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.muap module

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.

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 occurred 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.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, optional) – Name of coil .vtu file (incl. path).

  • info (str) – Information about the plot the settings belong 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 (np.ndarray) – (1, 3) Color of brain surface in RGB (0…1) for better visibility 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 (np.ndarray of float) – (N_vecs, 3) Array containing the RGB values between 0…1 of the vector groups in dataset to plot.

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

    • ’All Points’

    • ’Every Nth Point’

    • ’Uniform Spatial Distribution’

    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 in 3D space: [[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.subject.fill_from_dict(obj, d)

Set all attributes from d in obj.

Parameters:

  • obj (pynibs.Mesh or pynibs.roi.ROI) – Object to fill with d.

  • d (dict) – Dictionary containing the attributes to set.

Returns:

obj – Object with attributes set from d.

Return type:

pynibs.Mesh or pynibs.ROI

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

Wrapper for pkl and hdf5 subject loader

Parameters:

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

  • filetype (str) – Explicitly set file version.

Returns:

subject – Loaded Subject object.

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 – The 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) – MRI info.

  • mesh_dict (list of dict, optional) – Mesh info.

  • roi_dict (list of list of dict, optional) – Mesh info.

  • exp_dict (list of dict, optional) – Experiment info.

  • ps_dict (list of dict, optional) – Plot-settings info.

  • 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_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) – MRI info.

  • mesh_dict (list of dict, optional) – Mesh info.

  • roi_dict (list of list of dict, optional) – Mesh info.

  • exp_dict (list of dict or dict of dict, optional) – 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

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