nigsp package

nigsp.workflow - Primary workflows

nigsp(fname, scname[, atlasname, outname, ...])

Main workflow for nigsp, following the methods described in [Ra554358f60f2-1].

nigsp.operations.graph - Operations on graphs

zerocross(eigenvec)	Compute the amount of zero-crossing of an eigenvector matrix (for each eigenvector).
nodestrength(mtx[, mean])	Compute the node strength of a graph.

nigsp.operations.laplacian - Operations on/for Laplacians

compute_laplacian(mtx[, negval, selfloops])	Compute Laplacian (L) matrix from a square matrix.
normalisation(lapl, degree[, norm, fix_zeros])	Normalise a Laplacian (L) matrix using either symmetric or random walk normalisation.
symmetric_normalised_laplacian(mtx[, d, ...])	Compute symmetric normalised Laplacian (SNL) matrix.
decomposition(mtx)	Run a eigenvector decomposition on input.
recomposition(eigenval, eigenvec)	Recompose a matrix from its eigenvalues and eigenvectors.

nigsp.operations.metrics - Metrics computation

sdi(ts_split[, mean, keys])	Compute the Structural Decoupling Index (SDI).
gsdi(ts_split[, mean, keys])	Compute the generalised SDI.
functional_connectivity(timeseries[, mean])	Compute Functional Connectivity of timeseries.

nigsp.operations.nifti - Operations on nifti-like data

vol_to_mat(data)	Reshape <3D in 1D or 4D into 2D.
mat_to_vol(data[, shape, asdata])	Reshape nD data (normally 2D) using either shape or data shape).
apply_mask(data, mask)	Reduce shape and size of data based on mask.
unmask(data, mask[, shape, asdata])	Unmask 1D or 2D into an nD based on shape or asdata.
apply_atlas(data, atlas[, mask])	Extract average timeseries from an atlas.
unfold_atlas(data, atlas[, mask])	Return a lower dimensional matrix into a 3- or 4- D matrix based on an atlas.

nigsp.operations.surrogates - Surrogate computations and testing

random_sign(eigenvec[, n_surr, seed, stack])	Create surrogates by randomly switching signs of eigenvectors.
sc_informed(timeseries, eigenvec[, n_surr, ...])	Create surrogates informed by the real structural connectivity.
sc_uninformed(timeseries, lapl_mtx[, ...])	Create surrogates ignorant of the real structural connectivity.
test_significance(surr[, data, method, p, ...])	Test the significance of the empirical data against surrogates.

nigsp.operations.timeseries - Operations on/involving timeseries

normalise_ts(timeseries[, globally])	Normalise given timeseries (i.e. mean=0, std=1).
spc_ts(timeseries[, globally])	Express timeseries in signal percentage change.
demean_ts(timeseries[, globally])	Demean timeseries.
rescale_ts(timeseries[, vmin, vmax, globally])	Rescale given timeseries between given max and min value.
resize_ts(timeseries[, resize, globally])	Rescale timeseries with some methods.
graph_fourier_transform(timeseries, eigenvec)	Project a graph decomposition onto the timeseries.
median_cutoff_frequency_idx(energy)	Find the frequency that splits the energy of a timeseries in two roughly equal parts.
graph_filter(timeseries, eigenvec, freq_idx)	Filter a graph decomposition into two parts based on freq_idx.

nigsp.io - I/O functions: checks

check_ext(all_ext, fname[, scan, remove])	Check which extension a file has, and possibly remove it.
check_nifti_dim(fname, data[, dim])	Check number of dimensions in nifti file.
check_mtx_dim(fname, data[, shape])	Check dimensions of a matrix.

nigsp.io - I/O functions: load

load_nifti_get_mask(fname[, is_mask, ndim])	Load a nifti file and returns its data, its image, and a 3d mask.
load_txt(fname[, shape])	Read files in textual format.
load_mat(fname[, shape])	Read files in matlab format.
load_xls(fname[, shape])	Read files in xls format.

nigsp.io - I/O functions: export

export_nifti(data, img, fname)	Export a nifti file.
export_txt(data, fname[, ext])	Export data into a text-like or mat file.
export_mtx(data, fname[, ext])	Export data into a text-like or mat file.

nigsp.io - I/O functions: supported extensions

EXT_1D	Built-in mutable sequence.
EXT_MAT	Built-in mutable sequence.
EXT_NIFTI	Built-in mutable sequence.
EXT_XLS	Built-in mutable sequence.

nigsp.viz - Visualisations

plot_connectivity(mtx[, filename, closeplot])	Create a connectivity matrix plot.
plot_greyplot(timeseries[, filename, title, ...])	Create a greyplot (a.k.a.
plot_nodes(ns, atlas[, filename, thr, closeplot])	Create a marker plot in the MNI space.
plot_edges(mtx, atlas[, filename, thr, ...])	Create a connectivity plot in the MNI space.

nigsp.utils - Utility functions

pairwise(iterable)	Recreate itertools.pairwise behaviour for python < 3.10 compatibility.
change_var_type(var, dtype[, varname, stop, ...])	Make sure var is of type dtype.
prepare_ndim_iteration(data, idx)	Reshape data to have idx+1 dimensions.

nigsp.objects - Data objects

The main object of nigsp.

It holds all data information - graph, timeseries, decompositions, …

Contains duplications of operations, allowing a more object oriented approach to interact with nigsp.

Attributes

LGR

Logger

SCGraph(mtx, timeseries[, atlas, filename, ...])

Main module object, containing all data representing the graph.

nigsp.blocks - Workflow blocks

nifti_to_timeseries(fname, atlasname)	Read a nifti file and returns a normalised timeseries from an atlas.
export_metric(scgraph, outext, outprefix)	Export the metrics computed within the library.
plot_metric(scgraph, outprefix[, atlas, thr])	If possible, plot metrics as markerplot.