Python Reference API

netneurotools.datasets - Automatic dataset fetching

Functions for handling datasets.

To download templates

fetch_fsaverage([version, use_local, force, ...])	Download files for fsaverage FreeSurfer template.
fetch_fsaverage_curated([version, force, ...])	Download files for fsaverage FreeSurfer template.
fetch_hcp_standards([force, data_dir, verbose])	Fetch HCP standard mesh atlases for converting between FreeSurfer and HCP.
fetch_fslr_curated([version, force, ...])	Download files for HCP fsLR template.
fetch_civet([density, version, force, ...])	Fetch CIVET surface files.
fetch_civet_curated([version, force, ...])	Download files for CIVET template.
fetch_conte69([force, data_dir, verbose])	Download files for Van Essen et al., 2012 Conte69 template.
fetch_yerkes19([force, data_dir, verbose])	Download files for Donahue et al., 2016 Yerkes19 template.

To download atlases

fetch_cammoun2012([version, force, ...])	Download files for Cammoun et al., 2012 multiscale parcellation.
fetch_schaefer2018([version, force, ...])	Download FreeSurfer .annot files for Schaefer et al., 2018 parcellation.
fetch_mmpall([version, force, data_dir, verbose])	Download .label.gii files for Glasser et al., 2016 MMPAll atlas.
fetch_pauli2018([force, data_dir, verbose])	Download files for Pauli et al., 2018 subcortical parcellation.
fetch_ye2020()	Fetch Ye et al., 2020 subcortical parcellation.
fetch_voneconomo([force, data_dir, verbose])	Fetch von-Economo Koskinas probabilistic FreeSurfer atlas.

To download project-related data

fetch_vazquez_rodriguez2019([force, ...])	Download files from Vazquez-Rodriguez et al., 2019, PNAS.
fetch_mirchi2018([force, data_dir, verbose])	Download (and creates) dataset for replicating Mirchi et al., 2018, SCAN.
fetch_hansen_manynetworks([force, data_dir, ...])	Download files from Hansen et al., 2023, PLOS Biology.
fetch_hansen_receptors([force, data_dir, ...])	Download files from Hansen et al., 2022, Nature Neuroscience.
fetch_hansen_genescognition([force, ...])	Download files from Hansen et al., 2021, Nature Human Behaviour.
fetch_hansen_brainstemfc([force, data_dir, ...])	Download files from Hansen et al., 2024.
fetch_shafiei_megfmrimapping([force, ...])	Download files from Shafiei et al., 2022.
fetch_shafiei_megdynamics([force, data_dir, ...])	Download files from Shafiei et al., 2023.
fetch_suarez_mami([force, data_dir, verbose])	Download files from Suarez et al., 2022, eLife.
fetch_famous_gmat(dataset[, force, ...])	Download files from multi-species connectomes.
fetch_neurosynth()	Download Neurosynth data.

netneurotools.networks - Constructing networks

Functions for constucting networks.

To construct consensus networks

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.

To randomize networks

randmio_und(W, itr)	Optimized version of randmio_und.
match_length_degree_distribution(W, D[, ...])	Generate degree- and edge length-preserving surrogate connectomes.
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.

Convenient functions

binarize_network(network[, retain, keep_diag])	Keep top retain % of connections in network and binarizes.
threshold_network(network[, retain])	Keep top retain % of connections in network and binarizes.

netneurotools.plotting - Plotting brain data

Functions for making pretty plots and whatnot.

Pyvista

pv_plot_surface(vertex_data, template[, ...])

Plot surface data using PyVista.

PySurfer (deprecated)

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.

matplotlib

plot_point_brain(data, coords[, views, ...])	Plot data as a cloud of points in 3D space based on specified coords.
plot_mod_heatmap(data, communities, *[, ...])	Plot data as heatmap with borders drawn around communities.

Fun color & colormap stuff

available_cmaps()

Return list of available colormaps in module.

netneurotools.metrics - Calculating graph metrics

Magics on networks.

Brain network metrics

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.
navigation_wu(nav_dist_mat, sc_mat)	Compute network navigation.
get_navigation_path_length(nav_paths, ...)	Get navigation path length from navigation results.
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.
path_transitivity(D)	Calculate path transitivity.
search_information(W, D[, has_memory])	Calculate search information.
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.
flow_graph(W[, r, t])	Calculate flow graph.
assortativity(W[, r])	Calculate assortativity.
matching_ind_und(W)	Calculate undirected matching index.
rich_feeder_peripheral(x, sc[, stat])	Calculate connectivity values in rich, feeder, and peripheral edges.

Network spreading

simulate_atrophy()

Simulate atrophy in a network.

Statistical network metrics

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.modularity - Calculating network modularity

Functions for working with network modularity.

match_cluster_labels(source, target)	Align cluster labels in source to those in target.
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".
find_consensus(assignments[, null_func, ...])	Find consensus clustering labels from cluster solutions in assignments.
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.stats - General statistics functions

Functions for performing statistical operations.

Correlations

efficient_pearsonr(a, b[, ddof, nan_policy])	Compute correlation of matching columns in a and b.
weighted_pearsonr()	Calculate weighted Pearson correlation coefficient.
make_correlated_xy([corr, size, seed, tol])	Generate random vectors that are correlated to approximately corr.

Permutation tests

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().

Regressions

residualize(X, Y[, Xc, Yc, normalize, ...])	Return residuals of regression equation from Y ~ X.
get_dominance_stats(X, y[, ...])	Return the dominance analysis statistics for multilinear regression.

netneurotools.spatial - Spatial statistics

Functions for handling spatial brain data.

Calculating spatial statistics

morans_i()	Calculate Moran's I for spatial autocorrelation.
local_morans_i()	Calculate local Moran's I for spatial autocorrelation.

netneurotools.interface - Interface for external tools

Functions for interfacing with common tools.

netneurotools.experimental - Functions in alpha stage

Functions in alpha stage.