{
    "componentChunkName": "component---src-templates-notebook-tsx",
    "path": "/2025.1.3/examples/integration_tests/gradient_tests/gradient_test_tti",
    "result": {"data":{"site":{"siteMetadata":{"salvusDocVersions":{"current":"2026.5.0"}}},"jupyterNotebook":{"slug":"/2025.1.3/examples/integration_tests/gradient_tests/gradient_test_tti","notebook_widgets_root_path":"/jupyter_notebook_widgets","notebook_json":"{\"cells\":[{\"cell_type\":\"markdown\",\"id\":\"01e1c0b0\",\"metadata\":{},\"source\":[\"# Gradient Test TTI Media\"]},{\"cell_type\":\"code\",\"execution_count\":1,\"id\":\"73713695\",\"metadata\":{},\"outputs\":[{\"name\":\"stdout\",\"output_type\":\"stream\",\"text\":[]}],\"source\":[\"import os\\n\",\"import typing\\n\",\"from pathlib import Path\\n\",\"\\n\",\"import h5py\\n\",\"import matplotlib.pyplot as plt\\n\",\"import numpy as np\\n\",\"import pyasdf\\n\",\"import random\\n\",\"\\n\",\"from salvus.flow import api\\n\",\"import salvus.flow.simple_config as sc\\n\",\"import salvus.mesh\\n\",\"from salvus.mesh import mesh_block\\n\",\"from salvus.mesh.mesh_block import MeshBlockCollection\\n\",\"from gradient_helper_functions import (\\n\",\"    get_unit_cube_mesh,\\n\",\"    test_material_gradient,\\n\",\")\\n\",\"from salvus.mesh import simple_mesh\\n\",\"from salvus.mesh.unstructured_mesh import UnstructuredMesh\\n\",\"\\n\",\"import parameterization\\n\"]},{\"cell_type\":\"markdown\",\"id\":\"65521a96\",\"metadata\":{\"lines_to_next_cell\":2},\"source\":[\"### Build mesh\\n\",\"Choose either a cartesian or spherical reference frame.\"]},{\"cell_type\":\"code\",\"execution_count\":2,\"id\":\"2a19e74f\",\"metadata\":{},\"outputs\":[{\"name\":\"stdout\",\"output_type\":\"stream\",\"text\":[\"Running gradient test on site: local_f64\\n\"]}],\"source\":[\"model_order = random.choice([1, 2])\\n\",\"reference_frame = os.environ.get(\\\"REFERENCE_FRAME\\\", \\\"spherical\\\")\\n\",\"site_name = os.environ.get(\\\"SITE_NAME\\\", \\\"local_f64\\\")\\n\",\"print(f\\\"Running gradient test on site: {site_name}\\\")\\n\",\"\\n\",\"p1 = parameterization.ElasticTti3D(\\n\",\"    rho=2.6e3, vsh=4.0e3 * 1.2, vsv=4.0e3, vph=5.8e3 * 1.2, vpv=5.8e3, eta=1.0\\n\",\")\\n\",\"\\n\",\"# Set a random orientation vector.\\n\",\"if reference_frame == \\\"cartesian\\\":\\n\",\"    setattr(p1, \\\"_axis\\\", np.array([1.5, 0.75, 3.0]))\"]},{\"cell_type\":\"code\",\"execution_count\":3,\"id\":\"97d8d846\",\"metadata\":{},\"outputs\":[{\"name\":\"stdout\",\"output_type\":\"stream\",\"text\":[\"\\n\",\"SPHERICAL MESH\\n\",\"\\n\"]}],\"source\":[\"n_elem_per_dim = 2\\n\",\"if reference_frame == \\\"spherical\\\":\\n\",\"    print(\\\"\\\\nSPHERICAL MESH\\\\n\\\")\\n\",\"    receiver_type = \\\"classic\\\"\\n\",\"    try:\\n\",\"        mesh = MeshBlockCollection(\\n\",\"            mesh_block._generators.spherical.central_sphere_3d(\\n\",\"                element_count_lat=3, r_outer=np.sqrt(3)\\n\",\"            )\\n\",\"        ).get_unstructured_mesh()\\n\",\"    except Exception as e:\\n\",\"        # Backwards compatibility with 0.12.X\\n\",\"        print(e)\\n\",\"        mesh = MeshBlockCollection(\\n\",\"            mesh_block._generators.spherical.central_sphere_3d(\\n\",\"                nelem_lat=3, r_outer=np.sqrt(3)\\n\",\"            )\\n\",\"        ).get_unstructured_mesh()\\n\",\"\\n\",\"    mesh.attach_global_variable(\\\"reference_frame\\\", \\\"spherical\\\")\\n\",\"    mesh.change_tensor_order(model_order, interpolation_mode=\\\"spherical\\\")\\n\",\"    mesh.attach_field(\\\"fluid\\\", np.zeros(mesh.nelem))\\n\",\"\\n\",\"    for name, val in p1.parameters():\\n\",\"        if name != \\\"AXIS\\\":\\n\",\"            mesh.attach_field(\\n\",\"                name, np.ones_like(mesh.get_element_nodes()[:, :, 0]) * val\\n\",\"            )\\n\",\"        else:\\n\",\"            for i, a in enumerate(val):\\n\",\"                mesh.attach_global_variable(f\\\"a_{i}\\\", a)\\n\",\"\\n\",\"    mesh.find_side_sets(mode=\\\"spherical_full\\\")\\n\",\"\\n\",\"else:\\n\",\"    print(\\\"\\\\nCUBE MESH\\\\n\\\")\\n\",\"    receiver_type = \\\"block\\\"\\n\",\"    mesh = get_unit_cube_mesh(\\n\",\"        dim=3,\\n\",\"        model_order=model_order,\\n\",\"        par=p1,\\n\",\"        deform=False,\\n\",\"        n_elem_per_dim=n_elem_per_dim,\\n\",\"    )\"]},{\"cell_type\":\"markdown\",\"id\":\"78c3b9f0\",\"metadata\":{},\"source\":[\"### Setup sources and receivers\"]},{\"cell_type\":\"code\",\"execution_count\":4,\"id\":\"97eebf58\",\"metadata\":{\"lines_to_next_cell\":2},\"outputs\":[{\"name\":\"stdout\",\"output_type\":\"stream\",\"text\":[\"\\n\",\"SEISMOLOGICAL SOURCE\\n\",\"\\n\",\"\\n\",\"SEISMOLOGICAL RECEIVER\\n\",\"\\n\"]}],\"source\":[\"pval_s = 0.15\\n\",\"pval_r = 0.55\\n\",\"\\n\",\"r_loc = [(pval_r, pval_r, pval_r)]\\n\",\"s_loc = [(1 - pval_s, 1 - pval_s, 1 - pval_s)]\\n\",\"\\n\",\"if reference_frame == \\\"spherical\\\":\\n\",\"    print(\\\"\\\\nSEISMOLOGICAL SOURCE\\\\n\\\")\\n\",\"    sources = [\\n\",\"        sc.source.seismology.VectorPoint3D(\\n\",\"            fr=1e21,\\n\",\"            ft=1e21,\\n\",\"            fp=1e21,\\n\",\"            latitude=-47.5,\\n\",\"            radius_of_sphere_in_m=np.sqrt(3),\\n\",\"            depth_in_m=1,\\n\",\"            longitude=-112.7,\\n\",\"            source_time_function=sc.stf.Delta(),\\n\",\"        )\\n\",\"        for x in s_loc\\n\",\"    ]\\n\",\"\\n\",\"else:\\n\",\"    print(\\\"\\\\nCARTESIAN SOURCE\\\\n\\\")\\n\",\"    sources = [\\n\",\"        sc.source.cartesian.VectorPoint3D(\\n\",\"            fx=1e21,\\n\",\"            fy=1e21,\\n\",\"            fz=1e21,\\n\",\"            x=x[0],\\n\",\"            y=x[1],\\n\",\"            z=x[2],\\n\",\"            source_time_function=sc.stf.Delta(),\\n\",\"        )\\n\",\"        for x in s_loc\\n\",\"    ]\\n\",\"\\n\",\"if reference_frame == \\\"spherical\\\":\\n\",\"    print(\\\"\\\\nSEISMOLOGICAL RECEIVER\\\\n\\\")\\n\",\"    recs = [\\n\",\"        sc.receiver.seismology.Point3D(\\n\",\"            latitude=33.35,\\n\",\"            longitude=45,\\n\",\"            depth_in_m=np.sqrt(3) - 0.9526,\\n\",\"            radius_of_sphere_in_m=np.sqrt(3),\\n\",\"            station_code=f\\\"{i:03d}\\\",\\n\",\"            fields=[\\\"displacement\\\"],\\n\",\"        )\\n\",\"        for i, x in enumerate(r_loc)\\n\",\"    ]\\n\",\"\\n\",\"else:\\n\",\"    print(\\\"\\\\nCARTESIAN RECEIVER\\\\n\\\")\\n\",\"    recs = [\\n\",\"        sc.receiver.cartesian.Point3D(\\n\",\"            x=x[0],\\n\",\"            y=x[1],\\n\",\"            z=x[2],\\n\",\"            station_code=f\\\"{i:03d}\\\",\\n\",\"            fields=[\\\"displacement\\\"],\\n\",\"        )\\n\",\"        for i, x in enumerate(r_loc)\\n\",\"    ]\"]},{\"cell_type\":\"code\",\"execution_count\":5,\"id\":\"4ec04332\",\"metadata\":{},\"outputs\":[],\"source\":[\"w = sc.simulation.Waveform(mesh=mesh, sources=sources, receivers=recs)\\n\",\"\\n\",\"# Timing.\\n\",\"start_time, end_time, time_step = 0.0, 5e-4, 5e-6 / n_elem_per_dim\\n\",\"w.physics.wave_equation.end_time_in_seconds = end_time\\n\",\"w.physics.wave_equation.time_step_in_seconds = time_step\\n\",\"w.physics.wave_equation.start_time_in_seconds = start_time\\n\",\"\\n\",\"# For gradient computation.\\n\",\"w.output.volume_data.format = \\\"hdf5\\\"\\n\",\"w.output.volume_data.filename = \\\"output.h5\\\"\\n\",\"w.output.volume_data.fields = [\\\"adjoint-checkpoint\\\"]\\n\",\"w.output.volume_data.sampling_interval_in_time_steps = 10000\\n\",\"w.validate()\\n\",\"\\n\",\"mesh.write_h5(\\\"test.h5\\\")\\n\",\"\\n\",\"if reference_frame == \\\"spherical\\\":\\n\",\"    assert mesh.global_strings[\\\"reference_frame\\\"] == \\\"spherical\\\"\"]},{\"cell_type\":\"code\",\"execution_count\":6,\"id\":\"1fed9d79\",\"metadata\":{},\"outputs\":[{\"data\":{\"text/plain\":[\"<salvus.flow.executors.salvus_job.SalvusJob at 0x729c1f242210>\"]},\"execution_count\":6,\"metadata\":{},\"output_type\":\"execute_result\"}],\"source\":[\"api.run(\\n\",\"    ranks=2,\\n\",\"    input_file=w,\\n\",\"    get_all=True,\\n\",\"    site_name=site_name,\\n\",\"    overwrite=True,\\n\",\"    delete_remote_files=False,\\n\",\"    output_folder=\\\"fwd_output\\\",\\n\",\"    verbosity=0,\\n\",\")\"]},{\"cell_type\":\"code\",\"execution_count\":7,\"id\":\"0b449abc\",\"metadata\":{},\"outputs\":[],\"source\":[\"def compute_misfit(\\n\",\"    rec_file: Path, adj_src: typing.Union[Path, None] = None, rot_mat=None\\n\",\"):\\n\",\"    \\\"\\\"\\\"\\n\",\"    A function to compute the energy misfit and corresponding adjoin source.\\n\",\"    :param rec_file: File containing the receivers from the forward run.\\n\",\"    :param adj_src: File which, if provided, will contain the adjoint sources.\\n\",\"    :returns: A measure of misft. Will write adjoint sources if adj_src is a valid file path.\\n\",\"    \\\"\\\"\\\"\\n\",\"\\n\",\"    misfit = 0.0\\n\",\"    if adj_src is not None and adj_src.is_file():\\n\",\"        adj_src.unlink()\\n\",\"    adj_out = h5py.File(adj_src, mode=\\\"w\\\") if adj_src else None\\n\",\"    with pyasdf.ASDFDataSet(rec_file, mode=\\\"r\\\") as fh:\\n\",\"        for rec in fh.waveforms:\\n\",\"            u = rec.displacement\\n\",\"            adj = np.empty((u[0].stats.npts, len(u)))\\n\",\"\\n\",\"            if reference_frame == \\\"cartesian\\\":\\n\",\"                for _i, cmp in enumerate(u):\\n\",\"                    misfit += time_step * (cmp.data * cmp.data).sum()\\n\",\"                    adj[:, _i] = -time_step * cmp.data\\n\",\"            else:\\n\",\"                for _i, code in enumerate([\\\"Z\\\", \\\"N\\\", \\\"E\\\"]):\\n\",\"                    cmp = u.select(component=code)[0]\\n\",\"                    misfit += time_step * (cmp.data * cmp.data).sum()\\n\",\"                    adj[:, _i] = -time_step * cmp.data\\n\",\"\\n\",\"            if adj_out:\\n\",\"                rot_mat = np.eye(3)\\n\",\"                adj = adj[:, :, np.newaxis]\\n\",\"                if receiver_type == \\\"classic\\\":\\n\",\"                    dset = adj_out.create_dataset(\\n\",\"                        name=u[0].stats.station,\\n\",\"                        data=(np.matmul(rot_mat, adj)).squeeze(),\\n\",\"                    )\\n\",\"                    dset.attrs[\\\"starttime\\\"] = start_time * 1e9\\n\",\"                    dset.attrs[\\\"sampling_rate\\\"] = 1 / time_step\\n\",\"\\n\",\"                else:\\n\",\"                    ncmp = 3\\n\",\"                    stf = np.empty((1, ncmp, adj.shape[0]))\\n\",\"                    stf[0, :] = adj.T\\n\",\"                    group = adj_out.create_group(\\\"adjoint_sources\\\")\\n\",\"                    group.create_dataset(name=\\\"stf\\\", data=stf)\\n\",\"                    group.create_dataset(\\n\",\"                        name=\\\"coordinates\\\", data=np.atleast_2d(r_loc)\\n\",\"                    )\\n\",\"                    group.attrs[\\\"start_time_in_seconds\\\"] = start_time\\n\",\"                    group.attrs[\\\"sampling_rate_in_hertz\\\"] = 1 / time_step\\n\",\"                    group.attrs[\\\"spatial_type\\\"] = np.bytes_(\\\"vector\\\")\\n\",\"\\n\",\"    if adj_out:\\n\",\"        adj_out.close()\\n\",\"\\n\",\"    return misfit * 0.5\\n\",\"\\n\",\"\\n\",\"rx, ry, rz = w.output.point_data.receiver[0].location\\n\",\"misfit_00 = compute_misfit(\\n\",\"    Path(\\\"fwd_output/receivers.h5\\\"), Path(\\\"adjoint_sources.h5\\\")\\n\",\")\"]},{\"cell_type\":\"code\",\"execution_count\":8,\"id\":\"f4e74135\",\"metadata\":{},\"outputs\":[{\"name\":\"stdout\",\"output_type\":\"stream\",\"text\":[\"\\n\",\"ZNE ADJOINT SOURCE\\n\",\"\\n\"]}],\"source\":[\"if reference_frame == \\\"spherical\\\":\\n\",\"    print(\\\"\\\\nZNE ADJOINT SOURCE\\\\n\\\")\\n\",\"    adj_srcs = [\\n\",\"        sc.source.seismology.VectorPoint3DZNE(\\n\",\"            latitude=33.35,\\n\",\"            longitude=45,\\n\",\"            depth_in_m=np.sqrt(3) - 0.9526,\\n\",\"            radius_of_sphere_in_m=np.sqrt(3),\\n\",\"            fz=1,\\n\",\"            fn=1,\\n\",\"            fe=1,\\n\",\"            source_time_function=sc.stf.Custom(\\n\",\"                filename=\\\"adjoint_sources.h5\\\", dataset_name=\\\"000\\\"\\n\",\"            ),\\n\",\"        )\\n\",\"    ]\\n\",\"\\n\",\"else:\\n\",\"    print(\\\"\\\\nCARTESIAN ADJOINT SOURCE\\\\n\\\")\\n\",\"    adj_srcs = [\\n\",\"        sc.source.cartesian.VectorPoint3D(\\n\",\"            x=r_loc[0][0],\\n\",\"            y=r_loc[0][1],\\n\",\"            z=r_loc[0][2],\\n\",\"            fx=1,\\n\",\"            fy=1,\\n\",\"            fz=1,\\n\",\"            source_time_function=sc.stf.Custom(\\n\",\"                filename=\\\"adjoint_sources.h5\\\", dataset_name=\\\"000\\\"\\n\",\"            ),\\n\",\"        )\\n\",\"    ]\"]},{\"cell_type\":\"code\",\"execution_count\":9,\"id\":\"46358290\",\"metadata\":{},\"outputs\":[],\"source\":[\"w_adjoint = sc.simulation.Waveform(mesh=mesh)\\n\",\"\\n\",\"if receiver_type == \\\"classic\\\":\\n\",\"    w_adjoint.adjoint.point_source = adj_srcs\\n\",\"else:\\n\",\"    w_adjoint.adjoint.point_source_block = {\\n\",\"        \\\"filename\\\": \\\"adjoint_sources.h5\\\",\\n\",\"        \\\"groups\\\": [\\\"adjoint_sources\\\"],\\n\",\"    }\\n\",\"\\n\",\"w_adjoint.adjoint.gradient.output_filename = \\\"gradient.h5\\\"\\n\",\"w_adjoint.adjoint.forward_meta_json_filename = \\\"fwd_output/meta.json\\\"\\n\",\"w_adjoint.adjoint.gradient.parameterization = \\\"tti\\\"\\n\",\"\\n\",\"w_adjoint.validate()\"]},{\"cell_type\":\"code\",\"execution_count\":10,\"id\":\"86613376\",\"metadata\":{},\"outputs\":[],\"source\":[\"job = api.run(\\n\",\"    ranks=2,\\n\",\"    get_all=True,\\n\",\"    overwrite=True,\\n\",\"    site_name=site_name,\\n\",\"    delete_remote_files=False,\\n\",\"    input_file=w_adjoint,\\n\",\"    output_folder=\\\"adj_output\\\",\\n\",\"    verbosity=0,\\n\",\")\\n\",\"\\n\",\"# print(job.stdout)\"]},{\"cell_type\":\"code\",\"execution_count\":11,\"id\":\"e8ae66ae\",\"metadata\":{},\"outputs\":[],\"source\":[\"g = None\\n\",\"gradient = UnstructuredMesh.from_h5(\\\"adj_output/gradient.h5\\\")\"]},{\"cell_type\":\"code\",\"execution_count\":12,\"id\":\"4b170cdc\",\"metadata\":{},\"outputs\":[{\"name\":\"stdout\",\"output_type\":\"stream\",\"text\":[\"--------------------------Results (spherical, order=1)--------------------------\\n\",\"RHO                 1.28400432055686e-08          1         4.137711052104022e-06         0.014802470346100173          \\n\",\"VPH                 1.543376547349376e-07         1         2.202201933209572e-05         0.1094629471776331            \\n\",\"VPV                 9.344689423407555e-08         1         2.3899912624957423e-05        0.09986034125621927           \\n\",\"VSH                 7.903174729538969e-08         1         6.145018334421396e-05         0.013084950532224502          \\n\",\"VSV                 5.6838093319927477e-08        1         6.416408792661697e-05         0.07252946165575308           \\n\",\"ETA                 3.6744919313941284e-07        1         0.0006239027171050027         0.007930394326093678          \\n\",\"--------------------------------------------------------------------------------\\n\",\"\\n\"]},{\"data\":{\"text/plain\":[\"<matplotlib.legend.Legend at 0x729c15c42890>\"]},\"execution_count\":12,\"metadata\":{},\"output_type\":\"execute_result\"},{\"data\":{\"image/png\":\"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\",\"text/plain\":[\"<Figure size 500x500 with 1 Axes>\"]},\"metadata\":{},\"output_type\":\"display_data\"}],\"source\":[\"# Update model\\n\",\"h = np.logspace(-11, -2, 10)\\n\",\"pars = [x for x, y in p1.parameters()]\\n\",\"\\n\",\"nx = 1\\n\",\"ny = 1\\n\",\"f, a = plt.subplots(ny, nx, figsize=(5, 5))\\n\",\"\\n\",\"# Perturb parameters one at a time.\\n\",\"print(\\n\",\"    \\\"{:-^80}\\\".format(\\n\",\"        \\\"Results ({}, order={})\\\".format(reference_frame, model_order)\\n\",\"    )\\n\",\")\\n\",\"for _i, p in enumerate(pars):\\n\",\"    if p in {\\\"AXIS\\\", \\\"TTISymmetryAxis\\\"}:\\n\",\"        continue\\n\",\"\\n\",\"    # first try to get away with just three runs\\n\",\"    error = test_material_gradient(\\n\",\"        all_h=[h[0], h[3], h[-1]],\\n\",\"        model=mesh,\\n\",\"        gradient=gradient,\\n\",\"        parameter=p,\\n\",\"        simulation=w,\\n\",\"        misfit_function=compute_misfit,\\n\",\"        site_name=site_name,\\n\",\"        m0=misfit_00,\\n\",\"        ranks=2,\\n\",\"        quiet=True,\\n\",\"    )\\n\",\"\\n\",\"    try:\\n\",\"        assert np.min(error) < 1e-5\\n\",\"        assert error[0] > np.min(error)\\n\",\"        assert error[-1] > np.min(error)\\n\",\"        used_h = [h[0], h[3], h[-1]]\\n\",\"    except:\\n\",\"        # run again with more step lengths\\n\",\"        error = test_material_gradient(\\n\",\"            all_h=h,\\n\",\"            model=mesh,\\n\",\"            gradient=gradient,\\n\",\"            parameter=p,\\n\",\"            simulation=w,\\n\",\"            misfit_function=compute_misfit,\\n\",\"            site_name=site_name,\\n\",\"            m0=misfit_00,\\n\",\"            ranks=2,\\n\",\"            quiet=True,\\n\",\"        )\\n\",\"        used_h = h\\n\",\"        assert np.min(error) < 1e-5\\n\",\"        assert error[0] > np.min(error)\\n\",\"        assert error[-1] > np.min(error)\\n\",\"\\n\",\"    finally:\\n\",\"        # Generate a plot\\n\",\"        a.set_title(f\\\"Gradient Test TTI({reference_frame})\\\")\\n\",\"        a.loglog(used_h, error, label=p)\\n\",\"        a.set_xlabel(\\\"$h$\\\")\\n\",\"        a.set_ylabel(\\\"Relative Error\\\")\\n\",\"\\n\",\"        print(\\n\",\"            \\\"{0:<20}{1:<30}{2:<10}{3:<30}{4:<30}\\\".format(\\n\",\"                p, np.min(error), np.argmin(error), error[0], error[-1]\\n\",\"            )\\n\",\"        )\\n\",\"\\n\",\"print(\\\"{:-^80}\\\\n\\\".format(\\\"\\\"))\\n\",\"a.legend()\"]}],\"metadata\":{\"jupytext\":{\"formats\":\"ipynb,py:light\"},\"kernelspec\":{\"display_name\":\"Python 3 (ipykernel)\",\"language\":\"python\",\"name\":\"python3\"},\"language_info\":{\"codemirror_mode\":{\"name\":\"ipython\",\"version\":3},\"file_extension\":\".py\",\"mimetype\":\"text/x-python\",\"name\":\"python\",\"nbconvert_exporter\":\"python\",\"pygments_lexer\":\"ipython3\",\"version\":\"3.11.15\"}},\"nbformat\":4,\"nbformat_minor\":5}","notebook_widget_size_map":"{}","path_to_zip_file":"/2025.1.3/examples/integration_tests/gradient_tests/tutorial.zip","zip_file_size":"129.05 kB"}},"pageContext":{"slug":"/2025.1.3/examples/integration_tests/gradient_tests/gradient_test_tti","pagePath":"2025.1.3/examples/integration_tests/gradient_tests/gradient_test_tti"}},
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