.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "gallery/viser/07_gaussian_splats.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_gallery_viser_07_gaussian_splats.py: True Gaussian splats on the F3 demo =================================== Use Viser's experimental ``add_gaussian_splats`` primitive. Unlike ``06_splats.py``, each sample has a 3D covariance and opacity. .. image:: ../../_static/cigvis/viser/07.png :alt: image :align: center .. GENERATED FROM PYTHON SOURCE LINES 16-199 .. code-block:: Python # sphinx_gallery_thumbnail_path = '_static/cigvis/viser/07.png' from pathlib import Path import numpy as np from scipy.ndimage import binary_erosion from cigvis import viserplot from cigvis.io import load_skins def _extend(nodes, new_nodes): if new_nodes: nodes.extend(new_nodes) def salt_body_gaussians(salt, step=(5, 7, 4), max_points=22000): sample = np.asarray(salt[::step[0], ::step[1], ::step[2]]) > 0.0 if not np.any(sample): return [] eroded = binary_erosion( sample, structure=np.ones((3, 3, 3), dtype=bool), border_value=0, ) boundary = sample & ~eroded pos = np.argwhere(boundary).astype(np.float32) pos *= np.asarray(step, dtype=np.float32) return viserplot.create_gaussian_splats( pos, mode='volume', color=(0.0, 0.92, 0.95), radius=2.2, opacity=0.34, max_points=max_points, seed=21, ) def surface_gaussians(surface, zmax, step=10, cmap='viridis', opacity=0.42, max_points=42000): ii = np.arange(0, surface.shape[0], step, dtype=np.float32) jj = np.arange(0, surface.shape[1], step, dtype=np.float32) grid_i, grid_j = np.meshgrid(ii, jj, indexing='ij') depth = np.asarray(surface[np.ix_(ii.astype(int), jj.astype(int))], dtype=np.float32) valid = np.isfinite(depth) & (depth > 0.0) & (depth < zmax * 1.2) if not np.any(valid): return [] pos = np.column_stack([grid_i[valid], grid_j[valid], depth[valid]]) return viserplot.create_gaussian_splats( pos.astype(np.float32), values=depth[valid], mode='surface', cmap=cmap, radius=(5.0, 5.0, 0.75), opacity=opacity, max_points=max_points, seed=23, ) def fault_skin_gaussians(skin_dir, max_points=26000): vertices, _faces, likelihood = load_skins(str(skin_dir), endian='>', values_type='likelihood') return viserplot.create_gaussian_splats( vertices.astype(np.float32, copy=False), values=likelihood.astype(np.float32, copy=False), mode='surface', cmap='autumn', radius=1.7, opacity=0.55, max_points=max_points, seed=27, ) def well_log_gaussians(log_path, zmax, sample_step=14): nlog = 4 npoints = 2121 x = np.asarray([259, 619, 339, 141], dtype=np.float32) y = np.asarray([33, 545, 704, 84], dtype=np.float32) z = np.arange(0, 0.2 * npoints, 0.2, dtype=np.float32) raw = np.fromfile(log_path, np.float32).reshape(nlog, npoints) with np.errstate(divide='ignore', invalid='ignore'): values = 0.5 * np.log(raw) all_pos = [] all_values = [] for i in range(nlog): valid = np.isfinite(values[i]) & (raw[i] > 0.0) & (z < zmax * 1.2) valid_idx = np.flatnonzero(valid)[::sample_step] if valid_idx.size == 0: continue all_pos.append( np.column_stack([ np.full(valid_idx.size, x[i], dtype=np.float32), np.full(valid_idx.size, y[i], dtype=np.float32), z[valid_idx], ])) all_values.append(values[i, valid_idx]) if not all_pos: return [] pos = np.concatenate(all_pos).astype(np.float32) values = np.concatenate(all_values).astype(np.float32) return viserplot.create_gaussian_splats( pos, values=values, mode='point', cmap='viridis', radius=1.5, opacity=0.72, ) def pick_gaussians(): pos = np.asarray([ [192, 634.1855, 32.3816], [192, 616.5631, 139.5132], [192, 600.3925, 220.0604], ], dtype=np.float32) return viserplot.create_gaussian_splats( pos, mode='point', color=(0.0, 0.95, 1.0), radius=3.0, opacity=0.9, ) root = Path('/Volumes/T7/DATA/cigvisdata/F3/') seisp = root / 'seis.dat' saltp = root / 'salt.dat' hz2p = root / 'hz.dat' unc2p = root / 'unc2.dat' logp = root / 'logs.dat' skin_dir = root / 'skins' ni, nx, nt = 591, 951, 362 shape = (ni, nx, nt) seis = np.memmap(seisp, np.float32, 'c', shape=shape) nodes = viserplot.create_slices( seis, pos=[ni - 2, 25, nt - 2], cmap='gray', clim=[-2.0, 1.5], ) salt = np.memmap(saltp, np.float32, 'c', shape=shape) _extend(nodes, salt_body_gaussians(salt)) hz2 = np.fromfile(hz2p, np.float32).reshape(ni, nx) _extend(nodes, surface_gaussians(hz2, nt, step=10, cmap='viridis')) unc2 = np.fromfile(unc2p, np.float32).reshape(ni, nx) _extend(nodes, surface_gaussians(unc2, nt, step=12, cmap='cool', opacity=0.38)) _extend(nodes, fault_skin_gaussians(skin_dir)) _extend(nodes, well_log_gaussians(logp, nt)) _extend(nodes, pick_gaussians()) viserplot.plot3D(nodes, axis_scales=(1.0, 1.0, 1.7), fov=30.0, look_at=[0.328201, 1.022802, 0.363886], wxyz=[0.695484, 0.377461, 0.291647, 0.537371], position=[-1.709967, 1.554335, -1.005026], ) .. _sphx_glr_download_gallery_viser_07_gaussian_splats.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: 07_gaussian_splats.ipynb <07_gaussian_splats.ipynb>` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: 07_gaussian_splats.py <07_gaussian_splats.py>` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: 07_gaussian_splats.zip <07_gaussian_splats.zip>` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_