3]

pynibs.muap.create_muscle_fibre(x0, y0, L, n_fibre)

Creates muscle fibre coordinates (in z-direction)

Parameters:

  • x0 (float) – X-location of muscle fibre

  • y0 (float) – Y-location of muscle fibre

  • L (float) – Length of muscle fibre

  • n_fibre (float) – Number of discrete fibre elements

Returns:

fibre_coords – Coordinates of muscle fibre in z-direction (x, y, z)

Return type:

ndarray of float [n_fibre x 3]

pynibs.muap.create_sensor_matrix(electrode_coords, fibre_coords, sigma_r=1, sigma_z=1)

Create sensor matrix containing the inverse distances from the point electrodes to the fibre elements weighted by the anisotropy factor of the muscle tissue.

Parameters:

  • electrode_coords (ndarray of float [n_ele x 3]) – Coordinates of point electrodes (x, y, z)

  • fibre_coords (ndarray of float [n_fibre x 3]) – Coordinates of muscle fibre in z-direction (x, y, z)

  • sigma_r (float, optional, default: 1) – Radial conductivity of muscle

  • sigma_z (float, optional, default: 1) – Axial conductivity of muscle along fibre

Returns:

sensor_matrix – Sensor matrix containing the inverse distances weighted with the anisotropy of muscle tissue

Return type:

ndarray of float [n_fibre x n_ele]

pynibs.muap.create_signal_matrix(T, dt, fibre_coords, z_e, v)

Create signal matrix containing the travelling action potential on the fibre

Parameters:

  • T (float) – Total time

  • dt (float) – Time step

  • fibre_coords (ndarray of float [n_fibre x 3]) – Coordinates of muscle fibre in z-direction (x, y, z)

  • z_e (float) – Location of action potential generation

  • v (float) – Velocity of action potential

Returns:

signal_matrix – Signal matrix containing the action potential values for each time step in the rows

Return type:

ndarray of float [n_time x n_fibre]

pynibs.muap.dipole_potential(z, loc, response)

Returns dipole potential at given coordinates z (interpolates given dipole potential)

pynibs.muap.hermite_rodriguez_1st(t, tau0=0, tau=0, lam=0.002)

First order Hermite Rodriguez function to model surface MUAPs

Parameters:

  • t (ndarray of float [n_t]) – Time axis in s

  • tau0 (float, optional, default: 0) – initial shift to ensure causality in s

  • tau (float, optional, default: 0) – shift (firing time) in s

  • lam (float, optional, default: 2) – Timescale in s

Returns:

y – Surface MUAP

Return type:

ndarray of float [n_t]

pynibs.muap.sfap(z, sigma_i=1.01, d=5.4999999999999995e-05, alpha=0.5)

Single fibre propagating transmembrane current (second spatial derivative of transmembrane potential).

S. D. Nandedkar and E. V. Stalberg,“Simulation of single musclefiber action potentials” Med. Biol. Eng. Comput., vol. 21, pp. 158–165, Mar.1983.

J. Duchene and J.-Y. Hogrel,“A model of EMG generation,” IEEETrans. Biomed. Eng., vol. 47, no. 2, pp. 192–200, Feb. 2000

Hamilton-Wright, A., & Stashuk, D. W. (2005). Physiologically based simulation of clinical EMG signals. IEEE Transactions on biomedical engineering, 52(2), 171-183.

Parameters:

  • t (ndarray of float [n_t]) – Time in (ms)

  • sigma_i (float, optional, default: 1.01) – Intracellular conductivity in (S/m)

  • d (float, optional, default: 55*1e-6) – Diameter of muscle fibre in (m)

  • v (float, optional, default: 1) – Conduction velocity in (m/s)

  • alpha (float, optional, default: 0.5) – Scaling factor to adjust length of AP

Returns:

i – Transmembrane current of muscle fibre

Return type:

ndarray of float [n_t]

pynibs.muap.sfap_dip(z)
pynibs.muap.weight_signal_matrix(signal_matrix, fn_imp, t, z)

Weight signal matrix with impulse response from single dipole at every location

pynibs.para module

pynibs.para.ResetSession()

Resets Paraview session (needed if multiple plots are generated successively)

pynibs.para.b2rcw(cmin_input, cmax_input)

BLUEWHITERED Blue, white, and red color map. This function is designed to generate a blue to red colormap. The color of the colorbar is from blue to white and then to red, corresponding to the data values from negative to zero to positive, respectively. The color white always correspondes to value zero. The brightness of blue and red will change according to your setting, so that the brightness of the color corresponded to the color of his opposite number. e.g. b2rcw(-3,6) is from light blue to deep red e.g. b2rcw(-3,3) is from deep blue to deep red

Parameters:

  • cmin_input (float) – Minimum value of data

  • cmax_input (float) – Maximum value of data

Returns:

newmap

Return type:

nparray of float [N_RGB x 3]

pynibs.para.create_plot_settings_dict(plotfunction_type)

Creates a dictionary with default plotsettings.

Parameters:

plotfunction_type (str) –

Plot function the dictionary is generated for:

  • ’surface_vector_plot’

  • ’surface_vector_plot_vtu’

  • ’volume_plot’

  • ’volume_plot_vtu’

Returns:

  • ps (dict) – Dictionary containing the plotsettings:

  • axes (bool) – Show orientation axes.

  • background_color (nparray) – (1m 3) Set background color of exported image RGB (0…1).

  • calculator (str) – Format string with placeholder of the calculator expression the quantity to plot is modified with, e.g.: “{}^5”.

  • clip_coords (nparray of float) – (N_clips, 3) Coordinates of clip surface origins (x,y,z).

  • clip_normals (nparray of float) – (N_clips, 3) Surface normals of clip surfaces pointing in the direction where the volume is kept for clip_type = [‘clip’ …] (x,y,z).

  • clip_type (list of str) – Type of clipping:

    • ’clip’: cut geometry but keep volume behind

    • ’slice’: cut geometry and keep only the slice

  • coil_dipole_scaling (list [1 x 2]) – Specify the scaling type of the dipoles (2 entries): coil_dipole_scaling[0]:

    • ’uniform’: uniform scaling, i.e. all dipoles have the same size

    • ’scaled’: size scaled according to dipole magnitude

    coil_dipole_scaling[1]:

    • scalar scale parameter of dipole size

  • coil_dipole_color (str or list) – Color of the dipoles; either str to specify colormap (e.g. ‘jet’) or list of RGB values [1 x 3] (0…1)

  • coil_axes (bool, default: True) – Plot coil axes visualizing the principle direction and orientation of the coil.

  • colorbar_label (str) – Label of plotted data close to colorbar.

  • colorbar_position (list of float) – (1, 2) Position of colorbar (lower left corner) 0…1 [x_pos, y_pos].

  • colorbar_orientation (str) – Orientation of colorbar ('Vertical', 'Horizontal').

  • colorbar_aspectratio (int) – Aspectratio of colorbar (higher values make it thicker).

  • colorbar_titlefontsize (float) – Fontsize of colorbar title.

  • colorbar_labelfontsize (float) – Fontsize of colorbar labels (numbers).

  • colorbar_labelformat (str) – Format of colorbar labels (e.g.: ‘%-#6.3g’).

  • colorbar_numberoflabels (int) – maximum number of colorbar labels.

  • colorbar_labelcolor (list of float) – (1, 3) Color of colorbar labels in RGB (0…1).

  • colormap (str or nparray) – If nparray [1 x 4*N]: custom colormap providing data and corresponding RGB values

    \begin{bmatrix} data_{1} & R_1 & G_1 & B_1 \\ data_{2} & R_2 & G_2 & B_2 \\ ... & ... & ... & ... \\ data_{N} & R_N & G_N & B_N \\ \end{bmatrix}

    if str: colormap of plotted data chosen from included presets:

    • ’Cool to Warm’,

    • ’Cool to Warm (Extended)’,

    • ’Blue to Red Rainbow’,

    • ’X Ray’,

    • ’Grayscale’,

    • ’jet’,

    • ’hsv’,

    • ’erdc_iceFire_L’,

    • ’Plasma (matplotlib)’,

    • ’Viridis (matplotlib)’,

    • ’gray_Matlab’,

    • ’Spectral_lowBlue’,

    • ’BuRd’

    • ’Rainbow Blended White’

    • ’b2rcw’

  • colormap_categories (bool) – Use categorized (discrete) colormap.

  • datarange (list) – (1, 2) Minimum and Maximum of plotted datarange [MIN, MAX] (default: automatic).

  • domain_IDs (int or list of int) – Domain IDs surface plot: Index of surface where the data is plotted on (Default: 0) volume plot: Specify the domains IDs to show in plot (default: all) Attention! Has to be included in the dataset under the name ‘tissue’! e.g. for SimNIBS:

    • 1 -> white matter (WM)

    • 2 -> grey matter (GM)

    • 3 -> cerebrospinal fluid (CSF)

    • 4 -> skull

    • 5 -> skin

  • domain_label (str) – Label of the dataset which contains the domain IDs (default: ‘tissue_type’).

  • edges (BOOL) – Show edges of mesh.

  • fname_in (str or list of str) – Filenames of input files, 2 possibilities:

    • .xdmf-file: filename of .xmdf (needs the corresponding .hdf5 file(s) in the same folder)

    • .hdf5-file(s): filename(s) of .hdf5 file(s) containing the data and the geometry. The data can be provided in the first hdf5 file and the geometry can be provided in the second file. However, both can be also provided in a single hdf5 file.

  • fname_png (str) – Name of output .png file (incl. path).

  • fname_vtu_volume (str) – Name of .vtu volume file containing volume data (incl. path).

  • fname_vtu_surface (str) – Name of .vtu surface file containing surface data (incl. path) (to distinguish tissues).

  • fname_vtu_coil (str) – Name of coil .vtu file (incl. path) (optional).

  • info (str) – Information about the plot the settings are belonging to.

  • interpolate (bool) – Interpolate data for visual smoothness.

  • NanColor (list of float) –

    1. RGB color values for “Not a Number” values (range 0 … 1).

  • opacitymap (np.ndarray) – Points defining the piecewise linear opacity transfer function (transparency) (default: no transparency) connecting data values with opacity (alpha) values ranging from 0 (max. transparency) to 1 (no transparency).

    \begin{bmatrix} data_{1} & opac_1 & 0.5 & 0 \\ data_{2} & opac_2 & 0.5 & 0 \\ ... & ... & ... & ...\\ data_{N} & opac_N & 0.5 & 0 \\ \end{bmatrix}

  • plot_function (str) – Function the plot is generated with:

    • ’surface_vector_plot’

    • ’surface_vector_plot_vtu’

    • ’volume_plot’

    • ’volume_plot_vtu’

  • png_resolution (float) – Resolution parameter of output image (1…5)

  • quantity (str) – Label of magnitude dataset to plot

  • surface_color (nparray [1 x 3]) – Color of brain surface in RGB (0…1) for better visability of tissue borders

  • surface_smoothing (bool) – Smooth the plotted surface (True/False)

  • show_coil (bool, default: True) – show coil if present in dataset as block termed ‘coil’

  • vcolor (nparray of float [N_vecs x 3]) – Array containing the RGB values between 0…1 of the vector groups in dataset to plot

  • vector_mode (dict) – dict key determines the type how many vectors are shown:

    • ’All Points’

    • ’Every Nth Point’

    • ’Uniform Spatial Distribution’

    dict value (int) is the corresponding number of vectors

    • ’All Points’ (not set)

    • ’Every Nth Point’ (every Nth vector is shown in the grid)

    • ’Uniform Spatial Distribution’ (not set)

  • view (list) – Camera position and angle [[3 x CameraPosition], [3 x CameraFocalPoint], [3 x CameraViewUp], 1 x CameraParallelScale]

  • viewsize (nparray [1 x 2]) – Set size of exported image in pixel [width x height] will be extra scaled by parameter png_resolution

  • vlabels (list of str) – Labels of vector datasets to plot (other present datasets are ignored)

  • vscales (list of float) – Scale parameters of vector groups to plot

  • vscale_mode (list of str [N_vecs x 1]) – List containing the type of vector scaling:

    • ’off’: all vectors are normalized

    • ’vector’: vectors are scaled according to their magnitudeeee

pynibs.para.crop_data_hdf5_to_datarange(ps)

Crops the data (quantity) in .hdf5 data file to datarange and overwrites the original .hdf5 data file pointed by the .xdmf file.

Parameters:

ps (dict) – Plot settings dictionary created with create_plotsettings_dict(plot_function)

Returns:

  • fn_hdf5 (str) – Filename (incl. path) of data .hdf5 file (read from .xmdf file)

  • <File> (.hdf5 file) – *_backup.hdf5 backup file of original .hdf5 data file

  • <File> .hdf5 file – Cropped data

pynibs.para.crop_image(fname_image, fname_image_cropped)

Remove surrounding empty space around an image. This implemenation assumes that the surrounding space has the same colour as the top leftmost pixel.

Parameters:

fname_image (str) – Filename of image to be cropped

Returns:

<File> – Cropped image file saved as “fname_image_cropped”

Return type:

.png file

pynibs.para.surface_vector_plot(ps)

Generate plot with Paraview from data in .hdf5 file(s).

Parameters:

ps (dict) – Plot settings dict initialized with create_plot_settings_dict(plotfunction_type=’surface_vector_plot’)

Returns:

<File> – Generated plot

Return type:

.png file

pynibs.para.surface_vector_plot_vtu(ps)

Generate plot with Paraview from data in .vtu file.

Parameters:

ps (dict) – Plot settings dict initialized with create_plot_settings_dict(plotfunction_type=’surface_vector_plot_vtu’)

Returns:

<File> – Generated plot

Return type:

.png file

pynibs.para.volume_plot(ps)

Generate plot with Paraview from data in .hdf5 file.

Parameters:

ps (dict) – Plot settings dict initialized with create_plot_settings_dict(plotfunction_type=’’volume_plot’’)

Returns:

<File> – Generated plot

Return type:

.png file

pynibs.para.volume_plot_vtu(ps)

Generate plot with Paraview from data in .vtu file.

Parameters:

ps (dict) – Plot settings dict initialized with create_plot_settings_dict(plotfunction_type=’’volume_plot_vtu’’)

Returns:

<File> – Generated plot

Return type:

.png file

pynibs.para.write_vtu(fname, data_labels, points, connectivity, idx_start, data)

Writes data in tetrahedra centers into .vtu file, which can be loaded with Paraview.

Parameters:

  • fname (str) – Name of .vtu file (incl. path)

  • data_labels (list with N_data str) – Label of each dataset

  • points (array of float [N_points x 3]) – Coordinates of vertices

  • connectivity (array of int [N_tet x 4]) – Connectivity of points forming tetrahedra

  • idx_start (int) – Smallest index in connectivity matrix, defines offset w.r.t Python indexing, which starts at ‘0’

  • *data (array(s) [N_tet x N_comp(N_data)]) – Arrays containing data in tetrahedra center multiple components per dataset possible e.g. [Ex, Ey, Ez]

Returns:

<File> – Geometry and data information

Return type:

.vtu file

pynibs.para.write_vtu_coilpos(fname_geo, fname_vtu)

Read dipole data of coil (position and magnitude of each dipole) from geo file and store it as vtu file.

Parameters:

  • fname_geo (str) – .geo file from SimNIBS.

  • fname_vtu (str) – .vtu output file. Nodes and nodedata.

Returns:

<File> – Magnetic dipoles of the TMS coil

Return type:

.vtu file

pynibs.para.write_vtu_mult(fname, data_labels, points, triangles, tetrahedras, idx_start, *data)

Writes data in triangles and tetrahedra centers into .vtu file, which can be loaded with Paraview.

Parameters:

  • fname (str) – Name of .vtu file (incl. path)

  • data_labels (list of str [N_data]) – Label of each dataset

  • points (nparray of float [N_points x 3]) – Coordinates of vertices

  • triangles (nparray of int [N_tri x 3]) – Connectivity of points forming triangles

  • tetrahedras (nparray of int [N_tri x 4]) – Connectivity of points forming tetrahedra idx_start: int smallest index in connectivity matrix, defines offset w.r.t python indexing, which starts at ‘0’

  • *data (nparray(s) [N_tet x N_comp(N_data)]) – Arrays containing data in tetrahedra center multiple components per dataset possible e.g. [Ex, Ey, Ez]

Returns:

<File> – Geometry and data information

Return type:

.vtu file

pynibs.subject module

class pynibs.subject.Subject(subject_id, subject_folder)

Bases: object

Subject containing subject specific information, like mesh, roi, uncertainties, plot settings.

self.id

Subject id from MPI database

Type:

str

Notes

Initialization

sub = pynibs.subject(subject_ID, mesh)

Parameters

idstr

Subject id

fn_meshstr

.msh or .hdf5 file containing the mesh information

Subject.seg, segmentation information dictionary

fn_lh_wmstr

Filename of left hemisphere white matter surface

fn_rh_wmstr

Filename of right hemisphere white matter surface

fn_lh_gmstr

Filename of left hemisphere grey matter surface

fn_rh_gmstr

Filename of right hemisphere grey matter surface

fn_lh_curvstr

Filename of left hemisphere curvature data on grey matter surface

fn_rh_curvstr

Filename of right hemisphere curvature data on grey matter surface

Subject.mri, mri information dictionary

fn_mri_T1str

Filename of T1 image

fn_mri_T2str

Filename of T2 image

fn_mri_DTIstr

Filename of DTI dataset

fn_mri_DTI_bvecstr

Filename of DTI bvec file

fn_mri_DTI_bvalstr

Filename of DTI bval file

fn_mri_conformstr

Filename of conform T1 image resulting from SimNIBS mri2mesh function

Subject.ps, plot settings dictionary

see plot functions in para.py for more details

Subject.exp, experiment dictionary

infostr

General information about the experiment

datestr

Date of experiment (e.g. 01/01/2018)

fn_tms_navstr

Path to TMS navigator folder

fn_datastr

Path to data folder or files

fn_exp_csvstr

Filename of experimental data .csv file containing the merged experimental data information

fn_coilstr

Filename of .ccd or .nii file of coil used in the experiment (contains ID)

fn_mri_niistr

Filename of MRI .nii file used during the experiment

condstr or list of str

Conditions in the experiment in the recorded order (e.g. [‘PA-45’, ‘PP-00’])

experimenterstr

Name of experimenter who conducted the experiment

incidentsstr

Description of special events occured during the experiment

Subject.mesh, mesh dictionary

infostr

Information about the mesh (e.g. dicretization etc)

fn_mesh_mshstr

Filename of the .msh file containing the FEM mesh

fn_mesh_hdf5str

Filename of the .hdf5 file containing the FEM mesh

seg_idxint

Index indicating to which segmentation dictionary the mesh belongs

Subject.roi region of interest dictionary

typestr

Specify type of ROI (‘surface’, ‘volume’)

infostr

Info about the region of interest, e.g. “M1 midlayer from freesurfer mask xyz”

regionlist of str or float

Filename for freesurfer mask or [[X_min, X_max], [Y_min, Y_max], [Z_min, Z_max]]

deltafloat

Distance parameter between WM and GM (0 -> WM, 1 -> GM) (for surfaces only)

add_experiment_info(exp_dict)

Adding information about a particular experiment.

Parameters:

exp_dict (dict of dict or list of dict) – Dictionary containing information about the experiment

Notes

Adds Attributes

explist of dict

Dictionary containing information about the experiment

add_mesh_info(mesh_dict)

Adding filename information of the mesh to the subject object (multiple filenames possible).

Parameters:

mesh_dict (dict or list of dict) – Dictionary containing the mesh information

Notes

Adds Attributes

Subject.meshlist of dict

Dictionaries containing the mesh information

add_mri_info(mri_dict)

Adding MRI information to the subject object (multiple MRIs possible).

Parameters:

mri_dict (dict or list of dict) – Dictionary containing the MRI information of the subject

Notes

Adds Attributes

Subject.mrilist of dict

Dictionary containing the MRI information of the subject

add_plotsettings(ps_dict)

Adding ROI information to the subject object (multiple ROIs possible).

Parameters:

ps_dict (dict or list of dict) – Dictionary containing plot settings of the subject

Notes

Adds Attributes

Subject.pslist of dict

Dictionary containing plot settings of the subject

add_roi_info(roi_dict)

Adding ROI (surface) information of the mesh with mesh_index to the subject object (multiple ROIs possible).

Parameters:

roi_dict (dict of dict or list of dict) – Dictionary containing the ROI information of the mesh with mesh_index [mesh_idx][roi_idx]

Notes

Adds Attributes

Subject.mesh[mesh_index].roilist of dict

Dictionaries containing ROI information

pynibs.subject.check_file_and_format(fname)

Checking existence of file and transforming to list if necessary.

Parameters:

fname (str or list of str) – Filename(s) to check

Returns:

fname – Checked filename(s) as list

Return type:

list of str

pynibs.subject.fill_from_dict(obj, d)

Set all attributes from d in obj.

Parameters:

  • obj (pynibs.Mesh or pynibs.ROI) –

  • d (dict) –

Returns:

obj

Return type:

pynibs.Mesh or pynibs.ROI

pynibs.subject.load_subject(fname, filetype=None)

Wrapper for pkl and hdf5 subject loader

Parameters:

  • fname (str) – endwith(‘.pkl’) | endswith(‘.hdf5’)

  • filetype (str) – Explicitely set file version.

Returns:

subject

Return type:

pynibs.subject.Subject

pynibs.subject.load_subject_hdf5(fname)

Loading subject information from .hdf5 file and returning subject object.

Parameters:

fname (str) – Filename with .hdf5 extension (incl. path)

Returns:

subject – Loaded Subject object

Return type:

pynibs.subject.Subject

pynibs.subject.load_subject_pkl(fname)

Loading subject object from .pkl file.

Parameters:

fname (str) – Filename with .pkl extension

Returns:

subject – Loaded Subject object

Return type:

pynibs.subject.Subject

pynibs.subject.save_subject(subject_id, subject_folder, fname, mri_dict=None, mesh_dict=None, roi_dict=None, exp_dict=None, ps_dict=None, **kwargs)

Saves subject information in .pkl or .hdf5 format (preferred)

Parameters:

  • subject_id (str) – ID of subject

  • subject_folder (str) – Subject folder

  • fname (str) – Filename with .hdf5 or .pkl extension (incl. path)

  • mri_dict (list of dict, optional, default: None) – MRI info

  • mesh_dict (list of dict, optional, default: None) – Mesh info

  • roi_dict (list of list of dict, optional, default: None) – Mesh info

  • exp_dict (list of dict, optional, default: None) – Experiment info

  • ps_dict (list of dict, optional, default:None) – Plot-settings info

  • kwargs (str or np.array) – Additional information saved in the parent folder of the .hdf5 file

Returns:

<File> – Subject information

Return type:

.hdf5 file

pynibs.subject.save_subject_hdf5(subject_id, subject_folder, fname, mri_dict=None, mesh_dict=None, roi_dict=None, exp_dict=None, ps_dict=None, overwrite=True, check_file_exist=False, verbose=False, **kwargs)

Saving subject information in hdf5 file.

Parameters:

  • subject_id (str) – ID of subject

  • subject_folder (str) – Subject folder

  • fname (str) – Filename with .hdf5 extension (incl. path)

  • mri_dict (list of dict, optional, default: None) – MRI info

  • mesh_dict (list of dict, optional, default: None) – Mesh info

  • roi_dict (list of list of dict, optional, default: None) – Mesh info

  • exp_dict (list of dict or dict of dict, optional, default: None) – Experiment info

  • ps_dict (list of dict, optional, default:None) – Plot-settings info

  • overwrite (bool) – Overwrites existing .hdf5 file

  • check_file_exist (bool) – Hide warnings.

  • verbose (bool) – Print information about meshes and ROIs.

  • kwargs (str or np.ndarray) – Additional information saved in the parent folder of the .hdf5 file

Returns:

<File> – Subject information

Return type:

.hdf5 file

pynibs.subject.save_subject_pkl(sobj, fname)

Saving subject object as pickle file.

Parameters:

  • sobj (object) – Subject object to save

  • fname (str) – Filename with .pkl extension

Returns:

<File> – Subject object instance

Return type:

.pkl file

pynibs.tensor_scaling module

pynibs.tensor_scaling.ellipse_eccentricity(a, b)

Calculates the eccentricity of an 2D ellipse with the semi axis a and b. An eccentricity of 0 corresponds to a sphere and an eccentricity of 1 means complete eccentric (line) with full restriction to the other axis

Parameters:

  • a (float) – First semi axis parameter

  • b (float) – Second semi axis parameter

Returns:

e – Eccentricity (0…1)

Return type:

float

pynibs.tensor_scaling.rescale_lambda_centerized(D, s, tsc=False)

Rescales the eigenvalues of the matrix D according to their eccentricity. The scale factor is between 0…1 a scale factor of 0.5 would not alter the eigenvalues of the matrix D. A scale factor of 0 would unify all eigenvalues to one value such that it corresponds to a isotropic sphere. A scale factor of 1 alters the eigenvalues in such a way that the resulting ellipsoid is fully eccentric and anisotropic.

Parameters:

  • D (nparray of float) – (3, 3) Diffusion tensor.

  • s (float) – Scale parameter [0 (iso) … 0.5 (unaltered)… 1 (aniso)].

  • tsc (bool) – Tensor singularity correction.

Returns:

Ds – (3, 3) Scaled diffusion tensor.

Return type:

np.ndarray of float

pynibs.tensor_scaling.rescale_lambda_centerized_workhorse(D, s, tsc=False)

Rescales the eigenvalues of the matrix D according to their eccentricity. The scale factor is between 0…1 a scale factor of 0.5 would not alter the eigenvalues of the matrix D. A scale factor of 0 would unify all eigenvalues to one value such that it corresponds to a isotropic sphere. A scale factor of 1 alters the eigenvalues in such a way that the resulting ellipsoid is fully eccentric and anisotropic

Parameters:

  • D (ndarray of float) – (n, 9) Diffusion tensor.

  • s (float) – Scale parameter [0 (iso) … 0.5 (unaltered)… 1 (aniso)].

  • tsc (bool) – Tensor singularity correction.

Returns:

Ds – (3, 3) Scaled diffusion tensor

Return type:

list of nparray of float

pynibs.tms_pulse module

pynibs.tms_pulse.biphasic_pulse(t, R=0.0338, L=1.55e-05, C=0.0001936, alpha=1089.8, f=2900)

Returns normalized single biphasic pulse waveform of electric field (first derivative of coil current)

Parameters:

  • t (ndarray of float [n_t]) – Time array in seconds

  • R (float, optional, default: 0.0338 Ohm) – Resistance of coil in (Ohm)

  • L (float, optional, default: 15.5*1e-6 H) – Inductance of coil in (H)

  • C (float, optional, default: 193.6*1e-6) – Capacitance of coil in (F)

  • alpha (float, optional, default: 1089.8 1/s) – Damping coefficient in (1/s)

  • f (float, optional, default: 2900 Hz) – Frequency in (Hz)

Returns:

e – Normalized electric field time course (can be scaled with electric field)

Return type:

ndarray of float [n_t]

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