From 24760173951bf2c85d81d93a03e23e6e5ea67d69 Mon Sep 17 00:00:00 2001 From: Leonardo Uieda Date: Tue, 21 Oct 2025 18:13:03 -0300 Subject: [PATCH 01/20] Better comments in apply filter and a test notebook --- filter-test.ipynb | 1145 +++++++++++++++++++++++++++++++++++ harmonica/filters/_utils.py | 19 +- 2 files changed, 1154 insertions(+), 10 deletions(-) create mode 100644 filter-test.ipynb diff --git a/filter-test.ipynb b/filter-test.ipynb new file mode 100644 index 000000000..b8ef69bc8 --- /dev/null +++ b/filter-test.ipynb @@ -0,0 +1,1145 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "b5e97dfb-7beb-48b7-bb0f-ca9e811309be", + "metadata": {}, + "outputs": [], + "source": [ + "import ensaio\n", + "import harmonica as hm\n", + "import xarray as xr" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "b4bfbd03-65dd-457f-aa89-2b43531f9fe6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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+       "    source:        Interpolated from airborne magnetic line data using gradie...\n",
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  • Conventions :
    CF-1.8
    title :
    Magnetic total-field anomaly of the Lightning Creek sill complex, Australia
    crs :
    proj=utm zone=54 south datum=WGS84 units=m no_defs ellps=WGS84 towgs84=0,0,0
    source :
    Interpolated from airborne magnetic line data using gradient-boosted equivalent sources
    license :
    Creative Commons Attribution 4.0 International Licence
    references :
    Geophysical Acquisition & Processing Section 2019. MIM Data from Mt Isa Inlier, QLD (P1029), magnetic line data, AWAGS levelled. Geoscience Australia, Canberra. http://pid.geoscience.gov.au/dataset/ga/142419
    long_name :
    total-field magnetic anomaly
    units :
    nT
    actual_range :
    [-1785. 3798.]
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", 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "hm.upward_continuation(g, height_displacement=1000)" + "gu.plot()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c6ef65bb-a81c-40af-9d83-a30344979000", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/harmonica/_transformations.py b/harmonica/_transformations.py index d505d3645..10ef1a380 100644 --- a/harmonica/_transformations.py +++ b/harmonica/_transformations.py @@ -54,7 +54,7 @@ def derivative_upward(grid, order=1): -------- harmonica.filters.derivative_upward_kernel """ - return apply_filter(grid, derivative_upward_kernel, order=order) + return apply_filter(grid, derivative_upward_kernel, filter_kwargs={"order": order}) def derivative_easting(grid, order=1, method="finite-diff"): @@ -107,7 +107,9 @@ def derivative_easting(grid, order=1, method="finite-diff"): for _ in range(order): grid = grid.differentiate(coord=coordinate) elif method == "fft": - grid = apply_filter(grid, derivative_easting_kernel, order=order) + grid = apply_filter( + grid, derivative_easting_kernel, filter_kwargs={"order": order} + ) else: msg = ( f"Invalid method '{method}'. Please select one from 'finite-diff' or 'fft'." @@ -166,7 +168,9 @@ def derivative_northing(grid, order=1, method="finite-diff"): for _ in range(order): grid = grid.differentiate(coord=coordinate) elif method == "fft": - return apply_filter(grid, derivative_northing_kernel, order=order) + return apply_filter( + grid, derivative_northing_kernel, filter_kwargs={"order": order} + ) else: msg = ( f"Invalid method '{method}'. Please select one from 'finite-diff' or 'fft'." @@ -209,7 +213,9 @@ def upward_continuation(grid, height_displacement): harmonica.filters.upward_continuation_kernel """ return apply_filter( - grid, upward_continuation_kernel, height_displacement=height_displacement + grid, + upward_continuation_kernel, + filter_kwargs={"height_displacement": height_displacement}, ) @@ -245,7 +251,9 @@ def gaussian_lowpass(grid, wavelength): -------- harmonica.filters.gaussian_lowpass_kernel """ - return apply_filter(grid, gaussian_lowpass_kernel, wavelength=wavelength) + return apply_filter( + grid, gaussian_lowpass_kernel, filter_kwargs={"wavelength": wavelength} + ) def gaussian_highpass(grid, wavelength): @@ -280,7 +288,9 @@ def gaussian_highpass(grid, wavelength): -------- harmonica.filters.gaussian_highpass_kernel """ - return apply_filter(grid, gaussian_highpass_kernel, wavelength=wavelength) + return apply_filter( + grid, gaussian_highpass_kernel, filter_kwargs={"wavelength": wavelength} + ) def reduction_to_pole( @@ -335,10 +345,12 @@ def reduction_to_pole( return apply_filter( grid, reduction_to_pole_kernel, - inclination=inclination, - declination=declination, - magnetization_inclination=magnetization_inclination, - magnetization_declination=magnetization_declination, + filter_kwargs={ + "inclination": inclination, + "declination": declination, + "magnetization_inclination": magnetization_inclination, + "magnetization_declination": magnetization_declination, + }, ) diff --git a/harmonica/filters/_utils.py b/harmonica/filters/_utils.py index be0c8c71d..ca636fee4 100644 --- a/harmonica/filters/_utils.py +++ b/harmonica/filters/_utils.py @@ -9,11 +9,12 @@ """ import numpy as np +import xrft from ._fft import fft, ifft -def apply_filter(grid, fft_filter, **kwargs): +def apply_filter(grid, fft_filter, filter_kwargs=None, pad_kwargs=None): """ Apply a filter to a grid and return the transformed grid in spatial domain. @@ -39,20 +40,38 @@ def apply_filter(grid, fft_filter, **kwargs): A :class:`xarray.DataArray` with the filtered version of the passed ``grid``. Defined are in the spatial domain. """ + if filter_kwargs is None: + filter_kwargs = {} + if pad_kwargs is None: + pad_kwargs = {} grid_sanity_checks(grid) - fft_grid = fft(grid) - da_filter = fft_filter(fft_grid, **kwargs) + dims = grid.dims + # Need to remove non-dimensional coordinates before padding and FFT because + # xrft doesn't know what to do with them. + non_dim_coords = {c: grid[c] for c in grid.coords if c not in grid.indexes} + grid = grid.drop_vars(non_dim_coords.keys()) + # By default, use a padding width of 25% of the largest grid dimension. + # Fedi et al. (2012; doi:10.1111/j.1365-246X.2011.05259.x) suggest + # a padding of 100% but that seems exaggerated. + if "pad_width" not in pad_kwargs: + width = int(0.25 * max(grid[d].size for d in dims)) + pad_kwargs["pad_width"] = {d: width for d in dims} + if "mode" not in pad_kwargs: + pad_kwargs["mode"] = "edge" + # Has to be included explicitly as None or numpy complains about the + # argument being there. + pad_kwargs["constant_values"] = None + fft_grid = fft(xrft.pad(grid, **pad_kwargs)) # The filter convolution in the frequency domain is a multiplication - filtered_fft_grid = fft_grid * da_filter + filtered_fft_grid = fft_grid * fft_filter(fft_grid, **filter_kwargs) # Keep only the real part since the inverse transform returns complex # number by default - filtered_grid = ifft(filtered_fft_grid).real + filtered_grid = xrft.unpad(ifft(filtered_fft_grid).real, pad_kwargs["pad_width"]) # Restore the original coordinates to the grid because the inverse # transform calculates coordinates from the frequencies, which can lead to # rounding errors and coordinates that are slightly off. This causes errors # when doing operations with the transformed grids. Restoring the original # coordinates avoids these issues. - dims = grid.dims filtered_grid = filtered_grid.assign_coords( {dims[1]: grid[dims[1]].values, dims[0]: grid[dims[0]].values} ) From 238951c283614631d48c67f20668b7f5631bf8c3 Mon Sep 17 00:00:00 2001 From: Leonardo Uieda Date: Wed, 22 Oct 2025 17:31:47 -0300 Subject: [PATCH 04/20] Ignore a Ruff warning about replacing dict comp with fromkeys --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index d3aa46ba2..7deb56518 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -118,6 +118,7 @@ ignore = [ "RET504", # allow variable assignment only for return "PT001", # conventions for parenthesis on pytest.fixture "D200", # allow single line docstrings in their own line + "C420", # Replacing dict comprehension with `dict.fromkeys` is ugly ] [tool.ruff.lint.per-file-ignores] From bbf70f1f1c3c8e9a4d4080ac4645fc804096d2e9 Mon Sep 17 00:00:00 2001 From: Leonardo Uieda Date: Thu, 23 Oct 2025 09:58:37 -0300 Subject: [PATCH 05/20] Add more tests and update older ones They are failing but I don't know why yet --- doc/user_guide/transformations.rst | 89 ++--------- harmonica/_transformations.py | 36 ++--- harmonica/filters/_fft.py | 21 +-- harmonica/filters/_utils.py | 33 ++-- harmonica/tests/test_transformations.py | 204 +++++++----------------- 5 files changed, 108 insertions(+), 275 deletions(-) diff --git a/doc/user_guide/transformations.rst b/doc/user_guide/transformations.rst index ce9ac9734..8baf29a0c 100644 --- a/doc/user_guide/transformations.rst +++ b/doc/user_guide/transformations.rst @@ -42,41 +42,6 @@ And plot it: :func:`verde.project_grid` function and a map projection like the ones available in :mod:`pyproj`. -Since all the grid transformations we are going to apply are based on FFT -methods, we usually want to pad them in order their increase the accuracy. -We can easily do it through the :func:`xrft.pad` function. -First we need to define how much padding we want to add along each direction. -We will add one third of the width and height of the grid to each side: - -.. jupyter-execute:: - - pad_width = { - "easting": magnetic_grid.easting.size // 3, - "northing": magnetic_grid.northing.size // 3, - } - -And then we can pad it, but dropping the ``height`` coordinate first (this is -needed by the :func:`xrft.pad` function): - -.. jupyter-execute:: - - import xrft - - magnetic_grid_no_height = magnetic_grid.drop_vars("height") - magnetic_grid_padded = xrft.pad(magnetic_grid_no_height, pad_width) - magnetic_grid_padded - -.. jupyter-execute:: - - tmp = magnetic_grid_padded.plot(cmap="seismic", center=0, add_colorbar=False) - plt.gca().set_aspect("equal") - plt.title("Padded magnetic anomaly grid") - plt.gca().ticklabel_format(style="sci", scilimits=(0, 0)) - plt.colorbar(tmp, label="nT") - plt.show() - -Now that we have the padded grid, we can apply any grid transformation. - Upward derivative ----------------- @@ -88,15 +53,7 @@ magnetic anomaly grid using the :func:`harmonica.derivative_upward` function: import harmonica as hm - deriv_upward = hm.derivative_upward(magnetic_grid_padded) - deriv_upward - -This grid includes all the padding we added to the original magnetic grid, so -we better unpad it using :func:`xrft.unpad`: - -.. jupyter-execute:: - - deriv_upward = xrft.unpad(deriv_upward, pad_width) + deriv_upward = hm.derivative_upward(magnetic_grid) deriv_upward And plot it: @@ -160,14 +117,12 @@ frequency domain: .. jupyter-execute:: - deriv_easting = hm.derivative_easting(magnetic_grid_padded, method="fft") - deriv_easting = xrft.unpad(deriv_easting, pad_width) + deriv_easting = hm.derivative_easting(magnetic_grid, method="fft") deriv_easting .. jupyter-execute:: - deriv_northing = hm.derivative_northing(magnetic_grid_padded, method="fft") - deriv_northing = xrft.unpad(deriv_northing, pad_width) + deriv_northing = hm.derivative_northing(magnetic_grid, method="fft") deriv_northing .. jupyter-execute:: @@ -213,17 +168,9 @@ to upward continue it a height displacement of 500m: .. jupyter-execute:: upward_continued = hm.upward_continuation( - magnetic_grid_padded, height_displacement=500 + magnetic_grid, height_displacement=500 ) -This grid includes all the padding we added to the original magnetic grid, so -we better unpad it using :func:`xrft.unpad`: - -.. jupyter-execute:: - - upward_continued = xrft.unpad(upward_continued, pad_width) - upward_continued - And plot it: .. jupyter-execute:: @@ -269,11 +216,8 @@ remanence), then we can apply the reduction to the pole passing only the .. jupyter-execute:: rtp_grid = hm.reduction_to_pole( - magnetic_grid_padded, inclination=inclination, declination=declination + magnetic_grid, inclination=inclination, declination=declination ) - - # Unpad the reduced to the pole grid - rtp_grid = xrft.unpad(rtp_grid, pad_width) rtp_grid And plot it: @@ -296,15 +240,12 @@ magnetization vector of the sources, we can specify the mag_inclination, mag_declination = -25, 21 tmp = rtp_grid = hm.reduction_to_pole( - magnetic_grid_padded, + magnetic_grid, inclination=inclination, declination=declination, magnetization_inclination=mag_inclination, magnetization_declination=mag_declination, ) - - # Unpad the reduced to the pole grid - rtp_grid = xrft.unpad(rtp_grid, pad_width) rtp_grid .. jupyter-execute:: @@ -337,24 +278,17 @@ Let's define a cutoff wavelength of 5 kilometers: cutoff_wavelength = 5e3 -Then apply the two filters to our padded magnetic grid: +Then apply the two filters to our magnetic grid: .. jupyter-execute:: magnetic_low_freqs = hm.gaussian_lowpass( - magnetic_grid_padded, wavelength=cutoff_wavelength + magnetic_grid, wavelength=cutoff_wavelength ) magnetic_high_freqs = hm.gaussian_highpass( - magnetic_grid_padded, wavelength=cutoff_wavelength + magnetic_grid, wavelength=cutoff_wavelength ) -And unpad them: - -.. jupyter-execute:: - - magnetic_low_freqs = xrft.unpad(magnetic_low_freqs, pad_width) - magnetic_high_freqs = xrft.unpad(magnetic_high_freqs, pad_width) - .. jupyter-execute:: magnetic_low_freqs @@ -422,11 +356,8 @@ We can apply it through the :func:`harmonica.total_gradient_amplitude` function. .. jupyter-execute:: tga_grid = hm.total_gradient_amplitude( - magnetic_grid_padded + magnetic_grid ) - - # Unpad the total gradient amplitude grid - tga_grid = xrft.unpad(tga_grid, pad_width) tga_grid And plot it: diff --git a/harmonica/_transformations.py b/harmonica/_transformations.py index 10ef1a380..666a8dff8 100644 --- a/harmonica/_transformations.py +++ b/harmonica/_transformations.py @@ -252,7 +252,10 @@ def gaussian_lowpass(grid, wavelength): harmonica.filters.gaussian_lowpass_kernel """ return apply_filter( - grid, gaussian_lowpass_kernel, filter_kwargs={"wavelength": wavelength} + grid, + gaussian_lowpass_kernel, + pad=False, + filter_kwargs={"wavelength": wavelength}, ) @@ -289,7 +292,10 @@ def gaussian_highpass(grid, wavelength): harmonica.filters.gaussian_highpass_kernel """ return apply_filter( - grid, gaussian_highpass_kernel, filter_kwargs={"wavelength": wavelength} + grid, + gaussian_highpass_kernel, + pad=False, + filter_kwargs={"wavelength": wavelength}, ) @@ -373,8 +379,8 @@ def total_gradient_amplitude(grid): Returns ------- total_gradient_amplitude_grid : :class:`xarray.DataArray` - A :class:`xarray.DataArray` after calculating the - total gradient amplitude of the passed ``grid``. + A :class:`xarray.DataArray` after calculating the total gradient + amplitude of the passed ``grid``. Notes ----- @@ -410,10 +416,9 @@ def tilt_angle(grid): r""" Calculate the tilt angle of a potential field grid. - Compute the tilt of a regular gridded potential field - :math:`M`. The horizontal derivatives are calculated - through finite-differences while the upward derivative - is calculated using FFT. + Compute the tilt of a regular gridded potential field :math:`M`. The + horizontal derivatives are calculated through finite-differences while the + upward derivative is calculated using FFT. Parameters ---------- @@ -454,17 +459,12 @@ def tilt_angle(grid): [Blakely1995]_ [MillerSingh1994]_ """ - # Run sanity checks on the grid grid_sanity_checks(grid) - # Calculate the gradients of the grid - gradient = ( - derivative_easting(grid, order=1), - derivative_northing(grid, order=1), - derivative_upward(grid, order=1), - ) - # Calculate and return the tilt - horiz_deriv = np.sqrt(gradient[0] ** 2 + gradient[1] ** 2) - tilt = np.arctan2(gradient[2], horiz_deriv) + deriv_east = derivative_easting(grid, order=1) + deriv_north = derivative_northing(grid, order=1) + deriv_up = derivative_upward(grid, order=1) + horiz_deriv = np.hypot(deriv_east, deriv_north) + tilt = np.arctan2(deriv_up, horiz_deriv) return tilt diff --git a/harmonica/filters/_fft.py b/harmonica/filters/_fft.py index 9ded6428d..c2e3bf1f8 100644 --- a/harmonica/filters/_fft.py +++ b/harmonica/filters/_fft.py @@ -8,11 +8,10 @@ Wrap xrft functions to compute FFTs and inverse FFTs. """ -from xrft.xrft import fft as _fft -from xrft.xrft import ifft as _ifft +import xrft -def fft(grid, true_phase=True, true_amplitude=True, drop_bad_coords=True, **kwargs): +def fft(grid, true_phase=True, true_amplitude=True, **kwargs): """ Compute Fast Fourier Transform of a 2D regular grid. @@ -32,21 +31,15 @@ def fft(grid, true_phase=True, true_amplitude=True, drop_bad_coords=True, **kwar If True, the FFT is multiplied by the spacing of the transformed variables to match theoretical FT amplitude. Defaults to True. - drop_bad_coords : bool (optional) - If True, only the indexes of the array will be kept before passing it - to :func:`xrft.fft`. Any extra coordinate should be drooped, otherwise - :func:`xrft.fft` raises an error after finding *bad coordinates*. - Defaults to True. Returns ------- fourier_transform : :class:`xarray.DataArray` Array with the Fourier transform of the original grid. """ - if drop_bad_coords: - bad_coords = tuple(c for c in grid.coords if c not in grid.indexes) - grid = grid.drop(bad_coords) - return _fft(grid, true_phase=true_phase, true_amplitude=true_amplitude, **kwargs) + return xrft.fft( + grid, true_phase=true_phase, true_amplitude=true_amplitude, **kwargs + ) def ifft(fourier_transform, true_phase=True, true_amplitude=True, **kwargs): @@ -75,10 +68,10 @@ def ifft(fourier_transform, true_phase=True, true_amplitude=True, **kwargs): grid : :class:`xarray.DataArray` Array with the inverse Fourier transform of the passed grid. """ - return _ifft( + return xrft.ifft( fourier_transform, true_phase=true_phase, true_amplitude=true_amplitude, - lag=(None, None), # Mutes an annoying FutureWarning from xrft + lag=(0, 0), # Mutes an annoying FutureWarning from xrft **kwargs, ) diff --git a/harmonica/filters/_utils.py b/harmonica/filters/_utils.py index ca636fee4..3a598dedd 100644 --- a/harmonica/filters/_utils.py +++ b/harmonica/filters/_utils.py @@ -14,7 +14,7 @@ from ._fft import fft, ifft -def apply_filter(grid, fft_filter, filter_kwargs=None, pad_kwargs=None): +def apply_filter(grid, fft_filter, filter_kwargs=None, pad=True, pad_kwargs=None): """ Apply a filter to a grid and return the transformed grid in spatial domain. @@ -50,23 +50,28 @@ def apply_filter(grid, fft_filter, filter_kwargs=None, pad_kwargs=None): # xrft doesn't know what to do with them. non_dim_coords = {c: grid[c] for c in grid.coords if c not in grid.indexes} grid = grid.drop_vars(non_dim_coords.keys()) - # By default, use a padding width of 25% of the largest grid dimension. - # Fedi et al. (2012; doi:10.1111/j.1365-246X.2011.05259.x) suggest - # a padding of 100% but that seems exaggerated. - if "pad_width" not in pad_kwargs: - width = int(0.25 * max(grid[d].size for d in dims)) - pad_kwargs["pad_width"] = {d: width for d in dims} - if "mode" not in pad_kwargs: - pad_kwargs["mode"] = "edge" - # Has to be included explicitly as None or numpy complains about the - # argument being there. - pad_kwargs["constant_values"] = None - fft_grid = fft(xrft.pad(grid, **pad_kwargs)) + if pad: + # By default, use a padding width of 25% of the largest grid dimension. + # Fedi et al. (2012; doi:10.1111/j.1365-246X.2011.05259.x) suggest + # a padding of 100% but that seems exaggerated. + if "pad_width" not in pad_kwargs: + width = int(0.25 * max(grid[d].size for d in dims)) + pad_kwargs["pad_width"] = {d: width for d in dims} + if "mode" not in pad_kwargs: + pad_kwargs["mode"] = "edge" + # Has to be included explicitly as None or numpy complains about the + # argument being there. + pad_kwargs["constant_values"] = None + fft_grid = fft(xrft.pad(grid, **pad_kwargs)) + else: + fft_grid = fft(grid) # The filter convolution in the frequency domain is a multiplication filtered_fft_grid = fft_grid * fft_filter(fft_grid, **filter_kwargs) # Keep only the real part since the inverse transform returns complex # number by default - filtered_grid = xrft.unpad(ifft(filtered_fft_grid).real, pad_kwargs["pad_width"]) + filtered_grid = ifft(filtered_fft_grid).real + if pad: + filtered_grid = xrft.unpad(filtered_grid, pad_kwargs["pad_width"]) # Restore the original coordinates to the grid because the inverse # transform calculates coordinates from the frequencies, which can lead to # rounding errors and coordinates that are slightly off. This causes errors diff --git a/harmonica/tests/test_transformations.py b/harmonica/tests/test_transformations.py index 55d3ceaec..404a309cd 100644 --- a/harmonica/tests/test_transformations.py +++ b/harmonica/tests/test_transformations.py @@ -16,9 +16,13 @@ import verde as vd import xarray as xr import xarray.testing as xrt -import xrft -from .. import point_gravity +from .. import ( + dipole_magnetic, + magnetic_angles_to_vec, + point_gravity, + total_field_anomaly, +) from .._transformations import ( _get_dataarray_coordinate, derivative_easting, @@ -253,19 +257,7 @@ def test_derivative_upward(sample_potential, sample_g_z): """ Test derivative_upward function against the synthetic model. """ - # Pad the potential field grid to improve accuracy - pad_width = { - "easting": sample_potential.easting.size // 3, - "northing": sample_potential.northing.size // 3, - } - # need to drop upward coordinate (bug in xrft) - potential_padded = xrft.pad( - sample_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) + derivative = derivative_upward(sample_potential) # Compare against g_up (trim the borders to ignore boundary effects) trim = 6 derivative = derivative[trim:-trim, trim:-trim] @@ -281,19 +273,8 @@ def test_derivative_upward_order2(sample_potential, sample_g_zz): Note: We omit the minus sign here because the second derivative is positive for both downward (negative) and upward (positive) derivatives. """ - # Pad the potential field grid to improve accuracy - pad_width = { - "easting": sample_potential.easting.size // 3, - "northing": sample_potential.northing.size // 3, - } - # need to drop upward coordinate (bug in xrft) - potential_padded = xrft.pad( - sample_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) + second_deriv = derivative_upward(sample_potential, order=2) # Compare against g_zz (trim the borders to ignore boundary effects) trim = 6 second_deriv = second_deriv[trim:-trim, trim:-trim] @@ -347,19 +328,7 @@ def test_derivative_easting_fft(sample_potential, sample_g_e): """ Test derivative_easting function against the synthetic model using FFTs. """ - # Pad the potential field grid to improve accuracy - pad_width = { - "easting": sample_potential.easting.size // 3, - "northing": sample_potential.northing.size // 3, - } - # need to drop upward coordinate (bug in xrft) - potential_padded = xrft.pad( - sample_potential.drop_vars("upward"), - pad_width=pad_width, - ) - # Calculate easting derivative and unpad it - derivative = derivative_easting(potential_padded) - derivative = xrft.unpad(derivative, pad_width) + derivative = derivative_easting(sample_potential) # Compare against g_e (trim the borders to ignore boundary effects) trim = 6 derivative = derivative[trim:-trim, trim:-trim] @@ -372,19 +341,7 @@ def test_derivative_easting_order2(sample_potential, sample_g_ee): """ Test higher order of derivative_easting function against the sample grid. """ - # Pad the potential field grid to improve accuracy - pad_width = { - "easting": sample_potential.easting.size // 3, - "northing": sample_potential.northing.size // 3, - } - # need to drop upward coordinate (bug in xrft) - potential_padded = xrft.pad( - sample_potential.drop_vars("upward"), - pad_width=pad_width, - ) - # Calculate second easting derivative and unpad it - second_deriv = derivative_easting(potential_padded, order=2) - second_deriv = xrft.unpad(second_deriv, pad_width) + second_deriv = derivative_easting(sample_potential, order=2) # Compare against g_ee (trim the borders to ignore boundary effects) trim = 6 second_deriv = second_deriv[trim:-trim, trim:-trim] @@ -423,19 +380,7 @@ def test_derivative_northing(sample_potential, sample_g_n): """ Test derivative_northing function against the synthetic model. """ - # Pad the potential field grid to improve accuracy - pad_width = { - "easting": sample_potential.easting.size // 3, - "northing": sample_potential.northing.size // 3, - } - # need to drop upward coordinate (bug in xrft) - potential_padded = xrft.pad( - sample_potential.drop_vars("upward"), - pad_width=pad_width, - ) - # Calculate northing derivative and unpad it - derivative = derivative_northing(potential_padded) - derivative = xrft.unpad(derivative, pad_width) + derivative = derivative_northing(sample_potential) # Compare against g_n (trim the borders to ignore boundary effects) trim = 6 derivative = derivative[trim:-trim, trim:-trim] @@ -448,19 +393,7 @@ def test_derivative_northing_order2(sample_potential, sample_g_nn): """ Test higher order of derivative_northing function against the sample grid. """ - # Pad the potential field grid to improve accuracy - pad_width = { - "easting": sample_potential.easting.size // 3, - "northing": sample_potential.northing.size // 3, - } - # need to drop upward coordinate (bug in xrft) - potential_padded = xrft.pad( - sample_potential.drop_vars("upward"), - pad_width=pad_width, - ) - # Calculate second northing derivative and unpad it - second_deriv = derivative_northing(potential_padded, order=2) - second_deriv = xrft.unpad(second_deriv, pad_width) + second_deriv = derivative_northing(sample_potential, order=2) # Compare against g_nn (trim the borders to ignore boundary effects) trim = 6 second_deriv = second_deriv[trim:-trim, trim:-trim] @@ -475,24 +408,10 @@ def test_laplace_fft(sample_potential): We will use FFT computations only. """ - # Pad the potential field grid to improve accuracy - pad_width = { - "easting": sample_potential.easting.size // 3, - "northing": sample_potential.northing.size // 3, - } - # need to drop upward coordinate (bug in xrft) - potential_padded = xrft.pad( - sample_potential.drop_vars("upward"), - pad_width=pad_width, - ) - # Calculate second northing derivative and unpad it method = "fft" - second_deriv_ee = derivative_easting(potential_padded, order=2, method=method) - second_deriv_nn = derivative_northing(potential_padded, order=2, method=method) - second_deriv_zz = derivative_upward(potential_padded, order=2) - second_deriv_ee = xrft.unpad(second_deriv_ee, pad_width) - second_deriv_nn = xrft.unpad(second_deriv_nn, pad_width) - second_deriv_zz = xrft.unpad(second_deriv_zz, pad_width) + second_deriv_ee = derivative_easting(sample_potential, order=2, method=method) + second_deriv_nn = derivative_northing(sample_potential, order=2, method=method) + second_deriv_zz = derivative_upward(sample_potential, order=2) # Compare g_nn + g_ee against -g_zz (trim the borders to ignore boundary # effects) trim = 6 @@ -506,19 +425,7 @@ def test_upward_continuation(sample_g_z, sample_g_z_upward): """ Test upward_continuation function against the synthetic model. """ - # Pad the potential field grid to improve accuracy - pad_width = { - "easting": sample_g_z.easting.size // 3, - "northing": sample_g_z.northing.size // 3, - } - # need to drop upward coordinate (bug in xrft) - gravity_padded = xrft.pad( - sample_g_z.drop_vars("upward"), - pad_width=pad_width, - ) - # Calculate upward continuation and unpad it - continuation = upward_continuation(gravity_padded, 10e3) - continuation = xrft.unpad(continuation, pad_width) + continuation = upward_continuation(sample_g_z, 10e3) # Compare against g_z_upward (trim the borders to ignore boundary effects) trim = 6 continuation = continuation[trim:-trim, trim:-trim] @@ -528,14 +435,44 @@ def test_upward_continuation(sample_g_z, sample_g_z_upward): xrt.assert_allclose(continuation, g_z_upward, atol=1e-8) -def test_reduction_to_pole(sample_potential): +def test_reduction_to_pole(): + """ + Test reduction_to_pole function against an analytical solution. + """ + coordinates = vd.grid_coordinates( + (-100e3, 100e3, -120e2, 120e2), spacing=1e3, extra_coords=500 + ) + finc, fdec = -45, 13 + minc, mdec = -14, -24 + dipole = [-10e3, 20e3, -5000] + moment = 1e12 + magnetic_field_pole = dipole_magnetic( + coordinates, + dipoles=dipole, + magnetic_moments=magnetic_angles_to_vec(moment, 90, 0), + field="b", + ) + anomaly_pole = total_field_anomaly(magnetic_field_pole, 90, 0) + magnetic_field = dipole_magnetic( + coordinates, + dipoles=dipole, + magnetic_moments=magnetic_angles_to_vec(moment, minc, mdec), + field="b", + ) + anomaly = total_field_anomaly(magnetic_field, finc, fdec) + grid = vd.make_xarray_grid(coordinates[:2], anomaly, data_names="anomaly") + anomaly_reduced = reduction_to_pole(grid.anomaly, finc, fdec, minc, mdec) + np.testing.assert_allclose(anomaly_reduced, anomaly_pole) + + +def test_reduction_to_pole_dim_names(sample_potential): """ Test reduction_to_pole function with non-typical dim names. """ renamed_dims_grid = sample_potential.rename( {"easting": "name_one", "northing": "name_two"} ) - reduction_to_pole(renamed_dims_grid, 60, 45) + reduction_to_pole(renamed_dims_grid, 60, 45, 60, 45) class TestTotalGradientAmplitude: @@ -549,19 +486,7 @@ def test_against_synthetic( """ Test total_gradient_amplitude function against the synthetic model. """ - # Pad the potential field grid to improve accuracy - pad_width = { - "easting": sample_potential.easting.size // 3, - "northing": sample_potential.northing.size // 3, - } - # need to drop upward coordinate (bug in xrft) - potential_padded = xrft.pad( - sample_potential.drop_vars("upward"), - pad_width=pad_width, - ) - # Calculate total gradient amplitude and unpad it - tga = total_gradient_amplitude(potential_padded) - tga = xrft.unpad(tga, pad_width) + tga = total_gradient_amplitude(sample_potential) # Compare against g_tga (trim the borders to ignore boundary effects) trim = 6 tga = tga[trim:-trim, trim:-trim] @@ -619,31 +544,10 @@ def test_against_synthetic( """ Test tilt function against the synthetic model. """ - # Pad the potential field grid to improve accuracy - pad_width = { - "easting": sample_potential.easting.size // 3, - "northing": sample_potential.northing.size // 3, - } - # need to drop upward coordinate (bug in xrft) - potential_padded = xrft.pad( - sample_potential.drop_vars("upward"), - pad_width=pad_width, - ) - # Calculate the tilt and unpad it - tilt_grid = tilt_angle(potential_padded) - tilt_grid = xrft.unpad(tilt_grid, pad_width) - # Compare against g_tilt (trim the borders to ignore boundary effects) - trim = 6 - tilt_grid = tilt_grid[trim:-trim, trim:-trim] - g_e = sample_g_e[trim:-trim, trim:-trim] - g_n = sample_g_n[trim:-trim, trim:-trim] - g_z = sample_g_z[trim:-trim, trim:-trim] - g_horiz_deriv = np.sqrt(g_e**2 + g_n**2) - g_tilt = np.arctan2( - -g_z, g_horiz_deriv - ) # use -g_z to use the _upward_ derivative - rms = root_mean_square_error(tilt_grid, g_tilt) - assert rms / np.abs(tilt_grid).max() < 0.1 + numerical = tilt_angle(sample_potential) + # Use -g_z to use the upward derivative (g_z is downward) + analytical = np.arctan2(-sample_g_z, np.sqrt(sample_g_e**2 + sample_g_n**2)) + np.testing.assert_allclose(numerical, analytical) def test_invalid_grid_single_dimension(self): """ @@ -681,7 +585,7 @@ def test_invalid_grid_with_nans(self, sample_potential): tilt_angle(sample_potential) -class Testfilter: +class TestAgainstOasisMontaj: """ Test filter result against the output from oasis montaj. """ From 27b48583f439c16c40b9897eb30c78e483f8d174 Mon Sep 17 00:00:00 2001 From: Leonardo Uieda Date: Wed, 29 Oct 2025 14:07:57 -0300 Subject: [PATCH 06/20] Fix the reduction to the pole test --- harmonica/filters/_fft.py | 2 +- harmonica/tests/test_transformations.py | 18 +++++++++++++----- 2 files changed, 14 insertions(+), 6 deletions(-) diff --git a/harmonica/filters/_fft.py b/harmonica/filters/_fft.py index c2e3bf1f8..dafb0143e 100644 --- a/harmonica/filters/_fft.py +++ b/harmonica/filters/_fft.py @@ -72,6 +72,6 @@ def ifft(fourier_transform, true_phase=True, true_amplitude=True, **kwargs): fourier_transform, true_phase=true_phase, true_amplitude=true_amplitude, - lag=(0, 0), # Mutes an annoying FutureWarning from xrft + lag=(None, None), # Mutes an annoying FutureWarning from xrft **kwargs, ) diff --git a/harmonica/tests/test_transformations.py b/harmonica/tests/test_transformations.py index 404a309cd..a647a0ac7 100644 --- a/harmonica/tests/test_transformations.py +++ b/harmonica/tests/test_transformations.py @@ -440,11 +440,11 @@ def test_reduction_to_pole(): Test reduction_to_pole function against an analytical solution. """ coordinates = vd.grid_coordinates( - (-100e3, 100e3, -120e2, 120e2), spacing=1e3, extra_coords=500 + (-70e3, 20e3, -20e3, 60e3), spacing=0.5e3, extra_coords=500 ) finc, fdec = -45, 13 minc, mdec = -14, -24 - dipole = [-10e3, 20e3, -5000] + dipole = [-25e3, 20e3, -5000] moment = 1e12 magnetic_field_pole = dipole_magnetic( coordinates, @@ -462,7 +462,14 @@ def test_reduction_to_pole(): anomaly = total_field_anomaly(magnetic_field, finc, fdec) grid = vd.make_xarray_grid(coordinates[:2], anomaly, data_names="anomaly") anomaly_reduced = reduction_to_pole(grid.anomaly, finc, fdec, minc, mdec) - np.testing.assert_allclose(anomaly_reduced, anomaly_pole) + # Relative tol doesn't work because the anomaly at the pole is zero in + # a ring around the source and the rtol blows up at those points. + np.testing.assert_allclose( + anomaly_reduced.values, + anomaly_pole, + rtol=0, + atol=0.01 * np.abs(anomaly_pole).max(), + ) def test_reduction_to_pole_dim_names(sample_potential): @@ -547,7 +554,7 @@ def test_against_synthetic( numerical = tilt_angle(sample_potential) # Use -g_z to use the upward derivative (g_z is downward) analytical = np.arctan2(-sample_g_z, np.sqrt(sample_g_e**2 + sample_g_n**2)) - np.testing.assert_allclose(numerical, analytical) + np.testing.assert_allclose(numerical.values, analytical) def test_invalid_grid_single_dimension(self): """ @@ -613,7 +620,8 @@ def test_reduction_to_pole_grid(self): rtp = reduction_to_pole(self.expected_grid.filter_data, 60, 45) # Remove mean value to match OM result xrt.assert_allclose( - self.expected_grid.filter_rtp - self.expected_grid.filter_data.mean(), + # self.expected_grid.filter_rtp - self.expected_grid.filter_data.mean(), + self.expected_grid.filter_rtp, rtp, atol=1, ) From 38923f67dfc51503eb349f60d6593df38a3547d4 Mon Sep 17 00:00:00 2001 From: Leonardo Uieda Date: Thu, 30 Oct 2025 17:41:48 -0300 Subject: [PATCH 07/20] Remove Oasis Montaj test for reduction to the pole We have a better test now against a synthetic and this test didn't work very well before, since it required removing a mean from the Oasis result for some unspecified reason. --- harmonica/tests/test_transformations.py | 13 ------------- 1 file changed, 13 deletions(-) diff --git a/harmonica/tests/test_transformations.py b/harmonica/tests/test_transformations.py index a647a0ac7..0fc925460 100644 --- a/harmonica/tests/test_transformations.py +++ b/harmonica/tests/test_transformations.py @@ -612,16 +612,3 @@ def test_gaussian_highpass_grid(self): """ high_pass = gaussian_highpass(self.expected_grid.filter_data, 10) xrt.assert_allclose(self.expected_grid.filter_hp10, high_pass, atol=1e-6) - - def test_reduction_to_pole_grid(self): - """ - Test reduction_to_pole function against the output from oasis montaj. - """ - rtp = reduction_to_pole(self.expected_grid.filter_data, 60, 45) - # Remove mean value to match OM result - xrt.assert_allclose( - # self.expected_grid.filter_rtp - self.expected_grid.filter_data.mean(), - self.expected_grid.filter_rtp, - rtp, - atol=1, - ) From 4d2dd3084470bb98e3f79106b4b71791f6cfe123 Mon Sep 17 00:00:00 2001 From: Leonardo Uieda Date: Thu, 30 Oct 2025 17:43:36 -0300 Subject: [PATCH 08/20] Come up with a better test for gaussian filters --- filter-test.ipynb | 982 ++++++++++++++++------------------------------ 1 file changed, 344 insertions(+), 638 deletions(-) diff --git a/filter-test.ipynb b/filter-test.ipynb index c782aa2b6..e606eb071 100644 --- a/filter-test.ipynb +++ b/filter-test.ipynb @@ -2,578 +2,157 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, - "id": "b5e97dfb-7beb-48b7-bb0f-ca9e811309be", - "metadata": {}, - "outputs": [], - "source": [ - "import ensaio\n", - "import harmonica as hm\n", - "import xarray as xr" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "b4bfbd03-65dd-457f-aa89-2b43531f9fe6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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MIM Dat...\n", - " long_name: total-field magnetic anomaly\n", - " units: nT\n", - " actual_range: [-1785. 3798.]" + "" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "coordinates = vd.grid_coordinates(\n", + " (-70e3, 20e3, -20e3, 60e3), spacing=0.25e3,\n", + ")\n", + "d = np.cos(2 * np.pi * 1 / 5e3 * coordinates[0]) #* np.cos(2 * np.pi * 1 / 50e3 * coordinates[1])\n", + "d += np.cos(2 * np.pi * 1 / 0.7e3 * coordinates[0]) #* np.cos(2 * np.pi * 1 / 5e3 * coordinates[1])\n", + "d = vd.make_xarray_grid(coordinates, d, data_names=\"scalars\").scalars\n", + "d.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "51e3e063-0cdf-4890-9598-6fe8d569e84a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" ] }, - "execution_count": 2, + "execution_count": 80, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "g = xr.load_dataarray(ensaio.fetch_lightning_creek_magnetic(version=1))\n", - "g" + "abs(xrft.fft(d).sel(freq_northing=0)).plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "5338e88f-5f77-4ddb-b869-29cb0c799b7e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "a = xrft.isotropic_power_spectrum(d, nfactor=1)\n", + "a = a / a.max()\n", + "a.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "2c6e9bb9-8f8c-4ade-81e6-7fa6bab19e53", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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    -       "  * easting   (easting) float64 3kB 4.655e+05 4.656e+05 ... 4.827e+05 4.828e+05\n",
    -       "  * northing  (northing) float64 3kB 7.576e+06 7.576e+06 ... 7.595e+06 7.595e+06
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/ s.max(), t, rtol=0, atol=0.01)" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "a9abf24b-1689-4ab2-95f5-749b49421305", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "[]" ] }, - "execution_count": 4, + "execution_count": 61, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", "text/plain": [ - "
    " + "
    " ] }, "metadata": {}, @@ -1129,28 +726,28 @@ } ], "source": [ - "g.plot()" + "xrft.isotropic_power_spectrum(d, nfactor=1).plot()" ] }, { "cell_type": "code", - "execution_count": 5, - "id": "02f8e821-e37a-4153-89df-c9de8e950231", + "execution_count": 4, + "id": "8ab7933e-7762-4fc6-aa73-70db9424a836", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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    " ] @@ -1160,7 +757,86 @@ } ], "source": [ - "gu.plot()" + "np.abs(xrft.fft(d)).plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "02d32c3c-55fe-4e90-b495-7bebec7467ab", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0539aba-aa32-443f-9426-c4c902f7d5c6", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b364dcd2-6fde-44ed-80ea-480002d42a69", + "metadata": {}, + "outputs": [], + "source": [ + "g = xr.open_dataset(\"harmonica/tests/data/filter.nc\")\n", + "g" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b4bfbd03-65dd-457f-aa89-2b43531f9fe6", + "metadata": {}, + "outputs": [], + "source": [ + "coordinates = vd.grid_coordinates(\n", + " (-70e3, 20e3, -20e3, 60e3), spacing=0.22e3, extra_coords=500\n", + ")\n", + "finc, fdec = -45, 13\n", + "minc, mdec = -14, -24\n", + "dipole = [-25e3, 20e3, -5000]\n", + "moment = 1e12\n", + "magnetic_field_pole = hm.dipole_magnetic(\n", + " coordinates,\n", + " dipoles=dipole,\n", + " magnetic_moments=hm.magnetic_angles_to_vec(moment, 90, 0),\n", + " field=\"b\",\n", + ")\n", + "anomaly_pole = hm.total_field_anomaly(magnetic_field_pole, 90, 0)\n", + "magnetic_field = hm.dipole_magnetic(\n", + " coordinates,\n", + " dipoles=dipole,\n", + " magnetic_moments=hm.magnetic_angles_to_vec(moment, minc, mdec),\n", + " field=\"b\",\n", + ")\n", + "anomaly = hm.total_field_anomaly(magnetic_field, finc, fdec)\n", + "grid = vd.make_xarray_grid(coordinates[:2], (anomaly, anomaly_pole), data_names=[\"anomaly\", \"anomaly_pole\"])\n", + "anomaly_reduced = hm.reduction_to_pole(grid.anomaly, finc, fdec, minc, mdec)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d976b694-44ff-4a3e-9003-922a83e97b36", + "metadata": {}, + "outputs": [], + "source": [ + "(anomaly_reduced - grid.anomaly_pole).plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6e800e07-c141-4bc3-948d-f274592502a5", + "metadata": {}, + "outputs": [], + "source": [ + "(1 / np.abs(grid.anomaly_pole)).plot()" ] }, { @@ -1169,6 +845,36 @@ "id": "c6ef65bb-a81c-40af-9d83-a30344979000", "metadata": {}, "outputs": [], + "source": [ + "grid.anomaly.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b8ae01b7-2600-4ce9-a17b-49eee522aac4", + "metadata": {}, + "outputs": [], + "source": [ + "grid.anomaly_pole.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5b582491-5272-4a87-bd51-fb3d50ee1560", + "metadata": {}, + "outputs": [], + "source": [ + "anomaly_reduced.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "04a4108b-772e-448a-983f-cbdd4d4266a9", + "metadata": {}, + "outputs": [], "source": [] } ], From 2f886f63fde170ea2a262a2586a538dee66d10f0 Mon Sep 17 00:00:00 2001 From: Leonardo Uieda Date: Mon, 30 Mar 2026 15:20:03 -0300 Subject: [PATCH 09/20] Fix tilt angle test Was using the potential but it's too smooth and causes problems at the edges. Switch to gz instead and it works when looking at a smaller region inside the area. --- harmonica/tests/test_transformations.py | 53 ++++++++++++++++++++----- 1 file changed, 44 insertions(+), 9 deletions(-) diff --git a/harmonica/tests/test_transformations.py b/harmonica/tests/test_transformations.py index 0fc925460..fb8bd588b 100644 --- a/harmonica/tests/test_transformations.py +++ b/harmonica/tests/test_transformations.py @@ -62,7 +62,7 @@ def fixture_sample_grid_coords(): Define sample grid coordinates. """ grid_coords = vd.grid_coordinates( - region=(-150e3, 150e3, -150e3, 150e3), shape=(41, 41), extra_coords=0 + region=(-300e3, 300e3, -300e3, 300e3), spacing=5000, extra_coords=0 ) return grid_coords @@ -73,7 +73,7 @@ def fixture_upward_grid_coords(): Define upward grid coordinates. """ grid_coords = vd.grid_coordinates( - region=(-150e3, 150e3, -150e3, 150e3), shape=(41, 41), extra_coords=10e3 + region=(-300e3, 300e3, -300e3, 300e3), spacing=5000, extra_coords=10e3 ) return grid_coords @@ -206,6 +206,38 @@ def fixture_sample_g_ee(sample_grid_coords, sample_sources): return g_ee.g_ee +@pytest.fixture(name="sample_g_ez") +def fixture_sample_g_ez(sample_grid_coords, sample_sources): + """ + Return g_ez field of sample points on sample grid coords. + """ + points, masses = sample_sources + g_ez = point_gravity(sample_grid_coords, points, masses, field="g_ez") + g_ez = vd.make_xarray_grid( + sample_grid_coords, + g_ez, + data_names="g_ez", + extra_coords_names="upward", + ) + return g_ez.g_ez + + +@pytest.fixture(name="sample_g_nz") +def fixture_sample_g_nz(sample_grid_coords, sample_sources): + """ + Return g_nz field of sample points on sample grid coords. + """ + points, masses = sample_sources + g_nz = point_gravity(sample_grid_coords, points, masses, field="g_nz") + g_nz = vd.make_xarray_grid( + sample_grid_coords, + g_nz, + data_names="g_nz", + extra_coords_names="upward", + ) + return g_nz.g_nz + + @pytest.mark.parametrize( ("index", "expected_dimension"), [(1, "easting"), (0, "northing")] ) @@ -545,16 +577,19 @@ class TestTilt: Test tilt function. """ - def test_against_synthetic( - self, sample_potential, sample_g_n, sample_g_e, sample_g_z - ): + def test_against_synthetic(self, sample_g_z, sample_g_nz, sample_g_ez, sample_g_zz): """ Test tilt function against the synthetic model. """ - numerical = tilt_angle(sample_potential) - # Use -g_z to use the upward derivative (g_z is downward) - analytical = np.arctan2(-sample_g_z, np.sqrt(sample_g_e**2 + sample_g_n**2)) - np.testing.assert_allclose(numerical.values, analytical) + # Use the gz because the potential is too smooth and messes up at the edges + numerical = tilt_angle(sample_g_z) + # Use minus to use the upward derivative (z is downward) + analytical = np.arctan2(-sample_g_zz, np.sqrt(sample_g_ez**2 + sample_g_nz**2)) + # Compare only an inner region because the tilt is terrible at the edges + crop = [-125e3, 125e3, -125e3, 125e3] + numerical = numerical.sel(easting=slice(*crop[:2]), northing=slice(*crop[2:])) + analytical = analytical.sel(easting=slice(*crop[:2]), northing=slice(*crop[2:])) + np.testing.assert_allclose(numerical, analytical, atol=0.15) def test_invalid_grid_single_dimension(self): """ From 7169f6e4c682d5bf06614ac68b92c1ac2acd8bd2 Mon Sep 17 00:00:00 2001 From: Leonardo Uieda Date: Tue, 31 Mar 2026 08:37:26 -0300 Subject: [PATCH 10/20] Adjust Euler tests after the new filters --- harmonica/tests/test_euler_deconvolution.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/harmonica/tests/test_euler_deconvolution.py b/harmonica/tests/test_euler_deconvolution.py index 1338a23a2..ab7f51544 100644 --- a/harmonica/tests/test_euler_deconvolution.py +++ b/harmonica/tests/test_euler_deconvolution.py @@ -19,6 +19,7 @@ def test_euler_with_numeric_derivatives(): + "Check the Euler solution against a synthetic using numerical derivatives" # Add dipole source dipole_coordinates = (10e3, 15e3, -10e3) dipole_moments = magnetic_angles_to_vec(1.0e14, 0, 0) @@ -48,15 +49,16 @@ def test_euler_with_numeric_derivatives(): coordinates = (grid_table.easting, grid_table.northing, grid_table.upward) euler.fit( - (grid_table.easting, grid_table.northing, grid_table.upward), + coordinates, (grid_table.tfa, grid_table.d_east, grid_table.d_north, grid_table.d_up), ) - npt.assert_allclose(euler.location_, dipole_coordinates, atol=1.0e-3, rtol=1.0e-3) - npt.assert_allclose(euler.base_level_, true_base_level, atol=1.0e-3, rtol=1.0e-3) + npt.assert_allclose(euler.location_, dipole_coordinates, rtol=1.0e-2) + npt.assert_allclose(euler.base_level_, true_base_level, rtol=1.0e-3) def test_euler_with_analytic_derivatives(): + "Check the Euler solution against a synthetic using analytical derivatives" # Add dipole source masses_coordinates = (10e3, 15e3, -10e3) masses = 1.0e12 From d841ce31fd8fcdf9225092bc5451d39dc153fdb2 Mon Sep 17 00:00:00 2001 From: Leonardo Uieda Date: Tue, 31 Mar 2026 09:50:12 -0300 Subject: [PATCH 11/20] Fix broken tests and add missing tests --- harmonica/filters/_utils.py | 5 +- harmonica/tests/test_filters.py | 212 +++++++++++++----------- harmonica/tests/test_transformations.py | 40 ++++- 3 files changed, 158 insertions(+), 99 deletions(-) diff --git a/harmonica/filters/_utils.py b/harmonica/filters/_utils.py index 3a598dedd..2968e1d50 100644 --- a/harmonica/filters/_utils.py +++ b/harmonica/filters/_utils.py @@ -59,8 +59,9 @@ def apply_filter(grid, fft_filter, filter_kwargs=None, pad=True, pad_kwargs=None pad_kwargs["pad_width"] = {d: width for d in dims} if "mode" not in pad_kwargs: pad_kwargs["mode"] = "edge" - # Has to be included explicitly as None or numpy complains about the - # argument being there. + if "constant_values" not in pad_kwargs: + # Has to be included explicitly as None since xrft always passes + # it to xarray.DataArray.pad. pad_kwargs["constant_values"] = None fft_grid = fft(xrft.pad(grid, **pad_kwargs)) else: diff --git a/harmonica/tests/test_filters.py b/harmonica/tests/test_filters.py index 78576ed78..099f13f57 100644 --- a/harmonica/tests/test_filters.py +++ b/harmonica/tests/test_filters.py @@ -29,6 +29,10 @@ ) from ..filters._utils import apply_filter +# ------------------------------- +# Fixtures +# ------------------------------- + @pytest.fixture(name="region") def fixture_region(): @@ -48,21 +52,6 @@ def fixture_sample_grid(region): return make_xarray_grid((easting, northing), data, data_names="sample").sample -@pytest.fixture(name="sample_grid_upward") -def fixture_sample_grid_upward(region): - """ - Return a sample grid as an :class:`xarray.DataArray` with upward coord. - """ - easting, northing, upward = grid_coordinates(region, spacing=500, extra_coords=100) - data = np.sin(easting / 1e3) + np.cos(northing / 1e3) - return make_xarray_grid( - (easting, northing, upward), - data, - data_names="sample", - extra_coords_names="upward", - ).sample - - @pytest.fixture(name="sample_grid_multiple_coords") def fixture_sample_grid_multiple_coords(region): """ @@ -86,50 +75,76 @@ def fixture_sample_grid_multiple_coords(region): ) -def test_fft_round_trip(sample_grid): +@pytest.fixture(name="invalid_grid_single_dim") +def fixture_invalid_grid_single_dim(): """ - Test if the wrapped fft and ifft functions satisfy a round trip. + Return a sample grid with a single dimension. + + This fixture is meant to test if apply_filter raises an error on a grid + with a single dimension. """ - xrt.assert_allclose(sample_grid, ifft(fft(sample_grid))) + x = np.linspace(0, 10, 11) + y = x**2 + grid = xr.DataArray(y, coords={"x": x}, dims=("x",)) + return grid -def test_fft_round_trip_upward(sample_grid_upward): +@pytest.fixture(name="invalid_grid_3_dims") +def fixture_invalid_grid_3_dims(): """ - Test if the wrapped fft and ifft functions satisfy a round trip. + Return a sample grid with 3 dimensions. + + This fixture is meant to test if apply_filter raises an error on a grid + with 3 dimensions. """ - round_trip = ifft(fft(sample_grid_upward)) - assert "upward" not in round_trip - # Assert if both arrays are close enough, but dropping upward first - xrt.assert_allclose( - sample_grid_upward.drop("upward"), ifft(fft(sample_grid_upward)) + x = np.linspace(0, 10, 11) + y = np.linspace(-4, 4, 9) + z = np.linspace(20, 30, 5) + xx, yy, zz = np.meshgrid(x, y, z) + data = xx + yy + zz + grid = xr.DataArray(data, coords={"x": x, "y": y, "z": z}, dims=("y", "x", "z")) + return grid + + +@pytest.fixture(name="sample_fft_grid") +def fixture_sample_fft_grid(): + """ + Return a sample fft_grid to be used in test functions. + """ + domain = (-9e-4, 9e-4, -8e-4, 8e-4) + freq_easting, freq_northing = grid_coordinates(region=domain, spacing=8e-4) + dummy_fft = np.ones_like(freq_easting) + fft_grid = make_xarray_grid( + (freq_easting, freq_northing), + dummy_fft, + data_names=["sample_fft"], + dims=("freq_northing", "freq_easting"), ) + return fft_grid.sample_fft -def test_fft_no_drop_bad_coords(sample_grid_upward): +@pytest.fixture(name="invalid_grid_with_nans") +def fixture_invalid_grid_with_nans(sample_grid): """ - Check if no dropping bad coordinates raises ValueError on upward coord. + Return a sample grid with nans. - ``xrft`` complains when *bad coordinates* are present in the input array. - This test, along with the ``drop_bad_coordinates`` argument, should be - removed if ``xrft`` changes this behaviour + This fixture is meant to test if apply_filter raises an error on a grid + with a nans. """ - msg = "Please drop these coordinates" - with pytest.raises(ValueError, match=msg): - fft(sample_grid_upward, drop_bad_coords=False) + sample_grid[2, 4] = np.nan + return sample_grid -def test_fft_no_drop_bad_coords_multi(sample_grid_multiple_coords): - """ - Check if no dropping bad coordinates raises ValueError on multiple coords. +# ------------------------------- +# Tests for FFT functions +# ------------------------------- - This test should fail because ``xrft`` complains when *bad coordinates* are - present in the input array. - This test, along with the ``drop_bad_coordinates`` argument, should be - removed if ``xrft`` changes this behaviour + +def test_fft_round_trip(sample_grid): """ - msg = "Please drop these coordinates" - with pytest.raises(ValueError, match=msg): - fft(sample_grid_multiple_coords, drop_bad_coords=False) + Test if the wrapped fft and ifft functions satisfy a round trip. + """ + xrt.assert_allclose(sample_grid, ifft(fft(sample_grid))) # ------------------------------- @@ -148,7 +163,7 @@ def dummy_filter(fourier_transform): def test_apply_filter(sample_grid): """ - Test apply_filter function using the dummy_filter. + Test apply_filter function when the grid has no extra coordinates. """ # Apply the dummy filter filtered_grid = apply_filter(sample_grid, dummy_filter) @@ -157,35 +172,71 @@ def test_apply_filter(sample_grid): xrt.assert_allclose(filtered_grid, expected) -@pytest.fixture(name="invalid_grid_single_dim") -def fixture_invalid_grid_single_dim(): +def test_apply_filter_drop_coords(sample_grid_multiple_coords): """ - Return a sample grid with a single dimension. + Test apply_filter function on a grid with extra coordinates. + """ + # Apply the dummy filter + filtered_grid = apply_filter(sample_grid_multiple_coords, dummy_filter) + # Compare output with expected results + expected = sample_grid_multiple_coords * 0 + # Non-dimensional coordinates should have been dropped by apply_filter. + # We can't restore them because we can't know if they are still correct + # after filtering. + assert set(sample_grid_multiple_coords.dims) == set(filtered_grid.coords) + npt.assert_allclose(filtered_grid.values, expected.values) - This fixture is meant to test if apply_filter raises an error on a grid - with a single dimension. + +def test_apply_filter_no_padding(sample_grid): """ - x = np.linspace(0, 10, 11) - y = x**2 - grid = xr.DataArray(y, coords={"x": x}, dims=("x",)) - return grid + Test apply_filter function with padding off. + """ + # Apply the dummy filter + filtered_grid = apply_filter(sample_grid, dummy_filter, pad=False) + # Compare output with expected results + expected = sample_grid * 0 + xrt.assert_allclose(filtered_grid, expected) -@pytest.fixture(name="invalid_grid_3_dims") -def fixture_invalid_grid_3_dims(): +def test_apply_filter_pad_width(sample_grid): """ - Return a sample grid with 3 dimensions. + Test apply_filter function with custom padding width. + """ + # Apply the dummy filter + filtered_grid = apply_filter( + sample_grid, + dummy_filter, + pad_kwargs={"pad_width": {"easting": 20, "northing": 20}}, + ) + # Compare output with expected results + expected = sample_grid * 0 + xrt.assert_allclose(filtered_grid, expected) - This fixture is meant to test if apply_filter raises an error on a grid - with 3 dimensions. + +def test_apply_filter_pad_mode(sample_grid): """ - x = np.linspace(0, 10, 11) - y = np.linspace(-4, 4, 9) - z = np.linspace(20, 30, 5) - xx, yy, zz = np.meshgrid(x, y, z) - data = xx + yy + zz - grid = xr.DataArray(data, coords={"x": x, "y": y, "z": z}, dims=("y", "x", "z")) - return grid + Test apply_filter function with custom padding method. + """ + # Apply the dummy filter + filtered_grid = apply_filter( + sample_grid, dummy_filter, pad_kwargs={"mode": "median"} + ) + # Compare output with expected results + expected = sample_grid * 0 + xrt.assert_allclose(filtered_grid, expected) + + +def test_apply_filter_pad_with_constant(sample_grid): + """ + Test apply_filter function with the constant padding method. + """ + # Apply the dummy filter + filtered_grid = apply_filter( + sample_grid, dummy_filter, pad_kwargs={"mode": "constant", "constant_values": 120} + ) + # Compare output with expected results + expected = sample_grid * 0 + xrt.assert_allclose(filtered_grid, expected) def test_apply_filter_grid_single_dimension(invalid_grid_single_dim): @@ -206,18 +257,6 @@ def test_apply_filter_grid_three_dimensions(invalid_grid_3_dims): apply_filter(invalid_grid_3_dims, dummy_filter) -@pytest.fixture(name="invalid_grid_with_nans") -def fixture_invalid_grid_with_nans(sample_grid): - """ - Return a sample grid with nans. - - This fixture is meant to test if apply_filter raises an error on a grid - with a nans. - """ - sample_grid[2, 4] = np.nan - return sample_grid - - def test_apply_filter_grid_with_nans(invalid_grid_with_nans): """ Check if apply_filter raises error on grid with single dimension. @@ -239,27 +278,10 @@ def test_coordinate_rounding_fix(sample_grid): # ----------------------------- -# Test upward derivative filter +# Test filter functions # ----------------------------- -@pytest.fixture(name="sample_fft_grid") -def fixture_sample_fft_grid(): - """ - Return a sample fft_grid to be used in test functions. - """ - domain = (-9e-4, 9e-4, -8e-4, 8e-4) - freq_easting, freq_northing = grid_coordinates(region=domain, spacing=8e-4) - dummy_fft = np.ones_like(freq_easting) - fft_grid = make_xarray_grid( - (freq_easting, freq_northing), - dummy_fft, - data_names=["sample_fft"], - dims=("freq_northing", "freq_easting"), - ) - return fft_grid.sample_fft - - @pytest.mark.parametrize("order", [1, 2, 3]) def test_derivative_upward_kernel(sample_fft_grid, order): """ diff --git a/harmonica/tests/test_transformations.py b/harmonica/tests/test_transformations.py index fb8bd588b..743f56ec0 100644 --- a/harmonica/tests/test_transformations.py +++ b/harmonica/tests/test_transformations.py @@ -467,9 +467,9 @@ def test_upward_continuation(sample_g_z, sample_g_z_upward): xrt.assert_allclose(continuation, g_z_upward, atol=1e-8) -def test_reduction_to_pole(): +def test_reduction_to_pole_remanent(): """ - Test reduction_to_pole function against an analytical solution. + Test reduction_to_pole against an analytical solution with remanent magnetization. """ coordinates = vd.grid_coordinates( (-70e3, 20e3, -20e3, 60e3), spacing=0.5e3, extra_coords=500 @@ -504,6 +504,42 @@ def test_reduction_to_pole(): ) +def test_reduction_to_pole_induced(): + """ + Test reduction_to_pole against an analytical solution with induced magnetization. + """ + coordinates = vd.grid_coordinates( + (-70e3, 20e3, -20e3, 60e3), spacing=0.5e3, extra_coords=500 + ) + finc, fdec = -45, 13 + dipole = [-25e3, 20e3, -5000] + moment = 1e12 + magnetic_field_pole = dipole_magnetic( + coordinates, + dipoles=dipole, + magnetic_moments=magnetic_angles_to_vec(moment, 90, 0), + field="b", + ) + anomaly_pole = total_field_anomaly(magnetic_field_pole, 90, 0) + magnetic_field = dipole_magnetic( + coordinates, + dipoles=dipole, + magnetic_moments=magnetic_angles_to_vec(moment, finc, fdec), + field="b", + ) + anomaly = total_field_anomaly(magnetic_field, finc, fdec) + grid = vd.make_xarray_grid(coordinates[:2], anomaly, data_names="anomaly") + anomaly_reduced = reduction_to_pole(grid.anomaly, finc, fdec) + # Relative tol doesn't work because the anomaly at the pole is zero in + # a ring around the source and the rtol blows up at those points. + np.testing.assert_allclose( + anomaly_reduced.values, + anomaly_pole, + rtol=0, + atol=0.01 * np.abs(anomaly_pole).max(), + ) + + def test_reduction_to_pole_dim_names(sample_potential): """ Test reduction_to_pole function with non-typical dim names. From 6a3863c4f512fd012e550e7c5fc30fda3ee25e80 Mon Sep 17 00:00:00 2001 From: Leonardo Uieda Date: Tue, 31 Mar 2026 09:54:56 -0300 Subject: [PATCH 12/20] Fix broken formatting --- harmonica/tests/test_filters.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/harmonica/tests/test_filters.py b/harmonica/tests/test_filters.py index 099f13f57..ecb30901f 100644 --- a/harmonica/tests/test_filters.py +++ b/harmonica/tests/test_filters.py @@ -232,7 +232,9 @@ def test_apply_filter_pad_with_constant(sample_grid): """ # Apply the dummy filter filtered_grid = apply_filter( - sample_grid, dummy_filter, pad_kwargs={"mode": "constant", "constant_values": 120} + sample_grid, + dummy_filter, + pad_kwargs={"mode": "constant", "constant_values": 120}, ) # Compare output with expected results expected = sample_grid * 0 From 5f5b7979253ae3b0c3140e193133b66e2581dd28 Mon Sep 17 00:00:00 2001 From: Leonardo Uieda Date: Wed, 1 Apr 2026 12:06:18 -0300 Subject: [PATCH 13/20] Update docstrings and add option to control padding in transformations --- harmonica/_transformations.py | 126 +++++++++++++++++++++++++++++----- harmonica/filters/_fft.py | 4 ++ harmonica/filters/_utils.py | 21 +++++- 3 files changed, 130 insertions(+), 21 deletions(-) diff --git a/harmonica/_transformations.py b/harmonica/_transformations.py index 666a8dff8..7c9ef3db2 100644 --- a/harmonica/_transformations.py +++ b/harmonica/_transformations.py @@ -22,7 +22,7 @@ from .filters._utils import apply_filter, grid_sanity_checks -def derivative_upward(grid, order=1): +def derivative_upward(grid, order=1, pad=True, pad_kwargs=None): """ Calculate the derivative of a potential field grid in the upward direction. @@ -38,6 +38,16 @@ def derivative_upward(grid, order=1): same units. order : int The order of the derivative. Default to 1. + pad : bool + If True, will add padding to the grid before taking the Fourier Transform + and applying the filter and remove it after the inverse Fourier Transform. + Adding padding usually helps reduce edge effects from signal truncation. + Default is True. + pad_kwargs : dict or None + Any additional keyword arguments that should be passed to the + :meth:`xarray.DataArray.pad` function in the form of a dictionary. If none + are given, the default padding of 25% the dimensions of the grid will be + added using the "edge" method. Returns ------- @@ -54,10 +64,16 @@ def derivative_upward(grid, order=1): -------- harmonica.filters.derivative_upward_kernel """ - return apply_filter(grid, derivative_upward_kernel, filter_kwargs={"order": order}) + return apply_filter( + grid, + derivative_upward_kernel, + filter_kwargs={"order": order}, + pad=pad, + pad_kwargs=pad_kwargs, + ) -def derivative_easting(grid, order=1, method="finite-diff"): +def derivative_easting(grid, order=1, method="finite-diff", pad=True, pad_kwargs=None): """ Calculate the derivative of a regular grid in the easting direction. @@ -84,8 +100,17 @@ def derivative_easting(grid, order=1, method="finite-diff"): Method that will be used for computing the easting derivative. It can be either ``"finite-diff"``, for computing using :func:`xarray.differentiate`, or ``"fft"``, for using FFT-based - filters. - Default ``"finite-diff"``. + filters. Default ``"finite-diff"``. + pad : bool + If True, will add padding to the grid before taking the Fourier Transform + and applying the filter and remove it after the inverse Fourier Transform. + Adding padding usually helps reduce edge effects from signal truncation. + Default is True. + pad_kwargs : dict or None + Any additional keyword arguments that should be passed to the + :meth:`xarray.DataArray.pad` function in the form of a dictionary. If none + are given, the default padding of 25% the dimensions of the grid will be + added using the "edge" method. Returns ------- @@ -108,7 +133,11 @@ def derivative_easting(grid, order=1, method="finite-diff"): grid = grid.differentiate(coord=coordinate) elif method == "fft": grid = apply_filter( - grid, derivative_easting_kernel, filter_kwargs={"order": order} + grid, + derivative_easting_kernel, + filter_kwargs={"order": order}, + pad=pad, + pad_kwargs=pad_kwargs, ) else: msg = ( @@ -118,7 +147,7 @@ def derivative_easting(grid, order=1, method="finite-diff"): return grid -def derivative_northing(grid, order=1, method="finite-diff"): +def derivative_northing(grid, order=1, method="finite-diff", pad=True, pad_kwargs=None): """ Calculate the derivative of a regular grid in the northing direction. @@ -145,8 +174,17 @@ def derivative_northing(grid, order=1, method="finite-diff"): Method that will be used for computing the easting derivative. It can be either ``"finite-diff"``, for computing using :func:`xarray.differentiate`, or ``"fft"``, for using FFT-based - filters. - Default ``"finite-diff"``. + filters. Default ``"finite-diff"``. + pad : bool + If True, will add padding to the grid before taking the Fourier Transform + and applying the filter and remove it after the inverse Fourier Transform. + Adding padding usually helps reduce edge effects from signal truncation. + Default is True. + pad_kwargs : dict or None + Any additional keyword arguments that should be passed to the + :meth:`xarray.DataArray.pad` function in the form of a dictionary. If none + are given, the default padding of 25% the dimensions of the grid will be + added using the "edge" method. Returns ------- @@ -169,7 +207,11 @@ def derivative_northing(grid, order=1, method="finite-diff"): grid = grid.differentiate(coord=coordinate) elif method == "fft": return apply_filter( - grid, derivative_northing_kernel, filter_kwargs={"order": order} + grid, + derivative_northing_kernel, + filter_kwargs={"order": order}, + pad=pad, + pad_kwargs=pad_kwargs, ) else: msg = ( @@ -179,7 +221,7 @@ def derivative_northing(grid, order=1, method="finite-diff"): return grid -def upward_continuation(grid, height_displacement): +def upward_continuation(grid, height_displacement, pad=True, pad_kwargs=None): """ Calculate the upward continuation of a potential field grid. @@ -197,6 +239,16 @@ def upward_continuation(grid, height_displacement): The height displacement of upward continuation. For upward continuation, the height displacement should be positive. Its units are the same units of the ``grid`` coordinates. + pad : bool + If True, will add padding to the grid before taking the Fourier Transform + and applying the filter and remove it after the inverse Fourier Transform. + Adding padding usually helps reduce edge effects from signal truncation. + Default is True. + pad_kwargs : dict or None + Any additional keyword arguments that should be passed to the + :meth:`xarray.DataArray.pad` function in the form of a dictionary. If none + are given, the default padding of 25% the dimensions of the grid will be + added using the "edge" method. Returns ------- @@ -216,6 +268,8 @@ def upward_continuation(grid, height_displacement): grid, upward_continuation_kernel, filter_kwargs={"height_displacement": height_displacement}, + pad=pad, + pad_kwargs=pad_kwargs, ) @@ -305,6 +359,8 @@ def reduction_to_pole( declination, magnetization_inclination=None, magnetization_declination=None, + pad=True, + pad_kwargs=None, ): """ Calculate the reduction to the pole of a magnetic field grid. @@ -333,6 +389,16 @@ def reduction_to_pole( None, the ``magnetization_declination`` will be set equal to the ``declination``, neglecting remanent magnetization and self demagnetization. Default None. + pad : bool + If True, will add padding to the grid before taking the Fourier Transform + and applying the filter and remove it after the inverse Fourier Transform. + Adding padding usually helps reduce edge effects from signal truncation. + Default is True. + pad_kwargs : dict or None + Any additional keyword arguments that should be passed to the + :meth:`xarray.DataArray.pad` function in the form of a dictionary. If none + are given, the default padding of 25% the dimensions of the grid will be + added using the "edge" method. Returns ------- @@ -357,10 +423,12 @@ def reduction_to_pole( "magnetization_inclination": magnetization_inclination, "magnetization_declination": magnetization_declination, }, + pad=pad, + pad_kwargs=pad_kwargs, ) -def total_gradient_amplitude(grid): +def total_gradient_amplitude(grid, pad=True, pad_kwargs=None): r""" Calculate the total gradient amplitude of a potential field grid. @@ -375,6 +443,16 @@ def total_gradient_amplitude(grid): evenly spaced (regular grid). Its dimensions should be in the following order: *northing*, *easting*. Its coordinates should be defined in the same units. + pad : bool + If True, will add padding to the grid before taking the Fourier Transform + and applying the filter and remove it after the inverse Fourier Transform. + Adding padding usually helps reduce edge effects from signal truncation. + Default is True. + pad_kwargs : dict or None + Any additional keyword arguments that should be passed to the + :meth:`xarray.DataArray.pad` function in the form of a dictionary. If none + are given, the default padding of 25% the dimensions of the grid will be + added using the "edge" method. Returns ------- @@ -404,15 +482,15 @@ def total_gradient_amplitude(grid): grid_sanity_checks(grid) # Calculate the gradients of the grid gradient = ( - derivative_easting(grid, order=1), - derivative_northing(grid, order=1), - derivative_upward(grid, order=1), + derivative_easting(grid, order=1, pad=pad, pad_kwargs=pad_kwargs), + derivative_northing(grid, order=1, pad=pad, pad_kwargs=pad_kwargs), + derivative_upward(grid, order=1, pad=pad, pad_kwargs=pad_kwargs), ) # return the total gradient amplitude return np.sqrt(gradient[0] ** 2 + gradient[1] ** 2 + gradient[2] ** 2) -def tilt_angle(grid): +def tilt_angle(grid, pad=True, pad_kwargs=None): r""" Calculate the tilt angle of a potential field grid. @@ -427,6 +505,16 @@ def tilt_angle(grid): evenly spaced (regular grid). Its dimensions should be in the following order: *northing*, *easting*. Its coordinates should be defined in the same units. + pad : bool + If True, will add padding to the grid before taking the Fourier Transform + and applying the filter and remove it after the inverse Fourier Transform. + Adding padding usually helps reduce edge effects from signal truncation. + Default is True. + pad_kwargs : dict or None + Any additional keyword arguments that should be passed to the + :meth:`xarray.DataArray.pad` function in the form of a dictionary. If none + are given, the default padding of 25% the dimensions of the grid will be + added using the "edge" method. Returns ------- @@ -460,9 +548,9 @@ def tilt_angle(grid): [MillerSingh1994]_ """ grid_sanity_checks(grid) - deriv_east = derivative_easting(grid, order=1) - deriv_north = derivative_northing(grid, order=1) - deriv_up = derivative_upward(grid, order=1) + deriv_east = derivative_easting(grid, order=1, pad=pad, pad_kwargs=pad_kwargs) + deriv_north = derivative_northing(grid, order=1, pad=pad, pad_kwargs=pad_kwargs) + deriv_up = derivative_upward(grid, order=1, pad=pad, pad_kwargs=pad_kwargs) horiz_deriv = np.hypot(deriv_east, deriv_north) tilt = np.arctan2(deriv_up, horiz_deriv) return tilt diff --git a/harmonica/filters/_fft.py b/harmonica/filters/_fft.py index dafb0143e..d65d2de32 100644 --- a/harmonica/filters/_fft.py +++ b/harmonica/filters/_fft.py @@ -31,6 +31,8 @@ def fft(grid, true_phase=True, true_amplitude=True, **kwargs): If True, the FFT is multiplied by the spacing of the transformed variables to match theoretical FT amplitude. Defaults to True. + **kwargs + Any extra keyword arguments will be passed the :func:`xrft.fft` function. Returns ------- @@ -62,6 +64,8 @@ def ifft(fourier_transform, true_phase=True, true_amplitude=True, **kwargs): If True, output is divided by the spacing of the transformed variables to match theoretical IFT amplitude. Defaults to True. + **kwargs + Any extra keyword arguments will be passed the :func:`xrft.ifft` function. Returns ------- diff --git a/harmonica/filters/_utils.py b/harmonica/filters/_utils.py index 2968e1d50..da1cd5652 100644 --- a/harmonica/filters/_utils.py +++ b/harmonica/filters/_utils.py @@ -21,6 +21,13 @@ def apply_filter(grid, fft_filter, filter_kwargs=None, pad=True, pad_kwargs=None Computes the Fourier transform of the given grid, builds the filter, applies it and returns the inverse Fourier transform of the filtered grid. + .. note:: + + Any non-dimensional coordinates in the original grid will be dropped + from the filtered grid. This is because we can't know if the filter + invalidates the coordinate values (for example, upward continuation + would invalidate any height coordinates). So it's safer to drop them. + Parameters ---------- grid : :class:`xarray.DataArray` @@ -30,9 +37,19 @@ def apply_filter(grid, fft_filter, filter_kwargs=None, pad=True, pad_kwargs=None same units. fft_filter : func Callable that builds the filter in the frequency domain. - kwargs : + filter_kwargs : dict Any additional keyword argument that should be passed to the - ``fft_filter``. + ``fft_filter`` in the form of a dictionary. + pad : bool + If True, will add padding to the grid before taking the Fourier Transform + and applying the filter and remove it after the inverse Fourier Transform. + Adding padding usually helps reduce edge effects from signal truncation. + Default is True. + pad_kwargs : dict + Any additional keyword arguments that should be passed to the + :meth:`xarray.DataArray.pad` function. If none are given, the default + padding of 25% the dimensions of the grid will be added using the + "edge" method. Returns ------- From 85519056de1d52e784a500aedb2f477cacab30e6 Mon Sep 17 00:00:00 2001 From: Leonardo Uieda Date: Wed, 1 Apr 2026 12:06:42 -0300 Subject: [PATCH 14/20] Update gallery examples to use built-in padding --- .../transformations/reduction_to_pole.py | 17 +---------- doc/gallery_src/transformations/tga.py | 16 +--------- doc/gallery_src/transformations/tilt.py | 29 +++---------------- .../transformations/upward_continuation.py | 16 +--------- .../transformations/upward_derivative.py | 16 +--------- 5 files changed, 8 insertions(+), 86 deletions(-) diff --git a/doc/gallery_src/transformations/reduction_to_pole.py b/doc/gallery_src/transformations/reduction_to_pole.py index 8a785a36b..5a8f74165 100644 --- a/doc/gallery_src/transformations/reduction_to_pole.py +++ b/doc/gallery_src/transformations/reduction_to_pole.py @@ -13,7 +13,6 @@ import pygmt import verde as vd import xarray as xr -import xrft import harmonica as hm @@ -22,15 +21,6 @@ fname = ensaio.fetch_lightning_creek_magnetic(version=1) magnetic_grid = xr.load_dataarray(fname) -# Pad the grid to increase accuracy of the FFT filter -pad_width = { - "easting": magnetic_grid.easting.size // 3, - "northing": magnetic_grid.northing.size // 3, -} -# drop the extra height coordinate -magnetic_grid_no_height = magnetic_grid.drop_vars("height") -magnetic_grid_padded = xrft.pad(magnetic_grid_no_height, pad_width) - # Define the inclination and declination of the region by the time of the data # acquisition (1990). inclination, declination = -52.98, 6.51 @@ -39,16 +29,11 @@ # that the sources share the same inclination and declination as the # geomagnetic field. rtp_grid = hm.reduction_to_pole( - magnetic_grid_padded, inclination=inclination, declination=declination + magnetic_grid, inclination=inclination, declination=declination ) - -# Unpad the reduced to the pole grid -rtp_grid = xrft.unpad(rtp_grid, pad_width) - # Show the reduced to the pole grid print("\nReduced to the pole magnetic grid:\n", rtp_grid) - # Plot original magnetic anomaly and the reduced to the pole fig = pygmt.Figure() with fig.subplot(nrows=1, ncols=2, figsize=("28c", "15c"), sharey="l"): diff --git a/doc/gallery_src/transformations/tga.py b/doc/gallery_src/transformations/tga.py index 21c710e5a..24d3b1bbe 100644 --- a/doc/gallery_src/transformations/tga.py +++ b/doc/gallery_src/transformations/tga.py @@ -13,7 +13,6 @@ import pygmt import verde as vd import xarray as xr -import xrft import harmonica as hm @@ -22,21 +21,8 @@ fname = ensaio.fetch_lightning_creek_magnetic(version=1) magnetic_grid = xr.load_dataarray(fname) -# Pad the grid to increase accuracy of the FFT filter -pad_width = { - "easting": magnetic_grid.easting.size // 3, - "northing": magnetic_grid.northing.size // 3, -} -# drop the extra height coordinate -magnetic_grid_no_height = magnetic_grid.drop_vars("height") -magnetic_grid_padded = xrft.pad(magnetic_grid_no_height, pad_width) - # Compute the total gradient amplitude of the grid -tga = hm.total_gradient_amplitude(magnetic_grid_padded) - -# Unpad the total gradient amplitude grid -tga = xrft.unpad(tga, pad_width) - +tga = hm.total_gradient_amplitude(magnetic_grid) # Show the total gradient amplitude print("\nTotal Gradient Amplitude:\n", tga) diff --git a/doc/gallery_src/transformations/tilt.py b/doc/gallery_src/transformations/tilt.py index 9f9c1b579..56331eccb 100644 --- a/doc/gallery_src/transformations/tilt.py +++ b/doc/gallery_src/transformations/tilt.py @@ -13,7 +13,6 @@ import pygmt import verde as vd import xarray as xr -import xrft import harmonica as hm @@ -22,21 +21,8 @@ fname = ensaio.fetch_lightning_creek_magnetic(version=1) magnetic_grid = xr.load_dataarray(fname) -# Pad the grid to increase accuracy of the FFT filter -pad_width = { - "easting": magnetic_grid.easting.size // 3, - "northing": magnetic_grid.northing.size // 3, -} -# drop the extra height coordinate -magnetic_grid_no_height = magnetic_grid.drop_vars("height") -magnetic_grid_padded = xrft.pad(magnetic_grid_no_height, pad_width) - # Compute the tilt of the grid -tilt_grid = hm.tilt_angle(magnetic_grid_padded) - -# Unpad the tilt grid -tilt_grid = xrft.unpad(tilt_grid, pad_width) - +tilt_grid = hm.tilt_angle(magnetic_grid) # Show the tilt print("\nTilt:\n", tilt_grid) @@ -47,19 +33,12 @@ # Apply a reduction to the pole over the magnetic anomaly grid. We will assume # that the sources share the same inclination and declination as the # geomagnetic field. -rtp_grid_padded = hm.reduction_to_pole( - magnetic_grid_padded, inclination=inclination, declination=declination +rtp_grid = hm.reduction_to_pole( + magnetic_grid, inclination=inclination, declination=declination ) -# Unpad the reduced to the pole grid -rtp_grid = xrft.unpad(rtp_grid_padded, pad_width) - # Compute the tilt of the padded rtp grid -tilt_rtp_grid = hm.tilt_angle(rtp_grid_padded) - -# Unpad the tilt grid -tilt_rtp_grid = xrft.unpad(tilt_rtp_grid, pad_width) - +tilt_rtp_grid = hm.tilt_angle(rtp_grid) # Show the tilt from RTP print("\nTilt from RTP:\n", tilt_rtp_grid) diff --git a/doc/gallery_src/transformations/upward_continuation.py b/doc/gallery_src/transformations/upward_continuation.py index db6ce9829..182b29261 100644 --- a/doc/gallery_src/transformations/upward_continuation.py +++ b/doc/gallery_src/transformations/upward_continuation.py @@ -12,7 +12,6 @@ import ensaio import pygmt import xarray as xr -import xrft import harmonica as hm @@ -21,22 +20,9 @@ fname = ensaio.fetch_lightning_creek_magnetic(version=1) magnetic_grid = xr.load_dataarray(fname) -# Pad the grid to increase accuracy of the FFT filter -pad_width = { - "easting": magnetic_grid.easting.size // 3, - "northing": magnetic_grid.northing.size // 3, -} -# drop the extra height coordinate -magnetic_grid_no_height = magnetic_grid.drop_vars("height") -magnetic_grid_padded = xrft.pad(magnetic_grid_no_height, pad_width) - # Upward continue the magnetic grid, from 500 m to 1000 m # (a height displacement of 500m) -upward_continued = hm.upward_continuation(magnetic_grid_padded, height_displacement=500) - -# Unpad the upward continued grid -upward_continued = xrft.unpad(upward_continued, pad_width) - +upward_continued = hm.upward_continuation(magnetic_grid, height_displacement=500) # Show the upward continued grid print("\nUpward continued magnetic grid:\n", upward_continued) diff --git a/doc/gallery_src/transformations/upward_derivative.py b/doc/gallery_src/transformations/upward_derivative.py index 2d48b89db..b432a0b57 100644 --- a/doc/gallery_src/transformations/upward_derivative.py +++ b/doc/gallery_src/transformations/upward_derivative.py @@ -13,7 +13,6 @@ import pygmt import verde as vd import xarray as xr -import xrft import harmonica as hm @@ -22,21 +21,8 @@ fname = ensaio.fetch_lightning_creek_magnetic(version=1) magnetic_grid = xr.load_dataarray(fname) -# Pad the grid to increase accuracy of the FFT filter -pad_width = { - "easting": magnetic_grid.easting.size // 3, - "northing": magnetic_grid.northing.size // 3, -} -# drop the extra height coordinate -magnetic_grid_no_height = magnetic_grid.drop_vars("height") -magnetic_grid_padded = xrft.pad(magnetic_grid_no_height, pad_width) - # Compute the upward derivative of the grid -deriv_upward = hm.derivative_upward(magnetic_grid_padded) - -# Unpad the derivative grid -deriv_upward = xrft.unpad(deriv_upward, pad_width) - +deriv_upward = hm.derivative_upward(magnetic_grid) # Show the upward derivative print("\nUpward derivative:\n", deriv_upward) From 8b09bab01b773eac56af4445afa0011fcccf245b Mon Sep 17 00:00:00 2001 From: Leonardo Uieda Date: Thu, 2 Apr 2026 09:49:03 -0300 Subject: [PATCH 15/20] Use padding in Gaussian filters and don't drop extra coords always Introduce an argument to apply_filter for dropping coordinates. We only really need to do this for upward continuation. The other filters don't alter the location of points. Update the filter tests to use synthetics instead of Montaj results. --- doc/user_guide/transformations.rst | 72 +++++++++++++----------- harmonica/_transformations.py | 36 ++++++++++-- harmonica/filters/_utils.py | 15 ++++- harmonica/tests/test_filters.py | 20 ++++++- harmonica/tests/test_transformations.py | 75 ++++++++++++++++++++----- 5 files changed, 163 insertions(+), 55 deletions(-) diff --git a/doc/user_guide/transformations.rst b/doc/user_guide/transformations.rst index 8baf29a0c..98ac637ad 100644 --- a/doc/user_guide/transformations.rst +++ b/doc/user_guide/transformations.rst @@ -15,8 +15,11 @@ We can load the data file using :mod:`xarray`: .. jupyter-execute:: - import ensaio import xarray as xr + import matplotlib.pyplot as plt + import verde as vd + import harmonica as hm + import ensaio fname = ensaio.fetch_lightning_creek_magnetic(version=1) magnetic_grid = xr.load_dataarray(fname) @@ -26,8 +29,6 @@ And plot it: .. jupyter-execute:: - import matplotlib.pyplot as plt - tmp = magnetic_grid.plot(cmap="seismic", center=0, add_colorbar=False) plt.gca().set_aspect("equal") plt.title("Magnetic anomaly grid") @@ -51,8 +52,6 @@ magnetic anomaly grid using the :func:`harmonica.derivative_upward` function: .. jupyter-execute:: - import harmonica as hm - deriv_upward = hm.derivative_upward(magnetic_grid) deriv_upward @@ -153,11 +152,10 @@ frequency domain: and have less artifacts than their FFT-based counterpart. - Upward continuation ------------------- -We can also upward continue the original magnetic grid. +We can also upward continue the original magnetic grid using :func:`harmonica.upward_continuation`. This is, estimating the magnetic field generated by the same sources at a higher altitude. The original magnetic anomaly grid is located at 500 m above the ellipsoid, as @@ -167,11 +165,26 @@ to upward continue it a height displacement of 500m: .. jupyter-execute:: + change_in_height = 500 # meters upward_continued = hm.upward_continuation( - magnetic_grid, height_displacement=500 + magnetic_grid, height_displacement=change_in_height ) + upward_continued -And plot it: +Did you notice that the ``height`` coordinate is gone from the +upward-continued grid? We drop any non-dimensional coordinates when +doing upward continuation because we don't know the name of the +vertical coordinate and any values there would be wrong after continuation. +If we want it back, we need to assign an updated version of it: + +.. jupyter-execute:: + + upward_continued = upward_continued.assign_coords( + {"height": magnetic_grid.height + change_in_height} + ) + upward_continued + +Now we can plot it: .. jupyter-execute:: @@ -276,52 +289,49 @@ Let's define a cutoff wavelength of 5 kilometers: .. jupyter-execute:: - cutoff_wavelength = 5e3 + cutoff_wavelength = 5e3 # meters Then apply the two filters to our magnetic grid: .. jupyter-execute:: - magnetic_low_freqs = hm.gaussian_lowpass( - magnetic_grid, wavelength=cutoff_wavelength - ) - magnetic_high_freqs = hm.gaussian_highpass( + magnetic_low = hm.gaussian_lowpass( magnetic_grid, wavelength=cutoff_wavelength ) + magnetic_low .. jupyter-execute:: - magnetic_low_freqs - -.. jupyter-execute:: - - magnetic_high_freqs + magnetic_high = hm.gaussian_highpass( + magnetic_grid, wavelength=cutoff_wavelength + ) + magnetic_high Let's plot the results side by side: .. jupyter-execute:: - import verde as vd - - fig, (ax1, ax2) = plt.subplots( - nrows=1, ncols=2, sharey=True, figsize=(12, 8) + fig, (ax1, ax2, ax3) = plt.subplots( + nrows=1, ncols=3, sharey=True, figsize=(14, 8) ) - maxabs = vd.maxabs(magnetic_low_freqs, magnetic_high_freqs) + maxabs = vd.maxabs(magnetic_grid, magnetic_low, magnetic_high) kwargs = dict(cmap="seismic", vmin=-maxabs, vmax=maxabs, add_colorbar=False) - tmp = magnetic_low_freqs.plot(ax=ax1, **kwargs) - tmp = magnetic_high_freqs.plot(ax=ax2, **kwargs) + tmp = magnetic_grid.plot(ax=ax1, **kwargs) + tmp = magnetic_low.plot(ax=ax2, **kwargs) + tmp = magnetic_high.plot(ax=ax3, **kwargs) - ax1.set_title("Magnetic anomaly after low-pass filter") - ax2.set_title("Magnetic anomaly after high-pass filter") - for ax in (ax1, ax2): + ax1.set_title("Original") + ax2.set_title("After low-pass filter") + ax3.set_title("After high-pass filter") + for ax in (ax1, ax2, ax3): ax.set_aspect("equal") ax.ticklabel_format(style="sci", scilimits=(0, 0)) plt.colorbar( tmp, - ax=[ax1, ax2], + ax=[ax1, ax2, ax3], label="nT", orientation="horizontal", aspect=42, @@ -364,8 +374,6 @@ And plot it: .. jupyter-execute:: - import verde as vd - tmp = tga_grid.plot(cmap="viridis", add_colorbar=False) plt.gca().set_aspect("equal") plt.title("Total gradient amplitude of the magnetic anomaly") diff --git a/harmonica/_transformations.py b/harmonica/_transformations.py index 7c9ef3db2..300570fb7 100644 --- a/harmonica/_transformations.py +++ b/harmonica/_transformations.py @@ -228,6 +228,11 @@ def upward_continuation(grid, height_displacement, pad=True, pad_kwargs=None): Compute the upward continuation of regular gridded data using frequency domain calculations through Fast Fourier Transform. + .. note:: + + Any non-dimensional coordinates of the grid will be dropped since + upward continuation may have made them no longer correct. + Parameters ---------- grid : :class:`xarray.DataArray` @@ -270,10 +275,11 @@ def upward_continuation(grid, height_displacement, pad=True, pad_kwargs=None): filter_kwargs={"height_displacement": height_displacement}, pad=pad, pad_kwargs=pad_kwargs, + drop_coords=True, ) -def gaussian_lowpass(grid, wavelength): +def gaussian_lowpass(grid, wavelength, pad=True, pad_kwargs=None): """ Calculate the Gaussian low-pass of a potential field grid. @@ -290,6 +296,16 @@ def gaussian_lowpass(grid, wavelength): wavelength : float The cutoff wavelength in low-pass filter. Its units are the same units of the ``grid`` coordinates. + pad : bool + If True, will add padding to the grid before taking the Fourier Transform + and applying the filter and remove it after the inverse Fourier Transform. + Adding padding usually helps reduce edge effects from signal truncation. + Default is True. + pad_kwargs : dict or None + Any additional keyword arguments that should be passed to the + :meth:`xarray.DataArray.pad` function in the form of a dictionary. If none + are given, the default padding of 25% the dimensions of the grid will be + added using the "edge" method. Returns ------- @@ -308,12 +324,13 @@ def gaussian_lowpass(grid, wavelength): return apply_filter( grid, gaussian_lowpass_kernel, - pad=False, + pad=pad, + pad_kwargs=pad_kwargs, filter_kwargs={"wavelength": wavelength}, ) -def gaussian_highpass(grid, wavelength): +def gaussian_highpass(grid, wavelength, pad=True, pad_kwargs=None): """ Calculate the Gaussian high-pass of a potential field grid. @@ -330,6 +347,16 @@ def gaussian_highpass(grid, wavelength): wavelength : float The cutoff wavelength in high-pass filter. Its units are the same units of the ``grid`` coordinates. + pad : bool + If True, will add padding to the grid before taking the Fourier Transform + and applying the filter and remove it after the inverse Fourier Transform. + Adding padding usually helps reduce edge effects from signal truncation. + Default is True. + pad_kwargs : dict or None + Any additional keyword arguments that should be passed to the + :meth:`xarray.DataArray.pad` function in the form of a dictionary. If none + are given, the default padding of 25% the dimensions of the grid will be + added using the "edge" method. Returns ------- @@ -348,7 +375,8 @@ def gaussian_highpass(grid, wavelength): return apply_filter( grid, gaussian_highpass_kernel, - pad=False, + pad=pad, + pad_kwargs=pad_kwargs, filter_kwargs={"wavelength": wavelength}, ) diff --git a/harmonica/filters/_utils.py b/harmonica/filters/_utils.py index da1cd5652..ac9bb0238 100644 --- a/harmonica/filters/_utils.py +++ b/harmonica/filters/_utils.py @@ -14,7 +14,9 @@ from ._fft import fft, ifft -def apply_filter(grid, fft_filter, filter_kwargs=None, pad=True, pad_kwargs=None): +def apply_filter( + grid, fft_filter, filter_kwargs=None, pad=True, pad_kwargs=None, drop_coords=False +): """ Apply a filter to a grid and return the transformed grid in spatial domain. @@ -50,6 +52,10 @@ def apply_filter(grid, fft_filter, filter_kwargs=None, pad=True, pad_kwargs=None :meth:`xarray.DataArray.pad` function. If none are given, the default padding of 25% the dimensions of the grid will be added using the "edge" method. + drop_coords : bool + If True, non-dimensional coordinates of the grid will be dropped after + filtering. This is useful if the filter could move the grid, like in upward + continuation, which could make these coordinates incorrect. Returns ------- @@ -96,8 +102,13 @@ def apply_filter(grid, fft_filter, filter_kwargs=None, pad=True, pad_kwargs=None # when doing operations with the transformed grids. Restoring the original # coordinates avoids these issues. filtered_grid = filtered_grid.assign_coords( - {dims[1]: grid[dims[1]].values, dims[0]: grid[dims[0]].values} + {dims[1]: grid[dims[1]], dims[0]: grid[dims[0]]} ) + # Restore the non-dimensional coordinates if desired + if not drop_coords: + filtered_grid = filtered_grid.assign_coords( + {name: non_dim_coords[name] for name in non_dim_coords} + ) return filtered_grid diff --git a/harmonica/tests/test_filters.py b/harmonica/tests/test_filters.py index ecb30901f..60d71cda1 100644 --- a/harmonica/tests/test_filters.py +++ b/harmonica/tests/test_filters.py @@ -172,7 +172,7 @@ def test_apply_filter(sample_grid): xrt.assert_allclose(filtered_grid, expected) -def test_apply_filter_drop_coords(sample_grid_multiple_coords): +def test_apply_filter_keep_coords(sample_grid_multiple_coords): """ Test apply_filter function on a grid with extra coordinates. """ @@ -181,8 +181,22 @@ def test_apply_filter_drop_coords(sample_grid_multiple_coords): # Compare output with expected results expected = sample_grid_multiple_coords * 0 # Non-dimensional coordinates should have been dropped by apply_filter. - # We can't restore them because we can't know if they are still correct - # after filtering. + assert set(sample_grid_multiple_coords.coords) == set(filtered_grid.coords) + npt.assert_allclose(filtered_grid.values, expected.values) + + +def test_apply_filter_drop_coords(sample_grid_multiple_coords): + """ + Test apply_filter function on a grid with extra coordinates. + """ + # Apply the dummy filter + filtered_grid = apply_filter( + sample_grid_multiple_coords, dummy_filter, drop_coords=True + ) + # Compare output with expected results + expected = sample_grid_multiple_coords * 0 + # Non-dimensional coordinates should have been dropped by apply_filter. + assert set(sample_grid_multiple_coords.coords) != set(filtered_grid.coords) assert set(sample_grid_multiple_coords.dims) == set(filtered_grid.coords) npt.assert_allclose(filtered_grid.values, expected.values) diff --git a/harmonica/tests/test_transformations.py b/harmonica/tests/test_transformations.py index 743f56ec0..cab87b756 100644 --- a/harmonica/tests/test_transformations.py +++ b/harmonica/tests/test_transformations.py @@ -12,6 +12,7 @@ from pathlib import Path import numpy as np +import numpy.testing as npt import pytest import verde as vd import xarray as xr @@ -463,7 +464,7 @@ def test_upward_continuation(sample_g_z, sample_g_z_upward): continuation = continuation[trim:-trim, trim:-trim] g_z_upward = sample_g_z_upward[trim:-trim, trim:-trim] # Drop upward for comparison - g_z_upward = g_z_upward.drop("upward") + g_z_upward = g_z_upward.drop_vars("upward") xrt.assert_allclose(continuation, g_z_upward, atol=1e-8) @@ -663,23 +664,69 @@ def test_invalid_grid_with_nans(self, sample_potential): tilt_angle(sample_potential) -class TestAgainstOasisMontaj: +class TestGaussianFilters: """ - Test filter result against the output from oasis montaj. + Test the Gaussian low and high pass filters against a synthetic. """ - expected_grid = xr.open_dataset(TEST_DATA_DIR / "filter.nc") - - def test_gaussian_lowpass_grid(self): + def test_gaussian_lowpass(self): """ - Test gaussian_lowpass function against the output from oasis montaj. + Test gaussian_lowpass against a synthetic. """ - low_pass = gaussian_lowpass(self.expected_grid.filter_data, 10) - xrt.assert_allclose(self.expected_grid.filter_lp10, low_pass, atol=1e-6) - - def test_gaussian_highpass_grid(self): + # Make a synthetic with only 2 known wavelengths + coordinates = vd.grid_coordinates((0, 10, 0, 10), spacing=0.05) + wavelength_high = 0.5 + wavelength_low = 10 + component_low = ( + 5 + * np.sin(2 * np.pi / wavelength_low * coordinates[0]) + * np.cos(2 * np.pi / wavelength_low * coordinates[1]) + ) + component_high = np.cos(2 * np.pi / wavelength_high * coordinates[0]) * np.sin( + 2 * np.pi / wavelength_high * coordinates[1] + ) + grid_low = xr.DataArray( + component_low, + coords={"x": coordinates[0][0, :], "y": coordinates[1][:, 0]}, + dims=("y", "x"), + ) + grid_high = xr.DataArray( + component_high, + coords={"x": coordinates[0][0, :], "y": coordinates[1][:, 0]}, + dims=("y", "x"), + ) + grid = grid_low + grid_high + # Try to isolate the long wavelength component + low = gaussian_lowpass(grid, wavelength=1) + # This is about a 10% error tolerance + npt.assert_allclose(low, grid_low, atol=0.4) + + def test_gaussian_highpass(self): """ - Test gaussian_highpass function against the output from oasis montaj. + Test gaussian_highpass against a synthetic. """ - high_pass = gaussian_highpass(self.expected_grid.filter_data, 10) - xrt.assert_allclose(self.expected_grid.filter_hp10, high_pass, atol=1e-6) + # Make a synthetic with only 2 known wavelengths + coordinates = vd.grid_coordinates((0, 10, 0, 10), spacing=0.05) + wavelength_high = 0.5 + wavelength_low = 10 + component_low = np.sin(2 * np.pi / wavelength_low * coordinates[0]) * np.cos( + 2 * np.pi / wavelength_low * coordinates[1] + ) + component_high = np.cos(2 * np.pi / wavelength_high * coordinates[0]) * np.sin( + 2 * np.pi / wavelength_high * coordinates[1] + ) + grid_low = xr.DataArray( + component_low, + coords={"x": coordinates[0][0, :], "y": coordinates[1][:, 0]}, + dims=("y", "x"), + ) + grid_high = xr.DataArray( + component_high, + coords={"x": coordinates[0][0, :], "y": coordinates[1][:, 0]}, + dims=("y", "x"), + ) + grid = grid_low + grid_high + # Try to isolate the short wavelength component + high = gaussian_highpass(grid, wavelength=2) + # This is about a 10% error tolerance + npt.assert_allclose(high, grid_high, atol=0.14) From a99ea832037d58ed4559023de678db127b641f92 Mon Sep 17 00:00:00 2001 From: Leonardo Uieda Date: Thu, 2 Apr 2026 09:50:21 -0300 Subject: [PATCH 16/20] Remove test notebook --- filter-test.ipynb | 902 ---------------------------------------------- 1 file changed, 902 deletions(-) delete mode 100644 filter-test.ipynb diff --git a/filter-test.ipynb b/filter-test.ipynb deleted file mode 100644 index e606eb071..000000000 --- a/filter-test.ipynb +++ /dev/null @@ -1,902 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "id": "b5e97dfb-7beb-48b7-bb0f-ca9e811309be", - "metadata": {}, - "outputs": [], - "source": [ - "import ensaio\n", - "import harmonica as hm\n", - "import xarray as xr\n", - "import verde as vd\n", - "import numpy as np\n", - "import xrft" - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "id": "7dc8a2a5-2405-4839-86b8-9fd4e2cd4572", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 79, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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UpDyYgBEREZGECZg8OARJREREJDNWwIiIiEjCSfjyYAJGREREEltYOQQpmqwpdzQmYERERCSxsbICZmPFvncTzgEjIiIikhkrYERERCSx+ilIFsDMwgSMiIiIJFZPwucQpFk4BElEREQkM1bAiIiISMIhSHkwASMiIiIJhyDlwSFIIiIiIpmxAkZEREQSG4XCqnd58T1g5mECRkRERBKFrQIKG8uTKAUTMLNwCJKIiIhIZqyAERERkcTGVgEbKypgHII0DytgREREdJ2tDRRWLLC99dRi586dGDNmDLRaLRQKBb788ssG43fs2AGFQlFnOXHihIVfWn6tJgGLi4ur09EajUbaLoRAXFwctFotnJycMHjwYBw9etTkGHq9HtOnT4eHhwdcXFwwduxYnDlzxiSmqKgI4eHhUKvVUKvVCA8Px5UrV0xicnNzMWbMGLi4uMDDwwNRUVEwGAzN9t2JiIjkorBRVM8Ds3SxoHpWVlaG3r17Y9myZbe0X05ODvLz86WlW7dut3zultKqhiDvu+8+bN++Xfpsa2sr/XnRokVITExEUlISunfvjrfeegvDhw9HTk4O3NzcAADR0dHYvHkzkpOT0a5dO8ycOROjR49GVlaWdKywsDCcOXMGqampAIAXXngB4eHh2Lx5MwCgqqoKo0aNQvv27bFr1y5cunQJzz33HIQQWLp0qVxdQUREdMcYOXIkRo4cecv7eXp6ok2bNk3fIBm0qgTMzs7OpOpVQwiBJUuWYM6cORg3bhwAYO3atfDy8sKGDRswZcoUFBcXY/Xq1fjkk08wbNgwAMC6devg4+OD7du3IzQ0FMePH0dqair27NmDfv36AQBWrVqFoKAg5OTkwM/PD2lpaTh27Bjy8vKg1WoBAIsXL8akSZMwb948qFQqmXqDiIio6dnYKmBjxevsbVC9r06nM1mvVCqhVCqtalttffr0wbVr1+Dv749XX30VQ4YMadLjN6dWMwQJAL/++iu0Wi18fX0xceJE/PHHHwCAkydPoqCgACEhIVKsUqlEcHAwdu/eDQDIyspCRUWFSYxWq0VAQIAUk5mZCbVaLSVfANC/f3+o1WqTmICAACn5AoDQ0FDo9XpkZWXdtO16vR46nc5kISIiut0obGysXgDAx8dHms6jVqsRHx/fZG309vbGypUrkZKSgo0bN8LPzw9Dhw7Fzp07m+wcza3VVMD69euHjz/+GN27d8f58+fx1ltvYcCAATh69CgKCgoAAF5eXib7eHl54fTp0wCAgoICODg4wN3dvU5Mzf4FBQXw9PSsc25PT0+TmNrncXd3h4ODgxRTn/j4eLz55pu3+K2JiIhap7y8PJNRoaasfvn5+cHPz0/6HBQUhLy8PCQkJOCRRx5psvM0p1aTgN04NtyrVy8EBQXh3nvvxdq1a9G/f38AdV/+JoRo9IVwtWPqi7ckprbZs2cjJiZG+qzT6eDj49Ng24iIiOTWVEOQKpVK1mk5/fv3x7p162Q7n7Va1RDkjVxcXNCrVy/8+uuv0ryw2hWowsJCqVql0WhgMBhQVFTUYMz58+frnOvChQsmMbXPU1RUhIqKijqVsRsplUrpYpT7oiQiIjKXVU9A/rm0hEOHDsHb27tFzm2JVpuA6fV6HD9+HN7e3vD19YVGo0F6erq03WAwICMjAwMGDAAABAYGwt7e3iQmPz8fR44ckWKCgoJQXFyMffv2STF79+5FcXGxScyRI0eQn58vxaSlpUGpVCIwMLBZvzMREdGdqLS0FNnZ2cjOzgZQPbc7Ozsbubm5AKpHkZ599lkpfsmSJfjyyy/x66+/4ujRo5g9ezZSUlIwbdq0lmi+RVrNEGRsbCzGjBmDjh07orCwEG+99RZ0Oh2ee+45KBQKREdHY/78+ejWrRu6deuG+fPnw9nZGWFhYQAAtVqNyZMnY+bMmWjXrh3atm2L2NhY9OrVS3oqsmfPnhgxYgQiIiLw4YcfAqh+DcXo0aOlseaQkBD4+/sjPDwcb7/9Ni5fvozY2FhERESwqkVERK1edRXL8vqMAsZb3ufAgQMmTzDWTNl57rnnkJSUhPz8fCkZA6qLLLGxsTh79iycnJxw3333YevWrXjssccsbrfcWk0CdubMGTz99NO4ePEi2rdvj/79+2PPnj3o1KkTAGDWrFkoLy9HZGQkioqK0K9fP6SlpUnvAAOAd955B3Z2dhg/fjzKy8sxdOhQJCUlmbxPbP369YiKipKelhw7dqzJi+FsbW2xdetWREZGYuDAgXByckJYWBgSEhJk6gkiIqLm01RzwG7F4MGDIYS46fakpCSTz7NmzcKsWbNu+Ty3k1aTgCUnJze4XaFQIC4uDnFxcTeNcXR0xNKlSxt8YWrbtm0bncTXsWNHbNmypcEYIiIioptpNQkYERERNT+FwrKfE5L2N/LHuM3BBIyIiIgkNrY2sLFiDpiNaLXP98mKCRgRERFJrH2VhEKwAmYOpqlEREREMmMFjIiIiCSsgMmDCRgRERFJOAdMHuwlIiIiIpmxAkZERETXWft7jhyCNAsTMCIiIpLYKBSwseI9YDYKJmDm4BAkERERkcxYASMiIiKJwtbGuh/jNrK2Yw4mYERERCSx+se4+VNEZmGaSkRERCQzVsCIiIhIYvWLWFkBMwsTMCIiIpJwDpg8mIARERGRxMYWVs4Ba8LG3MGYphIRERHJjBUwIiIikihsFFBY8SJWa/a9mzABIyIiIomNjZU/xl3FwTVzsJeIiIiIZMYKGBEREUmsfg2FNT/kfRdhAkZEREQSq19DYcW+dxP2EhEREZHMWAEjIiIiicLGBgobKypgVux7N2ECRkRERBIbWyufguQQpFnYS0REREQyYwWMiIiIrrNyEj5YATMLEzAiIiKSKGysfAqSc8DMwgSMiIiIJJyELw/2EhEREZHMWAEjIiIiSfWLWG2t2L+qCVtz52ICRkRERBK+CV8e7CUiIiIimbECRkRERBIbGxvYWDGR3pp97ybsJSIiIpLUDEFas9yqnTt3YsyYMdBqtVAoFPjyyy8b3ScjIwOBgYFwdHREly5dsGLFCgu+bctptQlYfHw8FAoFoqOjpXVCCMTFxUGr1cLJyQmDBw/G0aNHTfbT6/WYPn06PDw84OLigrFjx+LMmTMmMUVFRQgPD4darYZarUZ4eDiuXLliEpObm4sxY8bAxcUFHh4eiIqKgsFgaK6vS0REdMcqKytD7969sWzZMrPiT548icceewyDBg3CoUOH8MorryAqKgopKSnN3NKm0yqHIPfv34+VK1fi/vvvN1m/aNEiJCYmIikpCd27d8dbb72F4cOHIycnB25ubgCA6OhobN68GcnJyWjXrh1mzpyJ0aNHIysrC7Z/PvURFhaGM2fOIDU1FQDwwgsvIDw8HJs3bwYAVFVVYdSoUWjfvj127dqFS5cu4bnnnoMQAkuXLpWxJ4iIiJpWS0zCHzlyJEaOHGl2/IoVK9CxY0csWbIEANCzZ08cOHAACQkJeOqpp275/C2h1VXASktL8cwzz2DVqlVwd3eX1gshsGTJEsyZMwfjxo1DQEAA1q5di6tXr2LDhg0AgOLiYqxevRqLFy/GsGHD0KdPH6xbtw6HDx/G9u3bAQDHjx9Hamoq/vOf/yAoKAhBQUFYtWoVtmzZgpycHABAWloajh07hnXr1qFPnz4YNmwYFi9ejFWrVkGn08nfKURERE1EobCRXsZq0aJo/tQiMzMTISEhJutCQ0Nx4MABVFRUNPv5m0KrS8BeeukljBo1CsOGDTNZf/LkSRQUFJj8hSiVSgQHB2P37t0AgKysLFRUVJjEaLVaBAQESDGZmZlQq9Xo16+fFNO/f3+o1WqTmICAAGi1WikmNDQUer0eWVlZ9bZbr9dDp9OZLERERHeq2vc8vV7fZMcuKCiAl5eXyTovLy9UVlbi4sWLTXae5tSqErDk5GQcPHgQ8fHxdbYVFBQAQL1/ITXbCgoK4ODgYFI5qy/G09OzzvE9PT1NYmqfx93dHQ4ODlJMbfHx8dKcMrVaDR8fH3O+MhERkayaahK+j4+PyX2vvnu3Ve1UKEw+CyHqXX+7ajVzwPLy8vDyyy8jLS0Njo6ON42r7y+ksb+M2jH1xVsSc6PZs2cjJiZG+qzT6ZiEERHRbaep5oDl5eVBpVJJ65VKpdVtq6HRaOoUPAoLC2FnZ4d27do12XmaU6upgGVlZaGwsBCBgYGws7ODnZ0dMjIy8N5778HOzk6qSNX3F1KzTaPRwGAwoKioqMGY8+fP1zn/hQsXTGJqn6eoqAgVFRV1KmM1lEolVCqVyUJERHS7sbG1sXoBUOee15QJWFBQENLT003WpaWloW/fvrC3t2+y8zSnVpOADR06FIcPH0Z2dra09O3bF8888wyys7PRpUsXaDQak78Qg8GAjIwMDBgwAAAQGBgIe3t7k5j8/HwcOXJEigkKCkJxcTH27dsnxezduxfFxcUmMUeOHEF+fr4Uk5aWBqVSicDAwGbtByIiojtNaWmpdG8Hqud1Z2dnIzc3F0D1KNKzzz4rxU+dOhWnT59GTEwMjh8/jo8++girV69GbGxsSzTfIq1mCNLNzQ0BAQEm61xcXNCuXTtpfXR0NObPn49u3bqhW7dumD9/PpydnREWFgYAUKvVmDx5MmbOnIl27dqhbdu2iI2NRa9evaRJ/T179sSIESMQERGBDz/8EED1ayhGjx4NPz8/AEBISAj8/f0RHh6Ot99+G5cvX0ZsbCwiIiJY2SIiolZNYaOAwoq32Stsbn0O1oEDBzBkyBDpc82Uneeeew5JSUnIz8+XkjEA8PX1xbZt2zBjxgy8//770Gq1eO+991rNKyiAVpSAmWPWrFkoLy9HZGQkioqK0K9fP6SlpUnvAAOAd955B3Z2dhg/fjzKy8sxdOhQJCUlSe8AA4D169cjKipKelpy7NixJi+Hs7W1xdatWxEZGYmBAwfCyckJYWFhSEhIkO/LEhERNYOWeA/Y4MGDpUn09UlKSqqzLjg4GAcPHrzlc90uWnUCtmPHDpPPCoUCcXFxiIuLu+k+jo6OWLp0aYMvTG3bti3WrVvX4Lk7duyILVu23EpziYiIiAC08gSMiIiImlZLVMDuRkzAiIiISFLzJnxr9qfGsZeIiIiIZMYKGBEREUkUtrawueHBNEv2p8YxASMiIiIJ54DJg71EREREJDNWwIiIiEjCCpg8mIARERGRRGFj5VOQVux7N2ECRkRERBJWwOTBXiIiIiKSGStgREREJFHYKKyrgFnwY9x3IyZgREREJOEcMHmwl4iIiIhkxgoYERERSRQ2tlDYWPEmfCv2vZswASMiIqLrbGyrF2v2p0ZxCJKIiIhIZqyAERER0XU2NtWLNftTo5iAERERkURhawuFrRVzwKzY927CNJWIiIhIZqyAERER0XWchC8LJmBERER0nY2NlQkYB9fMwQSMiIiIJHwTvjzYS0REREQyYwWMiIiIrlNYOQdMwTlg5mACRkRERNdxEr4sOARJREREJDNWwIiIiEjCSfjyYAJGRERE13EIUhZMU4mIiIhkxgoYERERXccXscqCCRgRERFJ+GPc8mCaSkRERCQzVsCIiIjoOhsb64YROQRpFiZgREREdB2fgpQFEzAiIiKSKGxsobAiibJm37tJq6kTLl++HPfffz9UKhVUKhWCgoLw9ddfS9uFEIiLi4NWq4WTkxMGDx6Mo0ePmhxDr9dj+vTp8PDwgIuLC8aOHYszZ86YxBQVFSE8PBxqtRpqtRrh4eG4cuWKSUxubi7GjBkDFxcXeHh4ICoqCgaDodm+OxER0Z3ugw8+gK+vLxwdHREYGIgffvjhprE7duyAQqGos5w4cULGFlun1SRgHTp0wIIFC3DgwAEcOHAAjz76KB5//HEpyVq0aBESExOxbNky7N+/HxqNBsOHD0dJSYl0jOjoaGzatAnJycnYtWsXSktLMXr0aFRVVUkxYWFhyM7ORmpqKlJTU5GdnY3w8HBpe1VVFUaNGoWysjLs2rULycnJSElJwcyZM+XrDCIiouaisLk+D8ySRXHrqcVnn32G6OhozJkzB4cOHcKgQYMwcuRI5ObmNrhfTk4O8vPzpaVbt26WfmvZtZohyDFjxph8njdvHpYvX449e/bA398fS5YswZw5czBu3DgAwNq1a+Hl5YUNGzZgypQpKC4uxurVq/HJJ59g2LBhAIB169bBx8cH27dvR2hoKI4fP47U1FTs2bMH/fr1AwCsWrUKQUFByMnJgZ+fH9LS0nDs2DHk5eVBq9UCABYvXoxJkyZh3rx5UKlUMvYKERFR02qJIcjExERMnjwZzz//PABgyZIl+Oabb7B8+XLEx8ffdD9PT0+0adPG0qa2qFZTAbtRVVUVkpOTUVZWhqCgIJw8eRIFBQUICQmRYpRKJYKDg7F7924AQFZWFioqKkxitFotAgICpJjMzEyo1Wop+QKA/v37Q61Wm8QEBARIyRcAhIaGQq/XIysr66Zt1uv10Ol0JgsREdGdqvY9T6/X1xtnMBiQlZVlcn8GgJCQEOneezN9+vSBt7c3hg4diu+//77J2i6HVpWAHT58GK6urlAqlZg6dSo2bdoEf39/FBQUAAC8vLxM4r28vKRtBQUFcHBwgLu7e4Mxnp6edc7r6elpElP7PO7u7nBwcJBi6hMfHy/NK1Or1fDx8bnFb09ERCSDmjfhW7xUpxY+Pj4m972bVbIuXryIqqqqBu/htXl7e2PlypVISUnBxo0b4efnh6FDh2Lnzp1N2xfNqNUMQQKAn58fsrOzceXKFaSkpOC5555DRkaGtF2hUJjECyHqrKutdkx98ZbE1DZ79mzExMRIn3U6HZMwIiK6/TTRe8Dy8vJMpuUolcoGd7uVe7ifnx/8/Pykz0FBQcjLy0NCQgIeeeQRS1suq1ZVAXNwcEDXrl3Rt29fxMfHo3fv3nj33Xeh0WgAoE6mXFhYKGXUGo0GBoMBRUVFDcacP3++znkvXLhgElP7PEVFRaioqKiTvd9IqVRKT3DWLERERHeq2ve8myVgHh4esLW1bfAebo7+/fvj119/tarNcmpVCVhtQgjo9Xr4+vpCo9EgPT1d2mYwGJCRkYEBAwYAAAIDA2Fvb28Sk5+fjyNHjkgxQUFBKC4uxr59+6SYvXv3ori42CTmyJEjyM/Pl2LS0tKgVCoRGBjYrN+XiIioudX8FqQ1y61wcHBAYGCgyf0ZANLT06V7rzkOHToEb2/vWzp3S2o1Q5CvvPIKRo4cCR8fH5SUlCA5ORk7duxAamoqFAoFoqOjMX/+fHTr1g3dunXD/Pnz4ezsjLCwMACAWq3G5MmTMXPmTLRr1w5t27ZFbGwsevXqJT0V2bNnT4wYMQIRERH48MMPAQAvvPACRo8eLZU6Q0JC4O/vj/DwcLz99tu4fPkyYmNjERERwaoWERG1fi3wJvyYmBiEh4ejb9++CAoKwsqVK5Gbm4upU6cCqJ7Gc/bsWXz88ccAqp+S7Ny5M+677z4YDAasW7cOKSkpSElJsbzdMms1Cdj58+cRHh6O/Px8qNVq3H///UhNTcXw4cMBALNmzUJ5eTkiIyNRVFSEfv36IS0tDW5ubtIx3nnnHdjZ2WH8+PEoLy/H0KFDkZSUBNsbsvX169cjKipKehpj7NixWLZsmbTd1tYWW7duRWRkJAYOHAgnJyeEhYUhISFBpp4gIiJqRi2QgE2YMAGXLl3C3LlzkZ+fj4CAAGzbtg2dOnUCUD1ideM7wQwGA2JjY3H27Fk4OTnhvvvuw9atW/HYY49Z3m6ZtZoEbPXq1Q1uVygUiIuLQ1xc3E1jHB0dsXTpUixduvSmMW3btsW6desaPFfHjh2xZcuWBmOIiIjIfJGRkYiMjKx3W1JSksnnWbNmYdasWTK0qvm0mgSMiIiImp/CxgYKK56CtGbfuwkTMCIiIrpOYeUQpII/xm0OpqlEREREMmMFjIiIiK5TKCz6QW2T/alRTMCIiIjoOoWNlQkYB9fMwV4iIiIikhkrYERERCQRChsIK6pY1ux7N2ECRkRERNdxCFIW7CUiIiIimbECRkRERNcpFNY9ycinIM3CBIyIiIius7GpXqzZnxrFBIyIiIgknIQvD/YSERERkcxYASMiIqLr+BSkLCxKwN5777161ysUCjg6OqJr16545JFHYGvLH+QkIiJqVZiAycKiBOydd97BhQsXcPXqVbi7u0MIgStXrsDZ2Rmurq4oLCxEly5d8P3338PHx6ep20xERETUqlmUps6fPx8PPvggfv31V1y6dAmXL1/GL7/8gn79+uHdd99Fbm4uNBoNZsyY0dTtJSIiouZUUwGzZqFGWVQBe/XVV5GSkoJ7771XWte1a1ckJCTgqaeewh9//IFFixbhqaeearKGEhERUfMTCoWVT0HeHe8Bu3LlCtq0aWPx/hb1cH5+PiorK+usr6ysREFBAQBAq9WipKTE4oYRERER3Q4WLlyIzz77TPo8fvx4tGvXDvfccw9++ukni45pUQI2ZMgQTJkyBYcOHZLWHTp0CC+++CIeffRRAMDhw4fh6+trUaOIiIiohXAIso4PP/xQmtOenp6O9PR0fP311xg5ciT+3//7fxYd06IhyNWrVyM8PByBgYGwt7cHUF39Gjp0KFavXg0AcHV1xeLFiy1qFBEREbUQ/hRRHfn5+VICtmXLFowfPx4hISHo3Lkz+vXrZ9ExLUrANBoN0tPTceLECfzyyy8QQqBHjx7w8/OTYoYMGWJRg4iIiIhuJ+7u7sjLy4OPjw9SU1Px1ltvAQCEEKiqqrLomFa9iLVHjx7o0aOHNYcgIiKi2wnfA1bHuHHjEBYWhm7duuHSpUsYOXIkACA7Oxtdu3a16JgWJWBVVVVISkrCt99+i8LCQhiNRpPt3333nUWNISIiopbF34Ks65133oGvry9yc3OxaNEiuLq6AqgemoyMjLTomBYlYC+//DKSkpIwatQoBAQEQHEHjvcSERHdlRQ2gA0rYDUqKirwwgsv4LXXXkOXLl1MtkVHR1t8XIsSsOTkZHz++ed47LHHLD4xERER0e3O3t4emzZtwmuvvdakx7UoTXVwcLB4zJOIiIhuY3wNRR1PPvkkvvzyyyY9pkUVsJkzZ+Ldd9/FsmXLOPxIRER0J+Ek/Dq6du2K//u//8Pu3bsRGBgIFxcXk+1RUVG3fEyLErBdu3bh+++/x9dff4377rtPehdYjY0bN1pyWCIiIqLbzn/+8x+0adMGWVlZyMrKMtmmUCjkS8DatGmDJ5980pJdiYiI6HbGClgdJ0+ebPJjWpSArVmzpqnbQURERLcB/hi3PKx6ESsRERHR3eDMmTP46quvkJubC4PBYLItMTHxlo9ndgL2l7/8Bd9++y3c3d3Rp0+fBiffHzx48JYbQkRERLcBDkHW8e2332Ls2LHw9fVFTk4OAgICcOrUKQgh8Je//MWiY5qdgD3++ONQKpUAgCeeeMKikxEREdFtjj/GXcfs2bMxc+ZMzJ07F25ubkhJSYGnpyeeeeYZjBgxwqJjmp2AvfHGG/X+mYiIiOhOdvz4cXz66acAADs7O5SXl8PV1RVz587F448/jhdffPGWj2lVndBgMODMmTPIzc01WZpDfHw8HnzwQbi5ucHT0xNPPPEEcnJyTGKEEIiLi4NWq4WTkxMGDx6Mo0ePmsTo9XpMnz4dHh4ecHFxwdixY3HmzBmTmKKiIoSHh0OtVkOtViM8PBxXrlwxicnNzcWYMWPg4uICDw8PREVF1RkTJiIianVa6EWsH3zwAXx9feHo6IjAwED88MMPDcZnZGQgMDAQjo6O6NKlC1asWGHRec3h4uICvV4PANBqtfj999+lbRcvXrTomBb10i+//IJBgwbByckJnTp1gq+vL3x9fdG5c2f4+vpa1JDGZGRk4KWXXsKePXuQnp6OyspKhISEoKysTIpZtGgREhMTsWzZMuzfvx8ajQbDhw9HSUmJFBMdHY1NmzYhOTkZu3btQmlpKUaPHo2qqiopJiwsDNnZ2UhNTUVqaiqys7MRHh4uba+qqsKoUaNQVlaGXbt2ITk5GSkpKZg5c2azfHciIiK51PwYtzXLrfrss88QHR2NOXPm4NChQxg0aBBGjhx506LOyZMn8dhjj2HQoEE4dOgQXnnlFURFRSElJcXar1+v/v3748cffwQAjBo1CjNnzsS8efPwz3/+E/3797fomBY9BfmPf/wDdnZ22LJlC7y9vWV5G35qaqrJ5zVr1sDT0xNZWVl45JFHIITAkiVLMGfOHIwbNw4AsHbtWnh5eWHDhg2YMmUKiouLsXr1anzyyScYNmwYAGDdunXw8fHB9u3bERoaiuPHjyM1NRV79uxBv379AACrVq1CUFAQcnJy4Ofnh7S0NBw7dgx5eXnQarUAgMWLF2PSpEmYN28eVCpVs/cHERFRs2iBSfiJiYmYPHkynn/+eQDAkiVL8M0332D58uWIj4+vE79ixQp07NgRS5YsAQD07NkTBw4cQEJCAp566inL295A+0pLSwEAcXFxKC0txWeffYauXbvinXfeseiYFiVg2dnZyMrKQo8ePSw6aVMoLi4GALRt2xZAdTZcUFCAkJAQKUapVCI4OBi7d+/GlClTkJWVhYqKCpMYrVaLgIAA7N69G6GhocjMzIRarZaSL6A681Wr1di9ezf8/PyQmZmJgIAAKfkCgNDQUOj1emRlZWHIkCF12qvX66XyJQDodLqm6wwiIqLbTO37nFKplB7mu5HBYEBWVhb+/e9/m6wPCQnB7t276z12Zmamyb0cqL4Pr169GhUVFXV+ocdaXbp0kf7s7OyMDz74wOpjWpTi+vv7Wzzm2RSEEIiJicHDDz+MgIAAAEBBQQEAwMvLyyTWy8tL2lZQUAAHBwe4u7s3GOPp6VnnnJ6eniYxtc/j7u4OBwcHKaa2+Ph4aU6ZWq2Gj4/PrX5tIiKiZlf9IlbrFgDw8fExue/VV8kCqudQVVVVNXj/rq2++7CXlxcqKytbND+5FWZXwG7MZBcuXIhZs2Zh/vz56NWrV51Ms7mH4KZNm4aff/4Zu3btqrOt9nCoEKLRIdLaMfXFWxJzo9mzZyMmJkb6rNPpmIQREdFtR4jqxZr9ASAvL88kH6iv+nWjW71/1xdf33pLubu7m32sy5cv3/LxzU7A2rRpY9IQIQSGDh1qElPTWTdOaG9q06dPx1dffYWdO3eiQ4cO0nqNRgOgOiv29vaW1hcWFkpZskajgcFgQFFRkUkVrLCwEAMGDJBizp8/X+e8Fy5cMDnO3r17TbYXFRWhoqKiTkZe42alVyIiojuRSqUyqyDj4eEBW1vbOtWuG+/ftWk0mnrj7ezs0K5dO8sbfYOa+WXNxewE7Pvvv2/OdjRKCIHp06dj06ZN2LFjR52nLX19faHRaJCeno4+ffoAqB5XzsjIwMKFCwEAgYGBsLe3R3p6OsaPHw8AyM/Px5EjR7Bo0SIAQFBQEIqLi7Fv3z489NBDAIC9e/eiuLhYStKCgoIwb9485OfnS8leWloalEolAgMDm78ziIiImolRCBitKIHd6r4ODg4IDAxEeno6nnzySWl9eno6Hn/88Xr3CQoKwubNm03WpaWloW/fvk02/+u5555rkuPcjNkJWHBwsPTn3Nxc+Pj41Fv+y8vLa7rW3eCll17Chg0b8L///Q9ubm5S5qtWq+Hk5ASFQoHo6GjMnz8f3bp1Q7du3TB//nw4OzsjLCxMip08eTJmzpyJdu3aoW3btoiNjUWvXr2kpyJ79uyJESNGICIiAh9++CEA4IUXXsDo0aPh5+cHoHpioL+/P8LDw/H222/j8uXLiI2NRUREBJ+AJCKiVk38uViz/62KiYlBeHg4+vbti6CgIKxcuRK5ubmYOnUqgOppPGfPnsXHH38MAJg6dSqWLVuGmJgYREREIDMzE6tXr5ZeltqcysvLUVFRYbLOknu/RU9B+vr6Ij8/v85k9cuXL8PX17dZhiCXL18OABg8eLDJ+jVr1mDSpEkAgFmzZqG8vByRkZEoKipCv379kJaWBjc3Nyn+nXfegZ2dHcaPH4/y8nIMHToUSUlJsLW1lWLWr1+PqKgo6QmLsWPHYtmyZdJ2W1tbbN26FZGRkRg4cCCcnJwQFhaGhISEJv/eREREd7oJEybg0qVLmDt3LvLz8xEQEIBt27ahU6dOAKpHq258J5ivry+2bduGGTNm4P3334dWq8V7773XLK+gAICysjL861//wueff45Lly7V2W5J3mNRAnaziXGlpaVwdHS05JBmnbMxCoUCcXFxiIuLu2mMo6Mjli5diqVLl940pm3btli3bl2D5+rYsSO2bNnSaJuIiIhaE6OoXqzZ3xKRkZGIjIysd1tSUlKddcHBwTh48KBlJ7tFs2bNwvfff48PPvgAzz77LN5//32cPXsWH374IRYsWGDRMW8pAat5ik+hUOC1116Ds7OztK2qqgp79+7FAw88YFFDiIiIqOUJIcwqejS0/51m8+bN+PjjjzF48GD885//xKBBg9C1a1d06tQJ69evxzPPPHPLx7ylBOzQoUMAqjv38OHDcHBwkLY5ODigd+/eiI2NveVGEBEREd2uaqZYAdXzvWpeO/Hwww9b9EPcwC0mYDVPQk6aNAlLly41mVtFRERErV9LDUHezrp06YJTp06hU6dO8Pf3x+eff46HHnoImzdvRps2bSw65i2/Cb+yshLr1q3D6dOnLTohERER3d6EFcud6B//+Ad++uknANVPZH7wwQdQKpWYMWMG/t//+38WHfOWJ+Hb2dmhU6dOzfqyVSIiImoZrIDVNWPGDOnPQ4YMwYkTJ3DgwAHce++96N27t0XHtOgpyFdffRWzZ8/GunXrpB/DJiIiIrobdOzYER07drTqGBYlYO+99x5+++03aLVadOrUCS4uLibb5XoslIiIiJoWn4KsKyoqCl27dkVUVJTJ+mXLluG3336z6GeLLErAnnjiCUt2IyIiotuc8c/Fmv3vNCkpKfjqq6/qrB8wYAAWLFggXwL2xhtvWLIbERERUatz6dIlqNXqOutVKhUuXrxo0TEtSsBqZGVl4fjx41AoFPD395d+BJuIiIhaJyGqF2v2v9N07doVqampmDZtmsn6r7/+Gl26dLHomBYlYIWFhZg4cSJ27NiBNm3aQAiB4uJiDBkyBMnJyWjfvr1FjSEiIqKWxacg64qJicG0adNw4cIFPProowCAb7/9FgkJCXj33XctOuYtvwcMAKZPnw6dToejR4/i8uXLKCoqwpEjR6DT6epMUCMiIiJqzf75z39i8eLFWL16NYYMGYIhQ4Zg/fr1WLFiBSIiIiw6pkUVsNTUVGzfvh09e/aU1vn7++P9999HSEiIRQ0hIiKilsenIOsqLy/Hc889hxdffBEXLlzA+fPnkZ6eDi8vL4uPaVEFzGg0wt7evs56e3t7GI134vMPREREdwdjEyx3mscffxwff/wxgOpcZ9iwYUhMTMQTTzyB5cuXW3RMixKwRx99FC+//DLOnTsnrTt79ixmzJiBoUOHWtQQIiIiotvRwYMHMWjQIADAF198AS8vL5w+fRoff/wx3nvvPYuOaVECtmzZMpSUlKBz586499570bVrV3Tu3BklJSUWN4SIiIhansD1JyEtWlr6CzSDq1evws3NDQCQlpaGcePGwcbGBv3797f4t7EtmgPm4+ODgwcPYvv27Th+/DiEEPD398ewYcMsagQRERHdHoxCwGjFPC5r9r1dde3aFV9++SWefPJJfPPNN9JvQxYWFkKlUll0TIvfA/btt9/iu+++Q2FhIYxGI7Kzs7FhwwYAwEcffWTpYYmIiKgFCVhXxbrz0i/g9ddfR1hYmDTVKigoCEB1NczSd6BalIC9+eabmDt3Lvr27Qtvb28oFAqLTk5ERER0u/vrX/+Khx9+GPn5+ejdu7e0fujQoXjyySctOqZFCdiKFSuQlJSE8PBwi05KREREtye+iLV+Go0GGo3GZN1DDz1k8fEsSsAMBgMGDBhg8UmJiIjoNmXlTxHdkWOQzcCipyCff/55ab4XEREREd0aiypg165dw8qVK7F9+3bcf//9dV7KmpiY2CSNIyIiInkZIWC0ooxlzb53E4sSsJ9//hkPPPAAAODIkSMm2zghn4iIqPUSVg5B3oFvoWgWFiVg33//fVO3g4iIiOiuYfF7wIiIiOjOw6cg5cEEjIiIiCQcgpSHRU9BEhEREZHlWAEjIiIiCZ+ClAcTMCIiIpJwCFIeTMCIiIhIYhQCRiuyKGv2vZtwDhgRERGRzFgBIyIiIkmVsXqxZn9qHBMwIiIiknAIUh4cgiQiIiKSWatKwHbu3IkxY8ZAq9VCoVDgyy+/NNkuhEBcXBy0Wi2cnJwwePBgHD161CRGr9dj+vTp8PDwgIuLC8aOHYszZ86YxBQVFSE8PBxqtRpqtRrh4eG4cuWKSUxubi7GjBkDFxcXeHh4ICoqCgaDoTm+NhERkWyMQqDKiqW5K2Dm3KNrmzRpEhQKhcnSv3//Zm1nY1pVAlZWVobevXtj2bJl9W5ftGgREhMTsWzZMuzfvx8ajQbDhw9HSUmJFBMdHY1NmzYhOTkZu3btQmlpKUaPHo2qqiopJiwsDNnZ2UhNTUVqaiqys7MRHh4uba+qqsKoUaNQVlaGXbt2ITk5GSkpKZg5c2bzfXkiIiIZVP8UkbBiad72NXaPvpkRI0YgPz9fWrZt29a8DW1Eq5oDNnLkSIwcObLebUIILFmyBHPmzMG4ceMAAGvXroWXlxc2bNiAKVOmoLi4GKtXr8Ynn3yCYcOGAQDWrVsHHx8fbN++HaGhoTh+/DhSU1OxZ88e9OvXDwCwatUqBAUFIScnB35+fkhLS8OxY8eQl5cHrVYLAFi8eDEmTZqEefPmQaVSydAbREREdxdz7tE3o1QqodFo5Gpqo1pVBawhJ0+eREFBAUJCQqR1SqUSwcHB2L17NwAgKysLFRUVJjFarRYBAQFSTGZmJtRqtfQXCwD9+/eHWq02iQkICJCSLwAIDQ2FXq9HVlZWve3T6/XQ6XQmCxER0e2m5ilIaxYAde55er3e6raZc4++mR07dsDT0xPdu3dHREQECgsLrW6PNe6YBKygoAAA4OXlZbLey8tL2lZQUAAHBwe4u7s3GOPp6Vnn+J6eniYxtc/j7u4OBwcHKaa2+Ph4abxarVbDx8fHgm9JRETUvKwbfrw+B8zHx8fkvhcfH29128y5R9dn5MiRWL9+Pb777jssXrwY+/fvx6OPPtokSaGlWtUQpDkUCoXJZyFEnXW11Y6pL96SmBvNnj0bMTEx0medTsckjIiI7lh5eXkmU3KUSuVNY+Pi4vDmm282eLz9+/cDuPX7LwBMmDBB+nNAQAD69u2LTp06YevWrdK0JbndMQlYzbhuQUEBvL29pfWFhYVStUqj0cBgMKCoqMikClZYWIgBAwZIMefPn69z/AsXLpgcZ+/evSbbi4qKUFFRUacyVkOpVDZ48REREd0Oap5mtGZ/AFCpVGbPiZ42bRomTpzYYEznzp3x888/N3qPNoe3tzc6deqEX3/91ex9mtodMwTp6+sLjUaD9PR0aZ3BYEBGRoaUXAUGBsLe3t4kJj8/H0eOHJFigoKCUFxcjH379kkxe/fuRXFxsUnMkSNHkJ+fL8WkpaVBqVQiMDCwWb8nERFRczKi5klICxcLzunh4YEePXo0uDg6Opp1jzbHpUuXkJeXZ1KwkVurSsBKS0uRnZ2N7OxsANUT77Ozs5GbmwuFQoHo6GjMnz8fmzZtwpEjRzBp0iQ4OzsjLCwMAKBWqzF58mTMnDkT3377LQ4dOoS///3v6NWrl/RUZM+ePTFixAhERERgz5492LNnDyIiIjB69Gjp6YqQkBD4+/sjPDwchw4dwrfffovY2FhERETwCUgiImrVqozC6qW5mHOPBoAePXpg06ZNAKpzh9jYWGRmZuLUqVPYsWMHxowZAw8PDzz55JPN1tbGtKohyAMHDmDIkCHS55o5Vc899xySkpIwa9YslJeXIzIyEkVFRejXrx/S0tLg5uYm7fPOO+/Azs4O48ePR3l5OYYOHYqkpCTY2tpKMevXr0dUVJT0tOTYsWNN3j1ma2uLrVu3IjIyEgMHDoSTkxPCwsKQkJDQ3F1ARER0V2vsHg0AOTk5KC4uBlB9zz58+DA+/vhjXLlyBd7e3hgyZAg+++wzk/xAbq0qARs8eDBEA+PSCoUCcXFxiIuLu2mMo6Mjli5diqVLl940pm3btli3bl2DbenYsSO2bNnSaJuJiIhaE2Hl2+wbuk83BXPu0Te2wcnJCd98802ztskSrSoBIyIiouZVJaoXa/anxrWqOWBEREREdwJWwIiIiEhitHIIsrl/jPtOwQSMiIiIJNY+ydicT0HeSTgESURERCQzVsCIiIhIwiFIeTABIyIiIgmfgpQHhyCJiIiIZMYKGBEREUk4BCkPJmBEREQkMRoFjFY8yWjNvncTJmBEREQkMVo5B4z5l3k4B4yIiIhIZqyAERERkYRzwOTBBIyIiIgkVUKgyookypp97yYcgiQiIiKSGStgREREJOFTkPJgAkZERESSKlj5Jvwma8mdjUOQRERERDJjBYyIiIgkfApSHkzAiIiISMKnIOXBIUgiIiIimbECRkRERBKjUaCKT0E2OyZgREREJKmyMgGzZt+7CRMwIiIikjABkwfngBERERHJjBUwIiIiklQZratiVRmbsDF3MCZgREREJOEQpDw4BElEREQkM1bAiIiISMIKmDyYgBEREZGE7wGTB4cgiYiIiGTGChgRERFJqoSVQ5D8LUizMAEjIiIiCeeAyYNDkEREREQyYwWMiIiIJKyAyYMVMCt98MEH8PX1haOjIwIDA/HDDz+0dJOIiIgsVmkUVi/Nad68eRgwYACcnZ3Rpk0bs/YRQiAuLg5arRZOTk4YPHgwjh492qztbAwTMCt89tlniI6Oxpw5c3Do0CEMGjQII0eORG5ubks3jYiIyCI1FTBrluZkMBjwt7/9DS+++KLZ+yxatAiJiYlYtmwZ9u/fD41Gg+HDh6OkpKQZW9owJmBWSExMxOTJk/H888+jZ8+eWLJkCXx8fLB8+fKWbhoREdEd6c0338SMGTPQq1cvs+KFEFiyZAnmzJmDcePGISAgAGvXrsXVq1exYcOGZm7tzTEBs5DBYEBWVhZCQkJM1oeEhGD37t114vV6PXQ6nclCRER0uzFaWf2qeRFr7XueXq9vke9z8uRJFBQUmNyvlUolgoOD671fy4UJmIUuXryIqqoqeHl5maz38vJCQUFBnfj4+Hio1Wpp8fHxkaupREREZqsSwuoFAHx8fEzue/Hx8S3yfWruyeber+XCBMxKCoXC5LMQos46AJg9ezaKi4ulJS8vT64mEhERyS4vL8/kvjd79uybxsbFxUGhUDS4HDhwwKr2mHu/lgtfQ2EhDw8P2Nra1smeCwsL62TZQHW5U6lUytU8IiIiizTVayhUKhVUKpVZ+0ybNg0TJ05sMKZz584WtUej0QCoroR5e3tL6292v5YLEzALOTg4IDAwEOnp6XjyySel9enp6Xj88cdbsGVERESWa4n3gHl4eMDDw8PiczbE19cXGo0G6enp6NOnD4DqedwZGRlYuHBhs5zTHByCtEJMTAz+85//4KOPPsLx48cxY8YM5ObmYurUqS3dNCIiojtSbm4usrOzkZubi6qqKmRnZyM7OxulpaVSTI8ePbBp0yYA1UOP0dHRmD9/PjZt2oQjR45g0qRJcHZ2RlhYWEt9DVbArDFhwgRcunQJc+fORX5+PgICArBt2zZ06tSppZtGRERkkdv9Tfivv/461q5dK32uqWp9//33GDx4MAAgJycHxcXFUsysWbNQXl6OyMhIFBUVoV+/fkhLS4Obm1uztrUhTMCsFBkZicjIyJZuBhERUZOoEkZUGY1W7d+ckpKSkJSU1GCMEKZJoEKhQFxcHOLi4pqvYbeIQ5BEREREMmMFjIiIiCRGK4cgjc08BHmnYAJGREREkiqjgM1tPAfsTsEEjIiIiCSVRkBhRRJV2bxTwO4YnANGREREJDNWwIiIiEjCIUh5MAEjIiIiCRMweXAIkoiIiEhmrIARERGRhBUweTABIyIiIgnfAyYPDkESERERyYwVMCIiIpJUGYVV7wHjEKR5mIARERGRRAgBYUUSVfuHsKl+HIIkIiIikhkrYERERCQxGoVVE+k5Cd88TMCIiIhIIoSwahiRQ5DmYQJGREREEmG0cg4YK2Bm4RwwIiIiIpmxAkZEREQSzgGTBxMwIiIikghj9WLN/tQ4DkESERERyYwVMCIiIpLwKUh5MAEjIiIiCeeAyYNDkEREREQyYwWMiIiIJHwPmDyYgBEREdF1ViZgYAJmFg5BEhEREcmMFTAiIiKSGIWAwoonGY18CtIsTMCIiIhIIoSVc8CYgJmFCRgRERFJOAlfHpwDRkRERCQzVsCIiIhIYjQCCqtexNqEjbmDMQEjIiIiCX+KSB4cgiQiIiKSGStgREREJBHG6sWa/alxraYCNm/ePAwYMADOzs5o06ZNvTG5ubkYM2YMXFxc4OHhgaioKBgMBpOYw4cPIzg4GE5OTrjnnnswd+7cOuXSjIwMBAYGwtHREV26dMGKFSvqnCslJQX+/v5QKpXw9/fHpk2bmuy7EhERtZSaH+O2ZmlO5uQDtU2aNAkKhcJk6d+/f7O2szGtJgEzGAz429/+hhdffLHe7VVVVRg1ahTKysqwa9cuJCcnIyUlBTNnzpRidDodhg8fDq1Wi/3792Pp0qVISEhAYmKiFHPy5Ek89thjGDRoEA4dOoRXXnkFUVFRSElJkWIyMzMxYcIEhIeH46effkJ4eDjGjx+PvXv3Nl8HEBERUaP5wM2MGDEC+fn50rJt27ZmaqF5Ws0Q5JtvvgkASEpKqnd7Wloajh07hry8PGi1WgDA4sWLMWnSJMybNw8qlQrr16/HtWvXkJSUBKVSiYCAAPzyyy9ITExETEwMFAoFVqxYgY4dO2LJkiUAgJ49e+LAgQNISEjAU089BQBYsmQJhg8fjtmzZwMAZs+ejYyMDCxZsgSffvpp83YEERFRM7rd3wPWWD5wM0qlEhqNphlaZJlWUwFrTGZmJgICAqTkCwBCQ0Oh1+uRlZUlxQQHB0OpVJrEnDt3DqdOnZJiQkJCTI4dGhqKAwcOoKKiosGY3bt337R9er0eOp3OZCEiIrrd1CRg1iy3ox07dsDT0xPdu3dHREQECgsLW7Q9d0wCVlBQAC8vL5N17u7ucHBwQEFBwU1jaj43FlNZWYmLFy82GFNzjPrEx8dDrVZLi4+PjwXfkoiIqHWoXXTQ6/Ut1paRI0di/fr1+O6777B48WLs378fjz76aIu2qUUTsLi4uDqT4movBw4cMPt4CoWizjohhMn62jE1E/CbIqa+89eYPXs2iouLpSUvL6+xr0NERCQ7oxBWLwDg4+NjUniIj4+/6TmbOh+obcKECRg1ahQCAgIwZswYfP311/jll1+wdetWi49prRadAzZt2jRMnDixwZjOnTubdSyNRlNnEnxRUREqKiqkapVGo6lTpaopQTYWY2dnh3bt2jUYU7sqdiOlUmky9ElERHQ7aqo5YHl5eVCpVNL6hu6BTZkPmMPb2xudOnXCr7/+2mTHvFUtmoB5eHjAw8OjSY4VFBSEefPmIT8/H97e3gCqJ+YrlUoEBgZKMa+88goMBgMcHBykGK1WK/3FBgUFYfPmzSbHTktLQ9++fWFvby/FpKenY8aMGSYxAwYMaJLvQkRE1FKEsDIB+7MCplKpTBKwhjRlPmCOS5cuIS8vT8oXWkKrmQOWm5uL7Oxs5ObmoqqqCtnZ2cjOzkZpaSkAICQkBP7+/ggPD8ehQ4fw7bffIjY2FhEREdIFEBYWBqVSiUmTJuHIkSPYtGkT5s+fLz0BCQBTp07F6dOnERMTg+PHj+Ojjz7C6tWrERsbK7Xl5ZdfRlpaGhYuXIgTJ05g4cKF2L59O6Kjo2XvFyIiortJY/kAAPTo0UN6P2dpaSliY2ORmZmJU6dOYceOHRgzZgw8PDzw5JNPttTXaD2voXj99dexdu1a6XOfPn0AAN9//z0GDx4MW1tbbN26FZGRkRg4cCCcnJwQFhaGhIQEaR+1Wo309HS89NJL6Nu3L9zd3RETE4OYmBgpxtfXF9u2bcOMGTPw/vvvQ6vV4r333pNeQQEAAwYMQHJyMl599VW89tpruPfee/HZZ5+hX79+MvQEERFR8xFWvky1uZ+CbCwfAICcnBwUFxcDAGxtbXH48GF8/PHHuHLlCry9vTFkyBB89tlncHNza9a2NqTVJGBJSUmNvvOjY8eO2LJlS4MxvXr1ws6dOxuMCQ4OxsGDBxuM+etf/4q//vWvDcYQERG1Nrf7j3Gbkw/c2AYnJyd88803zdomS7SaIUgiIiKiO0WrqYARERFR87vd34R/p2ACRkRERBKjUQBWJFHN/WPcdwoOQRIRERHJjBUwIiIikghjFYSxyqr9qXFMwIiIiEjCBEweHIIkIiIikhkrYERERCQRRqOVFTBjE7bmzsUEjIiIiCSiqgqiyooEzIp97yZMwIiIiEgihJVzwAQTMHNwDhgRERGRzFgBIyIiIgmfgpQHEzAiIiKSMAGTB4cgiYiIiGTGChgRERFJWAGTBxMwIiIikvA9YPLgECQRERGRzFgBIyIiIonRWAVYUQEzcgjSLEzAiIiISMI5YPLgECQRERGRzFgBIyIiIgkrYPJgAkZERETXVVVB2FiRRPHHuM3CBIyIiIgkQlg3CZ8/xm0ezgEjIiIikhkrYERERCQRRqN1FTC+iNUsTMCIiIhIIqx8Dxgn4ZuHQ5BEREREMmMFjIiIiCTVQ5CWDyNyCNI8TMCIiIhIwiFIeXAIkoiIiEhmrIARERGRhBUweTABIyIiIonRWAUFE7BmxyFIIiIiIpmxAkZEREQSUWUEFFZUwKr4FKQ5mIARERGRhL8FKY9WMQR56tQpTJ48Gb6+vnBycsK9996LN954AwaDwSQuNzcXY8aMgYuLCzw8PBAVFVUn5vDhwwgODoaTkxPuuecezJ07F0IIk5iMjAwEBgbC0dERXbp0wYoVK+q0KSUlBf7+/lAqlfD398emTZua/osTERHJTBirrF6oca0iATtx4gSMRiM+/PBDHD16FO+88w5WrFiBV155RYqpqqrCqFGjUFZWhl27diE5ORkpKSmYOXOmFKPT6TB8+HBotVrs378fS5cuRUJCAhITE6WYkydP4rHHHsOgQYNw6NAhvPLKK4iKikJKSooUk5mZiQkTJiA8PBw//fQTwsPDMX78eOzdu1eeDiEiIroLmVuQqU0Igbi4OGi1Wjg5OWHw4ME4evSoTK2uX6sYghwxYgRGjBghfe7SpQtycnKwfPlyJCQkAADS0tJw7Ngx5OXlQavVAgAWL16MSZMmYd68eVCpVFi/fj2uXbuGpKQkKJVKBAQE4JdffkFiYiJiYmKgUCiwYsUKdOzYEUuWLAEA9OzZEwcOHEBCQgKeeuopAMCSJUswfPhwzJ49GwAwe/ZsZGRkYMmSJfj0009l7BkiIqKmJYxV1s0Ba8YK2I0Fma5du+LIkSOIiIhAWVmZlA/UZ9GiRUhMTERSUhK6d++Ot956C8OHD0dOTg7c3Nyarb0NaRUVsPoUFxejbdu20ufMzEwEBARIyRcAhIaGQq/XIysrS4oJDg6GUqk0iTl37hxOnTolxYSEhJicKzQ0FAcOHEBFRUWDMbt3775pe/V6PXQ6nclCRER0u7mdhyBHjBiBNWvWICQkBF26dMHYsWMRGxuLjRs33vz7CIElS5Zgzpw5GDduHAICArB27VpcvXoVGzZsaLa2NqZVVMBq+/3337F06VIsXrxYWldQUAAvLy+TOHd3dzg4OKCgoECK6dy5s0lMzT4FBQXw9fWt9zheXl6orKzExYsX4e3tfdOYmvPUJz4+Hm+++Wad9brSMlSVXQUAlFVVQV9aAp1dKWz0ZTAarqJEp0N5aQlEZXUCZ7haCgCovFYG3dVyXC0tgU6nR1lVFXQ6HUoNFbhqrP5zWVVl9TmuXsPV0hKUlOtRVlpSvW9JCYyGq9V/1ulgrCgHAJSW6KAzlMJwVYGSa3pUXL2GSp0O14QRuqvXoCwpxbU/J1jqdDrYV5ajrFyPSlGG0hId7CtsIaoMKC2pbnfNcXUlpSirKMFVfSVKKyqh0+ngUHa1+ri6EpQaKqT22pToUHJND5uSmrbawHDVCKPhanVbDVelftCV2Ejfp+SaHqUl1d+71FABna6k+vh/9q9Bp0NpRSWulpbApsIOOvtSGCvKUV5agtISBUSVASV/Jsal16pQea0MJQo9jDodKuwqcE1UQVdSCv3Va3+2Wwe7q9cAANeu6WG4WgpdCVDqUN2fOp0Oldeq/x51f34XACj78+/iamkJdFevoayquj+uGqv+bLcOZVXX+/hqqQG6q+WovFYGw9XqdaJSj/LSEpTo7GE0XJX62GgQuFoqUFVVBV3ZVdiWlMIAI3Qlpagquyod15zrrKa95lxnuj/7wZzrrLSkuo+rrzM0eJ3V/I+KOdeZrqT6mjDnOrP5sw3mXGc111h1Hzd8ndVcYzV90dB1VnONATDrOrt2TV99HZtxnZWVllQf14zrrKaPzbnOavrYnOtM/2cbzLnOdFfLpT5r7Dq7esN3a+w60xlK/+yzxq8z+8rqNphzndVcYwAavc5qrjHpv9uNXGe6P2NrX2fKP9fXnrPcLKoqYNVZqqqLFbULDUql0qQA0lRqF2RqO3nyJAoKCkwKJ0qlEsHBwdi9ezemTJnS5G0yi2hBb7zxhgDQ4LJ//36Tfc6ePSu6du0qJk+ebLI+IiJChISE1DmHvb29+PTTT4UQQgwfPly88MILJtvPnDkjAIjMzEwhhBDdunUT8+fPN4nZtWuXACDy8/OlY27YsMEkZt26dUKpVN70u167dk0UFxdLy7Fjxxr97ly4cOHChcuNy++//97QbdUq5eXlQqPRNEk7XV1d66x74403mrzNv/32m1CpVGLVqlU3jfnxxx8FAHH27FmT9TfLG+TSohWwadOmYeLEiQ3G3FixOnfuHIYMGYKgoCCsXLnSJE6j0dSZBF9UVISKigqpWqXRaOpUqQoLCwGg0Rg7Ozu0a9euwZjaVbEb1c78XV1dkZeXBzc3NygUipvudzvR6XTw8fFBXl4eVCpVSzfnjsP+bV7s3+bF/m1excXF6NixY4OVHms5Ojri5MmTjU5oN4cQos69raHqV1xcXL2jRDfav38/+vbtK30+d+4cRowYgb/97W94/vnnG21T7fbU10Y5tWgC5uHhAQ8PD7Niz549iyFDhiAwMBBr1qyBjY3p9LWgoCDMmzcP+fn58Pb2BlA9MV+pVCIwMFCKeeWVV2AwGODg4CDFaLVaKdELCgrC5s2bTY6dlpaGvn37wt7eXopJT0/HjBkzTGIGDBhg9ne3sbFBhw4dzI6/nahUKv4Hthmxf5sX+7d5sX+bV+17X1NzdHSEo6Njs56jPk1ZkKlNo9EAqJ5qVJMfAI0XTppdi9XebkHNsOOjjz4qzpw5I/Lz86WlRmVlpQgICBBDhw4VBw8eFNu3bxcdOnQQ06ZNk2KuXLkivLy8xNNPPy0OHz4sNm7cKFQqlUhISJBi/vjjD+Hs7CxmzJghjh07JlavXi3s7e3FF198IcX8+OOPwtbWVixYsEAcP35cLFiwQNjZ2Yk9e/bI0yEtpLi4WAAQxcXFLd2UOxL7t3mxf5sX+7d5sX+vO3PmjOjWrZuYOHGiqKysbDTeaDQKjUYjFi5cKK3T6/VCrVaLFStWNGdTG9QqErA1a9bcdJz5RqdPnxajRo0STk5Oom3btmLatGni2rVrJjE///yzGDRokFAqlUKj0Yi4uDhhNBpNYnbs2CH69OkjHBwcROfOncXy5cvrtOm///2v8PPzE/b29qJHjx4iJSWl6b/4bYb/AWhe7N/mxf5tXuzf5sX+rWZOQUYIIfz8/MTGjRulzwsWLBBqtVps3LhRHD58WDz99NPC29tb6HQ6ub+CpFUkYHR7uHbtmnjjjTfqJLXUNNi/zYv927zYv82L/VvN3IIMALFmzRrps9FoFG+88YbQaDRCqVSKRx55RBw+fFjm1ptSCCHHM61EREREVKPVvoiViIiIqLViAkZEREQkMyZgRERERDJjAkZEREQkMyZgd6GtW7eiX79+cHJygoeHB8aNG2eyPTc3F2PGjIGLiws8PDwQFRVV583Ihw8fRnBwMJycnHDPPfdg7ty5dX6jLCMjA4GBgXB0dESXLl2wYsWKOm1JSUmBv78/lEol/P39sWnTpqb/wi1Ar9fjgQcegEKhQHZ2tsk29q9lTp06hcmTJ8PX1xdOTk6499578cYbb9TpO/av/D744AP4+vrC0dERgYGB+OGHH1q6SS0qPj4eDz74INzc3ODp6YknnngCOTk5JjFCCMTFxUGr1cLJyQmDBw/G0aNHTWL0ej2mT58ODw8PuLi4YOzYsThz5oxJTFFREcLDw6FWq6FWqxEeHo4rV66YxJjzb4JaQEs+gkny++KLL4S7u7tYvny5yMnJESdOnBD//e9/pe01L7QdMmSIOHjwoEhPTxdardbkhbbFxcXCy8tLTJw4URw+fFikpKQINze3el9o+/LLL4tjx46JVatW1Xmh7e7du4Wtra2YP3++OH78uJg/f/4d80LbqKgoMXLkSAFAHDp0SFrP/rXc119/LSZNmiS++eYb8fvvv4v//e9/wtPTU8ycOVOKYf/KLzk5Wdjb24tVq1aJY8eOiZdfflm4uLiI06dPt3TTWkxoaKhYs2aNOHLkiMjOzhajRo0SHTt2FKWlpVLMggULhJubm0hJSRGHDx8WEyZMqPNeqqlTp4p77rlHpKeni4MHD4ohQ4aI3r17m7x8dMSIESIgIEDs3r1b7N69WwQEBIjRo0dL2835N0EtgwnYXaSiokLcc8894j//+c9NY7Zt2yZsbGxMfrT0008/FUqlUnoB4AcffCDUarXJ+2ji4+OFVquVXmo7a9Ys0aNHD5NjT5kyRfTv31/6PH78eDFixAiTmNDQUDFx4kTLv+RtYNu2baJHjx7i6NGjdRIw9m/TWrRokfD19ZU+s3/l99BDD4mpU6earOvRo4f497//3UItuv0UFhYKACIjI0MIcf3N7AsWLJBirl27ZvJm9itXrgh7e3uRnJwsxZw9e1bY2NiI1NRUIYQQx44dEwBMkv7MzEwBQJw4cUIIYd6/CWoZHIK8ixw8eBBnz56FjY0N+vTpA29vb4wcOdKk7J2ZmYmAgABotVppXWhoKPR6PbKysqSY4OBgkx9WDQ0Nxblz53Dq1CkpJiQkxOT8oaGhOHDgACoqKhqM2b17d5N+bzmdP38eERER+OSTT+Ds7FxnO/u3aRUXF5v8ODH7V14GgwFZWVl1+iEkJOSu6ofGFBcXA4B0rZ48eRIFBQUm/aZUKhEcHCz1W1ZWFioqKkxitFotAgICpJjMzEyo1Wr069dPiunfvz/UarVJTGP/JqhlMAG7i/zxxx8Aqn91/tVXX8WWLVvg7u6O4OBgXL58GUD1j5XW/nFSd3d3ODg4oKCg4KYxNZ8bi6msrMTFixcbjKk5RmsjhMCkSZMwdepU9O3bt94Y9m/T+f3337F06VJMnTpVWsf+ldfFixdRVVV11/dDQ4QQiImJwcMPP4yAgAAA16+zhvqtoKAADg4OcHd3bzDG09Ozzjk9PT0bvJZr/5uglsEE7A4QFxcHhULR4HLgwAEYjUYAwJw5c/DUU08hMDAQa9asgUKhwH//+1/peAqFos45hBAm62vHiD8nMDdFTH3nb0nm9u/SpUuh0+kwe/bsBo/H/jVlbv/e6Ny5cxgxYgT+9re/4fnnnzfZxv6VH/vh5qZNm4aff/4Zn376aZ1tlvRbY9eypTEkP7uWbgBZb9q0aZg4cWKDMZ07d0ZJSQkAwN/fX1qvVCrRpUsX5ObmAgA0Gg327t1rsm9RUREqKiqk/4vSaDR1/s+psLAQABqNsbOzQ7t27RqMqf1/ay3N3P596623sGfPHpOhLQDo27cvnnnmGaxdu5b9Ww9z+7fGuXPnMGTIEAQFBWHlypUmcexfeXl4eMDW1vau74ebmT59Or766ivs3LkTHTp0kNZrNBoA1dUpb29vaf2N/abRaGAwGFBUVGRSBSssLMSAAQOkmPPnz9c574ULF0yO09i/CWohMs85oxZUXFwslEqlySR8g8EgPD09xYcffiiEuD5h89y5c1JMcnJynUnMbdq0EXq9XopZsGBBnUnMPXv2NDn/1KlT60xiHjlypEnMiBEjWu0k5tOnT4vDhw9LyzfffCMAiC+++ELk5eUJIdi/1jpz5ozo1q2bmDhxosmTYDXYv/J76KGHxIsvvmiyrmfPnnf1JHyj0SheeuklodVqxS+//FLvdo1GIxYuXCit0+v19U7C/+yzz6SYc+fO1TsJf+/evVLMnj176p2E39C/CWoZTMDuMi+//LK45557xDfffCNOnDghJk+eLDw9PcXly5eFENcfWR46dKg4ePCg2L59u+jQoYPJI8tXrlwRXl5e4umnnxaHDx8WGzduFCqVqt7H+GfMmCGOHTsmVq9eXecx/h9//FHY2tqKBQsWiOPHj4sFCxbcUY/xnzx58qavoWD/3rqzZ8+Krl27ikcffVScOXNG5OfnS0sN9q/8al5DsXr1anHs2DERHR0tXFxcxKlTp1q6aS3mxRdfFGq1WuzYscPkOr169aoUs2DBAqFWq8XGjRvF4cOHxdNPP13vayg6dOggtm/fLg4ePCgeffTRel9Dcf/994vMzEyRmZkpevXqVe9rKBr6N0EtgwnYXcZgMIiZM2cKT09P4ebmJoYNGyaOHDliEnP69GkxatQo4eTkJNq2bSumTZtm8si+EEL8/PPPYtCgQUKpVAqNRiPi4uKk6kGNHTt2iD59+ggHBwfRuXNnsXz58jrt+e9//yv8/PyEvb296NGjh0hJSWn6L91C6kvAhGD/WmrNmjUCQL3Ljdi/8nv//fdFp06dhIODg/jLX/4ivW7hbnWz63TNmjVSjNFoFG+88YbQaDRCqVSKRx55RBw+fNjkOOXl5WLatGmibdu2wsnJSYwePVrk5uaaxFy6dEk888wzws3NTbi5uYlnnnlGFBUVmcSY82+C5KcQotbrn4mIiIioWfEpSCIiIiKZMQEjIiIikhkTMCIiIiKZMQEjIiIikhkTMCIiIiKZMQEjIiIikhkTMCIiIiKZMQEjolZBoVDgyy+/bOlmEBE1CSZgRHRbiYuLwwMPPFBnfX5+PkaOHCl/g4iImoFdSzeAiMgcGo2mpZtARNRkWAEjolsmhMCiRYvQpUsXODk5oXfv3vjiiy8AAFVVVZg8eTJ8fX3h5OQEPz8/vPvuuyb779ixAw899BBcXFzQpk0bDBw4EKdPn0ZSUhLefPNN/PTTT1AoFFAoFEhKSgJgOgR56tQpKBQKbNy4EUOGDIGzszN69+6NzMxMk/OsWrUKPj4+cHZ2xpNPPonExES0adOmubuHiKhRrIAR0S179dVXsXHjRixfvhzdunXDzp078fe//x3t27fHgAED0KFDB3z++efw8PDA7t278cILL8Db2xvjx49HZWUlnnjiCURERODTTz+FwWDAvn37oFAoMGHCBBw5cgSpqanYvn07AECtVt+0HXPmzEFCQgK6deuGOXPm4Omnn8Zvv/0GOzs7/Pjjj5g6dSoWLlyIsWPHYvv27Xjttdfk6iIiogbxx7iJ6JaUlZXBw8MD3333HYKCgqT1zz//PK5evYoNGzbU2eell17C+fPn8cUXX+Dy5cto164dduzYgeDg4DqxcXFx+PLLL5GdnW2yXqFQYNOmTXjiiSdw6tQp+Pr64j//+Q8mT54MADh27Bjuu+8+HD9+HD169MDEiRNRWlqKLVu2SMf4+9//ji1btuDKlStN0xlERBbiECQR3ZJjx47h2rVrGD58OFxdXaXl448/xu+//w4AWLFiBfr27Yv27dvD1dUVq1atQm5uLgCgbdu2mDRpEkJDQzFmzBi8++67yM/Pt6gt999/v/Rnb29vAEBhYSEAICcnBw899JBJfO3PREQthQkYEd0So9EIANi6dSuys7Ol5dixY/jiiy/w+eefY8aMGfjnP/+JtLQ0ZGdn4x//+AcMBoN0jDVr1iAzMxMDBgzAZ599hu7du2PPnj233BZ7e3vpzwqFwqR9QghpXQ0W/InodsE5YER0S/z9/aFUKpGbm1vvEOKiRYswYMAAREZGSutqKmM36tOnD/r06YPZs2cjKCgIGzZsQP/+/eHg4ICqqiqr29mjRw/s27fPZN2BAwesPi4RUVNgAkZEt8TNzQ2xsbGYMWMGjEYjHn74Yeh0OuzevRuurq7o2rUrPv74Y3zzzTfw9fXFJ598gv3798PX1xcAcPLkSaxcuRJjx46FVqtFTk4OfvnlFzz77LMAgM6dO+PkyZPIzs5Ghw4d4ObmBqVSecvtnD59Oh555BEkJiZizJgx+O677/D111/XqYoREbUEDkES0S37v//7P7z++uuIj49Hz549ERoais2bN8PX1xdTp07FuHHjMGHCBPTr1w+XLl0yqYY5OzvjxIkTeOqpp9C9e3e88MILmDZtGqZMmQIAeOqppzBixAgMGTIE7du3x6effmpRGwcOHIgVK1YgMTERvXv3RmpqKmbMmAFHR8cm6QMiImvwKUgiumtERETgxIkT+OGHH1q6KUR0l+MQJBHdsRISEjB8+HC4uLjg66+/xtq1a/HBBx+0dLOIiFgBI6I71/jx47Fjxw6UlJSgS5cumD59OqZOndrSzSIiYgJGREREJDdOwiciIiKSGRMwIiIiIpkxASMiIiKSGRMwIiIiIpkxASMiIiKSGRMwIiIiIpkxASMiIiKSGRMwIiIiIpkxASMiIiKS2f8HvPeLTo4xq5cAAAAASUVORK5CYII=", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "coordinates = vd.grid_coordinates(\n", - " (-70e3, 20e3, -20e3, 60e3), spacing=0.25e3,\n", - ")\n", - "d = np.cos(2 * np.pi * 1 / 5e3 * coordinates[0]) #* np.cos(2 * np.pi * 1 / 50e3 * coordinates[1])\n", - "d += np.cos(2 * np.pi * 1 / 0.7e3 * coordinates[0]) #* np.cos(2 * np.pi * 1 / 5e3 * coordinates[1])\n", - "d = vd.make_xarray_grid(coordinates, d, data_names=\"scalars\").scalars\n", - "d.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "id": "51e3e063-0cdf-4890-9598-6fe8d569e84a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 80, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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WwyV45asfUVlVo3YopHH55y4BAE7U/paBPebzl1WOhLTuYFE5XvxyL4rLKtUOhcDERNfe3XAIS7cew7YjZ9UOhTROxtYQW8hsyqGmfLjtGD7Zno+v/ndK7VAITEx0zVRtcfhN5E7dl7yqYXhGplhJVaYq6znQXMNzoRYwMdGxui8ZnsGpCcL2S56yYotVqmSKVMGyoi1MTHRMyqtgUoWMJ24hYTJFKmER0RQmJnpWe+bmMUlNsX/JS1RYZIyZ1MH+SNriUWIyf/589OjRA+Hh4QgPD0d6ejrWrFnjdv2cnBwYDAann59++umKA6crxxoTUko0+C0Dey2PynGQ9tkSEp4LtcHPk5UTExPx+uuvo1OnTgCADz/8EKNHj0ZeXh66devmdruDBw8iPDzc/rhdu3bNDJdaEqu6Sam6E7c8ZYU1JqSUjIm3N/MoMRk1apTD49deew3z58/H9u3bG01MYmJi0KZNm2YFSFePjP0GSB0ynrjrYpYpalIDk1htaXYfk5qaGixfvhwVFRVIT09vdN3evXsjPj4eQ4YMwcaNG5t8bZPJhLKyMocfanl1NSZEjRMSZib8siGlmMRqi8eJyd69exEaGgqj0YiJEydi5cqVuO6661yuGx8fj4ULFyI7OxsrVqxAamoqhgwZgs2bNzf6HllZWYiIiLD/JCUleRomKVB34ubBSI2T88QtU6ykJvYx0RaPmnIAIDU1Fbt378aFCxeQnZ2N8ePHY9OmTS6Tk9TUVKSmptofp6en48SJE5gzZw5uueUWt+8xY8YMZGZm2h+XlZUxObkK2PmVFJPwxM3Em5Ri7bG2eJyYBAQE2Du/9uvXDzt27MA777yDv//974q2HzBgAD755JNG1zEajTAajZ6GRh6yXyXwcKQmSNiSw8SbFLOfA1lYNOGK5zERQsBkMilePy8vD/Hx8Vf6ttSCeCxSU2Tsr1GXeBM1jjUm2uJRjcnzzz+PESNGICkpCeXl5Vi+fDlycnKwdu1aANYmmIKCAnz00UcAgLlz5yI5ORndunWD2WzGJ598guzsbGRnZ7f8npDHLBJWz5M66uYEkaewsMaElLKVEQsLiyZ4lJicPn0aY8eORWFhISIiItCjRw+sXbsWQ4cOBQAUFhYiPz/fvr7ZbMa0adNQUFCAoKAgdOvWDatWrcLIkSNbdi+oWXiVQErJWWNS+5slnJrAqRO0xaPEZNGiRY0+v3TpUofH06dPx/Tp0z0OiloHp2EmpWRMYlljQkrJWL69Ge+Vo2Nsgyel6m5ELU9pYfkmpZjEagsTEx0TTn8QuSb1lzy/bagJbPbTFiYmesaDkTwk03c8q+dJORYWLWFiomOsviSlZJysjB0aSSkmsdrCxETHpK6ep1ZVN1xYHqyeJ6U4EEBbmJjoGGtMSCmphwtLFDOpg/fK0RYmJjrGK0pSSs4p6eWr5SF1yFi+vRkTEx1jGzwpVXdFKU9hYY0JKcWyoi1MTHSMHb5IKRnLSN1VsIzRU2tiWdEWJiY6JtjJhJSS8YqS9fOkEPuYaAsTE+J5m5ok4xUl+5gQyYmJiY7xKoGUkrGsyDj3CqmDZUVbmJjoGMfuk1IytoqwpZKUYu2atjAx0TF2fiWlZBy1wAkESSkZy7c3Y2KiYxwuTErVXVHKU1hYY0JKcU4nbWFiomOsMSGlZLyi5JcNKcWLNG1hYqJj7GNCSslYRFhjQkrxIk1bmJjoGE/Y5CmpkliZYiVVMYnVFiYmusbqS1JGyuHCtt8yBU3qEE5/kIqYmOgY2+BJKSmHC7N6nhRiHxNtYWKiY7Zj0MKDkZogZedXftmQQhYJy7c3Y2KiYzJWz5M6pBwuzBpBUqhuzhuWFS1gYqJjMt7/hNQhZY2JhDGTOtj5VVuYmOgYT9yklJR9TBr8JnKH/ZG0hYmJjnG0AiklJLykZFMlKSVh8fZqTEx0jMMpSTmZ7zsjZ9TUitjHRFOYmOgZm3JIIRmb/WSMmdTBaUy0hYmJjrENnpSSsaM0hwuTUuxjoi1MTHSMbfCklIxlhcOFSam6JJZlRQuYmOiYjFfBpA4Za9fYoZGUYo2JtjAx0TG2wZNSMpaVukmziBonY/n2ZkxMdExIPdKCWpOMM2OyxoSUkrFG0JsxMdExGeemIHVIOWqBfUxIobo+VCwrWsDERMfYrkqKSVhWpEymSFXMS7SBiYmOcTglKSXjZHzsY0JKcQSXtniUmMyfPx89evRAeHg4wsPDkZ6ejjVr1jS6zaZNm9C3b18EBgaiY8eOWLBgwRUFTC2HByMpJeVwYdtvmYImVfAiTVs8SkwSExPx+uuvIzc3F7m5ubj11lsxevRo/Pjjjy7XP3r0KEaOHImbb74ZeXl5eP755/Hkk08iOzu7RYKnK8MuJqSUjJ0D2VRJSnFUjrb4ebLyqFGjHB6/9tprmD9/PrZv345u3bo5rb9gwQK0b98ec+fOBQB07doVubm5mDNnDu6+++7mR00tglXdpJSMJ25eBZNSnNNJW5rdx6SmpgbLly9HRUUF0tPTXa6zbds2ZGRkOCwbNmwYcnNzUVVV1dy3phbCGhNSqm5ouTyFhTUmpJSMTZXezKMaEwDYu3cv0tPTUVlZidDQUKxcuRLXXXedy3WLiooQGxvrsCw2NhbV1dUoKSlBfHy8y+1MJhNMJpP9cVlZmadhkgLsY0JKSVljYo9ZoqBJFTI2VXozj2tMUlNTsXv3bmzfvh1/+MMfMH78eOzfv9/t+gaDweGx7STRcHl9WVlZiIiIsP8kJSV5GiZ5gkcjNUHmIiJz7NRKJEy8vZnHiUlAQAA6deqEfv36ISsrCz179sQ777zjct24uDgUFRU5LCsuLoafnx+ioqLcvseMGTNQWlpq/zlx4oSnYVIT6l9F8likJklY+yDYlkMK8WyoLR435TQkhHBodqkvPT0d//d//+ewbN26dejXrx/8/f3dvqbRaITRaLzS0KgR9b9fZPqyIXXIePsCdmgkpdjHRFs8qjF5/vnnsWXLFhw7dgx79+7FCy+8gJycHIwZMwaAtaZj3Lhx9vUnTpyI48ePIzMzEwcOHMDixYuxaNEiTJs2rWX3gjxW//jjwUhNkbuPibpxkPaxj4m2eFRjcvr0aYwdOxaFhYWIiIhAjx49sHbtWgwdOhQAUFhYiPz8fPv6KSkpWL16NZ555hm8//77SEhIwLvvvsuhwhrAphzyhIy1DxwuTEqxo7S2eJSYLFq0qNHnly5d6rRs4MCB2LVrl0dB0dXHGhPyhIxV3Rx1RkrJ2FTpzXivHJ1y6GPCw5GaIGNVN+fpIaXY7KctTEx0qn4ywoORmiLjiZuDckgplhVtYWKiUxyVQ80jU1mRr/mJ1FHXVMnCogVMTEiqrxpqfQ4dpSUqLHWxShQ0qYIlRFuYmOiUY42JenGQ9slaVtjHhJSSsanSmzEx0SmHPia8XqBGOIzgkqis8O7ZpJSMN6n0ZkxMdErWq2BqfdI25dh+yxQ0qYI1JtrCxESnhJu/iRqStaxwpAUpxWY/bWFiolOyXgVT65O1dk3GSeFIHZyMT1uYmOiUaOQRUX2y9keScVI4UguTWC1hYqJTsl4FU+sT0rbl1P5iAacmsNlPW5iY6BUTE2oGmYqKTLGSujjljbYwMdEpWavnqfXJOksw+5iQUnVDy1lYtICJiU6xKYeUckxi5VHXx0SmqEkNHJWjLUxMdErWbgPU+mRNYjk3BSnFPibawsREpzhcmJSSNYkVHGlBCvEmftrCxESnZJ1mnFqfYxIrT1nh3BSkFIeWawsTE52SdggotTpZiwr7DZBibPbTFCYmOiVrh0ZqfdImsew3QAqxxkRbmJjolaRDQEkF9cuKRKduwcyEFBLsKa0pTEx0StaLYGp9DrVrEhUW9jEhpVhjoi1MTHRK1iGg1PpkLSvsY0JKscJEW5iY6JRFsI8JKSPrCK662TyJGmcr1xZmJprAxESnHL5seDBSI2Sd86auxkSioEkVrDHRFiYmOiVYY0IKydofibN5klLsY6ItTEx0StohoNTqZO1jYiNjzNTK7DUmLCxawMSEpOo3QK1PSFhnwhpB8gTPgdrCxESn6l8YWCzqxUESkLDGxLFGUJKgSTUW9jHRFCYmOuU48yuPRnJPvvoSOWMm9dSN4GJp0QImJjole78Baj2OZUWOwiLrSCJSB+e80RYmJjrFK0pSSsb7Ksk69wqpgyO4tIWJiU7xipKUkrF2TcaYSX2y1Ah6OyYmOiUaeURUn4y1a7Le34daH0dwaQ8TE53iFSUp5Vi7JkdhcSjf6oVBEuCcTtrDxES3eJVAykiSi7glSzJF6mBeoj1MTHRKxpEWpD5ZiooscZL6ZKwR9HYeJSZZWVno378/wsLCEBMTgzvvvBMHDx5sdJucnBwYDAann59++umKAqcrw6sEUsqxWUSO0sI+JqQUz4Xa41FismnTJkyaNAnbt2/H+vXrUV1djYyMDFRUVDS57cGDB1FYWGj/ufbaa5sdNF059jEhpWT8kpcxmSJ18FyoPX6erLx27VqHx0uWLEFMTAx27tyJW265pdFtY2Ji0KZNG48DpKtDxrkpSB0ynrg5Iz0pxVmwteeK+piUlpYCACIjI5tct3fv3oiPj8eQIUOwcePGRtc1mUwoKytz+KGWxT4mpJSMk5VxCCgpJWPi7e2anZgIIZCZmYmbbroJaWlpbteLj4/HwoULkZ2djRUrViA1NRVDhgzB5s2b3W6TlZWFiIgI+09SUlJzwyQ3eACSUjJOxudYYyJJ0KQ6FhVt8Kgpp77Jkydjz549+O9//9voeqmpqUhNTbU/Tk9Px4kTJzBnzhy3zT8zZsxAZmam/XFZWRmTkxYmY78BUoeMnQM5jwkpxfOf9jSrxmTKlCn46quvsHHjRiQmJnq8/YABA3Do0CG3zxuNRoSHhzv8UMti50BSSsoJqGSMmVTheJHGwqIFHtWYCCEwZcoUrFy5Ejk5OUhJSWnWm+bl5SE+Pr5Z21LL47FIjZOvcyA7d5NSrF3THo8Sk0mTJuGf//wn/v3vfyMsLAxFRUUAgIiICAQFBQGwNsMUFBTgo48+AgDMnTsXycnJ6NatG8xmMz755BNkZ2cjOzu7hXeFPMEOX6SUjGWFnbtJKY7g0h6PEpP58+cDAAYNGuSwfMmSJZgwYQIAoLCwEPn5+fbnzGYzpk2bhoKCAgQFBaFbt25YtWoVRo4ceWWR0xXhEDlSSsZWERljJnU4juBiadECj5tymrJ06VKHx9OnT8f06dM9CoquPhmvgkkdMtY+yDiSiNTBGhPt4b1ydIpXlKSUjP01ZJx7hdTBPibaw8REpwSPRlJIxto1GWMmlbCsaA4TE53iFSUpJWMOy3l6SCnB+mPNYWKiU7yiJKWEjJeUkoRJ6uO5UHuYmOiWfP0GSB1y1pjU+5vfNtQI1pdoDxMTnZJxpAWpT5aiImMyRepwHMHF0qIFTEx0qv7hZ+GxSI2Q8fYF7GNCSlmYxGoOExOd4hUlKSXjl7yMyRSpQ8by7e2YmOiUYI8vUkjGosJJs0gxNmtrDhMTnWKHL1JKxrLiOM04kXsylm9vx8REpyycspsUskjYOZCdu0kpGWsEvR0TE71iGzwpJPvJWvb46epy7GPCwqIFTEx0im3wpJx8tWvs3E1KsaxoDxMTnWL1JSkl4wgXXgWTUrxI0x4mJjol4x1jSR0ynrh5FUxKOXaUZmnRAiYmOsXOgaSUjF/yMiZTpA7WHmsPExOd4vFHSsk4ZbeMMZP6WFK0gYmJTjmeuFUMhDRPxnkeZIyZ1CFYWDSHiYlOOR6LPBrJPRlP3DLGTOpw7G/HwqIFTEz0iu2qpJCcHaVljJnUwD4m2sPERKfk/LIhVUjYUZqdu0kpVq5pDxMTneKJm5SS8cQtY8ykDnaU1h4mJjol4xBQUoeMVd0yxkzqYBKrPUxMdEq4fUDkSMbOgTLGTOpgEqs9TEx0ireFJ6VkPHHLGDOphQVEa5iY6JTjzJg8MMk9GWdRZVMlKdWwTPN8qD4mJjrFEzcpJeOJ2qH5Rr7wqRU1LB4SFnevw8REtzjzKykjY+2ajHdEJnU41ZioEwbVw8REp3jiJsUkr3yQJJcilTQ8/8mSfHszJiY6JWO/AVKHkLB2jU2VpBRrTLSHiYlOcdQCKSVj7ZpjMiVHzKQO586v6sRBdZiY6JQsXzCkPhmTWNaYkFJOTTksMapjYqJT9U/cFlm+bUgVMs6MyaZKUoo1JtrDxESneOImpRzvJaJiIB5g8w0pxaKiPUxMdMpx5lcemeRew4puGTjPTSFH3NT6nEflqBQI2TExIR6I1CjZ+5i4ekxk4zwqh4VFbR4lJllZWejfvz/CwsIQExODO++8EwcPHmxyu02bNqFv374IDAxEx44dsWDBgmYHTC2DnQNJORnvqyRjPQ+pgTO/ao9HicmmTZswadIkbN++HevXr0d1dTUyMjJQUVHhdpujR49i5MiRuPnmm5GXl4fnn38eTz75JLKzs684eGo+GeemIHU41pjIUVh4/xNSqmHZYElRn58nK69du9bh8ZIlSxATE4OdO3filltucbnNggUL0L59e8ydOxcA0LVrV+Tm5mLOnDm4++67mxc1XTHHY5GHIrkn+6gcV4+JbNgfSXuuqI9JaWkpACAyMtLtOtu2bUNGRobDsmHDhiE3NxdVVVUutzGZTCgrK3P4oZYlY78BUoeMZYV9TEgpzvyqPc1OTIQQyMzMxE033YS0tDS36xUVFSE2NtZhWWxsLKqrq1FSUuJym6ysLERERNh/kpKSmhsmuSHjVTCpQ8ZZVJ2r5+WIm9TAUTla0+zEZPLkydizZw+WLVvW5LoGg8Hhse2k0XC5zYwZM1BaWmr/OXHiRHPDJDcc56bgkUjuydhRmh0aSSmnssGyojqP+pjYTJkyBV999RU2b96MxMTERteNi4tDUVGRw7Li4mL4+fkhKirK5TZGoxFGo7E5oZFCrDEhpWTsjsREhJRyzktYeNTmUY2JEAKTJ0/GihUrsGHDBqSkpDS5TXp6OtavX++wbN26dejXrx/8/f09i5ZajoT9BkgdjpPxyYGTZpFS7I+kPR4lJpMmTcInn3yCf/7znwgLC0NRURGKiopw+fJl+zozZszAuHHj7I8nTpyI48ePIzMzEwcOHMDixYuxaNEiTJs2reX2gjwmY78BUp80ZYWTZpFCHC6sPR4lJvPnz0dpaSkGDRqE+Ph4+89nn31mX6ewsBD5+fn2xykpKVi9ejVycnLQq1cv/PnPf8a7777LocIqk7HfAKlDxrLCPiakFIcLa49HfUyUfGBLly51WjZw4EDs2rXLk7eiq8zCTiakkIyT8XEIKCnVsKxYWFhUx3vl6JTDl42KcZD2OdaYyFFanPuYyBE3tT6nsiJJGfdmTEx0SsZpxkkdXjHBmjphkAw4XFhzmJjoFFtySCkZywr7mJBSzEu0h4mJXgn5+g2QOoSEvV+dagEliZtaH4cLaw8TE51yvArmkUjuyVhWOGkWKcU+JtrDxESnZOw3QCqRsazwKpgUYo2J9jAx0SkZZ/Mkdcg4gsv5KpjINfYx0R4mJjol4/1PSB0yjuByvgqWI25qfU4zv7KsqI6JiU7JODcFqUPKUTns+0oKcQSX9jAx0SmHLxseiNQIGfsj8cuGFGPZ0BwmJjrFPiaklIw1as43ZpNvH6h18E7U2sPEhGDhkUiNkLG/hlOE2g+ZVGKxOD5mEqs+JiY6JWP1PKlDxmYR9jEhpWQs396OiYlO8aqAFHNqFpEBq+dJGedmP1IbExOdkrF6ntThfEWp/bLiXGOi/ZhJHTKWb2/HxESnWH1JSsnYLMLyTUrJWL69HRMTneLBSEo5T0ClUiAeYPkm5eQr396OiYlOOQ+R49FIrsl4QzyWb1LKuWiwrKiNiYlO8YqSlJLxJmcyxkzqYLOf9jAxIQA8GMk9GYuGjDGTOniRpj1MTHSKM2OSUnL2MZEvZlIHZ37VHiYmOsWqbmouGZNYGWOm1sGh5drDxESneOiRUjImsTLGTOpgHxPtYWKiUzxxk1JOVd0qxeEJGWMmdbDZT3uYmOiU84mbRyO5JuMswTLGTNrAc6H6mJjoFGtMSCnneUy0jyMtSCmeC7WHiYlOyfhlQ+qQ8cTNfgOkFGtItIeJiV45tavy4CTXnE7cEhQV5/IsQdCkChkTb2/HxESneNompWQcTskaE1JKxvLt7ZiY6BSvEqi5pCgr8lXykEqYxGoPExOdkrF6ntThPEuw9nE2T1JKxvLt7ZiY6BSrL0kpGYfesnyTUs41JiwramNiolOsviSlZOyPxPJNirHyWHOYmOgU53kgpWTsjyRjzKQONvtpDxMTnXKehplHI7km4yzBMsZM6pCxqdLbeZyYbN68GaNGjUJCQgIMBgO+/PLLRtfPycmBwWBw+vnpp5+aGzO1ABmr50kdMk4JwhoTUkrC4u31/DzdoKKiAj179sSDDz6Iu+++W/F2Bw8eRHh4uP1xu3btPH1rakG8cRUpJeOJW4YYSRuYxGqPx4nJiBEjMGLECI/fKCYmBm3atPF4O7o6OGqBFJMxiZUxZlKFcx8TFha1tVofk969eyM+Ph5DhgzBxo0bG13XZDKhrKzM4YdaFkctkFLONSbaLywyxkzqsHAggOZc9cQkPj4eCxcuRHZ2NlasWIHU1FQMGTIEmzdvdrtNVlYWIiIi7D9JSUlXO0zdYfUlKSVjWZExZlIJa9c0x+OmHE+lpqYiNTXV/jg9PR0nTpzAnDlzcMstt7jcZsaMGcjMzLQ/LisrY3LSwjhqgZRyLivax9k8SSnWrmmPKsOFBwwYgEOHDrl93mg0Ijw83OGHWhavKEkpGYdTcjZPUkrGUWfeTpXEJC8vD/Hx8Wq8NbnBY5HckbE/EicQJKVYu6Y9HjflXLx4EYcPH7Y/Pnr0KHbv3o3IyEi0b98eM2bMQEFBAT766CMAwNy5c5GcnIxu3brBbDbjk08+QXZ2NrKzs1tuL8hjnGCNlJKxaMiYTJE6WFa0x+PEJDc3F4MHD7Y/tvUFGT9+PJYuXYrCwkLk5+fbnzebzZg2bRoKCgoQFBSEbt26YdWqVRg5cmQLhE/NxYORlJJxym7nRFuCoEkVnDpBezxOTAYNGtTo1fXSpUsdHk+fPh3Tp0/3ODC6umT4ciGN8IITN8s7ucOLNO3hvXJ0SsarYFKHjCdu9jEhpdjHRHuYmOgUqy9JKRlP3Ey8qbnY3059TEx0SsarYFKHlMOFJYyZ1MHaNe1hYqJTPBhJKRm7kcoYM6nDqbaYhUV1TEx0i8OFSRkZJ+OTMWZSB5u1tYeJiU6xxoSUcj5Ra7+08JYLpBSbtbWHiYlO8YqSlJKxrHAaE1JKxvLt7ZiY6JSMV8GkDTKWFBljptYh400qvR0TE53iVQIp5Xz7ApUC8YCMMZM6OIJLe5iY6BTrS0gpGW8Lzw6N1FwsKepjYqJTrDEhpWQsK+zQSEqxdk17mJjoFEctkFIyzqLKUWeklHN5ZmlRGxMTvZLwKpjUIWOziHMypf2YSR2sXdMeJiY6xYORlJKxrLDGhJRiWdEeJiY65XxjNh6O5JoMiUhDrJ0npWRsqvR2TEx0SsarYFKLhCduJt6kkIxNld6OiYlOSfHlQpog44mbiTcpxbKiPUxMdIoHIykl5XBhCWMmlTjVrpHamJjoVMM+JhaeuckNGafsblieZYiZ1GHhzK+aw8REpzjzKykl45TdzjWC2o+Z1CFD06TeMDHRKwm/bEgdMiaxHAJKSrHZT3uYmOiUjNXzpA4ZT9wcAkpKyXgvKG/HxESnZPyyIXU4n6glKCycyIQU4rlQe5iY6JRzZ1cejeSGhCdujjojpRom3g07w1LrY2KiU7xKIKVkTGGdZzYmcoP97TSHiYlOyfhlQ+qQ8bbwTLxJKZ4LtYeJiU7xxE1KyTj0lh0aSSmn8syiojomJrrF28KTMjKet5l4k1Iy3nLB2zEx0SkZv2xIHTJ2JOVweFJKxvLt7ZiY6BQPRlLKuSOp9guLjLPVkjp4kaY9TEx0SsYvG1IHpwQhb8bJ+LSHiYlO8cuGFJPwilLGkUSkDvYx0R4mJjrF6ktSSsYrSo7KoeaSoXx7OyYmOsU+JqSUjFeUHJVDSnEyPu1hYqJT7GNCSsn4JS9jLQ+pw7lZm4VFbUxMCACPRXJPxqG3bKokpVhWtMfjxGTz5s0YNWoUEhISYDAY8OWXXza5zaZNm9C3b18EBgaiY8eOWLBgQXNipRbEg5GUknHorYyz1ZI6WLumPR4nJhUVFejZsyf+9re/KVr/6NGjGDlyJG6++Wbk5eXh+eefx5NPPons7GyPg6WW43ww8mgk12S8lwgTb1JKxsTb2/l5usGIESMwYsQIxesvWLAA7du3x9y5cwEAXbt2RW5uLubMmYO7777b07enFsITNynldJ6WorCwgJMyUhZvL3fV+5hs27YNGRkZDsuGDRuG3NxcVFVVudzGZDKhrKzM4YdalpxfNqQO+TpKyziSiNQhY+dub3fVE5OioiLExsY6LIuNjUV1dTVKSkpcbpOVlYWIiAj7T1JS0tUOU3ecOzTyaCTXZDxxyxgzqUW+zt3erlVG5RgMBofHtja8hsttZsyYgdLSUvvPiRMnrnqMesMTNykl45w3Mo4kInWwj4n2eNzHxFNxcXEoKipyWFZcXAw/Pz9ERUW53MZoNMJoNF7t0HRNxi8bUoeME1Ax8SalWDa056rXmKSnp2P9+vUOy9atW4d+/frB39//ar89ucO+gaSQjENvOSU9KcXhwtrjcWJy8eJF7N69G7t37wZgHQ68e/du5OfnA7A2w4wbN86+/sSJE3H8+HFkZmbiwIEDWLx4MRYtWoRp06a1zB5QszQ8GC08GskNGUdwscaElLKwo7TmeNyUk5ubi8GDB9sfZ2ZmAgDGjx+PpUuXorCw0J6kAEBKSgpWr16NZ555Bu+//z4SEhLw7rvvcqiwynjiJqVkbPZjHxNSiudC7fE4MRk0aFCjVblLly51WjZw4EDs2rXL07eiq8j5E+TRSK45H+8SlBUZsylSBZNY7eG9cnTKqUMjj0ZSSIayImEqRWphjYnmMDHRKZ64SSk5+5gw8SZl2FFae5iY6BTbVUkpGUctyDiSiNTBJFZ7mJjoFK8SSCkZp3eXsZaH1MGyoT1MTPSKVwmkkIy1a+z7Skpx5lftYWKiU+xjQkrJOGpBxtlqSR1MYrWHiYlO8SqBlJKxrLCPCSnFJFZ7mJjolAz9BEgbpCwpUgZNamCNifYwMdEpGfsNkEokLCsyjiQilUjYudvbMTHRKRlHWpA6nPuYaL+ssHyTUkxitYeJiU7Zjj2DofYxD0ZyQ8baNRljJnXYyob9XKheKFSLiYlO2Tp8+dQejTxxkzsytsHLOJKI1GErzz68StMMJiY6VXcw1j5WLxTSOBlHLbDGhJSyJbG2c6GFZUV1TEx0ynYwGuw1JjwayTUZh95yZmNSqq4pp/ZcyLKiOiYmOsUaE1JKxundWWNCStmKhg9bcjSDiYlO1R2MzEyocU5FQ4qyIkWQpAEN+5iw5KiPiYlOOXV+5eFI7ghbs1/tQwnKitNIC14Gk1scCKA1TEx0isOFSamGtWsylBUZYyZ1OA8XZmFRGxMTvWL1JSkkY3+kuhrB2scqxkLaxmZt7WFiolPs8EVKOY/gUjMaZepqBOWJmdTBJFZ7mJjoFPuYkFLONSbaLysyxkzqcG72Y1lRGxMTneIVJSnlNGpBgrLCPiaklNM8JiwrqmNiolMy9hsgdTh9yasXimLONYJErjk1a6sWCdkwMdGpummYWX1JjRMNhgvLdEkpY8zUunjfMO1hYqJTTjUmPBipCTLVPnDSLFKK/ZG0h4mJTjm3q/JgJNcsDUctSFBUnG/MJkHQpAoZR515OyYmOudTWwJ4LJI7zp1ftV9aZOywS+qwlxV+G2oGPwqdYrsqKeU0gku9UBRzvmMskWsyJt7ejomJTsk40oLU4TQBlQSFpWFTjgwxkzqcBgKoGQwBYGKiW7zJGSklYxLr3PlVhqhJDRwIoD1MTHSq4VUCkVtOJ27tn7kbzk3BvITccU68WVjUxsREp3iVQEo17GMiBfYxIaXYUVpzmJjoFK8SSCl7HxPbCC4Jioq9RtAeswRBkyrqhgvbHpPamJjoFO8PQUrJmMRyuDApxbKiPUxMdIu3+iZlZDxxy9hhl9RhLys+DZeQWpqVmMybNw8pKSkIDAxE3759sWXLFrfr5uTkwGAwOP389NNPzQ6arpyMXzakDhmruhve34flm9zhnE7a43Fi8tlnn+Hpp5/GCy+8gLy8PNx8880YMWIE8vPzG93u4MGDKCwstP9ce+21zQ6arpzzHTV5NJJrMiaxMjY/kTqcJhBkUVGdx4nJX//6Vzz88MN45JFH0LVrV8ydOxdJSUmYP39+o9vFxMQgLi7O/uPr69vsoOnK1V1R8mCkxsl4kzOOOiOlZCzf3s6jxMRsNmPnzp3IyMhwWJ6RkYGtW7c2um3v3r0RHx+PIUOGYOPGjZ5HSi3KaZ4HoibIXGNC5I5T7ZoE5dvb+XmycklJCWpqahAbG+uwPDY2FkVFRS63iY+Px8KFC9G3b1+YTCZ8/PHHGDJkCHJycnDLLbe43MZkMsFkMtkfl5WVeRImKcD7Q5BSDWvXpOBUI8jyTW40vOWCiqGQlUeJiU3DE5QQwu1JKzU1FampqfbH6enpOHHiBObMmeM2McnKysLMmTObExopxA5fpJRTfyQJCotzHyoi19jHRHs8asqJjo6Gr6+vU+1IcXGxUy1KYwYMGIBDhw65fX7GjBkoLS21/5w4ccKTMEmBuoPR8TFRQ1J2fpUwZlIH+5hoj0eJSUBAAPr27Yv169c7LF+/fj1uvPFGxa+Tl5eH+Ph4t88bjUaEh4c7/FAL44mbFHK6U6+KsSjlHLMMUZManO4bxqKiOo+bcjIzMzF27Fj069cP6enpWLhwIfLz8zFx4kQA1tqOgoICfPTRRwCAuXPnIjk5Gd26dYPZbMYnn3yC7OxsZGdnt+yekEcaTirEEze5I+MswTLGTOpwvhM1qc3jxOTee+/F2bNn8eqrr6KwsBBpaWlYvXo1OnToAAAoLCx0mNPEbDZj2rRpKCgoQFBQELp164ZVq1Zh5MiRLbcX5DH2MSGlZJzzxrl6nsi1uiTW9pilRW3N6vz6xBNP4IknnnD53NKlSx0eT58+HdOnT2/O29BVZDv0fH14lUCNk7G/BoeAklI8F2oP75WjUw2/bHjmJvca1K6pGYpCDWsE5Yia1MDaY+1hYqJTFo7dJ4UaVnXLdOZm3k1K2c6FFhYW1TEx0SmO3SelZLxTr4zNT6QOp47SKsZCVkxM9Ipj90khe1W3bQSXBEXFPgSUo86oCQ2HlrOoqI+JiU41HLtv4cFIbsh4p17WmJBSloZlRYLy7e2YmOgUT9yklIxzgjg1VaoXCmkcO79qDxMTnXKekp5HI7kmJOwo7RSzDEGTKuomm2RiohVMTHTKaTglD0Zywz7Pg0RXlE4xs4CTO+xvpzlMTHSKd18lxZxGLUhQWhrELEPIpA5Oxqc9TEx0yrmPCY9Gcq1hEivDlzwTb1LKdu5jDqsdTEx0TqYOjaQOp86BagajkHOHRhmiJjWwxkR7mJjoUP2TNK8oqSlOd6KW4MztfPdsItca3vCRpUV9TEx0qP73Cq8SqClSDheWMGZSR8M5nVhW1MfERIfqH3ecGZOa4nTiVjMYhWSMmdTBKem1h4mJDjk25dTO/MqpX8mNhlXdMlxROscsQdCkCtu5j2VFO5iY6FD9wy4yJAAAcO5SlTrBkOZ5xZT0KsZC2iWEwLlLZgBAVO25kGVFfUxMdKj+BUFMmBEAcKa8UqVoSPPsVd21DyU6cxskGuJMre+iqRqVVRYAQLvwQABylW9vxcREh2rqNdvE1h6MZ8pNaoVDGtewv4YMnIc489uGnNnOe2FGP4QE+AIAqi0WNUMiMDHRpdNl1tqRQH8fpESHAGBiQu7J2F/DaYI17YdMKrCd99qFGdGutva4uIznQrUxMdGhUxcuAwAS2gQhJsxaY1JWWY3Kqho1wyKNcu5jon28ezYpceaiNQmJDjMioU0QAOv5UYbk25sxMdGhU6XWGpNr2gQhPMgPAb7WYlBykVcK5Kxuym55vuRtTTdS3d+HWl39GpOECGtiUmGuQVlltZph6R4TEx2y15hEBMFgMNirMNmcQ64433dG+1/yMg5xptZnT0xCjQgK8LWPUrSdI0kdTEx0qH5TDmCtxgSYmJAzi0XUm4DK+luGL/mGzU8WGYKmVle/xgQAEtpYm7aZmKiLiYkOFdQedPG1B2G70NrEhE051EBx7Ynb18eAyGDr1WSNBJPx2SbNiq4t26fZoZFcsJ3zbIlJfERdPxNSDxMTHSqs18cEAJtyyK3jZysAWMtKYmQwAODkeW2ftCtM1ThbYZ00a0DHSAB1+0FUX8MaE9s50dYPj9TBxERnhBBOTTlMTMid4+cuAQA6RAWjU0woAOBw8UU1Q2rSkTPW+KJDA9AjsQ0A66izC7UzfBLZ1O9jArApRyuYmOhM6eUqXDJbhwXHR9Q25TAxITdsNQ31E5OiskqUV2r3Fga2xOlX7UIRFOCL2HBr+T529pKaYZHG1FiEvWYtxt7HhE05WsDERGdOXbBWUUaHBiDQ3zrToe1q4TQTE2rgeO2XeYfIEIQH+ttP4FquNbHFZkukOkRaJxFkcw7Vd7bChBqLgMFQd8+wusSETTlqYmKiM3sLLgAAkmr7CwBA51jrCXz/qVJcNHH8PtXJr9eUA0CK5hynxKQ29nzWmFA9O46eBwCkRIfAr3Yup/a158VTpZdZg6wiJiY6s2pvEQDgtq6x9mUp0SFIjgpGVY3Afw+dUSs00qBjJbamHGutgz0xOaPhxOSM68SETTlU37c/nQYADOkSY18WHWpEz6Q2EAJY+2ORWqHpHhMTHTlfYcZ3h0sAACO7x9uXGwwG3NrFmqhs+KlYldhIey5cMttnwLRdSdq+7I9otMbEXG2xNz/ZYm1fm1Tln2NTDlnVWARyDlovwmznPpvfdI8DAKzac6rV4yIrJiY68vXeQtRYBLrGh9tv3mczpKv1qmHDT2dQXcO7axLw82lr8hETZp0VEwCujQkDAOw8fl6T91bacewcaiwCYUY/xNXeOTu5tsbkcPFFKeZgoatvV/55nKswIyzQD/2S2zo8Z7to++HoOXaCVQkTE504e9GEt9f/DAC4u881Ts/3T45EZEgASi6a8OG2460dHmnQ57knAADpv4qyL+uf3BaJbYNw/lIVvqh9XksWbDoCALirzzX2++R0jg1Dm2B/nL9UhW8OnFYzPNIAIQTeWncQADC0ayz8fR2/BhPbBuOGlEhYBPCnf+/jDf1UwMREByqravD0Z7txrsKMLnFhGH9jstM6AX4+mJaRCgB4e/3P7CiocyUXTfhqt7Uqu3558fP1wWO3dAQALNj0i6Y6S+ceO4cth0rg62PAozd3tC8P9PfF/de3BwAs/e6YStGRVnyRexLbfzkHo58Pnhna2eU6r45Og7+vAd8cKMY/thxt5QiJiYmXO3S6HPct3I4th0pg9PPBnN/3dLpCsLmvfxJ6JbXBRVM17lu4DYdOl7dytKQFQgjMXn0A5hoLeia1QZ/2jlXdv++bhJgwIwouXMZjH+VqYk6TA4VleOzjnQCA0b0SHEadAcDYAR3g62PAtl/OYs3eQjVCJA3I3nkSM1buBQBMubWTUzmxSY0LQ+ZQ64Xaa6sP4KUv92minOuFQUhQT1VWVoaIiAiUlpYiPDxc7XA0r7KqBtt+OYvsnSexZl+Rtc090A+LJ/RH/+TIRrc9XVaJ+z/Yjl/OVCDA1weP3JyCCb9ORkxYYCtFT2qqrKpB1uoD+HDbcfgYgI8eugE3XRvttN7/TlzA//tgOyrMNYiPCMSTQ67FHT0TEGL0a9V4T56/hI+3HceS747BXGNBj8QI/PPRAQh1Ecefv96PRf89CqOfD966pydu75HQqrGSevaeLMU73x6yN+Xd3ScRf/ldD/jabj/tghAC7288jDnrrE3gbYL9cW//JAzrFoeeiW0a3ZbqNOf7u1mJybx58/Dmm2+isLAQ3bp1w9y5c3HzzTe7XX/Tpk3IzMzEjz/+iISEBEyfPh0TJ05U/H5MTFyzWASKyipx7GwFjp+9hF/OXERe/gXsOVkKc70OrLd1jcUrd1yHxLaurw4aOlNuwh//9T97r3UfA9C3Q1vc1jUW/ZLbomt8OIIDWvcLiK6u02WV+HpPIZZuPYoT56wd/l67Kw1jbujgdpudx8/jmc922+c68fMxoFdSG9zQMRLXxoQhJToE8RGBaBMcgAC/K6ucNVXX4HSpCadKL+PImYvYV1CG/524gP2FZfZ1hnSJwZzf90Tb2smyGqqusWDiJzvxzQHryLMbfxWFMTd0wE2dohER7H9F8ZG2nL1oQl7+Bew4dg6bfj6Dn4qstb9+PgY8MbgTnh5yLXwUJhZbDp3By//+Eb+U1I3qigwJQJ/2bdEtIRypcWFIbBuEa9oEITIkwN63iaxaJTH57LPPMHbsWMybNw+//vWv8fe//x3/+Mc/sH//frRv395p/aNHjyItLQ2PPvooHn/8cXz33Xd44oknsGzZMtx9991XbcfUJoRAVY1AtcVi/V1j/V1VY0G1xfq7qsaC6hrb39bfl8w1uGSuRoW5BpdM1Q6PSy9X4exFE85VmHGuwozzl6rcjjKICTNiWLc43H99e1yX4Pn/TAiBdftP4++bjmBX/gWH5wwGIKltMBLbBtUekMFoF2ZE22B/tA0JQNvgALQN9kdooB8C/XwVnwDo6hFC4HJVDc5eNOPUhcs4VXoZBecv46eicuw5WWpPLgDrLQqy7uqO266LbeQVrS6ba/DJ9uP45w/5OFrifjhumNEPbUMCEOTvC6O/DwJ8fey/BQCLsCbaFiFQbRG4ZK7GxcpqXDRVo7yyGqZq1yPFDAZgQEoUHr4pBUO6xjT5pWCutuBvGw7h/Zwj9mPHxwD0SGyDrvHh6BQTivaRwYgJMyI2PBDRoQH2ybdIXabqGlysrEaFqQblpiqUXa5GcXklzpSbcLqsEsXlJhReqMThMxdxrsLxvkj+vgb8pns8nhjcCZ1jwzx+7xqLwPr9p/H1nlPY9PMZlFe67lsV6O9Tey4MQESQP9oGB6BNsD/CAv0QHOCHQH9fBAf4IsjfF0EB1r8D/X3h7+sDPx+D9bevAf4+1t/1/7at4+tjkCr5aZXE5IYbbkCfPn0wf/58+7KuXbvizjvvRFZWltP6zz77LL766iscOHDAvmzixIn43//+h23btil6z6uVmMz8vx/x7YFiWISAENaTt/UkWfsYtctE7TJYT57W5S7WF4CAgEW03q3h/XwMSIoMRoeoYCRHhaBbQjj6J0eiQ1RwixXegguX8c3+09j08xnsKyhFsYczIgbZDsYA228/BPn7wN/X9mOAn6/1S8rPxwB/Px/42w9SHwTUPu/rY4CPwQAfA+wHZ8O/fQwG+BoMMNj+9nH828eA2nWtf7vT2L/OgEafdEsIgRqLtbxYv4SBGiEghPUL2d1zNbWP639xm6otMFdbYKquqfe3BaaqGphrLDBVWXCpqgbll6tQerkKZZVVqKppvEz2ad8Gd/W+Bnf3TWxWjdiJc5fw3eES7D5xAUdLKnC0pAIlF01oqUPB6OeDhDZBSIoMRreEcHRLCMeAjlGIrr2lgidOnr+Ej7cfx7cHihudxdZgAEIC/BBi9EWo0Q+hRj+E1P4OCvC1l+GA2i8Of78Gj2u/aOqXz4Zl0MdggI9P48+7Ko9u/60unhAuFro689vOezWW2rInHMuedTlql9crq7XPW0TdY4fyXrttVY21rJprBMzVFvvjqhoLzPbn6pZfMtegwmRNUJsqvw11jA7BDR0jcX1KJG5NjW2xWrGqGgt2n7iAfQWl2H+qDIfPXETB+csenxevhF/tuRAGa2JtQF1ZQb0yU3ees5Ur2zJrgfLxcTyfvTzqOgzp2vQFiSeuemJiNpsRHByML774AnfddZd9+VNPPYXdu3dj06ZNTtvccsst6N27N9555x37spUrV+Kee+7BpUuX4O/vXFhMJhNMproPuaysDElJSS2emExZlof/+1/rTaLj51OXAfv71WXIti9mf18fBNd+eQcH+CIkwA/BRl8EB/ghOMAX4YH+iAoNQFSIEZEhAbV/t/4V3ZlyE46WVODk+Us4ed565X22woTzl6pwvsKM85fMKL1c1WJfSNRy/H0NSGgThISIIFzTNggp0SHomdgG3a+JuCrNGRaLQFllFc5WmHHhkhmVVbXJVJXFnkA1TCJ9fQwIMfohzOiH0EBrEhBm9Ed4kN9VuVIsuHAZucfO4dDpizhcfBGnSi+juMyEMxdNnPdEg4IDfK3lI9AP7UKtNVsxYUbEhFv//lW7UHRsF9Lqzc2m6hoUlVai5KIJFy5V4fylKly4ZD0fVphqcNlcg0tV1t+Xq6qtj801qKyqsdeuV9erVa+uEaiyWFwmkFfLu/f3xh09W7bvVXMSE48+uZKSEtTU1CA21jGjio2NRVGR6+l7i4qKXK5fXV2NkpISxMfHO22TlZWFmTNnehJaszxz27V48NfJMMAxwwTqMkv7b1ivYmoTUqf1fXxs61gzUD/fekmHj/W3TNVvjWkXZkS7MCOuT3HfkdZisTYdXDLbDkhrs1Rl7cF4uarG2sxVLWCusdQ1ddUuq7bUXjlV25rDLE41BxZRdzVmsV+ZweHKra7Goe6x/YrOXfCNnAkaO0c0dgIRELU1OXU1N7YrF1e1Og2fs9UWGQzWBNfo54sAPx8Y/Xwc/7Y3k/gi0M8HEUH+CA/yt/8OCfBt1XLo42NAm+AAtAl23e9DC65pE4RrejnP7WOxCJy7ZEZ5ZTUqapuUKkzVqDDXNS9V1VhQZb/ir6sFqF8D0LB8CuG+/AoX5ddWXht+bK4+xoa1ea7XabjAcUnDmkd7TWWDsunj4jlbGa9fk+nrU7eN0VajVO93gK+hwWPrhVuArw+CAnwRZqulCvRDSICfZjudGv180SEqxH77hpZiq2myJivWcmWx1NXO22r165cdx9p+x7IFOJ4r60tu4dibq1kpZcMTmxCi0ZOdq/VdLbeZMWMGMjMz7Y9tNSYtrWO70BZ/TbLyqb3qbe1RGkQtxcfHgOhQY7Oaiohaiq+PAb4+vmqH0ao8+taIjo6Gr6+vU+1IcXGxU62ITVxcnMv1/fz8EBUV5XIbo9EIo5EnAyIiIr3xqHNCQEAA+vbti/Xr1zssX79+PW688UaX26Snpzutv27dOvTr189l/xIiIiLSL497TWZmZuIf//gHFi9ejAMHDuCZZ55Bfn6+fV6SGTNmYNy4cfb1J06ciOPHjyMzMxMHDhzA4sWLsWjRIkybNq3l9oKIiIi8gscdAO69916cPXsWr776KgoLC5GWlobVq1ejQwfrREyFhYXIz8+3r5+SkoLVq1fjmWeewfvvv4+EhAS8++67iucwISIiIv3glPRERER0VTTn+5tTGhIREZFmMDEhIiIizWBiQkRERJrBxISIiIg0g4kJERERaQYTEyIiItIMJiZERESkGUxMiIiISDOYmBAREZFmSHFPetvktGVlZSpHQkRERErZvrc9mWReisSkvLwcAJCUlKRyJEREROSp8vJyREREKFpXinvlWCwWnDp1CmFhYTAYDC32umVlZUhKSsKJEye88h483r5/gPfvo7fvH+D9++jt+wd4/z56+/4BV28fhRAoLy9HQkICfHyU9R6RosbEx8cHiYmJV+31w8PDvbawAd6/f4D376O37x/g/fvo7fsHeP8+evv+AVdnH5XWlNiw8ysRERFpBhMTIiIi0gxdJyZGoxEvv/wyjEaj2qFcFd6+f4D376O37x/g/fvo7fsHeP8+evv+AdraRyk6vxIREZE+6LrGhIiIiLSFiQkRERFpBhMTIiIi0gwmJkRERKQZ0iYm58+fx9ixYxEREYGIiAiMHTsWFy5caHQbIQReeeUVJCQkICgoCIMGDcKPP/7osI7JZMKUKVMQHR2NkJAQ3HHHHTh58qT9+WPHjuHhhx9GSkoKgoKC8Ktf/Qovv/wyzGazw+vk5+dj1KhRCAkJQXR0NJ588kmndbS6jwDw2muv4cYbb0RwcDDatGnj8r0MBoPTz4IFC7xm/2T/DJW8t6ef4bx585CSkoLAwED07dsXW7ZsaXRfNm3ahL59+yIwMBAdO3Z0+drZ2dm47rrrYDQacd1112HlypUev6+S/5lSWt3HCRMmOH1WAwYMkGL/Nm/ejFGjRiEhIQEGgwFffvml02vI/hkq2UeZP8OsrCz0798fYWFhiImJwZ133omDBw86rNNin6GQ1PDhw0VaWprYunWr2Lp1q0hLSxO33357o9u8/vrrIiwsTGRnZ4u9e/eKe++9V8THx4uysjL7OhMnThTXXHONWL9+vdi1a5cYPHiw6Nmzp6iurhZCCLFmzRoxYcIE8Z///EccOXJE/Pvf/xYxMTFi6tSp9teorq4WaWlpYvDgwWLXrl1i/fr1IiEhQUyePFmKfRRCiD/96U/ir3/9q8jMzBQREREu3wuAWLJkiSgsLLT/XLp0ySv2zxs+QyXv7clnuHz5cuHv7y8++OADsX//fvHUU0+JkJAQcfz4cZfr//LLLyI4OFg89dRTYv/+/eKDDz4Q/v7+4l//+pd9na1btwpfX18xe/ZsceDAATF79mzh5+cntm/f7tH7KvmfKaHlfRw/frwYPny4w2d19uxZKfZv9erV4oUXXhDZ2dkCgFi5cqXTe8n+GSrZR5k/w2HDhoklS5aIffv2id27d4vf/OY3on379uLixYv2dVrqM5QyMdm/f78A4PBP27ZtmwAgfvrpJ5fbWCwWERcXJ15//XX7ssrKShERESEWLFgghBDiwoULwt/fXyxfvty+TkFBgfDx8RFr1651G89f/vIXkZKSYn+8evVq4ePjIwoKCuzLli1bJoxGoygtLZVqH5csWdJoYuLq4FNC6/sn+2eo9L09+Qyvv/56MXHiRIdlXbp0Ec8995zL9adPny66dOnisOzxxx8XAwYMsD++5557xPDhwx3WGTZsmLjvvvsUv6+S/5lSWt1HIaxfaqNHj/ZofxpSa//qc1XmvOEzrK+xxMQbPkMhhCguLhYAxKZNm4QQLfsZStmUs23bNkREROCGG26wLxswYAAiIiKwdetWl9scPXoURUVFyMjIsC8zGo0YOHCgfZudO3eiqqrKYZ2EhASkpaW5fV0AKC0tRWRkpEN8aWlpSEhIsC8bNmwYTCYTdu7cKeU+ujN58mRER0ejf//+WLBgASwWi1fsn+yfoSfvreQzNJvN2Llzp8N7AkBGRobbfdm2bZvT+sOGDUNubi6qqqoaXcf2mkreV8n/TAkt76NNTk4OYmJi0LlzZzz66KMoLi7W/P4pIftn6Alv+QxLS0sBwP7d11KfISBpH5OioiLExMQ4LY+JiUFRUZHbbQAgNjbWYXlsbKz9uaKiIgQEBKBt27Zu12noyJEjeO+99zBx4kSH92r4Pm3btkVAQIDb13EVr1b20Z0///nP+OKLL/DNN9/gvvvuw9SpUzF79mxF22p9/2T/DJW+t9LPsKSkBDU1NY3G5WpfXK1fXV2NkpKSRtexvaaS91XyP1NCy/sIACNGjMCnn36KDRs24K233sKOHTtw6623wmQyaXr/lJD9M1TKWz5DIQQyMzNx0003IS0tzf4atu2Uvo47mrq78CuvvIKZM2c2us6OHTsAWDvtNSSEcLm8vobPK9nG3TqnTp3C8OHD8fvf/x6PPPJIo+9je53s7Gz8v//3/xp9Py3tY2NefPFF+9+9evUCADz//PN46aWXGt1Olv2T/TNU8t6uPsNXX33VYfmVxOVq/YbLlbxmS62jhFb38d5777X/nZaWhn79+qFDhw5YtWoVfvvb3za2Sx69j5L1Gy5X63/fUq/TWvvoLZ/h5MmTsWfPHvz3v/+94thc0VRiMnnyZNx3332NrpOcnIw9e/bg9OnTTs+dOXPGKVuziYuLA2DN6uLj4+3Li4uL7dvExcXBbDbj/PnzDlejxcXFuPHGGx1e79SpUxg8eDDS09OxcOFCp/f6/vvvHZadP38eVVVVeOCBBzBr1iwp9tFTAwYMgNlsxpYtWxAdHe12PRn2T/bPMC4uzuP3BqyfYVlZGU6fPu2wXnR0NHx9fZ2ufOrH5WpfXK3v5+eHqKioRtexvaaS91XyP1NCy/voSnx8PDp06IBDhw5pev+UkP0zbC4ZP8MpU6bgq6++wubNm5GYmOjwPsCVf4aAxppyoqOj0aVLl0Z/AgMDkZ6ejtLSUvzwww/2bb///nuUlpa6/fJJSUlBXFwc1q9fb19mNpuxadMm+zZ9+/aFv7+/wzqFhYXYt2+fw+sWFBRg0KBB6NOnD5YsWQIfH8d/Y3p6Ovbt24fCwkL7snXr1sFoNOLWW2+VYh+bIy8vD4GBgejfv7/0+yf7Z9ic9wbqPsOGQ6gDAgLQt29fh/cEgPXr17t9vfT0dKf1161bh379+sHf37/RdWyvqeR9lfzPlNDyPrpy9uxZnDhxwuFLQIv7p4Tsn2FzyfQZCiEwefJkrFixAhs2bEBKSorD+i31GdreTErDhw8XPXr0ENu2bRPbtm0T3bt3dxoKmZqaKlasWGF//Prrr4uIiAixYsUKsXfvXnH//fe7HIaZmJgovvnmG7Fr1y5x6623OgzDLCgoEJ06dRK33nqrOHnypMOwLxvbUNMhQ4aIXbt2iW+++UYkJiY2a6ipGvsohBDHjx8XeXl5YubMmSI0NFTk5eWJvLw8UV5eLoQQ4quvvhILFy4Ue/fuFYcPHxYffPCBCA8PF08++aRX7J83fIZNvbenn6FtmOKiRYvE/v37xdNPPy1CQkLEsWPHhBBCPPfcc2Ls2LH29W3DFJ955hmxf/9+sWjRIqdhit99953w9fUVr7/+ujhw4IB4/fXX3Q6ldfe+Sv9nSmh1H8vLy8XUqVPF1q1bxdGjR8XGjRtFenq6uOaaazzaR7X2r7y83H6MARB//etfRV5entOQb5k/w6b2UfbP8A9/+IOIiIgQOTk5bqcXaKnPUNrE5OzZs2LMmDEiLCxMhIWFiTFjxojz5887rIPaORpsLBaLePnll0VcXJwwGo3illtuEXv37nXY5vLly2Ly5MkiMjJSBAUFidtvv13k5+fbn1+yZIkA4PKnvuPHj4vf/OY3IigoSERGRorJkyeLyspKKfZRCOuwNlf7uHHjRiGEdT6XXr16idDQUBEcHCzS0tLE3LlzRVVVlVfsnxDyf4ZNvXdzPsP3339fdOjQQQQEBIg+ffrYhwoKYf2fDhw40GH9nJwc0bt3bxEQECCSk5PF/PnznV7ziy++EKmpqcLf31906dJFZGdne/S+Sv9nSmlxHy9duiQyMjJEu3bthL+/v2jfvr0YP36802eu1f3buHGjy+Nt/Pjx9nVk/wyb2kfZP0N333uenruUMNS+IREREZHqNNXHhIiIiPSNiQkRERFpBhMTIiIi0gwmJkRERKQZTEyIiIhIM5iYEBERkWYwMSEiIiLNYGJC5MWEEHjssccQGRkJg8GA3bt3qx1Sqzl27Jju9pnIG3CCNSIvtmbNGowePRo5OTno2LEjoqOj4eenqXt3togJEybgwoUL+PLLL+3LampqcObMGa/dZyJvxaOVyIsdOXIE8fHxbm+iZTabERAQ0MpRtQ5fX1/7HU+JSB5syiHyUhMmTMCUKVOQn58Pg8GA5ORkDBo0CJMnT0ZmZiaio6MxdOhQAMD+/fsxcuRIhIaGIjY2FmPHjkVJSYn9tSoqKjBu3DiEhoYiPj4eb731FgYNGoSnn35aUSxmsxnTp0/HNddcg5CQENxwww3IycmxP3/27Fncf//9SExMRHBwMLp3745ly5Y5vMa//vUvdO/eHUFBQYiKisJtt92GiooKvPLKK/jwww/x73//GwaDAQaDATk5OU5NOTk5OTAYDPj222/Rr18/BAcH48Ybb8TBgwcd3mfWrFmIiYlBWFgYHnnkETz33HPo1auXx/9/ImoeJiZEXuqdd97Bq6++isTERBQWFmLHjh0AgA8//BB+fn747rvv8Pe//x2FhYUYOHAgevXqhdzcXKxduxanT5/GPffcY3+tP/7xj9i4cSNWrlyJdevWIScnBzt37lQcy4MPPojvvvsOy5cvx549e/D73/8ew4cPx6FDhwAAlZWV6Nu3L77++mvs27cPjz32GMaOHYvvv/8eAFBYWIj7778fDz30EA4cOICcnBz89re/hRAC06ZNwz333IPhw4ejsLAQhYWFjd5m/YUXXsBbb72F3Nxc+Pn54aGHHrI/9+mnn+K1117DG2+8gZ07d6J9+/aYP3++R/93IrpCHt/2j4ik8fbbb4sOHTrYHw8cOFD06tXLYZ2XXnpJZGRkOCw7ceKEACAOHjwoysvLRUBAgFi+fLn9+bNnz4qgoCDx1FNPNRnD4cOHhcFgEAUFBQ7LhwwZImbMmOF2u5EjR4qpU6cKIYTYuXOnAGC/tXtD48ePF6NHj3ZYdvToUQFA5OXlCSHq7v76zTff2NdZtWqVACAuX74shBDihhtuEJMmTXJ4nV//+teiZ8+eTe4nEbUM9jEh0pl+/fo5PN65cyc2btyI0NBQp3WPHDmCy5cvw2w2Iz093b48MjISqampit5v165dEEKgc+fODstNJhOioqIAWDuqvv766/jss89QUFAAk8kEk8mEkJAQAEDPnj0xZMgQdO/eHcOGDUNGRgZ+97vfoW3bth7tOwD06NHD/nd8fDwAoLi4GO3bt8fBgwfxxBNPOKx//fXXY8OGDR6/DxE1DxMTIp2xfdnbWCwWjBo1Cm+88YbTuvHx8fbmluayWCzw9fXFzp074evr6/CcLRl666238Pbbb2Pu3Lno3r07QkJC8PTTT8NsNgOwdmRdv349tm7dinXr1uG9997DCy+8gO+//x4pKSkexePv72//22Aw2GNsuMxGcOAiUatiHxMinevTpw9+/PFHJCcno1OnTg4/ISEh6NSpE/z9/bF9+3b7NufPn8fPP/+s6PV79+6NmpoaFBcXO72+bdTMli1bMHr0aDzwwAPo2bMnOnbs6JQQGQwG/PrXv8bMmTORl5eHgIAArFy5EgAQEBCAmpqaK/5fpKam4ocffnBYlpube8WvS0TKMTEh0rlJkybh3LlzuP/++/HDDz/gl19+wbp16/DQQw+hpqYGoaGhePjhh/HHP/4R3377Lfbt24cJEybAx0fZ6aNz584YM2YMxo0bhxUrVuDo0aPYsWMH3njjDaxevRoA0KlTJ3uNyIEDB/D444+jqKjI/hrff/89Zs+ejdzcXOTn52PFihU4c+YMunbtCgBITk7Gnj17cPDgQZSUlKCqqqpZ/4spU6Zg0aJF+PDDD3Ho0CHMmjULe/bscapFIaKrh005RDqXkJCA7777Ds8++yyGDRsGk8mEDh06YPjw4fbk480338TFixdxxx13ICwsDFOnTkVpaani91iyZAlmzZqFqVOnoqCgAFFRUUhPT8fIkSMBAC+99BKOHj2KYcOGITg4GI899hjuvPNO+3uEh4dj8+bNmDt3LsrKytChQwe89dZbGDFiBADg0UcfRU5ODvr164eLFy9i48aNSE5O9vh/MWbMGPzyyy+YNm0aKisrcc8992DChAlOtShEdPVw5lciapZBgwahV69emDt3rtqhXFVDhw5FXFwcPv74Y7VDIdIF1pgQEdW6dOkSFixYgGHDhsHX1xfLli3DN998g/Xr16sdGpFuMDEhoiuyZcsWe5OKKxcvXmzFaK6MwWDA6tWrMWvWLJhMJqSmpiI7Oxu33Xab2qER6Qabcojoily+fBkFBQVun+/UqVMrRkNEsmNiQkRERJrB4cJERESkGUxMiIiISDOYmBAREZFmMDEhIiIizWBiQkRERJrBxISIiIg0g4kJERERaQYTEyIiItKM/w+0tYq8KzAr1wAAAABJRU5ErkJggg==", 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "abs(xrft.fft(d).sel(freq_northing=0)).plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "id": "5338e88f-5f77-4ddb-b869-29cb0c799b7e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 81, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "a = xrft.isotropic_power_spectrum(d, nfactor=1)\n", - "a = a / a.max()\n", - "a.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "id": "2c6e9bb9-8f8c-4ade-81e6-7fa6bab19e53", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 85, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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"np.testing.assert_allclose(s / s.max(), t, rtol=0, atol=0.01)" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "id": "a9abf24b-1689-4ab2-95f5-749b49421305", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "np.abs(xrft.fft(d)).plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "02d32c3c-55fe-4e90-b495-7bebec7467ab", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a0539aba-aa32-443f-9426-c4c902f7d5c6", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b364dcd2-6fde-44ed-80ea-480002d42a69", - "metadata": {}, - "outputs": [], - "source": [ - "g = xr.open_dataset(\"harmonica/tests/data/filter.nc\")\n", - "g" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b4bfbd03-65dd-457f-aa89-2b43531f9fe6", - "metadata": {}, - "outputs": [], - "source": [ - "coordinates = vd.grid_coordinates(\n", - " (-70e3, 20e3, -20e3, 60e3), spacing=0.22e3, extra_coords=500\n", - ")\n", - "finc, fdec = -45, 13\n", - "minc, mdec = -14, -24\n", - "dipole = [-25e3, 20e3, -5000]\n", - "moment = 1e12\n", - "magnetic_field_pole = hm.dipole_magnetic(\n", - " coordinates,\n", - " dipoles=dipole,\n", - " magnetic_moments=hm.magnetic_angles_to_vec(moment, 90, 0),\n", - " field=\"b\",\n", - ")\n", - "anomaly_pole = hm.total_field_anomaly(magnetic_field_pole, 90, 0)\n", - "magnetic_field = hm.dipole_magnetic(\n", - " coordinates,\n", - " dipoles=dipole,\n", - " magnetic_moments=hm.magnetic_angles_to_vec(moment, minc, mdec),\n", - " field=\"b\",\n", - ")\n", - "anomaly = hm.total_field_anomaly(magnetic_field, finc, fdec)\n", - "grid = vd.make_xarray_grid(coordinates[:2], (anomaly, anomaly_pole), data_names=[\"anomaly\", \"anomaly_pole\"])\n", - "anomaly_reduced = hm.reduction_to_pole(grid.anomaly, finc, fdec, minc, mdec)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d976b694-44ff-4a3e-9003-922a83e97b36", - "metadata": {}, - "outputs": [], - "source": [ - "(anomaly_reduced - grid.anomaly_pole).plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6e800e07-c141-4bc3-948d-f274592502a5", - "metadata": {}, - "outputs": [], - "source": [ - "(1 / np.abs(grid.anomaly_pole)).plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c6ef65bb-a81c-40af-9d83-a30344979000", - "metadata": {}, - "outputs": [], - "source": [ - "grid.anomaly.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b8ae01b7-2600-4ce9-a17b-49eee522aac4", - "metadata": {}, - "outputs": [], - "source": [ - "grid.anomaly_pole.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5b582491-5272-4a87-bd51-fb3d50ee1560", - "metadata": {}, - "outputs": [], - "source": [ - "anomaly_reduced.plot()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "04a4108b-772e-448a-983f-cbdd4d4266a9", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:harmonica]", - "language": "python", - "name": "conda-env-harmonica-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.11" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 98ffd8125ace7f56e958cebda51c6d2bd62bf127 Mon Sep 17 00:00:00 2001 From: Leonardo Uieda Date: Thu, 2 Apr 2026 09:51:00 -0300 Subject: [PATCH 17/20] Remove Montaj testing grid --- harmonica/tests/data/filter.nc | Bin 126944 -> 0 bytes 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100644 harmonica/tests/data/filter.nc diff --git a/harmonica/tests/data/filter.nc b/harmonica/tests/data/filter.nc deleted file mode 100644 index ea70fc37b80fa82d5adf96ae6b032ca6ace0552d..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 126944 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Date: Mon, 6 Apr 2026 14:32:20 -0300 Subject: [PATCH 18/20] Apply suggestions from Santi Make arguments keyword only. Co-authored-by: Santiago Soler --- harmonica/_transformations.py | 55 ++++++++++++++++++----------------- harmonica/filters/_utils.py | 10 +++---- 2 files changed, 33 insertions(+), 32 deletions(-) diff --git a/harmonica/_transformations.py b/harmonica/_transformations.py index 300570fb7..c31aa1c8e 100644 --- a/harmonica/_transformations.py +++ b/harmonica/_transformations.py @@ -22,7 +22,7 @@ from .filters._utils import apply_filter, grid_sanity_checks -def derivative_upward(grid, order=1, pad=True, pad_kwargs=None): +def derivative_upward(grid, *, order=1, pad=True, pad_kwargs=None): """ Calculate the derivative of a potential field grid in the upward direction. @@ -36,14 +36,14 @@ def derivative_upward(grid, order=1, pad=True, pad_kwargs=None): evenly spaced (regular grid). Its dimensions should be in the following order: *northing*, *easting*. Its coordinates should be defined in the same units. - order : int + order : int, optional The order of the derivative. Default to 1. - pad : bool + pad : bool, optional If True, will add padding to the grid before taking the Fourier Transform and applying the filter and remove it after the inverse Fourier Transform. Adding padding usually helps reduce edge effects from signal truncation. Default is True. - pad_kwargs : dict or None + pad_kwargs : dict or None, optional Any additional keyword arguments that should be passed to the :meth:`xarray.DataArray.pad` function in the form of a dictionary. If none are given, the default padding of 25% the dimensions of the grid will be @@ -73,7 +73,7 @@ def derivative_upward(grid, order=1, pad=True, pad_kwargs=None): ) -def derivative_easting(grid, order=1, method="finite-diff", pad=True, pad_kwargs=None): +def derivative_easting(grid, *, order=1, method="finite-diff", pad=True, pad_kwargs=None): """ Calculate the derivative of a regular grid in the easting direction. @@ -101,12 +101,12 @@ def derivative_easting(grid, order=1, method="finite-diff", pad=True, pad_kwargs be either ``"finite-diff"``, for computing using :func:`xarray.differentiate`, or ``"fft"``, for using FFT-based filters. Default ``"finite-diff"``. - pad : bool + pad : bool, optional If True, will add padding to the grid before taking the Fourier Transform and applying the filter and remove it after the inverse Fourier Transform. Adding padding usually helps reduce edge effects from signal truncation. Default is True. - pad_kwargs : dict or None + pad_kwargs : dict or None, optional Any additional keyword arguments that should be passed to the :meth:`xarray.DataArray.pad` function in the form of a dictionary. If none are given, the default padding of 25% the dimensions of the grid will be @@ -147,7 +147,7 @@ def derivative_easting(grid, order=1, method="finite-diff", pad=True, pad_kwargs return grid -def derivative_northing(grid, order=1, method="finite-diff", pad=True, pad_kwargs=None): +def derivative_northing(grid, *, order=1, method="finite-diff", pad=True, pad_kwargs=None): """ Calculate the derivative of a regular grid in the northing direction. @@ -175,12 +175,12 @@ def derivative_northing(grid, order=1, method="finite-diff", pad=True, pad_kwarg be either ``"finite-diff"``, for computing using :func:`xarray.differentiate`, or ``"fft"``, for using FFT-based filters. Default ``"finite-diff"``. - pad : bool + pad : bool, optional If True, will add padding to the grid before taking the Fourier Transform and applying the filter and remove it after the inverse Fourier Transform. Adding padding usually helps reduce edge effects from signal truncation. Default is True. - pad_kwargs : dict or None + pad_kwargs : dict or None, optional Any additional keyword arguments that should be passed to the :meth:`xarray.DataArray.pad` function in the form of a dictionary. If none are given, the default padding of 25% the dimensions of the grid will be @@ -221,7 +221,7 @@ def derivative_northing(grid, order=1, method="finite-diff", pad=True, pad_kwarg return grid -def upward_continuation(grid, height_displacement, pad=True, pad_kwargs=None): +def upward_continuation(grid, height_displacement, *, pad=True, pad_kwargs=None): """ Calculate the upward continuation of a potential field grid. @@ -244,12 +244,12 @@ def upward_continuation(grid, height_displacement, pad=True, pad_kwargs=None): The height displacement of upward continuation. For upward continuation, the height displacement should be positive. Its units are the same units of the ``grid`` coordinates. - pad : bool + pad : bool, optional If True, will add padding to the grid before taking the Fourier Transform and applying the filter and remove it after the inverse Fourier Transform. Adding padding usually helps reduce edge effects from signal truncation. Default is True. - pad_kwargs : dict or None + pad_kwargs : dict or None, optional Any additional keyword arguments that should be passed to the :meth:`xarray.DataArray.pad` function in the form of a dictionary. If none are given, the default padding of 25% the dimensions of the grid will be @@ -279,7 +279,7 @@ def upward_continuation(grid, height_displacement, pad=True, pad_kwargs=None): ) -def gaussian_lowpass(grid, wavelength, pad=True, pad_kwargs=None): +def gaussian_lowpass(grid, wavelength, *, pad=True, pad_kwargs=None): """ Calculate the Gaussian low-pass of a potential field grid. @@ -296,12 +296,12 @@ def gaussian_lowpass(grid, wavelength, pad=True, pad_kwargs=None): wavelength : float The cutoff wavelength in low-pass filter. Its units are the same units of the ``grid`` coordinates. - pad : bool + pad : bool, optional If True, will add padding to the grid before taking the Fourier Transform and applying the filter and remove it after the inverse Fourier Transform. Adding padding usually helps reduce edge effects from signal truncation. Default is True. - pad_kwargs : dict or None + pad_kwargs : dict or None, optional Any additional keyword arguments that should be passed to the :meth:`xarray.DataArray.pad` function in the form of a dictionary. If none are given, the default padding of 25% the dimensions of the grid will be @@ -330,7 +330,7 @@ def gaussian_lowpass(grid, wavelength, pad=True, pad_kwargs=None): ) -def gaussian_highpass(grid, wavelength, pad=True, pad_kwargs=None): +def gaussian_highpass(grid, wavelength, *, pad=True, pad_kwargs=None): """ Calculate the Gaussian high-pass of a potential field grid. @@ -347,12 +347,12 @@ def gaussian_highpass(grid, wavelength, pad=True, pad_kwargs=None): wavelength : float The cutoff wavelength in high-pass filter. Its units are the same units of the ``grid`` coordinates. - pad : bool + pad : bool, optional If True, will add padding to the grid before taking the Fourier Transform and applying the filter and remove it after the inverse Fourier Transform. Adding padding usually helps reduce edge effects from signal truncation. Default is True. - pad_kwargs : dict or None + pad_kwargs : dict or None, optional Any additional keyword arguments that should be passed to the :meth:`xarray.DataArray.pad` function in the form of a dictionary. If none are given, the default padding of 25% the dimensions of the grid will be @@ -387,6 +387,7 @@ def reduction_to_pole( declination, magnetization_inclination=None, magnetization_declination=None, + *, pad=True, pad_kwargs=None, ): @@ -417,12 +418,12 @@ def reduction_to_pole( None, the ``magnetization_declination`` will be set equal to the ``declination``, neglecting remanent magnetization and self demagnetization. Default None. - pad : bool + pad : bool, optional If True, will add padding to the grid before taking the Fourier Transform and applying the filter and remove it after the inverse Fourier Transform. Adding padding usually helps reduce edge effects from signal truncation. Default is True. - pad_kwargs : dict or None + pad_kwargs : dict or None, optional Any additional keyword arguments that should be passed to the :meth:`xarray.DataArray.pad` function in the form of a dictionary. If none are given, the default padding of 25% the dimensions of the grid will be @@ -456,7 +457,7 @@ def reduction_to_pole( ) -def total_gradient_amplitude(grid, pad=True, pad_kwargs=None): +def total_gradient_amplitude(grid, *, pad=True, pad_kwargs=None): r""" Calculate the total gradient amplitude of a potential field grid. @@ -471,12 +472,12 @@ def total_gradient_amplitude(grid, pad=True, pad_kwargs=None): evenly spaced (regular grid). Its dimensions should be in the following order: *northing*, *easting*. Its coordinates should be defined in the same units. - pad : bool + pad : bool, optional If True, will add padding to the grid before taking the Fourier Transform and applying the filter and remove it after the inverse Fourier Transform. Adding padding usually helps reduce edge effects from signal truncation. Default is True. - pad_kwargs : dict or None + pad_kwargs : dict or None, optional Any additional keyword arguments that should be passed to the :meth:`xarray.DataArray.pad` function in the form of a dictionary. If none are given, the default padding of 25% the dimensions of the grid will be @@ -518,7 +519,7 @@ def total_gradient_amplitude(grid, pad=True, pad_kwargs=None): return np.sqrt(gradient[0] ** 2 + gradient[1] ** 2 + gradient[2] ** 2) -def tilt_angle(grid, pad=True, pad_kwargs=None): +def tilt_angle(grid, *, pad=True, pad_kwargs=None): r""" Calculate the tilt angle of a potential field grid. @@ -533,12 +534,12 @@ def tilt_angle(grid, pad=True, pad_kwargs=None): evenly spaced (regular grid). Its dimensions should be in the following order: *northing*, *easting*. Its coordinates should be defined in the same units. - pad : bool + pad : bool, optional If True, will add padding to the grid before taking the Fourier Transform and applying the filter and remove it after the inverse Fourier Transform. Adding padding usually helps reduce edge effects from signal truncation. Default is True. - pad_kwargs : dict or None + pad_kwargs : dict or None, optional Any additional keyword arguments that should be passed to the :meth:`xarray.DataArray.pad` function in the form of a dictionary. If none are given, the default padding of 25% the dimensions of the grid will be diff --git a/harmonica/filters/_utils.py b/harmonica/filters/_utils.py index ac9bb0238..696f01b88 100644 --- a/harmonica/filters/_utils.py +++ b/harmonica/filters/_utils.py @@ -15,7 +15,7 @@ def apply_filter( - grid, fft_filter, filter_kwargs=None, pad=True, pad_kwargs=None, drop_coords=False + grid, fft_filter, *, filter_kwargs=None, pad=True, pad_kwargs=None, drop_coords=False ): """ Apply a filter to a grid and return the transformed grid in spatial domain. @@ -39,20 +39,20 @@ def apply_filter( same units. fft_filter : func Callable that builds the filter in the frequency domain. - filter_kwargs : dict + filter_kwargs : dict or None, optional Any additional keyword argument that should be passed to the ``fft_filter`` in the form of a dictionary. - pad : bool + pad : bool, optional If True, will add padding to the grid before taking the Fourier Transform and applying the filter and remove it after the inverse Fourier Transform. Adding padding usually helps reduce edge effects from signal truncation. Default is True. - pad_kwargs : dict + pad_kwargs : dict or None, optional Any additional keyword arguments that should be passed to the :meth:`xarray.DataArray.pad` function. If none are given, the default padding of 25% the dimensions of the grid will be added using the "edge" method. - drop_coords : bool + drop_coords : bool, optional If True, non-dimensional coordinates of the grid will be dropped after filtering. This is useful if the filter could move the grid, like in upward continuation, which could make these coordinates incorrect. From 2680e9f560138a6ec802c3c4d86810dddcb228d2 Mon Sep 17 00:00:00 2001 From: Leonardo Uieda Date: Mon, 6 Apr 2026 14:34:25 -0300 Subject: [PATCH 19/20] Fix formatting issues after code review --- harmonica/_transformations.py | 8 ++++++-- harmonica/filters/_utils.py | 8 +++++++- 2 files changed, 13 insertions(+), 3 deletions(-) diff --git a/harmonica/_transformations.py b/harmonica/_transformations.py index c31aa1c8e..bca815bf8 100644 --- a/harmonica/_transformations.py +++ b/harmonica/_transformations.py @@ -73,7 +73,9 @@ def derivative_upward(grid, *, order=1, pad=True, pad_kwargs=None): ) -def derivative_easting(grid, *, order=1, method="finite-diff", pad=True, pad_kwargs=None): +def derivative_easting( + grid, *, order=1, method="finite-diff", pad=True, pad_kwargs=None +): """ Calculate the derivative of a regular grid in the easting direction. @@ -147,7 +149,9 @@ def derivative_easting(grid, *, order=1, method="finite-diff", pad=True, pad_kwa return grid -def derivative_northing(grid, *, order=1, method="finite-diff", pad=True, pad_kwargs=None): +def derivative_northing( + grid, *, order=1, method="finite-diff", pad=True, pad_kwargs=None +): """ Calculate the derivative of a regular grid in the northing direction. diff --git a/harmonica/filters/_utils.py b/harmonica/filters/_utils.py index 696f01b88..33ec8d9c1 100644 --- a/harmonica/filters/_utils.py +++ b/harmonica/filters/_utils.py @@ -15,7 +15,13 @@ def apply_filter( - grid, fft_filter, *, filter_kwargs=None, pad=True, pad_kwargs=None, drop_coords=False + grid, + fft_filter, + *, + filter_kwargs=None, + pad=True, + pad_kwargs=None, + drop_coords=False, ): """ Apply a filter to a grid and return the transformed grid in spatial domain. From a184d3eb7be1e7e1d192c018ded1539fe2eb2a45 Mon Sep 17 00:00:00 2001 From: Leonardo Uieda Date: Mon, 6 Apr 2026 14:44:41 -0300 Subject: [PATCH 20/20] Use 25% of each dimension instead of the largest It would cause problems for very asymmetric grids. --- harmonica/filters/_utils.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/harmonica/filters/_utils.py b/harmonica/filters/_utils.py index 33ec8d9c1..5971e4558 100644 --- a/harmonica/filters/_utils.py +++ b/harmonica/filters/_utils.py @@ -80,12 +80,11 @@ def apply_filter( non_dim_coords = {c: grid[c] for c in grid.coords if c not in grid.indexes} grid = grid.drop_vars(non_dim_coords.keys()) if pad: - # By default, use a padding width of 25% of the largest grid dimension. + # By default, use a padding width of 25% of each grid dimension. # Fedi et al. (2012; doi:10.1111/j.1365-246X.2011.05259.x) suggest # a padding of 100% but that seems exaggerated. if "pad_width" not in pad_kwargs: - width = int(0.25 * max(grid[d].size for d in dims)) - pad_kwargs["pad_width"] = {d: width for d in dims} + pad_kwargs["pad_width"] = {d: int(0.25 * grid[d].size) for d in dims} if "mode" not in pad_kwargs: pad_kwargs["mode"] = "edge" if "constant_values" not in pad_kwargs: