SciPy

pynibs package

Subpackages

Submodules

pynibs.coil module

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.ndarrays [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 (nparray of float [3]) – Location variation in normalized space (dx’, dy’, dz’), i.e. zero mean and projected on principal axes

  • ORI_var (nparray of float [3]) – Orientation variation expressed in Euler angles [alpha, beta, gamma] in deg

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

Returns

mat – Transformation matrix containing 3 axis and 1 location vector:

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

Return type

nparray of float [4 x 4]

pynibs.coil.check_coil_position(points, hull)

Check if magnetic dipoles are lying inside head region

Parameters

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

  • hull (Delaunay object or np.ndarray of float [N_surface_points x 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 (basestring) – Filename for the resulting .hdf5 file. The .xdmf is saved with the same basename. Folder should already exist.

  • datanames (basestring or list of basestring) – Dataset names for _data_. Default: None.

  • data (np.ndarray) – Dataset array with (len(poslist.pos), len(datanames()). Default: None.

  • overwrite (boolean) – Overwrite existing files. Default: False.

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

This takes a TMSLIST from simnibs and creates a .hdf5 + .xdmf tuple for all positions.

Centers and coil orientations are written so disk.

Parameters

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

  • datanames (basestring or list of basestring) – Dataset names for _data_. Default: None.

  • data (np.ndarray) – Dataset array with (len(poslist.pos), len(datanames()). Default: None.

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

  • overwrite (boolean) – Overwrite existing files. Default: False.

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 (string) – 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)

    [ | | | | ] [ x y z p ] [ | | | | ] [ 0 0 0 1 ]

  • datanames (string or list of string) – Dataset names for _data_. Default: None.

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

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

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

This takes a TMSLIST from simnibs and creates a .hdf5 + .xdmf tuple for all positions.

Centers and coil orientations are written so disk.

Parameters

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

  • datanames (basestring or list of basestring) – Dataset names for _data_. Default: None.

  • data (np.ndarray) – Dataset array with (len(poslist.pos), len(datanames()). Default: None.

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

  • overwrite (boolean) – Overwrite existing files. Default: False.

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, Default=None) – 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 (boolean) – If true, only one coil center per site is generated.

  • fix_angles (boolean) – 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, Default=None) – 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

Example::
pynibs.create_stimsite_hdf5(‘/data/pt_01756/probands/15484.08/exp/1/experiment_corrected.csv’,

‘/data/pt_01756/tmp/test’, 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 – Cartesian coordinates (x, y, z) of coil magnetic dipoles

Return type

nparray [N x 3]

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)

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, optional, default: False) – Print output messages

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

Returns

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 for all coil positions.

Centers and coil orientations are written to disk.

Parameters

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

  • centers (np.ndarray of float [n_pos x 3]) – Coil positions

  • m0 (np.ndarray of float [n_pos x 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 x 3]) – Coil orientation y-axis (looking at the active (patient) side of the coil pointing up away from the handle)

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

  • datanames (basestring or list of basestring [n_data]) – Dataset names for _data_. Default: None.

  • data (np.ndarray [n_pos, n_data]) – Dataset array with (len(poslist.pos), len(datanames()). Default: None.

  • overwrite (boolean) – Overwrite existing files. Default: False.

pynibs.freesurfer module

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.

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

Returns

output_xdmf

Return type

<file>

Other Parameters

coil_sorted (str) – Path to coil positions in sorted_fn

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 NaN) – Data substitute with this number for all points in the inflated brain, which do not belong to the given data set (Default: -1)

  • normalize (boolean or str) – Decide if individual datasets are normalized w.r.t. their maximum values before they are superimposed (Default: False) - ‘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 – Instance of TetrahedraLinear class

Return type

pynibs.mesh.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 object instance of TetrahedraLinear class.

Parameters

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

Returns

obj

Return type

pynibs.mesh.TetrahedraLinear

pynibs.hdf5_io.msh2hdf5(fn_msh=None, skip_roi=False, 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, default: None) – Filename of .msh file

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

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

  • subject (Subject object, optional, default: None) – Subject object

  • mesh_idx (int or list of int, optional, default: None) – Mesh index, the conversion from .msh to .hdf5 is conducted for

  • parameters (Depreciated) –

  • ---------------------- –

  • approach (str) – Approach the headmodel was created with (“mri2mesh” or “headreco”)

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)

Read array and transform to list: strings saved as np.bytes_ to str and ‘None’ to None

fn_hdf5: str

Filename of .hdf5 file

folder: str

Folder inside .hdf5 file to read

Returns

l – 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 (nparray of float [N_nodes]) – Electric potential in the nodes of the mesh

  • da_dt (nparray 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 (Subject object) – Subject object loaded from .pkl file

  • mesh_idx (int) – Mesh index

  • mode_xdmf (str, optional, 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, optional, default: False) – Print output messages

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

  • mid2roi (bool or string, optional, 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, S, 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 (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 (Subject object) – Subject object loaded from .pkl file

  • mesh_idx (int) – Mesh index

  • mode_xdmf (str, optional, 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, optional, default: False) – Print output messages

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

  • mid2roi (bool, list of string, or string, optional, 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 (If 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’.

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 (nparray 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, optional, 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)

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 (ndarray or list [N_points_ROI x 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 (boolean, optional, default: False) – Replace existing .hdf5 and .xdmf file completely

  • replace_array_in_file (boolean, optional, 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 | 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) – Filename of output .hdf5 file containing the geometry information

  • msh (TetrahedraLinear object instance) – pynibs.mesh.TetrahedraLinear object

  • roi_dict (dict of RegionOfInterestSurface and/or RegionOfInterestVolume object instance(s)) – Region of interest (surface and/or volume) class instance

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

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 (nparray [N_points x 3]) – Coordinates of nodes (x,y,z)

  • con (nparray [N_tri x 3]) – Connectivity list of triangles

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

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

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)

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) – Path within hdf5 to group of dataframes

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

  • overwrite_xdmf (boolean) – 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) –

  • verbose (boolean) – 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) – Filename of hdf5 file containing the geometry. Optional.

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

  • overwrite_array (bool) – Overwrite existing arrays if present. Default: False.

  • verbose (boolean) – Print output or not

  • mode (str, optional, 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.main module

pynibs.muap module

pynibs.neuron module

pynibs.opt module

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 (boolean) – Show orientation axes (TRUE / FALSE)

  • background_color (nparray [1 x 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 x 3]) – Coordinates of clip surface origins (x,y,z)

  • clip_normals (nparray of float [N_clips x 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 (boolean) – Plot coil axes visualizing the principle direction and orientation of the coil (Default: True)

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

  • colorbar_position (list of float [1 x 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 x 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 (boolean) – Use categorized (discrete) colormap

  • datarange (list [1 x 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 (boolean) – Show edges of mesh (TRUE / FALSE)

  • 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 (boolean) – Interpolate data for visual smoothness (TRUE / FALSE)

  • NanColor (list of float [3]) – RGB color values for “Not a Number” values (range 0 … 1)

  • opacitymap (nparray) – 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 (boolean) – show coil if present in dataset as block termed ‘coil’ (Default: True)

  • 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.postproc module

pynibs.regression module

pynibs.roi module

pynibs.simnibs module

pynibs.subject module

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 x 3]) – Diffusion tensor

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

  • tsc (boolean) – Tensor singularity correction

Returns

Ds – Scaled diffusion tensor

Return type

nparray of float [3 x 3]

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 x 9]) – Diffusion tensor

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

  • tsc (boolean) – Tensor singularity correction

Returns

Ds – Scaled diffusion tensor

Return type

list of nparray of float [3 x 3]

pynibs.test_match_instrument_marker_string module

pynibs.tms_pulse module

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