# ndslib function reference

# 24. `ndslib`

function reference¶

The `ndslib`

software library implements functions that we use in the book to
download datasets, and to encapsulate some computations. We provide descriptions
of the functions for each module below. Additional documentation can be found in
the ndslib library website.

ndslib.data.load_data(dataset, fname=None)

Loads data for use in examples.

- Parameters:

datasetstrThe name of a dataset. Can be one of:

“bold_numpy” : Read a BOLD time-series as a numpy array.

“bold_volume” : Read a single volume of a BOLD time-series as a numpy array

“afq” : Read diffusion MRI data in tabular format

“age_groups_fa” : Read AFQ data and return dataframe divided by age-groups

“abide2_saggitals”: Read ABIDE2 mid-saggitals as numpy arrays.

fnamestr, optional.If provided, data will be cached to this local path and retrieved from there on future calls with the same value.

ndslib.data.download_bids_dataset()

Makes a minimal BIDS dataset with one fMRI subject from OpenNeuro dataset “ds001233”.

ndslib.viz.imshow_with_annot(im, vmax=40)

Like imshow, but with added annotation of the array values.

- Parameters:

im: numpy array

ndslib.viz.plot_diffeomorphic_map(mapping, ax, delta=15, direct_grid_shape=None, direct_grid2world=- 1, inverse_grid_shape=None, inverse_grid2world=- 1)

Draw the effect of warping a regular lattice by a diffeomorphic map. Draws a diffeomorphic map by showing the effect of the deformation on a regular grid. The resulting figure contains two images: the direct transformation is plotted to the left, and the inverse transformation is plotted to the right.

- Parameters:

mappingDiffeomorphicMapthe diffeomorphic map to be drawn

deltaint, optionalthe size (in pixels) of the squares of the regular lattice to be used to plot the warping effects. Each square will be delta x delta pixels. By default, the size will be 10 pixels.

fnamestring, optionalthe name of the file the figure will be written to. If None (default), the figure will not be saved to disk.

direct_grid_shapetuple, shape (2,), optionalthe shape of the grid image after being deformed by the direct transformation. By default, the shape of the deformed grid is the same as the grid of the displacement field, which is by default equal to the shape of the fixed image. In other words, the resulting deformed grid (deformed by the direct transformation) will normally have the same shape as the fixed image.

direct_grid2worldarray, shape (3, 3), optionalthe affine transformation mapping the direct grid’s coordinates to physical space. By default, this transformation will correspond to the image-to-world transformation corresponding to the default direct_grid_shape (in general, if users specify a direct_grid_shape, they should also specify direct_grid2world).

inverse_grid_shapetuple, shape (2,), optionalthe shape of the grid image after being deformed by the inverse transformation. By default, the shape of the deformed grid under the inverse transform is the same as the image used as “moving” when the diffeomorphic map was generated by a registration algorithm (so it corresponds to the effect of warping the static image towards the moving).

inverse_grid2worldarray, shape (3, 3), optionalthe affine transformation mapping inverse grid’s coordinates to physical space. By default, this transformation will correspond to the image-to-world transformation corresponding to the default inverse_grid_shape (in general, if users specify an inverse_grid_shape, they should also specify inverse_grid2world).

- Returns:

warped_forwardarrayImage with the grid showing the effect of transforming the moving image to the static image. The shape will be

direct_grid_shapeif specified, otherwise the shape of the static image.warped_backwardarrayImage with the grid showing the effect of transforming the static image to the moving image. Shape will be

inverse_grid_shapeif specified, otherwise the shape of the moving image.Notes:

The default value for the affine transformation is “-1” to handle the case in which the user provides “None” as input meaning “identity”. If we used None as default, we wouldn’t know if the user specifically wants to use the identity (specifically passing None) or if it was left unspecified, meaning to use the appropriate default matrix.

ndslib.viz.plot_coef_path(estimator, X, y, alpha, **kwargs)

Plot the coefficient path for a sklearn estimator.

- Parameters:

estimatorsklearn estimatorFor example “Lasso()”

Xndarray (n, m)Feature matrix

yndarray (n,)Target matrix

- Returns:

ax: Matplotlib Axes object

ndslib.viz.plot_train_test(x_range, train_scores, test_scores, label, hlines=None)

Plot train/test \(R^2\)

- Parameters:

x_rangesequenceThe range of x values used (e.g., number of features, number of samples)

train_scoressequenceThe train r2_score corresponding to different x values

test_scoressequenceThe test r2_score corresponding to different x values

labelstrUsed in the legend labels.

hlinesdictA dictionary where keys are labels and values are y values for hlines.

- Returns:

ax: Matplotlib Axes object.

ndslib.viz.plot_learning_curves(estimators, X_sets, y, train_sizes, labels=None, errors=True, **kwargs)

Generate multi-panel plot displaying learning curves for multiple predictor sets and/or estimators.

- Parameters:

estimatorslist,A scikit-learn Estimator or list of estimators. If a list is provided, it must have the same number of elements as X_sets.

X_setslist,An NDArray or similar object, or list. If a list is passed, it must have the same number of elements as estimators.

yndarrayA 1-D numpy array (or pandas Series) representing the outcome variable to predict.

train_sizeslistList of ints providing the sample sizes at which to evaluate the estimator.

labelslist, optional.List of labels for the panels. Must have the same number of elements as X_sets.

errorsbool, optional.If True, plots error bars representing 1 StDev. Default: True.

kwargsdict, optionalOptional keyword arguments passed on to sklearn’s

learning_curveutility.

ndslib.viz.plot_graphviz_tree(tree, feature_names)

Takes a tree as input, calls Scikit-learn’s export_graphviz function to generate an image of the tree using “graphviz”, and then plots the result in-line.

- Parameters:

tree: sklearn tree objectfeature_names: sequence of strings

ndslib.image.gaussian_kernel(x=20, sigma=4)

Construct a 2D Gaussian kernel for image processing

- Parameters:

xint, optionalThe number of pixels on a side for the filter. Default : 20

sigmafloat, optionalThe standard deviation parameter for the Gaussian. Default : 4

- Returns:

gaussndarrayContains the values of the 2D Gaussian normalized to sum to 1.