Python Reference API

netneurotools.networks - Constructing networks

Functions for generating group-level networks from individual measurements.

func_consensus(data[, n_boot, ci, seed])	Calculate thresholded group consensus functional connectivity graph.
struct_consensus(data, distance, hemiid[, ...])	Calculate distance-dependent group consensus structural connectivity graph.
threshold_network(network[, retain])	Keep top retain % of connections in network and binarizes.
binarize_network(network[, retain, keep_diag])	Keep top retain % of connections in network and binarizes.
match_length_degree_distribution(W, D[, ...])	Generate degree- and edge length-preserving surrogate connectomes.
randmio_und(W, itr)	Optimized version of randmio_und.
strength_preserving_rand_sa(A[, ...])	Strength-preserving network randomization using simulated annealing.
strength_preserving_rand_sa_mse_opt(A[, ...])	Strength-preserving network randomization using simulated annealing.
strength_preserving_rand_sa_dir(A[, ...])	Strength-preserving network randomization using simulated annealing.

netneurotools.modularity - Calculating network modularity

Functions for working with network modularity.

consensus_modularity(adjacency[, gamma, B, ...])	Find community assignments from adjacency through consensus.
zrand(X, Y)	Calculate the z-Rand index of two community assignments.
get_modularity(adjacency, comm[, gamma])	Calculate modularity contribution for each community in comm.
get_modularity_z(adjacency, comm[, gamma, ...])	Calculate average z-score of community assignments by permutation.
get_modularity_sig(adjacency, comm[, gamma, ...])	Calculate significance of community assignments in comm by permutation.

netneurotools.cluster - Working with clusters

Functions for clustering and working with cluster solutions.

find_consensus(assignments[, null_func, ...])	Find consensus clustering labels from cluster solutions in assignments.
match_assignments(assignments[, target, seed])	Re-label clusters in columns of assignments to best match target.
reorder_assignments(assignments[, ...])	Relabel and reorders rows / columns of assignments to "look better".
match_cluster_labels(source, target)	Align cluster labels in source to those in target.

netneurotools.plotting - Plotting brain data

Functions for making pretty plots and whatnot.

sort_communities(consensus, communities)	Sort communities in consensus according to strength.
plot_mod_heatmap(data, communities, *[, ...])	Plot data as heatmap with borders drawn around communities.
plot_conte69(data, lhlabel, rhlabel[, surf, ...])	Plot surface data on Conte69 Atlas.
plot_fslr(data, lhlabel, rhlabel[, ...])	Plot surface data on a given fsLR32k atlas.
plot_fsaverage(data, *, lhannot, rhannot[, ...])	Plot data to fsaverage brain using annot as parcellation.
plot_fsvertex(data, *[, order, surf, views, ...])	Plot vertex-wise data to fsaverage brain.
plot_point_brain(data, coords[, views, ...])	Plot data as a cloud of points in 3D space based on specified coords.

netneurotools.stats - General statistics functions

Functions for performing statistical preprocessing and analyses.

gen_spinsamples(coords, hemiid[, n_rotate, ...])	Return a resampling array for coords obtained from rotations / spins.
residualize(X, Y[, Xc, Yc, normalize, ...])	Return residuals of regression equation from Y ~ X.
get_mad_outliers(data[, thresh])	Determine which samples in data are outliers.
efficient_pearsonr(a, b[, ddof, nan_policy])	Compute correlation of matching columns in a and b.
permtest_1samp(a, popmean[, axis, n_perm, seed])	Non-parametric equivalent of scipy.stats.ttest_1samp().
permtest_rel(a, b[, axis, n_perm, seed])	Non-parametric equivalent of scipy.stats.ttest_rel().
permtest_pearsonr(a, b[, axis, n_perm, ...])	Non-parametric equivalent of scipy.stats.pearsonr().
get_dominance_stats(X, y[, ...])	Return the dominance analysis statistics for multilinear regression.
network_pearsonr(annot1, annot2, weight)	Calculate pearson correlation between two annotation vectors.
network_pearsonr_numba(annot1, annot2, weight)	Numba version of netneurotools.stats.network_pearsonr().
network_pearsonr_pairwise(annot_mat, weight)	Calculate pairwise network correlation between rows of annot_mat.
effective_resistance(W[, directed])	Calculate effective resistance matrix.
network_polarisation(vec, W[, directed])	Calculate polarisation of a vector on a graph.
network_variance(vec, D)	Calculate variance of a vector on a graph.
network_variance_numba(vec, D)	Numba version of netneurotools.stats.network_variance().
network_covariance(joint_pmat, D[, ...])	Calculate covariance of a joint probability matrix on a graph.
network_covariance_numba(joint_pmat, D[, ...])	Numba version of netneurotools.stats.network_covariance().

netneurotools.metrics - Calculating graph metrics

Functions for calculating network metrics.

Uses naming conventions adopted from the Brain Connectivity Toolbox (https://sites.google.com/site/bctnet/).

_binarize(W)	Binarize a matrix.
degrees_und(W)	Compute the degree of each node in W.
degrees_dir(W)	Compute the in degree and out degree of each node in W.
distance_wei_floyd(D)	Compute the all-pairs shortest path length using Floyd-Warshall algorithm.
retrieve_shortest_path(s, t, p_mat)	Return the shortest paths between two nodes.
communicability_bin(adjacency[, normalize])	Compute the communicability of pairs of nodes in adjacency.
communicability_wei(adjacency)	Compute the communicability of pairs of nodes in adjacency.
rich_feeder_peripheral(x, sc[, stat])	Calculate connectivity values in rich, feeder, and peripheral edges.
navigation_wu(nav_dist_mat, sc_mat)	Compute network navigation.
get_navigation_path_length(nav_paths, ...)	Get navigation path length from navigation results.
search_information(W, D[, has_memory])	Calculate search information.
path_transitivity(D)	Calculate path transitivity.
flow_graph(W[, r, t])	Calculate flow graph.
mean_first_passage_time(W[, tol])	Calculate mean first passage time.
diffusion_efficiency(W)	Calculate diffusion efficiency.
resource_efficiency_bin(W_bin[, lambda_prob])	Calculate resource efficiency and shortest-path probability.
matching_ind_und(W)	Calculate undirected matching index.
_graph_laplacian(W)	Compute the graph Laplacian of a weighted adjacency matrix.

netneurotools.datasets - Automatic dataset fetching

Functions for fetching and generating datasets.

Functions to download atlases and templates

fetch_cammoun2012([version, data_dir, url, ...])	Download files for Cammoun et al., 2012 multiscale parcellation.
fetch_civet([density, version, data_dir, ...])	Fetch CIVET surface files.
fetch_conte69([data_dir, url, resume, verbose])	Download files for Van Essen et al., 2012 Conte69 template.
fetch_fsaverage([version, data_dir, url, ...])	Download files for fsaverage FreeSurfer template.
fetch_pauli2018([data_dir, url, resume, verbose])	Download files for Pauli et al., 2018 subcortical parcellation.
fetch_schaefer2018([version, data_dir, url, ...])	Download FreeSurfer .annot files for Schaefer et al., 2018 parcellation.
fetch_hcp_standards([data_dir, url, resume, ...])	Fetch HCP standard mesh atlases for converting between FreeSurfer and HCP.
fetch_voneconomo([data_dir, url, resume, ...])	Fetch von-Economo Koskinas probabilistic FreeSurfer atlas.

Functions to download real-world datasets

fetch_connectome(dataset[, data_dir, url, ...])	Download files from multi-species connectomes.
fetch_mirchi2018([data_dir, resume, verbose])	Download (and creates) dataset for replicating Mirchi et al., 2018, SCAN.
fetch_vazquez_rodriguez2019([data_dir, url, ...])	Download files from Vazquez-Rodriguez et al., 2019, PNAS.

Functions to generate (pseudo-random) datasets

make_correlated_xy([corr, size, seed, tol])

Generate random vectors that are correlated to approximately corr.

netneurotools.freesurfer - FreeSurfer compatibility functions

Functions for working with FreeSurfer data and parcellations.

apply_prob_atlas(subject_id, gcs, hemi, *[, ...])	Create an annotation file for subject_id by applying atlas in gcs.
find_parcel_centroids(*, lhannot, rhannot[, ...])	Return vertex coords corresponding to centroids of parcels in annotations.
parcels_to_vertices(data, *, lhannot, rhannot)	Project parcellated data to vertices defined in annotation files.
vertices_to_parcels(data, *, lhannot, rhannot)	Reduce vertex-level data to parcels defined in annotation files.
spin_data(data, *, lhannot, rhannot[, ...])	Project parcellated data to surface, rotates, and re-parcellates.
spin_parcels(*, lhannot, rhannot[, version, ...])	Rotate parcels in {lh,rh}annot and re-assigns based on maximum overlap.

netneurotools.civet - CIVET compatibility functions

Functions for working with CIVET data (ugh).

read_civet(fname)	Read a CIVET-style .obj geometry file.
civet_to_freesurfer(brainmap[, surface, ...])	Project brainmap in CIVET space to freesurfer fsaverage space.

netneurotools.utils - Miscellaneous, grab bag utilities

Miscellaneous functions of various utility.

run(cmd[, env, return_proc, quiet])	Run cmd via shell subprocess with provided environment env.
add_constant(data)	Add a constant (i.e., intercept) term to data.
get_triu(data[, k])	Return vectorized version of upper triangle from data.
get_centroids(img[, labels, image_space])	Find centroids of labels in img.

netneurotools.colors - Useful colormaps

Useful colormaps.

available_cmaps()

Return list of available colormaps in module.