""" ========================================================== Signal Reconstruction Using Spherical Harmonics ========================================================== This example shows how you can use a spherical harmonics (SH) function to reconstruct any spherical function using DIPY_. In order to generate a signal, we will need to generate an evenly distributed sphere. Let's import some standard packages. """ import numpy as np from dipy.core.sphere import disperse_charges, Sphere, HemiSphere """ We can first create some random points on a ``HemiSphere`` using spherical polar coordinates. """ n_pts = 64 theta = np.pi * np.random.rand(n_pts) phi = 2 * np.pi * np.random.rand(n_pts) hsph_initial = HemiSphere(theta=theta, phi=phi) """ Next, we call ``disperse_charges`` which will iteratively move the points so that the electrostatic potential energy is minimized. In ``hsph_updated`` we have the updated ``HemiSphere`` with the points nicely distributed on the hemisphere. """ hsph_updated, potential = disperse_charges(hsph_initial, 5000) sphere = Sphere(xyz=np.vstack((hsph_updated.vertices, -hsph_updated.vertices))) """ We now need to create our initial signal. To do so, we will use our sphere's vertices as the sampled points of our spherical function (SF). We will use ``multi_tensor_odf`` to simulate an ODF. For more information on how to use DIPY_ to simulate a signal and ODF, see :ref:`example_simulate_multi_tensor`. """ from dipy.sims.voxel import multi_tensor_odf mevals = np.array([[0.0015, 0.00015, 0.00015], [0.0015, 0.00015, 0.00015]]) angles = [(0, 0), (60, 0)] odf = multi_tensor_odf(sphere.vertices, mevals, angles, [50, 50]) from dipy.viz import window, actor # Enables/disables interactive visualization interactive = False scene = window.Scene() scene.SetBackground(1, 1, 1) odf_actor = actor.odf_slicer(odf[None, None, None, :], sphere=sphere) odf_actor.RotateX(90) scene.add(odf_actor) print('Saving illustration as symm_signal.png') window.record(scene, out_path='symm_signal.png', size=(300, 300)) if interactive: window.show(scene) """ .. figure:: symm_signal.png :align: center Illustration of the simulated signal sampled on a sphere of 64 points per hemisphere We can now express this signal as a series of SH coefficients using ``sf_to_sh``. This function converts a series of SF coefficients in a series of SH coefficients. For more information on SH basis, see :ref:`sh-basis`. For this example, we will use the ``descoteaux07`` basis up to a maximum SH order of 8. """ from dipy.reconst.shm import sf_to_sh # Change this value to try out other bases sh_basis = 'descoteaux07' # Change this value to try other maximum orders sh_order = 8 sh_coeffs = sf_to_sh(odf, sphere, sh_order, sh_basis) """ ``sh_coeffs`` is an array containing the SH coefficients multiplying the SH functions of the chosen basis. We can use it as input of ``sh_to_sf`` to reconstruct our original signal. We will now reproject our signal on a high resolution sphere using ``sh_to_sf``. """ from dipy.data import get_sphere from dipy.reconst.shm import sh_to_sf high_res_sph = get_sphere('symmetric724').subdivide(2) reconst = sh_to_sf(sh_coeffs, high_res_sph, sh_order, sh_basis) scene.clear() odf_actor = actor.odf_slicer(reconst[None, None, None, :], sphere=high_res_sph) odf_actor.RotateX(90) scene.add(odf_actor) print('Saving output as symm_reconst.png') window.record(scene, out_path='symm_reconst.png', size=(300, 300)) if interactive: window.show(scene) """ .. figure:: symm_reconst.png :align: center Reconstruction of a symmetric signal on a high resolution sphere using a symmetric basis While a symmetric SH basis works well for reconstructing symmetric SF, it fails to do so on asymmetric signals. We will now create such a signal by using a different ODF for each hemisphere of our sphere. """ mevals = np.array([[0.0015, 0.0003, 0.0003]]) angles = [(0, 0)] odf2 = multi_tensor_odf(sphere.vertices, mevals, angles, [100]) n_pts_hemisphere = int(sphere.vertices.shape[0] / 2) asym_odf = np.append(odf[:n_pts_hemisphere], odf2[n_pts_hemisphere:]) scene.clear() odf_actor = actor.odf_slicer(asym_odf[None, None, None, :], sphere=sphere) odf_actor.RotateX(90) scene.add(odf_actor) print('Saving output as asym_signal.png') window.record(scene, out_path='asym_signal.png', size=(300, 300)) if interactive: window.show(scene) """ .. figure:: asym_signal.png :align: center Illustration of an asymmetric signal sampled on a sphere of 64 points per hemisphere Let's try to reconstruct this SF using a symmetric SH basis. """ sh_coeffs = sf_to_sh(asym_odf, sphere, sh_order, sh_basis) reconst = sh_to_sf(sh_coeffs, high_res_sph, sh_order, sh_basis) scene.clear() odf_actor = actor.odf_slicer(reconst[None, None, None, :], sphere=high_res_sph) odf_actor.RotateX(90) scene.add(odf_actor) print('Saving output as asym_reconst.png') window.record(scene, out_path='asym_reconst.png', size=(300, 300)) if interactive: window.show(scene) """ .. figure:: asym_reconst.png :align: center Reconstruction of an asymmetric signal using a symmetric SH basis As we can see, a symmetric basis fails to properly represent asymmetric SF. Fortunately, DIPY_ also implements full SH bases, which can deal with symmetric as well as asymmetric signals. For this tutorial, we will demonstrate it using the ``descoteaux07`` full SH basis by setting ``full_basis=true``. """ sh_coeffs = sf_to_sh(asym_odf, sphere, sh_order, sh_basis, full_basis=True) reconst = sh_to_sf(sh_coeffs, high_res_sph, sh_order, sh_basis, full_basis=True) scene.clear() odf_actor = actor.odf_slicer(reconst[None, None, None, :], sphere=high_res_sph) odf_actor.RotateX(90) scene.add(odf_actor) print('Saving output as asym_reconst_full.png') window.record(scene, out_path='asym_reconst_full.png', size=(300, 300)) if interactive: window.show(scene) """ .. figure:: asym_reconst_full.png :align: center Reconstruction of an asymmetric signal using a full SH basis As we can see, a full SH basis properly reconstruct asymmetric signal. .. include:: ../links_names.inc """