Reference API

`nispace.api` – NiSpace main class 

NiSpace(x[, y, z, x_labels, y_labels, ...])

The NiSpace class.

`fetch_template`([template, res, desc, hemi, ...])	Fetch a brain template.
`fetch_parcellation`([parcellation, space, ...])	Fetch a parcellation.
`fetch_reference`(dataset[, maps, space, ...])
`fetch_collection`(collection[, dataset, ...])	Fetch a collection that defines a subset (and optional grouping) of maps.
`apply_collection`(data, collection)
`fetch_metadata`(dataset[, maps, collection, ...])
`fetch_example`(example[, parcellation, ...])	Fetch an example dataset.

`rank_array`(array)
`rank1d`(arr)	Rank a 1D array using mid-ranks (average rank) for tied values.
`rank2d`(arr)	Rank a 2D array column-wise using mid-ranks.
`corr`(x, y[, rank])	Compute Pearson or Spearman correlation for two 1D arrays.
`pearson`(x, y)	Compute Pearson correlation for two 1D arrays.
`partialcorr`(x, y, z[, rank])	Computes partial correlation between {x} and {y} controlled for {z}
`partialpearson`(x, y, z)	Computes partial Pearson correlation between {x} and {y} controlled for {z}
`mutualinfo`(x, y[, n_neighbors])	Compute mutual information between x and y using sklearn.
`mlr`(x, y[, adj_r2, intercept])	Compute Regression of predictor(s) x on target y.
`r2`(x, y[, adj_r2])	Compute R2 for Regression of predictor(s) x on target y.
`beta`(x, y[, intercept])	Compute beta coefficients for Regression of predictor(s) x on target y.
`dominance`(x, y[, adj_r2, verbose])
`pls`(x, y[, n_components])
`pcr`(x, y[, adj_r2, n_components])
`fast_pls1`(x, y, n_components)	Fast PLS (SIMPLS) for a single target.
`elasticnet`(x, y[, cv, seed])
`lasso`(x, y[, cv, seed, kwargs])
`ridge`(x, y[, cv, seed, kwargs])

`np_any_axis1`(x)	Numba compatible version of np.any(x, axis=1).
`residuals`(x, y[, decenter])	Compute residuals for Regression with dependent variable y and independent variable(s) x.
`residuals_nan`(x, y[, decenter])	Compute residuals for Regression with dependent variable y and independent variable(s) x.
`partial_residuals_nan`(x_nuisance, x_protect, y)	Partial residuals: regress x_nuisance from y while controlling for x_protect.
`rho_to_z`(array[, replace_1])	Fisher's z-transformation of correlation coefficients.
`z_to_rho`(array)	Inverse Fisher's z-transformation of correlation coefficients.
`zscore_df`(df[, along, force_df])	Z-standardizes array and returns pandas dataframe.
`permute_groups`(groups[, strategy, paired, ...])
`null_to_p`(test_value, null_array[, tail, ...])	Return p-value for test value(s) against null array.
`mc_correction`(p_array[, alpha, method, how, ...])
`compute_meff`(X[, method])	Effective number of independent tests from data matrix via eigendecomposition.
`meff_sidak_correction`(p_array, meff[, ...])	Sidak correction using Meff effective number of tests.
`maxT_correction`(obs_stats, null_colocs, stat)	Max-T FWER correction (Westfall & Young 1993).
`step_maxT_correction`(obs_stats, null_colocs, ...)	Step-down Max-T FWER correction (Westfall & Young 1993).

`parcellate_data`(data[, data_labels, ...])	Parcellates given imaging data using a specified parcellation.
`read_json`(json_path)
`write_json`(json_dict, json_path)
`load_img`(img[, override_file_format])
`load_labels`(labels[, concat, header, index])
`load_distmat`(distmat)
`load_spinmat`(spinmat)
`load_l2rmap`(l2rmap[, header, index, threshold])
`to_pickle`(obj, filepath[, use_dill])	Pickle, compress, and save to a file.
`from_pickle`(filepath[, use_dill])	Unpickle a python object.

Parcellater(parcellation, space[, ...])

Class for parcellating arbitrary volumetric / surface data.

`generate_null_maps`(method, data, parcellation)
`nulls_moran`(data_1d, dist_mat[, n_nulls, seed])
`nulls_burt2020`(data_1d, dist_mat[, n_nulls, ...])
`nulls_burt2018`(data_1d, dist_mat[, n_nulls, ...])
`nulls_random`(data_1d[, dist_mat, n_nulls, seed])
`generate_spins`(parc, parc_space[, n_perm, ...])	Generate spin resampling indices for a surface parcellation.
`apply_spins`(data_1d, spins_lh, spins_rh, ...)	Apply precomputed spin indices to a 1D data array.
`get_distance_matrix`(parc, parc_space[, ...])
`find_vol_parc_centroids`(parc[, affine, ...])
`find_surf_parc_centroids`(parc[, parc_space, ...])
`correlate_hemis_parc`(data[, parc_idc_lh, ...])
`find_parcel_hemispheres`(parcellation)

`brainplot`(data[, parcellation, kind, space, ...])	Plot brain maps onto surfaces or anatomical volumes.
`view_surf`([data, parcellation, hemi, ...])
`catplot`(fig, ax, data_long[, ...])
`nullplot`(fig, ax, data_long[, ...])
`heatmap`(ax[, data_colors, data_sizes, ...])
`nice_stats_labels`(string[, add_dollars])
`print_significance`(ax, p_values[, q_values, ...])	Annotate a categorical axis plot with significance markers or p-value text.
`move_legend_fig_to_ax`(fig, ax, loc[, ...])
`colors_from_values`(values, palette_name)
`hide_empty_axes`(axes)
`linewidth_from_data_units`(linewidth, axis[, ...])	Convert a linewidth in data units to linewidth in points.

`set_log`(lgr[, verbose])
`nan_detector`(*arrays)
`remove_nan`(data[, which])
`fill_nan`(data, idx[, idx_label, which])
`mean_by_set_df`(df[, mean_by_set, weighted, ...])
`print_arg_pairs`(**kwargs)
`get_column_names`(df_or_series[, force_list])	Get column names from a DataFrame, the name from a Series, or None if input is a numpy array.
`lower`(str_list)
`get_background_value`(img[, border_size])
`vect_to_vol_arr`(vect, parc_arr, parc_idc[, ...])
`vol_to_vect_arr`(vol_arr, parc_arr, parc_idc, ...)	Aggregate vol_arr into parcel means, excluding NaN and any values in bg_values.
`parc_vect_to_vol`(vect, parc)
`relabel_gifti_parc`(parc[, new_labels])
`relabel_nifti_parc`(parc[, new_order, ...])
`merge_parcellations`(parcellations[, labels, ...])
`mirror_nifti`(img[, affine, direction, ...])
`mirror_gifti`(img[, direction, match_r, mask])