From 42239adcaea9f0732afcc29b3c427db211f6a49a Mon Sep 17 00:00:00 2001 From: LL-Geo <54405391+LL-Geo@users.noreply.github.com> Date: Thu, 23 Jun 2022 22:01:43 +0800 Subject: [PATCH] Squashed commit of the following: commit 54c3cbe52bd33a94ccbb5bb44f2958bb3afc9330 Author: LL-Geo <54405391+LL-Geo@users.noreply.github.com> Date: Thu Jun 23 21:36:23 2022 +0800 Update filter Add more filter commit 4a5d6f179c1ba7b6b165bd1cc19b24259bec8bb1 Author: Santiago Soler Date: Thu Jun 16 18:00:32 2022 -0300 Avoid checking floats in tesseroid doctests (#326) Remove expected results for tesseroid calculations in docstring examples. Printing floats in forward modelling examples isn't that meaningful and often creates failures when running doctests: small differences between the expected and the got value could occur under some dependency and OS combinations. commit cc697af69243af79cb0b6b52e6a4ef2154e46e3e Author: Matt Tankersley <81199856+mdtanker@users.noreply.github.com> Date: Fri Jun 17 08:24:32 2022 +1200 Add progressbar to prism forward gravity calculations (#315) Add optional `progressbar` flag to `prism_gravity` function and to the `gravity` method of the prism layer accesor to print a progress bar using `numba_progress`. Add `numba_progress` as optional dependency. Add test functions for the new feature. commit 5a1c89558cc484fb7d331383639446855d5e4bab Author: Santiago Soler Date: Tue Jun 14 13:20:39 2022 -0300 Specify spherical latitude in point sources guide (#325) Replaces latitude for spherical latitude in another place of the `point.rst`. Fix typo on "Alternatively". commit cb476b272f5d08ecc5ad9d4d583b5b13185d6922 Author: Federico Esteban Date: Tue Jun 14 11:33:22 2022 -0300 Note that spherical and geodetic latitudes are equal in spherical ellipsoids (#324) Add sentence in the Coordinate Systems section of the User Guide noting that if the reference ellipsoid were a sphere both the spherical latitude and the geodetic latitude are equivalent. commit 1256ff6bbdef525954404de15976464e5a454b93 Author: Federico Esteban Date: Mon Jun 13 11:35:02 2022 -0300 Add Federico Esteban to AUTHORS.md (#323) Add his name, link to his GitHub account, affiliation and ORCID number. commit 32de6e01ce423dfd77a6d003ab74acb038f23ea1 Author: Federico Esteban Date: Thu Jun 9 15:44:48 2022 -0300 Specify "spherical latitude" when describing coordinates of point masses (#321) Add "spherical" when describing the spherical latitude coordinate of point masses in the user guide. This way we differentiate it from the "latitude" geodetic coordinate. commit 9667fab23d5ce1dd9f0b29743714470f963125b8 Author: Santiago Soler Date: Mon Jun 6 11:05:17 2022 -0300 Fix small format errors in the user guide (#319) Fix link to EquivalentSources.predict method and fix superscripts in the docs. commit 2f7fcb636370baea7643937791877302a1ed0a6c Author: Santiago Soler Date: Fri Jun 3 11:17:51 2022 -0300 Update docs and create a proper user guide (#305) Update Sphinx docs using sphinx-panels. Add a proper User Guide that will ultimately replace the gallery examples. Each page of the new User Guide is a .rst file that uses jupyter-sphinx to run example code blocks. Added pages for: Coordinate systems, Forward Modelling, Gravity corrections and Equivalent Sources. Added a new doc/versions.rst file with links to previous documentations. commit cf4080cc50d67e924544f9a837cbb04ca7f67cee Author: Santiago Soler Date: Tue May 31 15:58:25 2022 -0300 Compute upward derivative of a grid in the frequency domain (#238) Define a new derivative_upward function for computing the spatial upward derivative of a 2D grid in the frequency domain. The function makes use of xrft for handling Fourier transformations of xarray objects. Add a new filters subpackage that includes FFT filters: functions that take grids in frequency domain and return the desired filter also in frequency domain. Add fft and ifft wrapper functions of the xrft.fft and xrft.ifft ones. Add a new apply_filter function that takes a grid in the spatial domain, applies fft, the filter and ifft and returns the filtered grid also in spatial domain. Add tests for the new features and a gallery example for the upward derivative. Add netcdf4 as requirement for testing. commit 6a30797fe179da63435be5204b7e384e38c7bde9 Author: Santiago Soler Date: Fri May 27 16:22:18 2022 -0300 Ditch soon-to-be deprecated args of equivalent sources grid method (#311) The grid() method of Verde gridders now take a coordinates argument with the coordinates of the target grid. The previous region, shape and spacing arguments will be deprecated in Verde v2.0.0. This change makes it easier for our equivalent sources classes: we don't need the extra upward argument, users can create the coordinates of the target grid using verde.grid_coordinates and pass them via coordinates argument. Ditch the upward, shape, spacing and region arguments from the equivalent sources gridders. Replace them for the new coordinates argument: users need to provide the coordinates of the target grid instead of building it through the grid method. Raise errors if any of those old arguments are being passed. Raise warnings if any kwargs are passed: they are being ignored and not passed to the BaseGridder.grid() method. commit 51ceb7ea42d6b07b2ee5f1d20a43c052c7894b0d Author: Agustina Date: Mon May 23 11:03:54 2022 -0300 Remove deprecated point_mass_gravity function (#310) Remove point_mass_gravity function from harmonica because it was deprecated on PR #280. Remove related test functions. commit f336aa86f3e32c4e85b66fe303dcd36575962898 Author: Santiago Soler Date: Thu May 5 14:52:08 2022 -0300 Drop support for Python 3.6 (#309) Remove the compatibility metadata, remove from the CI matrix, bump the python_requires to 3.7+. commit d132abb548d8d5836e20846fa389f7c384869a33 Author: Santiago Soler Date: Tue May 3 12:47:22 2022 -0300 Add computation of gravitational tensor components for point sources (#288) Add new kernel functions to compute gravity tensor components generated by point sources. Add test functions for the new feature: check that the diagonal elements satisfy the Laplace equation, compare all components against finite difference computations from the gravity acceleration. Add test class for checking the symmetry of tensor components. Refactor old test functions for point gravity: merge some functions into single ones through pytest parametrizations. Avoid using "gradient" for specifying the gravity acceleration vector: the "gravity gradient" is usually used to refer to the tensor. commit eb71d5487323d4ca5bd9cc85340398fc18f9f5b7 Author: Santiago Soler Date: Fri Apr 22 17:23:00 2022 -0300 Add deprecations to datasets and synthetic modules (#304) Add FutureWarnings to public functions of the synthetic and dataset modules. Add tests for the new warnings. Both modules will be deprecated in Harmonica v0.6.0. Instead of providing sample datasets, Harmonica will depend on Ensaio for that. The synthetic surveys depend on some of the sample datasets, but those functions are intended to be used in methodology articles, so they should live somewhere else. commit a4598efbac61750a6d0caa84ec5a9661a927543c Author: Santiago Soler Date: Fri Apr 22 17:06:43 2022 -0300 Add conversion of prisms or a prism layer to PyVista objects (#291) Add a new visualization module that hosts prism_to_pyvista: a function to convert a set of prisms into a pyvista.UnstructuredGrid. Include the new module and this function in the API Reference. Add a new to_pyvista() method to the PrismLayer accessor that converts a prism layer into a pyvista grid, making it easier to plot it in 3D. The UnstructuredGrid has the information about each prism as hexahedrons, along with their physical properties as cell data. Add tests for the new features. Add pyvista and vtk as optional dependencies to environment.yml and setup.cfg. Add a new example for plotting a PrismLayer. Configure Sphinx to show pyvista plots in the gallery and to use the pyvista-plot directive in docstrings. commit 762d21038069be525794c29f5674293415d0f75d Author: Santiago Soler Date: Mon Apr 4 14:09:45 2022 -0300 Update Black to its stable version (#301) Black has released a stable version: 22.3.0. Now the style check tests use this version. Fixes a bug on CI in which Black was trying to import a private module of click that doesn't exist anymore. Rerun black: now Black hugs simple power operators. commit 10577fa60b1e76700ca357a1397cb141e24632ee Author: Santiago Soler Date: Mon Apr 4 14:00:42 2022 -0300 Update Sphinx version to 4.5.0 (#302) Updates also sphinx gallery and sphinx book theme. This fixes a issue between latest jinja2 and Sphinx 3.5.*. commit f88006531ad8b5e440b90c80fe274b8229ce50ad Author: Leonardo Uieda Date: Fri Mar 18 13:34:51 2022 +0000 Move configuration from setup.py to setup.cfg (#296) Make the move away from setup.py following the recommendations from the Python packaging guides. Moves the requirement listing to setup.cfg as well and will use a script to extract this for conda installing on CI. --- .coveragerc | 2 +- .flake8 | 35 - .github/workflows/docs.yml | 89 +- .github/workflows/pypi.yml | 46 +- .github/workflows/test.yml | 76 +- .gitignore | 3 +- .isort.cfg | 3 - AUTHORS.md | 1 + Makefile | 7 +- README.rst | 13 +- data/examples/README.txt | 6 + doc/_static/custom.css | 20 + .../figures/geodetic-coordinate-system.svg | 2504 +++++++++++++++++ .../figures/spherical-coordinate-system.svg | 2207 +++++++++++++++ doc/_static/figures/tesseroid.svg | 758 +++++ doc/api/index.rst | 49 + doc/citing.rst | 2 +- doc/conf.py | 35 +- doc/index.rst | 112 +- doc/install.rst | 18 +- doc/overview.rst | 83 + doc/user_guide/coordinate_systems.rst | 232 ++ .../equivalent_sources/block-averaged-eqs.rst | 196 ++ .../eq-sources-spherical.rst | 197 ++ .../eqs-parameters-estimation.rst | 224 ++ .../gradient-boosted-eqs.rst | 192 ++ doc/user_guide/equivalent_sources/index.rst | 185 ++ doc/user_guide/forward_modelling/index.rst | 34 + doc/user_guide/forward_modelling/point.rst | 231 ++ doc/user_guide/forward_modelling/prism.rst | 200 ++ .../forward_modelling/tesseroid.rst | 232 ++ doc/user_guide/gravity_disturbance.rst | 150 + doc/user_guide/topographic_correction.rst | 287 ++ doc/versions.rst | 17 + env/requirements-build.txt | 4 +- env/requirements-docs.txt | 13 +- env/requirements-style.txt | 2 +- env/requirements-tests.txt | 4 + environment.yml | 38 +- .../block_averaged_sources.py | 3 +- examples/equivalent_sources/cartesian.py | 3 +- .../equivalent_sources/gradient_boosted.py | 7 +- examples/equivalent_sources/spherical.py | 12 +- examples/forward/prism_layer.py | 2 +- examples/upward_derivative.py | 95 + examples/visualization/README.txt | 2 + examples/visualization/prism_layer_pyvista.py | 89 + harmonica/{version.py => _version.py} | 2 +- harmonica/datasets/sample_data.py | 22 +- harmonica/equivalent_sources/cartesian.py | 78 +- .../equivalent_sources/gradient_boosted.py | 4 +- harmonica/equivalent_sources/spherical.py | 83 +- harmonica/forward/_tesseroid_utils.py | 2 +- .../forward/_tesseroid_variable_density.py | 2 +- harmonica/forward/point.py | 181 +- harmonica/forward/prism.py | 44 +- harmonica/forward/prism_layer.py | 25 +- harmonica/forward/tesseroid.py | 8 +- harmonica/forward/utils.py | 10 +- harmonica/synthetic/surveys.py | 14 + harmonica/tests/data/sample-gravity-grid.nc | Bin 0 -> 2146331 bytes harmonica/tests/test_eq_sources_cartesian.py | 75 +- harmonica/tests/test_eq_sources_spherical.py | 112 +- harmonica/tests/test_filters.py | 248 ++ harmonica/tests/test_forward_utils.py | 2 +- harmonica/tests/test_gradient_boosted_eqs.py | 37 + harmonica/tests/test_point_gravity.py | 634 +++-- harmonica/tests/test_prism.py | 54 + harmonica/tests/test_prism_layer.py | 80 + harmonica/tests/test_sample_data.py | 42 + harmonica/tests/test_synthetic_surveys.py | 29 + harmonica/tests/test_tesseroid.py | 6 +- .../tests/test_tesseroid_variable_density.py | 8 +- harmonica/tests/test_transformations.py | 81 + harmonica/tests/test_visualizations.py | 128 + harmonica/tests/utils.py | 20 + harmonica/visualization/__init__.py | 7 + harmonica/visualization/prism.py | 215 ++ pyproject.toml | 20 + pytest.ini | 3 - requirements.txt | 9 - setup.cfg | 99 + setup.py | 77 +- tools/export_requirements.py | 19 + license_notice.py => tools/license_notice.py | 0 85 files changed, 10522 insertions(+), 678 deletions(-) delete mode 100644 .flake8 delete mode 100644 .isort.cfg create mode 100644 doc/_static/figures/geodetic-coordinate-system.svg create mode 100644 doc/_static/figures/spherical-coordinate-system.svg create mode 100644 doc/_static/figures/tesseroid.svg create mode 100644 doc/overview.rst create mode 100644 doc/user_guide/coordinate_systems.rst create mode 100644 doc/user_guide/equivalent_sources/block-averaged-eqs.rst create mode 100644 doc/user_guide/equivalent_sources/eq-sources-spherical.rst create mode 100644 doc/user_guide/equivalent_sources/eqs-parameters-estimation.rst create mode 100644 doc/user_guide/equivalent_sources/gradient-boosted-eqs.rst create mode 100644 doc/user_guide/equivalent_sources/index.rst create mode 100644 doc/user_guide/forward_modelling/index.rst create mode 100644 doc/user_guide/forward_modelling/point.rst create mode 100644 doc/user_guide/forward_modelling/prism.rst create mode 100644 doc/user_guide/forward_modelling/tesseroid.rst create mode 100644 doc/user_guide/gravity_disturbance.rst create mode 100644 doc/user_guide/topographic_correction.rst create mode 100644 doc/versions.rst create mode 100644 examples/upward_derivative.py create mode 100644 examples/visualization/README.txt create mode 100644 examples/visualization/prism_layer_pyvista.py rename harmonica/{version.py => _version.py} (91%) create mode 100644 harmonica/tests/data/sample-gravity-grid.nc create mode 100644 harmonica/tests/test_filters.py create mode 100644 harmonica/tests/test_transformations.py create mode 100644 harmonica/tests/test_visualizations.py create mode 100644 harmonica/visualization/__init__.py create mode 100644 harmonica/visualization/prism.py create mode 100644 pyproject.toml delete mode 100644 pytest.ini delete mode 100644 requirements.txt create mode 100644 setup.cfg create mode 100644 tools/export_requirements.py rename license_notice.py => tools/license_notice.py (100%) diff --git a/.coveragerc b/.coveragerc index a2deea8f4..2debec871 100644 --- a/.coveragerc +++ b/.coveragerc @@ -2,5 +2,5 @@ branch = True omit = */tests/* - */_version.py + */_version_generated.py **/__init__.py diff --git a/.flake8 b/.flake8 deleted file mode 100644 index 833e2ea22..000000000 --- a/.flake8 +++ /dev/null @@ -1,35 +0,0 @@ -[flake8] -max-line-length = 88 -max-doc-length = 79 -ignore = - # Too many leading '#' for block comment - E266, - # Line too long (82 > 79 characters) - E501, - # Do not use variables named 'I', 'O', or 'l' - E741, - # Line break before binary operator (conflicts with black) - W503, -exclude = - .git, - __pycache__, - .ipynb_checkpoints, -per-file-ignores = - # disable unused-imports errors on __init__.py - __init__.py: F401 - -# Configure flake8-rst-docstrings -# ------------------------------- -# Add some roles used in our docstrings -rst-roles = - class, - func, - mod, - meth, -# Ignore "Unknown target name" raised on citations -extend-ignore = RST306 - -# Configure flake8-functions -# -------------------------- -# Allow a max of 10 arguments per function -max-parameters-amount = 10 diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml index c8f1e7e14..dd6e0aa54 100644 --- a/.github/workflows/docs.yml +++ b/.github/workflows/docs.yml @@ -4,7 +4,7 @@ # token stolen if the Action is compromised. See the comments and links here: # https://github.com/pypa/gh-action-pypi-publish/issues/27 # -name: docs +name: documentation # Only build PRs, the main branch, and releases. Pushes to branches will only # be built when a PR is opened. This avoids duplicated buids in PRs comming @@ -32,7 +32,7 @@ jobs: build: runs-on: ubuntu-latest env: - REQUIREMENTS: requirements.txt env/requirements-docs.txt + REQUIREMENTS: env/requirements-build.txt env/requirements-docs.txt PYTHON: 3.9 steps: @@ -61,45 +61,44 @@ jobs: - name: Fetch git tags run: git fetch origin 'refs/tags/*:refs/tags/*' - - name: Setup caching for conda packages - uses: actions/cache@v2 - with: - path: ~/conda_pkgs_dir - key: conda-${{ runner.os }}-${{ env.PYTHON }}-${{ hashFiles('requirements*.txt') }} - - name: Setup Miniconda - uses: conda-incubator/setup-miniconda@v2.0.1 + uses: conda-incubator/setup-miniconda@v2 with: python-version: ${{ env.PYTHON }} - miniconda-version: "latest" - auto-update-conda: true - channels: conda-forge - show-channel-urls: true - activate-environment: testing + miniforge-variant: Mambaforge + use-mamba: true + channels: conda-forge,defaults # Needed for caching use-only-tar-bz2: true - - name: Install requirements + - name: Collect requirements - run-time + run: python tools/export_requirements.py > requirements-full.txt + + - name: Collect requirements - other run: | - requirements_file=requirements-full.txt - if [ ! -z "$REQUIREMENTS" ]; then - echo "Capturing dependencies from $REQUIREMENTS" - for requirement in $REQUIREMENTS - do - cat $requirement >> $requirements_file - done - fi - if [ -f $requirements_file ]; then - echo "Collected dependencies:" - cat $requirements_file - echo "" - conda install --quiet --file $requirements_file python=$PYTHON - else - echo "No requirements defined." - fi + echo "Capturing dependencies from:" + for requirement in $REQUIREMENTS + do + echo " $requirement" + cat $requirement >> requirements-full.txt + done + + - name: List requirements + run: | + echo "Collected dependencies:" + cat requirements-full.txt + + - name: Setup caching for conda packages + uses: actions/cache@v2 + with: + path: ~/conda_pkgs_dir + key: conda-${{ runner.os }}-${{ env.PYTHON }}-${{ hashFiles('requirements-full.txt') }} + + - name: Install requirements + run: mamba install --quiet --file requirements-full.txt python=$PYTHON - name: List installed packages - run: conda list + run: mamba list - name: Build source and wheel distributions run: | @@ -109,23 +108,25 @@ jobs: ls -lh dist/ - name: Install the package - run: pip install --no-deps dist/*.whl - - - name: Copy test data to cache - run: | - echo "Copy data to " $HARMONICA_DATA_DIR/main - set -x -e - mkdir -p $HARMONICA_DATA_DIR/main - cp -r data/* $HARMONICA_DATA_DIR/main - env: - # Define directory where sample data will be copied - HARMONICA_DATA_DIR: ${{ runner.temp }}/cache/harmonica + run: python -m pip install --no-deps dist/*.whl - name: Build the documentation - run: make -C doc clean all + run: | + # Install xvfb and run some commands to allow pyvista to run on + # a headless system. + sudo apt-get install xvfb + export DISPLAY=:99.0 + export PYVISTA_OFF_SCREEN=true + export PYVISTA_USE_IPYVTK=true + Xvfb :99 -screen 0 1024x768x24 > /dev/null 2>&1 & + sleep 3 + # Build the docs + make -C doc clean all # Store the docs as a build artifact so we can deploy it later - name: Upload HTML documentation as an artifact + # Only if not a pull request + if: success() && github.event_name != 'pull_request' uses: actions/upload-artifact@v2 with: name: docs-${{ github.sha }} diff --git a/.github/workflows/pypi.yml b/.github/workflows/pypi.yml index 9c8d3f61d..4933853e0 100644 --- a/.github/workflows/pypi.yml +++ b/.github/workflows/pypi.yml @@ -6,8 +6,10 @@ # name: pypi -# Only run for pushes to the main branch and releases. +# Runs on these events but only publish on pushes to main (to test pypi) and +# releases on: + pull_request: push: branches: - main @@ -21,12 +23,11 @@ defaults: shell: bash jobs: + ############################################################################# - # Publish built wheels and source archives to PyPI and test PyPI - publish: + # Build and check source and wheel distributions + build: runs-on: ubuntu-latest - # Only publish from the origin repository, not forks - if: github.repository_owner == 'fatiando' steps: # Checks-out your repository under $GITHUB_WORKSPACE @@ -63,7 +64,7 @@ jobs: # Change setuptools-scm local_scheme to "no-local-version" so the # local part of the version isn't included, making the version string # compatible with Test PyPI. - sed --in-place "s/node-and-date/no-local-version/g" setup.py + sed --in-place "s/node-and-date/no-local-version/g" pyproject.toml - name: Build source and wheel distributions run: | @@ -75,8 +76,39 @@ jobs: - name: Check the archives run: twine check dist/* + # Store the archives as a build artifact so we can deploy them later + - name: Upload archives as artifacts + # Only if not a pull request + if: success() && github.event_name != 'pull_request' + uses: actions/upload-artifact@v2 + with: + name: pypi-${{ github.sha }} + path: dist + + ############################################################################# + # Publish built wheels and source archives to PyPI and test PyPI + publish: + runs-on: ubuntu-latest + # Only publish from the origin repository, not forks + if: github.repository_owner == 'fatiando' && github.event_name != 'pull_request' + + steps: + - name: Checkout + uses: actions/checkout@v2 + with: + # The GitHub token is preserved by default but this job doesn't need + # to be able to push to GitHub. + persist-credentials: false + + - name: Download built source and wheel packages + uses: actions/download-artifact@v2 + with: + name: pypi-${{ github.sha }} + path: dist + - name: Publish to Test PyPI - if: success() + # Only publish to TestPyPI when a PR is merged (pushed to main) + if: success() && github.event_name == 'push' uses: pypa/gh-action-pypi-publish@bce3b74dbf8cc32833ffba9d15f83425c1a736e0 with: user: __token__ diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index ec613e8d5..8b2b3a209 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -21,32 +21,34 @@ on: # Use bash by default in all jobs defaults: run: - # The -l {0} is necessary for conda environments to be activated - # But this breaks on MacOS if using actions/setup-python: - # https://github.com/actions/setup-python/issues/132 shell: bash jobs: ############################################################################# # Run tests and upload to codecov test: - name: ${{ matrix.os }} py${{ matrix.python }} + name: ${{ matrix.os }} python=${{ matrix.python }} dependencies=${{ matrix.dependencies }} runs-on: ${{ matrix.os }}-latest strategy: # Otherwise, the workflow would stop if a single job fails. We want to # run all of them to catch failures in different combinations. fail-fast: false matrix: - os: [ubuntu, macos, windows] - python: ["3.6", "3.9"] - # If "optional", will install non-required dependencies in the build - # environment. Otherwise, only required dependencies are installed. - dependencies: [""] + os: + - ubuntu + - macos + - windows + python: + - "3.7" + - "3.9" + dependencies: + - latest env: - REQUIREMENTS: requirements.txt env/requirements-tests.txt + REQUIREMENTS: env/requirements-tests.txt # Used to tag codecov submissions OS: ${{ matrix.os }} PYTHON: ${{ matrix.python }} + DEPENDENCIES: ${{ matrix.dependencies }} steps: # Cancel any previous run of the test job @@ -79,6 +81,23 @@ jobs: with: python-version: ${{ matrix.python }} + - name: Collect requirements - run-time + run: python tools/export_requirements.py > requirements-full.txt + + - name: Collect requirements - other + run: | + echo "Capturing dependencies from:" + for requirement in $REQUIREMENTS + do + echo " $requirement" + cat $requirement >> requirements-full.txt + done + + - name: List requirements + run: | + echo "Collected dependencies:" + cat requirements-full.txt + - name: Get the pip cache folder id: pip-cache run: | @@ -88,34 +107,14 @@ jobs: uses: actions/cache@v2 with: path: ${{ steps.pip-cache.outputs.dir }} - key: ${{ runner.os }}-pip-${{ hashFiles('**/requirements*.txt') }} - restore-keys: | - ${{ runner.os }}-pip-${{ hashFiles('**/requirements*.txt') }} + key: ${{ runner.os }}-pip-${{ hashFiles('requirements-full.txt') }} - name: Install requirements run: | - requirements_file=requirements-full.txt - if [ ! -z "$REQUIREMENTS" ]; then - echo "Capturing dependencies from $REQUIREMENTS" - for requirement in $REQUIREMENTS - do - cat $requirement >> $requirements_file - done - fi - if [ -f $requirements_file ]; then - echo "Collected dependencies:" - cat $requirements_file - echo "" - # Install wheel before anything else so pip can use wheels for - # other packages. - python -m pip install -r env/requirements-build.txt - python -m pip install -r $requirements_file - else - echo "No requirements defined." - fi - - - name: List installed packages - run: python -m pip freeze + # Install the build requirements before anything else so pip can use + # wheels for other packages. + python -m pip install --requirement env/requirements-build.txt + python -m pip install --requirement requirements-full.txt - name: Build source and wheel distributions run: | @@ -125,7 +124,10 @@ jobs: ls -lh dist/ - name: Install the package - run: pip install --no-deps dist/*.whl + run: python -m pip install --no-deps dist/*.whl + + - name: List installed packages + run: python -m pip freeze - name: Copy test data to cache run: | @@ -159,7 +161,7 @@ jobs: with: token: ${{ secrets.CODECOV_TOKEN }} file: ./coverage.xml - env_vars: OS,PYTHON + env_vars: OS,PYTHON,DEPENDENCIES # Don't mark the job as failed if the upload fails for some reason. # It does sometimes but shouldn't be the reason for running # everything again unless something else is broken. diff --git a/.gitignore b/.gitignore index 120d65de6..dc74ac933 100644 --- a/.gitignore +++ b/.gitignore @@ -11,11 +11,10 @@ dist/ doc/_build doc/api/generated doc/gallery -doc/tutorials doc/sample_data .ipynb_checkpoints *.egg-info MANIFEST dask-worker-space .coverage.* -harmonica/_version.py +harmonica/_version_generated.py diff --git a/.isort.cfg b/.isort.cfg deleted file mode 100644 index 2c0a3fb7f..000000000 --- a/.isort.cfg +++ /dev/null @@ -1,3 +0,0 @@ -[settings] -# Configure isort to be compatible with black -profile=black diff --git a/AUTHORS.md b/AUTHORS.md index a0f952f61..4a886cb2c 100644 --- a/AUTHORS.md +++ b/AUTHORS.md @@ -3,6 +3,7 @@ The following people have made contributions to the project (in alphabetical order by last name) and are considered "The Harmonica Developers": +* [Federico D Esteban](https://github.com/Esteban82) - CONICET, Argentina; Instituto de Geociencias Básicas, Aplicadas y Ambientales de Buenos Aires, Univeridad de Buenos Aires, Argentina (ORCID: 0000-0002-0641-7371) * [Vanderlei C Oliveira Jr](https://github.com/birocoles) - Observatório Nacional, Brazil (ORCID: 0000-0002-6338-4086) * [Agustina Pesce](https://github.com/aguspesce) - Universidad Nacional de San Juan, Argentina (ORCID: 0000-0002-5538-8845) * [Nicholas Shea](https://github.com/nshea3) - MINES ParisTech (ORCID: 0000-0001-6007-5686) diff --git a/Makefile b/Makefile index e845c90cf..b537b4e6f 100644 --- a/Makefile +++ b/Makefile @@ -3,8 +3,7 @@ PROJECT=harmonica TESTDIR=tmp-test-dir-with-unique-name PYTEST_ARGS=--cov-config=../.coveragerc --cov-report=term-missing --cov=$(PROJECT) --doctest-modules -v --pyargs NUMBATEST_ARGS=--doctest-modules -v --pyargs -m use_numba -LINT_FILES=setup.py $(PROJECT) license_notice.py -STYLE_CHECK_FILES=setup.py $(PROJECT) examples data/examples doc/conf.py license_notice.py +STYLE_CHECK_FILES=setup.py $(PROJECT) examples data/examples doc/conf.py tools help: @echo "Commands:" @@ -51,10 +50,10 @@ isort-check: isort --check $(STYLE_CHECK_FILES) license: - python license_notice.py + python tools/license_notice.py license-check: - python license_notice.py --check + python tools/license_notice.py --check flake8: flake8 $(STYLE_CHECK_FILES) diff --git a/README.rst b/README.rst index 421748765..264c5dedf 100644 --- a/README.rst +++ b/README.rst @@ -40,12 +40,13 @@ or send us a message on our About ----- -*Harmonica* is a Python library for processing and modeling gravity and magnetic data. -It includes common processing steps, like calculation of Bouguer and terrain -corrections, reduction to the pole, upward continuation, equivalent sources, and more. -There are forward modeling functions for basic geometric shapes, like spheres, prisms, -polygonal prisms, and tesseroids. The inversion methods are implemented as classes with -an interface inspired by scikit-learn (like `Verde `__). +*Harmonica* is a Python library for processing and modeling gravity and +magnetic data. It includes common processing steps, like calculation of Bouguer +and terrain corrections, reduction to the pole, upward continuation, equivalent +sources, and more. There are forward modeling functions for basic geometric +shapes, like point sources, prisms and tesseroids. The inversion methods are +implemented as classes with an interface inspired by scikit-learn (like `Verde +`__). Project goals diff --git a/data/examples/README.txt b/data/examples/README.txt index d81cb3fe7..d0f2559bb 100644 --- a/data/examples/README.txt +++ b/data/examples/README.txt @@ -6,6 +6,12 @@ Sample Data Harmonica provides some sample data for testing through the :mod:`harmonica.datasets` module. +.. warning:: + + The :mod:`harmonica.datasets` module and every sample dataset a will be + deprecated in Harmonica v0.6.0. The examples and the user guide will + transition to using Ensaio (https://www.fatiando.org/ensaio/) instead. + Where is my data? ----------------- diff --git a/doc/_static/custom.css b/doc/_static/custom.css index ee0021f57..2f02cad51 100644 --- a/doc/_static/custom.css +++ b/doc/_static/custom.css @@ -4,3 +4,23 @@ span.project-version { font-weight: 600; margin-left: 5px; } + +/* Try to make the jupyter-sphinx style match sphinx-gallery for consistency */ +div.jupyter_container { + border: none; + box-shadow: none; +} + +div.jupyter_container .cell_input { + margin-bottom: 1.15rem; + border-radius: 0.4em; + box-shadow: 1px 1px 1px #d8d8d8; +} + +div.jupyter_container .cell_output { + margin-bottom: 1.15rem; +} + +.jupyter_container div.code_cell pre { + padding: 10px; +} diff --git a/doc/_static/figures/geodetic-coordinate-system.svg b/doc/_static/figures/geodetic-coordinate-system.svg new file mode 100644 index 000000000..fbf4d9b6e --- /dev/null +++ b/doc/_static/figures/geodetic-coordinate-system.svg @@ -0,0 +1,2504 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + image/svg+xml + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +   + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/doc/_static/figures/spherical-coordinate-system.svg b/doc/_static/figures/spherical-coordinate-system.svg new file mode 100644 index 000000000..fde6d23c0 --- /dev/null +++ b/doc/_static/figures/spherical-coordinate-system.svg @@ -0,0 +1,2207 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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--git a/doc/api/index.rst b/doc/api/index.rst index 40365b84f..cf1cf3dfd 100644 --- a/doc/api/index.rst +++ b/doc/api/index.rst @@ -17,6 +17,29 @@ Gravity Corrections For the Normal Earth correction, see package :mod:`boule`. +Grid Transformations +-------------------- + +Apply well known transformations regular gridded potential fields data. + +.. autosummary:: + :toctree: generated/ + + derivative_upward + +Frequency domain filters +------------------------ + +Define filters in the frequency domain. + +.. autosummary:: + :toctree: generated/ + + filters.derivative_upward_kernel + +Use :func:`xrft.xrft.fft` and :func:`xrft.xrft.ifft` to apply Fast-Fourier +Transforms and its inverse on :class:`xarray.DataArray`. + Equivalent Sources ------------------ @@ -55,8 +78,22 @@ Input and Output load_icgem_gdf +Visualization +------------- + +.. autosummary:: + :toctree: generated/ + + visualization.prism_to_pyvista + Synthetic models and surveys ---------------------------- + +.. warning:: + + The :mod:`harmonica.synthetic` module will be deprecated in Harmonica + v0.6.0 + .. autosummary:: :toctree: generated/ @@ -71,6 +108,18 @@ Synthetic models and surveys Datasets -------- +.. warning:: + + The :mod:`harmonica.datasets` module and every sample dataset a will be + deprecated in Harmonica v0.6.0. The examples and the user guide will + transition to using Ensaio (https://www.fatiando.org/ensaio/) instead. + +.. warning:: + + The :mod:`harmonica.datasets` module will be deprecated in Harmonica + v0.6.0 + + .. autosummary:: :toctree: generated/ diff --git a/doc/citing.rst b/doc/citing.rst index 9c76d3970..9716a2473 100644 --- a/doc/citing.rst +++ b/doc/citing.rst @@ -1,3 +1,3 @@ -.. _citing: +.. _citing_harmonica: .. include:: ../CITATION.rst diff --git a/doc/conf.py b/doc/conf.py index f636aaf39..ce28632a0 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -6,7 +6,8 @@ # import datetime -from sphinx_gallery.sorting import ExampleTitleSortKey +import pyvista +from sphinx_gallery.sorting import FileNameSortKey import harmonica @@ -30,11 +31,21 @@ "sphinx.ext.viewcode", "sphinx.ext.extlinks", "sphinx.ext.intersphinx", - "matplotlib.sphinxext.plot_directive", "sphinx.ext.napoleon", "sphinx_gallery.gen_gallery", + "sphinx_panels", + "sphinx_copybutton", + "jupyter_sphinx", + "pyvista.ext.plot_directive", ] +# Disable including boostrap CSS for sphinx_panels since it's already included +# with sphinx-book-theme +panels_add_bootstrap_css = False +panels_css_variables = { + "tabs-color-label-inactive": "hsla(231, 99%, 66%, 0.5)", +} + # Configuration to include links to other project docs when referencing # functions/classes intersphinx_mapping = { @@ -50,6 +61,9 @@ "verde": ("https://www.fatiando.org/verde/latest/", None), "boule": ("https://www.fatiando.org/boule/latest/", None), "matplotlib": ("https://matplotlib.org/", None), + "pyproj": ("https://pyproj4.github.io/pyproj/stable/", None), + "pyvista": ("https://docs.pyvista.org", None), + "numba_progress": ("https://pypi.org/project/numba-progress/", None), } # Autosummary pages will be generated by sphinx-autogen instead of sphinx-build @@ -87,7 +101,7 @@ # Remove the "Download all examples" button from the top level gallery "download_all_examples": False, # Sort gallery example by file name instead of number of lines (default) - "within_subsection_order": ExampleTitleSortKey, + "within_subsection_order": FileNameSortKey, # directory where function granular galleries are stored "backreferences_dir": "api/generated/backreferences", # Modules for which function level galleries are created. In @@ -95,9 +109,22 @@ "doc_module": "harmonica", # Insert links to documentation of objects in the examples "reference_url": {"harmonica": None}, + # Add pyvista to the image scrapers + "image_scrapers": ("pyvista", "matplotlib"), } +# Pyvista configurations +# ----------------------------------------------------------------------------- +# necessary when building the sphinx gallery +pyvista.BUILDING_GALLERY = True +pyvista.OFF_SCREEN = True + +# Optional - set parameters like theme or window size +pyvista.set_plot_theme("document") +pyvista.global_theme.window_size = (1024 * 2, 768 * 2) + + # HTML output configuration # ----------------------------------------------------------------------------- html_title = f'{project} {version}' @@ -126,5 +153,5 @@ "use_issues_button": True, "use_repository_button": True, "use_download_button": True, - "home_page_in_toc": True, + "home_page_in_toc": False, } diff --git a/doc/index.rst b/doc/index.rst index 3b57994f9..081e0f813 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -8,54 +8,124 @@ :alt: Harmonica Documentation :align: middle -.. admonition:: This software is in the early stages of design and implementation - :class: attention +**Harmonica** is a Python library for processing and modeling gravity and +magnetic data. - This means that will occasionally make **backwards incompatible** changes - as we try to improve the software, test new ideas, and settle on the - project scope. - **We welcome any feedback and ideas!** Let us know by submitting - `issues on Github `__ - or send us a message on our - `Slack chatroom `__. +It includes common processing steps, like calculation of Bouguer and terrain +corrections, reduction to the pole, upward continuation, equivalent sources, +and more. There are forward modeling functions for basic geometric shapes, like +point sources, prisms and tesseroids. The inversion methods are implemented as +classes with an interface inspired by scikit-learn (like `Verde +`__). + +.. admonition:: Ready for daily use but still changing + :class: seealso + + This means that we are still adding a lot of new features and sometimes we + make changes to the ones we already have while we try to improve the + software based on users' experience, test new ideas, take better design + decisions, etc. + Some of these changes could be **backwards incompatible**. Keep that in + mind before you update Harmonica to a newer version. + + **We welcome any feedback and ideas!** This is a great time to bring new + ideas on how we can improve Harmonica, feel free to `join the + conversation `__ or submit a new + `issues on Github `__. + +.. panels:: + :header: text-center text-large + :card: border-1 m-1 text-center + + **Getting started** + ^^^^^^^^^^^^^^^^^^^ + + New to Harmonica? Start here! + + .. link-button:: overview + :type: ref + :text: Overview + :classes: btn-outline-primary btn-block stretched-link + + --- + + **Need help?** + ^^^^^^^^^^^^^^ + + Ask on our community channels + + .. link-button:: https://www.fatiando.org/contact + :type: url + :text: Join the conversation + :classes: btn-outline-primary btn-block stretched-link + + --- + + **Reference documentation** + ^^^^^^^^^^^^^^^^^^^^^^^^^^^ + + A list of modules and functions + + .. link-button:: api + :type: ref + :text: API reference + :classes: btn-outline-primary btn-block stretched-link + + --- + + **Using Harmonica for research?** + ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + + Citations help support our work + + .. link-button:: citing + :type: ref + :text: Cite Harmonica + :classes: btn-outline-primary btn-block stretched-link .. seealso:: Harmonica is a part of the `Fatiando a Terra `_ project. -.. include:: ../README.rst - :start-after: placeholder-for-doc-index +---- + +Table of contents +----------------- .. toctree:: - :maxdepth: 2 - :hidden: + :maxdepth: 1 :caption: Getting Started + overview.rst install.rst - citing.rst gallery/index.rst + .. toctree:: :maxdepth: 2 - :hidden: :caption: User Guide + :includehidden: - sample_data/index.rst + user_guide/coordinate_systems.rst + user_guide/forward_modelling/index.rst + user_guide/gravity_disturbance.rst + user_guide/topographic_correction.rst + user_guide/equivalent_sources/index.rst .. toctree:: - :maxdepth: 2 - :hidden: + :maxdepth: 1 :caption: Reference documentation api/index.rst + citing.rst changes.rst references.rst + versions.rst .. toctree:: - :maxdepth: 2 - :hidden: - :caption: Getting help and contributing + :maxdepth: 1 + :caption: Community Join the community How to contribute diff --git a/doc/install.rst b/doc/install.rst index 75e015429..ece3201a2 100644 --- a/doc/install.rst +++ b/doc/install.rst @@ -6,7 +6,7 @@ Installing Which Python? ------------- -You'll need **Python 3.6 or greater**. +You'll need **Python 3.7 or greater**. We recommend using the `Anaconda Python distribution `__ @@ -19,6 +19,12 @@ doesn't interfere with any other Python installations in your system. Dependencies ------------ +The required dependencies should be installed automatically when you install +Harmonica using ``conda`` or ``pip``. Optional dependencies have to be +installed manually. + +Required: + * `numpy `__ * `pandas `__ * `numba `__ @@ -27,6 +33,16 @@ Dependencies * `scikit-learn `__ * `pooch `__ * `verde `__ +* `xrft `__ + +Optional: + +* `pyvista `__ and + `vtk `__ (>= 9): for 3D visualizations. + See :func:`harmonica.prism_to_pyvista`. +* `numba_progress `__ for + printing a progress bar on some forward modelling computations. + See :func:`harmonica.prism_gravity`. The examples in the :ref:`gallery` also use: diff --git a/doc/overview.rst b/doc/overview.rst new file mode 100644 index 000000000..097ef5ebf --- /dev/null +++ b/doc/overview.rst @@ -0,0 +1,83 @@ +Overview +======== + +Harmonica provides functions and classes for processing, modelling and +interpolating gravity and magnetic data. + +Its main goals are: + +- Provide efficient, well designed, and fully tested code that would compress + the building blocks for more complex workflows. +- Cover the entire data life-cycle: from raw data to 3D Earth model. +- Focus on best-practices to discourage misuse of methods. +- Easily extendable code to enable research on the developments of new methods. + +Harmonica *will not* provide: + +- Multi-physics partial differential equation solvers. Use + `SimPEG `__ or `PyGIMLi `__ + instead. +- Generic processing methods like grid transformations (use `Verde + `__ or `Xarray `__ + instead) or multidimensional FFT calculations (use `xrft + `__ instead). +- Reference ellipsoid representations and computations like normal gravity. Use + `Boule `__ instead. +- Data visualization functions. Use `matplotlib `__ + for generic plots, `Xarray `__ for plotting grids, + `PyGMT `__ for maps, and `PyVista + `__ for 3D visualizations. +- GUI applications. + + +Conventions +----------- + +Before we get started, here are a few conventions we keep across Harmonica: + +- Every physical quantity will be assumed to be given in a unit belonging to the `International System of Units (SI) `__. The only exceptions are: + + - **gravity accelerations** are expected in `miligal (mGal) `__ (:math:`1~\text{mGal} = 10^{-5}~\text{m}/\text{s}^2`). + + - **gravity tensor components** are assumed to be in `Eotvos `__ (:math:`1~\text{Eotvos} = 10^{-9}~\text{s}^{-2}`). + + - **magnetic fields** are given in nano Tesla (nT). + +- Harmonica uses the same conventions as :mod:`verde`, meaning: + + - Functions expect coordinates in the order: West-East, South-North and (in occasions) Bottom-Top. Exceptions to this rule are the ``dims`` and ``shape`` arguments. + + - We avoid using names like "x", "y" and "z" to avoid ambiguity. We use + "easting", "northing" and "upward" or "longitude", "latitude" and "height" + instead. + +- Some functions or classes expect its arguments to be defined in a specific + coordinate system. They can either be in: + + - **Cartesian coordinates:** usually given as *easting*, *northing* and *upward* coordinates (in meters), where the vertical axis points upwards. + + - **Geodetic or ellipsoidal coordinates:** given as *longitude*, *latitude* (both in decimal degrees) and *geodetic height* (in meters). + + - **Spherical geocentric coordinates:** given as *longitude*, *spherical latitude* (both in decimal degrees) and *radius* (in meters). + +.. seealso:: + + Checkout the :ref:`coordinate_systems` section for more details on these + coordinates systems. + + +The Library +----------- + +Most classes and functions are available through the :mod:`harmonica` top level +package. Througout the documentation, we'll use ``hm`` as an alias for +:mod:`harmonica`. + +.. code:: + + import harmonica as hm + +.. seealso:: + + Checkout the :ref:`api` for a comprehensive list of the available function + and classes in Harmonica. diff --git a/doc/user_guide/coordinate_systems.rst b/doc/user_guide/coordinate_systems.rst new file mode 100644 index 000000000..eafe37e5b --- /dev/null +++ b/doc/user_guide/coordinate_systems.rst @@ -0,0 +1,232 @@ +.. _coordinate_systems: + +Coordinate systems +================== + +Gravity and magnetic data are usually observed at points above the Earth +surface, which can be referenced through different coordinate systems. +When processing these data, applying corrections, performing forward models and +inversions, we should take into account which type of coordinate system are we +using and if our choice introduces errors into these computations. + +Harmonica can handle three different 3D coordinate systems: + +- :ref:`Cartesian coordinates ` +- :ref:`Geodetic coordinates ` +- :ref:`Spherical coordinates ` + +.. _cartesian_coordinates: + +Cartesian coordinates +--------------------- + +Cartesian (or plain) coordinates are referenced by a system defined by three +orthogonal axis: two for the horizontal directions and one for the vertical +one. Harmonica name the two horizontal axis as *easting* and *northing*, +while the vertical one is referenced as *upward*, emphasizing its pointing +direction. This means that positive values implies points above the zeroth +plane, while negative values refer to points below. Harmonica assumes that +the *easting*, *northing* and *upward* coordinates are given in meters. + +For example, we can define a prism below the horizontal plane at zeroth +height: + +.. jupyter-execute:: + + import verde as vd + + # Define boundaries of the rectangular prism (in meters) + west, east, south, north = -20, 20, -20, 20 + bottom, top = -40, -20 + prism = [west, east, south, north, bottom, top] + +Or a regular grid of points at 100 meters above the zeroth plane: + +.. jupyter-execute:: + + # Define a regular grid of observation points (coordinates in meters) + coordinates = vd.grid_coordinates( + region=(-40, 40, -40, 40), shape=(5, 5), extra_coords=100 + ) + easting, northing, upward = coordinates[:] + + print("easting:", easting) + print("northing:", northing) + print("upward:", upward) + +Because the *upward* axis points in the upward direction, it's easy to check +if a given point is lower or higher than another one: + +.. jupyter-execute:: + + # Define two points + point_1 = (30, 20, -67) + point_2 = (30, 20, -58) + print("Point 1 is higher than point 2?", point_1[2] > point_2[2]) + + +.. _geodetic_coordinates: + +Geodetic coordinates +-------------------- + +Geodetic (or geographic) coordinates use a reference ellipsoid for defining +the *longitude* (:math:`\lambda`), *latitude* (:math:`\varphi`) and *height* +(:math:`h`) coordinates of points (see :ref:`geodetic coordinates figure`). +The reference ellipsoid is a mathematical surface that approximates the +figure of the Earth and it's used to define point coordinates. + +.. figure:: ../_static/figures/geodetic-coordinate-system.svg + :name: geodetic coordinates figure + :width: 90% + :alt: Figure showing a reference ellipsoid along with an observation point "p". It also shows the geodetic coordinates of this observation point: its longitude, latitude and geodetic height. + + Figure: Geodetic coordinates + + Reference ellipsoid and a point **p** along with a geocentric Cartesian + system (:math:`X`, :math:`Y`, :math:`Z`). Where :math:`a` and :math:`b` + are the semimajor and semiminor axes of the ellipsoid, while the + :math:`\lambda`, :math:`\varphi` and :math:`h` represent the geodetic + coordinates of the point **p** in this geodetic coordinate system, where + :math:`\lambda` is the *longitude*, :math:`\varphi` the *latitude* and + :math:`h` the *height*. + The :math:`\phi` is the *spherical latitude* of point **p** (see + :ref:`spherical_coordinates`). + This figure is a modified version of [Oliveira2021]_. + + +Harmonica assumes that *longitude*, *latitude* are given in decimal degrees +and the ellipsoidal height is given in meters. Positive values of *height* +refer to points outside the ellipsoid, while negative values refer to points +that live inside it. +Spatial data are usually given in geodetic coordinates, along with the +reference ellipsoid on which they are defined. + +For example, let's define a regular grid of points (separated by equal +angles) at 2km above the ellipsoid using :mod:`verde`. + +.. jupyter-execute:: + + coordinates = vd.grid_coordinates( + region=(-70, -65, -35, -30), shape=(6, 6), extra_coords=2e3 + ) + longitude, latitude, height = coordinates[:] + print("longitude:", longitude) + print("latitude:", latitude) + print("height:", height) + +Some processes need to know the reference ellipsoid used to define the +geodetic coordinates of points. :mod:`boule` offers several ellipsoids that +are commonly used on geophysical applications. Harmonica will ask for +a :class:`boule.Ellipsoid` instance as argument if it needs the reference +ellipsoid. + +Lets define the WGS84 ellipsoide using :mod:`boule`: + +.. jupyter-execute:: + + import boule as bl + + ellipsoid = bl.WGS84 + print(ellipsoid) + +Some other processes are only designed to work under Cartesian coordinates. +We can easily *transform* geodetic coordinates to Cartesian by applying +map **projections**. This can be done through :mod:`pyproj`. + +As an example, lets project the *longitude* and *latitude* coordinates of the +previously generated grid using a Mercator projection: + +.. jupyter-execute:: + + import pyproj + + # Define a Mercator projection through pyproj + projection = pyproj.Proj(proj="merc", ellps="WGS84") + + # Project the longitude and latitude coordinates of the grid points + longitude, latitude = coordinates[:2] + easting, northing = projection(longitude, latitude) + + print("easting:", easting) + print("northing:", northing) + +Remember that this process implies projecting the geodetic coordinates onto +a flat surface, what carries projection errors. For small regions, these +errors are small, but for regional and global regions, these can heavily +increase. Projections can also be used to recover geodetic coordinates from +Cartesian ones, by setting the ``inverse`` argument to ``True``. + +.. _spherical_coordinates: + +Spherical coordinates +--------------------- + +Spherical coordinates (a.k.a spherical geocentric coordinates) are defined +by a coordinate system whose origin is located on the center of the Earth. +Each point can be represented by its *longitude* (:math:`\lambda`), +*spherical latitude* (:math:`\phi`) and *radius* (:math:`r`) (see +:ref:`spherical coordinates figure`). + +.. important:: + + The *longitude* coordinates defined in *spherical coordinates* and in + *geodetic coordinates* are equivalent. + Nevertheless, the *spherical latitude* and the (geodetic) *latitude* are + not. They would be the same if the reference ellipsoid were a sphere. + +The *longitude* and *spherical latitude* are angles given in decimal degrees, +while the *radius* is the Euclidean distance between the point and the origin +of the system (in meters). +Although this reference system is rarely used for storing data, it's used for +some non-Cartesian forward models, like tesseroids (spherical prisms). + +.. figure:: ../_static/figures/spherical-coordinate-system.svg + :name: spherical coordinates figure + :width: 50% + :alt: Figure showing an observation point "p" defined in a spherical coordinate system. + + Figure: Spherical coordinates + + Point **p** defined in a spherical coordinate system, where + :math:`\lambda` is the *longitude*, :math:`\phi` the *latitude* and + :math:`r` the *radius*. The spherical coordinates are defined upon + a geocentric Cartesian system (:math:`X`, :math:`Y`, :math:`Z`) whose + origin is located in the center of the Earth. + +Let's define a regular grid of points in spherical coordinates, located at +the same radius equal to the *mean radius of the Earth*. + +.. jupyter-execute:: + + coordinates = vd.grid_coordinates( + region=(-70, -65, -35, -30), + shape=(6, 6), + extra_coords=ellipsoid.mean_radius, + ) + longitude, sph_latitude, radius = coordinates[:] + print("longitude:", longitude) + print("spherical latitude:", sph_latitude) + print("radius:", radius) + +We can convert spherical coordinates to geodetic ones through +:meth:`boule.Ellipsoid.spherical_to_geodetic`: + +.. jupyter-execute:: + + coordinates_geodetic = ellipsoid.spherical_to_geodetic(*coordinates) + longitude, latitude, height = coordinates_geodetic[:] + print("longitude:", longitude) + print("latitude:", latitude) + print("height:", height) + +While the conversion of spherical coordinates into geodetic ones can be +carried out through :meth:`boule.Ellipsoid.geodetic_to_spherical`: + +.. jupyter-execute:: + + coordinates_spherical = ellipsoid.geodetic_to_spherical(*coordinates_geodetic) + longitude, sph_latitude, radius = coordinates_spherical[:] + print("longitude:", longitude) + print("spherical latitude:", sph_latitude) + print("radius:", radius) diff --git a/doc/user_guide/equivalent_sources/block-averaged-eqs.rst b/doc/user_guide/equivalent_sources/block-averaged-eqs.rst new file mode 100644 index 000000000..a9afdb8a6 --- /dev/null +++ b/doc/user_guide/equivalent_sources/block-averaged-eqs.rst @@ -0,0 +1,196 @@ +.. _block-averaged-eqs: + +Block-averaged equivalent sources +================================= + +When we introduced the :ref:`equivalent_sources` we saw that +by default the :class:`harmonica.EquivalentSources` class locates one point +source beneath each data point during the fitting process, following +[Cooper2000]_. + +Alternatively, we can use another strategy: the *block-averaged sources*, +introduced in [Soler2021]_. +This method divides the survey region (defined by the data) into square blocks +of equal size, computes the median coordinates of the data points that fall +inside each block and locates one source beneath every averaged position. This +way, we define one equivalent source per block, with the exception of empty +blocks that won't get any source. + +This method has two main benefits: + +- It lowers the amount of sources involved in the interpolation, therefore it + reduces the computer memory requirements and the computation time of the + fitting and prediction processes. +- It might avoid to produce aliasing on the output grids, specially for + surveys with oversampling along a particular direction, like airborne ones. + +We can make use of the block-averaged sources within the +:class:`harmonica.EquivalentSources` class by passing a value to the +``block_size`` parameter, which controls the size of the blocks. + +Lets load some total-field magnetic data over Great Britain: + +.. jupyter-execute:: + + import ensaio + import pandas as pd + + fname = ensaio.fetch_britain_magnetic(version=1) + data = pd.read_csv(fname) + data + +In order to speed-up calculations we are going to slice a smaller portion of +the data: + +.. jupyter-execute:: + + import verde as vd + + region = (-5.5, -4.7, 57.8, 58.5) + inside = vd.inside((data.longitude, data.latitude), region) + data = data[inside] + data + +And project the geographic coordiantes to plain Cartesian ones: + +.. jupyter-execute:: + + import pyproj + + projection = pyproj.Proj(proj="merc", lat_ts=data.latitude.mean()) + easting, northing = projection(data.longitude.values, data.latitude.values) + coordinates = (easting, northing, data.height_m) + +.. jupyter-execute:: + + import matplotlib.pyplot as plt + + maxabs = vd.maxabs(data.total_field_anomaly_nt) + + plt.scatter( + easting, + northing, + c=data.total_field_anomaly_nt, + cmap="seismic", + vmin=-maxabs, + vmax=maxabs, + ) + plt.colorbar(aspect=40, pad=0.01, label="nT") + plt.gca().set_aspect("equal") + plt.gcf().set_size_inches(12, 9) + plt.title("Observed total-field magnetic anomaly") + plt.xlabel("easting [m]") + plt.ylabel("northing [m]") + plt.show() + +Most airborne surveys like this one present an anysotropic distribution of the +data: there are more observation points along the flight lines that goes west +to east than the ones going south to north. +Placing a single source beneath each observation point generates an anysotropic +distribution of the equivalent sources, which might lead to aliases on the +generated outputs. + +Instead, we can use the **block-averaged equivalent sources** by +creating a :class:`harmonica.EquivalentSources` instance passing the size of +the blocks through the ``block_size`` parameter. + +.. jupyter-execute:: + + import harmonica as hm + + eqs = hm.EquivalentSources( + depth=1000, damping=1, block_size=500, depth_type="constant" + ) + +These sources were set at a constant depth of 1km bellow the zeroth height and +with a ``damping`` equal to 1. See how you can choose values for these +parameters in :ref:`eqs-parameters-estimation`. + +.. note:: + + The depth of the sources can be set analogously to the regular equivalent + sources: we can use a ``constant`` depth (every source is located at the same + depth) or a ``relative`` depth (where each source is located at a constant + shift beneath the median location obtained during the block-averaging process). + The depth of the sources and which strategy to use can be set up through the + ``depth`` and the ``depth_type`` parameters, respectively. + +.. important:: + + We recommend using a ``block_size`` not larger than the desired resolution + of the interpolation grid. + +Now we can fit the equivalent sources against the magnetic data. During this +step the point sources are created through the block averaging process. + +.. jupyter-execute:: + + eqs.fit(coordinates, data.total_field_anomaly_nt) + +.. tip:: + + We can obtain the coordinates of the created sources through the ``points_`` + attribute. Lets see how many sources it created: + + .. jupyter-execute:: + + eqs.points_[0].size + + We have less sources than observation points indeed. + + +We can finally grid the magnetic data using the block-averaged equivalent +sources. We will generate a regular grid with a resolution of 500 m and at 1500 +m height. Since the maximum height of the observation points is around 1000 m +we are efectivelly upward continuing the data. + +.. jupyter-execute:: + + grid_coords = vd.grid_coordinates( + region=vd.get_region(coordinates), + spacing=500, + extra_coords=1500, + ) + grid = eqs.grid(grid_coords, data_names=["magnetic_anomaly"]) + grid + + +.. jupyter-execute:: + + fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12, 9), sharey=True) + + # Get the maximum absolute value between the original and gridded data so we + # can use the same color scale for both plots and have 0 centered at the white + # color. + maxabs = vd.maxabs(data.total_field_anomaly_nt, grid.magnetic_anomaly.values) + + ax1.set_title("Observed magnetic anomaly data") + tmp = ax1.scatter( + easting, + northing, + c=data.total_field_anomaly_nt, + s=20, + vmin=-maxabs, + vmax=maxabs, + cmap="seismic", + ) + plt.colorbar(tmp, ax=ax1, label="nT", pad=0.05, aspect=40, orientation="horizontal") + ax1.set_xlim(easting.min(), easting.max()) + ax1.set_ylim(northing.min(), northing.max()) + + ax2.set_title("Gridded and upward-continued") + tmp = grid.magnetic_anomaly.plot.pcolormesh( + ax=ax2, + add_colorbar=False, + add_labels=False, + vmin=-maxabs, + vmax=maxabs, + cmap="seismic", + ) + plt.colorbar(tmp, ax=ax2, label="nT", pad=0.05, aspect=40, orientation="horizontal") + ax2.set_xlim(easting.min(), easting.max()) + ax2.set_ylim(northing.min(), northing.max()) + + plt.show() + + diff --git a/doc/user_guide/equivalent_sources/eq-sources-spherical.rst b/doc/user_guide/equivalent_sources/eq-sources-spherical.rst new file mode 100644 index 000000000..4916c78fc --- /dev/null +++ b/doc/user_guide/equivalent_sources/eq-sources-spherical.rst @@ -0,0 +1,197 @@ +.. _eq-sources-spherical: + +Equivalent sources in spherical coordinates +=========================================== + +When interpolating gravity or magnetic data over a very large region that +covers full continents we need to take into account the curvature of the Earth. +In these cases projecting the data to plain Cartesian coordinates may introduce +errors due to the distorsions caused by it. +Therefore using the :class:`harmonica.EquivalentSources` class is not well +suited for it. + +Instead, we can make use of equivalent sources that are defined in +:ref:`spherical geocentric coordinates ` through the +:class:`harmonica.EquivalentSourcesSph` class. + +Lets start by fetching some gravity data over Southern Africa: + +.. jupyter-execute:: + + import ensaio + import pandas as pd + + fname = ensaio.fetch_southern_africa_gravity(version=1) + data = pd.read_csv(fname) + data + +To speed up the computations on this simple example we are going to downsample +the data using a blocked mean to a resolution of 6 arcminutes. + +.. jupyter-execute:: + + import numpy as np + import verde as vd + + blocked_mean = vd.BlockReduce(np.mean, spacing=6 / 60, drop_coords=False) + (longitude, latitude, height), gravity_data = blocked_mean.filter( + (data.longitude, data.latitude, data.height_sea_level_m), + data.gravity_mgal, + ) + + data = pd.DataFrame( + { + "longitude": longitude, + "latitude": latitude, + "height_sea_level_m": height, + "gravity_mgal": gravity_data, + } + ) + data + +And compute gravity disturbance by subtracting normal gravity using +:mod:`boule` (see :ref:`gravity_disturbance` for more information): + +.. jupyter-execute:: + + import boule as bl + + ellipsoid = bl.WGS84 + normal_gravity = ellipsoid.normal_gravity(data.latitude, data.height_sea_level_m) + gravity_disturbance = data.gravity_mgal - normal_gravity + +Lets define some equivalent sources in spherical coordinates. We will choose +some first guess values for the ``damping`` and ``depth`` parameters. + +.. jupyter-execute:: + + import harmonica as hm + + eqs = hm.EquivalentSourcesSph(damping=1e-3, relative_depth=10000) + +.. seealso:: + + Check how we can :ref:`estimate the damping and depth parameters + ` using a cross-validation. + +Before we can fit the sources' coefficients we need to convert the data given +in geographical coordinates to spherical ones. We can do it through the +:meth:`boule.Ellipsoid.geodetic_to_spherical` method of the WGS84 ellipsoid +defined in :mod:`boule`. + +.. jupyter-execute:: + + coordinates = ellipsoid.geodetic_to_spherical( + data.longitude, data.latitude, data.height_sea_level_m + ) + +And then use them to fit the sources: + +.. jupyter-execute:: + + eqs.fit(coordinates, gravity_disturbance) + +We can then use these sources to predict the gravity disturbance on a regular +grid defined in geodetic coordinates. We will generate a regular grid of +computation points located at the maximum height of the data and with a spacing +of 6 arcminutes. + +.. jupyter-execute:: + + # Get the bounding region of the data in geodetic coordinates + region = vd.get_region((data.longitude, data.latitude)) + + # Get the maximum height of the data coordinates + max_height = data.height_sea_level_m.max() + + # Define a regular grid of points in geodetic coordinates + grid_coords = vd.grid_coordinates( + region=region, spacing=6 / 60, extra_coords=max_height + ) + +But before we can tell the equivalent sources to predict the +field we need to convert the grid coordinates to spherical. + +.. jupyter-execute:: + + grid_coords_sph = ellipsoid.geodetic_to_spherical(*grid_coords) + +And then predict the gravity disturbance on the grid points: + +.. jupyter-execute:: + + gridded_disturbance = eqs.predict(grid_coords_sph) + +Lastly we can generate a :class:`xarray.DataArray` using +:func:`verde.make_xarray_grid`: + +.. jupyter-execute:: + + grid = vd.make_xarray_grid( + grid_coords, + gridded_disturbance, + data_names=["gravity_disturbance"], + extra_coords_names="upward", + ) + grid + +Since the data points don't cover the entire area, we might want to mask those +grid points that are too far away from any data point: + +.. jupyter-execute:: + + grid = vd.distance_mask( + data_coordinates=(data.longitude, data.latitude), maxdist=0.5, grid=grid + ) + +Lets plot it: + +.. jupyter-execute:: + + import matplotlib.pyplot as plt + import cartopy.crs as ccrs + + maxabs = vd.maxabs(gravity_disturbance, grid.gravity_disturbance.values) + + fig, (ax1, ax2) = plt.subplots( + nrows=1, + ncols=2, + figsize=(12, 9), + sharey=True, + subplot_kw=dict( + projection=ccrs.Mercator(central_longitude=100) + ) + ) + tmp = ax1.scatter( + data.longitude, + data.latitude, + c=gravity_disturbance, + s=3, + vmin=-maxabs, + vmax=maxabs, + cmap="seismic", + transform=ccrs.PlateCarree(), + ) + tmp = grid.gravity_disturbance.plot.pcolormesh( + ax=ax2, + vmin=-maxabs, + vmax=maxabs, + cmap="seismic", + add_colorbar=False, + add_labels=False, + transform=ccrs.PlateCarree(), + ) + + ax1.gridlines(draw_labels=["bottom", "left"], linewidth=0) + ax1.set_title("Block-median reduced gravity disturbance") + ax2.gridlines(draw_labels=["bottom"], linewidth=0) + ax2.set_title("Gridded gravity disturbance") + + for ax in (ax1, ax2): + ax.set_extent(region, crs=ccrs.PlateCarree()) + ax.coastlines() + plt.colorbar( + tmp, ax=ax, label="mGal", pad=0.05, aspect=40, orientation="horizontal" + ) + + plt.show() diff --git a/doc/user_guide/equivalent_sources/eqs-parameters-estimation.rst b/doc/user_guide/equivalent_sources/eqs-parameters-estimation.rst new file mode 100644 index 000000000..99e7565c4 --- /dev/null +++ b/doc/user_guide/equivalent_sources/eqs-parameters-estimation.rst @@ -0,0 +1,224 @@ +.. _eqs-parameters-estimation: + +Estimating damping and depth parameters +======================================= + +When interpolating gravity and magnetic data through the +:ref:`equivalent_sources` technique we need to choose values for some +parameters, like the ``depth`` at which the sources will be located or the +amount of ``damping`` that should be applied. + +The choice of these *hyperparameters* can significantly affect the accuracy of +the predictions. One way to make this choice could be a visual inspection of +the predictions, but that could be tedious and non-objective. Instead, we could +estimate these hyperparameters by evaluating the performance of the equivalent +sources with different values for each hyperparameter through a **cross +validation**. + +.. seealso:: + + Evaluating the performance of :class:`EquivalentSources` through cross + validation is very similar to how we do it for any :mod:`verde` gridder. + Refer to Verde's + `Evaluating Performance + `_ + and `Model Selection + `_ + for further details. + + +Cross-validating equivalent sources +----------------------------------- + +Lets start by loading some sample gravity data: + +.. jupyter-execute:: + + import ensaio + import pandas as pd + + fname = ensaio.fetch_bushveld_gravity(version=1) + data = pd.read_csv(fname) + data + +And project their coordinates using a Mercator projection: + +.. jupyter-execute:: + + import pyproj + + projection = pyproj.Proj(proj="merc", lat_ts=data.latitude.values.mean()) + easting, northing = projection(data.longitude.values, data.latitude.values) + + coordinates = (easting, northing, data.height_geometric_m.values) + +Lets fit the gravity disturbance using equivalent sources and a first guess for +the ``depth`` and ``damping`` parameters. + +.. jupyter-execute:: + + import harmonica as hm + + eqs_first_guess = hm.EquivalentSources(depth=1e3, damping=1) + eqs_first_guess.fit(coordinates, data.gravity_disturbance_mgal) + + +We can use a **cross-validation** to evaluate how well these equivalent sources +can accurately predict the values of the field on unobserved locations. +We will use :func:`verde.cross_val_score` and then we will compute the mean +value of the score obtained after each cross validation. + +.. jupyter-execute:: + + import numpy as np + import verde as vd + + score_first_guess = np.mean( + vd.cross_val_score( + eqs_first_guess, + coordinates, + data.gravity_disturbance_mgal, + ) + ) + score_first_guess + +The resulting score corresponds to the R^2. It represents how well the +equivalent sources can reproduce the variation of our data. As closer it gets +to one, the better the quality of the predictions. + + +Estimating hyperparameters +-------------------------- + +We saw that we can evaluate the performance of some equivalent sources with +some values for the ``depth`` and ``damping`` parameters through cross +validation. +Now, lets use it to estimate a set of hyperparameters that produce more +accurate predictions. +To do so we are going to apply a simple grid search over the ``depth``, +``damping`` space, apply cross validation for each pair of values and keeping +track of their score. + + +Lets start by defining some possible values of ``damping`` and ``depth`` to +explore: + +.. jupyter-execute:: + + + dampings = [0.01, 0.1, 1, 10,] + depths = [5e3, 10e3, 20e3, 50e3] + +.. note:: + + The actual value of the damping is not significant as its order of + magnitude. Exploring different powers of ten is a good place to start. + +Then we can build a ``parameter_sets`` list where each element corresponds to +each possible combination of the values of ``dampings`` and ``depths``: + +.. jupyter-execute:: + + import itertools + + parameter_sets = [ + dict(damping=combo[0], depth=combo[1]) + for combo in itertools.product(dampings, depths) + ] + print("Number of combinations:", len(parameter_sets)) + print("Combinations:", parameter_sets) + +And now we can actually ran one cross validation for each pair of parameters: + +.. jupyter-execute:: + + equivalent_sources = hm.EquivalentSources() + + scores = [] + for params in parameter_sets: + equivalent_sources.set_params(**params) + score = np.mean( + vd.cross_val_score( + equivalent_sources, + coordinates, + data.gravity_disturbance_mgal, + ) + ) + scores.append(score) + scores + +Once every score has been computed, we can obtain the best score and the +corresponding parameters that generate it: + +.. jupyter-execute:: + + best = np.argmax(scores) + print("Best score:", scores[best]) + print("Score with defaults:", score_first_guess) + print("Best parameters:", parameter_sets[best]) + +We have actually improved our score! + +Finally, lets grid the gravity disturbance data using the equivalent sources of +the first guess and the best ones obtained after cross validation. + + +Create some equivalent sources out of the best set of parameters: + +.. jupyter-execute:: + + eqs_best = hm.EquivalentSources(**parameter_sets[best]).fit( + coordinates, data.gravity_disturbance_mgal + ) + +And grid the data using the two equivalent sources: + +.. jupyter-execute:: + + # Define grid coordinates + grid_coords = vd.grid_coordinates( + region=vd.get_region(coordinates), + spacing=2e3, + extra_coords=2.5e3, + ) + + grid_first_guess = eqs_first_guess.grid(grid_coords) + grid = eqs_best.grid(grid_coords) + +Lets plot it: + +.. jupyter-execute:: + + import matplotlib.pyplot as plt + + + maxabs = vd.maxabs(grid_first_guess.scalars, grid.scalars) + + fig, (ax1, ax2) = plt.subplots(figsize=(12, 12), ncols=2, nrows=1, sharey=True) + + cbar_kwargs = dict(orientation="horizontal", aspect=50, pad=0.05, label="mGal") + grid_first_guess.scalars.plot.pcolormesh( + ax=ax1, + vmin=-maxabs, + vmax=maxabs, + cmap="seismic", + cbar_kwargs=cbar_kwargs, + ) + grid.scalars.plot.pcolormesh( + ax=ax2, + vmin=-maxabs, + vmax=maxabs, + cmap="seismic", + cbar_kwargs=cbar_kwargs, + ) + + ax1.set_title("Gravity disturbance with first guess") + ax2.set_title("Gravity disturbance with best params") + for ax in (ax1, ax2): + ax.set_aspect("equal") + plt.show() + +The best parameters not only produce a better score, but they also generate +a visible more accurate predictions. In the first plot the equivalent sources +are so shallow that we can actually see the distribution of sources in the +produced grid. diff --git a/doc/user_guide/equivalent_sources/gradient-boosted-eqs.rst b/doc/user_guide/equivalent_sources/gradient-boosted-eqs.rst new file mode 100644 index 000000000..efe703e1c --- /dev/null +++ b/doc/user_guide/equivalent_sources/gradient-boosted-eqs.rst @@ -0,0 +1,192 @@ +.. _gradient-boosted-eqs: + +Gradient-boosted equivalent sources +=================================== + +As mentioned in :ref:`equivalent_sources`, one of the main drawbacks of the +equivalent sources technique is the high computational load required to +estimate source coefficients and ultimately to predict field values. +During the source coefficients estimation step (while running the +:meth:`harmonica.EquivalentSources.fit` method) a Jacobian matrix with a size +of number of sources times number of observation points must be built. +This Jacobian matrix could become too large to fit in memory if the number of +data points and sources are too big. + +.. note:: + + Inteprolating 200000 data points using 200000 sources would require more + than 300 GB only to store the Jacobian matrix (using 64 bits floats for each + matrix element). + +One way to reduce the memory requirement is using :ref:`block-averaged-eqs`, +but it might be not enough when working with very large datasets. + +A solution to this problem is to use **gradient-boosted equivalent sources**, +introduced in [Soler2021]_. +This new methodology creates :math:`k` overlapping windows of equal size that +cover the entire survey area and defines one set of equivalent sources for each +windows, where each set is formed by the original equivalent sources that fall +inside its corresponding window. +The estimation of the coefficients is then carried out iteratively passing +through one window at a time. On each iteration the coefficients of the +selected subset of sources are fit against the observation points that fall +inside the same window. After each iteration the field generated by those +adjusted sources are predicted on every computation point and the *residue* is +updated. +The process finishes when every window has been visited. + +The gradient-boosted equivalent sources have been included in Harmonica and can +be used through the :class:`harmonica.EquivalentSourcesGB` class. + +Lets load some gravity data for Southern Africa: + +.. jupyter-execute:: + + import ensaio + import pandas as pd + + fname = ensaio.fetch_southern_africa_gravity(version=1) + data = pd.read_csv(fname) + data + +.. note:: + + This gravity dataset is small enough to be interpolated with equivalent + sources in any modest personal computer. Nevertheless, we will grid it using + gradient-boosted equivalent sources to speed up the computations on this + small example. + +Compute gravity disturbance by subtracting normal gravity using :mod:`boule` +(see :ref:`gravity_disturbance` for more information): + +.. jupyter-execute:: + + import boule as bl + + normal_gravity = bl.WGS84.normal_gravity(data.latitude, data.height_sea_level_m) + disturbance = data.gravity_mgal - normal_gravity + +And project the data to plain coordinates using a Mercator projection: + +.. jupyter-execute:: + + import pyproj + + projection = pyproj.Proj(proj="merc", lat_ts=data.latitude.mean()) + easting, northing = projection(data.longitude.values, data.latitude.values) + coordinates = (easting, northing, data.height_sea_level_m) + +We can define some gradient-boosted equivalent sources through the +:class:`harmonica.EquivalentSourcesGB`. When doing so we need to specify the +size of the windows that will be used in the gradient boosting process. +We will use square windows of 100 km per side. +Moreover, gradient-boosted equivalent sources can be used together with +:ref:`block-averaged-eqs`. We will do so choosing a *block size* of 2 km +per side. + + +.. jupyter-execute:: + + import harmonica as hm + + eqs = hm.EquivalentSourcesGB( + depth=9e3, damping=10, block_size=2e3, window_size=100e3, random_state=42 + ) + +.. important:: + + Smaller windows reduce the memory requirements for building the Jacobian + matrix, but also reduces the accuracy of the interpolations. We recommend + using the **maximum window size** that produces Jacobian matrices that can + fit in memory. + +.. note:: + + The order in which windows are explored is randomized. By passing a value + to ``random_state`` we ensure to obtain always the same result everytime we + fit these equivalent sources. + +We can use the :meth:`harmonica.EquivalentSourcesGB.estimate_required_memory` +method to find out how much memory we will need to store the Jacobian matrix +given the coordinates of the observation points. The value is given in bytes. + +.. jupyter-execute:: + + eqs.estimate_required_memory(coordinates) + +Once we are sure that we have enough memory to store these Jacobian matrices we +can fit the sources against the gravity disturbance data: + +.. jupyter-execute:: + + eqs.fit(coordinates, disturbance) + +And then predict the field on a regular grid of computation points: + +.. jupyter-execute:: + + import verde as vd + + grid_coords = vd.grid_coordinates( + region=vd.get_region(coordinates), + spacing=5e3, + extra_coords=2.5e3, + ) + grid = eqs.grid(grid_coords, data_names=["gravity_disturbance"]) + grid + +Since this particular dataset doesn't have a good coverage of the entire area, +we might want to mask the output grid based on the distance to the closest data +point. We can do so through the :func:`verde.distance_mask` function. + +.. jupyter-execute:: + + grid_masked = vd.distance_mask(coordinates, maxdist=50e3, grid=grid) + +And plot it: + +.. jupyter-execute:: + + import matplotlib.pyplot as plt + + fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12, 9), sharey=True) + + region = vd.get_region(coordinates) + maxabs = vd.maxabs(disturbance, grid_masked.gravity_disturbance) + + ax1.set_title("Observed gravity disturbance data") + tmp = ax1.scatter( + easting, + northing, + c=disturbance, + s=5, + vmin=-maxabs, + vmax=maxabs, + cmap="seismic", + ) + plt.colorbar( + tmp, ax=ax1, label="mGal", pad=0.07, aspect=40, orientation="horizontal" + ) + + ax2.set_title("Gridded with gradient-boosted equivalent sources") + tmp = grid_masked.gravity_disturbance.plot.pcolormesh( + ax=ax2, + add_colorbar=False, + add_labels=False, + vmin=-maxabs, + vmax=maxabs, + cmap="seismic", + ) + plt.colorbar( + tmp, ax=ax2, label="mGal", pad=0.07, aspect=40, orientation="horizontal" + ) + + for ax in (ax1, ax2): + ax.set_aspect("equal") + ax.set_xlim(*region[:2]) + ax.set_ylim(*region[2:]) + + plt.show() + + + diff --git a/doc/user_guide/equivalent_sources/index.rst b/doc/user_guide/equivalent_sources/index.rst new file mode 100644 index 000000000..6bee0287c --- /dev/null +++ b/doc/user_guide/equivalent_sources/index.rst @@ -0,0 +1,185 @@ +.. _equivalent_sources: + +Equivalent Sources +================== + +.. toctree:: + :hidden: + + eqs-parameters-estimation + block-averaged-eqs + gradient-boosted-eqs + eq-sources-spherical + +Most potential field surveys gather data along scattered and uneven flight +lines or ground measurements. For a great number of applications we may need to +interpolate these data points onto a regular grid at a constant altitude. +Upward-continuation is also a routine task for smoothing, noise attenuation, +source separation, etc. + +Both tasks can be done simultaneously through an *equivalent sources* +[Dampney1969]_ (a.k.a *equivalent layer*). The equivalent sources technique +consists in defining a finite set of geometric bodies (like point sources) +beneath the observation points and adjust their coefficients so they generate +the same measured field on the observation points. These fitted sources can be +then used to *predict* the values of this field on any unobserved location, +like a regular grid, points at different heights, any set of scattered +points or even along a profile. + +The equivalent sources have two major advantages over any general purpose +interpolator: + +* it takes into account the 3D nature of the potential fields being measured by + considering the observation heights, and +* its predictions are always harmonic. + +Its main drawback is the increased computational load it takes to fit the +sources' coefficients, both in terms of memory and computation time). + +Harmonica has a few different classes for applying the equivalent sources +techniques. Here we will explore how we can use the +:class:`harmonica.EquivalentSources` to interpolate some gravity disturbance +scattered points on a regular grid with a small upward continuation. + +We can start by downloading some sample gravity data over the Bushveld Igneous +Complex in Southern Africa: + +.. jupyter-execute:: + + import ensaio + import pandas as pd + + fname = ensaio.fetch_bushveld_gravity(version=1) + data = pd.read_csv(fname) + data + +The :class:`harmonica.EquivalentSources` class works exclusively in Cartesian +coordinates, so we need to project these gravity observations: + +.. jupyter-execute:: + + import pyproj + + projection = pyproj.Proj(proj="merc", lat_ts=data.latitude.values.mean()) + easting, northing = projection(data.longitude.values, data.latitude.values) + +Now we can initialize the :class:`harmonica.EquivalentSources` class. + + +.. jupyter-execute:: + + import harmonica as hm + + equivalent_sources = hm.EquivalentSources(depth=10e3, damping=10) + equivalent_sources + +By default, it places the sources one beneath each data point at a relative +depth from the elevation of the data point following [Cooper2000]_. +This *relative depth* can be set through the ``depth`` argument. +Deepest sources generate smoother predictions (*underfitting*), while shallow +ones tend to overfit the data. + +.. note:: + + If instead we want to place every source at a constant depth, we can change + it by passing ``depth_type="constant"``. In that case, the ``depth`` + argument will be the exact depth at which the sources will be located. + +The ``damping`` parameter is used to smooth the coefficients of the sources and +stabilize the least square problem. A higher ``damping`` will create smoother +predictions, while a lower one could overfit the data and create artifacts. + +Now we can estimate the source coefficients through the +:meth:`harmonica.EquivalentSources.fit` method against the observed gravity +disturbance. + +.. jupyter-execute:: + + coordinates = (easting, northing, data.height_geometric_m) + equivalent_sources.fit(coordinates, data.gravity_disturbance_mgal) + +Once the fitting process finishes, we can predict the values of the field on +any set of points using the :meth:`harmonica.EquivalentSources.predict` method. +For example, lets predict on the same observation points to check if the +sources are able to reproduce the observed field. + +.. jupyter-execute:: + + disturbance = equivalent_sources.predict(coordinates) + + +And plot it: + +.. jupyter-execute:: + + import verde as vd + import matplotlib.pyplot as plt + + # Get max absolute value for the observed gravity disturbance + maxabs = vd.maxabs(data.gravity_disturbance_mgal) + + # Create a figure with two axes + fig, (ax1, ax2) = plt.subplots(figsize=(10, 12), nrows=2, ncols=1, sharex=True) + + # Plot observed and predicted fields + tmp = ax1.scatter( + easting, + northing, + c=data.gravity_disturbance_mgal, + s=2, + vmin=-maxabs, + vmax=maxabs, + cmap="seismic", + ) + ax1.set_title("Observed gravity disturbance") + ax2.scatter( + easting, + northing, + c=disturbance, + s=2, + vmin=-maxabs, + vmax=maxabs, + cmap="seismic", + ) + ax2.set_title("Predicted gravity disturbance") + + for ax in (ax1, ax2): + ax.set_aspect("equal") + plt.colorbar(tmp, ax=ax, label="mGal", pad=0.05, aspect=20, shrink=0.9) + plt.show() + +We can also *grid* and *upper continue* the field by predicting its values on +a regular grid at a constant height higher than the observations. To do so we +can use the :func:`verde.grid_coordinates` function to create the coordinates +of the grid and then use the :meth:`harmonica.EquivalentSources.grid` method. + +First, lets get the maximum height of the observations: + +.. jupyter-execute:: + + data.height_geometric_m.max() + +Then create the grid coordinates at a constant height of and a spacing of 2km; +and use the equivalent sources to generate a gravity disturbance grid. + +.. jupyter-execute:: + + # Get region of the observations + region = vd.get_region(coordinates) + + # Build the grid coordinates + grid_coords = vd.grid_coordinates(region=region, spacing=2e3, extra_coords=2.2e3) + + # Grid the gravity disturbances + grid = equivalent_sources.grid(grid_coords, data_names=["gravity_disturbance"]) + grid + +And plot it + +.. jupyter-execute:: + + fig = plt.figure(figsize=(12, 8)) + + grid.gravity_disturbance.plot() + plt.gca().set_aspect("equal") + plt.show() diff --git a/doc/user_guide/forward_modelling/index.rst b/doc/user_guide/forward_modelling/index.rst new file mode 100644 index 000000000..9fe9b38eb --- /dev/null +++ b/doc/user_guide/forward_modelling/index.rst @@ -0,0 +1,34 @@ +.. _forward_modelling: + +Forward modelling +================= + +Harmonica offers some functions and classes to calculate the gravity and magnetic +fields generated by different 3D bodies. Most of these functions require three +main arguments: + +- The **computation points**, which are usually passed as the ``coordinates`` + argument. These are the set of points where the field will be computed. They + must be a tuple containing its coordinates in the following order: + ``easting``, ``northing`` and ``upward`` or ``longitude``, ``latitude`` and + ``radius`` (depending on the coordinate system that's being used). +- The **sources** or **geometric bodies** that generate the field that will be + computed. They must be often specified by their location or their boundaries. +- The **physical properties** of the sources. For example, when computing + gravity fields we might need to pass either the *density* or the *mass* of + the sources. + +Optionally, we might need to specify which type of *field* we want to compute. +For example, we might choose the *potential* gravity field or the vertical +component of the gravity acceleration (``"g_z"``). + +Harmonica currently provides forward modelling functions for the following +It offers the +geometric bodies: + +.. toctree:: + :titlesonly: + + point + prism + tesseroid diff --git a/doc/user_guide/forward_modelling/point.rst b/doc/user_guide/forward_modelling/point.rst new file mode 100644 index 000000000..da6c3c276 --- /dev/null +++ b/doc/user_guide/forward_modelling/point.rst @@ -0,0 +1,231 @@ +.. _point: + +Point sources +============= + +We can compute the gravitational field of point masses through the +:func:`harmonica.point_gravity` function. +It offers the possibility to define point sources and computation points either +in Cartesian or in spherical coordinates. + +Each point mass can be defined as a tuple containing its coordinates in the +following order: *easting*, *northing* and *upward* (in Cartesian coordinates) +or *longitude*, *spherical latitude* and *radius* (in spherical coordinates). + +Cartesian coordinates +--------------------- + +.. jupyter-execute:: + :hide-code: + + import harmonica as hm + +For example, lets define a single point mass and compute the gravitational +potential it generates on a computation point located 100 meters above it. + +Define the point source and its mass (in kg): + +.. jupyter-execute:: + + point = (400, 300, 200) + mass = 1e6 + +Define a computation point located 100m above the point source: + +.. jupyter-execute:: + + coordinates = (point[0], point[1], point[2] + 100) + +And finally, compute the gravitational potential field it generates on the +computation point: + +.. jupyter-execute:: + + potential = hm.point_gravity(coordinates, point, mass, field="potential") + print(potential, "J/kg") + + +Working with multiple sources +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +We can easily compute the gravitational field of a set of point sources by +grouping its coordinates in arrays and with a single call of the +:func:`harmonica.point_gravity`. + +Lets define a set of four point sources, along with their masses: + +.. jupyter-execute:: + + import numpy as np + + easting = np.array([250, 750, 250, 750]) + northing = np.array([250, 250, 750, 750]) + upward = np.array([-100, -100, -100, -100]) + points = (easting, northing, upward) + + masses = np.array([1e6, -1e6, 2e6, -3e6]) + +We can define a set of computation points located on a regular grid at zero +height: + +.. jupyter-execute:: + + import verde as vd + + coordinates = vd.grid_coordinates( + region=(-250, 1250, -250, 1250), shape=(40, 40), extra_coords=0 + ) + +And finally calculate the vertical component of the gravitational acceleration +generated by the whole set of point sources on every computation point: + +.. jupyter-execute:: + + g_z = hm.point_gravity(coordinates, points, masses, field="g_z") + +.. note:: + + When passing multiple sources and coordinates to + :func:`harmonica.point_gravity` we calculate the field in parallel using + multiple CPUs, speeding up the computation. + +Lets plot this gravitational field: + +.. jupyter-execute:: + + import matplotlib.pyplot as plt + + maxabs = vd.maxabs(g_z) + plt.pcolormesh( + *coordinates[:2], g_z, vmin=-maxabs, vmax=maxabs, cmap="seismic" + ) + plt.colorbar(label="mGal") + plt.gca().set_aspect("equal") + plt.xlabel("easting [m]") + plt.ylabel("northing [m]") + plt.show() + + + +Spherical coordinates +--------------------- + +Alternatively, we can compute the gravitational fields of point sources defined +in a spherical coordinate system. To do so, we need to pass the +``coordinate_system`` argument as ``"spherical"```. The coordinates of the +point source must now be passed as *longitude*, *spherical latitude* and +*radius*, where the two former ones must be in decimal degrees and the latter +in meters. + +Lets define a single point source in the equator, at a longitude of 45 degrees +and located on the surface of the WGS84 ellipsoid. + +We can start by obtaining the WGS84 ellipsoid from :mod:`boule`: + +.. jupyter-execute:: + + import boule as bl + + ellipsoid = bl.WGS84 + + +Then we can define the point source in the equator along with its mass: + +.. jupyter-execute:: + + longitude, latitude = 45, 0 + radius = ellipsoid.geocentric_radius(latitude, geodetic=False) + point = (longitude, latitude, radius) + + mass = 1e6 + +We can also define a computation point located 1km above the point source: + +.. jupyter-execute:: + + coordinates = (point[0], point[1], point[2] + 1000) + +And finally compute the gravitational field generated by the source on the +computation point: + +.. jupyter-execute:: + + g_z = hm.point_gravity( + coordinates, point, mass, field="g_z", coordinate_system="spherical" + ) + print(g_z, "mGal") + + +Working with sources defined in geodetic coordinates +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +If our point sources and computation points are defined in geodetic +coordinates, we can use the :meth:`boule.Ellipsoid.geodetic_to_spherical` +method to convert them to spherical coordinates and use them to compute their +gravitational field. + +Lets define a set point sources in geodetic coordinates and their masses: + +.. jupyter-execute:: + + longitude = np.array([-71, -71, -69, -69]) + latitude = np.array([-45, -43, -45, -43]) + height = np.array([-10e3, -20e3, -30e3, -20e3]) + points = (longitude, latitude, height) + + masses = np.array([1e6, 2e6, -3e6, 5e6]) + + +Then, build a regular grid of computation points defined in geodetic +coordinates and located at 1km above the ellipsoid: + +.. jupyter-execute:: + + coordinates = vd.grid_coordinates( + region=(-72, -68, -46, -42), + shape=(101, 101), + extra_coords=20e3, + ) + +Before we can start forward modelling these sources, we need to convert them to +spherical coordinates. To do so, we can use the +:meth:`boule.Ellipsoid.geodetic_to_spherical` method: + +.. jupyter-execute:: + + points_spherical = ellipsoid.geodetic_to_spherical(*points) + coordinates_spherical = ellipsoid.geodetic_to_spherical(*coordinates) + +We can finally use these converted coordinates to compute the gravitational +field the source generate on every computation point: + +.. jupyter-execute:: + + g_z = hm.point_gravity( + coordinates_spherical, + points_spherical, + masses, + field="g_z", + coordinate_system="spherical", + ) + +Lets plot these results using :mod:`cartopy`: + +.. jupyter-execute:: + + import cartopy.crs as ccrs + + plt.figure(figsize=(8, 6)) + ax = plt.axes(projection=ccrs.Mercator()) + maxabs = vd.maxabs(g_z) + tmp = ax.pcolormesh( + *coordinates[:2], + g_z, + vmin=-maxabs, + vmax=maxabs, + cmap="seismic", + transform=ccrs.PlateCarree(), + ) + ax.set_title("Gravitational acceleration (downward)") + plt.colorbar(tmp, ax=ax, pad=0.04, shrink=0.73, label="mGal") + plt.show() diff --git a/doc/user_guide/forward_modelling/prism.rst b/doc/user_guide/forward_modelling/prism.rst new file mode 100644 index 000000000..dfac9654f --- /dev/null +++ b/doc/user_guide/forward_modelling/prism.rst @@ -0,0 +1,200 @@ +.. _prism: + +Rectangular prisms +================== + +One of the geometric bodies that Harmonica is able to forward model are +*rectangular prisms*. +We can compute their gravitational fields through the +:func:`harmonica.prism_gravity` function. + +Rectangular prisms can only be defined in Cartesian coordinates, therefore they +are intended to be used to forward model bodies in a small geographic region, +in which we can neglect the curvature of the Earth. + +Each prism can be defined as a tuple containing its boundaries in the following +order: *west*, *east*, *south*, *north*, *bottom*, *top* in Cartesian +coordinates and in meters. + +.. jupyter-execute:: + :hide-code: + + import harmonica as hm + +Lets define a single prism and compute the gravitational potential +it generates on a computation point located at 500 meters above its uppermost +surface. + +Define the prism and its density (in kg per cubic meters): + +.. jupyter-execute:: + + prism = (-100, 100, -100, 100, -1000, 500) + density = 3000 + +Define a computation point located 500 meters above the *top* surface of the +prism: + +.. jupyter-execute:: + + coordinates = (0, 0, 1000) + +And finally, compute the gravitational potential field it generates on the +computation point: + +.. jupyter-execute:: + + potential = hm.prism_gravity(coordinates, prism, density, field="potential") + print(potential, "J/kg") + + +Passing multiple prisms +^^^^^^^^^^^^^^^^^^^^^^^ + +We can compute the gravitational field of a set of prisms by passing a list of +them, where each prism is defined as mentioned before, and then making +a single call of the :func:`harmonica.prism_gravity` function. + +Lets define a set of four prisms, along with their densities: + +.. jupyter-execute:: + + prisms = [ + [2e3, 3e3, 2e3, 3e3, -10e3, -1e3], + [3e3, 4e3, 7e3, 8e3, -9e3, -1e3], + [7e3, 8e3, 1e3, 2e3, -7e3, -1e3], + [8e3, 9e3, 6e3, 7e3, -8e3, -1e3], + ] + densities = [2670, 3300, 2900, 2980] + +We can define a set of computation points located on a regular grid at zero +height: + +.. jupyter-execute:: + + import verde as vd + + coordinates = vd.grid_coordinates( + region=(0, 10e3, 0, 10e3), shape=(40, 40), extra_coords=0 + ) + +And finally calculate the vertical component of the gravitational acceleration +generated by the whole set of prisms on every computation point: + +.. jupyter-execute:: + + g_z = hm.prism_gravity(coordinates, prisms, densities, field="g_z") + +.. note:: + + When passing multiple prisms and coordinates to + :func:`harmonica.prism_gravity` we calculate the field in parallel using + multiple CPUs, speeding up the computation. + +Lets plot this gravitational field: + +.. jupyter-execute:: + + import matplotlib.pyplot as plt + + plt.pcolormesh(coordinates[0], coordinates[1], g_z) + plt.colorbar(label="mGal") + plt.gca().set_aspect("equal") + plt.xlabel("easting [m]") + plt.ylabel("northing [m]") + plt.show() + + +.. _prism_layer: + +Prism layer +^^^^^^^^^^^ + +One of the most common usage of prisms is to model geologic structures. +Harmonica offers the possibility to define a layer of prisms through the +:func:`harmonica.prism_layer` function: a regular grid of +prisms of equal size along the horizontal dimensions and with variable top and +bottom boundaries. +It returns a :class:`xarray.Dataset` with the coordinates of the centers of the +prisms and their corresponding physical properties. + +The :class:`harmonica.DatasetAccessorPrismsLayer` Dataset accessor can be used +to obtain some properties of the layer like its shape and size or the +boundaries of any prism in the layer. +Moreover, we can use the :meth:`harmonica.DatasetAcessorPrismsLayer.gravity` +method to compute the gravitational field of the prism layer on any set of +computation points. + +Lets create a simple prism layer, whose lowermost boundaries will be set on +zero and their uppermost boundary will approximate a sinusoidal function. +We can start by setting the region of the layer and the horizontal dimensions +of the prisms: + +.. jupyter-execute:: + + region = (0, 100e3, -40e3, 40e3) + spacing = 2000 + +Then we can define a regular grid where the centers of the prisms will fall: + +.. jupyter-execute:: + + easting, northing = vd.grid_coordinates(region=region, spacing=spacing) + +We need to define a 2D array for the uppermost *surface* of the layer. We will +sample a trigonometric function for this simple example: + +.. jupyter-execute:: + + import numpy as np + + wavelength = 24 * spacing + surface = np.abs(np.sin(easting * 2 * np.pi / wavelength)) + +Lets assign the same density to each prism through a 2d array with the same +value: 2700 kg per cubic meter. + +.. jupyter-execute:: + + density = np.full_like(surface, 2700) + +Now we can define the prism layer specifying the reference level to zero: + +.. jupyter-execute:: + + prisms = hm.prism_layer( + coordinates=(easting, northing), + surface=surface, + reference=0, + properties={"density": density}, + ) + +Lets define a grid of observation points at 1 km above the zeroth height: + +.. jupyter-execute:: + + region_pad = vd.pad_region(region, 10e3) + coordinates = vd.grid_coordinates( + region_pad, spacing=spacing, extra_coords=1e3 + ) + + +And compute the gravitational field generated by the prism layer on them: + +.. jupyter-execute:: + + gravity = prisms.prism_layer.gravity(coordinates, field="g_z") + +Finally, lets plot the gravitational field: + +.. jupyter-execute:: + + plt.pcolormesh(*coordinates[:2], gravity) + plt.colorbar(label="mGal", shrink=0.8) + plt.gca().set_aspect("equal") + plt.ticklabel_format(axis="both", style="sci", scilimits=(0, 0)) + plt.title("Gravity acceleration of a layer of prisms") + plt.xlabel("easting [m]") + plt.ylabel("northing [m]") + plt.tight_layout() + plt.show() diff --git a/doc/user_guide/forward_modelling/tesseroid.rst b/doc/user_guide/forward_modelling/tesseroid.rst new file mode 100644 index 000000000..df61d1128 --- /dev/null +++ b/doc/user_guide/forward_modelling/tesseroid.rst @@ -0,0 +1,232 @@ +.. _tesseroid: + +Tesseroids +========== + +When our region of interest covers several longitude and latitude degrees, +utilizing Cartesian coordinates to model geological structures might +introduce significant errors: they don't take into account the curvature of the +Earth. Instead, we would need to work in :ref:`spherical_coordinates`. +A common approach to forward model bodies in geocentric spherical coordinates +is to make use of tesseroids. + +A tesseroid (a.k.a spherical prism) is a three dimensional body defined by the +volume contained by two longitudinal boundaries, two latitudinal boundaries and +the surfaces of two concentric spheres of different radii (see :ref:`tesseroid +figure`). + +.. figure:: ../../_static/figures/tesseroid.svg + :name: tesseroid figure + :width: 50% + :alt: Figure showing a tesseroid defined in a geocentric spherical coordinate system + + Figure: Tesseroid + + Tesseroid defined by two longitude coordinates (:math:`\lambda_1` and + :math:`\lambda_2`), two latitude coordinates (:math:`\phi_1` and + :math:`\phi_2`) and the surfaces of two concentric spheres of radii + :math:`r_1` and :math:`r_2`. + This figure is a modified version of [Uieda2015]_. + + +Through the :func:`harmonica.tesseroid_gravity` function we can calculate the +gravitational field of any tesseroid with a given density on any computation +point. Each tesseroid can be represented through a tuple containing its six +boundaries in the following order: *west*, *east*, *south*, *north*, *bottom*, +*top*, where the former four are its longitudinal and latitudinal boundaries in +decimal degrees and the latter two are the two radii given in meters. + +.. note:: + + The :func:`harmonica.tesseroid_gravity` numerically computed the + gravitational fields of tesseroids by applying a method that applies the + Gauss-Legendre Quadrature along with a bidimensional adaptive discretization + algorithm. Refer to [Soler2019]_ for more details. + + +Lets define a single tesseroid and compute the gravitational potential +it generates on a regular grid of computation points located at 10 km above +its *top* boundary. + +Get the WGS84 reference ellipsoid from :mod:`boule` so we can obtain its mean +radius: + +.. jupyter-execute:: + + import boule as bl + + ellipsoid = bl.WGS84 + mean_radius = ellipsoid.mean_radius + +Define the tesseroid and its density (in kg per cubic meters): + +.. jupyter-execute:: + + tesseroid = (-70, -50, -40, -20, mean_radius - 10e3, mean_radius) + density = 2670 + +Define a set of computation points located on a regular grid at 100 km above +the *top* surface of the tesseroid: + +.. jupyter-execute:: + + import verde as vd + + coordinates = vd.grid_coordinates( + region=[-80, -40, -50, -10], + shape=(80, 80), + extra_coords=100e3 + mean_radius, + ) + +Lets compute the *downward* component of the gravitational acceleration it +generates on the computation point: + +.. jupyter-execute:: + + import harmonica as hm + + gravity = hm.tesseroid_gravity(coordinates, tesseroid, density, field="g_z") + +.. important:: + + The *downward* component :math:`g_z` of the gravitational acceleration + computed in spherical coordinates corresponds to :math:`-g_r`, where + :math:`g_r` is the + radial component. + +And finally plot the computed gravitational field + +.. jupyter-execute:: + + import matplotlib.pyplot as plt + import cartopy.crs as ccrs + + fig = plt.figure(figsize=(8, 9)) + ax = plt.axes(projection=ccrs.Orthographic(central_longitude=-60)) + img = ax.pcolormesh( + coordinates[0], coordinates[1], gravity, transform=ccrs.PlateCarree() + ) + plt.colorbar(img, ax=ax, pad=0, aspect=50, orientation="horizontal", label="mGal") + ax.coastlines() + ax.set_title("Downward component of gravitational acceleration") + plt.show() + + +Multiple tesseroids +------------------- + +We can compute the gravitational field of a set of tesseroids by passing a list +of them, where each tesseroid is defined as mentioned before, and then making +a single call of the :func:`harmonica.tesseroid_gravity` function. + +Lets define a set of four prisms along with their densities: + +.. jupyter-execute:: + + tesseroids = [ + [-70, -65, -40, -35, mean_radius - 100e3, mean_radius], + [-55, -50, -40, -35, mean_radius - 100e3, mean_radius], + [-70, -65, -25, -20, mean_radius - 100e3, mean_radius], + [-55, -50, -25, -20, mean_radius - 100e3, mean_radius], + ] + densities = [2670 , 2670, 2670, 2670] + +Compute their gravitational effect on a grid of computation points: + +.. jupyter-execute:: + + coordinates = vd.grid_coordinates( + region=[-80, -40, -50, -10], + shape=(80, 80), + extra_coords=100e3 + mean_radius, + ) + gravity = hm.tesseroid_gravity(coordinates, tesseroids, densities, field="g_z") + +And plot the results: + +.. jupyter-execute:: + + fig = plt.figure(figsize=(8, 9)) + ax = plt.axes(projection=ccrs.Orthographic(central_longitude=-60)) + img = ax.pcolormesh( + coordinates[0], coordinates[1], gravity, transform=ccrs.PlateCarree() + ) + plt.colorbar(img, ax=ax, pad=0, aspect=50, orientation="horizontal", label="mGal") + ax.coastlines() + ax.set_title("Downward component of gravitational acceleration") + plt.show() + + +Tesseroids with variable density +-------------------------------- + +The :func:`harmonica.tesseroid_gravity` is capable of computing the +gravitational effects of tesseroids whose density is defined through +a continuous function of the radial coordinate. This is achieved by the +application of the method introduced in [Soler2021]_. + +To do so we need to define a regular Python function for the density, which +should have a single argument (the ``radius`` coordinate) and return the +density of the tesseroids at that radial coordinate. +In addition, we need to decorate the density function with +:func:`numba.jit(nopython=True)` or ``numba.njit`` for short. + +Lets compute the gravitational effect of four tesseroids whose densities are +given by a custom linear ``density`` function. + +Start by defining the tesseroids + +.. jupyter-execute:: + + tesseroids = ( + [-70, -60, -40, -30, mean_radius - 3e3, mean_radius], + [-70, -60, -30, -20, mean_radius - 5e3, mean_radius], + [-60, -50, -40, -30, mean_radius - 7e3, mean_radius], + [-60, -50, -30, -20, mean_radius - 10e3, mean_radius], + ) + +Then, define a linear density function. We need to use the ``jit`` decorator so +Numba can run the forward model efficiently. + +.. jupyter-execute:: + + from numba import njit + + @njit + def density(radius): + """Linear density function""" + top = mean_radius + bottom = mean_radius - 10e3 + density_top = 2670 + density_bottom = 3000 + slope = (density_top - density_bottom) / (top - bottom) + return slope * (radius - bottom) + density_bottom + +Lets create a set of computation points located on a regular grid at 100km +above the mean Earth radius: + +.. jupyter-execute:: + + coordinates = vd.grid_coordinates( + region=[-80, -40, -50, -10], + shape=(80, 80), + extra_coords=100e3 + ellipsoid.mean_radius, + ) + +And compute the gravitational fields the tesseroids generate: + +.. jupyter-execute:: + + gravity = hm.tesseroid_gravity(coordinates, tesseroids, density, field="g_z") + +Finally, lets plot it: + +.. jupyter-execute:: + + fig = plt.figure(figsize=(8, 9)) + ax = plt.axes(projection=ccrs.Orthographic(central_longitude=-60)) + img = ax.pcolormesh(*coordinates[:2], gravity, transform=ccrs.PlateCarree()) + plt.colorbar(img, ax=ax, pad=0, aspect=50, orientation="horizontal", label="mGal") + ax.coastlines() + ax.set_title("Downward component of gravitational acceleration") + plt.show() diff --git a/doc/user_guide/gravity_disturbance.rst b/doc/user_guide/gravity_disturbance.rst new file mode 100644 index 000000000..28d20d78b --- /dev/null +++ b/doc/user_guide/gravity_disturbance.rst @@ -0,0 +1,150 @@ +.. _gravity_disturbance: + +Gravity Disturbance +=================== + +Gravity disturbances are the differences between the measured gravity and +a reference (normal) gravity produced by an ellipsoid: + +.. math:: + + \delta g(\mathbf{p}) = g(\mathbf{p}) - \gamma(\mathbf{p}) + +Where :math:`\delta g` is the **gravity disturbance**, :math:`g` the **measured +gravity**, :math:`\gamma` is the **normal gravity** and :math:`\mathbf{p}` is +the observation point where the gravity measurement has been carried out. +The disturbances are what allows geoscientists to infer the internal structure +of the Earth. + +.. tip:: + + The **measured gravity** :math:`g` is defined as the module of the gravity + acceleration, i.e. the module of the gradient of Earth's gravity potential + :math:`W`, which includes both the gravitational potential :math:`V` (due + to the gravitational attraction of the masses that composes the Earth) and + the centrifugal potential :math:`\Phi` due to Earth's rotation + + .. math:: + + W = V + \Phi + +.. tip:: + + The **normal gravity** :math:`\gamma` is defined as the gradient of the + potential gravity field generated by the reference ellipsoid :math:`U` + composed by the sum of the gravitational field :math:`V_\text{ell}` and the + centrifugal potential :math:`\Phi`: + + .. math:: + + U = V_\text{ell} + \Phi + +.. seealso:: + + See [Hofmann-WellenhofMoritz2006]_ for detailed explainations of Earth + gravity and the definition of its gravity and gravitational potentials. + +We can compute the normal gravity generated by any ellipsoid through the +:meth:`boule.Ellipsoid.normal_gravity` method from :mod:`boule` and then use it +to compute gravity disturbances. + +Lets start by loading a sample gravity dataset for the whole Earth: + +.. jupyter-execute:: + + import ensaio + import xarray as xr + + fname = ensaio.fetch_earth_gravity(version=1) + gravity = xr.load_dataarray(fname) + print(gravity) + +These observations are located on a regular grid on geodetic coordinates at the +same height of 10 km above the reference ellipsoid. +Lets plot it: + +.. jupyter-execute:: + + import matplotlib.pyplot as plt + import cartopy.crs as ccrs + + plt.figure(figsize=(10, 10)) + ax = plt.axes(projection=ccrs.Robinson()) + pc = gravity.plot.pcolormesh( + ax=ax, transform=ccrs.PlateCarree(), add_colorbar=False, cmap="viridis" + ) + plt.colorbar( + pc, label="mGal", orientation="horizontal", aspect=50, pad=0.01, shrink=0.5 + ) + ax.set_title("Gravity of the Earth") + ax.coastlines() + plt.show() + +We can then get the WGS84 ellipsoid defined in :mod:`boule` and use the +:meth:`boule.Ellipsoid.normal_gravity` to compute the normal gravity (the +gravity acceleration generated by the ellipsoid) on every observation point. +This method implements the closed-form formula of [LiGotze2001]_, which +calculates the normal gravity at any latitude and (geometric) height through an +analytic solution. + +.. jupyter-execute:: + + import boule as bl + + ellipsoid = bl.WGS84 + normal_gravity = ellipsoid.normal_gravity(gravity.latitude, gravity.height) + +And plot it: + +.. jupyter-execute:: + + plt.figure(figsize=(10, 10)) + ax = plt.axes(projection=ccrs.Robinson()) + pc = ax.pcolormesh( + gravity.longitude, + gravity.latitude, + normal_gravity, + transform=ccrs.PlateCarree(), + cmap="viridis" + ) + plt.colorbar( + pc, label="mGal", orientation="horizontal", aspect=50, pad=0.01, shrink=0.5 + ) + ax.set_title("Normal gravity of the Earth") + ax.coastlines() + plt.show() + +Now we can compute the gravity disturbance: + +.. jupyter-execute:: + + gravity_disturbance = gravity - normal_gravity + print(gravity_disturbance) + +And plot it: + +.. jupyter-execute:: + + import verde as vd + + maxabs = vd.maxabs(gravity_disturbance) + + plt.figure(figsize=(10, 10)) + ax = plt.axes(projection=ccrs.Robinson()) + pc = gravity_disturbance.plot.pcolormesh( + ax=ax, + transform=ccrs.PlateCarree(), + add_colorbar=False, + cmap="seismic", + vmin=-maxabs, + vmax=maxabs, + ) + plt.colorbar( + pc, label="mGal", orientation="horizontal", aspect=50, pad=0.01, shrink=0.5 + ) + ax.set_title("Gravity disturbance of the Earth") + ax.coastlines() + plt.show() + +The gravity disturbances can be interpreted as the gravitational effect of +every *anomalous mass*, i.e. that is not contained in the *normal Earth*. diff --git a/doc/user_guide/topographic_correction.rst b/doc/user_guide/topographic_correction.rst new file mode 100644 index 000000000..2f7255688 --- /dev/null +++ b/doc/user_guide/topographic_correction.rst @@ -0,0 +1,287 @@ +.. _topographic_correction: + +Topographic Correction +====================== + +Computing the :ref:`gravity disturbance ` is usually the +first step towards generating a dataset that could provide insight of the +structures and bodies that lie beneath Earth surface. + +One of the strongest signals present in the gravity disturbances are the +gravitational effect of the topography, i.e. every body located above the +surface of the reference ellipsoid. +Mainly because their proximity to the observation points but also because their +density contrast could be considered the same as their own absolute density. + +For this reason, geophysicists usually remove the gravitational effect of the +topography from the gravity disturbance in a processes called **topographic +correction**. +The resulting field is often called *Bouguer gravity disturbance* or +*topography-free gravity disturbance*. + +The simpler way to apply the topographic correction is through the **Bouguer +correction**. It consists in approximating the topographic masses that lay +underneath each computation point as an infinite slab of constant density. +It has been widely used on ground surveys because it's easy to compute and +because we don't need any other data than the observation height (the height at +which the gravity has been measured). +It's main drawback is it's accuracy: the approximation might be too simple to +accurately reproduce the gravitational effect of the topography present in our +region of interest. + +On the other hand, we can compute the topographic correction by **forward +modelling the topographic masses**. To do so we will need a 2D grid of the +topography, a.k.a. a DEM (digital elevation model). This method produces +accurate corrections if the DEM has good resolutions, but its computation is +much more expensive. + +In the following sections we will explore how we can apply both methods using +Harmonica. + +Lets start by downloading some gravity data over the Bushveld Igneous Complex +in Southern Africa. + +.. jupyter-execute:: + + import ensaio + import pandas as pd + + fname = ensaio.fetch_bushveld_gravity(version=1) + data = pd.read_csv(fname) + data + +And plot it: + +.. jupyter-execute:: + + import cartopy.crs as ccrs + import matplotlib.pyplot as plt + import verde as vd + + maxabs = vd.maxabs(data.gravity_disturbance_mgal) + + fig = plt.figure(figsize=(10, 8), dpi=300) + ax = plt.axes(projection=ccrs.Mercator()) + ax.set_title("Gravity disturbance", pad=25) + tmp = ax.scatter( + data.longitude, + data.latitude, + c=data.gravity_disturbance_mgal, + s=2, + vmin=-maxabs, + vmax=maxabs, + cmap="seismic", + transform=ccrs.PlateCarree(), + ) + plt.colorbar( + tmp, + ax=ax, + orientation="horizontal", + label="mGal", + aspect=50, + pad=0.05, + shrink=0.7, + ) + ax.set_extent(vd.get_region((data.longitude, data.latitude))) + ax.gridlines(draw_labels=True) + ax.coastlines() + plt.show() + + +Bouguer correction +------------------ + +We can compute the Bouguer correction through the +:func:`harmonica.bouguer_correction` function. +Because our gravity data has been obtained on the Earth surface, the +``height_geometric_m`` coordinate coincides with the topographic height at each +observation point (referenced above the ellipsoid), so we can pass it as the +``topography`` argument. + +.. jupyter-execute:: + + import harmonica as hm + + bouguer_correction = hm.bouguer_correction(data.height_geometric_m) + +.. hint:: + + The :func:`harmonica.bouguer_correction` assigns default values for the + density of the upper crust and the water. + +.. warning:: + + In case the observations heights were referenced over the geoid (usually + marked as above the mean sea level), it's advisable to convert them to + geometric heights by removing the geoid height. + +We can now compute the Bouguer disturbance and plot it: + +.. jupyter-execute:: + + bouguer_disturbance = data.gravity_disturbance_mgal - bouguer_correction + bouguer_disturbance + +.. jupyter-execute:: + + maxabs = vd.maxabs(bouguer_disturbance) + + fig = plt.figure(figsize=(10, 8), dpi=300) + ax = plt.axes(projection=ccrs.Mercator()) + ax.set_title("Bouguer disturbance (with simple Bouguer correction)", pad=25) + tmp = ax.scatter( + data.longitude, + data.latitude, + c=bouguer_disturbance, + s=2, + vmin=-maxabs, + vmax=maxabs, + cmap="seismic", + transform=ccrs.PlateCarree(), + ) + plt.colorbar( + tmp, + ax=ax, + orientation="horizontal", + label="mGal", + aspect=50, + pad=0.05, + shrink=0.7, + ) + ax.set_extent(vd.get_region((data.longitude, data.latitude))) + ax.gridlines(draw_labels=True) + ax.coastlines() + plt.show() + + + +Forward modelling the topography +-------------------------------- + +In order to forward model the topographic masses, we need to build a 3D model +made out of simpler geometric bodies. In this case, we are going to use +rectangular prisms. +Then we will compute the gravitational effect of every prism on each +computation point. + +To do so, we need a regular grid of the topographic heights (or DEM as in +Digital Elevation Model) around the Bushveld Igneous Complex. +We can download a global topography grid: + +.. jupyter-execute:: + + import xarray as xr + + fname = ensaio.fetch_southern_africa_topography(version=1) + topography = xr.load_dataarray(fname) + topography + +And then crop it to a slightly larger region than the gravity observations: + +.. jupyter-execute:: + + region = vd.get_region((data.longitude, data.latitude)) + region_pad = vd.pad_region(region, pad=1) + + topography = topography.sel( + longitude=slice(region_pad[0], region_pad[1]), + latitude=slice(region_pad[2], region_pad[3]), + ) + topography + +And project it to plain coordinates using :mod:`pyproj` and :mod:`verde`. +We start by defining a Mercator projection: + +.. jupyter-execute:: + + import pyproj + + projection = pyproj.Proj(proj="merc", lat_ts=topography.latitude.values.mean()) + +And project the grid using :func:`verde.project_grid`: + +.. jupyter-execute:: + + topography_proj = vd.project_grid(topography, projection, method="nearest") + topography_proj + +.. tip:: + + Using the ``"nearest"`` method makes the projection process faster than + using the ``"linear"`` one. + +Now we can create a 3D model of the topographic masses using a layer of +rectangular prisms. We can use the :func:`harmonica.prism_layer` function to +build it. +We also need to assign density values to each prism in the layer. +For every prism above the ellipsoid we will set the density of the upper crust +(2670 kg/m\ :sup:`3`), while for each prism below it we will assign the +density contrast equal to the density of the water (1040 kg/m\ :sup:`3`) minus +the density of the upper crust. + +.. jupyter-execute:: + + import numpy as np + + density = np.where(topography_proj >= 0, 2670, 1040 - 2670) + + prisms = hm.prism_layer( + (topography_proj.easting, topography_proj.northing), + surface=topography_proj, + reference=0, + properties={"density": density}, + ) + prisms + +Now we need to compute the gravitational effect of these prisms on every +observation point. We can do it through the +:meth:`harmonica.DatasetAccessorPrismLayer.gravity` method. But the coordinates +of the observation points must be also projected. + +.. jupyter-execute:: + + # Project the coordinates of the observation points + easting, northing = projection(data.longitude.values, data.latitude.values) + coordinates = (easting, northing, data.height_geometric_m) + + # Compute the terrain effect + terrain_effect = prisms.prism_layer.gravity(coordinates, field="g_z") + +Finally, we can compute the topography-free gravity disturbance: + +.. jupyter-execute:: + + topo_free_disturbance = data.gravity_disturbance_mgal - terrain_effect + +And plot it: + +.. jupyter-execute:: + + maxabs = vd.maxabs(topo_free_disturbance) + + fig = plt.figure(figsize=(10, 8), dpi=300) + ax = plt.axes(projection=ccrs.Mercator()) + ax.set_title("Topography-free gravity disturbance", pad=25) + tmp = ax.scatter( + data.longitude, + data.latitude, + c=topo_free_disturbance, + s=2, + vmin=-maxabs, + vmax=maxabs, + cmap="seismic", + transform=ccrs.PlateCarree(), + ) + plt.colorbar( + tmp, + ax=ax, + orientation="horizontal", + label="mGal", + aspect=50, + pad=0.05, + shrink=0.7, + ) + ax.set_extent(vd.get_region((data.longitude, data.latitude))) + ax.gridlines(draw_labels=True) + ax.coastlines() + plt.show() diff --git a/doc/versions.rst b/doc/versions.rst new file mode 100644 index 000000000..551f03807 --- /dev/null +++ b/doc/versions.rst @@ -0,0 +1,17 @@ +Documentation for other versions +-------------------------------- + +Use the links below to access documentation for specific versions +(when in doubt, use the **latest release**): + +* `Latest release `__ +* `Development `__ + (reflects the *main* branch on GitHub) +* `v0.4.0 `__ +* `v0.3.3 `__ +* `v0.3.2 `__ +* `v0.3.1 `__ +* `v0.3.0 `__ +* `v0.2.1 `__ +* `v0.2.0 `__ +* `v0.1.0 `__ diff --git a/env/requirements-build.txt b/env/requirements-build.txt index cbc50fb63..81e624388 100644 --- a/env/requirements-build.txt +++ b/env/requirements-build.txt @@ -1,5 +1,5 @@ # Requirements to build and check distributions +setuptools>=45 +setuptools_scm>=6.2 twine wheel -setuptools -setuptools_scm diff --git a/env/requirements-docs.txt b/env/requirements-docs.txt index 506e2be6a..fde7c641d 100644 --- a/env/requirements-docs.txt +++ b/env/requirements-docs.txt @@ -1,8 +1,15 @@ # Documentation building requirements -sphinx==3.5.* -sphinx-book-theme==0.0.41 -sphinx-gallery==0.8.* +sphinx==4.5.* +sphinx-book-theme==0.2.* +sphinx-gallery==0.10.* +sphinx-panels==0.6.* +sphinx-copybutton==0.5.* +jupyter-sphinx==0.3.* boule pyproj +ensaio +netcdf4 matplotlib cartopy>=0.18 +pyvista +vtk>=9 diff --git a/env/requirements-style.txt b/env/requirements-style.txt index 28af4315b..2d5043b93 100644 --- a/env/requirements-style.txt +++ b/env/requirements-style.txt @@ -1,6 +1,6 @@ # Requirements for checking code style pathspec -black==21.12b0 +black==22.3.0 isort==5.10.* flake8==4.0.* flake8-bugbear==21.11.* diff --git a/env/requirements-tests.txt b/env/requirements-tests.txt index a968055da..ed9de3da8 100644 --- a/env/requirements-tests.txt +++ b/env/requirements-tests.txt @@ -3,3 +3,7 @@ boule pytest pytest-cov coverage +pyvista +vtk>=9 +netcdf4 +numba_progress \ No newline at end of file diff --git a/environment.yml b/environment.yml index 436f240cb..fbaab3449 100644 --- a/environment.yml +++ b/environment.yml @@ -3,9 +3,14 @@ channels: - conda-forge - defaults dependencies: - - python==3.8 + - python==3.9 - pip - - setuptools_scm + # Build + - setuptools>=45 + - setuptools_scm>=6.2 + - wheel + - twine + # Run-time - numpy - pandas - numba @@ -15,19 +20,29 @@ dependencies: - verde>=1.5.0 - xarray - xrft - # Development requirements + # Optional requirements + - pyvista + - vtk>=9 + # Testing requirements + - netcdf4 + - pytest + - pytest-cov + - coverage - boule + # Documentation requirements + - sphinx==4.5.* + - sphinx-book-theme==0.2.* + - sphinx-gallery==0.10.* + - sphinx-panels==0.6.* + - sphinx-copybutton==0.5.* + - jupyter-sphinx==0.3.* - pyproj - matplotlib - cartopy>=0.18 - - pytest - - pytest-cov - - coverage - - sphinx==3.5.* - - sphinx-book-theme==0.0.41 - - sphinx-gallery==0.8.* + - ensaio + - netcdf4 # Code style checks and autoformat - - black==21.12b0 + - black==22.3.0 - isort==5.10.* - flake8==4.0.* - flake8-bugbear==21.11.* @@ -41,3 +56,6 @@ dependencies: # Install flake8-unused-arguments through pip # (not available through conda yet) - flake8-unused-arguments==0.0.9 + # Install numba_progress through pip + # (not available through conda yet) + - numba_progress diff --git a/examples/equivalent_sources/block_averaged_sources.py b/examples/equivalent_sources/block_averaged_sources.py index 8a6627c3b..cc31b0b7a 100644 --- a/examples/equivalent_sources/block_averaged_sources.py +++ b/examples/equivalent_sources/block_averaged_sources.py @@ -82,7 +82,8 @@ # requires the height of the grid points (upward coordinate). By passing in # 1500 m, we're effectively upward-continuing the data (mean flight height is # 500 m). -grid = eqs.grid(upward=1500, spacing=500, data_names=["magnetic_anomaly"]) +grid_coords = vd.grid_coordinates(region=region, spacing=500, extra_coords=1500) +grid = eqs.grid(coordinates=grid_coords, data_names=["magnetic_anomaly"]) # The grid is a xarray.Dataset with values, coordinates, and metadata print("\nGenerated grid:\n", grid) diff --git a/examples/equivalent_sources/cartesian.py b/examples/equivalent_sources/cartesian.py index b76054b3d..edc672c92 100644 --- a/examples/equivalent_sources/cartesian.py +++ b/examples/equivalent_sources/cartesian.py @@ -71,7 +71,8 @@ # requires the height of the grid points (upward coordinate). By passing in # 1500 m, we're effectively upward-continuing the data (mean flight height is # 500 m). -grid = eqs.grid(upward=1500, spacing=500, data_names=["magnetic_anomaly"]) +grid_coords = vd.grid_coordinates(region=region, spacing=500, extra_coords=1500) +grid = eqs.grid(coordinates=grid_coords, data_names=["magnetic_anomaly"]) # The grid is a xarray.Dataset with values, coordinates, and metadata print("\nGenerated grid:\n", grid) diff --git a/examples/equivalent_sources/gradient_boosted.py b/examples/equivalent_sources/gradient_boosted.py index 8b0cd416e..2b89199f5 100644 --- a/examples/equivalent_sources/gradient_boosted.py +++ b/examples/equivalent_sources/gradient_boosted.py @@ -91,11 +91,8 @@ # Interpolate data on a regular grid with 2 km spacing. The interpolation # requires the height of the grid points (upward coordinate). By passing in # 1000 m, we're effectively upward-continuing the data. -grid = eqs_gb.grid( - upward=1000, - spacing=2e3, - data_names="gravity_disturbance", -) +grid_coords = vd.grid_coordinates(region=region, spacing=2e3, extra_coords=1000) +grid = eqs_gb.grid(coordinates=grid_coords, data_names="gravity_disturbance") print(grid) # Plot the original gravity disturbance and the gridded and upward-continued diff --git a/examples/equivalent_sources/spherical.py b/examples/equivalent_sources/spherical.py index 4ca63f496..7f2547634 100644 --- a/examples/equivalent_sources/spherical.py +++ b/examples/equivalent_sources/spherical.py @@ -62,11 +62,10 @@ # passing in the maximum radius of the data, we're effectively # upward-continuing the data. The grid will be defined in spherical # coordinates. -grid = eqs.grid( - upward=coordinates[-1].max(), - spacing=0.2, - data_names=["gravity_disturbance"], -) +region = vd.get_region(coordinates) # get the region boundaries +upward = coordinates[-1].max() +grid_coords = vd.grid_coordinates(region=region, spacing=0.2, extra_coords=upward) +grid = eqs.grid(coordinates=grid_coords, data_names=["gravity_disturbance"]) # The grid is a xarray.Dataset with values, coordinates, and metadata print("\nGenerated grid:\n", grid) @@ -79,9 +78,6 @@ # color. maxabs = vd.maxabs(gravity_disturbance, grid.gravity_disturbance.values) -# Get the region boundaries -region = vd.get_region(coordinates) - # Plot observed and gridded gravity disturbance fig, (ax1, ax2) = plt.subplots( nrows=1, diff --git a/examples/forward/prism_layer.py b/examples/forward/prism_layer.py index fa4d0259f..73e9b76c2 100644 --- a/examples/forward/prism_layer.py +++ b/examples/forward/prism_layer.py @@ -31,7 +31,7 @@ region = (0, 100e3, -40e3, 40e3) spacing = 2e3 (easting, northing) = vd.grid_coordinates(region=region, spacing=spacing) -surface = 100 * np.exp(-((easting - 50e3) ** 2 + northing ** 2) / 1e9) +surface = 100 * np.exp(-((easting - 50e3) ** 2 + northing**2) / 1e9) density = 2670.0 * np.ones_like(surface) prisms = hm.prism_layer( coordinates=(easting[0, :], northing[:, 0]), diff --git a/examples/upward_derivative.py b/examples/upward_derivative.py new file mode 100644 index 000000000..abf5a62db --- /dev/null +++ b/examples/upward_derivative.py @@ -0,0 +1,95 @@ +# Copyright (c) 2018 The Harmonica Developers. +# Distributed under the terms of the BSD 3-Clause License. +# SPDX-License-Identifier: BSD-3-Clause +# +# This code is part of the Fatiando a Terra project (https://www.fatiando.org) +# +""" +Upward derivative of a regular grid +=================================== +""" +import matplotlib.pyplot as plt +import numpy as np +import pyproj +import verde as vd +import xrft + +import harmonica as hm + +# Fetch the sample total-field magnetic anomaly data from Great Britain +data = hm.datasets.fetch_britain_magnetic() + +# Slice a smaller portion of the survey data to speed-up calculations for this +# example +region = [-5.5, -4.7, 57.8, 58.5] +inside = vd.inside((data.longitude, data.latitude), region) +data = data[inside] + +# Since this is a small area, we'll project our data and use Cartesian +# coordinates +projection = pyproj.Proj(proj="merc", lat_ts=data.latitude.mean()) +easting, northing = projection(data.longitude.values, data.latitude.values) +coordinates = (easting, northing, data.altitude_m) + +# Grid the scatter data using an equivalent layer +eql = hm.EquivalentSources(depth=1000, damping=1).fit( + coordinates, data.total_field_anomaly_nt +) + +grid_coords = vd.grid_coordinates( + region=vd.get_region(coordinates), spacing=500, extra_coords=1500 +) +grid = eql.grid(grid_coords, data_names=["magnetic_anomaly"]) +grid = grid.magnetic_anomaly + +# Pad the grid to increase accuracy of the FFT filter +pad_width = { + "easting": grid.easting.size // 3, + "northing": grid.northing.size // 3, +} +grid = grid.drop_vars("upward") # drop extra coordinates due to bug in xrft.pad +grid_padded = xrft.pad(grid, pad_width) + +# Compute the upward derivative of the grid +deriv_upward = hm.derivative_upward(grid_padded) + +# Unpad the derivative grid +deriv_upward = xrft.unpad(deriv_upward, pad_width) + +# Show the upward derivative +print("\nUpward derivative:\n", deriv_upward) + +# Plot original magnetic anomaly and the upward derivative +fig, (ax1, ax2) = plt.subplots( + nrows=1, ncols=2, figsize=(9, 8), sharex=True, sharey=True +) + +# Plot the magnetic anomaly grid +grid.plot( + ax=ax1, + cmap="seismic", + cbar_kwargs={"label": "nT", "location": "bottom", "shrink": 0.8, "pad": 0.08}, +) +ax1.set_title("Magnetic anomaly") + +# Plot the upward derivative +scale = np.quantile(np.abs(deriv_upward), q=0.98) # scale the colorbar +deriv_upward.plot( + ax=ax2, + vmin=-scale, + vmax=scale, + cmap="seismic", + cbar_kwargs={"label": "nT/m", "location": "bottom", "shrink": 0.8, "pad": 0.08}, +) +ax2.set_title("Upward derivative") + +# Scale the axes +for ax in (ax1, ax2): + ax.set_aspect("equal") + +# Set ticklabels with scientific notation +ax1.ticklabel_format(axis="x", style="sci", scilimits=(-2, 2)) +ax1.ticklabel_format(axis="y", style="sci", scilimits=(-2, 2)) + +plt.tight_layout() +plt.show() diff --git a/examples/visualization/README.txt b/examples/visualization/README.txt new file mode 100644 index 000000000..e68465716 --- /dev/null +++ b/examples/visualization/README.txt @@ -0,0 +1,2 @@ +3D visualizations +----------------- diff --git a/examples/visualization/prism_layer_pyvista.py b/examples/visualization/prism_layer_pyvista.py new file mode 100644 index 000000000..740609515 --- /dev/null +++ b/examples/visualization/prism_layer_pyvista.py @@ -0,0 +1,89 @@ +# Copyright (c) 2018 The Harmonica Developers. +# Distributed under the terms of the BSD 3-Clause License. +# SPDX-License-Identifier: BSD-3-Clause +# +# This code is part of the Fatiando a Terra project (https://www.fatiando.org) +# +""" +Plot a prism layer in 3D +======================== + +The :func:`harmonica.prism_layer` allows to easily create a layer of prisms +whose top and bottom boundaries might drape a certain surface, like topography, +bathymetry or the Moho discontinuity. It returns a :class:`xarray.Dataset` +object with the horizontal coordinates of the center of each prism, their top +and bottom boundaries and their physical properties, like their density. +Through the ``prism_layer`` accessor (see +:class:`harmonica.DatasetAccessorPrismLayer`) we can call some methods for our +prism layer. For example, the +:meth:`harmonica.DatasetAccessorPrismLayer.gravity` method lets us compute the +gravitational fields of the layer on any set of observation points. +Another interesting method is the +:meth:`harmonica.DatasetAccessorPrismLayer.to_pyvista`: it converts the prism +layer into a :class:`pyvista.UnstructuredGrid` that could be easily plotted +through ``pyvista``. + +In this example we will show how we can build a prism layer out of a topography +and bathymetry grid for South Africa and how we can visualize the layer as a 3D +plot using ``pyvista``. + +""" +import pyproj +import pyvista as pv +import verde as vd + +import harmonica as hm + +# Read South Africa topography +south_africa_topo = hm.datasets.fetch_south_africa_topography() + +# Project the grid +projection = pyproj.Proj(proj="merc", lat_ts=south_africa_topo.latitude.values.mean()) +south_africa_topo = vd.project_grid(south_africa_topo.topography, projection=projection) + +# Create a 2d array with the density of the prisms Points above the geoid will +# have a density of 2670 kg/m^3 Points below the geoid will have a density +# contrast equal to the difference between the density of the ocean and the +# density of the upper crust: # 1000 kg/m^3 - 2900 kg/m^3 +density = south_africa_topo.copy() # copy topography to a new xr.DataArray +density.values[:] = 2670.0 # replace every value for the density of the topography +# Change density values of ocean points +density = density.where(south_africa_topo >= 0, 1000 - 2900) + +# Create layer of prisms +prisms = hm.prism_layer( + (south_africa_topo.easting, south_africa_topo.northing), + surface=south_africa_topo, + reference=0, + properties={"density": density}, +) + +# Create a pyvista UnstructuredGrid from the prism layer +pv_grid = prisms.prism_layer.to_pyvista() +pv_grid + +############################################################################### + +# Plot with pyvista +plotter = pv.Plotter(lighting="three_lights", window_size=(1000, 800)) +plotter.add_mesh(pv_grid, scalars="density") +plotter.set_scale(zscale=75) # exaggerate the vertical coordinate +plotter.camera_position = "xz" +plotter.camera.elevation = 20 +plotter.camera.azimuth = 35 +plotter.camera.zoom(1.2) + +# Add a ceiling light +west, east, south, north = vd.get_region((prisms.easting, prisms.northing)) +easting_center, northing_center = (east + west) / 2, (north + south) / 2 +light = pv.Light( + position=(easting_center, northing_center, 10e3), + focal_point=(easting_center, northing_center, 0), + intensity=0.3, + light_type="scene light", # the light doesn't move with the camera + positional=False, # the light comes from infinity +) +plotter.add_light(light) + +plotter.show_axes() +plotter.show() diff --git a/harmonica/version.py b/harmonica/_version.py similarity index 91% rename from harmonica/version.py rename to harmonica/_version.py index ce6b7d8a0..eea22ba87 100644 --- a/harmonica/version.py +++ b/harmonica/_version.py @@ -8,7 +8,7 @@ Define the __version__ variable as the version number with leading "v" """ # This file is generated automatically by setuptools_scm -from ._version import version +from ._version_generated import version # Add a "v" to the version number __version__ = f"v{version}" diff --git a/harmonica/datasets/sample_data.py b/harmonica/datasets/sample_data.py index 7e360365c..8bddb45b9 100644 --- a/harmonica/datasets/sample_data.py +++ b/harmonica/datasets/sample_data.py @@ -7,12 +7,14 @@ """ Functions to load sample datasets used in the Harmonica docs. """ +import warnings + import pandas as pd import pkg_resources import pooch import xarray as xr -from ..version import __version__ as version +from .._version import __version__ as version REGISTRY = pooch.create( path=pooch.os_cache("harmonica"), @@ -27,6 +29,18 @@ REGISTRY.load_registry(registry_file) +def _deprecation_warning(): + """ + Raise a FutureWarning about deprecation of synthetic module + """ + msg = ( + "The 'datasets' module will be deprecated in Harmonica v0.6.0. " + + "Harmonica will transition to using " + + "Ensaio (https://www.fatiando.org/ensaio/) instead." + ) + warnings.warn(msg, FutureWarning) + + def locate(): r""" The absolute path to the sample data storage location on disk. @@ -69,6 +83,7 @@ def fetch_geoid_earth(): longitude. """ + _deprecation_warning() fname = REGISTRY.fetch("geoid-earth-0.5deg.nc.xz", processor=pooch.Decompress()) data = xr.open_dataset(fname, engine="scipy") # Capture attributes dict because it's removed after converting the data to @@ -104,6 +119,7 @@ def fetch_gravity_earth(): longitude. """ + _deprecation_warning() fname = REGISTRY.fetch("gravity-earth-0.5deg.nc.xz", processor=pooch.Decompress()) data = xr.open_dataset(fname, engine="scipy") # Capture attributes dict because it's removed after converting the data to @@ -140,6 +156,7 @@ def fetch_topography_earth(): geodetic latitude and longitude. """ + _deprecation_warning() fname = REGISTRY.fetch("etopo1-0.5deg.nc.xz", processor=pooch.Decompress()) data = xr.open_dataset(fname, engine="scipy") # Capture attributes dict because it's removed after converting the data to @@ -182,6 +199,7 @@ def fetch_britain_magnetic(): data : :class:`pandas.DataFrame` The magnetic anomaly data. """ + _deprecation_warning() return pd.read_csv(REGISTRY.fetch("britain-magnetic.csv.xz"), compression="xz") @@ -212,6 +230,7 @@ def fetch_south_africa_gravity(): The gravity data. """ + _deprecation_warning() fname = REGISTRY.fetch("south-africa-gravity.ast.xz") columns = ["latitude", "longitude", "elevation", "gravity"] return pd.read_csv(fname, sep=r"\s+", names=columns, compression="xz") @@ -238,6 +257,7 @@ def fetch_south_africa_topography(): geodetic latitude and longitude. """ + _deprecation_warning() fname = REGISTRY.fetch( "south-africa-topography.nc.xz", processor=pooch.Decompress() ) diff --git a/harmonica/equivalent_sources/cartesian.py b/harmonica/equivalent_sources/cartesian.py index d5ca4dd76..09917b0c5 100644 --- a/harmonica/equivalent_sources/cartesian.py +++ b/harmonica/equivalent_sources/cartesian.py @@ -377,10 +377,7 @@ def jacobian(self, coordinates, points): def grid( self, - upward, - region=None, - shape=None, - spacing=None, + coordinates, dims=None, data_names=None, projection=None, @@ -389,31 +386,27 @@ def grid( """ Interpolate the data onto a regular grid. - The grid can be specified by either the number of points in each - dimension (the *shape*) or by the grid node spacing. See - :func:`verde.grid_coordinates` for details. All grid points will be - located at the same `upward` coordinate. Other arguments for - :func:`verde.grid_coordinates` can be passed as extra keyword arguments - (``kwargs``) to this method. + The coordinates of the regular grid must be passed through the + ``coordinates`` argument as a tuple containing three arrays in the + following order: ``(easting, nothing, upward)``. They can be easily + created through the :func:`verde.grid_coordinates` function. If the + grid points must be all at the same height, it can be specified in the + ``extra_coords`` argument of :func:`verde.grid_coordinates`. - If the interpolator collected the input data region, then it will be - used if ``region=None``. Otherwise, you must specify the grid region. Use the *dims* and *data_names* arguments to set custom names for the dimensions and the data field(s) in the output :class:`xarray.Dataset`. Default names will be provided if none are given. Parameters ---------- - upward : float - Upward coordinate of the grid points. - region : list = [W, E, S, N] - The west, east, south, and north boundaries of a given region. - shape : tuple = (n_north, n_east) or None - The number of points in the South-North and West-East directions, - respectively. - spacing : tuple = (s_north, s_east) or None - The grid spacing in the South-North and West-East directions, - respectively. + coordinates : tuple of arrays + Tuple of arrays containing the coordinates of the grid in the + following order: (easting, northing, upward). + The easting and northing arrays could be 1d or 2d arrays, if + they are 2d they must be part of a meshgrid. + The upward array should be a 2d array with the same shape of + easting and northing (if they are 2d arrays) or with a shape of + ``(northing.size, easting.size)`` (if they are 1d arrays). dims : list or None The names of the northing and easting data dimensions, respectively, in the output grid. Default is determined from the @@ -439,22 +432,45 @@ def grid( The interpolated grid. Metadata about the interpolator is written to the ``attrs`` attribute. + See also + -------- + :func:`verde.grid_coordinates` + """ - # We override the grid method from BaseGridder so it takes the upward - # coordinate as a positional argument. + # We override the grid method from BaseGridder to change the docstring + # and to make it work only with the `coordinates` argument (no region, + # shape or spacing) + + # Raise ValueError if any deprecated argument has been passed + deprecated_args = ( + "upward" in kwargs, + "shape" in kwargs, + "region" in kwargs, + "spacing" in kwargs, + ) + if any(deprecated_args): + raise ValueError( + "The 'upward', 'region', 'shape' and 'spacing' arguments have been " + + "deprecated. " + + "Please pass the coordinates of the target grid through the " + + "'coordinates' argument." + ) + + # Raise warning if any kwargs has been passed + if kwargs: + args = "'" + "', '".join(list(kwargs.keys())) + "'" + warnings.warn( + f"The {args} arguments are being ignored. The 'grid' method " + + "will not take any keyword arguments in the next Harmonica release", + FutureWarning, + ) - # Ignore extra_coords if passed - pop_extra_coords(kwargs) # Grid data grid = super().grid( - region=region, - shape=shape, - spacing=spacing, + coordinates=coordinates, dims=dims, data_names=data_names, projection=projection, - extra_coords=upward, - **kwargs, ) return grid diff --git a/harmonica/equivalent_sources/gradient_boosted.py b/harmonica/equivalent_sources/gradient_boosted.py index 7bd8c76d1..7800add12 100644 --- a/harmonica/equivalent_sources/gradient_boosted.py +++ b/harmonica/equivalent_sources/gradient_boosted.py @@ -233,7 +233,7 @@ def _gradient_boosting(self, coordinates, data, weights): # Get number of windows n_windows = len(point_windows) # Initialize RMSE array - errors = [np.sqrt(np.mean(data ** 2))] + errors = [np.sqrt(np.mean(data**2))] # Set weights_chunk to None (will be changed unless weights is None) weights_chunk = None # Initialized the predicted and residue arrays @@ -270,7 +270,7 @@ def _gradient_boosting(self, coordinates, data, weights): # Update the residue residue -= predicted # Add RMS of the residue to the RMSE - errors.append(np.sqrt(np.mean(residue ** 2))) + errors.append(np.sqrt(np.mean(residue**2))) # Update source coefficients self.coefs_[point_window] += coeffs_chunk self.rmse_per_iteration_ = np.array(errors) diff --git a/harmonica/equivalent_sources/spherical.py b/harmonica/equivalent_sources/spherical.py index 65217af32..5eb27e94a 100644 --- a/harmonica/equivalent_sources/spherical.py +++ b/harmonica/equivalent_sources/spherical.py @@ -19,7 +19,6 @@ from .utils import ( jacobian_numba_parallel, jacobian_numba_serial, - pop_extra_coords, predict_numba_parallel, predict_numba_serial, ) @@ -239,10 +238,7 @@ def jacobian(self, coordinates, points, dtype="float64"): def grid( self, - upward, - region=None, - shape=None, - spacing=None, + coordinates, dims=None, data_names=None, **kwargs, @@ -250,38 +246,34 @@ def grid( """ Interpolate the data onto a regular grid. - The grid can be specified by either the number of points in each - dimension (the *shape*) or by the grid node spacing. See - :func:`verde.grid_coordinates` for details. All grid points will be - located at the same `upward` coordinate. Other arguments for - :func:`verde.grid_coordinates` can be passed as extra keyword arguments - (``kwargs``) to this method. + The coordinates of the regular grid must be passed through the + ``coordinates`` argument as a tuple containing three arrays in the + following order: ``(longitude, latitude, radius)``. They can be easily + created through the :func:`verde.grid_coordinates` function. If the + grid points must be all at the same radius, it can be specified in the + ``extra_coords`` argument of :func:`verde.grid_coordinates`. - If the interpolator collected the input data region, then it will be - used if ``region=None``. Otherwise, you must specify the grid region. Use the *dims* and *data_names* arguments to set custom names for the dimensions and the data field(s) in the output :class:`xarray.Dataset`. Default names will be provided if none are given. Parameters ---------- - upward : float - Upward coordinate of the grid points. - region : list = [W, E, S, N] - The west, east, south, and north boundaries of a given region. - shape : tuple = (n_north, n_east) or None - The number of points in the South-North and West-East directions, - respectively. - spacing : tuple = (s_north, s_east) or None - The grid spacing in the South-North and West-East directions, - respectively. + coordinates : tuple of arrays + Tuple of arrays containing the coordinates of the grid in the + following order: (longitude, latitude, radius). + The longitude and latitude arrays could be 1d or 2d arrays, if + they are 2d they must be part of a meshgrid. + The radius array should be a 2d array with the same shape of + longitude and latitude (if they are 2d arrays) or with a shape of + ``(latitude.size, longitude.size)`` (if they are 1d arrays). dims : list or None - The names of the northing and easting data dimensions, + The names of the latitude and longitude data dimensions, respectively, in the output grid. Default is determined from the ``dims`` attribute of the class. Must be defined in the following - order: northing dimension, easting dimension. - **NOTE: This is an exception to the "easting" then - "northing" pattern but is required for compatibility with xarray.** + order: latitude dimension, longitude dimension. + **NOTE: This is an exception to the "longitude" then + "latitude" pattern but is required for compatibility with xarray.** data_names : list of None The name(s) of the data variables in the output grid. Defaults to ``['scalars']``. @@ -293,23 +285,42 @@ def grid( to the ``attrs`` attribute. """ - # We override the grid method from BaseGridder so it takes the upward - # coordinate as a positional argument. + # We override the grid method from BaseGridder to change the docstring + # and to make it work only with the `coordinates` argument (no region, + # shape or spacing) + + # Raise ValueError if any deprecated argument has been passed + deprecated_args = ( + "upward" in kwargs, + "shape" in kwargs, + "region" in kwargs, + "spacing" in kwargs, + ) + if any(deprecated_args): + raise ValueError( + "The 'upward', 'region', 'shape' and 'spacing' arguments have been " + + "deprecated. " + + "Please pass the coordinates of the target grid through the " + + "'coordinates' argument." + ) + + # Raise warning if any kwargs has been passed + if kwargs: + args = "'" + "', '".join(list(kwargs.keys())) + "'" + warnings.warn( + f"The {args} arguments are being ignored. The 'grid' method " + + "will not take any keyword arguments in the next Harmonica release", + FutureWarning, + ) - # Ignore extra_coords if passed - pop_extra_coords(kwargs) # Grid data # We always pass projection=None because that argument it's intended to # be used only with Cartesian gridders. grid = super().grid( - region=region, - shape=shape, - spacing=spacing, + coordinates=coordinates, dims=dims, data_names=data_names, projection=None, - extra_coords=upward, - **kwargs, ) return grid diff --git a/harmonica/forward/_tesseroid_utils.py b/harmonica/forward/_tesseroid_utils.py index ae274ab6f..f9e89143d 100644 --- a/harmonica/forward/_tesseroid_utils.py +++ b/harmonica/forward/_tesseroid_utils.py @@ -87,7 +87,7 @@ def gauss_legendre_quadrature( # Get the radius of the point mass radius_p = 0.5 * (top - bottom) * rad_node + 0.5 * (top + bottom) # Get kappa constant for the point mass - kappa = radius_p ** 2 * cosphi_p + kappa = radius_p**2 * cosphi_p for i, lon_node in enumerate(lon_nodes): # Get the longitude of the point mass longitude_p = np.radians(0.5 * (e - w) * lon_node + 0.5 * (e + w)) diff --git a/harmonica/forward/_tesseroid_variable_density.py b/harmonica/forward/_tesseroid_variable_density.py index 1429f8089..d2be1c8fe 100644 --- a/harmonica/forward/_tesseroid_variable_density.py +++ b/harmonica/forward/_tesseroid_variable_density.py @@ -88,7 +88,7 @@ def gauss_legendre_quadrature_variable_density( radius_p = 0.5 * (top - bottom) * rad_node + 0.5 * (top + bottom) density_p = density(radius_p) # Get kappa constant for the point mass - kappa = radius_p ** 2 * cosphi_p + kappa = radius_p**2 * cosphi_p for i, lon_node in enumerate(lon_nodes): # Get the longitude of the point mass longitude_p = np.radians(0.5 * (e - w) * lon_node + 0.5 * (e + w)) diff --git a/harmonica/forward/point.py b/harmonica/forward/point.py index add0bea32..c48405be2 100644 --- a/harmonica/forward/point.py +++ b/harmonica/forward/point.py @@ -7,7 +7,6 @@ """ Forward modelling for point masses """ -import warnings import numpy as np from numba import jit, prange @@ -164,6 +163,13 @@ def point_gravity( - Downward acceleration: ``g_z`` - Northing acceleration: ``g_northing`` - Easting acceleration: ``g_easting`` + - Tensor components: + - ``g_ee`` + - ``g_nn`` + - ``g_zz`` + - ``g_en`` + - ``g_ez`` + - ``g_nz`` coordinate_system : str (optional) Coordinate system of the coordinates of the computation points and the @@ -214,40 +220,12 @@ def point_gravity( # Convert to more convenient units if field in ("g_easting", "g_northing", "g_z"): result *= 1e5 # SI to mGal + tensors = ("g_ee", "g_nn", "g_zz", "g_en", "g_ez", "g_nz", "g_ne", "g_ze", "g_zn") + if field in tensors: + result *= 1e9 # SI to Eotvos return result.reshape(cast.shape) -def point_mass_gravity( - coordinates, - points, - masses, - field, - coordinate_system="cartesian", - parallel=True, - dtype="float64", -): - """ - DEPRECATED. Use :func:`harmonica.point_gravity` instead. - - This function exists to support backward compatibility until next release. - """ - warnings.warn( - "The 'point_mass_gravity' function has been renamed to 'point_gravity' " - + "and will be deprecated on the next release, " - + "please use 'point_gravity' instead.", - FutureWarning, - ) - return point_gravity( - coordinates=coordinates, - points=points, - masses=masses, - field=field, - coordinate_system=coordinate_system, - parallel=parallel, - dtype=dtype, - ) - - def dispatcher(coordinate_system, parallel): """ Return the appropriate forward model function @@ -275,6 +253,17 @@ def get_kernel(coordinate_system, field): "g_z": kernel_g_z_cartesian, "g_northing": kernel_g_northing_cartesian, "g_easting": kernel_g_easting_cartesian, + # diagonal tensor components + "g_ee": kernel_g_ee_cartesian, + "g_nn": kernel_g_nn_cartesian, + "g_zz": kernel_g_zz_cartesian, + # non-diagonal tensor components + "g_en": kernel_g_en_cartesian, + "g_ez": kernel_g_ez_cartesian, + "g_nz": kernel_g_nz_cartesian, + "g_ne": kernel_g_en_cartesian, + "g_ze": kernel_g_ez_cartesian, + "g_zn": kernel_g_nz_cartesian, }, "spherical": { "potential": kernel_potential_spherical, @@ -291,6 +280,11 @@ def get_kernel(coordinate_system, field): return kernel +# ------------------------------------------ +# Kernel functions for Cartesian coordinates +# ------------------------------------------ + + @jit(nopython=True) def kernel_potential_cartesian( easting, northing, upward, easting_p, northing_p, upward_p @@ -304,10 +298,16 @@ def kernel_potential_cartesian( return 1 / distance +# Acceleration components +# ----------------------- + + @jit(nopython=True) def kernel_g_z_cartesian(easting, northing, upward, easting_p, northing_p, upward_p): """ - Kernel for downward component of gravitational gradient in Cartesian coords + Kernel for downward component of gravitational acceleration + + Use Cartesian coords. """ distance = distance_cartesian( (easting, northing, upward), (easting_p, northing_p, upward_p) @@ -316,7 +316,7 @@ def kernel_g_z_cartesian(easting, northing, upward, easting_p, northing_p, upwar # gravitational acceleration. As a consequence, it is multiplied by -1. # Notice that the ``g_z`` does not have the minus signal observed at the # compoents ``g_northing`` and ``g_easting``. - return (upward - upward_p) / distance ** 3 + return (upward - upward_p) / distance**3 @jit(nopython=True) @@ -324,13 +324,14 @@ def kernel_g_northing_cartesian( easting, northing, upward, easting_p, northing_p, upward_p ): """ - Kernel function for northing component of gravitational gradient in - Cartesian coordinates + Kernel function for northing component of gravitational acceleration + + Use Cartesian coordinates """ distance = distance_cartesian( (easting, northing, upward), (easting_p, northing_p, upward_p) ) - return -(northing - northing_p) / distance ** 3 + return -(northing - northing_p) / distance**3 @jit(nopython=True) @@ -338,13 +339,103 @@ def kernel_g_easting_cartesian( easting, northing, upward, easting_p, northing_p, upward_p ): """ - Kernel function for easting component of gravitational gradient in - Cartesian coordinates + Kernel function for easting component of gravitational acceleration + + Use Cartesian coordinates + """ + distance = distance_cartesian( + (easting, northing, upward), (easting_p, northing_p, upward_p) + ) + return -(easting - easting_p) / distance**3 + + +# Tensor components +# ----------------- + + +@jit(nopython=True) +def kernel_g_ee_cartesian(easting, northing, upward, easting_p, northing_p, upward_p): + """ + Kernel function for g_ee component of gravitational tensor + + Use Cartesian coordinates + """ + distance = distance_cartesian( + (easting, northing, upward), (easting_p, northing_p, upward_p) + ) + return 3 * (easting - easting_p) ** 2 / distance**5 - 1 / distance**3 + + +@jit(nopython=True) +def kernel_g_nn_cartesian(easting, northing, upward, easting_p, northing_p, upward_p): + """ + Kernel function for g_nn component of gravitational tensor + + Use Cartesian coordinates """ distance = distance_cartesian( (easting, northing, upward), (easting_p, northing_p, upward_p) ) - return -(easting - easting_p) / distance ** 3 + return 3 * (northing - northing_p) ** 2 / distance**5 - 1 / distance**3 + + +@jit(nopython=True) +def kernel_g_zz_cartesian(easting, northing, upward, easting_p, northing_p, upward_p): + """ + Kernel function for g_zz component of gravitational tensor + + Use Cartesian coordinates + """ + distance = distance_cartesian( + (easting, northing, upward), (easting_p, northing_p, upward_p) + ) + return 3 * (upward - upward_p) ** 2 / distance**5 - 1 / distance**3 + + +@jit(nopython=True) +def kernel_g_en_cartesian(easting, northing, upward, easting_p, northing_p, upward_p): + """ + Kernel function for g_en component of gravitational tensor + + Use Cartesian coordinates + """ + distance = distance_cartesian( + (easting, northing, upward), (easting_p, northing_p, upward_p) + ) + return 3 * (easting - easting_p) * (northing - northing_p) / distance**5 + + +@jit(nopython=True) +def kernel_g_ez_cartesian(easting, northing, upward, easting_p, northing_p, upward_p): + """ + Kernel function for g_ez component of gravitational tensor + + Use Cartesian coordinates + """ + distance = distance_cartesian( + (easting, northing, upward), (easting_p, northing_p, upward_p) + ) + # Add a minus sign to account that the z axis points downwards. + return -3 * (easting - easting_p) * (upward - upward_p) / distance**5 + + +@jit(nopython=True) +def kernel_g_nz_cartesian(easting, northing, upward, easting_p, northing_p, upward_p): + """ + Kernel function for g_nz component of gravitational tensor + + Use Cartesian coordinates + """ + distance = distance_cartesian( + (easting, northing, upward), (easting_p, northing_p, upward_p) + ) + # Add a minus sign to account that the z axis points downwards. + return -3 * (northing - northing_p) * (upward - upward_p) / distance**5 + + +# ------------------------------------------ +# Kernel functions for Cartesian coordinates +# ------------------------------------------ @jit(nopython=True) @@ -360,18 +451,24 @@ def kernel_potential_spherical( return 1 / distance +# Acceleration components +# ------------------- + + @jit(nopython=True) def kernel_g_z_spherical( longitude, cosphi, sinphi, radius, longitude_p, cosphi_p, sinphi_p, radius_p ): """ - Kernel for downward component of gravitational gradient in spherical coords + Kernel for downward component of gravitational acceleration + + Use spherical coordinates """ distance, cospsi, _ = distance_spherical_core( longitude, cosphi, sinphi, radius, longitude_p, cosphi_p, sinphi_p, radius_p ) delta_z = radius - radius_p * cospsi - return delta_z / distance ** 3 + return delta_z / distance**3 def point_mass_cartesian( diff --git a/harmonica/forward/prism.py b/harmonica/forward/prism.py index 25553087f..4d22c29c9 100644 --- a/harmonica/forward/prism.py +++ b/harmonica/forward/prism.py @@ -10,6 +10,12 @@ import numpy as np from numba import jit, prange +# Attempt to import numba_progress +try: + from numba_progress import ProgressBar +except ImportError: + ProgressBar = None + from ..constants import GRAVITATIONAL_CONST @@ -20,6 +26,7 @@ def prism_gravity( field, parallel=True, dtype="float64", + progressbar=False, disable_checks=False, ): """ @@ -73,6 +80,10 @@ def prism_gravity( dtype : data-type (optional) Data type assigned to the resulting gravitational field. Default to ``np.float64``. + progressbar : bool (optional) + If True, a progress bar of the computation will be printed to standard + error (stderr). Requires :mod:`numba_progress` to be installed. + Default to ``False``. disable_checks : bool (optional) Flag that controls whether to perform a sanity check on the model. Should be set to ``True`` only when it is certain that the input model @@ -130,9 +141,23 @@ def prism_gravity( + "mismatch the number of prisms ({})".format(prisms.shape[0]) ) _check_prisms(prisms) + # Show progress bar for 'jit_prism_gravity' function + if progressbar: + if ProgressBar is None: + raise ImportError( + "Missing optional dependency 'numba_progress' required if progressbar=True" + ) + progress_proxy = ProgressBar(total=coordinates[0].size) + else: + progress_proxy = None # Compute gravitational field - dispatcher(parallel)(coordinates, prisms, density, kernels[field], result) + dispatcher(parallel)( + coordinates, prisms, density, kernels[field], result, progress_proxy + ) result *= GRAVITATIONAL_CONST + # Close previously created progress bars + if progressbar: + progress_proxy.close() # Convert to more convenient units if field == "g_z": result *= 1e5 # SI to mGal @@ -186,7 +211,7 @@ def _check_prisms(prisms): raise ValueError(err_msg) -def jit_prism_gravity(coordinates, prisms, density, kernel, out): +def jit_prism_gravity(coordinates, prisms, density, kernel, out, progress_proxy=None): """ Compute gravitational field of prisms on computations points @@ -210,6 +235,8 @@ def jit_prism_gravity(coordinates, prisms, density, kernel, out): Array where the resulting field values will be stored. Must have the same size as the arrays contained on ``coordinates``. """ + # Check if we need to update the progressbar on each iteration + update_progressbar = progress_proxy is not None # Iterate over computation points and prisms for l in prange(coordinates[0].size): for m in range(prisms.shape[0]): @@ -233,6 +260,9 @@ def jit_prism_gravity(coordinates, prisms, density, kernel, out): shift_upward - coordinates[2][l], ) ) + # Update progress bar if called + if update_progressbar: + progress_proxy.update(1) @jit(nopython=True) @@ -240,14 +270,14 @@ def kernel_potential(easting, northing, upward): """ Kernel function for potential gravitational field generated by a prism """ - radius = np.sqrt(easting ** 2 + northing ** 2 + upward ** 2) + radius = np.sqrt(easting**2 + northing**2 + upward**2) kernel = ( easting * northing * safe_log(upward + radius) + northing * upward * safe_log(easting + radius) + easting * upward * safe_log(northing + radius) - - 0.5 * easting ** 2 * safe_atan2(upward * northing, easting * radius) - - 0.5 * northing ** 2 * safe_atan2(upward * easting, northing * radius) - - 0.5 * upward ** 2 * safe_atan2(easting * northing, upward * radius) + - 0.5 * easting**2 * safe_atan2(upward * northing, easting * radius) + - 0.5 * northing**2 * safe_atan2(upward * easting, northing * radius) + - 0.5 * upward**2 * safe_atan2(easting * northing, upward * radius) ) return kernel @@ -257,7 +287,7 @@ def kernel_g_z(easting, northing, upward): """ Kernel for downward component of gravitational acceleration of a prism """ - radius = np.sqrt(easting ** 2 + northing ** 2 + upward ** 2) + radius = np.sqrt(easting**2 + northing**2 + upward**2) kernel = ( easting * safe_log(northing + radius) + northing * safe_log(easting + radius) diff --git a/harmonica/forward/prism_layer.py b/harmonica/forward/prism_layer.py index 8063f67ef..1f3359b0c 100644 --- a/harmonica/forward/prism_layer.py +++ b/harmonica/forward/prism_layer.py @@ -13,6 +13,7 @@ import verde as vd import xarray as xr +from ..visualization.prism import prism_to_pyvista from .prism import prism_gravity @@ -304,7 +305,9 @@ def update_top_bottom(self, surface, reference): self._obj.coords["top"] = (self.dims, top) self._obj.coords["bottom"] = (self.dims, bottom) - def gravity(self, coordinates, field, density_name="density", **kwargs): + def gravity( + self, coordinates, field, progressbar=False, density_name="density", **kwargs + ): """ Computes the gravity generated by the layer of prisms @@ -357,6 +360,7 @@ def gravity(self, coordinates, field, density_name="density", **kwargs): prisms=boundaries, density=density, field=field, + progressbar=progressbar, **kwargs, ) @@ -446,3 +450,22 @@ def get_prism(self, indices): bottom = self._obj.bottom.values[indices] top = self._obj.top.values[indices] return west, east, south, north, bottom, top + + def to_pyvista(self): + """ + Return a pyvista UnstructuredGrid to plot the PrismLayer + + Returns + ------- + pv_grid : :class:`pyvista.UnstructuredGrid` + :class:`pyvista.UnstructuredGrid` containing each prism of the + layer as a hexahedron along with their properties. + """ + prisms = self._to_prisms() + properties = None + if self._obj.data_vars: + properties = { + data_var: np.asarray(self._obj[data_var]).ravel() + for data_var in self._obj.data_vars + } + return prism_to_pyvista(prisms, properties=properties) diff --git a/harmonica/forward/tesseroid.py b/harmonica/forward/tesseroid.py index b6f74e105..3a3cbb45d 100644 --- a/harmonica/forward/tesseroid.py +++ b/harmonica/forward/tesseroid.py @@ -126,8 +126,7 @@ def tesseroid_gravity( >>> # Define computation point located on the top surface of the tesseroid >>> coordinates = [0, 0, ellipsoid.mean_radius] >>> # Compute radial component of the gravitational gradient in mGal - >>> tesseroid_gravity(coordinates, tesseroid, density, field="g_z") - array(112.54539933) + >>> g_z = tesseroid_gravity(coordinates, tesseroid, density, field="g_z") >>> # Define a linear density function for the same tesseroid. >>> # It should be decorated with numba.njit @@ -139,8 +138,9 @@ def tesseroid_gravity( ... slope = (density_top - density_bottom) / (top - bottom) ... return slope * (radius - bottom) + density_bottom >>> # Compute the downward acceleration it generates - >>> tesseroid_gravity(coordinates, tesseroid, linear_density, field="g_z") - array(125.80498632) + >>> g_z = tesseroid_gravity( + ... coordinates, tesseroid, linear_density, field="g_z" + ... ) """ kernels = {"potential": kernel_potential_spherical, "g_z": kernel_g_z_spherical} diff --git a/harmonica/forward/utils.py b/harmonica/forward/utils.py index f707f6e06..ea2117d82 100644 --- a/harmonica/forward/utils.py +++ b/harmonica/forward/utils.py @@ -265,7 +265,7 @@ def distance_geodetic(point_p, point_q, ellipsoid): coslambda, prime_vertical_radius, prime_vertical_radius_p, - ellipsoid.first_eccentricity ** 2, + ellipsoid.first_eccentricity**2, ) @@ -311,11 +311,11 @@ def geodetic_distance_core( upward_sum = prime_vertical_radius + height upward_sum_p = prime_vertical_radius_p + height_p dist = np.sqrt( - upward_sum_p ** 2 * cosphi_p ** 2 - + upward_sum ** 2 * cosphi ** 2 + upward_sum_p**2 * cosphi_p**2 + + upward_sum**2 * cosphi**2 - 2 * upward_sum * upward_sum_p * cosphi * cosphi_p * coslambda - + (upward_sum_p - ecc_sq * prime_vertical_radius_p) ** 2 * sinphi_p ** 2 - + (upward_sum - ecc_sq * prime_vertical_radius) ** 2 * sinphi ** 2 + + (upward_sum_p - ecc_sq * prime_vertical_radius_p) ** 2 * sinphi_p**2 + + (upward_sum - ecc_sq * prime_vertical_radius) ** 2 * sinphi**2 - ( 2 * (upward_sum_p - ecc_sq * prime_vertical_radius_p) diff --git a/harmonica/synthetic/surveys.py b/harmonica/synthetic/surveys.py index 5df6f298f..6b1a2686b 100644 --- a/harmonica/synthetic/surveys.py +++ b/harmonica/synthetic/surveys.py @@ -7,12 +7,24 @@ """ Create synthetic surveys for gravity and magnetic observations """ +import warnings + from verde import get_region, inside from verde.coordinates import check_region from ..datasets import fetch_britain_magnetic, fetch_south_africa_gravity +def _deprecation_warning(): + """ + Raise a FutureWarning about deprecation of synthetic module + """ + warnings.warn( + "The 'synthetic' module will be deprecated in Harmonica v0.6.0.", + FutureWarning, + ) + + def airborne_survey(region=None, data_region=(-5.0, -4.0, 56.0, 56.5)): """ Create measurement locations for a synthetic airborne survey @@ -51,6 +63,7 @@ def airborne_survey(region=None, data_region=(-5.0, -4.0, 56.0, 56.5)): -------- harmonica.datasets.fetch_britain_magnetic """ + _deprecation_warning() # Sanity checks for region and data_region if region is not None: check_region(region[:4]) @@ -103,6 +116,7 @@ def ground_survey(region=None, data_region=(13.60, 20.30, -24.20, -17.5)): -------- harmonica.datasets.fetch_south_africa_gravity """ + _deprecation_warning() # Sanity checks for region and data_region if region is not None: check_region(region[:4]) diff --git a/harmonica/tests/data/sample-gravity-grid.nc 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+@pytest.mark.filterwarnings( + "ignore:The '(synthetic|datasets)' module will be deprecated:FutureWarning" +) def test_data_region_airborne_survey(): """ Test if a different cut_region produces a different airborne survey diff --git a/harmonica/tests/test_tesseroid.py b/harmonica/tests/test_tesseroid.py index 17fd8e230..9db9533dd 100644 --- a/harmonica/tests/test_tesseroid.py +++ b/harmonica/tests/test_tesseroid.py @@ -362,7 +362,7 @@ def test_split_tesseroid(): tesseroid, n_lon=2, n_lat=2, n_rad=2, stack=stack, stack_top=stack_top ) splitted = np.array([tess for tess in stack if not np.all(tess == 0)]) - assert splitted.shape[0] == 2 ** 3 + assert splitted.shape[0] == 2**3 assert splitted.shape[0] == stack_top + 1 # Check if the tesseroid hasn't been split on each direction assert not (splitted[0, lon_indexes] == splitted[:, lon_indexes]).all() @@ -460,7 +460,7 @@ def test_split_tesseroid_only_horizontal(): tesseroid, n_lon=2, n_lat=2, n_rad=1, stack=stack, stack_top=stack_top ) splitted = np.array([tess for tess in stack if not np.all(tess == 0)]) - assert splitted.shape[0] == 2 ** 2 + assert splitted.shape[0] == 2**2 assert splitted.shape[0] == stack_top + 1 # Check if the tesseroid hasn't been split on radial direction assert (splitted[0, radial_indexes] == splitted[:, radial_indexes]).all() @@ -558,7 +558,7 @@ def spherical_shell_analytical(top, bottom, density, radius): * np.pi * GRAVITATIONAL_CONST * density - * (top ** 3 - bottom ** 3) + * (top**3 - bottom**3) / radius ) analytical = { diff --git a/harmonica/tests/test_tesseroid_variable_density.py b/harmonica/tests/test_tesseroid_variable_density.py index 3531f9ca2..6eff3e312 100644 --- a/harmonica/tests/test_tesseroid_variable_density.py +++ b/harmonica/tests/test_tesseroid_variable_density.py @@ -351,8 +351,8 @@ def analytical_spherical_shell_linear(radius, bottom, top, slope, constant_term) Analytical solutions of a spherical shell with linear density """ constant = np.pi * GRAVITATIONAL_CONST * slope * ( - top ** 4 - bottom ** 4 - ) + 4 / 3.0 * np.pi * GRAVITATIONAL_CONST * constant_term * (top ** 3 - bottom ** 3) + top**4 - bottom**4 + ) + 4 / 3.0 * np.pi * GRAVITATIONAL_CONST * constant_term * (top**3 - bottom**3) potential = constant / radius data = { "potential": potential, @@ -377,7 +377,7 @@ def analytical_spherical_shell_exponential( * np.pi * GRAVITATIONAL_CONST * a_factor - / k ** 3 + / k**3 / radius * ( ((bottom * k) ** 2 + 2 * bottom * k + 2) @@ -388,7 +388,7 @@ def analytical_spherical_shell_exponential( * np.pi * GRAVITATIONAL_CONST * constant_term - * (top ** 3 - bottom ** 3) + * (top**3 - bottom**3) / radius ) data = { diff --git a/harmonica/tests/test_transformations.py b/harmonica/tests/test_transformations.py new file mode 100644 index 000000000..5d4bad896 --- /dev/null +++ b/harmonica/tests/test_transformations.py @@ -0,0 +1,81 @@ +# Copyright (c) 2018 The Harmonica Developers. +# Distributed under the terms of the BSD 3-Clause License. +# SPDX-License-Identifier: BSD-3-Clause +# +# This code is part of the Fatiando a Terra project (https://www.fatiando.org) +# +""" +Test functions for regular grid transformations +""" +import os +from pathlib import Path + +import numpy as np +import pytest +import xarray as xr +import xrft + +from ..transformations import derivative_upward +from .utils import root_mean_square_error + +MODULE_DIR = Path(os.path.dirname(__file__)) +TEST_DATA_DIR = MODULE_DIR / "data" + + +@pytest.fixture(name="sample_gravity_grid") +def fixture_sample_gravity_grid(): + """ + Return a sample gravity grid with potential and acceleration components + """ + # Read grid from file + fname = TEST_DATA_DIR / "sample-gravity-grid.nc" + grid = xr.load_dataset(fname) + return grid + + +def test_derivative_upward(sample_gravity_grid): + """ + Test derivative_upward function against the sample grid + """ + potential = sample_gravity_grid.potential + # Pad the potential field grid to improve accuracy + pad_width = { + "easting": potential.easting.size // 3, + "northing": potential.northing.size // 3, + } + # need to drop upward coordinate (bug in xrft) + potential_padded = xrft.pad(potential.drop_vars("upward"), pad_width=pad_width) + # Calculate upward derivative and unpad it + derivative = derivative_upward(potential_padded) + derivative = xrft.unpad(derivative, pad_width) + # Compare against g_z (trim the borders to ignore boundary effects) + trim = 5 + derivative = derivative[trim:-trim, trim:-trim] + g_z = sample_gravity_grid.g_z[trim:-trim, trim:-trim] * 1e-5 # convert to SI units + rms = root_mean_square_error(derivative, g_z) + assert rms / np.abs(g_z).max() < 0.015 + + +def test_derivative_upward_order_2(sample_gravity_grid): + """ + Test higher order of derivative_upward function against the sample grid + """ + potential = sample_gravity_grid.potential + # Pad the potential field grid to improve accuracy + pad_width = { + "easting": potential.easting.size // 3, + "northing": potential.northing.size // 3, + } + # need to drop upward coordinate (bug in xrft) + potential_padded = xrft.pad(potential.drop_vars("upward"), pad_width=pad_width) + # Calculate second upward derivative and unpad it + second_deriv = derivative_upward(potential_padded, order=2) + second_deriv = xrft.unpad(second_deriv, pad_width) + # Compare against g_zz (trim the borders to ignore boundary effects) + trim = 10 + second_deriv = second_deriv[trim:-trim, trim:-trim] + g_zz = ( + sample_gravity_grid.g_zz[trim:-trim, trim:-trim] * 1e-9 + ) # convert to SI units + rms = root_mean_square_error(second_deriv, g_zz) + assert rms / np.abs(g_zz).max() < 0.015 diff --git a/harmonica/tests/test_visualizations.py b/harmonica/tests/test_visualizations.py new file mode 100644 index 000000000..cad20a320 --- /dev/null +++ b/harmonica/tests/test_visualizations.py @@ -0,0 +1,128 @@ +# Copyright (c) 2018 The Harmonica Developers. +# Distributed under the terms of the BSD 3-Clause License. +# SPDX-License-Identifier: BSD-3-Clause +# +# This code is part of the Fatiando a Terra project (https://www.fatiando.org) +# +""" +Test functions from the visualization module. +""" +from unittest.mock import patch + +import numpy as np +import numpy.testing as npt +import pytest +import xarray as xr + +from ..visualization.prism import prism_to_pyvista + +try: + import pyvista +except ImportError: + pyvista = None + + +@pytest.mark.skipif(pyvista is not None, reason="pyvista must be missing") +def test_prism_to_pyvista_missing_pyvista(): + """ + Check error raise after calling prism_to_pyvista when pyvista is missing + """ + prism = [0, 1, 0, 1, 0, 1] + with pytest.raises(ImportError) as exception: + prism_to_pyvista(prism) + assert "'pyvista'" in str(exception.value) + + +@pytest.fixture(name="prisms") +def fixture_prisms(): + """ + Define a set of sample prisms + """ + return [ + [1e3, 5e3, -2e3, 4e3, -10e3, -7e3], + [200, 500, 600, 900, 100, 300], + [-2e3, 0, 5e3, 15e3, -4e3, 2e3], + [-10e3, -8e3, -10e3, -8e3, -10e3, -8e3], + ] + + +@pytest.fixture(name="density", params=("numpy", "xarray")) +def fixture_density(request): + """ + Return a density array for the sample prisms + """ + density = [2670.0, 2900.0, 3300.0, 3200.0] + if request.param == "xarray": + density = xr.DataArray(density) + return density + + +@pytest.fixture(name="susceptibility", params=("numpy", "xarray")) +def fixture_susceptibility(request): + """ + Return a susceptibility array for the sample prisms + """ + susceptibility = [2e-4, 2.5e-4, 5e-4, 1e-3] + if request.param == "xarray": + susceptibility = xr.DataArray(susceptibility) + return susceptibility + + +@pytest.mark.skipif(pyvista is None, reason="requires pyvista") +def test_prism_to_pyvista(prisms, density): + """ + Test output of prism_to_pyvista + """ + pv_grid = prism_to_pyvista(prisms, properties={"density": density}) + assert pv_grid.n_cells == 4 + assert pv_grid.n_points == 32 + # Check coordinates of prisms + for i, prism in enumerate(prisms): + npt.assert_allclose(prism, pv_grid.cell_bounds(i)) + # Check properties of the prisms + assert pv_grid.n_arrays == 1 + assert pv_grid.array_names == ["density"] + npt.assert_allclose(pv_grid.get_array("density"), density) + + +@pytest.mark.skipif(pyvista is None, reason="requires pyvista") +@pytest.mark.parametrize("n_props", [0, 1, 2]) +def test_prism_to_pyvista_properties(n_props, prisms, density, susceptibility): + """ + Test prism_to_pyvista for different number of properties + """ + properties = None + if n_props == 1: + properties = {"density": density} + elif n_props == 2: + properties = {"density": density, "susceptibility": susceptibility} + pv_grid = prism_to_pyvista(prisms, properties=properties) + # Check if the grid has the right properties + if properties is None: + assert pv_grid.n_arrays == 0 + assert pv_grid.array_names == [] + else: + assert pv_grid.n_arrays == len(properties) + assert set(pv_grid.array_names) == set(properties.keys()) + for prop in properties: + npt.assert_allclose(pv_grid.get_array(prop), properties[prop]) + + +@pytest.mark.skipif(pyvista is None, reason="requires pyvista") +def test_prism_to_pyvista_error_2d_property(prisms, density): + """ + Test if prism_to_pyvista raises error on property as 2D array + """ + density_2d = np.array(density).reshape((2, 2)) + with pytest.raises(ValueError): + prism_to_pyvista(prisms, properties={"density": density_2d}) + + +@patch("harmonica.visualization.prism.pyvista", None) +def test_prisms_pyvista_missing_error(prisms, density): + """ + Check if prism_to_pyvista raises error if pyvista is missing + """ + # Check if error is raised + with pytest.raises(ImportError): + prism_to_pyvista(prisms, properties={"density": density}) diff --git a/harmonica/tests/utils.py b/harmonica/tests/utils.py index 8199280db..58cb27b67 100644 --- a/harmonica/tests/utils.py +++ b/harmonica/tests/utils.py @@ -9,9 +9,29 @@ """ import os +import numpy as np import pytest +def root_mean_square_error(x, y): + """ + Compute root mean square error between two arrays + + Parameters + ---------- + x : array + y : array + + Returns + ------- + rms : float + Root mean square error between the two arrays + """ + x = np.asarray(x) + y = np.asarray(y) + return np.sqrt(np.mean((x - y) ** 2)) + + def combine_decorators(*decorators): """ Combine several decorators into a single one diff --git a/harmonica/visualization/__init__.py b/harmonica/visualization/__init__.py new file mode 100644 index 000000000..d08adbdde --- /dev/null +++ b/harmonica/visualization/__init__.py @@ -0,0 +1,7 @@ +# Copyright (c) 2018 The Harmonica Developers. +# Distributed under the terms of the BSD 3-Clause License. +# SPDX-License-Identifier: BSD-3-Clause +# +# This code is part of the Fatiando a Terra project (https://www.fatiando.org) +# +from .prism import prism_to_pyvista diff --git a/harmonica/visualization/prism.py b/harmonica/visualization/prism.py new file mode 100644 index 000000000..1553bdd8a --- /dev/null +++ b/harmonica/visualization/prism.py @@ -0,0 +1,215 @@ +# Copyright (c) 2018 The Harmonica Developers. +# Distributed under the terms of the BSD 3-Clause License. +# SPDX-License-Identifier: BSD-3-Clause +# +# This code is part of the Fatiando a Terra project (https://www.fatiando.org) +# +""" +Functions for visualizing prisms through pyvista +""" +import numpy as np + +try: + import pyvista +except ImportError: + pyvista = None +else: + import vtk + + +def prism_to_pyvista(prisms, properties=None): + """ + Create a :class:`pyvista.UnstructuredGrid` out of prisms + + Builds a :class:`pyvista.UnstructuredGrid` out of a set of prisms that + could be used to plot a 3D representation through :mod:`pyvista`. + + Parameters + ---------- + prisms : list or 2d-array + List or 2d-array with the boundaries of the prisms. + Each row contains the boundaries of each prism in the following order: + ``west``, ``east``, ``south``, ``north``, ``bottom``, ``top``. + properties : dict or None (optional) + Dictionary with the physical properties of the prisms. + Each key should be a string and its corresponding value a 1D array. + If None, no property will be added to the + :class:`pyvista.UnstructuredGrid`. + Default to None. + + Returns + ------- + pv_grid : :class:`pyvista.UnstructuredGrid` + :class:`pyvista.UnstructuredGrid` that represents the prisms with their + properties (if any). + + Examples + -------- + + .. pyvista-plot:: + + Define a set of prisms and their densities: + + >>> prisms = [ + ... [0, 4, 0, 5, -10, 0], + ... [0, 4, 7, 9, -12, -3], + ... [6, 9, 2, 6, -7, 3], + ... ] + >>> densities = [2900, 3000, 2670] + + Generate a :class:`pyvista.UnstructuredGrid` out of them: + + >>> import harmonica as hm + >>> pv_grid = hm.visualization.prism_to_pyvista( + ... prisms, properties={"density": densities} + ... ) + >>> pv_grid # doctest: +SKIP + + Plot it using :mod:`pyvista`: + + >>> pv_grid.plot() # doctest: +SKIP + + """ + # Check if pyvista are installed + if pyvista is None: + raise ImportError( + "Missing optional dependency 'pyvista' required for building pyvista grids." + ) + # Get prisms and number of prisms + prisms = np.atleast_2d(prisms) + n_prisms = prisms.shape[0] + # Get the vertices of the prisms + vertices = _prisms_boundaries_to_vertices(prisms) + # Generate the cells for the hexahedrons + cells = np.arange(n_prisms * 8).reshape([n_prisms, 8]) + # Build the UnstructuredGrid + pv_grid = pyvista.UnstructuredGrid({vtk.VTK_HEXAHEDRON: cells}, vertices) + # Add properties to the grid + if properties is not None: + for name, prop in properties.items(): + # Check if the property is given as 1d array + prop = np.atleast_1d(prop) + if prop.ndim > 1: + raise ValueError( + f"Multidimensional array found in '{name}' property. " + + "Please, pass prism properties as 1d arrays." + ) + # Assign the property to the cell_data + pv_grid.cell_data[name] = prop + return pv_grid + + +def _prisms_boundaries_to_vertices(prisms): + """ + Converts prisms boundaries to sets of vertices for each prism + + The vertices for each prism will be in the following order + + .. code-block:: + + 7-------6 + /| /| + / | / | + 4-------5 | + up northing | | | | + ^ ~ | 3----|--2 + | / | / | / + | / |/ |/ + ------> easting 0-------1 + + So the vertices of a single prism should be like: + + .. code-block:: python + + [w, s, bottom], + [e, s, bottom], + [e, n, bottom], + [w, n, bottom], + [w, s, top], + [e, s, top], + [e, n, top], + [w, n, top], + + + Parameters + ---------- + prisms : 2d-array + 2d-array with the boundaries of a set of prisms. Each row of the array + should contain the boundaries of a single prism in the following + order: ``west``, ``east``, ``south``, ``north``, ``bottom``, ``top``. + + Returns + ------- + vertices : 2d-array + 2D array with the coordinates of the vertices of the prism. Each row of + the array corresponds to the coordinate of a single vertex in the + following order: ``easting``, ``northing``, ``upward``. + The shape of this array is ``(M, 3)``, where ``M`` is the total number + of vertices in the whole set of prisms (number of prisms times 8). + The order of the vertices is fixed to be compatible with VTK. + + Examples + -------- + >>> _prisms_boundaries_to_vertices(np.array([[-1, 1, -2, 2, -3, 3]])) + array([[-1., -2., -3.], + [ 1., -2., -3.], + [ 1., 2., -3.], + [-1., 2., -3.], + [-1., -2., 3.], + [ 1., -2., 3.], + [ 1., 2., 3.], + [-1., 2., 3.]]) + >>> _prisms_boundaries_to_vertices( + ... np.array([[-1, 1, -2, 2, -3, 3], [-4, 4, -5, 5, -6, 6]]) + ... ) + array([[-1., -2., -3.], + [ 1., -2., -3.], + [ 1., 2., -3.], + [-1., 2., -3.], + [-1., -2., 3.], + [ 1., -2., 3.], + [ 1., 2., 3.], + [-1., 2., 3.], + [-4., -5., -6.], + [ 4., -5., -6.], + [ 4., 5., -6.], + [-4., 5., -6.], + [-4., -5., 6.], + [ 4., -5., 6.], + [ 4., 5., 6.], + [-4., 5., 6.]]) + """ + # Get number of prisms + n_prisms = prisms.shape[0] + + # Allocate vertices array + vertices = np.empty((n_prisms, 8, 3)) + + # Define a dictionary with the indices of the vertices that contain each + # boundary of the prism. + # For example, the west boundary is present only in the vertices + # number 0, 3, 4 and 7. + indices = { + "west": (0, 3, 4, 7), + "east": (1, 2, 5, 6), + "south": (0, 1, 4, 5), + "north": (2, 3, 6, 7), + "bottom": (0, 1, 2, 3), + "top": (4, 5, 6, 7), + } + + # Assign the values to each vertex + for i, boundary in enumerate(indices): + # Determine at which component of the vertices should the current + # boundary be assigned to. + # The west and east (i = 0 and i = 1) should go to the component 0. + # The south and north (i = 2 and i = 3) should go to the component 1. + # The bottom and top (i = 4 and i = 5) should go to the component 2. + component = i // 2 + # Assign vertices components + for vertex in indices[boundary]: + vertices[:, vertex, component] = prisms[:, i] + + # Reshape the vertices array so it has (M, 3) shape, where M is the total + # number of vertices. + return vertices.reshape((n_prisms * 8, 3)) diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 000000000..6a5b0e2aa --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,20 @@ +# Specify that we use setuptools and setuptools_scm (to generate the version +# string). Actual configuration is in setup.py and setup.cfg. +[build-system] +requires = ["setuptools>=45", "wheel", "setuptools_scm[toml]>=6.2"] +build-backend = "setuptools.build_meta" + +[tool.setuptools_scm] +version_scheme = "post-release" +local_scheme = "node-and-date" +write_to = "harmonica/_version_generated.py" + +[tool.pytest.ini_options] +markers = [ + "use_numba: mark test functions that call Numba jitted functions" +] + +# Make sure isort and Black are compatible +[tool.isort] +profile = "black" +multi_line_output = 3 diff --git a/pytest.ini b/pytest.ini deleted file mode 100644 index 9ce91daec..000000000 --- a/pytest.ini +++ /dev/null @@ -1,3 +0,0 @@ -[pytest] -markers = - use_numba: mark test functions that call Numba jitted functions diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index c93c915f3..000000000 --- a/requirements.txt +++ /dev/null @@ -1,9 +0,0 @@ -numpy -pandas -scipy -scikit-learn -numba -pooch>=0.7.0 -xarray -xrft -verde>=1.5.0 diff --git a/setup.cfg b/setup.cfg new file mode 100644 index 000000000..386aae5e3 --- /dev/null +++ b/setup.cfg @@ -0,0 +1,99 @@ +[metadata] +name = harmonica +fullname = Harmonica +description = "Forward modeling, inversion, and processing gravity and magnetic data" +long_description = file: README.rst +long_description_content_type = text/x-rst +author = The Harmonica Developers +author_email = fatiandoaterra@protonmail.com +maintainer = "Santiago Soler" +maintainer_email = santiago.r.soler@gmail.com +license = BSD 3-Clause License +license_file = LICENSE.txt +platform = any +keywords = geoscience, geophysics +classifiers = + Development Status :: 5 - Production/Stable + Intended Audience :: Science/Research + Intended Audience :: Developers + Intended Audience :: Education + License :: OSI Approved :: BSD License + Natural Language :: English + Operating System :: OS Independent + Topic :: Scientific/Engineering + Topic :: Software Development :: Libraries + Programming Language :: Python :: 3 :: Only + Programming Language :: Python :: 3.7 + Programming Language :: Python :: 3.8 + Programming Language :: Python :: 3.9 +url = https://github.com/fatiando/harmonica +project_urls = + Documentation = https://www.fatiando.org/harmonica + Release Notes = https://github.com/fatiando/harmonica/releases + Bug Tracker = https://github.com/fatiando/harmonica/issues + Source Code = https://github.com/fatiando/harmonica + +[options] +zip_safe = True +include_package_data = True +packages = find: +python_requires = >=3.7 +install_requires = + numpy + pandas + scipy + scikit-learn + numba + pooch>=0.7.0 + xarray + verde>=1.5.0 + xrft + +[options.extras_require] +visualizations = + pyvista + vtk>=9 + +[options.package_data] +harmonica.tests = data/*, baseline/* +harmonica.datasets = registry.txt + +[flake8] +max-line-length = 88 +max-doc-length = 79 +ignore = + # Too many leading '#' for block comment + E266, + # Line too long (82 > 79 characters) + E501, + # Do not use variables named 'I', 'O', or 'l' + E741, + # Line break before binary operator (conflicts with black) + W503, +exclude = + .git, + __pycache__, + .ipynb_checkpoints, +per-file-ignores = + # disable unused-imports errors on __init__.py + __init__.py: F401 + +# Configure flake8-rst-docstrings +# ------------------------------- +# Add some roles used in our docstrings +rst-roles = + class, + func, + mod, + meth, + ref, +# Add some directives used in our docstrings +rst-directives = + pyvista-plot, +# Ignore "Unknown target name" raised on citations +extend-ignore = RST306 + +# Configure flake8-functions +# -------------------------- +# Allow a max of 10 arguments per function +max-parameters-amount = 10 diff --git a/setup.py b/setup.py index 70c718617..a275f1301 100644 --- a/setup.py +++ b/setup.py @@ -5,80 +5,11 @@ # This code is part of the Fatiando a Terra project (https://www.fatiando.org) # """ -Build and install the project. - +Setup script for the Python package. +Metadata and build configuration are defined in setup.cfg Uses setuptools-scm to manage version numbers using git tags. """ -from setuptools import find_packages, setup - -NAME = "harmonica" -FULLNAME = "Harmonica" -AUTHOR = "The Harmonica Developers" -AUTHOR_EMAIL = "leouieda@gmail.com" -MAINTAINER = "Leonardo Uieda" -MAINTAINER_EMAIL = AUTHOR_EMAIL -LICENSE = "BSD License" -URL = "https://github.com/fatiando/harmonica" -DESCRIPTION = "Forward modeling, inversion, and processing gravity and magnetic data " -KEYWORDS = "" -with open("README.rst") as f: - LONG_DESCRIPTION = "".join(f.readlines()) -CLASSIFIERS = [ - "Development Status :: 3 - Alpha", - "Intended Audience :: Science/Research", - "Intended Audience :: Developers", - "Intended Audience :: Education", - "Topic :: Scientific/Engineering", - "Topic :: Software Development :: Libraries", - "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3.6", - "Programming Language :: Python :: 3.7", - "Programming Language :: Python :: 3.8", - "Programming Language :: Python :: 3.9", - "Programming Language :: Python :: 3 :: Only", - f"License :: OSI Approved :: {LICENSE}", -] -PLATFORMS = "Any" -PACKAGES = find_packages(exclude=["doc"]) -SCRIPTS = [] -PACKAGE_DATA = { - "harmonica.datasets": ["registry.txt"], - "harmonica.tests": ["data/*", "baseline/*"], -} -with open("requirements.txt") as f: - INSTALL_REQUIRES = f.readlines() -PYTHON_REQUIRES = ">=3.6" - -# Configuration for setuptools-scm -SETUP_REQUIRES = ["setuptools_scm"] -USE_SCM_VERSION = { - "relative_to": __file__, - "version_scheme": "post-release", - "local_scheme": "node-and-date", - "write_to": f"{NAME}/_version.py", -} - +from setuptools import setup if __name__ == "__main__": - setup( - name=NAME, - fullname=FULLNAME, - description=DESCRIPTION, - long_description=LONG_DESCRIPTION, - use_scm_version=USE_SCM_VERSION, - author=AUTHOR, - author_email=AUTHOR_EMAIL, - maintainer=MAINTAINER, - maintainer_email=MAINTAINER_EMAIL, - license=LICENSE, - url=URL, - platforms=PLATFORMS, - scripts=SCRIPTS, - packages=PACKAGES, - package_data=PACKAGE_DATA, - classifiers=CLASSIFIERS, - keywords=KEYWORDS, - install_requires=INSTALL_REQUIRES, - python_requires=PYTHON_REQUIRES, - setup_requires=SETUP_REQUIRES, - ) + setup() diff --git a/tools/export_requirements.py b/tools/export_requirements.py new file mode 100644 index 000000000..bf3d9028d --- /dev/null +++ b/tools/export_requirements.py @@ -0,0 +1,19 @@ +# Copyright (c) 2018 The Harmonica Developers. +# Distributed under the terms of the BSD 3-Clause License. +# SPDX-License-Identifier: BSD-3-Clause +# +# This code is part of the Fatiando a Terra project (https://www.fatiando.org) +# +""" +Export the run-time requirements from setup.cfg to a requirement.txt format. +Modified from https://github.com/Unidata/MetPy +""" +import configparser + +# Read the setup.cfg +config = configparser.ConfigParser() +config.read("setup.cfg") + +print("# Run-time dependencies") +for package in config["options"]["install_requires"].strip().split("\n"): + print(package.strip()) diff --git a/license_notice.py b/tools/license_notice.py similarity index 100% rename from license_notice.py rename to tools/license_notice.py

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